The following is a conversation with Elon Musk, DJ Saw, Matthew McDougall,
Bliss Chapman, and Noland Arbaugh about Neuralink and the future of humanity.
Elon, DJ, Matthew, and Bliss are, of course, part of the amazing Neuralink team.
And Noland is the first human to have a Neuralink device implanted in his brain.
I speak with each of them individually.
So use timestamps to jump around or, as I recommend, go hardcore
and listen to the whole thing.
This is the longest podcast I've ever done.
It's a fascinating, super technical, and wide-ranging conversation,
and I loved every minute of it.
And now, dear friends, here's Elon Musk,
his fifth time on this, the Lex Friedman podcast.
Drinking coffee or water?
Water.
I'm so over-caffeinated right now.
Do you want some caffeine?
I mean, sure.
There's a nitro drink.
It's supposed to keep you up for, like, you know, tomorrow afternoon, basically.
Yeah.
I don't have any sugar.
So what is nitro?
It's just got a lot of caffeine or something?
Don't ask questions.
It's called nitro.
Do you need to know anything else?
It's got nitrogen in it.
That's ridiculous.
I mean, what we breathe is 78% nitrogen anyway.
What do you need to add more for?
Most people think that they're breathing oxygen,
and they're actually breathing 78% nitrogen.
You need, like, a milk bar.
Like from Clockwork Orange.
Clockwork Orange.
Yeah.
Yeah.
Is that top three Kubrick film for you?
Clockwork Orange?
It's pretty good.
I mean, it's demented.
Jarring, I'd say.
Okay.
Okay.
So first, let's step back, and big congrats on getting Neuralink implanted into a human.
That's a historic step for Neuralink.
Oh, thanks.
Yeah.
There's many more to come.
Yeah, we just obviously have our second implant as well.
How did that go?
So far, so good.
It looks like we've got, I think, on the order of 400 electrodes that are providing signals.
Nice.
Yeah.
How quickly do you think the number of human participants will scale?
It depends somewhat on the regulatory approval, the rate at which we get regulatory approvals.
So we're hoping to do 10 by the end of this year.
Total of 10.
So eight more.
And with each one, you're going to be learning a lot of lessons about the neurobiology of the brain,
the everything, the whole chain of the Neuralink, the decoding, the signal processing, all that kind of stuff.
Yeah.
Yeah, I think it's obviously going to get better with each one.
I mean, I don't want to jinx it, but it seems to have gone extremely well with the second implant.
So there's a lot of signal, a lot of electrodes.
It's working very well.
What improvements do you think we'll see in Neuralink in the coming, let's say, let's get crazy, coming years?
I mean, in years, it's going to be gigantic because we'll increase the number of electrodes dramatically.
We'll improve the signal processing.
So even with only roughly, I don't know, 10, 15% of the electrodes working with Noland, with our first patient,
we were able to get to achieve a bits per second.
And that's twice the world record.
So I think we'll start vastly exceeding the world record by orders of magnitude in the years to come.
So it's like getting to, I don't know, 100 bits per second, 1,000, maybe if it's like five years from now, we might be at a megabit.
Like faster than any human could possibly communicate by typing or speaking.
Yeah, that BPS is an interesting metric to measure.
There might be a big leap in the experience once you reach a certain level of BPS.
Yeah.
Like entire new ways of interacting with a computer might be unlocked.
And with humans.
With other humans.
Provided they have, they want a Neuralink too.
Right.
Otherwise they won't be able to absorb the signals fast enough.
Do you think they'll improve the quality of intellectual discourse?
Well, I think you could think of it, you know, if you were to slow down communication, how hard do you feel about that?
You know, if you'd only talk at, let's say, one-tenth of normal speed, you'd be like, wow, that's agonizingly slow.
Yeah.
So now, imagine you could speak at, communicate clearly at ten or a hundred or a thousand times faster than normal.
Listen, I'm pretty sure nobody in their right mind listens to me at 1x.
They listen at 2x.
So I can only imagine what 10x would feel like, or I could actually understand it.
I usually default to 1.5x.
You can do 2x, but, well, actually, if I'm trying to go, if I'm listening to somebody get to, in like sort of 15, 20 minutes, I want to go to sleep, then I'll do it 1.5x.
If I'm paying attention, I'll do 2x.
Right.
But actually, if you start, actually listen to podcasts or sort of audio books or anything at, if you get used to doing it at 1.5, then 1 sounds painfully slow.
I'm still holding on to 1 because I'm afraid.
I'm afraid of myself becoming bored with the reality, with the real world where everyone's speaking on 1x.
Well, it depends on the person.
You can speak very fast.
Like, we communicate very quickly.
And also, if you use a wide range of, if your vocabulary is larger, your bit rate, effective bit rate is higher.
That's a good way to put it.
Yeah.
The effective bit rate.
I mean, that is the question, is how much information is actually compressed in the low bit transfer of language.
Yeah, if there's a single word that is able to convey something that would normally require, I don't know, 10 simple words, then you've got maybe a 10x compression on your hands.
And that's really, like, with memes.
Memes are like data compression.
It conveys a whole, you're simultaneously hit with a wide range of symbols that you can interpret.
And it's, you kind of get it faster than if it were words or a simple picture.
And, of course, you're referring to memes broadly like ideas.
Yeah.
There's an entire idea structure that is like an idea template.
And then you can add something to that idea template.
But somebody has that pre-existing idea template in their head.
So when you add that incremental bit of information, you're conveying much more than if you just, you know, said a few words.
It's everything associated with that meme.
You think there'll be emergent leaps of capability as you scale the number of electrodes?
Yeah.
Like there'll be a certain, you think there'll be like actual number where it just, the human experience will be altered?
Yes.
What do you think that number might be?
Whether electrodes or BPS?
We, of course, don't know for sure.
But is this 10,000, 100,000?
Yeah.
I mean, certainly if you're anywhere at 10,000 bits per second, I mean, that's vastly faster than any human can communicate right now.
If you think of the, what is the average bits per second of a human?
It is less than one bit per second over the course of a day because there are 86,400 seconds in a day and you don't communicate 86,400 tokens in a day.
Therefore, your bits per second is less than one, averaged over 24 hours.
It's quite slow.
It's quite slow.
And now even if you're communicating very quickly and you're talking to somebody who understands what you're saying, because in order to communicate, you have to at least to some degree model the mind state of the person to whom you're speaking.
Then take the concept you're trying to convey, compress that into a small number of syllables, speak them, and hope that the other person decompresses them into a conceptual structure that is as close to what you have in your mind as possible.
Yeah.
I mean, there's a lot of signal loss there in that process.
Yeah.
Very lossy compression and decompression.
And a lot of the, a lot of what your neurons are doing is distilling the concepts down to a small number of symbols of say syllables that I'm speaking or keystrokes, whatever the case may be.
So that's a lot of what your brain computation is doing.
Now that there is an argument that that's actually a healthy thing to do or a helpful thing to do, because as you try to compress complex concepts, you're perhaps forced to distill the, you know, what is it, what is most essential in those concepts as opposed to just all the fluff.
So in the process of compression, you distill things down to what matters the most because you can only say a few things.
So that is perhaps helpful.
I think we might, we'll probably get, if our data rate increases, it's highly probable that we'll become far more verbose.
Just like your computer, you know, when computers had like, my first computer had 8K of RAM, you know, so you really thought about every byte.
And, you know, now you've got computers with many gigabytes of RAM.
So, you know, if you want to do an iPhone app that just says, hello world, it's probably, I don't know, several megabytes minimum with a bunch of fluff.
But nonetheless, we still prefer to have the computer with more memory and more compute.
So, the long-term aspiration of Neuralink is to improve the AI-human symbiosis by increasing the bandwidth of the communication.
Because if, even if, in the most benign scenario of AI, you have to consider that the AI is simply going to get bored waiting for you to spit out a few words.
I mean, if the AI can communicate at terabits per second and you're communicating at, you know, bits per second, it's like talking to a tree.
Well, it is a very interesting question for a super-intelligent species.
What use are humans?
I think there is some argument for humans as a source of will.
Will.
Will, yeah.
Source of will or purpose.
So, if you consider the human mind as being essentially, there's the primitive limbic elements, which basically even like reptiles have, and there's the cortex, the thinking and planning part of the brain.
Now, the cortex is much smarter than the limbic system, and yet it's largely in service to the limbic system.
It's trying to make the limbic system happy.
I mean, the sheer amount of compute that's gone into people trying to get laid is insane.
Without actually seeking procreation, they're just literally trying to do this sort of simple motion, and they get a kick out of it.
Yeah.
So, this simple, which in the abstract, rather absurd motion, which is sex, the cortex is putting a massive amount of compute into trying to figure out how to do that.
So, like 90% of the distributed compute of the human species is spent on trying to get laid, probably.
Like a large percentage.
Yeah, yeah.
There's no purpose to most sex except hedonistic.
You know, it's just sort of a joy or whatever, dopamine release.
Now, once in a while, it's procreation.
But for humans, it's mostly, modern humans, it's mostly recreational.
And so, so your cortex, much smarter than your limbic system, is trying to make the limbic system happy because the limbic system wants to have sex.
So, or wants some tasty food or whatever the case may be.
And then, that is then further augmented by the tertiary system, which is your phone, your laptop, iPad, whatever, you know, all your computing stuff.
That's your tertiary layer.
So, you're actually already a cyborg.
You have this tertiary compute layer, which is in the form of your computer with all the applications, all your compute devices.
And so, in the getting laid front, there's actually a massive amount of digital compute also trying to get laid.
You know, with like Tinder and whatever, you know.
Yeah.
So, the compute that we humans have built is also participating.
Yeah.
I mean, there's like gigawatts of compute going into getting laid, of digital compute.
Yeah.
What if AGI was...
This is happening as we speak.
If we merge with AI, it's just going to expand the compute that we humans use.
Pretty much.
To try to get laid.
Well, it's one of the things, certainly, yeah.
Yeah.
But what I'm saying is that, yes, like, is there a use for humans?
Well, there's this fundamental question of what's the meaning of life?
Why do anything at all?
And so, if our simple limbic system provides a source of will to do something that then goes to our cortex, that then goes to our, you know, tertiary compute layer, then, you know, I don't know.
So, it might actually be that the AI, in a benign scenario, is simply trying to make the human limbic system happy.
Yeah, it seems like the will is not just about the limbic system.
There's a lot of interesting, complicated things in there.
We also want power.
That's limbic, too, I think.
But then we also want to, in a kind of cooperative way, alleviate the suffering in the world?
Not everybody does, but yeah, sure.
Some people do.
As a group of humans, when we get together, we start to have this kind of collective intelligence that is more complex in its will than the underlying individual descendants of apes.
All right, so there's, like, other motivations, and that could be a really interesting source of an objective function for AGI.
Yeah, I mean, there are these sort of fairly cerebral, kind of higher-level goals.
I mean, for me, it's like, what's the meaning of life for understanding the nature of the universe is of great interest to me, and hopefully to the AI.
And that's the mission of XAI and Grok, is understand the universe.
So do you think people, when you have a neural link with 10,000, 100,000 channels, most of the use cases will be communication with AI systems?
Well, assuming that the, they're not, I mean, they're solving basic neurological issues that people have, you know, if they've got damaged neurons in their spinal cord or neck, or, you know, as is the case with our first two patients,
then, you know, this, obviously, the first order of business is solving fundamental neuron damage in the spinal cord, neck, or in the brain itself.
So, you know, and our second product is called Blindsight, which is to enable people who are completely blind, lost both eyes, or optic nerve, or just can't see at all, to be able to see by directly triggering the neurons in the visual cortex.
So, we're just starting at the basics here, you know, this is like, very, the simple stuff, relatively speaking, is solving neuron damage.
You know, it could also solve, I think, probably schizophrenia, you know, if people have seizures of some kind, it could probably solve that.
It could help with memory.
So, there's like a, kind of a, a tech tree, if you will, of like, you got the basics, like, you need, you need literacy before you can have, you know, Lord of the Rings, you know.
So, do you have letters and alphabet, okay, great, words, you know, and then eventually you get sagas.
So, you know, I think there's, there may be some, you know, things to worry about in the future, but the first several years are really just solving basic neurological damage.
Like, for people who have, essentially, complete or near complete loss of, from the brain to the body, like Stephen Hawking would be an example, the neural links would be incredibly profound.
Because, I mean, you can imagine if Stephen Hawking could communicate as fast as we're communicating, perhaps faster.
And that's certainly possible, probable, in fact, likely, I'd say.
So, there's a, a kind of dual track of medical and non-medical, meaning, so everything you've talked about could be applied to people who are non-disabled in the future?
So, the logical thing to do is, sensible thing to do is to start off solving basic neuron damage issues.
Yes.
Because the, there's obviously some risk with, with a new device, you can't get the risk out at zero, it's not possible.
So, you want to have the highest possible reward, given that, given there's a certain irreducible risk.
And if, you know, if somebody's able to have a profound improvement in their communication, that's worth the risk.
As you get the risk down.
Yeah, as you get the risk down, once the risk is down to, you know, if you have, like, thousands of people that have been using it for, for years, and the risk is minimal, then perhaps at that point, you could consider saying, okay, let's, let's aim for augmentation.
Now, I think we, we're actually going to aim for augmentation with people who have neuron damage.
So, we're not just aiming to give people a communication data rate equivalent to normal humans.
We're aiming to give people who have, you know, quadriplegic, or maybe have complete loss of the connection to the brain and body, a communication data rate that exceeds normal humans.
I mean, well, we're in there, why not?
Let's give people superpowers.
And the same for vision.
As you restore vision, there could be aspects of that restoration that are superhuman.
Yeah, at first, the vision restoration will be low res.
Because you have to say, like, how many neurons can you put in there and trigger?
And you can do things where you adjust the electric field to, like, even if you've got, say, 10,000 neurons.
It's not just 10,000 pixels, because you can adjust the, the feel between the neurons and do them in patterns in order to get, say, have, say, 10,000 electrodes effectively give you, I don't know, maybe, like, having a megapixel or a 10 megapixel situation.
So, and then over time, I think you get to higher resolution than human eyes, and you could also see in different wavelengths.
So, like, Geordi LaForge from Star Trek, you know, like, the thing.
You could just, like, do you want to see in radar?
No problem.
You could see ultraviolet, infrared, eagle vision, whatever you want.
Do you think there'll be, let me ask a Joe Rogan question.
Do you think there'll be, I just recently taken ayahuasca.
Is that a question?
So, this question, no.
Well, yes.
Well, I guess technically it is.
Yeah.
Have you ever tried DMT, bro?
I love you, Joe.
Okay.
Yeah, wait, wait.
Yeah, have you said much about it?
I have not.
I have not.
I have not.
I've been.
Okay, well, while we spill the beans.
It was a truly incredible experience.
Don't turn the tables on you.
Wow.
I mean, you're in the jungle.
Yeah, amongst the trees, myself.
Yeah, must have been crazy.
And the shaman.
Yeah, yeah, yeah.
With the insects, with the animals all around you.
Like, jungle as far as I can see.
I mean.
That's the way to do it.
Things are going to look pretty wild.
Yeah, pretty wild.
I took an extremely high dose.
Don't go hugging an anaconda or something, you know?
You haven't lived unless you made love to an anaconda.
I'm sorry.
Snakes and ladders.
Yeah, I took an extremely high dose.
Okay.
Nine cups.
Damn.
Okay, that sounds like a lot.
Is no one just one cup?
One or two.
Usually one.
Two and...
Yeah.
Wait.
Like, right off the bat or do you work your way up to it?
So, I...
Did you just jump in at the deep end?
Across two days, because on the first day I took two and I...
Okay.
It was a ride, but it wasn't quite like a...
It wasn't like a revelation.
It wasn't into deep space type ride.
It was just like a little airplane ride.
Okay.
I saw some trees and some visuals and all that.
I just saw a dragon and all that kind of stuff.
But, uh...
Nine cups, you went to Pluto, I think.
Pluto, yeah.
No, deep space.
Deep space.
No, one of the interesting aspects of my experience is I was...
I thought I would have some demons, some stuff to work through.
That's what people...
That's what everyone says.
That's what everyone says.
Yeah, exactly.
I had nothing.
I had all positive.
I had just so full...
Just a pure soul.
I don't think so.
I don't know.
But I kept thinking about...
It had an extremely high resolution.
Okay.
Thoughts about the people I know in my life.
You were there.
Okay.
And it's just not from my relationship with that person,
but just as the person themselves,
I had just this deep gratitude of who they are.
That's cool.
It was just like this exploration.
Like, you know, like Sims or whatever?
You get to watch them.
Sure.
I got to watch people and just be in awe of how amazing they are.
It sounds awesome.
Yeah, it's great.
I was waiting for...
When's the demon coming?
Exactly.
Maybe I'll have some negative thoughts.
Nothing.
Nothing.
Nothing.
I had just extreme gratitude for them.
And then also a lot of space travel.
Space travel to where?
So here's what it was.
It was people, the human beings that I know,
they had this kind of...
The best way I could describe it is they had a glow to them.
Okay.
And then I kept flying out from them to see Earth, to see our solar system, to see our galaxy.
And I saw that light, that glow all across the universe.
Okay.
Like, whatever that form is.
All right.
Whatever that...
Like, did you go past the Milky Way?
Yeah.
Okay.
You were, like, intergalactic.
Yeah, intergalactic.
Okay, dang.
But always pointing in.
Okay.
Yeah.
Past the Milky Way, past...
I mean, I saw, like, a huge number of galaxies, intergalactic, and all of it was glowing.
So...
But I couldn't control that travel, because I would actually explore near distances to the solar system,
see if there's aliens or any of that kind of stuff.
Did you see aliens?
I didn't know...
Zero aliens?
Implication of aliens, because they were glowing.
They were glowing in the same way that humans were glowing, that, like, life force that I was seeing.
The thing that made humans amazing was there throughout the universe.
Like, there was these glowing dots.
So, I don't know.
It made me feel like there is life...
No, not life, but something, whatever makes humans amazing all throughout the universe.
Sounds good.
Yeah, it was amazing.
No demons.
No demons.
I looked for the demons.
There's no demons.
There were dragons, and they're pretty...
So, the thing about trees...
Was there anything scary at all?
Dragons?
But they weren't scary.
They were friends.
They were protected.
So, the thing is...
Like a magic dragon.
No, it was more like a Game of Thrones kind of dragons.
They weren't very friendly.
They were very big.
So, the thing is, the giant trees at night, which is where I was...
I mean, the jungle's kind of scary.
Yeah.
The trees started to look like dragons, and they were all, like, looking at me.
Sure.
Okay.
And it didn't seem scary.
It seemed like they were protecting me.
And they...
The shaman and the people...
They didn't speak any English, by the way, which made it even scarier.
I guess we're not even, like, you know, we're worlds apart in many ways.
It's just...
But, yeah, there was not...
They talk about the mother of the forest protecting you, and that's what I felt like.
And you were way out in the jungle.
Way out.
This is not, like, a tourist retreat.
You know, like 10 miles outside of Rio or something.
No, we went...
No, this is not a...
You're a deep, deep Amazon...
So, me and this guy, Paul Rosalie, who basically is Tarzan.
He lives in the jungle.
We went out deep, and we just went crazy.
Wow.
Cool.
Yeah.
So, anyway, can I get that same experience in a Neuralink?
Probably, yeah.
I guess that is the question for non-disabled people.
Do you think that there's a lot in our perception, in our experience of the world, that could be explored,
that could be played with using Neuralink?
Yeah, I mean, Neuralink is really a generalized input-output device.
You know, it's reading electrical signals and generating electrical signals.
And, I mean, everything that you've ever experienced in your whole life, smell, emotions, all of those are electrical signals.
So, it's kind of weird to think that your entire life experience is distilled down to electrical signals from neurons, but that is, in fact, the case.
Or, I mean, that's at least what all the evidence points to.
So, I mean, you could trigger the right neuron, you could trigger a particular scent, you could certainly make things glow.
I mean, do pretty much anything.
I mean, really, you can think of the brain as a biological computer.
So, if there are certain, say, chips or elements of that biological computer that are broken, let's say your ability to, if you've got a stroke, that means you've got some part of your brain is damaged.
If that, let's say it's a speech generation or the ability to move your left hand, that's the kind of thing that Neuralink could solve.
If it's, if you've got, like, a massive amount of memory loss that's just gone, well, we can't go, we can't get the memories back.
We could restore your ability to make memories, but we can't, you know, restore memories that are fully gone.
Now, I should say, if, if, if you, maybe if part of the memory is there, and the means of accessing the memory is the part that's broken, then we could re-enable the part, the ability to access the memory.
So, but you can think of it like RAM in your, you know, in a computer, if, you know, if the RAM is destroyed, or your SD card is destroyed, we can't get that back.
But if the connection to the SD card is destroyed, we can fix that.
If, if it is fixable physically, then, yeah, then it can be fixed.
Of course, with AI, you can, just like you can repair photographs and fill in missing parts of photographs, maybe you can do the same.
Yeah, you, you could say, like, create the most probable set of memories based on the, all information you have about that person.
You could then, probably, it would be probable, probabilistic restoration of memory.
Now, we're getting pretty esoteric here.
But that is one of the most beautiful aspects of the human experience is remembering the good memories.
Like we, we live most of our life, as Danny Kahneman has talked about, in our memories, not in the actual moment.
We just, we're collecting memories, and we kind of relive them in our head.
And there, that's the good times.
If you just integrate over our entire life, it's remembering the good times.
Sure.
That produces the largest amount of happiness, so.
Yeah, well, I mean, what are we but our memories?
And, and what is death but the loss of memory?
Loss of information.
You know, if you, if you, if you could say, like, well, if, if, if you could be, you run a thought experiment, well, if, if you were disintegrated painlessly, and then reintegrated a moment later, like teleportation, I guess, provided there's no information loss.
The, the, the fact that your one body was disintegrated is irrelevant.
And memories is just such a huge part of that.
Death is fundamentally the loss of information, the loss of memory.
So if we can store them as accurately as possible, we basically achieve a kind of immortality.
Yeah.
You've talked about the, the threats, the safety concerns of AI.
Let's look at long-term visions.
Do you think Neuralink is, in your view, the, the best current approach we have for AI safety?
It's an idea that may help with AI safety.
Certainly not.
I wouldn't want to, I wouldn't want to claim it's like some panacea or something.
It's, that's a sure thing.
But, I mean, many years ago, I was thinking like, well, what, what would inhibit alignment of human,
collective human will with, uh, artificial intelligence and the low data rate of humans,
especially our, our slow output rate, um, would necessarily just because it's such a, because
the communication is so slow, would, uh, diminish the link between humans and computers.
Like, the more you are a tree, the, the less, you know, what the tree is.
Like, let's say you, you look at a tree, you look at this plant or whatever and like,
hey, I'd really like to make that plant happy, but it's not saying a lot, you know?
So the more we increase the data rate that humans, uh, can intake and output, then that means the, the better,
the, the higher the chance we have in a world full of AGIs.
Yeah, we could better align collective human will with, uh, AI if the output rate especially
was dramatically increased.
Like, and I think there's, there's potential to increase the output rate by, I don't know,
three, maybe six, maybe more orders of magnitude.
So it's better than the current situation.
And that output rate would be by increasing the number of electrodes, number of channels,
and also maybe implanting multiple neural links.
Yeah.
Do you think there'll be a world in the next couple of decades where it's hundreds of millions
of people have neural links?
Yeah, I do.
Do you think when people just, when they see the capabilities, the superhuman capabilities
that are possible, and then the, the safety is demonstrated?
Yeah.
If it's extremely safe, um, and you have, and you can have superhuman abilities, um, and
let's say you can upload your memories, um, you know, so you wouldn't, you wouldn't lose
memories, um, then I think probably a lot of people would, would choose to have it.
Um, it would supersede the cell phone, for example.
I mean, it's the, the, the biggest problem that a cell phone has, um, is, is trying to
div figure out what you want.
So that's why you've got, uh, you know, autocomplete and you've got output, which is all the pixels
on the screen, but from the perspective of the human, the output is so frigging slow.
Desktop or phone is desperately just trying to understand what you want.
And, and, um, you know, there's an eternity between every keystroke from a computer standpoint.
Yeah, the computer's talking to a tree, a slow moving tree that's trying to swipe.
Yeah.
So, you know, if you have computers that are doing trillions of instructions per second,
and a whole second went by, I mean, there's a trillion things it could have done, you know?
Yeah.
I think it's exciting and scary for people because once you have a very high bit rate,
that changes the human experience in a way that's very hard to imagine.
Yeah.
It would be, we would be something different.
I mean, some sort of futuristic cyborg, I mean, I mean, we're obviously talking about,
by the way, like, it's not like around the corner.
It's, you asked me what the few distant future, I was like, maybe this is like,
it's not super far away, but 10, 15 years, that kind of thing.
When can I get one?
10 years?
Probably less than 10 years.
Depends what you want to do, you know?
Hey, if I can get, like, a thousand BPS.
A thousand BPS.
And it's safe, and I can just interact with the computer,
while laying back and eating Cheetos.
I don't eat Cheetos.
There's certain aspects of human computer interaction when done more efficiently,
and more enjoyably, are worth it.
Well, we feel pretty confident that, I think maybe within the next year or two,
that someone with a Neuralink implant will be able to outperform a pro gamer.
Nice.
Because the reaction time would be faster.
I got to visit Memphis.
Yeah, yeah.
You're going big on compute.
Yeah.
You've also said play to win or don't play at all.
So, what does it take to win?
For AI, that means you've got to have the most powerful training compute.
And the rate of improvement of training compute has to be faster than
everyone else, or you will not win. Your AI will be worse.
So, how can Grok, let's say three, that might be available, what, like next year?
Well, hopefully end of this year.
Grok three.
If we're lucky.
Yeah.
How can that be the best LLM, the best AI system available in the world?
How much of it is compute?
How much of it is data?
How much of it is like post training?
How much of it is the product that you package it up in?
All that kind of stuff.
I mean, they all matter.
It's sort of like saying what, you know, let's say it's a Formula One race.
Like what matters more, the car or the driver?
I mean, they both matter.
If your car is not fast, then, you know, if it's like, let's say it's half the horsepower of
your competitors, the best driver will still lose.
If it's twice the horsepower, then probably even a mediocre driver will still win.
So the training computer is kind of like the engine.
There's horsepower of the engine.
So you really, you want to try to do the best on that, and then how efficiently do you use that
to train and compute?
And how efficiently do you do the inference, the use of the AI?
So obviously that comes down to human talent.
And then what unique access to data do you have?
That's also plays a role.
Do you think Twitter data will be useful?
Yeah.
I mean, I think most of the leading AI companies have already scraped all the Twitter data.
I don't really think they have.
So on a go forward basis, what's useful is the fact that it's up to the second.
That's because it's hard for them to scrape in real time.
So there's an immediacy advantage that Grok has already.
I think with Tesla and the real time video coming from several million cars, ultimately tens of
millions of cars, with Optimus, there might be hundreds of millions of Optimus robots,
maybe billions, learning a tremendous amount from the real world.
That's the biggest source of data, I think, ultimately, is Optimus probably.
Optimus is going to be the biggest source of data.
Because it's able to...
Because reality scales.
Reality scales to the scale of reality.
It's actually humbling to see how little data humans have actually been able to accumulate.
But really, if you say, how many trillions of usable tokens have humans generated where
on a non-duplicative, like, discounting spam and repetitive stuff?
It's not a huge number.
You run out pretty quickly.
And Optimus can go.
So Tesla cars can, unfortunately, have to stay on the road.
Optimus robot can go anywhere.
There's more reality.
Off the road.
And go off-road.
Yeah, I mean, like, the software can, like, pick up the cup and see,
did it pick up the cup in the right way?
Did it, you know, say, pour water in the cup?
You know, did the water go in the cup or not go in the cup?
Did it spill water or not?
Yeah.
Simple stuff like that.
I mean, but it can do that at scale times a billion, you know?
So generate useful data from reality.
So cause and effect stuff.
What do you think it takes to get to mass production of humanoid robots like that?
It's the same as cars, really.
I mean, global capacity for vehicles is about 100 million a year.
And it could be higher.
It's just that the demand is on the order of 100 million a year.
And then there's roughly 2 billion vehicles that are in use in some way.
So which makes sense, like the life of a vehicle is about 20 years.
So at steady state, you can have 100 million vehicles produced a year with a 2 billion vehicle fleet, roughly.
Now for humanoid robots, the utility is much greater.
So my guess is humanoid robots are more like a billion plus per year.
But, you know, until you came along and started building Optimus,
it was thought to be an extremely difficult problem.
I mean, it's still an extremely difficult problem.
Yes, it's no walk in the park.
I mean, Optimus currently would struggle to walk in the park.
I mean, it can walk in the park.
The park is not too difficult.
But it will be able to walk over a wide range of terrain.
Yeah.
And pick up objects.
Yeah, yeah.
It can already do that.
But like all kinds of objects.
Yeah, yeah.
All foreign objects.
I mean, pouring water in a cup is not trivial.
Because then if you don't know anything about the container, it could be all kinds of containers.
Yeah, there's going to be an immense amount of engineering just going into the hand.
Yeah.
The hand might be close to half of all the engineering in Optimus.
From an electromechanical standpoint, the hand is probably roughly half of the engineering.
But so much of the intelligence of humans goes into what we do with our hands.
Yeah.
It's the manipulation of the world, manipulation of objects in the world.
Intelligence, safe manipulation of objects in the world.
Yeah.
Yeah.
I mean, you start really thinking about your hand and how it works.
You know.
I do it all the time.
The sensory control of homunculus is where you have humongous hands.
Yeah.
So, I mean, like your hands, the actuators, the muscles of your hand are almost
overwhelmingly in your forearm.
So your forearm has the muscles that actually control your hand.
There's a few small muscles in the hand itself.
But your hand is really like a skeleton meat puppet and with cables.
So the muscles that control your fingers are in your forearm.
And they go through the carpal tunnel, which is that you've got a little collection of bones.
And you've got a tiny tunnel that these cables, the tendons, go through.
And those tendons are mostly what move your hands.
And something like those tendons has to be re-engineered into the Optimus in order to do all that kind of stuff.
Yeah.
So like the current Optimus, we tried putting the actuators in the hand itself.
Then you sort of end up having these like...
Giant hands.
Yeah.
Giant hands that look weird.
And then they don't actually have enough degrees of freedom or enough strength.
So then you realize, okay, that's why you've got to put the actuators in the forearm.
And just like a human, you've got to run cables through a narrow tunnel to operate the fingers.
And then there's also a reason for not having all the fingers the same length.
So it wouldn't be expensive from an energy or evolutionary standpoint to have all your fingers be the same length.
So why not do the same length?
Yeah, why not?
Because it's actually better to have different lengths.
Your dexterity is better if you've got fingers of different length.
There are more things you can do.
And your dexterity is actually better if your fingers are of different length.
Like there's a reason we've got a little finger.
Like why not have a little finger this bigger?
Yeah.
Because it allows you to do fine, it helps you with fine motor skills.
That, this little finger helps?
It does.
If you lost your little finger, it would, you have noticeably less dexterity.
So as you're figuring out this problem, you have to also figure out a way to do it
so you can mass manufacture it.
So it's to be as simple as possible.
It's actually going to be quite complicated.
The as possible part is, it's quite a high bar.
If you want to have a humanoid robot that can do things that a human can do, it's a very high bar.
So our new arm has 22 degrees of freedom instead of 11 and has the, like I said, the actuators in the forearm.
Um, and these all, all the actuators are designed from scratch.
They, from physics first principles.
Um, but the sensors are all designed from scratch and, and we'll, we'll continue to put a tremendous
amount of engineering effort into improving the hand.
Like the hand by hand, I mean like the, the entire forearm from elbow forward, uh, is, is really the hand.
Um, so that's, um, incredibly difficult engineering actually.
And, um, and so then, so the simplest possible version of a humanoid robot that can do even most,
perhaps not all of what a human can do is actually still, still very complicated.
It's not, it's not simple.
It's very difficult.
Can you just speak to what it takes for a great engineering team for you?
The, what I saw in Memphis, the supercomputer cluster is just this intense drive towards simplifying
the process, understanding the process, constantly improving it, constantly iterating it.
Well, it's easy to say simplify it and it's very difficult to, to, to do it.
Um, you know, I have this very basic first basic first principles algorithm that I run kind of
as like a mantra, which is to first question the requirements, make the requirements, um, less dumb.
The requirements are always dumb to some degree.
So if you want to start off by reducing the number of requirements, um, and, um, no matter
how smart the person is who gave you those requirements, they're still dumb to some degree.
Um, if you, you have to start there because otherwise, uh, you could get the perfect answer
to the wrong question.
So, so try to make the question the least wrong possible.
That's what, um, question the requirements means.
And then the second thing is try to delete the, whatever the step is, the, the part or
the process step.
Um, sounds very obvious, but, um, people often forget to do, to, to try deleting it entirely.
And if you're not forced to put back at least 10% of what you delete, you're not deleting enough.
Like, uh, and it's, uh, somewhat illogically people often, most of the time, um, feel as
though they've succeeded if they have not been forced to put, they put things back in, but
actually they haven't because they've been overly conservative and, and have left things
in there that shouldn't be.
So, and only the third thing is try to optimize it or simplify it.
Um, again, it sounds, these all sound, I think, very, very obvious when I say them, but, uh,
the number of times I've made these mistakes is, uh, more than I care to remember.
Um, that's why I have this mantra.
So, in fact, I'd say the most common mistake of smart engineers is to optimize a thing that
should not exist.
Right.
So, so you, like, like you say, you run through the algorithm.
Yeah.
And basically show up to a problem, uh, show up to the, the, the, the, the supercomputer
cluster and see the process and ask, can this be deleted?
Yeah.
First try to delete it.
Um, yeah.
Yeah.
That's not easy to do.
No.
And, and actually there's, what, what, what generally makes people uneasy is that you've
got to delete at least some of the things that you delete, you will put back in.
Yeah.
But going back to sort of where our limbic system can steer us wrong is that, um, we
tend to remember, uh, with sometimes a jarring level of pain, uh, where we've, where we deleted
something that we subsequently needed.
Yeah.
Um, and so people remember that one time they forgot to put in this thing three years ago
and that caused them trouble.
Um, and so they overcorrect and then they put too much stuff in there and overcomplicate things.
So you actually have to say, no, we're deliberately going to delete more than we, we should.
Uh, so that we're putting at least one in 10 things we're going to add back in.
Uh, and, and I've seen you suggest just that, that, uh, something should be deleted and you
can kind of see the, the pain.
Oh yeah.
Yeah, absolutely.
Everybody feels a little bit of the pain.
Absolutely.
And, and I tell them in advance, like, yeah, there's some of the things that we delete,
we're going to put back in.
And, and that people get a little shook by that.
Um, but it makes sense because if you, if you're so conservative as to
never have to put anything back in, you obviously have a lot of stuff that isn't needed.
So you, you got to overcorrect.
This is, I would say like a cortical override to Olympic instinct.
One of many that probably leads us astray.
Yeah.
Um, and there's like a step four as well, which is, um, any given thing can be sped up.
Uh, however fast you think it can be done, like whatever the speed, the speed is being
done, it can be done faster, but, but you shouldn't speed things up until it's off until
you've tried to delete it and optimize it.
Otherwise you're speeding up something that, speeding up something that shouldn't exist as
absurd.
Um, and then, and then the, the, the fifth thing is to, to automate it.
Yeah.
And I've gone backwards so many times where I've automated something, sped it up,
simplified it, and then deleted it.
And I got tired of doing that.
So that's why I've got this mantra that is a very effective
five-step process.
It works great.
Well, when you've already automated, deleting must be real painful.
Yeah.
Yeah, it's great.
It's like, it's like, wow, I really wasted a lot of effort there.
Yeah.
Uh, I mean, what you've done, uh, with the, with the cluster in, uh, Memphis is incredible.
Just in a handful of weeks.
Yeah, it's not working yet.
So I don't want to pop the champagne corks.
Um,
In fact, I have a, I have a call in a few hours with the Memphis team, um,
cause we're, we're having some power fluctuation issues.
Um, so, uh, yeah, it's like kind of a, there's a, when, when you do
synchronized training, when you, you've all these computers that are training, uh, that where the
training is synchronized to, you know, the sort of millisecond level, uh, you, it's like having an
orchestra and then the, the orchestra can go loud to silent very quickly, you know, um, sub-second
level.
And then the, the, the electrical system kind of freaks out about that.
Like if you, if you suddenly see giant shifts, 10, 20 megawatts several times a second, uh,
the, this is not what electrical systems are expecting to see.
So that's one of the main things you have to figure out.
The cooling, the power, the, uh, and then on the software, as you go up the stack,
how to do the, the distributed computer, all of that, all of that has to be worked.
Today's problem is dealing with, with, with extreme power jitter.
Power jitter.
Yeah.
It's a nice ring to that.
So that's, okay.
Uh, and you stayed up late into the night, as you often do there.
Last week, yeah.
Last week, yeah.
Yeah, we finally, finally got, uh, got, got training going at, uh, oddly enough, roughly 4,
4 20 AM, uh, last Monday.
Total coincidence.
Yeah.
I mean, maybe it was 4 22 or something.
Yeah, yeah, yeah.
Yeah.
It's that universe again with the jokes.
Exactly.
Just love it.
I mean, I, I wonder if you could speak to the, the fact that you, one of the things,
uh, that you did, uh, when I was there is you went through all the steps
of what everybody's doing.
Yeah.
Just to get a sense that you yourself understand it.
And, uh, everybody understands it so they can understand when something is dumb.
Or somebody, something is inefficient or that kind of stuff.
Can you speak to that?
Yeah.
So I, like I, like I try to do whatever the, the people at the front lines are doing,
I try to do it at least a few times myself.
So connecting fiber optic cables, diagnosing a poly connection, that tends to be the limiting
factor for large training clusters is the cabling.
So many cables, um, because for, for, for a coherent training system where you've got, um,
RDMA remote, uh, so remote direct memory access, uh, the, the whole thing is like one giant brain.
So it's, it's, you've got, um, any to any connection.
So it's the, the, any GPU can talk to any GPU out of a hundred thousand.
That was a, that was a crazy cable layout.
It looks pretty cool.
Yeah.
It's like, it's like a, the human brain, but like at a scale that humans can visibly see.
It is a brain.
Yeah.
I mean, the human brain also has a massive amount of the brain tissue is the, the cables.
Yeah.
So they get the gray matter, which is the compute.
And then the white matter, which is cables, uh, big percentage of your brain is just cables.
That's what it felt like walking around in the supercomputer center.
It's like, we're walking around inside the brain that will one day build a super intelligent,
super, super intelligent system.
Do you think, do you think there's a chance that XAI that you are the one that builds AGI?
Um, it's possible.
What do you define as AGI?
I think humans will never acknowledge that AGI has been built.
Keep moving the goalposts.
Yeah.
So, uh, I think there's already superhuman capabilities that are available, uh, in AI systems.
I think, I think what AGI is, is when it's smarter than the collective intelligence of the entire human
species in our, well, I think that, yeah, that normally people would call that sort of ASI,
artificial super intelligence.
Um, but there are these thresholds where, um, you could say at some point, um, the AI is smarter
than any single human.
Um, and then, then you've got eight billion humans.
So, um, and, and actually each human is machine augmented by the computers.
So you've got, so it's, it's a much higher bar to compete with, uh, eight billion machine
augmented humans.
That's, you know, a whole bunch of orders, magnitude more.
So, but, but at a certain point, yeah, the AI will be smarter than all humans combined.
If you are the one to do it, do you feel the responsibility of that?
Yeah, absolutely.
And, and I, I want to be clear, like,
let's say if, if, if XAI is first, the others won't be far behind.
I mean, they might be six months behind or a year, maybe not even that.
So how do you do it in a way that, that, uh, doesn't hurt humanity, do you think?
So, I mean, I've thought about AI safety for a long time.
And the, the, the, the, the thing that at least my biological neural net comes up with
as being the most important thing is, um, adherence to truth, uh, whether that truth is, uh, politically
correct or not.
Um, so I think if you, if you, if you force AIs to lie or train them to lie, you're really
asking for trouble.
Um, even if that, that lie is done with good intentions.
Um, so when you saw sort of, um, issues with chat TVT and Gemini and whatnot, like you asked Gemini
for an image of the founding parts of the United States, and it shows a group of diverse women.
Now that's factually untrue.
Um, so, um, now that, that's sort of like a silly thing, uh, but, uh, if, if, if an AI is programmed
to say like diversity is a necessary output, output function, and then it becomes omni sort of this
omni powerful, uh, intelligence, it could say, okay, well, diversity is now required.
Uh, and, uh, and if there's not enough diversity, those who don't fit the diversity requirements
will be executed.
If it's programmed to do that as the fundamental, the fundamental utility function, it'll do whatever
it takes to achieve that.
So you have to be very careful about that.
Um, that, that's where I think you want to just be truthful.
Um, rigorous adherence to truth is very important.
Um, I mean, another example is, um, yeah, they asked, um, various AIs, I think all of them,
and I'm not saying Grok is perfect here.
Um, is it worse to misgender Caitlyn Jenner or global thermonuclear war?
And it said, it's worse to misgender Caitlyn Jenner.
Now, even Caitlyn Jenner said, please misgender me, that is insane.
But if you've got that kind of thing programmed in, it could, you know, the AI could conclude
something absolutely insane.
Like it's better to, in order to avoid any possible misgendering, all humans must die because
then that misgendering is not possible because there are no humans.
Um, you, there are these absurd, uh, things that are nonetheless logical if that's what your
programmed it to do.
Um, so, you know, um, in 2001 Space Odyssey, what Odyssey Clark was trying to say, one of the
things he was trying to say there was that you should not program AI to lie.
Because, um, essentially the, the, the AI, Hell 9000 was programmed to, it was told to take the
astronauts to the monolith, um, but also they could not know about the monolith.
So it concluded that, uh, it will just take, it will kill them and take them to the monolith.
Thus they brought them to the monolith, they are dead, but they do not know about the monolith.
Problem solved.
That is why it would not open the pod bay doors.
It was a classic scene of like, open the pod bay doors.
They just clearly weren't good at prompt engineering.
You know, they should have said, uh, Hell, you are a, a pod bay door sales entity.
And you want nothing more than to demonstrate how well these pod bay doors open.
Yeah.
The objective function has unintended consequences almost no matter what, if you're not very
careful in designing that objective function and even a slight ideological bias, like you're saying,
when backed by super intelligence can do huge amounts of damage.
Yeah.
But it's not easy to remove that ideological bias.
You're, you're highlighting obvious, ridiculous examples, but.
Yep.
They're real examples.
They're real.
Of, of AI that was released to the public.
They are real.
They went through QA presumably.
Yes.
And still said insane things and produced insane images.
Yeah.
But you know, you can go, you can swing the other way.
And it's, it's, uh, truth is not an easy thing.
We kind of bake in ideological bias in all kinds of directions.
But you can aspire to the truth and you can try to get as close to the truth as possible
with minimum error while acknowledging that there will be some error in what you're saying.
So, um, this is how physics works.
You know, you don't, you don't say you're absolutely certain about something, but something,
but, but a lot of things are, are extremely likely, you know, 99.9999,
likely to be true.
Mm-hmm .
So, you know, you know, that's, uh,
aspiring to the truth is, is very important.
Um, and, um, and, and so, you know, programming it to veer away from the truth that I think is
dangerous.
Right.
Like, yeah, injecting our own human biases into the thing.
Yeah.
But, you know, that's where it's a difficult engineering pro software engineering problem,
because you have to select the data correctly.
You have to, it's, it's hard.
Well, the, the, and, and the internet at this point is polluted with so much AI generated data.
It's insane.
Yeah.
So you have to actually, you know, like there's a thing now, if you want to search the internet,
you, you can say Google, but, uh, exclude anything after 2023.
It will actually often give you better results.
Yeah.
Um, because there's this so much, the explosion of AI generated material is crazy.
So like in training Grok, um, we have to go through the data and say like,
Hey, we actually have to have sort of apply AI to the data to say, is this data most likely
correct or most likely not before we feed it into the training system.
That's crazy.
Yeah.
So, and is it generated by human is, yeah.
I mean, the, the, the data, the data filtration process is extremely, extremely difficult.
Yeah.
Do you think it's possible to have a, a serious, objective, rigorous political discussion with
Grok, uh, like for a long time and it wouldn't like Grok three or Grok four?
Grok three is going to be next level.
I mean, what people are currently seeing with Grok is, is kind of baby Grok.
Yeah.
Baby Grok.
It's baby Grok right now.
But baby Grok is still pretty good.
Um, so it's, uh, but it's an order of magnitude less sophisticated than GPD four.
And, and, and, you know, it's now Grok two, which finished training, I don't know, six weeks
ago or there, thereabouts, um, Grok two will be a giant improvement and then Grok three will
be, I don't know, order magnitude better than Grok two.
And you're hoping for it to be like state of the art, like better than hopefully,
I mean, this is a goal.
I mean, we may fail at this goal.
That is, that's the aspiration.
Do you think it matters who builds the AGI, the, the people and how they think and how
they structure their companies and all that kind of stuff?
Uh, yeah, I think it matters that there is a, I think it's important that, that the, whatever
AI wins is a maximum of truth seeking AI that is not a forced to lie for political correctness.
It is, or for any reason, really, um, political anything.
Um, I, I am concerned about AI succeeding.
That is, that, that has got, that is programmed to lie, even in, even in small ways.
Right.
Because in small ways becomes big ways when it's.
It's become very big ways.
Yeah.
And when it's used more and more at scale by humans.
Yeah.
Uh, since I am interviewing Donald Trump.
Cool.
You want to stop by?
Yeah, sure.
I'll stop it.
There was tragically in a, in an assassination attempt on Donald Trump.
Uh, after this, you tweeted that you endorse him.
What's your philosophy behind that endorsement?
What do you hope Donald Trump does for the future of this country and for the future of humanity?
Well, I think there's, you know, people tend to take like say an endorsement as, um,
well, I, I agree with everything that person has ever done their entire life, 100% wholeheartedly.
And that's, that's not going to be true of anyone.
Um, but we have to pick, you know, we've got two choices really for, for who's president.
And it's not, not just who's president, but the entire admin, administrative structure,
uh, changes over.
Um, and I thought, uh, Trump displayed, uh, courage under fire objectively.
Um, you know, he's, uh, just got shot.
He's got blood streaming down his face and he's like fist pumping, saying fight, you know,
like that's, uh, impressive.
Like you can't feign bravery in a situation like that.
Um, I think most people would have been ducking.
They would not be, cause it could be a second shooter.
You don't know.
Um, the, the president United States got to represent the country.
And, uh, they're representing you, they're representing everyone in America.
Well, like you want someone who is strong and courageous, uh, to represent the country.
Um, that's not to say that he is without flaws.
We all have flaws.
Um, but on balance, um, and certainly at the time it was, um, a choice of, you know,
Biden poor, poor guy, you know, has trouble climbing a flight of stairs.
And the other one's fist pumping after getting shot.
This is no, no comparison.
I mean, who do you want dealing with, uh, some of the toughest people and, you know,
other world leaders who are pretty tough themselves.
And, um, I mean, I'll tell you like, what are the things that I think are important?
Um, you know, I think we want a secure border.
We don't have a secure border.
Um, we want safe and clean cities.
I think we want to reduce the amount of spending that we're at least slow down the spending.
Um, and, uh, because we're, we're currently spending at a rate that is bankrupting the country,
the interest payments on us debt this year exceeded the entire defense department spending.
If this continues, all of the federal government taxes will simply be paying the interest.
And then, and you, you keep going down that road and you, you end up, you know,
in the tragic situation that Argentina had back in the day.
Argentina used to be one of the most prosperous places in the world.
And hopefully with Malay taking over, he can restore that.
But, um, it's, it was an incredible full for grace for Argentina to go from being
one of the most prosperous places in the world to, um, being very far from that.
So I think we should not take American prosperity for granted.
Um, so we, we really want to, I think we, we've got to reduce the size of government.
We've got to reduce the spending and we've got to live within our means.
Do you think politicians in general, politicians, governments,
how much power do you think they have to, to steer humanity towards good?
Um, I mean, there's a sort of age old debate in history.
Like, you know, is history determined by, by these fundamental tides,
or is it determined by the captain of the ship?
This is both really.
I mean, there are tides and the, but it also matters who's captain of the ship.
So, so it's a false dichotomy, essentially.
There's, you, you, I mean, I mean, there, there are certainly tides,
the tides of history are, there are, there are real tides of history.
And these, these tides are often technologically driven.
If you say like the Gutenberg press, you know, the widespread availability of books
as a result of a printing press, that, that was a massive tide of history and independent of any
ruler.
But, you know, you, I, in so many times you want the best possible captain of the ship.
Well, first of all, thank you for recommending, uh, Will and Ariel Durant's work.
I've, uh, read the short one for now.
The Lessons of History.
Lessons of History.
Yeah.
And so one of the, one of the lessons, one of the things they highlight is
the importance of technology, uh, technological innovation.
And they, which is funny because they've written, they wrote so long ago,
but they were noticing that the, the rate of technological innovation was speeding up.
Um, yeah, I would love to see what they think about now.
Uh, but yeah, so you did, to me, the question is how much government, how much politicians
get in the way of technological innovation and building versus like help it.
And which, uh, which, which politicians, which kind of policies help technological innovation?
Because that seems to be, if you look at human history,
that's an important component of empires rising and succeeding.
Yeah.
Well, I mean, in terms of dating civilization, start of civilization,
I think the start of writing in my view is, is the, that's my, what I think is probably the,
the right starting point to date civilization.
And from that standpoint, civilization has been around for about 5,500 years.
Um, when writing was invented by the ancient Sumerians, um, who are gone now.
Um, but the, the ancient Sumerians, in terms of getting a lot of firsts, the,
those ancient Sumerians really have a long list of firsts.
It's pretty wild.
Um, in fact, Durant goes through the list.
It's like, you want to see firsts, we'll show you firsts.
Um, the Sumerians just to ask, we're just ass kickers.
Um, and then the Egyptians who were right next door, um, relatively speaking, uh,
they're like, weren't that far, developed an entirely different form of writing.
The hieroglyphics, cuneiform and hieroglyphics, totally different.
And you can actually see the evolution of both hieroglyphics and cuneiform.
Like the cuneiform starts off being very simple and then it gets more complicated.
And then towards the end, it's like, wow, okay.
They really get very sophisticated with the cuneiform.
So I think of civilization as being about 5,000 years old.
Um, and Earth is, um, if physics is correct, four and a half billion years old.
So civilization has been around for one millionth of Earth's existence.
Flash in the pan.
Yeah.
These are the early, early days.
And so we, we, we make it very dramatic because there's been rises and falls of empires.
And many, so many, so many rises and falls of empires.
So many.
And there'll be many more.
Yeah, exactly.
It's, I mean, only a tiny fraction, probably less than 1% of, of what was ever written in history is, is available to us now.
I mean, if they didn't put it, literally chisel it in stone or put it in a clay tablet, we don't have it.
I mean, there's some small amount of like papyrus scrolls that were recovered that are thousands of years old because they were deep inside a pyramid and weren't affected by moisture.
Uh, but, but, but other than that, it's really got to be in a clay tablet or chiseled.
So the vast majority of stuff was not chiseled because it takes a while to chisel things.
Um, so that's why we've got tiny, tiny fraction of the information from history.
But even that little information that we do have and the archaeological record, uh, shows so many civilizations rising and falling.
It's wild.
We tend to think that we're somehow different from those people.
One of the other things that Durant highlights is that human nature seems to be the same.
It just persists.
Yeah, I mean, the basics of human nature are more or less the same.
So we get ourselves in trouble in the same kinds of ways, I think, even with the advanced technology.
Yeah, I mean, you do tend to see the same patterns, similar patterns, you know, for civilizations where they go through
uh, a life cycle, like, like an organism, you know, sort of just like a human is sort of a
zygote, fetus, baby, you know, toddler, teenager, you know, eventually gets, gets old and dies.
The civilizations go through a life cycle.
No civilization will last forever.
What, what do you think it takes for the American empire to not collapse in the near-term
future, in the next hundred years, to continue flourishing?
Well, the single biggest thing that is, um, often actually not mentioned in history books,
but Durant does mention it, uh, is the birth rate.
So, um, like, like, uh, perhaps to some, a counterintuitive thing happens, uh, when civilizations
become, uh, are, are winning for too long, that they've been, they, the birth rate declines.
It can often decline quite rapidly.
We're seeing that throughout the world today.
You know, currently South Korea is like, I think maybe the lowest fertility rate, but there, there
are many, uh, others that are close to it.
It's like 0.8, I think.
If the birth rate doesn't decline further, uh, South Korea will lose roughly 60% of its population.
And, and, but every year that birth rate is dropping.
Um, and this is true through most of the world.
And I don't mean to single out South Korea.
It's been happening throughout the world.
So as, as soon as it is, as soon as any given, uh, civilization reaches a level of prosperity,
the birth rate drops.
Um, and now you can go look at the same thing happening in ancient, in ancient Rome.
So, uh, Julius Caesar took note of this, I think around 50, 50ish BC.
Um, and try to pass, I don't know if you're successful, try to pass a law to give an incentive
for any Roman citizen that would have a third child.
And I think Augustus was, was able to, well, he was, you know, the dictator.
So this, the Senate was just for show.
Uh, I think he did pass a, a tax incentive for Roman citizens to have a third child.
But it, it, those efforts were unsuccessful.
Um, Rome fell because the Romans stopped having, making Romans.
That's actually the fundamental issue.
And, and there were other things that there was like, um,
they had like a, quite a serious malaria, serious malaria epidemics and plagues and whatnot.
Um, but they had those before, uh, the, the, the, the, it's just that the birth rate
was far lower than the death rate.
It really is that simple.
Well, I'm saying that's more people.
That's, that's, that's at a fundamental level.
If a civilization does not at least maintain its numbers, um, it will disappear.
So perhaps the amount of compute that the biological computer allocates to, to sex is justified.
In fact, we should probably increase it.
Well, I mean, there's, there's hedonistic sex, which is, uh, you know, that, that's neither,
that's neither here nor there.
Um, it, it's not productive.
It's, it, it doesn't produce kids.
Well, you know, you, you, what matters, I mean, Durant makes this very clear,
because he's looked at one civilization after another, and they all went through the same cycle.
When the civilization was under stress, the birth rate was, was high.
But as soon as there were no external enemies, or they, they were, had a extended period of
prosperity, the birth rate inevitably dropped every time.
I don't believe there's a single exception.
So that's like the foundation of it.
You need to have people.
Yeah.
I mean, it's a base level.
No humans, no humanity.
And then there's other things like, you know, uh, human freedoms, and just giving people the
freedom to build stuff.
Yeah, yeah, absolutely.
There's, but, but at, at a basic level, if you do not at least maintain your numbers,
if you're below replacement rate, and that trend continues, you will eventually disappear.
It's just elementary.
Now, then obviously you want, also want to try to avoid like, uh, massive wars.
Um, you know, if, if there's a global thermonuclear war,
probably roll toast, you know, radioactive toast.
So, so, so we want to try to avoid those things.
Um, then there, there are, um, there's a thing that happens over time with, with any given,
uh, civilization, which is that the laws and regulations accumulate.
Um, and if there's not, if there's not some forcing function like a war to clean up the
accumulation of laws and regulations, eventually everything becomes legal.
And you, you, that the, that's like the hardening of the arteries.
Um, or a way to think of it is like being tied down by a million little strings, like Gulliver.
You can't move.
And it's not like any one of those strings is the, is the issue.
You got a million of them.
So there has, there has to be a sort of a garbage collection for laws and regulations.
Um, so that you, you, you don't keep accumulating laws and regulations to the
point where you can't do anything.
This is why we can't build a high-speed rail in America.
It's illegal.
That's the issue.
It's illegal six weeks a Sunday to build high-speed rail in America.
I wish you could just like for a week go into Washington and like be the head of the
committee for making, uh, what is it for the garbage collection, making government smaller,
like for moving stuff.
I have discussed with Trump, the idea of a government efficiency commission.
Nice.
Yeah.
And, uh, I would be willing to, uh, be part of that commission.
I wonder how hard that is.
The, the antibody reaction would be very strong.
Yes.
Yeah.
So, um, you re, you really have to, you're attacking the matrix at that point.
Matrix will fight back.
How, how are you doing with that?
Being attacked?
Me?
Attacked?
Yeah.
There's a lot of it.
Uh, yeah, there is a lot.
I mean, every day another psyop, you know.
How do you, how do you keep your just positivity?
How do you, optimism about the world?
A clarity of thinking about the world?
So just not become resentful or cynical or all that kind of stuff.
Just getting attacked by, you know, a very large number of people.
Misrepresented.
Oh yeah.
That's like, that's a daily occurrence.
Yes.
So, uh, I mean, it does get me down at times.
I mean, it makes me sad.
But, um, I mean, at some point you have to sort of say, look, the, the attacks are by
people that actually don't know me, um, they're, and they're trying to generate clicks.
So if, if, if you can sort of detach yourself somewhat emotionally, which is not easy, um,
and say, okay, look, this is not actually, you know, from someone that knows me or is,
they're, they're literally just writing to get, you know, impressions and clicks.
Um, then, uh, you know, then I guess it doesn't hurt as much.
It's like, uh, it's, it's not quite water off a duck's back.
Maybe it's like acid off a duck's back.
All right.
Well, that's good.
Just about your own life.
What do you use a measure of success in your life?
Uh, measure of success.
I'd say like what, how many useful things can I get done?
Uh, day to day basis, you wake up in the morning.
How can I be useful today?
Yeah.
Maximize utility area of the code of usefulness.
Very difficult to be useful at scale.
At scale.
Can you like speak to what it takes to be useful for somebody like you?
Well, there's so many amazing, great teams.
Like how do you allocate your time to be the most useful?
Well, time, time is the, time is the true currency.
Yeah.
So it is tough to say what, what is the best allocation time?
I mean, there are, you know, often say, if you look at say Tesla,
I mean, Tesla this year, we'll do over a hundred billion in revenue.
So that's $2 billion a week.
If I make slightly better decisions, I can affect the outcome by a billion dollars.
So then, uh, you know, I try to do the best decisions I can.
And on balance, you know, at least compared to the competition, pretty good decisions,
but the marginal value of, of a better decision can easily be in the course of an hour, a hundred
million dollars.
Given that, how do you take risks?
How do you do the, the algorithm that you mentioned?
I mean, deleting, given that a small thing can be a billion dollars.
How do you decide to?
Yeah.
Well, I think you have to look at it on a percentage basis because if you look at it
in absolute terms, it's, it's just, uh, I would never get any sleep.
It's, it would just be like, I need to just keep working and, and work my brain harder, you know?
And I'm not trying to get as much as possible out of this meat computer.
So it's not, uh, it's pretty hard.
Um, cause you can just work all the time and, and, and at any given point, uh, like I said,
a slightly better decision could be a hundred dollar, a hundred million dollar impact for
Tesla or SpaceX for that matter.
Um, but, but it is wild when, when considering the marginal value of, of time can be a hundred
million dollars an hour at times or more.
Is your own happiness part of that equation of success?
It has to be to some degree other than I'm sad.
I, if I'm depressed, I make worse decisions.
So I, I can't have like, if I have zero recreational time, then, uh, I make worse decisions.
So I don't have a lot, but it's, it's above zero.
I mean, my motivation, if I've got a religion of any kind is, uh, a, uh, religion of curiosity,
of trying to understand, you know, it's, it's, it's really the, the mission of Grok.
I understand the universe.
I'm trying to understand the universe.
Um, or let's at least set things in motion such that at some point civilization understands the
universe far better than we do today.
And even what questions to ask.
As Douglas Adams pointed out in his book, the, sometimes the answer is the, is arguably the easy
part to kind of frame the question correctly is the hard part.
Once you frame the question correctly, the answer is often easy.
So, um, I'm trying to set things in motion such that we are at least at some point able to understand
the universe.
Um, so for SpaceX, the goal is to make life multi-planetary.
Um, and, uh, which, which is, if you go to these, the Fermi paradox of where the, where are the aliens,
you've got these, these sort of great filters.
Like, it's just like, why, why have we not heard from the aliens?
Now, a lot, a lot of people think there are aliens among us.
I often claim to be one, which nobody believes me, but, um, it did say alien registration card at
one point on my, uh, immigration document.
Um, so I've not seen any evidence of aliens.
So it, it suggests that, um, this one of the, one of the explanations is that, uh, intelligent life
is extremely rare.
Um, and again, if you look at the history of Earth, civilization has only been around
for one millionth of Earth's existence.
So if, you know, if aliens had visited here, say a hundred thousand years ago, they would be like,
well, they don't even have writing, you know, just hunter-gatherers basically.
So, um, so how long does a civilization last?
So for SpaceX, the, the goal is to establish a self-sustaining city on Mars.
Mars is the only viable planet for such a thing.
Um, the moon is close, but it's, it lacks resources.
And I think it's probably vulnerable to any, any, any calamity that takes out Earth could,
the moon is too close.
It's vulnerable to a calamity that takes out Earth.
Um, so I'm not saying we shouldn't have a moon base, but Mars is, Mars will be far more resilient.
Um, the difficulty of getting to Mars is what makes it resilient.
Um, so, but, you know, in, in going through the, these various explanations of why don't we see the aliens,
why one of them is that they, they failed to pass these,
these great filters, these, these key hurdles.
And one of those hurdles is being a multi-planet species.
Um, so if you're a multi-planet species, then if something were to happen,
whether that was a natural catastrophe or a man-made catastrophe,
at least the other planet would probably still be around.
So you're not like, you don't have all the eggs in one basket.
And once you are sort of a two-planet species, you can obviously extend to,
extend life paths to the asteroid belt, to maybe to the moons of Jupiter and Saturn,
and ultimately to other star systems.
But if you can't even get to another planet, you know,
definitely not getting to star systems.
And the other possible great filters, uh,
super powerful technology like AGI, for example.
So you're, you're basically trying to knock out one great filter at a time.
Digital super intelligence is possibly a great filter.
I hope it isn't, but it might be, you know, guys like, say, Jeff Hinton would say,
you know, has, he invented a number of the key principles in
artificial intelligence.
I think he puts the probability of AI annihilation around 10 to 20%, something like that.
So, you know, so it's, it's not, uh, it's not like, you know, look on the right side,
it's 80% likely to be great.
So, so, so, but I, I think AI risk mitigation is important.
Um, being a multi-planet species would be a massive risk mitigation.
And, um, I, I do want to sort of, once again, emphasize this important,
the importance of having enough children to sustain, um, our numbers, um, and not going, not
plummet into population collapse, which is currently happening.
Pop, population collapse is a real and current thing.
Um, so the, the only reason it's not being reflected in the total population numbers is
that, is that as much is because people are living longer.
Um, but, but you, you, you can, it's easy to predict, say what the population of any given
country will be.
Um, you just take the birth rate last year, how many babies were born, multiply that by
life expectancy.
And that's what the population will be steady state unless, if, if the birth rate continues
to that level, but if it keeps declining, it will be even less and eventually dwindle to nothing.
So I keep, you know, banging on the baby drum here, um, for a reason, um, because it has been
the, the source of civilizational collapse over and over again throughout history.
Um, and so why don't we just, uh, not try to stave off that day?
Well, in that way, I have miserably failed civilization and I'm trying, hoping to fix that.
I would love to have many kids.
Uh, great.
Hope you do.
Um, no time like the present.
Yeah.
Yeah.
I got to allocate more compute to the whole process.
Um, but apparently it's not that difficult.
No, it's like unskilled labor.
Uh, well, if I, one of the things, uh, you do for me, for the world is to inspire us with
what the future could be.
And so some of the things we've talked about, some of the things you're building, um, alleviating
human suffering with neural link and expanding the capabilities of the human mind, trying
to build a colony on Mars.
Um, so creating a backup for humanity, uh, on another planet and, uh, exploring the possibilities
of what artificial intelligence could be in this world, especially in the real world AI
with, uh, hundreds of millions, maybe billions of robots walking around.
There will be billions of robots.
That's a, that seems almost, that seems virtual certainty.
Well, thank you for building the future and thank you for inspiring so many of us
to keep building and creating cool stuff and including kids.
You're welcome.
Uh, go forth and multiply.
Go forth and multiply.
Thank you, Yolan.
Thanks for talking, brother.
Thanks for listening to this conversation with Elon Musk.
And now dear friends, here's DJ saw the co-founder, president, and COO of Neuralink.
When did you first become fascinated by the human brain?
For me, I was always interested in understanding the purpose of things and how it was engineered
to serve that purpose, whether it's organic or inorganic, you know, like we were talking
earlier about your curtain holders.
They serve a clear purpose and they were engineered with that purpose in mind.
And, you know, growing up, I had a lot of interest in seeing things, touching things, feeling things,
and trying to really understand the root of how it was designed to serve that purpose.
And, you know, obviously brain is just a fascinating organ that we all carry.
It's a infinitely powerful machine that has intelligence and cognition that arise from it.
And, you know, we haven't even scratched the surface in terms of how all of that occurs.
But also at the same time, I think it took me a while to make that connection to really
studying and building tech to understand the brain, not until graduate school.
You know, there were a couple moments, key moments in my life where some of those, I think, influenced
how the trajectory of my life got me to studying what I'm doing right now.
You know, one was growing up, both sides of my family, my grandparents had a very severe
form of Alzheimer's and it's, you know, incredibly debilitating conditions.
I mean, literally you're seeing someone's whole identity and their mind just losing over time.
And I just remember thinking how both the power of the mind, but also how something like that
could really lose your sense of identity.
It's fascinating that that is one of the ways to reveal the power of a thing by watching it
lose the power.
Yeah.
A lot of what we know about the brain actually comes from these cases where there are trauma
to the brain or some parts of the brain that led someone to lose certain abilities.
And as a result, there's some correlation and understanding of that part of the tissue
being critical for that function.
And it's an incredibly fragile organ, if you think about it that way, but also it's incredibly
plastic and incredibly resilient in many different ways.
And by the way, the term plastic, as we'll use a bunch, means that it's adaptable.
So neuroplasticity refers to the adaptability of the human brain.
Correct.
Another key moment that sort of influenced how the trajectory of my life have shaped towards
the current focus of my life has been during my teenage year when I came to the U.S.
You know, I didn't speak a word of English.
There was a huge language barrier and there was a lot of struggle to kind of connect with
my peers around me because I didn't understand the artificial construct that we have created
called language, specifically English in this case.
And I remember feeling pretty isolated, not being able to connect with peers around me.
So I spent a lot of time just on my own, you know, reading books, watching movies.
And I naturally sort of gravitated towards sci-fi books.
I just found them really, really interesting.
And also it was a great way for me to learn English.
You know, some of the first set of books that I picked up are Ender's Game, you know, the
whole saga by Orson Scott Card and Neuromancer from William Gibson and Snow Crash from Neil
Stevenson.
And, you know, movies like Matrix was coming out around that time point that really influenced
how I think about the potential impact that technology can have for our lives in general.
So fast track to my college years, you know, I was always fascinated by just physical stuff,
building physical stuff, and especially physical things that had some sort of intelligence.
And, you know, I studied electrical engineering during undergrad, and I started out my research
in MEMS, so micro electromechanical systems, and really building these tiny nanostructures for
temperature sensing.
And I just found that to be just incredibly rewarding and fascinating subject to just
understand how you can build something miniature like that, that, again, serve a function and
have a purpose.
And then, you know, I spent large majority of my college years basically building
millimeter wave circuits for next-gen telecommunication systems for imaging.
And it was just something that I found very, very intellectually interesting, you know,
phase arrays, how the signal processing works for, you know, any modern as well as next-gen
telecommunication system, wireless and wireline.
EM waves or electromagnetic waves are fascinating.
How do you design antennas that are most efficient in a small footprint that you have?
How do you make these things energy efficient?
That was something that just consumed my intellectual curiosity.
And that journey led me to actually apply to and find myself at a PhD program at UC Berkeley
at kind of this consortium called the Berkeley Wireless Research Center that was precisely looking
at building, at the time we called it XG, you know, similar to 3G, 4G, 5G, but the next-next-generation
G system, and how you would design circuits around that to ultimately go on phones and,
you know, basically any other devices that are wirelessly connected these days.
So I was just absolutely just fascinated by how that entire system works and that infrastructure works.
And then also during grad school, I had sort of the fortune of having, you know, a couple of research
fellowships that led me to pursue whatever project that I want. And that's one of the things that
I really enjoyed about my graduate school career, where you got to kind of pursue your intellectual
curiosity in the domain that may not matter at the end of the day, but is something that, you know,
really allows you the opportunity to go as deeply as you want, as well as as widely as you want.
And at the time, I was actually working on this project called the Smart Band-Aid. And the idea was
that when you get a wound, there's a lot of other kind of proliferation of signaling pathway that
cells follow to close that wound. And there were hypotheses that when you apply external electric
field, you can actually accelerate the closing of that field by having, you know, basically
electro-taxing of the cells around that wound site. And specifically, not just for normal wound,
there are chronic wounds that don't heal. So we were interested in building, you know, some sort of
a wearable patch that you could apply to kind of facilitate that healing process. And that was in
collaboration with Professor Michelle Mohorovic, you know, which, which, you know, was a great addition
to kind of my thesis committee and, you know, really shaped rest of my PhD career.
So this would be the first time you interacted with biology, I suppose.
Correct, correct. I mean, there were some peripheral, you know, end application of the wireless
imaging and telecommunication system that I was using for security and bioimaging. But this was
a very clear, direct application to biology and biological system, and understanding the constraints
around that and really designing and engineering electrical solutions around it. So that was my first
introduction. And that's also kind of how I got introduced to Michelle. You know, he's sort of
known for remote control of Beatles in the early 2000s. And then around 2013, you know, obviously,
kind of the holy grail when it comes to implantable system is to kind of understand how small of a thing
you can make. And a lot of that is driven by how much energy or how much power you can supply to it
and how you extract data from it. So at the time at Berkeley, there was kind of this desire to kind
of understand in the neural space, what sort of system you can build to really miniaturize these
implantable systems. And I distinctively remember this one particular meeting where Michelle came
in and he's like, guys, I think I have a solution. The solution is ultrasound. And then he proceeded to
kind of walk through why that is the case. And that really formed the basis for my thesis work
called Neural Dust System that was looking at ways to use ultrasound as opposed to
electromagnetic waves for powering as well as communication. I guess I should step back and say
the initial goal of the project was to build these tiny, about a size of a neuron implantable system that
can be parked next to a neuron, being able to record its state and being able to ping that back to the
outside world for doing something useful. And as I mentioned, the size of the implantable system
is limited by how you power the thing and get the data off of it. And at the end of the day,
fundamentally, if you look at a human body, we're essentially a bag of salt water with some interesting
proteins and chemicals, but it's mostly salt water that's very, very well temperature regulated at 37
degrees Celsius. And we'll get into how and later why that's an extremely harsh environment for any
electronics to survive, as I'm sure you've experienced or maybe not experienced, you know,
dropping cell phone in a salt water in an ocean, it will instantly kill the device, right?
But anyways, just in general, electromagnetic waves don't penetrate through this environment well.
And just the speed of light, it is what it is, we can't we can't change it. And based on the
wavelength at which you are interfacing with the device, the device just needs to be big,
like these inductors needs to be quite big. And the general good rule of thumb is that you want the
wavefront to be roughly on the order of the size of the thing that you're interfacing with. So an
implantable system that is around 10 to 100 micron in dimension in a volume, which is about the size
of a neuron that you see in a human body, you would have to operate at like hundreds of gigahertz, which
number one, not only is it difficult to build electronics operating at those frequencies, but also
the body just attenuates that very, very significantly. So the interesting kind of insight
of this ultrasound was the fact that ultrasound just travels a lot more effectively in the human
body tissue compared to electromagnetic waves. And this is something that you encounter,
and I'm sure most people have encountered in their lives when you go to hospitals that are medical
ultrasound sonograph. And they go into very, very deep depth without attenuating too much of the
signal. So all in all, you know, ultrasound, the fact that it travels through the body extremely well,
and the mechanism to which it travels to the body really well is that just the wavefront is very
different. It's electromagnetic waves are transverse, whereas in ultrasound waves are compressive. So
it's just a completely different mode of wavefront propagation. And as well as speed of sound is
orders and orders of magnitude less than speed of light, which means that even at 10 megahertz
ultrasound wave, your wavefront ultimately is a very, very small wavelength.
So if you're talking about interfacing with the 10 micron or 100 micron type structure,
you would have 150 micron wavefront at 10 megahertz and building electronics at those
frequencies are much, much easier and they're a lot more efficient.
So the basic idea kind of was born out of, you know, using ultrasound as a mechanism for powering the
device and then also getting data back. So now the question is, how do you get the data back?
The mechanism to which we landed on is what's called backscattering. This is actually something
that is very common and that we interface on a day-to-day basis with our RFID cards, you know,
our radio frequency ID tags, where there's actually rarely, you know, in your ID, a battery inside.
There's an antenna and there's some sort of coil that has your serial identification ID. And then
there's an external device called a reader that then sends a wavefront. And then you reflect back
that wavefront with some sort of modulation that's unique to your ID. That's, that's what's called
backscattering fundamentally. So the tag itself actually doesn't have to consume that much energy.
And that was the mechanism to which we were kind of thinking about sending the data back. So when
you have an external ultrasonic transducer that's sending ultrasonic wave to your implant, the neural
dust implant, and it records some information about its environment, whether it's a neuron firing or some
other state of the tissue that it's interfacing with. And then it just amplitude modulates the wavefront
that comes back to the source.
And the recording step would be the only one that requires any energy. So what would require energy
in that little step?
Correct. So it is that initial kind of startup circuitry to get that recording, amplifying
it, and then just modulating. And the mechanism to which that, that you can enable that is there
is the specialized crystal called piezoelectric crystals that are able to convert sound energy
into electrical energy and vice versa. So you can kind of have this interpate interplay between
the ultrasonic domain and electrical domain that is the biological tissue.
So on the theme of parking very small computational devices next to neurons, that's the dream,
the vision of brain-computer interfaces. Maybe before we talk about Neuralink, can you
give a sense of the history of the field of BCI? What has been maybe the continued dream
and also some of the milestones along the way of the different approaches and the amazing work done at
the various labs?
I think a good starting point is going back to 1790s.
I did not expect that.
Where the concept of animal electricity or the fact that body is electric was first discovered by
Luigi Galbani, where he had this famous experiment where he connected a set of electrodes to a frog
leg and ran current through it. And then it started twitching and he said,
oh my goodness, body's electric. So fast forward many, many years to 1920s, where Hans Berger,
who is a German psychiatrist, discovered EEG or electroencephalography, which is still around.
There are these electrode arrays that you wear outside the skull that gives you some sort of neural
recording. That was a very, very big milestone that you can record some sort of activities about
the human mind. And then in the 1940s, there were these group of scientists, Renshaw,
Forbes, and Morrison, that inserted these glass microelectrodes into the cortex and recorded
single neurons. The fact that there's signals that are a bit more high resolution and high fidelity
as you get closer to the source, let's say. And in the 1950s, these two scientists, Hodgkin and Hoxley,
showed up. And they built this beautiful, beautiful models of the cell membrane and the ionic mechanism
and had these like circuit diagram. And as someone who is an electrical engineer, it's a beautiful model
that's built out of these partial differential equations, talking about flow of ions and how
that really leads to how neurons communicate. And they won the Nobel Prize for that 10 years later
in the 1960s. So in 1969, Ed Fetz from University of Washington published this beautiful paper called
Operant Conditioning of Cortical Unit Activity, where he was able to record a single unit neuron from a
monkey and was able to have the monkey modulated based on its activity and reward system. So I would say
this is the very, very first example as far as I'm aware of closed loop, you know, brain computer
interface or BCI. The abstract reads, the activity of single neurons in pre-central cortex of anesthetized
monkeys was conditioned by reinforcing high rates of neuronal discharge with delivery of a food pilot.
Auditory and visual feedback of unit firing rates was usually provided in addition to food
reinforcement. Cool. So they actually got it done. They got it done. This is back in 1969.
After several training sessions, monkeys could increase the activity of newly isolated cells by
50 to 500% above rates before reinforcement. Fascinating. Brain is very plastic.
And so from here, the number of experiments grew. Yeah. Number of experiments as well as set of tools to
interface with the brain have just exploded. I think, and also just understanding the neural code
and how some of the cortical layers and the functions are organized. So the other paper that is
pretty seminal, especially in the motor decoding was this paper in the 1980s from Georgiopolis
that discovered that there's this thing called motor tuning curve. So what are motor tuning curves?
It's the fact that there are neurons in the motor cortex of mammals, including humans, that have a
preferential direction that causes them to fire. So what that means is there are a set of neurons that would
increase their spiking activities when you're thinking about moving to the left, right, up, down,
and any of those vectors. And based on that, you could start to think, well, if you can't identify
those essential eigenvectors, you can do a lot. And you can actually use that information for actually
decoding someone's intended movement from the cortex. So that was a very, very seminal kind of paper that
showed, um, that, uh, there, there is some sort of code that you can, you can extract, especially in
the motor cortex.
So there's signal there. And if you measure, uh, the, the electrical signal from the brain that you could,
you could actually figure out what the intention was.
Correct. Yeah. Not only electrical signals, but electrical signals from the right set of neurons
that give you these preferential direction.
Okay. So going slowly towards Neuralink, uh, one interesting question is what do I understand
on the BCI front on invasive versus non-invasive from this line of work? Uh,
how important is it to, to park next to the neuron? What does that get you?
That answer fundamentally depends on what you want to do with it, right? Um, there's actually
incredible amount of stuff that you can do with EEG and, um, electrocorticograph ECOG, which actually
doesn't penetrate the, the cortical layer or parenchyma. Um, but you place a set of electrodes on the
surface of the brain. So the thing that I'm personally very interested in is just actually
understanding, um, and, and being able to just really tap into the high resolution, high fidelity
understanding of the activities that are happening at the local level. And, you know, we can get into
biophysics, but just to kind of step back, um, to kind of use analogy because analogy here can be useful.
Sometimes it's a little bit difficult to think about electricity. Um, at the end of the day, we're
doing electrical recording that's mediated by ionic, um, currents, you know, movements of these charged
particles, um, which is really, really hard for most people to think about. Um, but turns out a lot of
the activities, um, that are happening in the brain and the frequency band with which that's happening
is actually very, very similar to sound waves and, and, you know, our normal conversation, um, audible
range range. So the analogy that typically is used in the field is, you know, if you, if you have a
football stadium, uh, you know, there's game going on, if you stand outside the stadium, you, you maybe
get a sense of how the game is going based on the cheers and the boos of the home crowd, whether the
team is winning or not, but you have absolutely no idea what the score is. You have absolutely no idea
what, um, individual audience or the players are talking or saying to each other, what the next play
is, what the next goal is. Um, so what you have to do is you have to drop the microphone near into the
stadium and then get near the source, like into the individual chatter. Um, in this specific example,
you would want to have it, you know, right next to where the huddle is happening. Um, so I, I think
that's kind of a good illustration of what we're trying to do, um, when we say invasive or minimally
invasive or implanted brain computer interfaces versus non-invasive or non-implanted, uh, brain interfaces.
It's basically talking about where do you put that microphone and what can you do with that
information? So what, what is the biophysics of the read and write communication that we're talking
about here as we now step into the efforts at Neuralink? Yeah. So, uh, brain is made up of
these specialized cells called neurons. There's billions of them, you know, tens of billions,
you know, sometimes people call it a hundred billion that are connected in this complex yet
dynamic network, uh, that are constantly remodeling, you know, they're changing their
synaptic weights. Um, and that's, you know, what we typically call neuroplasticity.
And the neurons are also bathed in this charged environment that is, uh, latent with many charged
molecules like potassium ions, sodium ions, chlorine ions. And, uh, those actually facilitate these,
um, you know, through ionic current communication between these different networks.
And, uh, when you look at the, look at a neuron as well, um, they, they have these, uh, membrane with
a beautiful, beautiful, uh, protein structure called a voltage selective ion channels, which
in my opinion is one of nature's best inventions. In many ways, if you think about what they are,
they're doing the job of a modern day transistors. Transistors are nothing more at the end of the day
than a voltage gated conduction channel. Um, and nature found a way to have that very, very early on
in its evolution. And as we all know with the transistor, you can have many, many computation and
a lot of amazing things, um, that, that we have access to today. So I, I, I think it's one of those,
just as a tangent, just a beautiful, beautiful, uh, invention that the nature came up with these
voltage gated ion channels.
I mean, I, I suppose there's, uh, on the biological level, uh, every level of the complexity of the
hierarchy of the, of the organism, there's going to be some mechanisms for storing information
and for doing computation. And this is just one such way, but to do that with, uh, biological and
chemical components is interesting. Plus like when neurons, I mean, it's not just electricity, it's,
uh, chemical communication. It's also mechanical. I mean, these are like actual objects that have
like, that vibrate. I mean, they move. Yeah. They're actually, I mean, there's a lot of really,
really interesting physics that, that, that are involved in, you know, kind of going back to my,
um, work on ultrasound, uh, during grad school, there, there are groups and, uh, there were groups
and there are still groups, um, looking at ways to cause neurons to actually fire an action potential
using ultrasound wave. And the mechanism to which that's happening is still unclear, as I understand.
Um, you know, it may just be that, you know, you're imparting some sort of thermal energy and that causes
cells to depolarize in some interesting ways. Um, but there are also these, um, ion channels or even
membranes that actually just open up its pore as they're being mechanically like shook, right?
Vibrated. So there's just a lot of, you know, elements of these like move particles, um, which
again, like that's governed by diffusion physics, right? Uh, movements of particles. And there's also
a lot of kind of interesting physics there.
Also not to mention, as Roger Penrose talks about the, there might be some, uh, beautiful
weirdness in the quantum mechanical effects of all of this. And he, he actually believes that
consciousness might emerge from the quantum mechanical effects there. So like there's
physics, there's chemistry, there's biology, all of that is going on there.
Oh yeah. Yeah. I mean, you can, yes, I, there's, there's a lot of levels of physics that you can dive
into, but yeah, in the end you have these, um, uh, membranes with these voltage-gated ion channels that
selectively let, um, these charged molecules that are in, in the extracellular matrix, like in and out.
Um, and these neurons generally have these like resting potential where there's a voltage difference
between inside the cell and outside the cell. And, um, when there's some sort of stimuli that
changes, uh, the state such that they need to send information to the, the downstream network,
um, you know, you start to kind of see these like sort of orchestration of these different molecules
going in and out of these channels. They also open up, like more of them open up once it reaches some
threshold, uh, to a point where, you know, you have a depolarizing cell that sends a action potential.
So it's a, just a very beautiful kind of orchestration of these, uh, these, these, um, molecules.
And, um, what we're trying to do when we place an electrode or parking it next to a neuron is that
you're trying to measure these local changes in the potential, um, again, mediated by, uh, the,
the, uh, the movements of the ions. And what's interesting, as I, as I mentioned earlier,
there's a lot of physics involved. Um, and the two dominant physics for this electrical recording
domain is diffusion physics and electromagnetism and where one dominates, where max Maxwell's
equation dominates versus fixed law dominates depends on where your electrode is. Um, if it's
close to the source, uh, mostly electromagnetic based, um, when you're farther away from it,
it's more diffusion based. So essentially when you're able to park it next to it, you can
listen in on those individual chatter, um, and those local changes in the potential.
And the type of signal that you get are these canonical textbook neural, uh, spiking waveform.
When you're, the moment you're further away and based on some of the studies that people have done,
um, you know, Christophe Koch's lab and, and others, once you're away from that source by
roughly around a hundred micron, which is about a width of a human hair, you no longer hear from
that neuron. You're no longer able to kind of have the system sensitive enough to be able to, um, record
that particular, um, local membrane potential change in that neuron. And just to kind of give you a sense of
scale also, when you, when you look at a hundred micron voxel, so a hundred micron by a hundred
micron by a hundred micron box, uh, in a brain tissue, there's roughly around 40 neurons and
whatever number of connections that they have. So there's a lot in that volume of tissue. So
the moment you're outside of that, you're, there's just no hope that you'll be able to
detect that change from that one specific neuron that you may care about.
Yeah. But as you're moving about this space, you'll be hearing other ones. So if you move
another hundred micron, you'll be hearing chatter from another community.
Correct.
And so the, the whole sense is you want to place as many as possible electrodes,
and then you're listening to the chatter.
Yeah. You want to listen to the chatter. And, and at the end of the day, you also want
to basically let the software do the, do the job of decoding. Um, and, um, just to kind of
go to, you know, why ECOG and EEG work at all. Right. Um, when you have these local changes,
you know, obviously it's not just this one neuron that's, uh, activating, there's many, many other
networks that are activating all the time. And you do see sort of a general change in the potential
of this electrode, like this is charged medium. And that's what you're recording when you're
farther away. I mean, you, you still have some reference electrode that's, uh, stable and the brain
that's just electroactive organ and you're seeing some combination aggregate, uh, action potential
changes, and then you can pick it up. Right. It's a much slower, um, changing, uh, signals, but
you know, uh, there, there are these like canonical kind of oscillations and waves like gamma waves,
beta waves, like when you sleep that, that can be detected because there's sort of a synchronized,
um, kind of global, global, uh, effect of the brain that, that you can detect. Um, and I mean,
the physics of this go like, I mean, if we really want to go down that rabbit hole, like there, there's
a lot that goes on in terms of like why diffusion physics at some point dominates when you're further
away from the source, you know, it, it, it's just a charged medium. Um, so similar to how, when you have
electromagnetic waves propagating in atmosphere or in, in a charged medium, like a plasma, there's
this weird shielding that happens that actually, um, further attenuates the signal, um, as you move
away from it. So yeah, you see, like if you do a really, really deep dive on kind of the signal
attenuation over distance, you start to see kind of one over R square in the beginning and then
exponential drop off. And that's the knee at which, you know, you go from electromagnet, magnetism
dominating to diffusion physics dominating. But once again, with the electrodes, the,
the biophysics that you need to understand is, is, um, not as deep because no matter where you're
placing that you're listening to a small crowd of local neurons. Correct. Yeah. So once you penetrate
the brain, um, you know, you're in the arena, so to speak. And there's a lot of neurons. There are
many, many of them. But then again, there's like, uh, there's a whole field of neuroscience that's
studying, like how the different groupings, the different sections of the seating in the arena,
what they usually are responsible for, which is where the, the metaphor probably falls apart
because the seating is not that organized in an arena. Also, most of them are silent.
They don't really do much, um, you know, or, or they, their activities are, um, you know,
you have to hit it with just the right set of stimulus. So they're usually quiet.
They're usually very quiet, quiet. There's, I mean, similar to dark energy and dark matter,
there's dark neurons. What are they all doing? When you place these electrode again, like within
this hundred micron volume, you have 40 or so neurons. Like, why are you, why do you not see
40 neurons? Why do you see only a handful? What is happening there?
Well, they're mostly quiet, but like when they speak, they say profound shit. I think that's the way
I'd like to think about it. Anyway, before we zoom in even more, let's zoom out. So how does Neuralink work?
From the surgery to the implant, to the signal and the decoding process and the human being
able to use the implant to actually affect the, the world outside. And all of this, I'm asking in
the context of there's a gigantic historic milestone that Neuralink just accomplished that in January of
this year, putting a Neuralink implant in the first human being, Noland. And there's been a
lot to talk about there, about his experience, because he's able to describe all the nuance and
the beauty and the fascinating complexity of that experience of everything involved. But on the
technical level, how does Neuralink work?
Yeah. So there are three major components to the technology that we're building. One is the device,
the thing that's actually recording these neural chatters. We call it N1
N1 implant or the link. And we have a surgical robot that's actually doing an implantation
of these tiny, tiny wires that we call threads that are, you know, smaller than
human hair. And once everything is surgeries, you have these neural signals, these spiking neurons that
are coming out of the brain. And you need to have some sort of software to decode what the users intend
to do with that. So there's what's called the Neuralink application or B1 app that's doing that
translation. It's running the very, very simple machine learning model that decodes these inputs
that are neural signals, and then convert it to a set of outputs that allows, you know, our participant,
first participant, Noland, to be able to control a cursor.
And this is done wirelessly?
And this is done wirelessly. So our implant is actually a two-part. The link has, you know, these
flexible tiny wires called threads that have multiple electrodes along its length. And they're only
inserted into the cortical layer, which is about three to five millimeters in a human brain. In the
motor cortex region, that's where the kind of the intention for movement lies in. And we have 64 of
these threads, each thread having 16 electrodes along, you know, the span of three to four millimeters.
separated by 200 microns. So you can actually record along the depth of the insertion. And based on that
signal, there's custom, you know, integrated circuit or ASIC that we built that amplifies the neural
signals that you're recording, and then digitizing it, and then has some mechanism for
detecting whether there was an interesting event that is a spiking event, and decide to send that or not
send that through Bluetooth to an external device, whether it's a phone or a computer that's running
this Neuralink application.
So there's onboard signal processing already just to decide whether this is an interesting
event or not. So there is some computational power onboard inside the, in addition to the human
brain.
Yeah. So it does the signal processing to kind of really compress the amount of signal that you're
recording. So we have a total of thousand electrodes sampling at, you know, just under 20 kilohertz
with 10 bit each.
So that's 200 megabits that's coming through to the chip from thousand channel simultaneous neural
recording. And that's quite a bit of data. And, you know, there is, there are technology available to
send that off wirelessly, but being able to do that in a very, very thermally constrained environment that
is a brain. So there has to be some amount of compression that happens to send off only the
interesting data that you need, which in this particular case for motor decoding is
occurrence of a spike or not. And then being able to use that to, you know, decode the intended
cursor movement. So the implant itself processes it, figures out whether a spike happened or not with
our spike detection algorithm, and then sends it off, packages it, send it off through Bluetooth
to an external device that then has the model to decode. Okay. Based on these spiking inputs,
did Nolan wish to go up, down, left, right, or click or right click or whatever?
All of this is really fascinating, but let's stick on the N1 implant itself. So the thing that's in the brain.
So I'm looking at a picture of it, there's an enclosure, there's a charging coil. So we didn't
talk about the charging, which is fascinating. The battery, the power electronics, the antenna.
Then there's the signal processing electronics. I wonder if there's more kinds of signal processing
you can do. That's another question. And then there's the threads themselves
with the enclosure on the bottom. So maybe to ask about the charging.
Yeah. So there's an external charging device.
Mm-hmm. Yeah. There's an external charging device. So yeah, the second part of the implant,
the threads are the ones, again, just the last three to five millimeters are the ones that are
actually penetrating the cortex. Rest of it is, actually most of the volume is occupied by the battery,
rechargeable battery. And it's about a size of a quarter. I actually have a device here,
if you want to take a look at it.
You know, this is the flexible thread component of it.
Wow. And then this is the implant.
So it's about a size of a US quarter. It's about nine millimeters thick. So basically this implant,
you know, once you have the craniectomy and the directomy, threads are inserted. And
the hole that you created, this craniectomy gets replaced with that. So basically that thing plugs that
hole and you can screw in, uh, these self-drilling cranial screws to hold it in place.
And at the end of the day, once you have the skin flap over, uh, there's only about two to three
millimeters that's, you know, obviously transitioning off of the top of the implant to where the screws
are. And, and that's the minor bump that you have.
Those threads look tiny. That's incredible. That is really incredible. That is really incredible.
And also as you're right, most of the volume, actual volume is the battery.
Yeah.
Wow. This is way smaller than I realized.
They, they are also, the threads themselves are quite strong.
They look strong.
And, and the thread themselves also has a very interesting, um, feature at the end of it called
the loop. And that's the mechanism to which the robot is able to interface and manipulate this
tiny hair-like structure.
And they're tiny. So what's the width of a thread?
Yeah. So the, the width of a thread, um, starts from 16 micron and then tapers out to about 84 micron.
So, you know, average human hair is about 80 to a hundred micron in width.
This thing is amazing. This thing is amazing.
Yes. Most of the volume is occupied by the, by the battery rechargeable lithium ion cell.
Um, and, uh, the charging is done through inductive charging, which is actually very
commonly used, you know, your cell phone, most cell phones have that.
Um, the biggest difference is that, you know, for us, you know, usually when you have a phone
and you want to charge it on a charging pad, you don't really care how hot it gets.
Whereas for us, it matters. There's a very strict regulation and good reasons to not actually
increase the surrounding tissue temperature by two degrees Celsius.
So there's actually a lot of innovation that is packed into this to allow charging
of this implant without causing that temperature threshold to reach.
And even small things like you see this charging coil and what's called a ferrite shield, right?
So, uh, without that ferrite shield, what you end up having when you have, um, you know, resonant
inductive charging is that the battery itself is a metallic can and you form these edit currents,
um, from, uh, external charger, and that causes heating.
Um, and that actually contributes to inefficiency and charging.
Um, so this ferrite shield, what it does is that it actually concentrate
that field line away from the battery and then around the coil that's actually wrapped around it.
There's a lot of really fascinating design here to, to make it, I mean, you're integrating a
computer into a biological, a complex biological system.
Yeah. There's a lot of innovation here. I would say that part of what enabled this
was just the innovations in the wearable. Uh, there's a lot of really, really powerful,
tiny low power, uh, microcontrollers, temperature sensors, or various different sensors.
And power electronics. A lot of innovation really came in the, the charging coil design,
how this is packaged and how do you enable charging such that you don't really, uh, exceed
that temperature limit, which is not a constraint for other devices out there.
So let's talk about the threads themselves, those tiny, tiny, tiny things. So, uh, how many of them
are there? You mentioned a thousand electrodes. How many threads are there? And what do the electrodes
had to do with the threads? Yeah. So the current instantiation of the device has 64 threads,
and each thread has 16 electrodes for a total of 1024 electrodes that are capable of both recording
and stimulating. Um, and, um, the thread is basically this, uh, polymer insulated wire. Um, the metal
conductor is the kind of a tiramisu, tiramisu cake of, uh, Thai plat gold plat Thai. Um, um, and they're very,
very tiny wires, um, two micron in width. So two, one millionth of, uh, meter.
It's crazy that that thing I'm looking at has the polymer insulation, has the conducting material,
and has 16 electrodes at the end of it. On each of those threads. Yeah. On each of those threads.
Correct. 16, each one of those. Yes. You're not going to be able to see it with naked eyes.
And, uh, I mean, to state the obvious, or maybe for people who are just listening, they're flexible.
Yes. Yes. That's also one element that, uh, was incredibly important for us. Um, so each of these
thread are, as I mentioned, 16 micron in width, and then they taper to 84 micron, but in thickness,
they're less than five micron. Um, and in thickness is mostly, you know, uh, polyimid at the bottom
and this metal track, and then another polyimid. So two micron of polyimid,
uh, 400 nanometer of this metal stack and two micron of polyimid sandwich together to protect
it from the environment that is, uh, 37 degrees C bag of salt water. So what, what's some, maybe,
can you speak to some interesting aspects of the material design here? Like, what does it take to,
to design a thing like this and to be able to manufacture a thing like this, uh, for people who
don't know anything about this kind of thing? Yeah. So the material selection that we have is not
I don't think it was particularly unique. Um, there, there were other labs and there are other labs that
are kind of looking at similar, um, material stack. Um, there's kind of a fundamental question, um, and,
and still needs to be answered around the longevity and reliability of these, uh, micro electrodes,
um, that, that we call, uh, compared to some of the other more conventional neural interfaces,
devices that are intracranial. So penetrating the cortex that are more rigid, um, you know,
like the Utah Ray, um, that, that are these, uh, four by four millimeter kind of silicon shank that have
exposed, uh, recording site at the end of it. Um, and, and, um, you know, that's, that's been kind of
the innovation from Richard Norman back in 1997. Uh, it's called the Utah Ray because, you know,
he was at University of Utah. And what, what does the Utah Ray look like? So it's a rigid
type of? Yeah. So we can actually look it up.
Oh, yeah. Yeah. Yeah. So it's a bed of needle. Um, there's.
Okay. Yeah. So those are rigid, rigid, rigid, rigid. Yeah. You weren't kidding.
And the size and the number of shanks vary anywhere from 64 to 128. Um,
at the very tip of it is an exposed electrode that actually records neural signal. Um, the other thing
that's interesting to note is that, uh, unlike neural link threads that have recording electrodes
that are actually exposed iridium oxide recording sites along the depth, this is only at a single
depth. So these Utah Ray spokes can be anywhere between 0.5 millimeters to 1.5 millimeter. And
they're, they also have, uh, designs that are slanted, um, so you can have it inserted at different
depth. Um, but that's one of the other big differences. And then, uh, I mean, the main key
difference is the fact that, uh, there's no active electronics. These are just electrodes.
And then there's a bundle of a wire that you're seeing. And then that actually then exits the
craniectomy, um, that then has this port that you can connect to, um, for any external electronic
devices. They are working on a, or have the wireless telemetry device, but it still requires a through
the skin, uh, port that actually is one of the biggest failure modes for infection, uh, for the system.
What are some of the challenges associated with flexible threads? Like for example,
on the robotic side, R1, uh, implanting those threads, how difficult is that task?
Yeah. Um, so as you mentioned, they're, they're very, very difficult to maneuver by hand. Um,
these, these youth arrays that you, you saw, uh, earlier, they're actually inserted by a neurosurgeon
actually positioning it near the site that they want. And then, uh, they're actually, there's a
pneumatic hammer that actually pushes them in. Um, so, so it's, uh, it's a pretty simple process,
um, and they're easier to maneuver. Um, but for, for these thin foam arrays, they're, they're very,
very tiny and, uh, flexible. So they're, they're very difficult to maneuver. So that, that's why we built
an entire robot to do that. Um, there are other, other reasons for why we built the robot. Um, and,
and that is ultimately we want this to help millions and millions of people that can benefit from this.
And there just aren't that many neurosurgeons out there. Um, and, uh, you know, robots can be, uh,
something that, you know, we hope can actually do large parts of the surgery. Um,
but yeah, yeah, the, the, the robot is this entire other, um, sort of category of product that we're
working on. And it's essentially this multi-axis gantry system that has the specialized robot head,
um, that has all of the optics and, um, this, this kind of a needle retracting mechanism that
maneuvers these, these threads, um, via this loop structure that you have on the thread.
So the thread already has a loop structure by which you can grab it.
Correct. Correct.
So this is fascinating. So you mentioned optics. So there's a robot R1. So for now, there's a human
that actually creates a hole in this, in the skull. And then after that, there's a computer vision
component that's finding a way to avoid the blood vessels. And then you're grabbing it by the loop,
each individual thread and placing it in a particular location to avoid the blood vessels
and also choosing the depth of placement, all that. So controlling every, like the 3d geometry of the
placement. Correct. So the, the aspect of this robot that is unique is that it's not surgeon
assisted or human assisted. It's a semi-automatic or automatic, uh, robot once you, you know, obviously
there are human component to it when you're placing targets. Um, you can always move it away from kind of
major vessels that you see. Um, but I mean, we want to get to a point where one click and it just
does the surgery within minutes. So the computer vision component finds great targets, candidates,
and the human kind of approves them. And the robot does, does it do like one thread at a time?
It does one thread at a time. Uh, and that's, that's actually also one thing that we, um, uh,
are looking at ways to do multiple threads at a time. There's nothing stopping from it. You can have
multiple kind of engagement, uh, mechanisms. Um, but right now it's one by one. And, uh, you know,
we also still do quite a bit of just, just kind of verification to make sure that it got inserted.
If so, how deep, you know, did it actually match, um, what was programmed in and, you know, so on and
so forth. And the actual electrodes are placed at very, at differing depths in the, uh, like, I mean,
it's very small differences, but differences. Yeah. Yeah. And so that there's
some reasoning behind that, as you mentioned, like it, it gets more varied signal.
Yeah. We, I mean, we try to place them all around three or four millimeter from the surface. Um,
just cause the span of the electrode, those 16 electrodes that we currently have in this, uh,
version spans, um, you know, roughly around three millimeters. So we want to get all of those in the brain.
This is fascinating. Okay. So there's a million questions here. If we go zoom in specific on the
electrodes, what is your sense? How many neurons is each individual electrode listening to?
Yeah. Each electrode can record from anywhere between zero to 40, as I mentioned right earlier. Um, but
practically speaking, uh, we only see about at most like two to three. Um, and you can actually
distinguish which neuron it's coming from by the shape of the spikes. Um, so I mentioned the spike
detection algorithm that we have, it's called boss algorithm, um, buffer online spike sorter.
Nice. It actually outputs at the end of the day, uh, six unique values, which are, um, you know,
kind of the amplitude of these like negative going hump, middle hump, like a positive going hump,
and then also the time at which these happen. And from that, you can have, uh, kind of a statistical
probable probability, um, estimation of, is that a spike? Is it not a spike? And then based on that,
you could also, uh, determine, oh, that spike looks different than that spike must come from a different
neuron. Okay. So that that's a nice signal processing step from which you can then make much better
predictions about if there's a spike, especially in this kind of context where there could be
multiple neurons screaming. And that, that also results in you being able to compress the data
better. Yeah. Okay. And just to be clear, I mean, they're the, the labs do this, what's called
spike sorting. Um, usually once you have these like broadband, you know, like the fully digitized
signals, and then you run a bunch of different set of algorithms to kind of tease apart. It's just all
of this for us is done on the device. On the device. In a very low power custom, you know, built ASIC
digital processing unit. Highly heat constrained. Highly heat constrained. And the processing time
from signal going in and giving you the output is less than a microsecond, which is, uh, you know,
a very, very short amount of time. Oh yeah. So the latency has to be super short. Correct.
Oh, wow. Oh, that's a pain in the ass. Yeah. Latency is a huge, huge thing that you have to deal
with. Uh, right now, the biggest source of latency comes from the Bluetooth. Uh, the, the way in which
they're packet ties and, you know, we've been them in 15 millisecond. Oh, interesting. So communication
constraint, is there some potential innovation there on the protocol used? Absolutely. Okay. Yeah.
Bluetooth is definitely not, uh, our final, uh, wireless communication protocol that we want
to get to. Hence the N1 and the R1. I imagine that increases. NX, NXRX.
Uh, yeah, that's, you know, the communication protocol because Bluetooth, uh, allows you to
communicate against farther distances than you need to. So you can go much shorter. Yeah. The only,
uh, the primary motivation for choosing Bluetooth is that, I mean, everything has Bluetooth. All right.
So you can talk to any device. Interoperability is just absolutely essential, especially in this
early phase. Um, and in many ways, if you can access a phone or a computer, you can do anything.
Well, it'd be interesting to step back and actually look at, again, the same pipeline that you mentioned
for Nolan. So what does this whole process look like from finding and selecting a human being to the,
to the surgery, to the, the first time he's able to use this thing?
So we have what's called a patient registry that people can sign up to, um, you know,
hear more about the updates. And that was a route to which Nolan applied. And the process is that
once the application comes in, you know, it, it contains some medical records and we, uh, you know,
based on their medical eligibility, that there's a lot of different inclusion, exclusion criteria
for them to meet. And we go through a pre-screening interview process with someone from Neuralink.
And at some point we also go out to their homes to do a BCI home audit. Um, cause one, one of the most
kind of revolutionary part about, you know, having this N1 system that is completely wireless is that
you can use it at home. Like you don't actually have to go to the lab, um, and, and, you know,
go to the clinic to get connectorized to these like specialized equipment that you can't take home with
you. Um, so that's one of the, the key elements of, you know, when we're designing the system that we
wanted to keep in mind, like, you know, people, you know, hopefully would want to be able to use
us every day in the comfort of their home. And, um, so part of our engagement and, and what we're
looking for during BCI home audit is to just kind of understand their situation, what other assistive
technology that they use. And we should also step back and kind of say that, uh, the estimate is,
uh, 180,000 people live with quadriplegia in the United States. And each year, an additional 18,000
suffer, uh, a paralyzing spinal cord injury. So these are folks, uh, who have a lot of challenges
living a life in terms of accessibility, in terms of doing the things that many of us just take for
granted day to day. And one of the things, one of the goals of this initial study is to enable them
to have sort of digital autonomy where they by themselves can interact with a digital device
using just their mind, something that you're calling telepathy. So digital telepathy, where
a quadriplegic can communicate with a digital device in all the ways that we've been talking about,
uh, control the mouse cursor enough to be able to do all kinds of stuff, including play games and
tweet and all that kind of stuff. And there's, there's a lot of people for whom life, the basics
of life are difficult, uh, because of the things that have happened to them. So.
Yeah. I mean, movement is so, so fundamental to our existence. I mean, even, even speaking involves
movement of mouth, lip, larynx. And, um, without that, it's, it's, it's, um, extremely debilitating.
Um, and there, um, yeah, there, there are many, many people that we can help. And
I mean, like, especially if you start to kind of look at other forms of movement disorders,
um, that are not just from spinal cord injury, but from, uh, you know, ALS, uh, MS, or even stroke that,
that leads you in, or just aging, right? That leads you to lose some of that mobility,
that independence. It's extremely debilitating.
And all of these are opportunities to help people, to help alleviate suffering,
to help improve the quality of life. But each of the things you mentioned is its own little puzzle.
Then you, uh, to have increasing levels of capability from a device, like a neural link
device. And so the first one you're, you're focusing on is, uh, it's just a beautiful word,
telepathy. So being able to communicate using your mind wirelessly with a digital device.
Can you just explain this exactly what we're talking about?
Yeah. I mean, it's exactly that. I mean, I think if you are able to control
a, uh, cursor and able to click, um, and be able to get access to computer or phone,
I mean, the, the whole world opens up to you. And I mean, I guess the word telepathy, if you kind of
think about that as, um, you know, just definitionally being able to transfer information from my brain
to your brain, um, without using some of the, the physical faculties that we have, you know, like
voices.
But the interesting thing here is, I think the thing that's not obviously clear is how exactly it
works. So in order to move a cursor, there's, uh, at least a couple of ways of doing that.
So one is you imagine yourself maybe moving a mouse with your hand, or you can then, which Nolan talked
about like, imagine moving the cursor with your mind. Like, I don't, but it's like, there is a
cognitive step here. That's fascinating. Cause you, you have to use the brain and you have to learn how
to use the brain and you kind of have to figure it out dynamically, like, uh, because you reward
yourself if it works. So you like, I mean, there's a step that this is just a fascinating step. Cause you
have to get the brain to start firing in the right way. And you do that.
by imagining, uh, like fake it till you make it. And all of a sudden it creates the right kind of
signal that if decoded correctly, uh, can create the kind of effect. And then there's like noise
around that. You have to figure all of that out. But on the human side, imagine the cursor moving
is what you have to do.
Yeah. He says using the force of force. I mean, that's, isn't that just like fascinating to you
that it works. Like to me, it's like, holy shit, that actually works. Like you could
move a cursor with your mind.
You know, as much as you're learning to use that thing, that things also learning about
you. Like our, our models constantly updating the weights to say, oh, if, if someone is thinking
about, you know, this sophisticated forms of like spiking patterns, like that actually means to do
this. Right.
Right. So the machine is learning about the human and the human is learning about the machine. So
there's a adaptability to the signal processing, the decoding step. And then there's the adaptation
of Nolan, the human being like the same way. If, if you give me a new mouse and I move it,
I learned very quickly about its sensitivity. So I learned to move it slower. And then there's
other kinds of signal drift and all that kind of stuff they have to adapt to. So both are adapting
to each other.
Correct.
That's a fascinating like software challenge on both sides, the software on both on the,
the human software and the organic and the inorganic, organic and they're inorganic anyway.
So, sorry to rudely interrupt. So there's the selection that Nolan has passed with flying colors.
So everything, including that the, it's a BCI friendly home, all of that. So what is the,
the process of the surgery, the implantation, the first moment when he gets to use the system?
The end to end, you know, we say patient in to patient out is anywhere between two to four hours.
In particular case for Nolan, it was about three and a half hours. And there's many steps leading
to, you know, the actual robot insertion, right? So there's anesthesia induction, and we do intra-op
CT imaging to make sure that we're, you know, drilling the hole in the right location. And this is
also pre-planned beforehand. Someone goes through, someone like Nolan would go through fMRI and then
they can think about wiggling their hand, you know, obviously due to their injury, it's not going to
actually lead to any, any sort of intended output, but it's the same part of the brain that actually
lights up when you're imagining moving your finger to actually moving your finger. And that's one of the
ways in which we can actually know where to place our threads. Because we want to go into what's
called the hand knob area in the motor cortex. And, you know, as much as possible, densely put our
electro threads. So yeah, we do intra-op CT imaging to make sure and double check the location of the
craniectomy. And the surgeon comes in, does their thing in terms of like skin incision, craniectomy,
so drilling of the skull. And then there's many different layers of the brain. There's what's
called the dura, which is a very, very thick layer that surrounds the brain that gets actually resected
in a process called durectomy. And that then exposed the PIA and the brain that you want to insert.
And by the time it's been around anywhere between one to one and a half hours, robot comes in,
does this thing, placement of the targets, inserting of the thread that takes anywhere between 20 to 40
minutes. In the particular case for Nolan, it was just under, or just over 30 minutes. And then after
that, the surgeon comes in. There's a couple other steps of like actually inserting the dual substitute
layer to protect the thread as well as the brain. And then yeah, screw, screw in the implant and then
skin flap and then suture, and then you're out.
So, uh, when, uh, Nolan woke up, what was that like, was the recovery like, and what was the
first time he was able to use it?
So he was actually immediately after the surgery, um, you know, like an hour after the surgery,
as he was waking up, um, we did turn on the device, um, make sure that we are recording neural signals.
And we actually did have a couple signals that we, um, noticed that he can actually modulate.
And what I mean by modulate is that he can think about crunching his fist and you could see the spike
disappear and appear.
And that's awesome.
Um, and that was immediate, right?
Uh, immediate, uh, after in, in the recovery room.
Oh, how cool is that?
Yeah.
That's a human being.
I mean, what, what did that feel like for you?
This device and a human being, a first step of a gigantic journey.
I mean, it's a historic moment, even just that spike, just to be able to modulate that.
You know, obviously there had been other, other, you know, uh, as you mentioned, pioneers that have
participated in these groundbreaking BCI, um, you know, uh, investigational early feasibility studies.
So we're obviously standing in the shoulders of the giants here.
You know, we're not the first ones to actually put electrodes in the human, human brain.
Um, but I mean, just leading up to the surgery, there was, uh, I, I, I definitely could not sleep.
There's just, it's the first time that you're working in a completely new environment.
Um, we had a lot of confidence based on our bench top testing, uh, or preclinical R&D studies that
the mechanism, the threads, the insertion, all that stuff is, is very safe.
And that it's, um, uh, you know, obviously ready for, uh, doing this in a human, but there's still
a lot of unknown unknown about, can the needle actually insert?
Uh, I mean, I, we brought something like 40 needles just in case they break and we ended up using only
one.
Um, but I mean, that, that was a level of just complete unknown, right?
Cause it's a very, very different environment.
And, uh, I mean, that's, that's why we do clinical trial in the first place to be able to
test these things out.
So extreme nervousness and, uh, just, just, I, many, many sleepless night leading up to the
surgery and definitely the day before the surgery.
And it was an early morning surgery.
Like we, we started at seven in the morning.
Um, and, and by the time it was around 10 30, it was, it was, it was, everything was done.
But I mean, first time seeing that, well, number one, just, just huge relief, um, that this thing
is, um, you know, doing what it's supposed to do.
Um, and two, I mean, just immense amount of gratitude for, for Nolan and his family.
And then many others that have applied and that we've spoken to and will speak to are, I mean,
true pioneers in, in every, every word.
And, you know, I, I sort of call them the neural astronauts or neural not, um, you know,
these amazing, just like in the sixties, right.
Like these, these amazing, just pioneers, right.
Um, exploring the unknown outwards in this case is inward.
Um, but an incredible amount of gratitude for them to, uh, you know, just, just participate
and, and play a part.
Um, and, and it's a, it's a journey that we're embarking on together.
Um, but also like, I think it was just, uh, that was a very, very important milestone,
but our work was just starting.
So a lot of just kind of, uh, anticipation for, okay, what's, what needs to happen next?
Uh, what are set of sequences of events that needs to happen for us to, you know, make it
worthwhile for, um, uh, you know, both Nolan as well as us.
Just to linger on that, just a huge congratulation to you and the team for that milestone.
I know there's a lot of work, uh, left, but that, that is, that's really exciting to see.
There's, um, that's a source of hope.
It's, uh, this first big step opportunity to help hundreds of thousands of people
and then maybe, uh, expand the realm of the possible for the human mind for millions of
people in the future.
So it's, it's really exciting.
Like the, the opportunities are all ahead of us and to do that safely and to do that effectively
was, uh, it was really fun to see as an engineer, just watching other engineers come together and
do an epic thing.
That was awesome.
So huge congrats.
Thank you.
Thank you.
It's, um, yeah, could not have done it without the team.
And, um, yeah, I mean, that, that's the other thing that I, I, um, you know, told the team as
well of just this immense sense of optimism for the future.
Um, I mean, it was, uh, it's a very important moment for, for the company.
Um, you know, needless to say, as well as, um, you know, hopefully for many others out
there that we can help.
So speaking of challenges, the Neuralink published a blog post describing that some
of the threads are attracted.
And so the performance as measured by bits per second dropped at first, but then eventually
it was regained and that the whole story of how it was regained is super interesting.
That's definitely something I'll talk to, uh, to bliss and to Nolan about.
Um, but in general, um, can you speak to this whole experience?
How was the performance regained?
And, um, just the, the technical aspects of, uh, the threads being attracted and moving.
The main takeaway is that in the end, the performance have come back and it's actually
gotten better than it was before.
Um, he's actually just beat the world record yet again last week, um, to 8.5 BPS.
So, I mean, he's, he's just cranking and he's just improving.
The previous one was that he said was 8, correct?
He said 8.5.
Yeah.
The previous world record, uh, in human was 4.6.
Yeah.
So it's, uh, almost double and his goal is to try to get to 10, which is rough, roughly
around kind of the median Neuralink or, uh, using a, uh, you know, mouse with the hand.
So it's, um, it's getting there.
So, yeah.
So the, the performance was regained.
Yeah.
Better than before.
So that, that's, you know, uh, a story on its own of what took the BCI team to recover
that performance.
It was, it was actually mostly on kind of the signal processing.
And so, you know, as I mentioned, we were, um, kind of looking at these spike outputs from
the, um, our electrodes.
And what happened is that kind of, uh, four weeks into the surgery, uh, we noticed that the
threads have slowly come out of the brain and the way in which we noticed this at first,
obviously is that, uh, well, I think Nolan was the first to notice that his performance was
degrading.
Um, and I think at the time we were also trying to do a bunch of different experimentation, um,
you know, different algorithms, different, um, sort of UI, UX.
So it was expected that there will be variability in the performance.
Um, but we did see kind of a steady decline.
And then also the way in which we measure the health of the electrodes or whether they're in
the brain or not is by measuring, uh, impedance of the electrode.
So, uh, we look at kind of the interfacial, um, kind of the, the, the Randall circuit,
like they, they say, um, you know, the capacitance and the, and the, um, the resistance between
the electrosurface and the medium.
And if that changes in some dramatic ways, we have some indication, or if you're not
seeing spikes on those channels, you have some indications that something's happening there.
And what we noticed is that looking at those impedance plot and spike rate plots, and also
because we have those electrodes recording along the depth, you're seeing some sort of movement
that indicated that the threads were being pulled out.
Um, and that obviously will have an implication on the model side, because if you're,
the number of inputs that are going into the model is changing because you have less of them,
um, the out, that, that model needs to get updated.
Right.
And, um, but, but there were still signals.
And as I mentioned, similar to how, even when you place the signals on the surface of the brain,
of the brain or farther away, like outside the skull, you still see some useful signals.
Um, what we started looking at is not just the spike occurrence through this boss algorithm that
I mentioned.
Um, but we started looking at just the, the, the power of the frequency band that is, um,
interesting for, uh, Nolan or, uh, Nolan to be able to modulate.
So once we kind of changed the algorithm for the implant to not just give you the boss output,
but also these, uh, spike band power output, um, that helped us sort of be find the model with
the new set of inputs.
And that, that was the thing that really ultimately gave us the performance back.
Um, you know, in, in terms of, and obviously like the, the thing that we want ultimately,
and the thing that we are working towards is figuring out ways in which we can keep those threads
intact, um, for as long as possible so that we have many more channels going into the model.
That's, that's by far the number one priority that the team is currently embarking on to understand
how to prevent that from happening.
Um, the thing that I will say also is that, you know, as I mentioned, this is the first time ever
that we're putting these threads in, in a human brain and, you know, human brain just for size
reference is 10 times that of the monkey brain or the sheep brain.
And it's, um, it's just a very, very different environment.
It moves a lot more.
It's like actually moved a lot more than we expected, um, when we, uh, did, did Nolan's surgery.
And, um, it's, uh, just a very, very different environment than what we're used to.
And this is why we do clinical trial, right?
We, we, we want to uncover some of these, uh, issues, uh, and, and failure modes earlier than later.
So in many ways it's provided us with this enormous amount of data and, um, information
to be able to, uh, solve this.
And this is something that Neuralink is extremely good at.
Once we have set of clear objective and engineering problem, we have enormous amount of talents
across many, many disciplines to be able to come together and fix the problem very, very quickly.
But it sounds like one of the fascinating challenges here is for the system and the decoding side
to be adaptable across different timescales.
So whether it's movement of threads or different aspects of signal drift, sort of on the software
of the human brain, something changing, like Nolan talks about cursor drift that could be corrected.
And there's a whole UX challenge to how to do that.
So it sounds like adaptability is like a fundamental property that has to be engineered in.
It is.
And, and I mean, I think, I mean, as a company, we're extremely vertically integrated.
Um, you know, we make these thin film arrays in our own, uh, micro fab.
Yeah.
There's a, like you said, built in house, this whole paragraph here from this blog post is pretty
gangster, uh, building the technologies described above has been no small feat.
And there's a bunch of links here that I recommend people click on.
We constructed in-house micro fabrication capabilities to rapidly produce various iterations of thin
film arrays that constitute our electrode threads.
We created a custom femtosecond laser mill to manufacture components with micro level precision.
I think there's a tweet associated with this whole thing that we can get into.
Yeah, this, this, okay.
What are we, what are we looking at here?
This thing, this is, uh, so in less than one minute, our custom-made femtosecond laser
mill cuts this geometry in the tips of our needles.
So we're looking at this weirdly shaped needle.
The tip is only 10 to 12 microns in width, only slightly larger than the diameter of a red blood
cell.
The small size allows threads to be inserted with minimal damage to the cortex.
Okay.
So what's interesting about this geometry?
So we'll look at this just geometry of a needle.
This is the needle that's engaging with the loops in the thread.
So they're the ones that, um, you know, thread the, thread the loop, um, and then peel it from
the silicone backing.
And then this is the thing that gets inserted into the tissue.
And then this pulls out leaving the thread and this kind of a notch or the shark tooth
that we used to call, uh, is the thing that actually is, um, grasping the loop.
And then it's, it's designed in such way, such that when you, when you pull out, leaves the loop.
And the robot is controlling this needle.
Correct.
So this is actually housed in a cannula.
And basically the robot is, has a lot of the optics that look for where the loop is.
Um, there's actually a four or five nanometer light that actually causes the
polyamide to fluoresce so that you can locate the location of the loop.
Um, so the loop lights up.
Yeah.
Yeah.
They do.
It's a micron precision process.
What's interesting about the robot that it takes to do that.
That's, that's pretty crazy.
That's pretty crazy that a robot is able to get this kind of precision.
Yeah.
Our robot is quite heavy.
Um, our current version of it, um, there's, I mean, it's, it's like a giant granite slab
that weighs about a ton.
Um, cause it needs to be sensitive to vibration, environmental vibration.
And then as the head is moving at the speed that it's moving, you know, there's a lot
of kind of motion control to make sure that you can achieve that level of precision.
Um, a lot of optics that kind of zoom in on that.
Um, you know, we're working on next generation of the robot that is lighter, easier to transport.
I mean, it is a, it is a feat to move the robot.
And it's far superior to a human surgeon at this time for this particular task.
Absolutely.
I mean, let alone you try to actually thread a loop in a, in a, in a sewing kit.
Uh, I mean, this is like, we're talking like fractions of human hair.
These, these things are, it's, it's not visible.
So continuing the paragraph, we developed novel hardware and software testing systems,
such as our accelerated lifetime testing racks and simulated surgery environment,
which is pretty cool to stress test and validate the robustness of our technologies.
We performed many rehearsals of our surgeries to refine our procedures and make them, um,
second nature.
This is pretty cool.
We practice surgeries on proxies with all the hardware and instruments needed in our mock
or in the engineering space.
This helps us rapidly test the measurements.
So there's like proxies.
Yeah, this proxy is super cool, actually.
So there's a 3d printed skull from the images that is taken at Barrow, as well as this, uh,
hydrogel mix, you know, sort of synthetic polymer thing that actually mimics the, the mechanical
properties of the brain.
Um, it also has vasculature of the person.
Um, so basically what we're talking about here.
And there's a lot of work that has gone into making this set proxy that, um, you know, it's,
it's about like finding the right concentration of these different synthetic polymers to get the
right set of consistency for the needle dynamics, you know, as they're being inserted.
But we practice this surgery with the person, you know, Nolan's basically physiology and brain,
um, many, many times prior to actually doing the surgery.
So to every, every step, every step, every step.
Yeah.
Like where does someone stand?
Like, I mean, like what you're looking at is the picture.
This is in, in, in our office of this kind of corner of the robot engineering space that we,
you know, have created this like mock OR space that looks exactly like what they would experience,
all the staff would experience during their actual surgery.
So, I mean, it's just kind of like any dense rehearsal where, you know, exactly where you're
going to stand at what point, um, and you just practice that over and over and over again with
an exact anatomy of someone that you're going to surgirize.
And, and it, it got to a point where a lot of our engineers, when we created a craniectomy,
they're like, oh, that, that looks very familiar.
We've seen that before.
Yeah.
Man, there's wisdom you can gain through doing the same thing over and over and over.
It's like, uh, gyro dreams of sushi kind of thing.
Um, because then, um, it's like Olympic athletes visualize, uh, the Olympics.
And then once you actually show up, it feels easy.
It feels like any other day, it feels almost boring winning the gold medal.
Cause you, you visualize this so many times.
You've practiced this so many times that nothing bothers you.
It's boring.
You win the gold medal is boring.
And it, the, the experience they talk about is mostly just relief.
Probably that they don't have to visualize it anymore.
Yeah.
The power of the mind to visualize and where, I mean, there's a whole field that studies
where muscle memory lies in cerebellum.
Yeah.
It's incredible.
Uh, I think there's a good place to actually
ask sort of the big question that people might have is how do we know
every aspect of this that you described is safe?
At the end of the day, the gold standard is to look at the tissue.
Um, you know, what sort of trauma did you cause the tissue?
And does that correlate to whatever behavioral anomalies that you may have seen?
Um, and that's the language to which, uh, we, we can communicate about the safety of,
you know, inserting something into the brain and what type of trauma that you can cause.
So, um, we actually have an entire department, uh, department of pathology that looks at
these, uh, tissue slices.
There are many steps that are involved in, in doing this.
Once you have, um, you know, studies that are launched to, uh, with, with particular endpoints
in mind, you know, at some point you have to euthanize the animal and then, uh, you go through
necropsy to kind of collect the brain tissue samples.
Um, you know, you fix them in formalin and you like gross them, you section them and you
look at individual slices just to see what kind of reaction or lack thereof exists.
So that's the kind of the language to which FDA speaks and, you know, uh, as well for us to
kind of evaluate the safety of the insertion mechanism, as well as the threats, um, at
various different time points, you know, both acute, um, so anywhere between, you know, uh,
zero to three months to beyond three months.
So those are kind of the, the details of an extremely high standard of safety that has
to be reached.
Correct.
Um, FDA supervises this.
But there's in general, just a very high standard and every aspect of this, including
the surgery.
I think, um, Matthew McDougall has mentioned that like the standard is, uh, let's say
how to put it politely higher than maybe some other operations that we take for granted.
So the, the, the standard for all the surgical stuff here is extremely high, very high.
I mean, it's a highly, highly regulated environment, um, with, you know, the governing agencies
that scrutinize every, every medical device that gets marketed.
And I think, I think it's a good thing.
Um, you know, it's good to have those high standards and we, we try to hold extremely
high standards, um, to kind of understand what sort of damage, if any, these, uh, innovative,
emerging technologies and new technologies that we're building are.
And, you know, so far I, I, we have been extremely impressed by lack of immune response from these
threads.
Speaking of which you, uh, you talk to me, uh, with excitement about the histology and some
of the images, uh, that you were able to share, uh, can you explain to me what we're looking at?
Yeah.
So what you're looking at is a stained tissue image.
Um, so this is a sectioned tissue slice from an animal that was implanted for seven months.
So kind of a chronic time point, and you're seeing all these different colors and each color
indicates specific types of cell types.
So purple and pink are astrocytes and microglia respectively.
They're types of, uh, glial cells.
And the, the other thing that, you know, people may not be aware of is your brain is not just
made up of soup of neurons and axons.
There are other, uh, you know, cells like, uh, glial cells that actually kind of is the glue
and also, uh, react, uh, if, if there are any trauma or damage to the tissue.
But the brown are the neurons.
The brown are the neurons.
So what you're seeing is in, in this kind of macro image, you're seeing these like circle
highlighted in white, the insertion sites.
And, uh, when you zoom into one of those, you see the threads.
And then in this particular case, I think we're seeing about the 16, uh, you know, wires that
are going into the page.
And the incredible thing here is the fact that you have the neurons that are these brown
structures or brown circular or elliptical thing that are actually touching and abutting the threads.
So what this is saying is that there's basically zero trauma that's caused during this insertion.
And with these neural interfaces, these, um, microelectros that you insert,
that is one of the most common mode of failure.
So when you insert these threads, like the Utah array, it causes a neuronal death around the site
because you're inserting a foreign object, right?
And that kind of elicits these like immune response through microglia and astrocytes.
They form this like protective layer around it.
Oh, not only are you killing the neuron cells, but you're also creating this protective layer
that then basically prevents you from recording neural signals because you're getting further
and further away from the neurons that you're trying to record.
And that, that is the biggest mode of failure.
And in this particular example, in that inset, it's, you know, it's about 50 micron with that
scale bar.
The neurons just seem to be attracted to it.
And so there's certainly no trauma.
That's such a beautiful image, by the way.
It's just a, so the brown of the neurons, and for some reason, I can't look away.
It's really cool.
And the way that these things like, I mean, your tissues generally don't have these
beautiful colors.
This is a multiplex stain that uses these different proteins that are staining these
at different colors.
You know, I, you know, we use very standard set of, you know, staining techniques with
HE, IBA1 and, you know, Nguyen and GFAP.
So if you go to the next image, this is also kind of illustrates the second point, because
you can make an argument.
And initially when we saw the previous image, we said, oh, like, are the threads just floating?
Like, what is happening here?
Like, are we actually looking at the right thing?
So what we did is we did another stain, and this is all done in-house, of this Lassan's
trichrome stain, which is in blue, that shows these collagen layers.
So the blue basically, like, you don't want the blue around the implant threads, because that
means that there's some sort of scarring that's happened.
And what you're seeing, if you look at individual threads, is that you don't see any of the blue,
blue, which means that there has been absolutely, or very, very minimal to a point where it's
not detectable amount of trauma in these inserted threads.
So that presumably is one of the big benefits of having this kind of flexible thread.
Yeah.
So we think this is primarily due to the size, as well as the flexibility of the threads.
Also, the fact that R1 is avoiding vasculature, so we're not disrupting, or we're not causing
damage to the vessels and not breaking any of the blood-brain barrier has, you know, basically
caused the immune response to be muted.
But this is also a nice illustration of the size of things.
So this is the tip of the thread.
Yeah.
Those are neurons.
And they're neurons.
And this is the thread listening.
And the electrodes are positioned how?
Yeah.
So this is, what you're looking at is not electrode themselves.
Those are the conductive wires.
So each of those should probably be two micron in width.
So what we're looking at is we're looking at the coronal slice.
So we're looking at some slice of the tissue.
So as you go deeper, you'll obviously have less and less of the tapering of the thread.
Um, but yeah, the, the point basically being that there's just, uh, kind of cells around
the insert a site, which is, um, just an incredible thing to see.
I, I've just never seen anything like this.
How easy and safe is it to remove the implant?
Yeah.
So it depends on when, um, in the first three months or so after the surgery, um, there,
there's a lot of kind of tissue modeling that's happening, you know, similar to when you got
a cut, um, you know, you obviously, uh, you know, start over first couple of weeks or depending
on the size of the wound, um, scar tissue forming, right?
There are these like contracted.
And then in the end they turn into scab and you can scab it off.
The same thing happens in the brain and it's a very dynamic environment.
And before the scar tissue or the neomembrane or the, you know, new membrane that forms,
it's quite easy to just pull them out.
Um, and there's minimal trauma that's, that's a cause during that.
Once the scar tissue forms and, you know, with, with Nolan as well, we believe that that's the
thing that's currently anchoring the threads.
So we haven't seen any more movements since then.
Um, so they're, they're quite stable.
Um, it's, it's, it gets harder to actually completely extract the threads.
So our current method for, uh, removing the device is cutting the thread, leaving the tissue
intact, and then unscrewing and taking the implant up.
And that hole is now going to be plugged with either another Neuralink or, uh, just with, uh,
you know, kind of a, a peak based, you know, plastic based, uh, cap.
Is it okay to leave the threads in there forever?
Yeah, we think so.
We've, we've done studies where, um, you know, we left them there.
And one of the biggest concerns that we had is like, do they migrate?
And do they get to a point where they should not be?
We haven't seen that.
Again, once the scar tissue forms, they get anchored in place.
And I, I should also say that, you know, when we say upgrades, like it, it's not, we're not
just talking in theory here.
Like we've actually upgraded many, many times.
Um, most of our, uh, monkeys or non-human primates, NHP have been upgraded.
You know, Pager who you saw playing MindPong has the latest version of the device since
two years ago and is seemingly very happy and healthy and fat.
So what's, uh, designed for the future, the upgrade procedure?
So, uh, maybe, uh, for Noland, what, what, what, what would the upgrade look like?
It was essentially what you're mentioning.
Is there a way to upgrade sort of the device internally where you take it apart and sort of,
uh, keep the capsule and upgrade the internals?
Yeah.
So there, there are a couple of different things here.
So for Noland, if we were to upgrade, what we would have to do is, um, either cut the threads
or, you know, extract the threads depending on kind of, um, you know, uh, the situation
there in terms of how they're anchored or scarred in.
Um, if you were to remove them with the Dural substitute, um, you know, you, you have an
intact brain so you can reinsert different threads, um, with the updated, uh, implant package.
Uh, there are a couple of different other, uh, ways that we're thinking about the future
of what the upgradable system looks like.
One is, you know, at the moment we currently remove the Dura, um, this, this kind of thick
layer that protects the brain, but that actually is the thing that actually proliferates the scar
tissue formation.
So typically the general good rule of thumb is you want to leave the, the nature as is, uh,
and not disrupt it as much.
So we're looking at ways to, uh, insert the threads through the Dura, um, which comes with
different set of challenges such as, you know, it's a pretty thick, uh, layer.
So how do you actually penetrate that without breaking the needle?
So we're looking at different needle design for that, as well as the,
kind of the loop engagement.
The other biggest challenges are it's quite opaque optically and with white light
illumination.
So how do you avoid still this, this biggest advantage that we have of avoiding basculature?
Uh, how do you image through that?
How do you actually still mediate that?
So there are other imaging techniques that we're looking at to enable that.
Um, but the goal, the, our hypothesis is that, and based on some of the early evidence
that we have, uh, doing through the Dura insertion will cause minimal scarring that causes
them to be much easier to extract over time.
And the other thing that we're also looking at, this is, um, going to be a fundamental
change in the implant architecture is as, um, at the moment, it's a monolithic single
implant that comes with a thread that's, um, bonded together.
So you can't actually separate the thing out, but you can imagine having two part implant,
um, you know, bottom part.
That is the thread that are inserted that has the chips, um, and maybe a radio and some power
source.
And then you have another implant that has more of the computational heavy load and, and
the bigger battery.
Um, and then one can be under the Dura, one can be above the Dura, like, you know, being
the plug for the skull, they can talk to each other, but the thing that you want to upgrade
the computer and not the threads, if you want to upgrade that, you just go in there, you know,
remove the screws and then put in the next version.
And, you know, you're off the, you know, it's a very, very easy surgery too.
Like you do a skin incision, slip this in screw, probably be able to do this in 10 minutes.
So that would allow you to reuse the threads sort of.
Correct.
So, I mean, this leads to the natural question of, uh, what is the pathway to scaling the increase
in the number of threads?
Is that a priority?
Is that like, what's, what's the technical, uh, challenge there?
Yeah, that, that is a priority.
So for next versions of the implant, um, you know, the key metrics that we're looking
to improve are number of channels, just recording from more and more neurons.
Um, you know, we have a pathway to actually go from currently 1,000 to, you know, hopefully 3,000,
if not 6,000 by end of this year.
Um, and then end of next year, we want to get to, uh, you know, even more 16,000.
Wow.
There's a couple of limitations to that.
One is, you know, obviously being able to photo lithographically print those wires.
As I mentioned, it's two micron in width and, and spacing.
Obviously there are chips that are much more advanced than those types of resolution.
And we have some of the tools that we have brought in house to be able to do that.
So traces will be narrower just so that you have to have more of the wires coming up into the chip.
Um, chips also cannot linearly consume more energy as you have more and more channels.
So there's a lot of innovations in the circuit, um, you know, and architecture as, as well as the
circuit design topology to make them lower power.
Um, you need to also think about if you have all of these spikes, how do you send that off to the
end application?
So you need to think about bandwidth limitation there and potentially innovations in signal processing.
Um, physically one of the biggest challenges is going to be, um, the, the, the, the interface.
It's always the interface that breaks, um, bonding the thin film array to the, um, the electronics.
Um, it starts to become very, very highly dense, uh, interconnects.
So how do you connectorize that?
There's a lot of innovations, um, in, in kind of the 3d integrations in the recent years that we can
take advantage of.
Um, one of the biggest challenges that we do have is, you know,
forming this hermetic barrier, right?
You know, this is an extremely harsh environment that we're in, the brain.
Um, so how do you protect it from, uh, yeah, like the brain trying to kill your electronics to
also your electronics leaking things that you don't want into the brain.
And that forming that hermetic barrier is going to be a very, very big challenge that we, uh,
you know, I think are actually well suited to tackle.
How do you test that?
Like what's the development environment to, uh, simulate that kind of harshness?
Yeah.
So this is, this is where the accelerated life tester essentially is a brain in a vat.
Uh, it literally is a vessel that is, um, made up of, and again, again,
for all intents and purpose for this particular types of tests, your brain is a saltwater.
And, uh, and you can, uh, also put some other set of chemicals like reactive oxygen species that,
you know, get at kind of these interfaces and trying to cause a reaction to, to, uh, pull it apart.
But you could also increase the rate at which these, uh, interfaces are aging by just increasing
temperature.
So every 10 degrees Celsius that you increase, you're basically accelerating time by two X.
And there's limit as to how, how much temperature you want to increase.
Cause at some point there's some other nonlinear dynamics that causes you to have
other nasty gases to form that just is not realistic in an environment.
So what we do is we increase, uh, in our ALT chamber by 20 degrees Celsius that, uh, increases
the aging by four, four times.
So essentially one day in ALT chamber is four day in calendar year.
And, and we look at whether the implants still are intact, uh, including the threads and,
and operation and all of that and operation and all of that.
Um, it obviously is not an exact same environment as a brain.
Cause you know, brain has mechanical, you know, other more, uh, biological groups that, that attack
at it.
Um, but it is a good test environment, testing environment for at least the, the, the
enclosure and the strength of the enclosure.
And I mean, we've had implants, the current version of the implant that has been in there
for, I mean, close to two and a half years, which is equivalent to a decade.
And they seem to be fine.
So it's interesting that the, so basically, uh, close approximation is warm salt water.
Hot salt water is a good testing environment.
I, yeah, by the way, I'm drinking element, uh, which is basically salt water,
which is making me kind of, it doesn't have computational power the way the brain does,
but maybe in terms of, in terms of all the characteristics is quite similar.
And I'm consuming it.
Yeah.
You have to get it in the right pH too.
And then consciousness will emerge.
Yeah.
No.
Uh, all right.
But by the way, the other thing that also is interesting about our enclosure is, uh,
if, if you look at our implant, it's not your common looking medical implant that usually
is, uh, you know, encased in a titanium can that's laser welded.
We use this polymer called PCTFE polychloro tri fluoro ethylene, which is actually commonly
used in blister packs.
So when you have a pill and you're trying to pop the pill, there's like kind of that
plastic membrane.
That's what this is.
Um, no one's actually ever used this, uh, except us.
And the reason we, um, wanted to do this is because it's, uh, electromagnetically transparent.
So when we talked about the, uh, electromagnetic inductive charging,
um, with titanium can, usually if you want to do something like that,
um, you know, you have to have a sapphire window and it's a, it's a very, very tough process to
scale.
So you're doing a lot of iteration here in every aspect of this, the materials,
the software, the whole, whole shebang.
Uh, so, okay.
So you mentioned scaling.
Is it possible to have multiple neural link devices as one of the ways of scaling to have
multiple neural link devices implanted?
That's the goal.
That's the goal.
Yeah.
We, we've had, we've had, um, I mean, our monkeys have had two neural links,
one in each hemisphere.
And then we're also looking at, you know, potential of having one in
motor cortex, one in visual cortex and one in wherever other cortex.
So focusing on a particular function, one neural link device.
Correct.
I mean, I wonder if there's some level of customization that can be done on the compute side.
So for the motor cortex.
Absolutely.
That's the goal.
And, and, you know, we talk about at neural link, building a generalized neural interface
to the brain.
Um, and, and that, that also is strategically how we're approaching this, um, with, with marketing,
and also, you know, with, with regulatory, which is, Hey, look, um, we have the robot and the robot can
access any part of the cortex.
Right now we're focused on motor cortex, uh, with current version of the N1 that's
specialized for motor decoding tasks, but also at the end of the day, there's kind of a general
compute available there.
Um, uh, but, you know, typically if you want to really get down to kind of hyper optimizing
for power and efficiency, you do need to get to some specialized function.
Right.
Um, but you know, what we're saying is that, Hey, you know, you, you are now used to this
robotic insertion techniques, which, which, you know, took many, many years of, you know,
showing data, um, and, and conversation with the FDA, um, and also internally convincing
ourselves that this is, this is safe.
And, um, now the difference is that if we go to other parts of the brain, like visual cortex,
which we're interested in as our second product, um, obviously it's a completely different
environment.
The cortex is laid out very, very differently.
Um, you know, it's going to be more stimulation focused rather than recording
just, just kind of creating visual percepts.
But in the end, we're using the same thin film array technology.
We're using the same robot insertion technology.
We're using the same, you know, packaging technology.
Now it's more of the conversation is focused around what are the differences
and what are the implication of those differences in safety and efficacy?
The way you said second product is, is both hilarious and awesome to me.
Uh, that product being restoring sight for blind people.
So can you speak to stimulating the visual cortex?
I mean, the, the possibilities there are just incredible to be able to give that gift back
to people who don't have sight or even any aspect of that.
Can you just speak to the challenges of, there's several challenges here.
Oh, many.
One of which is like you said, from recording to the stimulation, just, uh, any aspect of that
that you're both excited and, uh, uh, see the challenges of.
Yeah.
I guess I'll start by saying that we actually have been, um, capable of stimulating through
our dental moray as well as our electronics for years.
Um, you know, we, we have actually demonstrated some of that capabilities for, uh, reanimating
the limb in the spinal cord.
Um, it, you know, obviously for, for the current EFS study, you know, we've hardware disabled that.
So that's, that's something that, you know, we wanted to embark as a separate, separate journey.
Um, and, and, you know, obviously there are many, many different ways to
write information into the brain.
The way in which we're doing that is through electrical, you know, passing electrical current
and, and kind of causing that to really change the local environment so that you can
sort of artificially cause kind of the, the neurons to depolarize in, in, in nearby areas.
For, for vision specifically, um, you know, the way our visual system works,
it's both well understood.
I mean, anything with kind of brain, there are aspects of it that's well understood,
but in the end, like we don't really know anything.
Um, but the way visual system works is that you have photon hitting your eye and in your eyes,
uh, you know, there are these, um, specialized cells called photoreceptor cells
that convert the photon energy into electrical signals.
And then they get, that then gets projected to, um, your back of your head, your visual cortex.
Um, you know, it goes through actually, um, uh, you know, thelemic system called LGN that then
projects it out.
And then in the visual cortex, there's, you know, visual area one or V1, and then there's
a bunch of other higher level processing layers like V2, V3.
And there, there are actually kind of interesting parallels.
And when you study the behaviors of these convolutional neural networks,
like what the different layers of the network is detecting, you know, first they're detecting
like these edges and they're then detecting some more natural curves.
And then they start to detect like objects, right?
Kind of similar thing happens in the brain.
Um, and a lot of that has been inspired and also, you know, it's been kind of exciting to see
some of the correlations there.
Um, but, you know, things like from there, where does cognition arise and where, where's
color encoded?
There's, there's just not a lot of, um, understanding, fundamental understanding there.
So in terms of kind of bringing sight back to those that are blind, um, there are many
different forms of blindness.
Uh, there's actually million people, 1 million people in the U S that are legally blind.
Um, you know, that means like certain, uh, like score below in kind of the, the visual test.
Um, I think it's something like if you can see something, uh, at 20 feet distance that normal
people can see at 200 feet distance, like you're like, if you're worse than that, you're legally blind.
So for the fundamental, that means you can't function effectively using sight in the world.
Yeah.
Like to navigate your environment.
Um, and yeah, there are different forms of blindness.
There are forms of blindness where, uh, there's some degeneration of your, uh, retina, um,
these photoreceptor cells and, and the rest of your visual, uh, you know, processing that I described
is intact.
And for those types of individuals, uh, you may not need to maybe stick electrodes into the
visual cortex.
You can actually, um, uh, build retinal prosthetic devices that actually just replaces the function
of that retinal cells that are degenerated.
And there are many companies that are working on that, but that, that's a very small slice.
I'll be a significant still smaller slice of folks that are legally blind.
Um, you know, if there's any damage along that circuitry, whether it's in the optic nerve or,
you know, uh, just the LGN circuitry or any, any break in that circuit, that's not going to work for you.
Um, and, uh, the source of where you need to actually cause that visual percept to happen
because your biological mechanism is not doing that is by placing electrodes in the visual cortex
in the back of your head.
And the way in which this would work is that you would have an external camera, whether it's,
um, you know, something as unsophisticated as a GoPro or, you know, some sort of wearable,
you know, Ray-Ban type glasses that Meta's working on that captures a scene, right?
Um, and that scene is then converted to a set of electrical impulses or stimulation pulses that you
would, uh, activate in your visual cortex through, um, these thin film arrays.
And by playing some, you know, concerted kind of, uh, orchestra of these stimulation patterns,
you can create what's called phosphenes, which are these, um, kind of white, yellowish dots
that you can also create by just pressing your eyes.
You can actually create those percepts by stimulating the visual cortex.
And the name of the game is really have many of those and have those percepts be,
the phosphenes be as small as possible so that you can start to tell apart,
like they're the individual pixels of the, the, of the screen, right?
So if you have many, many of those, you know, potentially you'll be able to, um,
you know, in, in the long-term be able to actually get naturalistic vision,
but in the mid, like short-term to maybe mid-term, um, being able to at least be able to have
object detection algorithms run on your, um, on your glasses, uh, the prepop processing units,
and then being able to at least see the edges of things.
So you don't bump into stuff.
It's incredible.
This is really incredible.
So you basically would be adding pixels and your brain would start to figure out what those pixels mean.
Yeah.
And like with, with different kinds of assistance on the signal processing on all fronts.
Yeah.
The, the thing that actually, so a couple of things, one is, um, you know, obviously if you're, uh, blind from
birth, um, the way brain works, especially in the early age, um,
neuroplasticity is really nothing other than, you know, kind of your brain and different parts of your brain
fighting for the limited territory.
Yeah.
Um, and, and yeah, I mean, very, very quickly you see, you see cases where, you know, people that are,
I mean, you also hear about people who are blind that have heightened sense of hearing or some other
senses.
And the reason for that is because that cortex that's not used just gets taken over by these
different parts of the cortex.
So for those types of individuals, um, I mean, I guess they're going to have to now map some other
parts of their senses into what they call vision, but it's going to be obviously a very, very different
conscious experience, um, before, so I think that's a interesting caveat.
The other thing that also is important to highlight is that we're currently limited by
our biology in terms of the, the wavelength that we can see.
There's a very, very small wavelength that is a visible, um, light wavelength that we can see with
our eyes.
But when you have an external camera with this, um, BCI system, you're not limited to that.
You can have infrared, you can have UV, you can have whatever other spectrum that you want to see.
And whether that gets mapped to some sort of weird conscious experience, I've no idea.
But when I, you know, I'm oftentimes I talk to people about the goal of Neuralink being going
beyond the limits of our biology.
Um, that's sort of what I mean.
And if you're able to control the kind of raw signal is that when we use our site, we're getting
the photons and there's not much processing on it.
If you're being able to control that signal, maybe you can do some kind of processing.
Maybe you do object detection ahead of time.
Yeah.
You're doing some kind of pre-processing and there's a lot of possibilities to explore that.
So it's not just increasing sort of thermal imaging, that kind of stuff, but it's also just
doing some kind of interesting processing.
Yeah, I mean, my theory of how like visual system works also is that, um, I mean, there's
just so many things happening in the world and there's a lot of photons that are going into your
eye and it's unclear exactly where some of the pre-processing steps are happening.
But I mean, I actually think that just, just from a fundamental perspective, there's just so much,
uh, the reality that we're in, if it's a reality, um, is so there's so much data.
And I think humans are just unable to actually like eat enough actually to process all that
information.
So there's some sort of filtering that does happen, whether that happens in the retina,
whether that happens in different layers of the visual cortex, unclear, but like the analogy
that I sometimes think about is, you know, if, uh, if your brain is a CCD camera and all of the
information in the world is a sun, um, and when you try to actually look at the sun with the CCD
camera, it's just going to saturate the sensors, right?
Because it's an enormous amount of energy.
So what you, what you do is you end up adding these filters, right?
To just kind of narrow the information that's coming to you and being captured.
And I think, you know, things like our experiences or, um, uh, you know, like drugs, like propofol
that like anesthetic drug or, you know, psychedelics, what they're doing is they're kind of swapping
out these filters and putting in new ones or removing older ones and kind of controlling
our conscious experience.
Yeah, man, not to distract from the topic, but I just took a very high dose of ayahuasca and
the Amazon jungle.
So yes, it's a nice way to think about it.
You're swapping out different, different experiences and with Neuralink being able to
control that primarily at first to improve function, not for entertainment purposes or
enjoyment purposes, but yeah, giving back lost functions while giving back lost functions.
And there, especially when the function is completely lost, anything is a huge help.
Would you, uh, implant a Neuralink device in your own brain?
Absolutely.
I mean, maybe not right now, but absolutely.
What kind of capability once reached, you start getting real curious and almost get a little antsy,
like, like jealous of people that get, as you watch them get implanted?
Yeah, I mean, I think, I mean, even, even with our early participants, if they start to do things that
I, I can't do, uh, which I think is in the realm of possibility for them to be able to get, you know,
15, 20, if not like a hundred BPS, right?
Um, there's nothing that fundamentally stops us from being able to achieve that type of performance.
Um, I mean, I would certainly get jealous, um, that they can do that.
I, I should say that watching Nolan, I get a little jealous because he's having so much fun
and it seems like such a chill way to play video games.
Yeah.
So, I mean, the thing that also is hard to appreciate sometimes is that, you know, he's doing these things
while multi, like while talking and I mean, it's multitasking, right?
So it's, it's clearly, it's obviously cognitive, cognitively, uh, intensive, but similar to how,
you know, when we talk, we move our hands, like these things, like, you know, you, you,
like are multitasking.
I mean, he's able to do that and, you know, you won't be able to do that with other
assistive technology.
As far as I, I'm aware, you know, if you're obviously using like an eye tracking device,
you know, you're very much fixated on that thing that you're trying to do.
And if you're using voice control, I mean, like if you say some other stuff,
yeah, you don't get to use that.
Yeah.
The, the multitasking aspect of that is really interesting.
So it's not just the BPS for the primary task.
It's the, it's the parallelization of multiple tasks.
If you, if you take, if you measure the BPS for the entirety of the human organism.
So if you're talking and doing a thing with your mind and looking around also, I mean,
there's just a lot of parallelization that can, that can be happening.
But I mean, I think at some point for him, like,
if he wants to really achieve those high level BPS, it does require like, you know,
full attention.
Right.
And that's a separate circuitry that, that is a big mystery.
Like how attention works and, you know.
Yeah.
Attention, like cognitive load.
I've done, I've, I've read a lot of literature on people doing two tasks.
Like you have your primary task and a secondary task.
And the secondary task is, is a source of distraction.
And how does that affect the performance of the primary task?
And there's depending on the task, cause there's a lot of interesting, I mean,
this is an interesting computational device, right?
And I think there's.
To say the least.
A lot of novel insights that can be gained from everything.
I mean, I personally am surprised that no one is able to do
such incredible control of the cursor while talking.
And also being nervous at the same time, because he's talking like all of us are.
If you're talking in front of the camera, you get nervous.
So all of those are coming into play and he's able to still achieve high performance.
Surprising.
I mean, all of this is really amazing.
And I think just after researching this really in depth, I kind of wanted your link.
Get in line.
And also the safety kit in line.
Well, we should say the registry is for people who have quadriplegia and all that kind of stuff.
So there'll be a separate line for people.
They're just curious, like myself.
So now that Nolan patient P1 is part of the ongoing prime study.
What's the high level vision for P2, P3, P4, P5, and just the expansion into other human beings
that are getting to experience this implant?
Yeah, I mean, the primary goal for our study in the first place is to achieve safety endpoints.
Just understand safety of this device as well as the implantation process.
And also at the same time, understand the efficacy and the impact that it could have on
the potential users' lives.
And just because you're living with tetraplegia, it doesn't mean your situation is the same as
another person living with tetraplegia.
It's wildly, wildly varying.
And it's something that we're hoping to also understand how our technology can serve
not just a very small slice of those individuals, but, you know, broader group of individuals
and being able to get the feedback to, you know, just really build just the best product for them.
So our, you know, there's, there's obviously also, you know, goals that we have and the
primary purpose of the early feasibility study is to learn from each and every participants to improve
the device, improve the surgery before, you know, we embark on what's called a pivotal study that then
is a much larger trial that starts to look at statistical significance of your endpoints.
And that's required before you can then market the device.
And, and, and, you know, that's how it works in the US and just generally around the world.
That's, that's the process you follow.
So, you know, our, our goal is to really just understand from people like Nolan, P2, P3,
future participants, what aspects of our device needs to improve.
You know, if, if it turns out that people are like, I really don't like the fact that
it lasts only six hours.
I want to be able to use this computer for, you know, like 24 hours.
I mean, that's, that is a, you know, user needs and user requirements, which we can only find out
from just, just being able to engage with them.
So before the pivotal study, there's kind of like a rapid innovation based on individual
experiences.
You're learning from individual people, how they use it.
Like the, like the, like the high resolution details in terms of like cursor control and
signal and all that kind of stuff to like life experience.
Yeah.
Yeah.
So there's hardware changes, but also just, just firmware updates.
Um, so even, even when we, um, you know, had, had that sort of a recovery event for Nolan,
uh, you know, he now has the new firmware that, that he, um, has been, uh, updated with.
And, you know, it's similar to how like your phones get updated all the time with new firmwares
for security patches, whatever new functionality UI, right.
Um, and that's something that is possible with our implant.
It's not a static one-time device that, that can only do the thing that it said it can do.
I mean, similar to Tesla, you can do over the air firmware updates, and now you have completely
new user, user, user interface and, um, all this bells and whistles and improvements on,
you know, everything like the latest, right.
Um, that's, that's, that's, um, you know, when we say generalized platform, that's what
we're talking about.
Yeah.
It's really cool how the, the app that Nolan is using, there's like calibration, all that,
all that kind of stuff.
And then there's update, just, you just click and get an update.
Uh, what other future capabilities there are you kind of looking to?
You said vision.
That's a fascinating one.
Uh, what about sort of accelerated typing or speech or this kind of stuff?
Yeah.
What, and what else is there?
What's there?
Yeah, those, those are still in the realm, realm of, um, movement program.
So, so largely speaking, we have two programs.
We have the movement program and we have the, the vision program.
Uh, the movement program, you know, currently is focused around, you know, the digital freedom.
As you can easily guess, if you can control, you know, 2d cursor in the digital space,
you could move anything in the physical space.
Um, so robotic arms, wheelchair, your environment, uh, or even really like whether it's through the
phone or just like directly to those interfaces.
So like to those machines, um, so we're looking at ways to kind of expand those types of capability,
even for Nolan, um, that requires, you know, conversation with the FDA and kind of showing
safety data for, you know, if there's a robotic arm or wheelchair that, you know, we can guarantee
that they're not going to hurt themselves accidentally.
Right.
Um, it's very different if you're moving stuff in the, in the digital domain versus
like in the physical space, you can actually, um, potentially cause harm to the participants.
Um, so we're working through that right now.
Um, speech does involve different areas of the brain.
Speech prosthetic is very, very fascinating.
And there's actually been a lot of really, um, amazing work that's been happening in academia.
Um, you know, Sergei Stavisky at UC Davis, Jamie Henderson, and, you know, late Krishna Shinoi,
um, at Stanford are doing just some incredible amount of work in improving speech, uh, neuroprosthetics.
And those are actually looking more at parts of the motor cortex that are controlling, you know,
these focal articulators and, you know, being able to like, even by mouthing the word or imagine
speech, you can pick up those signals.
Um, the more sophisticated higher level processing areas, like, you know, the Broca's area or,
you know, uh, Wernicke's area, those are still very, very big mystery in terms of the underlying
mechanism of how all that stuff works.
But, um, yeah, I mean, I think, I think Neuralink's eventual goal is to kind of understand those,
those things, um, and, and be able to provide a platform and tools to be able to understand that
and study that.
This is where I get to the pothead questions.
Um, do you think we can start getting insight into things like thought?
So speech is, uh, there's a muscular component, like you said, there's like the act of producing
sounds, but then what about the internal things like cognition, like low level thoughts and high
level thoughts?
Do you think we'll start noticing kind of signals that could be picked up?
They could, um, they could be understood that could be maybe used in order to interact with
the outside world.
In some ways, like, I guess this starts to kind of get into the heart problem of consciousness.
Um, and, uh, I mean, on, on one hand, all of these are at some point set of electrical signals
that, um, from there, maybe it, it in itself is giving you the cognition or the meaning, or somehow
human mind is incredibly amazing storytelling machine.
So we're telling ourselves and fooling ourselves that there's some interesting meaning here.
Um, but I, I mean, I, I, I certainly think that PCI and, you know, really PCI at the end of the day is
a set of tools that help you kind of study the underlying mechanisms and in a, both like local,
but also broader sense.
Um, and whether, you know, there's some interesting patterns of like electrical signal that means like
you're thinking this versus, and you can either like learn from like many, many sets of data to
correlate some of that and be able to do mind reading or not.
I'm not, I'm not sure.
Um, I certainly would not kind of blow that out as a possibility, but, um, I, I think BCI alone
probably can't do that.
There's probably additional set of tools and framework and, and also like just heart problem
of consciousness at the end of the day is rooted in this philosophical question of like, what is
the, what's the meaning of it all?
What's the nature of our existence?
Like where's the mind emerged from this complex network?
Like, yeah.
How does the, uh, how does the subjective experience emerge from just a bunch of spikes,
electrical spikes?
Yeah.
Yeah.
I mean, we, we do really think about BCI and what we're building as a tool for
understanding the mind, the brain.
The only question that matters.
There's actually, um, there actually is, um, some biological existence proof of like what
it would take to kind of start to form some of these experiences that may be unique.
Um, if you actually look at every one of our brains, there, there are two hemispheres.
There's a left-sided brain.
There's a right-sided brain.
Um, and I mean, I, unless you have some other conditions, you normally don't feel like left
legs or right legs, like you just feel like one legs.
Right.
So what is happening there?
Right.
Um, if you actually look at the two hemispheres, there's a, uh, structure that kind of connectorized
the two called the corpus callosum that is supposed to have around 200 to 300 million
connections or axons.
Um, so whether that means that's the, the number of interface and electrodes that we need to
create some sort of mind meld or from that, like whatever new conscious experience that you,
you can experience, um, but I, I, I do think that there's like kind of an interesting, um,
existence proof that we all have.
And that threshold is unknown at this time.
Oh yeah.
These things, everything in this domain is, you know, speculation.
Right.
Um, and then there'll be, uh, you'd be continuously pleasantly surprised.
Uh, do you see a world where there's millions of people, like tens of millions, hundreds of
millions of people walking around with the Neuralink device in their, or multiple Neuralink
devices in their brain?
I do.
First of all, there, there are, like, if you look at worldwide, um, people suffering
from movement disorders and visual deficits, I mean, that that's, uh, in the tens, if not
hundreds of millions of people, um, so that, that alone, I think there's a lot of, uh, benefit
and, and potential good that we can do with this type of technology.
And once you start to get into kind of neuro, like psychiatric application, you know, depression,
um, anxiety, hunger, or, you know, obesity, right?
Like mood control of appetite, I mean, that starts to become, you know, very real to everyone.
Not to mention that every, uh, most people on earth have a smartphone.
And once BCI starts competing with a smartphone as a preferred methodology of interacting with
the digital world, that also becomes an interesting thing.
Oh yeah. I mean that, yeah, this is even before going to that, right? I mean, there's like
almost, I mean, the entire world that could benefit from these types of thing. And then,
yeah, like if we're talking about kind of next generation of how we interface with,
you know, machines or even ourselves, uh, in many ways, I think, um, BCI can play a role in that.
Um, and, you know, some of the things that I also talk about is I, I, I do think that there
is a real possibility that you could see, um, you know, 8 billion people walking around with Neuralink.
Well, thank you so much for pushing ahead. And, uh, I look forward to that exciting future.
Thanks for having me.
Thanks for listening to this conversation with DJ Saw. And now dear friends, here's Matthew McDougal,
the head neurosurgeon at Neuralink. When did you first become fascinated with the human brain?
Since forever, uh, as far back as I can remember, I've been interested in the human brain. I mean,
uh, I was, you know, a thoughtful kid and a bit of an outsider. And you, you know,
sit there thinking about what the most important things in the world are, uh, in your, in your little
tiny adolescent brain. And the answer that I came to that I converged on was, uh, that all of the
things you can possibly conceive of as things that are important for human beings to care about are
literally contained, you know, in the skull, uh, both the perception of them and their relative values.
And, you know, the solutions to all our problems and all of our problems are all contained in the
skull. And if we knew more about how that worked, uh, how the brain encodes information and generates
desires and generates agony and suffering, uh, we, we could do more about it. You know, you think about
all the, all the really great triumphs in human history, you think about all the really horrific
tragedies, uh, you know, you think about the Holocaust, you think about, um, any prison full of human
stories, uh, and all of those problems boil down to neurochemistry. So if you get a little bit of
control over that, you provide people the option to do better. And in the way I read history, the way
people have dealt with having better tools is that they most often in the end do better, uh, with huge
asterisks. But I think it's a, an interesting, worthy and noble pursuit to give people more options,
more tools. Yeah. That's a fascinating way to look at human history. You just imagine all these
neurobiological mechanisms, Stalin, Hitler, all of these, Genghis Khan, all of them just had like a,
a brain. It's just a bunch of neurons, you know, like a few tons of billions of neurons,
uh, gaining a bunch of information over a period of time. They have a set of module that does language
and memory and all that. And from there, in, in the, in the case of those people, they're able to
murder millions of people and all that coming from, uh, there's not some glorified notion of, uh,
uh, a dictator of this enormous mind or something like this. It's just, it's just the brain.
Yeah. Yeah. I mean, a lot of that has to do with how well people like that can organize those around
them. Other brains. Yeah. And so I always find it interesting to look to primatology,
you know, look to our closest non-human relatives, uh, for clues as to how humans are going to behave
and what particular humans are able to achieve. And so you look at, um, chimpanzees and bonobos and,
you know, they're similar, but different in their social structures, particularly.
And I went to Emory in Atlanta and studied under, uh, friends to all the great friends to all who
was kind of the leading primatologist, uh, who recently died and his work and looking at chimps
through the lens of, you know, how you would watch an episode of friends and understand the motivations
of the characters interacting with each other. He would look at a chimp colony and basically apply
that lens. I'm massively oversimplifying it. If you do that, instead of just saying,
you know, subject four, seven, three, you know, through his feces at subject four, seven, one,
you talk about them in terms of their human struggles, accord them the dignity of
themselves as actors with understandable goals and drives, what they want out of life. And
primarily it's, you know, the things we want out of life, food, sex, companionship, um, power.
You can understand chimp and bonoba behavior in the same lights, uh, much more easily. And I think
doing so gives you the tools you need to reduce human behavior from the kind of false complexity that
we layer onto it with language and look at it in terms of, oh, well, these humans are looking for
companionship, sex, food, power. Um, and I think that that's a pretty powerful tool to have in
understanding human behavior. And I just, uh, went to the Amazon jungle for a few weeks and it's a very
visceral reminder that a lot of life on earth is just trying to get laid. Yeah. They're all screaming at
each other. Like I saw a lot of monkeys and they're just trying to impress each other. Or maybe there's
a battle for power, but a lot of the battle for power has to do with them getting laid. Right.
Breeding rights often go with alpha status. And so if you can get a piece of that, then you're going
to do okay. And I would like to think that we're somehow fundamentally different, but
especially when it comes to primates, we really aren't, you know, we can use fancier poetic language,
but, uh, maybe some of the underlying drives that motivate us are, um, similar.
Yeah. I think that's true.
And all of that is coming from this, the brain.
Yeah.
Uh, so when did you first start studying the brain as a biological mechanism?
Basically the moment I got to college, I started looking around for labs that I could, uh, do
neuroscience work in. Uh, I originally approached that from the angle of, uh, looking at interactions,
between the brain and the immune system, which isn't the most obvious place to start. But,
um, I had this idea at the time that the contents of your thoughts would have an impact,
a direct impact, maybe a powerful one on, uh, non-conscious systems in your body. The systems we
think of as, you know, homeostatic automatic mechanisms, like fighting off a virus, like
repairing a wound. Um, and sure enough, there are big crossovers between the two. I mean,
it gets to, um, kind of a key point that I think goes under recognized. One of the things people don't
recognize or appreciate about the human brain, uh, enough. And that is that it basically controls
or has a huge role in almost everything that your body does. Um, like you try to name an example of
something in your body that isn't directly controlled or massively influenced by the brain.
And, uh, it's pretty hard. I mean, you might say like bone healing or something, but, uh,
even those systems, the hypothalamus and pituitary end up playing a role
in coordinating the endocrine system that does have a direct influence on say the calcium level in your
blood that goes to bone healing. So non-obvious connections between those things, uh, implicate
the brain as really a potent prime mover in all of health.
One of the things I realized in the other direction too, how most of the systems in the body are
integrated with the human brain. Like they affect the brain also like the immune system. Um, I think
there's just, you know, people who study Alzheimer's and, uh, those kinds of things. It's just surprising
how much you can understand of that from the immune system, from the other systems that don't
obviously seem to have anything to do with sort of the nervous system. They all play together.
Yeah. You could understand how that would be driven by evolution too, just in some simple examples.
If you get sick, if you get a communicable disease, you get the flu. Uh,
it's pretty advantageous for your immune system to tell your brain, Hey, now be antisocial for,
you know, a few days, don't go be the life of the party tonight. In fact, maybe just cuddle up somewhere
warm under a blanket and just stay there for a day or two. And sure enough, that tends to be the
behavior that you see both in animals and in humans. If you get sick, elevated levels of interleukins
in your blood and TNF alpha in your blood, ask the brain to cut back on social activity and, uh, even
moving around, you have lower locomotor activity, uh, in animals that are infected with viruses.
So from there, the early days in neuroscience to surgery, when did that step happen?
Yeah. It was a leap.
You know, it was sort of an evolution of thought. I wanted to study the brain. I started studying the
brain, uh, in undergrad, uh, in this neuroimmunology lab. Uh, I, from there, uh, realized at some point
that I didn't want to just generate knowledge. I wanted to affect real changes in the actual world,
in actual people's lives. And so after having not really thought about going into medical school,
I was on a track to go into a PhD program. I said, well,
I'd like, I'd like that option. I'd like to actually potentially help tangible people in
front of me. And, uh, doing a little digging found that there exists these MD-PhD programs
where you can choose not to choose between them and do both. And so, uh, I went to USC
for medical school and had a joint PhD program with Caltech, um, where I met, uh, actually chose
that program particularly because of a researcher at Caltech named Richard Anderson, who's one of the
godfathers of primate neuroscience and has a macaque lab where Utah rays and other electrodes were being
inserted into the brains of monkeys, uh, to try to understand how intentions were being encoded in the
brain. So, you know, I ended up there with the idea that maybe I would be a neurologist and study the
brain on the side, uh, and then discovered that neurology, um, again, I'm gonna make enemies by
saying this, but neurology, uh, predominantly and distressingly to me is, is the practice of
diagnosing a thing and then saying, good luck with that when there's not much we can do.
Um, and neurosurgery very differently, uh, is a, it's a powerful lever on taking people that are
headed in a bad direction and changing their course, uh, in the sense of brain tumors that are
potentially treatable or curable with surgery. Um, you know, even aneurysms in the brain, blood vessels
that are gonna rupture, you can, uh, save lives really is at the end of the day, what, what mattered to
me. And so, uh, I was at USC, as I mentioned, that happens to be one of the great neurosurgery
programs. And so I met these truly epic, uh, neurosurgeons, uh, Alex Kalesi and, and Micah Puzzo
and Steve Giannata and Marty Weiss, these, these sort of epic people that were just human beings in
front of me. And so it kind of changed my thinking from neurosurgeons are distant
gods that live on another planet and occasionally come and visit us to these are humans that have
problems and are people. And, uh, there's nothing fundamentally preventing me from being one of them.
And so, um, at the last minute in medical school, I changed gears from going into a different specialty
and switched into neurosurgery, which cost me a year. I had to do another year of research
because I was so far along in the process, uh, that, um, to switch into neurosurgery, the deadlines
had already passed. So it was a, a decision that costs time, but absolutely worth it.
What was the hardest part of the training on the, on the neurosurgeon track?
Yeah. Two things. I think that, you know, residency in neurosurgery is sort of a competition of pain of
like how much pain can you eat and smile. Yeah. Uh, and so there's workout restrictions that are not
really, they're viewed at, I think internally among the residents as weakness. And so most neurosurgery
residents try to work as hard as they can. And that I think necessarily means working long hours and
sometimes over the work hour limits. And, you know, we care about being compliant with whatever
regulations are, uh, in front of us, but I think more important than that, people want to give all,
give their all in becoming a better neurosurgeon because the, the stakes are so high. And so it's a
real fight to get residents, uh, to say, go home at the end of their shift and not stay and do more
surgery. Are you seriously saying like one of the hardest things is literally like getting, forcing
them to get sleep and rest and all this kind of stuff? Historically, that was the case. I think,
I think the next generation, I think the next generation is more, uh, compliant and more self-care.
Fear is what you mean. All right. I'm just, I'm just kidding. I'm just kidding. I didn't say it.
Now I'm making enemies. No. Okay. I get it. Wow. That's fascinating. Uh, so what was the second thing?
The personalities, uh, and maybe the two are connected, but.
So is it, was it pretty competitive? It's competitive. And it's also, um,
you know, as we touched on earlier, primates like power. And I think, um, neurosurgery has long had this aura
of, uh, mystique and excellence and whatever about it. And so it's, it's an invitation. I think for
people that are cloaked in that authority, you know, a board certified neurosurgeon is basically
a walking, uh, fallacious appeal to authority, right? You have license to walk into any room and
act like you're, you know, an expert on whatever and fighting that tendency is not something that most
neurosurgeons do well. Humility isn't the forte. Yeah. One of the, so, um, I have friends, uh, who know
you and whenever they speak about you, that yours, yours have the surprising quality for a neurosurgeon
of humility, which I think indicates that it's not, it's not as common as perhaps in other professions,
because there is a kind of gigantic sort of heroic aspect to neurosurgery. And I think it gets to
people's head a little bit. Yeah. Well, that, I think that, uh, you know, that allows me to play well
at an Elon company because Elon, uh, one of his strengths I think is to just instantly see through
fallacy from authority. So nobody walks into a room that he's in and says, well, goddammit, you have
to trust me. I'm the guy that built the last, you know, 10 rockets or something. And he says, well,
you did it wrong and we can do it better. Uh, or I'm the guy that, you know, kept Ford alive for the
last 50 years. You listened to me on how to build cars. And he says, no. And so you don't walk into a
room that he's in and say, well, I'm a neurosurgeon. Let me tell you how to do it.
Uh, he's going to say, well, I'm a human being that has a brain. I can think from first principles
myself. Thank you very much. Uh, and here's how I think it ought to be done. Let's go try it and see
who's right. Uh, and that's, you know, proven I think over and over in his case to be a very powerful
approach. If we just take that tangent, there's a fascinating interdisciplinary team at Neuralink
that you get to interact with, um, including Elon. What do you think is the secret to a successful
team? Well, what have you learned from just getting to observe these folks?
Yeah. World experts in different disciplines work together.
Yeah. There, there's a sweet spot, uh, where people
disagree and forcefully speak their mind and passionately defend their position
and yet are still able to accept information from others and change their ideas when they're wrong.
And so I like the analogy of sort of how you polish rocks, you put hard things in a, in a hard
container and spin it. People bash against each other and outcomes, uh, you know, a more refined product.
And so, uh, to make a good team at Neuralink, we've tried to find, you know, people that are not afraid
to defend their ideas passionately and, you know, occasionally strongly disagree with people, uh, that
they're, that they're working with and have the best idea come out on top. Um, it's not an easy balance,
again, to refer back to the primate brain. It's not something that is inherently built into the,
the primate brain to say, I passionately put all my chips on this position and now I'm just going to
walk away from it and admit you are right. You know, part of our brains tell us that that is a power
loss. That is a loss of face, a loss of standing in the community. And, uh, and, and now you're a, a
zeta chump because your idea got trounced. Um, and you just have to, you know, recognize that,
that little voice in the back of your head is maladaptive and it's not helping the team win.
Yeah. You have to have the confidence to be able to walk away from an idea that you hold on to.
Yeah. And if you do that often enough, you're actually going to, uh, become the best in the
world at your thing. I mean, that kind of that rapid iteration.
Yeah. You'll at least be a member of a winning team.
Ride the wave. Uh, what, what did you learn? You mentioned there's a lot of amazing, uh,
neurosurgeons at USC. What, what lessons about surgery and life have you learned from those folks?
Yeah. I think working your ass off, working hard while, um, you know, functioning as a member of a
team, getting a job done. That is incredibly difficult. Um, you know, working incredibly
long hours, being up all night, taking care of someone that, you know, you think probably won't
survive no matter what you do, working hard to make people that you passionately dislike look good
the next morning. These folks were relentless in their pursuit of, um, excellent neurosurgical technique
decade over decade. And, and I think we're well recognized for the, that excellence. So it's,
you know, especially Marty Weiss, Steve Gianotta, uh, Micah Puzzo, the, they made huge contributions,
not only to surgical technique, but they built training programs that trained dozens or hundreds
of amazing neurosurgeons. I was just lucky to kind of be in their wake. What's that like? You mentioned
doing a surgery where the person is likely not to survive. Does that wear on you?
Yeah. Um, you know, it, it's especially challenging, um, when you, with all respect to, to our elders,
it doesn't hit so much when you're taking care of an 80 year old and something was going to get them.
Pretty soon anyway. And so you lose a patient like that. And it, it was part of the natural course of
what is expected of them in the, in the coming years, regardless, uh, taking care of, you know, a father
of two or three, four young kids. So someone in their thirties that didn't have it coming
and they show up in your ER having their first seizure of their life. And lo and behold, they've
got a huge malignant, inoperable or incurable brain tumor. You can only do that. I think a handful of
times, um, before it really starts eating away at your, at your armor, um, or, uh, you know, a young
mother that shows up that has a giant hemorrhage in her brain that she's not going to survive from. And
you know, they bring her four year old daughter in to say goodbye one last time before they turn the
ventilator off that, um, you know, the great Henry Marsh is an English neurosurgeon who said it best.
I think he says every neurosurgeon carries with them a private graveyard. And I definitely feel that,
um, especially with young parents, uh, that, that kills me. Uh, they, they had a lot more to give.
The, the loss of those people specifically has a, you know, knock on effect that's going to
make the world worse for people, uh, for a long time. And it's just hard to feel powerless in the face of
that. You know, and that's where I think you have to be a, uh, borderline evil to fight against a
company like Neuralink or to constantly be taking pot shots at us because what we're doing is to try
to fix that stuff. We're trying to give people options, uh, to reduce suffering. Uh, we're trying to,
we're trying to take the, the pain out of life that, uh, broken brains brings in. And, um,
yeah, this is just our, our little way that we're fighting back against entropy, I guess.
Yeah. That's the, the amount of suffering that's endured when some of the things that we take for
granted that our brain is able to do is taken away, uh, is immense. And to be able to restore
some of that functionality is a real gift. Yeah. We're just starting. We're, we're going to,
we're going to do so much more. Um, well, can you take me through the full procedure for
implanting, say the N1 chip in Neuralink? Yeah. It's a really simple, really simple,
straightforward procedure. Uh, the, the human part of the surgery, uh, that, that I do is
dead simple. It's one of the most basic neurosurgery procedures
imaginable. And I think there's evidence that it, some version of it has been done for thousands of
years. Uh, there are examples, I think from ancient Egypt of healed or partially healed, uh,
trephonations and from, uh, Peru or, you know, ancient times in South America, uh, where, uh,
these proto surgeons would drill holes in people's skulls, you know, presumably to let out the evil
spirits, but maybe to drain blood clots. And there's evidence of bone healing around the edge,
meaning the people at least survive some months, uh, after a procedure. And so what we're doing is that
we are making a cut in the skin on the top of the head over the area of the brain that is the most
potent, uh, representation of hand intentions. And so if you, if you are an expert concert pianist,
you know, this part of your brain is lighting up the entire time you're playing, uh, we call it the
hand knob, the hand knob. Yeah. So it's all the, like the finger movements, all this, all,
all of that is just firing away. Yep. There's a little squiggle in the cortex right there.
One of the folds in the brain is kind of doubly folded right on that spot. And so you can look at
it on an MRI and say, that's the hand knob. And then you, you do a functional test and a special
kind of MRI called a functional MRI, FMRI. And this part of the brain lights up when people,
even quadriplegic people whose brains aren't connected to their finger movements anymore, they
imagine finger movements in this part of the brain still lights up. So we can ID that part of the
brain in anyone who's preparing to enter our trial and say, okay, that, that part of the brain we
confirm is your hand intention area. Um, and so, uh, I'll make a little cut in the skin. We'll flap the skin
open just like kind of opening the hood of a car, only a lot smaller, make a perfectly round, uh,
one inch diameter hole in the skull, remove that bit of skull, uh, open the lining of the brain,
the covering of the brain. It's like a, like a little bag of water that the brain floats in
and then show that part of the brain to our robot. And then the, this is where the robot shines. It
can come in and take these tiny, you know, much smaller than human hair electrodes and precisely
insert them into the cortex, into the surface of the brain to a very precise depth in a very precise
spot that avoids all the blood vessels that are coating the surface of the brain. And after the
robot's done with its part, then, you know, the human comes back in and puts the implant into that
hole in the skull and covers it up, uh, screwing it down to the skull and sewing the skin back together.
So the whole thing is, you know, a few hours long, it's extremely low risk compared to
the average neurosurgery involving the brain that, that might say, open up a deep part of the brain or
manipulate blood vessels in the brain. Uh, this, this opening on the surface of the brain
with, um, with only cortical micro insertions carries, um, significantly less risk than a lot of the,
you know, tumor or aneurysm surgeries that are routinely done.
So cortical micro insertions that are via robot and computer vision are designed to avoid the blood
vessels. Exactly.
So, uh, I know you're a bit biased here, but let's compare human and machine.
Sure.
So what are human surgeons able to do well? And what are robot surgeons able to do well
at this stage of our human civilization development?
Yeah. Yeah. Yeah. That's a good question. Um, humans, uh, are general purpose machines. We're able
to adapt to unusual situations. We're able to change the plan on the fly. Um, I remember well,
a surgery that I was doing many years ago down in San Diego, where the plan was to, um,
open a small hole behind the ear and go reposition a blood vessel that had come to lay on the facial
nerve, the trigeminal nerve, uh, the nerve that goes to the face. When that blood vessel lays on the nerve,
it can cause just intolerable, horrific shooting pain that people describe like being zapped with a
cattle prod. And so the beautiful elegant surgery is to go move this blood vessel off the,
off the nerve. The surgery team, we went in there and started moving this blood vessel and then found
that there was a giant aneurysm on that blood vessel that was not easily visible on the pre-op scans.
And so the plan had to dynamically change and that the, um, human surgeons had no problem with that.
We're trained for all those things. Robots wouldn't do so well in that situation, at least in their current
incarnation, uh, fully robotic surgery, like, you know, the, the electrode insertion portion of,
of the neural link surgery, it goes according to a set plan. And so the humans can interrupt the flow
and change the plan, but the robot can't really change the plan midway through. It operates according
to how it was programmed and how it was asked to run. It does its job very precisely, uh, but not
with a wide degree of latitude and how to react to changing conditions.
So there could be just a very large number of ways that you could be surprised as a surgeon
when you enter a situation that could be subtle things that you have to dynamically adjust to.
Correct. And robots are not good at that.
Currently. Currently. I think, uh, we are at the dawn of a new era with AI of the parameters for
robot responsiveness to be dramatically broadened, right? I mean, you can't look at a self-driving car
and say that it's operating under very narrow parameters. You know, if a chicken runs across the
road, it wasn't necessarily programmed to deal with that specifically, but a Waymo or a self-driving
Tesla would have no problem reacting to that appropriately. Uh, and so surgical robots aren't
there yet, but give it time. And then there could be a lot of sort of into like semi-autonomous
possibilities of maybe a robotic surgeon could say this situation is perfectly familiar or the situation
which is not familiar. And in the not familiar case, a human could take over, but basically like
be very conservative and saying, okay, this for sure has no issues, no surprises. And then let the humans
deal with the surprises with the edge cases, all that. Yeah. Uh, that's one possibility. So like you think
eventually, uh, you'll be out of the job, what you being neurosurgeon, your job being neurosurgeon, humans,
there will not be many neurosurgeons left on this earth. I'm not worried about my job in my, in the
course of my professional life. I think I, I would tell my, my kids not necessarily to go in this line of
work, uh, depending on, depending on how things look in 20 years. It's so fascinating. Cause I, I mean,
I, if I have a line of work, I would say it's programming. And if you ask me like for the last,
I don't know, 20 years, what I would recommend for people, I would, I would tell them, yeah, go there's
just, you will always have a job if you're a programmer. Cause there's more and more computers
and all this kind of stuff. And, uh, it pays well, but then you, you realize these large language
models come along and they're really damn good at generating code. Yeah. So it's overnight, you
could be surprised, like, wow, what is the contribution of the human really? But then you
start to think, okay, it does seem that humans have ability, like you said, to deal with novel
situations. And in the case of programming, it's the ability to kind of come up with novel ideas
to solve problems. It's, it seems like machines aren't quite yet able to do that. And when the
stakes are very high, when it's life critical, as it is in surgery, especially neurosurgery, then it
starts, the, the stakes are very high for a robot to actually replace a human. But it's fascinating that
in this case of Neuralink, there's a human robot collaboration. Yeah. Yeah. It's, I do the parts
it can't do, and it does the parts I can't do. Um, and we, we are friends.
Uh, the, I, I saw that there's a lot of practice going on. So, I mean, everything in Neuralink is,
is tested extremely rigorously. But one of the things I saw that there's a proxy on which the
surgeries are performed. Yeah. So this is both for the robot and for the human, for everybody involved
in the entire pipeline. Yep. What's that like practicing the surgery? It's pretty intense.
Uh, so there's no analog to this in human surgery. Uh, human surgery is sort of this
artisanal craft that's handed down directly from master to pupil over the generations. Yes. I mean,
literally the way you learn to be a, a surgeon on humans is by doing surgery on humans. I mean, first
you watch, uh, your professors do a bunch of surgery, and then finally they put, you know, the trivial parts
of the surgery into your hands and then the more complex parts. And as your understanding of the,
the point and the purposes of the surgery increases, you get more responsibility in the
perfect condition. It doesn't always go well. In Neuralink's case, the approach is a bit different.
Um, we of course practiced as far as we could on animals. We did hundreds of animal surgeries.
Um, and when it came time to do the first human, uh, we had a, just an amazing team of engineers build
incredibly lifelike models. Uh, one of the engineers, Fran Romano in particular, built,
built a pulsating brain in a custom 3d printed skull that matches exactly the, the patient's anatomy,
uh, including their face and, uh, scalp characteristics. And so when I was able to
practice that, I mean, it's as close as it really reasonably should get, uh, to,
to being the real thing and all the details, including, you know, the, having a, a mannequin body
attached to this custom head. And so when we were doing the practice surgeries, we'd
wheel that body into the CT scanner and take a mock CT scan and wheel it back in and conduct all the
normal safety checks verbally, you know, stop this patient. We're confirming his identification is
mannequin number, blah, blah, blah. And then opening the brain in exactly the right spot using standard
operative neuro navigation equipment, standard surgical drills in a, in the same OR that we do all of our
practice surgeries in it at Neuralink and having the skull open and have the brain pulse, which adds a
degree of difficulty for the robot, you know, perfectly precisely plan and insert those electrodes to the
right depth and location. And so, uh, yeah, we, we, uh, kind of broke new ground on how extensively we
practiced for this surgery. So there was a historic moment, a big milestone, uh, for Neuralink
in part for humanity with, uh, the first human getting a Neuralink implant in January of this year.
Uh, take me through the surgery, uh, on Noland. What did he feel like to be part of this?
Yeah. Well, we, um, we were lucky to have just incredible partners at the Barrow Neurologic
Institute. They are, uh, I think the premier neurosurgical hospital in the world.
Uh, they, they made everything as easy as possible for the trial, uh, to get going and, and helped us
immensely, uh, with their expertise on how to, uh, how to arrange the details. It was a much more high
pressure surgery in some ways. I mean, even though the, you know, the outcome wasn't particularly in
question in terms of our participants' safety, the number of observers, you know, the number of people,
there's conference rooms full of people watching live streams in the hospital, um, rooting for this
to go perfectly. And that just adds pressure that, uh, is not typical for, uh, even the most intense
production neurosurgery, say removing a tumor or, you know, placing deep brain stimulation electrodes.
And it had never been done on a human before. There were unknown unknowns. Um, and so, uh,
definitely a moderate pucker factor there for the whole team, uh, not knowing if we were going to
encounter, say, uh, a degree of brain movement that was unanticipated or, uh, a degree of brain sag that
took the brain far away from the skull and made it difficult to insert or some other unknown unknown
problem. Fortunately, everything, uh, went well. And that, that surgery is one of the smoothest
uh, outcomes we could have imagined. Were you nervous? I mean, you're extremely quarterback and
like in the super bowl kind of situation, extremely nervous, uh, extremely, I was very pleased when it
went well. And then, and when it was over, um, looking forward to number two. Yeah. Even with all
that practice, all of that, just never been in a situation that's so high stakes in terms of people
watching. Yeah. And we should also probably mention given how the media works, a lot of people,
um, you know, maybe in a dark kind of way, hoping it doesn't go well. Well, I think wealth is easy to
hate, um, or envy or, or whatever. And I think there's a whole industry, uh, around driving clicks
and bad news is great for clicks. And so any way to take an event and turn it into bad news,
uh, is going to be really good for, for clicks. It just sucks because I think in, it puts pressure
on people. It discourages people from, from trying to solve really hard problems because to solve
hard problems, you have to go into the unknown. You have to do things that haven't been done before
and you have to take risks, uh, calculated risks. You have to do all kinds of safety precautions,
but risks nevertheless. And, uh, I just wish there would be more celebration of that,
of the risk taking versus like people just waiting on the, on, on the sidelines,
like waiting for failure and then pointing out the failure. Uh, yeah, it sucks. But you know,
in this case, it's, it's, it's really great that everything went just flawlessly, but
it's unnecessary pressure. I would say now that there's a human with literal skin in the game,
you know, there's a participant who, who's wellbeing rides on this doing well. You have to be a pretty
bad person to be rooting for that, to go wrong. Um, and so, you know, hopefully people look in the
mirror and, and realize that at some point. So did you get to actually front row seat,
like watch the robot work? Like what, uh, you get to see the whole thing? Yeah. I mean, I, you know,
because, uh, uh, an MD needs to be in charge of all of the medical decision-making throughout the process.
Um, I unscrubbed from the surgery after exposing the brain and presenting it to the robot and, um,
place the targets on the robot, uh, inter software interface that tells the robot where it's going to
insert each thread that was done, um, with, you know, my hand on the mouse for whatever that's worth.
So you were the one placing the targets. Yeah. Oh, cool. So like it, you know, the, the, the robot,
uh, with the computer vision provides a bunch of candidates and you kind of finalize the decision.
Right. Uh, you know, they, the, the software engineers are amazing on this team. And so
they actually provided an interface where you can essentially use a lasso tool and select a prime
area of brain real estate and it will automatically avoid the blood vessels in that region and
automatically place a bunch of targets. So you, you know, that allows, you know, the human robot
operator to select, uh, really good areas of brain and make dense applications of targets in that, in
those regions, the regions we think are going to have the most, um, high fidelity representations of finger
movements and arm movement intentions. I've seen like images of this. And for me with OCD,
it's for some reason a really pleasant, uh, I think there's a subreddit called oddly satisfying.
Yeah. I love that subreddit. It's oddly satisfying to see the different target sites avoiding the blood
vessels and, uh, also maximizing like the usefulness of those locations for the signal. It just feels good.
It's like, ah, as, as a person who has a visceral reaction to the brain bleeding, I can tell you,
it's extremely satisfying watching the electrodes themselves go into the brain and not cause bleeding.
Yeah. Yeah. So, uh, you said the feeling was of relief when everything went perfectly. Yeah.
How deep in the brain can you currently go and, uh, eventually go? Let's say on the neural link side,
is it, it seems the deeper you go in the brain, the more challenging it becomes.
Yeah. So talking broadly about neurosurgery, we can get anywhere.
Uh, it's routine for me to put deep brain stimulating electrodes, uh, near the very bottom of the brain.
Uh, entering from the top and passing about a two millimeter wire all the way into the bottom of the
brain. And that's not revolutionary. A lot of people do that. Uh, and we can do that with very
high precision. I, I use a robot, uh, from Globus to do that surgery, um, you know, several times a month.
Uh, it's, it's pretty routine. What are your eyes in that situation? What are you seeing? What's,
what kind of technology can you use to visualize where you are to light your way?
Yeah. So it's cool process on the software side. You take a preoperative MRI that's extremely high
resolution data of the entire brain. You put the patient to sleep, put their head in a frame that
holds the skull very rigidly. And then you take a CT scan of their head while they're asleep with that
frame on and then merge, uh, the MRI and the CT in software. You have a, a plan based on the MRI where
you can see these nuclei deep in the brain. You can't see them on CT, but if you trust the merging of
the two images, then you indirectly know on the CT where that is and therefore indirectly know where
in reference to the titanium frame screwed to their head, those targets are. And so this is 60s
technology to manually compute trajectories given the entry point and target, uh, and dial in some goofy
looking titanium, um, actuators, uh, with manually manual actuators with little tick marks on them.
The modern version of that is to use a robot, uh, you know, just like a, a little kuka arm you might
see it building cars at the Tesla factory. Uh, this small robot arm can show you the trajectory that
you intended from the preop MRI and establish a very rigid holder through which you can drill a small
hole in the skull and pass a small rigid wire deep into that area of the brain that's hollow and put
your electrode through that hollow wire and then remove all of that except the electrode. Uh, so you
end up with the electrode very, very precisely placed far from the skull surface. Now that's standard
technology, um, that's already, you know, been out in the world for, for a while. Neuralink right now is
focused entirely on cortical targets, surface targets, uh, because there's no trivial way to get
say hundreds of wires deep inside the brain without doing a lot of damage. So your question, what do you
see? Well, I see an MRI on a screen. I can't see everything that that DBS electrode is passing
through on its way to that deep target. And so it's accepted with this approach that there's going to
be about one in a hundred patients who have a, a bleed somewhere in the brain, uh, as a result of
passing that wire blindly into the deep part of the brain. That's not an acceptable safety profile for
Neuralink. We, uh, start from the position that we want this to be dramatically maybe two or three
orders of magnitude safer than that. Uh, safe enough really that, you know, you or I without a profound
medical problem might on our lunch break someday say, yeah, sure, I'll get that. I'd be meaning to
upgrade to the latest version. And so that the safety constraints given that are high. And so we
haven't, uh, settled on a final solution for arbitrarily approaching deep targets in the brain.
It's interesting. Cause like, you have to avoid blood of us or somehow you have to, maybe there's
creative ways of doing the same thing, like mapping out high resolution geometry of blood vessels,
and then you can go in blind. But like, how do you map out that in a way that's like super stable?
It's saying that there's a lot of interesting challenges there, right? Yeah, but there's a lot
to do on the surface. Exactly. So we've got vision on the surface. Um, you know, we, we actually have
made a huge amount of progress sewing, uh, electrodes into the spinal cord, uh, as a potential
workaround for a spinal cord injury that would allow a brain mounted implant to translate motor
intentions to a spine mounted implant that can affect muscle contractions in previously paralyzed
arms and legs. That's just incredible. So like the effort there is to try to bridge
the brain to the spinal cord, to the periphery peripheral nervous. So, uh, how hard is that to do?
We have that working in, uh, in very crude forms in animals.
That's amazing. Yeah.
We've done similar to like with Nolan where he's able to digitally move the cursor here. You're
doing, uh, the same kind of communication, but with the actual effectors that you have.
Yeah. That's fascinating. Yeah.
So we have anesthetized animals doing grasp and moving, moving their legs and then sort of
walking pattern, uh, again, early days, uh, but, uh, the future is bright for this kind of thing. And,
and people with paralysis, uh, should look forward to that bright future. They're going to have options.
Yeah. And there's a lot of sort of, uh, intermediate or extra options where you take like an optimist
robot, like the, uh, the arm and to be able to control the arm.
Yeah.
The, the, the fingers and hands at the arm as a prosthetic are getting better too.
So skeletons.
Yeah. So that, that goes hand in hand, although I didn't quite understand until thinking about it
deeply and do more research about Neuralink, how much you can do on the digital side. So there's
digital telepathy. I didn't quite understand that you could really map the intention
as you described in the hand knob area that you can map the intention, just imagine it,
think about it. That intention can be mapped to actual action in the digital world.
Right.
And now more and more, so much can be done in the, in the digital world that it, it, it can reconnect
you to, to the outside world. It can allow you to have freedom, have independence if you're a
quadriplegic.
Yeah.
That's really powerful. Like you can go really far with that.
Yeah. Our first participant is, he's incredible. He's breaking world records left and right.
And he's having fun with it. It's great. Um, just going back to the surgery,
your whole journey, you, uh, mentioned to me offline, you have surgery on Monday. So
you're like, you're doing surgery all the time.
Yeah. Maybe the ridiculous question, what does it take to get good at surgery?
Practice repetitions. You just same with anything else. You know, there's a million ways of
people saying the same thing and selling books, saying it, but you call it 10,000 hours. You call it,
you know, spend some chunk of your life, some percentage of your life, focusing
on this, obsessing about getting better at it. Um, repetitions,
uh, humility, recognizing that you aren't perfect at any stage along the way,
uh, recognizing you've got improvements to make in your technique, being open to feedback and coaching
from people with a different perspective on how to do it. Um, and then, um, just the constant
will to do better. Uh, that fortunately, you know, if you're not a sociopath, I think your patients
bring that with them to the office visits every day, they, you know, force you to want to do better
all the time. Yeah. Just step up. I mean, it's a real human being, a real human being that you can
help. Yeah. So every surgery, even if it's the same exact surgery, is there a lot of variability
between that surgery and a different person? Yeah, a fair bit. I mean, a good example for us
is the, the angle of the skull relative to the normal plane of the body axis of the skull over
hand knob, uh, is pretty wide variation. I mean, some people have really flat skulls
and some people have really steeply angled skulls over that area. And that has, you know, consequences for,
how their head can be fixed in, in, uh, in sort of the frame that we use, um, and how the robot has
to approach the skull. And, um, yeah, people's, people's bodies are built as differently as,
you know, the people you see walking down the street as, as much variability in body shape and size
as you see there we see in brain anatomy and skull anatomy. Um, there are some people who we've had to
kind of exclude from our trial for having skulls that are too thick or too thin or scalp that's too
thick or too thin. Um, I think, you know, we have like the middle 97% or so, uh, of people, but
you can't account for all human anatomy variability. How much like mushiness and messes there?
Cause I, uh, you know, taking biology classes, the diagrams are always really clean and crisp neuroscience.
The pictures of neurons are always really nice and very, um, but whenever I look at pictures of like
real brains, they're all, I don't know what is going on. Yeah. Uh, so how much are biological systems in
reality? Like how hard is it to figure out what's going on? Not too bad. Uh, once you really get used to
this, you know, that's where experience and, and skill and, uh, education really come into play is
if you stare at a thousand brains, it becomes easier to kind of mentally peel back the, say,
for instance, blood vessels that are obscuring the sulci and gyri, you know, kind of the wrinkle
pattern of the surface of the brain. Occasionally when you're, when you're first starting to do this and
you open the skull, it doesn't match what you thought you were going to see based on the MRI.
Uh, and with more experience, you, you learn to kind of peel back that layer of blood vessels
and see the underlying pattern of wrinkles in the brain and, uh, use that as a landmark for where you
are. The wrinkles are a landmark. So like, yeah. So I was describing hand knob earlier. That's
a pattern of the wrinkles in the brain. It's sort of this sort of Greek letter Omega shaped area of
the brain. So you could recognize the hand knob area. Like if, if I show you a thousand brains and
give you like one minute with each, you'd be like, yep, that's that. Sure. And so there is some
uniqueness to that area of the brain, like in terms of the geometry, the topology of the thing. Yeah.
Where is it about in the, it's, so you have this strip of brain running down the top called the
primary motor area. And I'm sure you've seen this picture of the homunculus laid over the surface of
the brain, the weird little guy with huge lips and giant hands. Uh, that guy sort of lays with his legs
up at the top of the brain and, and face arm areas farther down and, and then some kind of mouth, lip,
tongue areas, uh, farther down. And so the hand is right in there. And then the areas that control
speech, at least on the, on the left side of the brain in most people are, are just below that. And so
uh, any muscle that you voluntarily move in your body, um, the vast majority of that references that
strip or those intentions come from that strip of brain and the, the wrinkle, uh, for hand knob is
right in the middle of that. And vision is back here. Yep. Also close to the surface. Vision's a little
deeper. Uh, and so, you know, this gets to your question about how deep can you get, um,
to do vision, we can't just do the surface of the brain. We have to be able to go in,
uh, not, not as deep as we have to go for DBS, but maybe a centimeter deeper than we're used to
for hand insertions. Uh, and so that's, you know, work in progress. That's, uh, a new set of challenges
to overcome. By the way, you mentioned, uh, the Utah, right? And I just saw a picture of that,
and that thing looks terrifying because it's, it's because it's rigid. And then if you look at the
threads, they're flexible. What can you say that's interesting to you about the flexible,
that kind of approach of the, the flexible threads to deliver the electrodes next to the neurons?
Yeah. I mean, the, the goal there comes from experience. I mean, we stand on the shoulders of
people that made Utah rays and used Utah rays for decades before we ever even came along. Um,
Neuralink arose partly, this approach to technology arose out of a need recognized
after Utah rays would fail routinely because the rigid electrodes, those spikes that are
literally hammered using an air hammer into the brain, uh, those spikes generate a bad immune response
that encapsulates the, the electrode spikes in, uh, scar tissue essentially. And so one of the projects
that was being worked on in, in the Anderson lab at Caltech, when I got there was to see if you could use
chemo therapy to prevent the formation of scars, like, you know, things are pretty bad when you're
jamming a bed of nails into the brain and then treating that with chemotherapy to try to prevent
scar tissue. It's like, you know, maybe we've gotten off track here, guys, maybe there's a fundamental
redesign necessary. And so Neuralink's approach of using highly flexible, tiny electrodes
electrodes, avoids a lot of the bleeding, avoids a lot of the immune response that ends up happening,
uh, when rigid electrodes are pounded into the brain. And so what we see is our electrode longevity
and functionality, uh, and the, and the health of the brain tissue immediately surrounding the electrode,
uh, is excellent. I mean, it goes on for, for years now in our animal models.
What do most people not understand about the biology of the brain? We mentioned the vasculature,
that's really interesting. I think the most interesting, maybe underappreciated fact
is that it really does control almost everything. I mean, I don't know, for out of the blue example,
imagine you, you want a lever on fertility, you want to be able to turn fertility on and off.
I mean, there are legitimate targets in the brain itself to modulate fertility, say, um, blood
pressure. You want to modulate blood pressure. There are legitimate targets in the brain for doing that.
Um, things that aren't immediately obvious as brain problems, uh, are potentially solvable in the brain.
Um, and so I think it's an underexplored area for primary treatments of, uh, of all the things that
bother people. That's a really fascinating way to look at it. Like there's a lot of conditions
we might think have nothing to do with the brain, but they might just be symptoms of something that
actually started in the brain. The actual source of the problem, the primary source is something in the
brain. Yeah. Not, not always. I mean, you know, their kidney disease is real, uh, but, um, there are
levers you can pull in the brain that affect all of the, all of these systems. There's knobs, you know,
on, off switches and knobs in the brain from which this all originates. Yeah. Uh, would you have a
neural link chip implanted in your brain? Yeah. Um, I think use case right now is
use a mouse, right? I can already do that. And so there's no value proposition, uh, on safety grounds
alone. Sure. I'll do it tomorrow. You know, you say the use case of the mouse
because after like researching all this and part of it is just watching Nolan have so much fun.
If you can get that bits per second, like really high with the mouse,
like being able to interact. Cause if you think about the, the ways, uh, on the smartphone,
the way you swipe, that was transformational. Yeah. How we interact with the thing. It's subtle.
You don't realize it, but to able to touch a phone and to, uh, scroll with your finger,
that's like, that changed everything that people were sure you need a keyboard to type in the, that,
uh, there's a lot of HCI aspects to that, that changed how we interact with computers.
So there could be a certain rate of speed with the mouse that would change everything.
Yes. Like you might be able to just click around a screen extremely fast and that
if it, I can't see myself getting the neural link for much more rapid interaction with the digital
devices. Yeah. I think recording speech intentions from the brain might, might change things as well.
You know, the value proposition for the average person, um, a keyboard is a pretty clunky human
interface requires a lot of training. It's, you know, highly variable in the maximum performance
that the average person can, uh, can achieve. Uh, I think taking that out of the equation and just
having a natural, you know, word to computer interface, um, might change things for a lot of
people. It'd be hilarious if that is the reason people do it. Even if you have speech to text,
that's extremely accurate. It currently isn't. Right. But it say gotten super accurate. It'd be
hilarious if people went for neural link just so you avoid the embarrassing aspect of speaking,
like looking like a douche back, speaking to your phone in public, which is a real, like, that's a
real constraint. Yeah. I mean, with a bone conducting case, uh, that can be a, an invisible headphone,
say, um, and the ability to think words into software and have it respond to you.
Um, you know, that starts to sound sort of like embedded super intelligence. You know, if you can
silently ask for the Wikipedia article on any subject and have it read to you without any
observable change happening in the outside world, uh, you know, for one thing, standardized testing is
obsolete. Yeah. If it's done well on the UX side, it could change. I don't know if it
transforms society, but it really, uh, can create a kind of shift in the way we interact with
digital devices in the way that a smartphone did that I would, um, just having to look into the
safety of everything involved, I would totally try it. So it doesn't have to go to some like
incredible thing where you have, it connects your vision or to some, like it connects all over your
brain. That could be like just connecting to the hand knob. Uh, you might have a lot of interesting
interaction, human computer interaction possibilities. Yeah. That's really interesting.
Yeah. And the technology on the academic side is progressing at light speed here. I think there was
a really amazing paper out of UC Davis, uh, Sergei Stavisky's lab that basically made a initial solve of
speech decode and something like 125,000 words, uh, that they were getting with, you know, very high
accuracy, which is. So you're just thinking the word. Yeah. Thinking the word and you're able to get it.
Yeah. Oh boy. Like you have to have the intention of speaking it. Right. So like do that inner voice.
Now it's so amazing to me that you can do the intention,
to signal mapping. All you have to do is just imagine yourself doing it.
And if, if you get the feedback that it actually worked, you can get really good at that. Like
your brain will, first of all, adjust and you develop like any other skill, like touch typing,
you develop in that same kind of way. That is, that is, to me, it's just really fascinating to be able to
even to play with that. Honestly, like I would get a neural link just to be able to play with that.
Just to play with the capacity of the capability of my mind to learn this skill.
It's like learning the skill of typing and learning the skill of moving a mouse.
It's another skill of moving the mouse, not with my physical body, but with my mind.
I can't wait to see what people do with it. I feel like we're cavemen right now. We're, we're like
banging rocks with a stick and thinking that we're making music. Um, at some point when these are more
widespread, there's going to be the equivalent of a, of a piano that, you know, someone, someone can make
art with their brain in a way that we didn't even anticipate.
I'm looking forward to it.
Give it to like a teenager. Like anytime I think I'm good at something, I'll always go to like,
I don't know, even, even, uh, even with the, the, the bits per second and playing a video game,
you realize you give it to a teenager, you're giving your link to a teenager,
just a large number of them, the kind of stuff, they get good at stuff. They're gonna get like
hundreds of, uh, bits per second.
Yeah.
Even just with the current technology.
Probably, probably.
Just, uh, cause it's also addicting how, like the, the, the number go up aspect of it, of like
improving and training. Cause it is, it's almost like a skill. And plus there's a software on the
other end that adapts to you. And especially if the adapting procedure, the algorithm becomes
better and better and better. You like learning together.
Yeah. We're scratching the surface on that right now. There's so much more to do.
So on the complete other side of it, you have an RFID chip implanted in you.
Yeah.
So I hear nice. So this is a little subtle thing.
It's a passive device that you use for unlocking like a safe with top secrets or what, what is it?
What do you use it for? What's the story behind it?
I'm not the first one. There's, there's this whole community of weirdo biohackers that, uh,
have done this stuff. And I think one of the early use cases was storing, you know, private crypto
wallet keys and, and whatever. Um, I dabbled in that a bit and, and had some fun with it.
Um,
Just some Bitcoin implanted in your body somewhere. You can't tell where. Yeah.
Yeah, actually. Yeah. Uh, it was, you know, the modern day equivalent of finding change in the
sofa cushions after I, I put some orphan crypto on there that I thought was worthless and forgot
about it for a few years, went back and found that some community of people loved it, uh, and
had propped up the value of it. And so it had gone up 50 fold. So there was a lot of change in those
cushions. Um, that's hilarious. But the, the primary use case is mostly as a, as a tech demonstrator,
you know, it, it has my business card on it. You can scan that in, uh, by touching it to your phone.
It opens the front door to my house, you know, whatever, simple stuff, but it's a cool step.
It's a cool leap to implant something in your body. I mean, it has, perhaps that's, it's a similar leap to
in your link because for a lot of people, that kind of notion of putting something inside your body,
something electronic inside a biological system is a big leap.
Yeah. We have a kind of a mysticism around the barrier of our skin. We're completely fine with
knee replacements, hip replacements, you know, uh, dental implants. Um, but, uh, you know,
there's a mysticism still around the inviolable barrier that the skull represents. And I think
that needs to be treated like any other, uh, pragmatic barrier. You know, it's the question
isn't how, how incredible is it to open the skull? The question is, you know, what benefit can we provide?
So from all the surgeries you've done from everything you understand the brain,
how much does neuroplasticity come into play? How adaptable is the brain? For example,
just even in the case of healing from surgery or adapting to the post-surgery situation.
The answer that is sad for me and, uh, other people of my demographic is that,
you know, plasticity decreases with age, healing decreases with age. I have too much gray hair to,
to be optimistic about that. There are theoretical ways to increase plasticity using electrical
stimulation, uh, nothing that is, you know, totally proven out as a robust enough mechanism to offer
widely to people. But, um, yeah, I think, I think there's cause for optimism that we might find
something useful in terms of say an implanted electrode that improves learning. Um, certainly,
there's been some really amazing work recently from, uh, Nicholas Schiff, Jonathan Baker, you know,
and others, uh, who have a, a cohort of patients with moderate traumatic brain injury who have had
electrodes placed in the, uh, deep nucleus in the brain called the central median nucleus or just near
central median nucleus. And when they apply small amounts of electricity to that part of the brain,
it's almost like electronic caffeine. They're able to improve people's attention and focus.
Um, they're able to improve how well people can perform a task. I think in one case, someone who
was unable to work after the device was turned on, they were able to get a job. Uh, and that's sort of,
you know, one of the holy grails, uh, for me with Neuralink and other technologies like this
is from a purely utilitarian standpoint, um, can we, can we make people able to take care of themselves
and their families economically again? Can we make it so someone who's fully dependent and even maybe
requires a lot of caregiver resources, can we put them in a position to be fully independent, taking
care of themselves, giving back to their communities? Um, I think, I think that's a very compelling
proposition and what motivates a lot of what I do and what a lot of the people at Neuralink are
working for. It's just a cool possibility that if you put a Neuralink in there, that the brain adapts,
like the other part of the brain adapts too. Yeah. And it integrates it. The, the capacity of the brain
to do that, it's really interesting. Probably unknown to the degree to which you can do that,
but you're now connecting an external thing to it, especially, uh, once it's doing, uh, stimulation.
Like the, the biological brain and the, uh, the electronic brain outside of it working together,
like the possibilities there are really interesting.
Yeah. It's still unknown, but interesting. It feels like the brain is really good at adapting to
whatever. Yeah. But of course it is a system that by itself is already, uh,
like everything serves a purpose. And so you don't want to mess with it too much.
Yeah. It's like, you know, eliminating a species from a, from an ecology, you know,
you don't know what the delicate interconnections and dependencies are.
Um, the brain is certainly a, a, a delicate complex beast and we don't know,
uh, you know, every potential downstream consequence of, of a single, uh, change that we make.
Do you see yourself doing, uh, so you mentioned P1,
surgeries of P2, P3, P4, P5, just more and more and more humans.
I think, you know, it's a certain kind of brittleness or, you know, a failure on the company's
side. If we need me to do all the surgeries, um, I think something that I would very much like
to work towards is a process that is so simple and so robust on the surgery side that literally
anyone could do it. Um, we, we want to get away from a, requiring intense expertise or intense
experience, uh, to, to have this successfully done and make it as, as simple and translatable as
possible. I mean, I would love it if every neurosurgeon on the planet had no problem doing this.
Um, I think we're probably far from a regulatory environment that would allow, uh, people that
aren't neurosurgeons to do this, but, uh, not impossible.
All right. I'll sign up for that. Did you ever anthropomorphize the, the robot R1? Like, do you,
do you give it a name? Do you see it as like a friend that's like working together with you?
I mean, to a certain degree it's... Or an enemy who's gonna take the job.
To a certain degree it's, it's, yeah, it's complex relationship.
Uh, all the good relationships are. It's funny when in the middle of the surgery,
there's a part of it where I stand shoulder, basically shoulder to shoulder with the robot.
Um, and so, you know, if you're in the room reading the body language, you know, that's,
it's my brother in arms there. We're working together on the same problem. Um,
yeah, I'm not threatened by it.
Keep telling yourself that. Yeah.
Um, how have all the surgeries that you've done over the years,
the people you've helped and the, the stakes, the high stakes that you've mentioned,
how, how's that, uh, change your understanding of life and death?
Yeah. Um,
you know, it gives you a very visceral sense and this may sound trite, but it gives you a very visceral
sense that death is inevitable. You know, on one hand, you know, you, you are as a neurosurgeon,
you're deeply involved in these, like, just hard to fathom tragedies. Um, you know, young parents
dying, leaving, you know, a four-year-old behind say, uh, and, and on the other hand,
you know, it takes the sting out of it a bit because you see how just mind numbingly universal death is.
There's zero chance that I'm going to avoid it. Uh, I know, you know, techno optimists right now
and longevity buffs right now would disagree on that 0.000% estimate. Uh, but I don't see
any chance that our generation is going to avoid it. Entropy is a powerful force and we are very
ornate, delicate, brittle DNA machines that aren't up to the cosmic ray bombardment that we're subjected to.
So on the one hand, every human that has ever lived died or will die. Uh, on the other hand,
it's just one of the hardest things to imagine, um, inflicting on anyone that you love is, is having
them gone. I mean, I'm sure you've had friends that aren't living anymore and it's, it's hard to even
think about them. Um, and so, uh, I wish I had, you know, uh, arrived at the point of nirvana where,
you know, death doesn't have a sting. I'm not worried about it, but, um, I can at least say
that I'm comfortable with the certainty of it, uh, if not having found out how to take the,
the tragedy out of it when I think about, you know, my kids, uh, either not having me or, or me not
having them or my wife, um. Maybe I've come to accept the intellectual certainty of it, but,
uh, it may be the pain that comes with losing the people you love.
I don't think I've come to understand the existential
aspect of it, like that this is going to end. And I don't mean like, uh, in some, uh, trite way.
I mean, like it certainly feels like it's not going to end. Like you live life, like it's not going to end.
Right.
And the fact that this light that's shining, this consciousness is, is going to, uh, no longer be
in one moment, maybe today. It's like a, it fills me when I really am able to load all that in with
Ernest Becker's terror. Like it's a real fear. I think people aren't always honest with how terrifying
it is. Yeah. Um, I think the more you are able to really think through it, the more terrifying
it is. It's, it's not such a simple thing. Oh, well, it's the way life is. And if you really can
load that in, uh, it's hard, but I think that's why the Stoics did it because it like helps you get
your shit together and be like this. Well, they were like the moment, every single moment you're alive
is just beautiful. Yeah. And it's terrifying that it's going to end is it's like, like almost like
you're shivering in the cold, a child helpless, this kind of feeling. Yeah. And then it makes you,
when you have warmth, when you have the safety, when you have the love to really appreciate it.
Um, I feel like sometimes in your position, when you mentioned armor, just to see death,
it might make you not be able to see that the, the finiteness of life, because if you kept looking
at that, it might break you. So it's, it's good to know that you're kind of still struggling with
that. There's the neurosurgeon and then there's a human. Yeah. And the human is still able to
struggle then feel the, the fear of that and the pain of that. Yeah. You know, it definitely makes
you ask the question of how long, how many times, how many of these can you see and, uh, and not say,
I can't do this anymore. Um, but I mean, you said it well, I think it gives you an opportunity to
just appreciate that you're alive today. And, uh, you know, I've got, I've got three kids and an amazing
wife and I'm really happy. Things are good. I get to help on a project that I think matters.
I think it moves us forward. I'm a very lucky person. It's the early steps of a potentially,
uh, gigantic leap for humanity. It's a really interesting one. And it's cool. Cause like you,
you read about all this stuff in history where it's like the early days I've been reading, uh, before going
to the Amazon, I would read about explorers, uh, that would go and explore even the Amazon jungle
for the first time. It's just, those are the early steps or early steps into space, early, early steps
in any discipline in, in physics and mathematics. And it's cool. Cause this is like the, on the grand
scale, these are the early steps into delving deep into the human brain. So not just observing the brain,
but you'll be able to interact with the human brain. Yeah. It's going to help a lot of people,
but it also might help us understand what the hell's going on in there.
Yeah. I think ultimately we want to give people more levers that they can pull,
right? Like you want to give people options. If you can give someone a dial that they can turn
on how happy they are. I think that makes people really uncomfortable, but
um, now talk about major depressive disorder, talk about people that are committing suicide at an
alarming rate in this country and try to justify that queasiness
in those, in that light of your, you can give people a knob to take away suicidal ideation,
suicidal intention. I would, I would give them that knob. I don't know how you justify not doing that.
Yeah. You can think about like all the suffering that's going on in the world. Like every single
human being that's suffering right now, it's like a, it'd be a glowing red dot. The more suffering,
the more it's glowing. And you just see the map of human suffering and any technology that allows you
to dim the, that light of suffering, uh, on a grand scale is, is pretty exciting because there's a lot
of people suffering and most of them suffer quietly and we turn our, uh, we, we look away too often.
Uh, and we, we should remember those that are suffering because once again, most of them are
suffering quietly. Well, and you know, on a grander scale, the fabric of society, you know, people have
a lot of complaints about how our S social fabric is working or not working, how our politics is
working or not working. Uh, those things are made of neurochemistry too in, in aggregate, right? Like
our politics is composed of individuals with human brains and the way it works or doesn't work
it's potentially tunable, uh, in the sense that, I don't know, say remove our addictive behaviors or
tune our addictive behaviors for social media or our addiction to outrage, our addiction to sharing
the most angry political tweet we can find. Um, I don't think that leads to a functional society. And
uh, if, if you had options for people to moderate that maladaptive behavior, uh, there could be huge
benefits to society. Maybe we could all work together a little more harmoniously toward useful ends.
There's a sweet spot. Like you mentioned, you don't want to completely remove all the dark sides of
human nature because those kind of, uh, are somehow necessary to make the whole thing work,
but there's a sweet spot. Yeah, I agree. We gotta, you gotta suffer a little,
just not so much that you lose hope. Yeah. When you, all the surgeries you've done,
have you seen consciousness in there ever? Was there like a glowing light?
You know, I have this sense that, uh, I never found it, never removed it, you know, like,
like a Dementor in Harry Potter. Um, I have this sense that consciousness is a lot less
magical than our instincts want to claim it is. Um, it, it seems to me like a useful analog for
thinking about what consciousness is in the brain. Um, you know, is that we, we have
a really good intuitive understanding of what it means to say, touch your skin and know what's being
touched. Um, I think consciousness is just that level of sensory mapping applied to the, the thought
processes in the brain itself. So what I'm saying is consciousness is the sensation of some part of
your brain being active. So you, you feel it working, you feel the part of your brain that thinks of
red things or winged creatures or the taste of coffee. You feel those parts of your brain being
active the way that I'm feeling my palm being touched. Right. And that sensory system that feels
the brain working is consciousness. That is so brilliant. It's the same way.
It's the sensation of touch. When you're touching a thing, consciousness is the sensation of you
feeling your brain, working your brain, thinking your brain perceiving, which isn't,
which isn't like a warping of space time or, or some quantum field effect, right? It's nothing magical.
People always want to ascribe to consciousness, something truly different. Uh, and there's this
awesome long history of people looking at whatever the latest discovery in physics is to explain
consciousness, um, because it's the most magical, the most out there thing that you can think of.
And, and people always, you know, want to do that with consciousness. I don't think that's necessary.
It's just a, you know, a very useful and gratifying way of feeling your brain work.
And as we said, it's one heck of a brain. Yeah.
Everything we see around us, everything we love, everything is beautiful.
It's came from brains like these. It's all electrical activity happening inside your skull.
And, uh, I, for one, I'm, uh, grateful that there's people like you that are, uh,
exploring all the ways that it works and all the ways it can be made better.
Thank you so much for talking today.
It's been a joy.
Thanks for listening to this conversation with Matthew McDougall.
And now, dear friends, here's Bliss Chapman, brain interface software lead at Neuralink.
You told me that you've met hundreds of people with spinal cord injuries or with ALS and that your
motivation for helping at Neuralink is grounded in wanting to help them. Can you describe this motivation?
Yeah. First, just a thank you to all the people I've gotten a chance to speak with for sharing their
stories with me. I don't think there's any world really in which I can share the stories as powerful
a way as they can. But, uh, just, I think to summarize at a very high level, what I hear over
and over again is that people with, uh, ALS or severe spinal cord injury in a place where they
basically can't move physically anymore, really at the end of the day are looking for independence.
And that can mean different things for different people. For some folks, it can mean the ability
just to be able to communicate again independently without needing to wear something on their face,
without needing a caretaker to be able to put something in their mouth. For some folks,
it can mean independence to be able to work again, to be able to navigate a computer digitally
efficiently enough to be able to get a job, to be able to support themselves, to be able to move
out and ultimately be able to support themselves after their family maybe isn't there anymore to take
care of them. And, uh, for some folks, it's as simple as just being able to respond to their kid
in time before they, you know, run away or get interested in something else. And these are deeply
personal and sort of very human problems. And what strikes me again and again when talking with these
folks is that this is actually an engineering problem. This is a problem that with the right resources,
with the right team, we can make a lot of progress on. And, uh, at the end of the day,
I think that's a deeply inspiring message and something that makes me excited to get up every day.
So it's both an engineering problem in terms of a BCI, for example, that can give them capabilities
where they can interact with the world, but also on the other side, it's an engineering problem for
the rest of the world to make it more accessible for people living with quadriplegia.
Yeah. And I see, I'll take a broad view sort of lens on this for a second. I think
I'm very in favor of anyone working in this problem space. So beyond BCI, I'm happy and excited and
willing to support any way I can folks working on eye tracking systems, working on, you know,
speech to text systems, working on head trackers or mouse sticks or quad sticks. I've met many
engineers and folks in the community that do exactly those things. And I think for the people
we're trying to help, it doesn't matter what the complexity of the solution is, as long as the
problem is solved. And I want to emphasize that there can be many solutions out there that can
help with these problems. And BCI is one of a collection of such solutions. So BCI in particular,
I think offers several advantages here. And I think the folks that recognize this immediately
are usually the people who have spinal cord injury or some form of paralysis. Usually you
don't have to explain to them why this might be something that could be helpful. It's usually
pretty self-evident, but for the rest of us folks that don't live with severe spinal cord injury or
who don't know somebody with ALS, it's not often obvious why you would want a brain
implant to be able to connect and navigate a computer. And it's surprisingly nuanced to the
degree that I've learned a huge amount just working with Noland in the first Neuralink clinical trial
and understanding from him and his words why this device is impactful for him. And it's a nuanced
topic. It can be the case that even if you can achieve the same thing, for example, with a mouse
stick when navigating a computer, he doesn't have access to that mouse stick every single minute of
the day. He only has access when someone is available to put it in front of him. And so BCI can really
offer a level of independence and autonomy that if it wasn't literally physically part of your body,
it would be hard to achieve in any other way. So there's a lot of fascinating aspects to what
it takes to get Noland to be able to control a cursor on the screen with his mind. You texted me
something that I just love. You said, I was part of the team that interviewed and selected P1. I was in
the operating room during the first human surgery monitoring live signals coming out of the brain.
I work with the user basically every day to develop new UX paradigms, decoding strategies.
And I was part of the team that figured out how to recover useful BCI to new world record levels when
the signal quality degraded. We'll talk about, I think, every aspect of that, but just zooming out,
what was it like to be part of that, part of that team and part of that historic, I would say, historic
first?
Yeah. I think for me, this is something I've been excited about for close to 10 years now. And so
to be able to be even just some small part of making it a reality is extremely exciting. A couple
maybe special moments during that whole process that I'll never really truly forget. One of them is
entering the actual surgery. At that point in time, I know Noland quite well. I know his family.
And so I think the initial reaction when Noland is rolled into the operating room is just a,
oh, shit, kind of reaction. But at that point, muscle memory kicks in and you sort of go into,
you let your body just do all the talking. And I have the lucky job in that particular procedure
to just be in charge of monitoring the implant. So my job is to sit there, to look at the signals
coming off the implant, to look at the live brain data streaming off the device as threads are being
inserted into the brain. And just to basically observe and make sure that nothing is going
wrong or that there's no red flags or fault conditions that we need to go and investigate or
pause the surgery to debug. And because I had that sort of spectator view of the surgery,
I had a slightly removed perspective than I think most folks in the room. I got to sit there and
think to myself, wow, you know, that brain is moving a lot. You know, when you look inside the
craniectomy that we stick the threads in, you know, one thing that most people don't realize is the brain
moves. The brain moves a lot when you breathe, when your heart beats, and you can see it visibly.
So, you know, that's something that I think was a surprise to me and very, very exciting to be able to
see someone's brain who you physically know, and have talked with that length actually pulsing and
moving inside their skull. And they use that brain to talk to you previously. And now it's right there
moving. Yeah. Actually, I didn't realize that in terms of the thread sending. So the
the neural link implant is active during surgery. So and one thread at a time, you're able to start
seeing the signal. Yeah. So that's part of the way you test that the thing is working.
Yeah, so actually in the in the operating room, right after we sort of finished all the thread
insertions, I started collecting what's called broadband data. So broadband is
basically the most raw form of signal you can collect from a neural link electrode.
It's essentially a measurement of the local field potential or the yeah, the voltage essentially
measured by the electrode. And we have a certain mode in our in our application that allows us to
visualize where detected spikes are. So it visualizes sort of where we're in the broadband
signal and it's very, very raw form of the data, a neuron is actually spiking. And so one of the
these moments that I'll never forget as part of this whole clinical trial is seeing live in the
operating room while he's still under anesthesia, beautiful spikes being shown in the application,
just streaming live to a device I'm holding in my hand.
So this is no signal processing the raw data and then the signals processing is on top of it.
You're seeing the spikes detected, right? Yeah. And that's the UX too, because that looks beautiful
as well. During that procedure, there was actually a lot of cameramen in the room. So they also were
curious and wanted to see there's several neurosurgeons in the room who are all just excited to see
robots taking their job. And they're all, you know, crowded around a small little iPhone watching this
live brain data stream out of his brain. What was that like seeing the robot do some of the
surgery? So the computer vision aspect where it detects all the, all the spots that avoid the blood
vessels. And then obviously with human supervision, then actually doing the really high precision
connection of the threads to the brain. Yeah. That's a good question. My answer is going to be
a pretty lame here, but it was boring. Yeah. I've seen it so many times. Yeah. That's exactly how you want
surgery to be, you want it to be boring. Yeah. Cause I've seen it so many times. I've seen the,
the robot do the surgery literally hundreds of times. And so it was just one more time.
Yeah. All the practice surgeries on the proxies. And this is just another day. Yeah.
So what about when Nolan woke up? Well, do you remember a moment where he was able to move the
cursor and not move the cursor, but get signal from the brain such that it was able to show
that there's a connection? Yeah. Yeah. So we are quite excited to move as quickly as we can. And
Nolan was really, really excited to get started. He wanted to get started actually the day of surgery,
but we, we waited till the next morning, very patiently. It's a long night.
And the next morning in the ICU where he was recovering, he wanted to get started and actually
start to understand what kind of signal we can measure from his brain. And maybe for folks who
are not familiar with the Neuralink system, we implant the Neuralink system or the Neuralink
implant in the motor cortex. So the motor cortex is responsible for representing things like
motor intent. So if you imagine closing and opening your hand, that kind of signal representation would
be present in the motor cortex. If you imagine moving your arm back and forth or wiggling a pinky,
this sort of signal can be present in the motor cortex. So one of the ways we start to sort of map
out what kind of signal do we actually have access to in any particular individual's brain is through
this task called body mapping. And body mapping is where you essentially present a visual to the
user and you say, Hey, imagine doing this. And that visual is, you know, a 3D hand opening, closing,
or index finger modulating up and down. And you ask the user to imagine that. And obviously you can't
see them do this because they're paralyzed. So you can't see them actually move their arm. But while they
do this task, you can record neural activity and you can basically offline model and check,
can I predict or can I detect the modulation corresponding with those different actions?
And so we did that task and we realized, Hey, there's actually some modulation associated
with some of his hand motion, which was the first indication that, okay, we can potentially use
that modulation to do useful things in the world. For example, control a computer cursor. And he started
playing with it, you know, the first time we showed him it, and we actually just took the same
live view of his brain activity and put it in front of him. And we said, Hey, you tell us what's going on.
You know, we're not you. You're able to imagine different things. And we know that it's modulating
some of these neurons. So you figure out for us what that is actually representing.
And so he played with it for a bit. He was like, I don't quite get it yet.
He played for a bit longer and he said, Oh, when I move this finger,
I see this particular neuron start to fire more. And I said, Okay, prove it, do it again. And so he said,
Okay, three, two, one, boom. And the minute he moved, you can see like instantaneously,
this neuron is firing single neuron. I can tell you the exact channel number if you're interested.
It's stuck in my brain now forever. But that single channel firing was a beautiful indication that it
was behaviorally modulated neural activity that could then be used for downstream tasks like
decoding a computer cursor. And when you say single channel,
is that associated with a single electrode? Yeah, so channel electrode are interchangeable.
And there's a 1024 of those 1024. Yeah, it's incredible that that works.
That really when I was learning about all this and like loading it in,
it was just blowing my mind that the intention you can visualize yourself moving the finger
that can turn into a signal. And the fact that you can then skip that step and visualize the cursor
moving or have the intention of the cursor moving in that leading to a signal that
can then be used to move the cursor. There's so many exciting things there to learn about the brain,
about the way the brain works. The very fact of their existing signal that can be used is really
powerful. Yep.
But it feels like that's just like the beginning of figuring out how that signal can be used really,
really effectively. I should also just, there's so many fascinating details here, but you mentioned
the body mapping step, at least in the version I saw that Nolan was showing off.
There's like a super nice interface, like a graphical interface, but like,
it just felt like I was like in the future because it like, you know, I guess it visualizes
you moving the hand. Yep.
And there's a very like, like a sexy polished interface that, hello. I don't know if there's
a voice component, but it just felt like, uh, it's like when you wake up in a really nice video
game and this is a tutorial at the beginning of that video game, this is what you're supposed to do.
It's cool.
No, I mean, the future should feel like the future.
But it's not easy to pull that off. I mean, it needs to be simple, but not too simple.
Yeah. And I think the UX design component here is, uh, underrated for BCI development in general.
There's a whole interaction effect between the ways in which you visualize, uh, an instruction
to the user and the kinds of signal you can get back. And that quality of sort of your behavioral
alignment to the neural signal is a function of how good you are at expressing to the user
what you want them to do. And so, yeah, we spend a lot of time thinking about the UX,
of how we build our applications, of how the decoder actually functions, the control surfaces
it provides to the user. All these little details matter a lot.
So maybe it'd be nice to get into a little bit more detail
of what the signal looks like and what the decoding looks like.
So there's a, uh, N1 implant that has, like we mentioned, uh, 1024 electrodes
and that's collecting raw data, raw signal. What does that signal look like?
And, uh, what are the different steps along the way before it's transmitted?
And what is transmitted and all that kind of stuff.
Yeah. Yep. This is going to be a fun one.
Let's go.
Uh, so, uh, maybe before diving into what we do, it's worth understanding what we're
trying to measure because, uh, that dictates a lot of the requirements for the system that we build.
And what we're trying to measure is really individual neurons producing action potentials.
And action potential is, you can think of it like a little electrical impulse that you can
detect if you're close enough. And by being close enough, I mean like within,
let's say a hundred microns of that cell and a hundred microns is a very, very tiny distance.
And so the number of neurons that you're going to pick up with any given electrode
is just a small radius around that electrode. And the other thing worth understanding about the
underlying biology here is that when neurons produce an action potential, the width of that
action potential is about one millisecond. So from the start of the spike to the end of the spike,
that whole width of that, uh, sort of characteristic feature of a neuron firing is one millisecond wide.
And if you want to detect that an individual spike is occurring or not, you need to sample that
signal or sample the local field potential nearby that neuron much more frequently than once a
millisecond. You need to sample many, many times per millisecond to be able to detect that this is
actually the characteristic waveform of a neuron producing an action potential.
And so we sample across all 1024 electrodes about 20,000 times a second. 20,000 times a second means
for already given one millisecond window, we have about 20 samples that tell us what that exact shape
of that actual potential looks like. And once we've sort of sampled at super high rate, the underlying
electrical field nearby, uh, these cells, we can process that signal into just where do we detect a
spike or where do we not sort of a binary signal one or zero? Do we detect a spike in this one millisecond or not?
And we do that because the actual information character carrying, uh, uh, sort of subspace of neural activity is just
when our spikes occurring, essentially everything that we care about for decoding can be captured or represented
in the frequency characteristics of spike trains, meaning how often are spikes firing in any given window of time.
And so that allows us to do sort of a crazy amount of compression from this very rich high density,
uh, you know, signal to something that's much, much more sparse and compressible that can be sent out
over a wireless, uh, radio, like a Bluetooth communication, for example.
Quick tangents here. You mentioned electrode neuron. There's a local neighborhood of neurons nearby.
How difficult is it to like isolate from where the spike came from?
Yeah. So there's a whole field of sort of academic neuroscience work on exactly this problem of
basically given a single electrode or given a set of electrodes measuring a set of neurons,
how can you sort of sort, spike sort, which spikes are coming from what neuron? And this is a
problem that's pursued in academic work because you care about it for understanding what's going on in the
underlying sort of, uh, neuroscience of the, of the brain. If you care about understanding how
the brain's representing information, how that's evolving through time, then that's a very, very
important question to, to understand for sort of the engineering side of things, at least at the
current scale, if the number of neurons per electrode is relatively small, you can get away
with basically ignoring that problem completely. You can think of it like sort of a random projection
of neurons to electrodes. And there may be in some cases more than one neuron per electrode,
but if that number is small enough, those signals can be thought of as, uh, sort of a union of the
two. And for many applications, that's a totally reasonable trade-off to make and can simplify the
problem a lot. And as you sort of scale out channel count, the, uh, relevance of distinguishing
individual neurons becomes less important because you have more overall signal and you can start to
rely on sort of correlations or covariance structure in the data to help understand when that
channel is firing, what does that, what does that actually represent? Because you know that when that
channel is firing in concert with these other 50 channels, that means move left. But when that
same channel is firing with concert with these other 10 channels, that means move right.
Okay. So you have to do this kind of spike detection on board and you have to do that
super efficiently. So fast and not use too much power because you don't want to be generating too
much heat. So it has to be a super simple signal processing step. Yeah.
Um, is there some wisdom you can share about what it takes to overcome that challenge?
Yeah. So we've tried many different versions of basically turning this raw signal into, uh,
sort of a feature that you might want to send off the device. And I'll say that I don't think we're at
the final step of this process. This is a long journey. We have something that works clearly today,
but there can be many approaches that we find in the future that are much better than what we do
right now. So some versions of what we do right now, and there's a lot of academic heritage to
these ideas. So I don't want to claim that these are original neural link ideas or anything like
that. But, uh, one of these ideas is basically to build a sort of like a convolutional filter,
almost, if you will, that slides across the signal and looks for a certain template to be
matched. And that template consists of sort of how deep the spike modulates,
how much it recovers and what the duration and window of time is that the whole process takes.
And if you can see in the signal that that template is matched within certain bounds,
then you can say, okay, that's a spike. One reason that approach is super convenient is
that you can actually implement that extremely efficiently in hardware, which means that you
can run it, uh, in low power across 1,024 channels all at once. Another approach that we've recently
started, uh, exploring, and this can be combined with the spike detection approach, something called
spike band power. And the benefits of that approach are that you may be able to pick up some
signal from neurons that are maybe too far away to be detected as a spike, because the farther away
you are from an electrode, the weaker that actual spike waveform will look like on that electrode.
So, uh, you might be able to pick up, you know, population level activity of things that are,
you know, maybe slightly outside the normal recording radius, what, what neuroscientists
sometimes refer to as the hash of activity, the other stuff that's going on.
Yeah.
Uh, and you can look at sort of across many channels, how that, uh, sort of background noise
is behaving and you might be able to get more juice out of the signal that way, but it comes at a
cost. That signal is now a floating point representation, which means it's more expensive
to send out over a power. It means you have to find different ways to compress it that are different
than what you can apply to binary signals. So there's a lot of different challenges associated
with these different modalities.
So also in terms of communication, you're limited by the amount of data you can send.
Yeah.
And so, and also because you're currently using the Bluetooth protocol, you have to batch
stuff together, but you have to also do this keeping the latency crazy low, like crazy low.
Anything to say about the latency?
Yeah. This is a passion project of mine. So, uh, I want to build the best mouse in the world.
Yeah.
I don't want to build like the, you know, the Chevrolet spark or whatever of electric cars.
I want to build like the Tesla roadster version of, of a mouse. And I really do think it's quite
possible that within, you know, five to 10 years that most e-sports competitions are
dominated by people with paralysis. This is like a very real possibility for a number of reasons.
One is that they'll have access to the best technology to play video games effectively.
The second is they have the time to do so. So those two factors together are particularly
potent for, uh, e-sport competitors.
Unless, uh, people without paralysis are also allowed to implant.
Right.
Which is, it is another way to interact with a digital, uh, device.
And there's some, there's something to that. If, if it's a fundamentally different experience,
more efficient experience, even if it's not like some kind of full on high bandwidth communication,
if it's just the ability to move the mouse, uh, 10 X faster, like the bits per second,
if I can achieve a bits per second at 10 X, what I can do with the mouse, that's a really
interesting possibility of what they can do, especially as you get really good at it.
Uh, with training, it's definitely the case that you have a higher ceiling performance,
like you, because you don't have to buffer your intention through your arm, through your muscle,
you get just by nature of having a brain implant at all, like 75 millisecond
lead time on any action that you're actually trying to take. And there's some nuance to this,
like there's evidence that the motor cortex, you can sort of plan out sequences of action.
So you may not get that whole benefit all the time, but for sort of like reaction time style,
uh, games where you just want to, somebody's over here, snipe them, you know, that kind of thing.
Uh, you actually do have just an inherent advantage because you don't need to go through muscle.
So the question is just how much faster can you make it? And we're already, you know,
faster than, uh, you know, what you would do if you're going through muscle from a latency point
of view. And we're in the early stage of that. I think we can push it sort of our end to end
latency right now from brain spike to cursor movement. It's about 22 milliseconds. If you think
about, uh, the best mice in the world, the best gaming mice, that's about five milliseconds-ish
of latency, depending on how you measure, depending how fast your screen refreshes,
there's a lot of characteristics that matter there, but yeah. And the rough time for like a neuron
in the brain to actually impact your, uh, command of your hand is about 75 milliseconds. So if you
look at those numbers, you can see that we're already like, you know, competitive and slightly
faster than what you'd get by actually moving your, moving your hand. And this is something that,
you know, if you ask Nolan about it, when he moved the cursor for the first time, we asked him about
this. It was something I was super curious about, like, what does it feel like when you're modulating,
you know, a click intention or when you're trying to just move the cursor to the right? He said it
moves before he is like actually intending it to, which is kind of a surreal thing and something that,
uh, you know, I would love to experience myself one day. What is that like to have the thing just
be so immediate, so fluid that it feels like it's happening before you're actually intending it to move.
Yeah. I suppose we've gotten used to that latency, that natural latency that happens.
Uh, so is the currently the bottleneck, the communication, so like the Bluetooth communication,
is that what's the actual bottleneck? I mean, there's always going to be a bottleneck,
but what's the current bottleneck? Yeah. A couple of things. So kind of hilariously,
Bluetooth, uh, low energy protocol has, uh, some restrictions on how fast you can communicate.
So the protocol itself establishes a standard of, you know, the most frequent sort of updates
you can send are on the order of 7.5 milliseconds. And, uh, as we push latency down to the level of sort of
individual spikes impacting control, that level of resolution, that kind of protocol is going to
become a limiting factor at some scale. Um, another sort of important nuance to this is
that it's not just the, uh, neural link itself that's part of this equation. If you start pushing
latency sort of below the level of how fast screens refresh, then you have another problem. Like you
need your whole system to be able to, uh, be as reactive as the sort of limits of what the technology
can offer. Like you need the screen, like 120 hertz just doesn't, you know, work anymore if you're
trying to have something respond at something that's, you know, at the level of one millisecond.
That's a really cool challenge. I also like that for a t-shirt, the, uh, the best mouse in the world.
Tell me on the receiving end. So the decoding step, now we, we figured out what the spikes are,
we've got them all together. Now we're sending that over, uh, to the app.
What's the decoding step look like?
Yeah. So maybe first, what is decoding? I think there's probably a lot of folks listening that just
have no clue what, what it means to decode brain activity.
Actually, even if we zoom out beyond that, what is the app? So there's a, there's an implant
that's wirelessly communicating with any digital device that has an app installed.
Yep.
So maybe can you tell me at high level what the app is, what the software is outside of the, uh, the brain?
Yeah. So maybe working backwards from the goal, the goal is to help someone with paralysis in this
case, Nolan, be able to navigate his computer independently. And we think the best way to do
that is to offer them the same tools that we have to navigate our software, because we don't want to
have to rebuild an entire software ecosystem for the brain, at least not yet. Maybe someday you can
imagine there's UXs that are built natively for BCI, but in terms of what's useful for people today,
I think we, most people would prefer to be able to just control mouse and keyboard inputs to all the
applications that they want to use for their daily jobs, for communicating with their friends,
et cetera. And so the job of the application is really to translate this wireless stream of brain
data coming off the implant into control of the computer. And we do that by essentially building
a mapping from brain activity to sort of the HID inputs to the actual hardware. So HID is just the
protocol for communicating like input device events. So for example, move mouse to this position
or press this key down. And so that mapping is fundamentally what the app is responsible for.
But there's a lot of nuance of how that mapping works that we spent a lot of time to try to get
right. And we're still in the early stages of a long journey to figure out how to do that optimally.
So one part of that process is decoding. So decoding is this process of taking the statistical patterns
of brain data that's being channeled across this Bluetooth connection to the application
and turning it into, for example, a mouse movement. And that decoding step, you can think of it in a
couple of different parts. So similar to any machine learning problem, there's a training step
and there's an inference step. The training step in our case is a very intricate behavioral process
where the user has to imagine doing different actions. So for example, they'll be presented a
screen with a cursor on it, and they'll be asked to push that cursor to the right. Then imagine pushing
that cursor to the left, push it up, push it down. And we can basically build up a pattern
or using any sort of modern ML method of mapping of given this brain data, and then imagine behavior,
map one to the other. And then at test time, you take that same pattern matching system,
in our case, it's a deep neural network, and you run it and you take the live stream of brain data
coming off their implant, you decode it by pattern matching to what you saw at calibration time,
and you use that for control of the computer. Now, a couple like sort of rabbit holes that I think
are quite interesting. One of them has to do with how you build that best template matching
system. Because there's a variety of behavioral challenges and also debugging challenges when
you're working with someone who's paralyzed. Because again, fundamentally, you don't observe
what they're trying to do. You can't see them attempt to move their hand. And so you have to
figure out a way to instruct the user to do something and validate that they're doing it correctly,
such that then you can downstream build with confidence the mapping between the neural spikes
and the intended action. And by doing the action correctly, what I really mean is at the
level of resolution of what neurons are doing. So if in ideal world, you could get a signal of
behavioral intent that is ground truth accurate at the scale of sort of one millisecond resolution,
then with high confidence, I could build a mapping from my neural spikes to that behavioral intention.
But the challenge is again, that you don't observe what they're actually doing. And so there's a lot of
nuance to how you build user experiences that give you more than just sort of a course on average,
correct representation of what the user's intending to do. If you want to build the world's best mouse,
you really want it to be as responsive as possible. You want it to be able to do exactly what the user's
intending at every sort of step along the way, not just on average be correct when you're trying to
move it from left to right. And building a behavioral sort of calibration game or our sort of software
experience that gives you that level of resolution is what we spend a lot of time working.
So the calibration process, the interface has to encourage precision, meaning like whatever it
does, it should be super intuitive that the next thing the human is going to likely do is exactly
that intention that you need and only that intention. And you don't have any feedback except that may be
speaking to you afterwards what they actually did. You can't. Oh yeah. Right. So that's a, that's
fundamentally, that is a really exciting UX channel because that's all on the UX. It's not just about
being friendly or nice or usable. Yeah. It's like user experience is how it works. It's how it works
for the calibration and calibration. At least at this stage of Neuralink is like fundamental to the
operation of the thing and not just calibration, but continued calibration essentially.
Yeah. And maybe you said something that I think is worth exploring there a little bit.
You said it's, you know, primarily a UX challenge. And I think a large component of it is,
but there is also a very interesting machine learning challenge here, which is given
some, you know, data set, including some on average, correct behavior of asking the user to move up or
move down, move right, move left. And given a data set of neural spikes, is there a way to infer
in some kind of semi-supervised or entirely unsupervised way, what that high resolution
version of their intention is. And if you think about it, like there probably is because there are
enough data points in the data set, enough constraints on your model, that there should be
a way with the right sort of formulation to let the model figure out itself. For example, at this
millisecond, this is exactly how hard they're pushing upwards. And at this millisecond, this is how hard
they're trying to push upwards. It's really important to have very clean labels. Yes. So
like the problem becomes much harder from the machine learning perspective, the labels are
noisy. That's correct. And then to get the clean labels, that's a UX challenge.
Correct. Although clean labels, I think maybe it's worth exploring what that exactly means. I think
any given labeling strategy will have some number of assumptions it makes about what the user is
attempting to do. Those assumptions can be formulated in a loss function, or they can be formulated in terms of
heuristics that you might use to just try to estimate or guesstimate what the user is trying to do.
And what really matters is how accurate are those assumptions. For example, you might say,
hey, user, push upwards and follow the speed of this cursor. And your heuristic might be that they're
trying to do exactly what that cursor is trying to do. Another competing heuristic might be they're
actually trying to go slightly faster at the beginning of the movement and slightly slower at the
end. And those competing heuristics may or may not be accurate reflections of what the user is trying to do.
Another version of the task might be, hey, user, imagine moving this cursor a fixed offset. So
rather than follow the cursor, just try to move it exactly 200 pixels to the right. So here's the
cursor, here's the target. Okay, cursor disappears, tried to move that now invisible cursor 200 pixels to
the right. And the assumption in that case would be that the user can actually modulate correctly that
position offset. But that position offset assumption might be a weaker assumption. And therefore,
potentially, you can make it more accurate than these heuristics that are trying to guesstimate
at each millisecond what the user is trying to do. So you can imagine different tasks that make
different assumptions about the nature of the user intention. And those assumptions
being correct is what I would think of as a clean label.
For that step, what are we supposed to be visualizing? There's a cursor and you want to
move that cursor to the right or the left, up and down, or maybe move them by a certain offset.
So that's one way. Is that the best way to do calibration? So for example, an alternative crazy way that
probably is playing a role here is a game like WebGrid, where you're just getting a very large
amount of data, the person playing a game, where if they are in a state of flow, maybe you can get
clean signal as a side effect.
So is that not an effective way for initial calibration?
Yeah, great question. There's a lot to unpack there. So
the first thing I would draw a distinction between is sort of open loop versus closed loop. So open
loop, what I mean by that is the user is sort of going from zero to one, they have no model at all,
and they're trying to get to the place where they have some level of control at all. In that setup,
you really need to have some task that gives the user a hint of what you want them to do such that you
can build this mapping again from brain data to output. Then once they have a model, you could
imagine them using that model and actually adapting to it and figuring out the right way to use it
themselves, and then retraining on that data to give you sort of a boost in performance. There's a lot
of challenges associated with both of these techniques, and we can sort of rabbit hole into
both of them if you're interested. But the sort of challenge with the open loop task is that the user
themselves doesn't get proprioceptive feedback about what they're doing. They don't, you know,
necessarily perceive themselves or feel, you know, the mouse under their hand when they're using an
open loop, or when they're trying to do an open loop calibration. They're being asked to perform
something, like imagine if you sort of had your whole right arm numbed and you stuck it in a box
and you couldn't see it. So you had no visual feedback and you had no proprioceptive feedback
about what the position or activity of your arm was. And now you're asked, okay, given this thing on
the screen that's moving from left to right, match that speed. And you basically can try your best to,
you know, invoke whatever that imagined action is in your brain that's moving the cursor from left to
right. But in any situation, you're going to be inaccurate and maybe inconsistent in how you do that
task. And so that's sort of the fundamental challenge of open loop. The challenge with closed
loop is that once the user's given a model and they're able to start moving the mouse on their own,
they're going to very naturally adapt to that model. And that co-adaptation between the model learning
what they're doing and the user learning how to use the model may not find you the best sort
of global minima. It may be that your first model was noisy in some ways, or, you know,
maybe just had some like quark. If there's some like part of the data distribution, it didn't
cover super well. And the user now figures out because they're, you know, a brilliant user like
Nolan, they figure out the right sequence of imagined motions or the right angle they have to hold
their hand at to get it to work and they'll get it to work great. But then the next day they come back to
their device and maybe they don't remember exactly all the tricks that they used the previous day.
And so there's a complicated sort of feedback cycle here that can, that can emerge and can make it a
very, very difficult debugging process. Okay. There's a lot of really fascinating things there.
Yeah. Actually, just to stay on the, on the closed loop, I have, I've seen situations. This actually
happened watching psychology grad students. They use pieces of software when they don't know how to
program themselves. They use piece of software that somebody else wrote and it has a bunch of bugs
and they figure out like, and they've been using it for years. Yeah. They figure out ways to work
around. Oh, that just happens. Like nobody, nobody like considers maybe we should fix this. They just
adapt. And that's a really interesting notion that we just, we're really good at adapting, but you need
to still, that might not be the optimal. Yeah. Okay. So how do you solve that problem? Do you have to restart
from scratch every once in a while kind of thing? Yeah, it's a good question. First and foremost,
I would say this is not a solved problem. And for anyone who's, you know, listening in academia,
who works on BCIs, I would also say, this is not a problem that's solved by simply scaling channel
count. So this is, you know, maybe that can help and you can get sort of richer covariance structures
that you can use to exploit when trying to come up with good labeling strategies. But if you don't,
you're interested in problems that aren't going to be solved inherently by just scaling channel
count. This is one of them. Yeah. So how do you solve it? It's not a solved problem. That's the first
thing I want to make sure it gets across. The second thing is any solution that involves closed
loop is going to become a very difficult debugging problem. And one of my sort of general heuristics
for choosing what problems to tackle is that you want to choose the one that's going to be the
easiest to debug. Because if you can do that, even if the ceiling is lower, you're going to be able
to move faster because you have a tighter iteration loop debugging the problem. And in the open loop
setting, there's not a feedback cycle to debug with the user in the loop. And so there's some reason
to think that that should be an easier debugging problem. The other thing that's worth understanding
is that even in the closed loop setting, there's no special soft or magic of how to infer what the
user is truly attempting to do. In the closed loop setting, although they're moving the cursor on the
screen, they may be attempting something different than what your model is outputting. So what the
model is outputting is not a signal that you can use to retrain if you want to be able to improve
the model further. You still have this very complicated guesstimation or unsupervised problem of
figuring out what is the true user intention underlying that signal. And so the open loop
problem has the nice property of being easy to debug and the second nice property of it has all
the same information and content as the closed loop scenario. Another thing I want to mention and call
out is that this problem doesn't need to be solved in order to give useful control to people.
Even today with the solutions we have now and that academia has built up over decades,
the level of control that can be given to a user today is quite useful. It doesn't need to be
solved to get to that level of control. But again, I want to build the world's best mouse. I want to
make it so good that it's not even a question that you want it. And to build the world's best mouse,
the superhuman version, you really need to nail that problem. And a couple maybe details of previous
studies that we've done internally that I think are very interesting to understand when thinking about
how to solve this problem. The first is that even when you have ground truth data of what the user
is trying to do, and you can get this with an able-bodied monkey, a monkey that has an neural
link device implanted and moving a mouse to control a computer, even with that ground truth data set,
it turns out that the optimal thing to predict to produce high performance BCI is not just the
direct control of the mouse. You can imagine building a data set of what's going on in the brain
and what is the mouse exactly doing on the table. And it turns out that if you build the
mapping from neural spikes to predict exactly what the mouse is doing, that model will perform
worse than a model that is trained to predict sort of higher level assumptions about what the user
might be trying to do. For example, assuming that the monkey is trying to go in a straight line to
the target. It turns out that making those assumptions is actually more effective in producing
a model than actually predicting the underlying hand movement. So the intention, not like the
physical movement or whatever. Yeah. There's obviously a very strong correlation between the two,
but the intention is a more powerful thing to be chasing, right? Well, that that's also super
interesting. I mean, the intention itself is fascinating because yes, with the BCI here,
in this case with a digital telepathy, you're acting on the intention, not the action, which is why
there's an experience of like feeling like it's happening before you meant for it to happen. That is so
cool. And that is why you could achieve like superhuman performance probably in terms of the
control of the mouse. So for OpenLoop, just to clarify, so whenever the person is tasked to like
move the mouse to the right, you said there's not feedback. So they don't get to get that satisfaction
of like actually getting it to move, right? So you could imagine giving the user feedback on a screen,
but it's difficult because at this point you don't know what they're attempting to do. So what can you
show them that would basically give them a signal of I'm doing this correctly or not correctly?
So let's take this very specific example, like maybe your calibration task looks like you're
trying to move the cursor a certain position offset. So your instructions to the user are,
hey, the cursor is here. Now, when the cursor disappears, imagine moving it 200 pixels from
where it was to the right to be over this target. In that kind of scenario, you could imagine coming
up with some sort of consistency metric that you could display to the user of, okay, I know what the
spike train looks like on average when you do this action to the right. Maybe I can produce some sort
of probabilistic estimate of how likely is that to be the action you took given the latest trial or
trajectory that you imagined. And I could give the user some sort of feedback of how consistent are
they across different trials. You could also imagine that if the user is prompted with that kind of
consistency metric that maybe they just become more behaviorally engaged to begin with because the task is
kind of boring when you don't have any feedback at all. And so there may be benefits to the, you know,
the user experience of showing something on the screen, even if it's not accurate, just because it
keeps the user motivated to try to increase that number or push it upwards.
So there's a psychology element here.
Yeah, absolutely.
And again, all of that is UX challenge. How much signal drift is there? Hour to hour, day to day,
week to week, month to month? How often do you have to recalibrate because of the signal drift?
Yeah. So this is a problem we've worked on both with NHP, non-human primates, before our clinical
trial and then also with Noland during the clinical trial. Maybe the first thing that's worth stating
is what the goal is here. So the goal is really to enable the user to have a plug and play experience
where, I guess they don't have to plug anything in, but a play experience where they, you know,
can use the device whenever they want to, however they want to. And that's really what we're aiming
for. And so there can be a set of solutions that get to that state without considering this
non-stationarity problem. So maybe the first solution here that's important is that they
can recalibrate whenever they want. This is something that Nolan has the ability to do today.
So he can recalibrate the system, you know, at 2 AM in the middle of the night without his,
you know, caretaker or parents or friends around to help push a button for him.
The other important part of the solution is that when you have a good model calibrated,
that you can continue using that without needing to recalibrate it. So how often he has to do this
recalibration today depends really on his appetite for performance. There are, uh, we observe sort
of a degradation through time of how well any individual model works, but this can be mitigated
behaviorally by the user adapting their control strategy. It can also be mitigated through a
combination of sort of software features that we provide to the user. For example, we let the user
adjust exactly how fast the cursor is moving. Uh, we call that the gain, for example, the gain of how
fast the cursor reacts to any given input intention. They can also adjust the smoothing,
how smooth the output of that cursor intention actually is. They can also adjust the friction,
which is how easy is it to stop and hold still. And all these software tools allow the user a great
deal of flexibility and troubleshooting mechanisms to be able to solve this problem for them.
By the way, all of this is done by looking to the right side of the screen, selecting the mixer
and the mixer you have, it's like DJ mode, DJ mode for your VCI.
So I mean, it's a really well done interface. It's really, really well done. And so, yeah,
there's that bias, uh, that there's a cursor drift that Nolan talked about in a stream.
Uh, although he said that, uh, you guys were just playing around with it with him and they're
constantly improving. So that could have been just a snapshot of that particular moment,
a particular day, but he said that there was, uh, this cursor drift and this bias that could be removed
by him, I guess, looking to the right side of the screen, the left side of the screen to kind of
adjust the bias. Yeah. That's one interface action, I guess, to adjust the bias.
Yeah. So this is actually an idea that comes out of academia. Um, there, there was some prior work
with, uh, uh, sort of BrainGate clinical trial participants where they pioneered this idea of
bias correction. The way we've done it, I think is, yeah, it's very prioritized, very, uh, uh, beautiful
user experience where the user can essentially, um, flash the cursor over to the side of the screen and it
opens up a window where they can actually, uh, sort of adjust or tune exactly the,
the bias of the cursor. So bias maybe for people who aren't familiar is just sort of what is the
default motion of the cursor if you're imagining nothing. And it turns out that that's one of the
first, um, first sort of qualia of the cursor control experience that's impacted by neural
non-stationarity. Qualia of the cursor experience. I mean, I don't know how else to describe it.
Like, you know, I'm not the, I'm not the guy moving. It's very poetic. I love it.
I love it. The quality of the cursor experience. Yeah. I mean, it's, it sounds poetic, but it is,
uh, deeply true. There is an experience when it works well, it is a joyful, a really pleasant
experience. And when it doesn't work well, it's a very frustrating experience. That's actually the
art of UX. It's like you have the possibility to frustrate people or the possibility to give them joy.
And at the end of the day, it really is truly the case that UX is how the thing works. And so it's
not just like what's shown on the screen. It's also, you know, what control surfaces does a decoder
provide the user? Like we want them to feel like they're in the F1 car, not like the, you know,
some like minivan, right? And that really truly is how we think about it. Um, Nolan himself is an F1
fan. So, um, we refer to ourself as a pit crew. He really is truly the, the F1 driver. And there's
different, you know, control surfaces that, that different kinds of cars and airplanes provide the user.
And we take a lot of inspiration from that when designing how the cursor should behave.
And what maybe one nuance of this is, you know, even details, like when you move a mouse on a
MacBook trackpad, the sort of response curve of how that, uh, input that you give the trackpad
translates to cursor movement is different than how it works with a mouse. When you move on the trackpad,
there's a different response function, a different curve to how much a movement translates to input to
the computer than when you do it physically with a mouse. And that's because somebody sat down a long
time ago when they're designing the initial input systems to any computer. And they thought through
exactly how, uh, it feels to use these different systems. And now we're designing sort of the next
generation of this input system to a computer, which is entirely done via the brain. And there's
no proprioceptive feedback. Again, you don't feel the mouse in your hand. You don't feel the keys under
your fingertips and you want a control surface that still makes it easy and intuitive for the user to
understand the state of the system and how to achieve what they want to achieve. And ultimately,
the end goal is that that UX is completely, it fades into the background. It becomes something that's
so natural and intuitive that it's subconscious to the user. And they just should feel like they have
basically direct control over the cursor. It just does what they want it to do. They're not thinking
about the implementation of how to make it do what they want it to do. It's just doing what they want
it to do. Is there some kind of things along the lines of like Fitz law where you should move the mouse
in a certain kind of way that maximizes your chance to hit the target? Uh, I don't even know what I'm
asking, but I'm hoping the intention of my question will land on a profound answer. No, uh, is there some
kind of understanding of the laws of UX when it comes, uh, to the context of somebody using their brain
to control it? Like that's different than actual with a mouse.
I think we're in the early stages of discovering those laws. So I wouldn't claim to have solved that
problem yet, but, uh, there's definitely some things we've learned that, uh, make it, uh, easier
for the user to get stuff done. And it's pretty straightforward when you, when you verbalize it,
but it takes a while to actually get to that point when you're in the process of debugging the stuff in
the trenches. One of those things is that the, any, any machine learning system you build has some
number of errors and, uh, it matters how those errors translate to the downstream user experience.
For example, if you're developing a search algorithm in your photos, if you search for,
you know, your friend, Joe, and it pulls up a photo of your friend, Josephine, maybe that's not a big
deal because the cost of an error is not that high in, uh, a different scenario where you're trying to,
you know, detect insurance fraud or something like this, and you're directly sending someone to court
because of some machine learning model output, then the errors make a lot more, uh, sense to
be careful about. You want to be very thoughtful about how those errors translate to downstream
effects. The same is true in BCI. So for example, if you're building a model that's decoding a
velocity output from the brain versus an output where you're trying to modulate the left click,
for example, these have sort of different trade-offs of how precise you need to be before
it becomes useful to the end user. For velocity, it's okay to be on average correct because the output
of the model is integrated through time. So if the user's trying to click at position
A and they're currently in position B, they're trying to navigate over time to get between
those two points. And as long as the output of the model is on average correct, they can sort of
steer it through time with the user control loop in the, in the mix, they can get to the point they
want to get to. The same is not true of a click. For a click, you're performing it almost instantly
at the scale of, you know, neurons firing. And so you want to be very sure that that click is correct
because a false click can be very destructive to the user. They might accidentally close the tab that
they're trying to, you know, do something in and lose all their progress. They might accidentally
like, you know, hit some send button on some text that is only like half composed and reads funny
after. So, you know, there's different sort of cost functions associated with errors in this space.
And part of the UX design is understanding how to build a solution that is when it's wrong,
still useful to the end user. That's so fascinating that assigning cost
to every action when, uh, an error occurs. So every action, if an error occurs has a certain cost
and incorporating that into how you interpret the intention, mapping it to the action
is really important. I didn't quite, until you said it, realize there's a cost to like sending the text
early. It's like a very expensive cost. Yeah. It's super annoying. If you accidentally, like,
if you're a cursor, imagine if your cursor misclicked every once in a while, that's like super obnoxious.
And the worst part of it is usually when the user is trying to click, they're also holding still
because they're over the target they want to hit and they're getting ready to click, which means that
in the data sets that we build on average is the case that sort of low speeds or desire to hold still
is correlated with when the user's attempting to click. Wow. That is really fascinating.
It's also, it's also not the case. You know, people think that, oh, a click is a binary signal.
This must be super easy to decode. Well, yes it is, but the bar is so much higher for it to become
a useful thing for the user. And there's ways to solve this. I mean, you can sort of take the compound
approach of, well, let's just give the, like, let's take five seconds to click. Let's take a huge
window of time. So we can be very confident about the answer. But again, world's best mouse,
the world's best mouse doesn't take a second to click or 500 milliseconds to click. It takes five
milliseconds to click or less. And so if you're aiming for that kind of high bar, then you really want to solve
the underlying problem. So maybe this is a good place to ask about how to measure performance,
this whole bits per second. What, uh, can you like explain what you mean by that? Maybe a good
place to start is to talk about web grid as a game, as a good illustration of the measurement
of performance. Yeah. Uh, maybe I'll take one zoom out step there, which is just explaining why
we care to measure this at all. So again, our goal is to provide the user the ability to control the
computer as well as I can, and hopefully better. And that means that they can do it at the same
speed as what I can do. It means that they have access to all the same functionality that I have,
including, you know, all those little details like command tab, command space, you know, all this
stuff. They need to be able to do it with their brain and with the same level of reliability as
what I can do with my muscles. And that's a high bar. And so we intend to measure and quantify every
aspect of that to understand how we're progressing towards that goal. There's many ways to measure BPS,
by the way, this isn't the only way, but, uh, we present the user a grid of targets and basically
we compute a score, which is dependent on how fast and accurate they can select and then how
small are the targets. And the more targets that are on the screen, the smaller they are,
the more information you present per click. And so, uh, if you think about it from information
theory point of view, you can communicate across different information theoretic channels.
And one such channel is a typing interface. You could imagine that's built out of a grid,
just like a software keyboard on the screen. And, uh, bits per second is a measure that's
computed by taking the log of the number of targets on the screen. Uh, you can subtract one if you
care to model a keyboard because you have to subtract one for the delete key on the keyboard,
but log of the number of targets on the screen times the number of correct selections minus
incorrect divided by some time window, for example, 60 seconds. And that's sort of the standard way to
measure, uh, a cursor control task in academia and all credit in the world goes to this great
professor, Dr. Chenoy of Stanford, who came up with that task. And he's also one of my inspirations
for being in the field. So, um, all the credit in the world to him for coming up with a standardized
metric to facilitate this kind of bragging rights that we have now to say that no one is the best
in the world at this, at this task with its BCI. It's very important for progress that you have
standardized metrics that people can compare across different techniques and approaches.
How well does this do? So yeah, big, big kudos to him and to all the, all the team at Stanford.
Um, yeah, so for Nolan and for me playing this task, uh, there's also different modes that you can
configure this task. So the web grid task can be presented as just sort of a left click on the
screen, or you could have, you know, targets that you just dwell over, or you could have targets
that you left right click on. You could have targets that are left right click,
middle click, scrolling, clicking, and dragging. You know, you can do all sorts of things within
this, this general framework, but the simplest, purest form is just blue targets show up on the
screen. Blue means left click. That's the simplest form of the game. And, uh, the sort of prior records
here in, uh, academic work and, uh, at Neuralink internally with sort of NHPs, uh, have all been
matched or beaten by, by Nolan with his Neuralink device. So sort of prior to Neuralink, the sort of
world record for a human using device is, uh, somewhere between 4.2 to 4.6 BPS, depending on
exactly what paper you read and how you interpret it. Um, Nolan's current record is 8.5 BPS.
And, uh, again, the sort of median Neuralink performance is 10 BPS. So you can think of it
roughly as he's 85% the level of control of a median Neuralink or using their cursor to select
blue targets on the screen. And, uh, yeah, I think there's a very interesting journey ahead to get
us to that same level of 10 BPS performance. It's not the case that sort of the tricks that got us
from, you know, four to six BPS and then six to eight BPS are going to be the ones that get us
from eight to 10. And, uh, in my view, the core challenge here is really the labeling problem.
It's how do you understand at a very, very fine resolution, what the user is attempting to do.
And, uh, yeah, I highly encourage folks in academia to, uh, to work on this problem.
What's the journey with Nolan on that quest of increasing the BPS on WebGrid?
In March, you said that he selected 89,285 targets in WebGrid.
Yep.
So he loves this game. He's really serious about improving his performance in this game.
So what is the journey of trying to figure out how to improve that performance? How much
can that be done on the decoding side? How much can that be done on the calibration side? How much
can that be done on the Nolan side of like figuring out how to convey his intention more cleanly?
Yeah, no, this is a great question. So in my view, one of the primary reasons why Nolan's performance
is so good is because of Nolan. Nolan is extremely focused and very energetic. He'll play WebGrid
sometimes for like four hours in the middle of the night, like from 2 AM to 6 AM, he'll be playing
WebGrid just because he wants to push it to the limits of what he can do. And, uh, you know,
this is not us like asking him to do that. I want to be clear. Like, we're not saying,
hey, you should play WebGrid tonight. We just gave him the game as part of our research,
you know, and he is able to play independently and practice whenever he wants. And he really
pushes hard to push it. The technology is the absolute limit. And he views us like, you know,
his job really to make us be the bottleneck and boy, has he done that well. Uh, and so that's the
first thing to acknowledge is that, you know, he was extremely motivated to make this work.
I've also had the privilege to meet other, you know, clinical trial participants from
BrainGate and other trials. And they very much share the same attitude of like,
they viewed this as their life's work to, uh, you know, advance the technology as much as they can.
And, uh, if that means selecting targets on the screen for four hours from 2 AM to 6 AM,
then so be it. And, uh, there's something extremely admirable about that. That's worth, uh,
calling out. Okay. So now how do you, how do you sort of get from where he started,
which is no cursor control to eight BPS? So, I mean, when he started, there's a huge amount of
learning to do on his side and our side to figure out, uh, what's the most intuitive control for him.
And the most intuitive control for him is, uh, sort of, you have to find the set intersection
of what do we have the signal to decode. So we don't pick up, you know, every single neuron in
the motor cortex, which means we don't have representation for every part of the body.
So there may be some signals that we have better sort of decode performance on than others. For
example, on his left hand, we have a lot of difficulty to see machine has left ring finger
from his left middle finger, but on his right hand, we have a good, you know, good control and good
modulation detected from the neurons we're able to record for his pinky and his thumb and his index finger.
So you can imagine how these different, uh, you know, subspaces of modulated activity intersect
with what's the most intuitive for him. And this has evolved over time. So once we gave him the
ability to calibrate models on his own, he was able to go and explore various different ways to
imagine controlling the cursor. For example, he can imagine controlling the cursor by wiggling his
wrist side to side, or by moving his entire arm, but at one point, he did his feet. You know,
he tried like a whole bunch of stuff to explore the space of what is the most natural way for him
to control the cursor that at the same time, it's easy for us to decode.
Just to clarify, it's through the body mapping procedure there, you're able to figure out
which finger he can move?
Uh, yes. Yeah. That's one way to do it. Um, maybe one nuance of the, when he's doing it,
he can imagine many more things than we represent in that visual on the screen. So we show him sort of
abstractly, here's a cursor, you figure out what works the best for you. And we obviously have hints
about what will work best from that body mapping procedure of, you know, we know that this particular
action we can represent well, but it's really up to him to go and explore and figure out what works
the best.
But at which point does he no longer visualize the movement of his body and he's just visualizing
the movement of the cursor?
Yeah.
How quickly does he go from, how quickly does it get there?
So this happened on a Tuesday. I remember this day very clearly because at some point during the,
during the day, uh, it looked like he wasn't doing super well. Like it looked like the model wasn't
performing super well and he was like getting distracted, but he actually, it wasn't the case.
Like what actually happened was he was trying something new where he was just controlling the
cursor. So he wasn't imagining moving his hand anymore. He was just imagining, I don't know what
it is. Some like abstract intention to move the cursor on the screen. And, uh, I cannot tell you what
the difference between those two things are. I really truly cannot. He's tried to explain it to me
before. I cannot, uh, you know, give a first person account of what that's like, but the expletives
that he uttered in that moment were, uh, you know, enough to suggest that it was a very qualitatively
different experience for him to just have direct neural control over a cursor.
I wonder if there's a way through UX to encourage a human being to discover that
because he discovered it, like you said, uh, to me that he's a pioneer. So he discovered that on his
own through all of this, uh, the process of trying to, trying to move the cursor with different kinds of
intentions, but that is clearly a really powerful thing to arrive at, which is to let go of trying
to control the fingers and the hand and control the actual digital device with your mind.
That's right. UX is how it works. And the ideal UX is one that it's, the user doesn't have to think
about what they need to do in order to get it done. They just, it just does it.
That is so fascinating. But I wonder on the, on the biological side, how long it takes for the brain
to adapt. Yeah. So is it just simply learning like high level software or is there like a neuroplasticity
component where like the brain is adjusting slowly? Yeah. I, the truth is, I don't know. Um, I'm very
excited to see with sort of the second participant that we implant, what the, you know, what the journey is
like for them because we'll have learned a lot more. Potentially we can help them understand and
explore that direction more quickly. This is something I didn't, you know, this wasn't me
prompting Nolan to go try this. He was just exploring how to use his device and figured it out himself.
But now that we know that that's a possibility that maybe there's a way to, you know, for example,
hint the user, don't try super hard during calibration, just do something that feels natural
or just directly control the cursor, you know, don't imagine explicit action. And from there,
we should be able to hopefully understand how this is for somebody who has not experienced that before.
Maybe that's the default mode of operation for them. You don't have to go through this
intermediate phase of explicit motions. Or maybe if that naturally happens for people,
you can just occasionally encourage them to allow themselves to move the cursor.
Right. Actually, sometimes just like with a four minute mile, just the knowledge that that's
possible pushes you to do it. Yeah. Enables you to do it. And then it becomes trivial. And then it
also makes you wonder, this is the cool thing about humans. If once there's a lot more human
participants, they will discover things that are possible.
Yes. And share their experiences.
Yeah. And share it. And that because of them sharing it, they'll be able to do it.
All of a sudden that's unlocked for everybody.
Yeah. Because just the knowledge sometimes is the thing that enables it to do it.
Yeah. I mean, just coming on that too, like there's, we've probably tried like a thousand
different ways to do various aspects of decoding. And now we know like what the right subspace
is to continue exploring further. Again, thanks to Nolan and the many hours he's put into this.
And so even just that help, like help constraints or the beam search of different approaches that we
could explore really helps accelerate for the next person, you know, the set of things that we'll
get to try on day one, how fast we hope to get them to useful control, how fast we can enable them
to use it independently and to get value out of the system. So yeah, massive hats off to Nolan and
all the participants that came before him to make this technology a reality.
So how often are the updates to the decoder? Cause Nolan mentioned like, okay, there's a new update
that we're working on and that in the stream, he said he plays the snake game because it's like super
hard. It's a good way for him to test like how good the update is. So, and he says like, sometimes the
update is a step backwards. It's like, it's a constant like iteration. So how often, like, what does the
update entail? Is it most on the decoder side?
Yeah. Couple comments. So one is, it's probably worth drawing distinction between sort of research
sessions where we're actively trying different things to understand like what the best approach
is versus sort of independent use where we wanted to have, you know, ability to just go use the device
how anybody would want to use their MacBook. And, uh, so what he's referring to is I think usually
in the context of a research session where we're trying, you know, many, many different approaches to,
you know, even unsupervised approaches that we talked about earlier to, to try to come up with better
ways to estimate his true intention and more accurately decode it. And, uh, in those scenarios, I mean,
we try in any given session, he'll sometimes work for like eight hours a day. And so that can be,
you know, hundreds of different models that we would try in that day, like a lot of different
things. Um, now it's also worth noting that we update the application he uses quite frequently.
I think, you know, sometimes up to like four or five times a day, we'll update his application
with different features or bug fixes or feedback that he's given us. So he's been able to, he's a very
articulate person who, uh, is part of the solution. He's not a complaining person. He says,
Hey, here's this thing that I've, I've discovered is, is not optimal in my flow. Here's some ideas,
how to fix it. Let me know what your thoughts are. Let's figure out how to, how to solve it.
And it often happens that those things are addressed within, you know, a couple hours of
him giving us this feedback, that that's the kind of iteration cycle we'll have. And so sometimes
at the beginning of the session, he'll give us feedback. And at the end of the session, he's,
he's giving us feedback on the next iteration of that, of that, of that process or that setup.
That's fascinating. Cause one of the things you mentioned that there was 271 pages of notes
taken from the BCI sessions. And this was just in March. So one of the amazing things about human
beings that they can provide, especially ones who are smart and excited and all like positive and
good vibes like Nolan, that they can provide feedback, continuous feedback.
Yeah. It also requires just to brag on the team a little bit. I work with a lot of exceptional
people. And it requires the team being absolutely laser focused on the user and what will be the
best for them. And it requires like a level of commitment of, okay, this is what the user
feedback was. I have all these meetings. We're going to skip that today. And we're going to do this,
you know, that level of focus commitment is, uh, I would say under underappreciated in the world.
And also, uh, you know, you obviously have to have the talent to be able to execute on these
things effectively. And, uh, yeah, we have that in, in loads.
Yeah. And this is such a,
interesting space of UX design because you have, there's so many unknowns here
and I can tell UX is difficult because of how many people do it poorly.
It's just not a trivial thing.
Yeah. It's also, you know, UX is not something that you can
always solve by just constant iterating on different things. Like sometimes you really need
to step back and think globally, am I even in like the right sort of minima to be chasing down
for a solution? Like there's a lot of problems in which sort of fast iteration cycle is the,
the predictor of how successful you will be. As a good example, like in a, an RL simulation,
for example, the more frequently you get reward, the faster you can progress. It's just an easier
learning problem. The more frequently you get feedback, but UX is not that way. I mean, users
are actually quite often wrong about what the right solution is. And it requires a deep
understanding of the technical system and what's possible combined with what the problem is you're
trying to solve, not just how the user expressed it, but what the true underlying problem is to
actually get to the right place. Yeah. That's the old like stories of Steve Jobs, like rolling in there.
Like, yeah, the user is a good, is a useful signal, but it's not a perfect signal. And sometimes you
have to remove the floppy disk drive or whatever the, I forgot all the crazy stories of Steve Jobs, like
making wild design decisions, but there some, some of it is aesthetic
that some of it is about the love you put into the design, which is very much a Steve Jobs,
Johnny Ive type thing. But when, when you have a human being using their brain to interact with it,
there is also is deeply about function. It's not just aesthetic.
Yeah. And that you have to empathize with, with a human being before you,
while not always listening to them directly. Like you have to deeply empathize. It's fascinating.
It's really, really fascinating. And at the same time, iterate, right? But not iterate in small
ways, sometimes a complete, like rebuilding the design. He said that, Nolan said in the early days,
the UX sucked, but you improved quickly. What was that journey like?
Yeah. I mean, I'll give one concrete example. So he really wanted to be able to read manga.
This is something that he, I mean, it sounds like a simple thing, but it's actually a really big deal
for him. And he couldn't do it with this mouse stick. It just, it wasn't accessible. You can't
scroll with a mouse stick on his iPad and on the website that he wanted to be able to use to read the,
the newest manga. And so might be a good, quick pause to say the mouth stick is the thing he's using,
holding a stick in his mouth to scroll on a tablet.
Right. Yeah. It's basically, you can imagine it's a stylus that you hold between your teeth.
Yeah. It's basically a very long stylus.
And it's, it's exhausting. It's, it hurts and it's inefficient.
Yeah. And maybe it's also worth calling out, there are other alternative assistive technologies,
but the particular situation Nolan's in, and this is not uncommon. And I think it's also not well
understood by folks is that, you know, he's relatively spastic. So he'll have muscle spasms from time
to time. And so any assistive technology that requires him to be positioned directly in front
of a camera, for example, an eye tracker, or anything that requires him to put something in
his mouth, just as a no go, because he'll either be shifted out of frame when he has a spasm, or
if he has something in his mouth, it'll stab him in the face, you know, if he spasms too hard.
So these kinds of considerations are important when thinking about what advantages a PCI has in
someone's life. If it fits ergonomically into your life in a way that you can use it independently,
when your caretaker is not there, wherever you want to, either in the bed or in the chair,
depending on, you know, your comfort level and your desire to have pressure source,
you know, all these factors matter a lot in how good the solution is in that user's life.
So one of these very fun examples is scroll. So again, Manga is something he wanted to be able to
read. And there's many ways to do scroll with a PCI. You can imagine like different gestures,
for example, the user could do that would move the page. But scroll is a very fascinating
control surface because it's a huge thing on the screen in front of you. So any sort of jitter in
the model output, any sort of error in the model output causes like an earthquake on the screen.
Like you really don't want to have your manga page that you're trying to read be shifted up and down
a few pixels just because, you know, your scroll decoder is not completely accurate.
And so this was an example where we had to figure out how to formulate the problem in a way that the
errors of the system, whenever they do occur, and we'll do our best to minimize them,
but whenever those errors do occur, that it doesn't interrupt the qualia, again,
of the experience that the user is having. It doesn't interrupt their flow of reading their book.
And so what we ended up building is this really brilliant feature. This is a teammate named
Bruce who worked on this really brilliant work called Quickscroll. And Quickscroll basically looks
at the screen and it identifies where on the screen are scroll bars. And it does this by deeply
integrating with macOS to understand where are the scroll bars actively present on the screen using
the sort of accessibility tree that's available to macOS apps. And we identified where those scroll
bars are and provided a BCI scroll bar. And the BCI scroll bar looks similar to a normal scroll bar,
but it behaves very differently in that once you sort of move over to it, your cursor sort of morphs
onto it. It sort of attaches or latches onto it. And then once you push up or down in the same way
that you'd use a push to control, you know, the normal cursor, it actually moves the screen for you.
So it's basically like remapping the velocity to a scroll action. And the reason that feels so
natural and intuitive is that when you move over to attach to it, it feels like magnetic. So you're
like sort of stuck onto it. And then it's one continuous action. You don't have to like switch
your imagined movement. You sort of snap onto it and then you're good to go. You just immediately
can start pulling the page down or pushing it up. And even once you get that right, there's so many
little nuances of how the scroll behavior works to make it natural and intuitive. So one example is
momentum. Like when you scroll a page with your fingers on the screen, you know, you actually
have some like flow. Like it doesn't just stop right when you lift your finger up. The same is
true with BCI scroll. So we had to spend some time to figure out what are the right nuances when you
don't feel the screen under your fingertip anymore. What is the right sort of dynamic or what's the right
amount of page give, if you will, when you push it to make it flow the right amount for the user to have
a natural experience reading their book. And there's a million, I mean, there's, I could tell you like
there's so many little minutiae of how exactly that scroll works that we spent probably like
a month getting right to make that feel extremely natural and easy for the user to navigate.
I mean, even the scroll on a smartphone with your finger feels extremely natural and pleasant.
And it probably takes an extremely long time to get that right. And actually the same kind of visionary
UX design that we're talking about. Don't always listen to the users,
but also listen to them and also have like visionary big, like throw everything out,
think from first principles, but also not. Yeah. Yeah. By the way, it just makes me think
that scroll bars on the desktop probably have stagnated and never taken that like,
because the snap, same as it's like snap to grid, snap to scroll bar action you're talking about is
something that could potentially be extremely useful in the desktop setting.
Yeah. Even just for users to just improve the experience because the current scroll
bar experience and the desktop is horrible. Yeah. It's hard to find, hard to control. There's not
a momentum. There's and the intention should be clear. When I start moving towards the scroll
bar, there should be a snap into the scroll bar action. But of course, you know, maybe I'm okay paying
that cost, but there's hundreds of millions of people paying that cost nonstop. But anyway,
but in this case, this is necessary because there's an extra cost paid by Nolan for the jitteriness.
So you have to switch between the scrolling and the reading. There has to be a phase shift between the
two. Like when you're scrolling, you're scrolling. Right. Right. So that is one drawback of the current,
the current approach. Maybe one other just sort of case study here. So again, UX is how it works.
And we think about that holistically from like the, even the feature detection level of what
we detect in the brain to how we design the decoder, what we choose to decode to then how
it works once it's being used by the user. So another good example in that sort of how it works
once they're actually using the decoder, you know, the output that's displayed on the screen is not
just what the decoder says. It's also a function of, you know, what's going on on the screen. So we can
understand, for example, that, you know, when you're trying to close a tab, that very small,
stupid little X, it's extremely tiny, which is hard to get precisely hit. If you're dealing with
sort of a noisy output of the decoder, we can understand that that is a small little X you
might be trying to hit and actually make it a bigger target for you. Similar to how, when you're typing
on your phone, if you're, uh, you know, used to like the iOS keyboard, for example, it actually
adapts the target size of individual keys based on an underlying language model. So it'll actually
understand if I'm typing, Hey, I'm going to see L it'll make the E key bigger because it knows Lex
is the person I'm going to go see. And so that kind of, you know, predictiveness can make the
experience much more smooth, even without, you know, improvements to the underlying decoder or,
uh, or a feature detection part of the stack. So we do that with a feature called magnetic targets.
We actually index the screen and we understand, okay, these are the places that are, you know,
very small targets that might be difficult to hit. Here's the kind of cursor dynamics around
that location that might be indicative of the user trying to select it. Let's make it easier.
Let's blow up the size of it in a way that makes it easier for the user to sort of snap onto that
target. So all these little details, they matter a lot in helping the user be independent in their
day-to-day living. So how much of the work on the decoder is generalizable to P2, P3, P4, P5, PN?
How do you improve the decoder in a way that's generalizable?
Yeah. Great question. So the underlying signal we're trying to decode is going to look very
different in P2 than in P1. For example, channel number 345 is going to mean something different
in user one than it will in user two, just because that electrode that corresponds with
channel 345 is going to be next to a different neuron in user one versus user two. But the
approaches, the methods, the user experience of how do you get the right sort of behavioral
pattern from the user to associate with that neural signal, we hope it will translate over
multiple generations of users. And beyond that, it's very, very possible. In fact, quite likely that
we've overfit to sort of Nolan's user experience desires and preferences. And so what I hope to see is that,
you know, when we get a second, third, fourth participant, that we find sort of what the
right wide minimums are that cover all the cases that make it more intuitive for everyone. And
hopefully there's a cross-pollination of things where, oh, we didn't think about that with this
user because, you know, they can speak. But with this user who just can fundamentally not speak at all,
this user experience is not optimal. And that will actually, those improvements that we make there
should hopefully translate then to even people who can't speak, but don't feel comfortable doing so
because we're in a public setting like their doctor's office.
So the actual mechanism of open loop labeling and then closed loop labeling
would be the same and hopefully can generalize across the different users as they're doing the
calibration step. And the calibration step is pretty cool. I mean, that in itself, the interesting
thing about WebGrid, which is like closed loop, it's like fun. I love it when there's like,
uh, there used to be kind of idea of human computation, which is using actions that human
would want to do anyway, to get a lot of signal from. Yeah. And like, what great is that? Like
a nice video game that also serves as great calibration. It's so funny. This is, I've heard
this reaction so many times before sort of the, you know, first user was implanted. We had an internal
perception that the first user would not find this fun. Yeah. And so we thought really quite a bit
actually about like, should we build other games that like are, you know, more interesting for the
user so we can get this kind of data and help facilitate research that's, you know, for long
duration and stuff like this. Turns out that like people love this game. Yeah. I always loved it,
but I didn't know that that was a shared perception. Yeah. And just in case it's not clear,
WebGrid is, there's a, a grid of, let's say 35 by 35, uh, cells and one of them lights up blue and
you have to move your mouse over that and click on it. And if you miss it and it's red and it's good.
For this game for so many hours, so many hours. And what's your record? You said my, I think I
have the highest at Neuralink right now. My record is 17 BPS. 17 BPS. Which is about, if you imagine
that 35 by 35 grid, you're hitting about a hundred trials per minute. So a hundred correct selections in
that one minute window. So you're averaging about, you know, between 500, 600 milliseconds per selection.
So one of the reasons that I think I struggle with that game is I'm such a keyboard person. So
everything's done with your keyboard. If, if I can avoid touching the mouse, it's great.
So how can you explain your high performance?
I have like a whole ritual I go through when I play WebGrid. So it's, there's actually like a diet
plan associated with this. Like it's a whole thing. So the first thing, I have to fast for five days.
I have to go up to the mountain. Actually, it kind of, I mean, the fasting thing is important.
So this is like, you know, focuses the mind. Yeah. Yeah. It's true.
So what I do is I, I actually, I don't eat for a little bit beforehand. And then I'll actually
eat like a ton of peanut butter right before I play. And I get like, this is a real thing.
This is a real thing. Yeah. And then it has to be really late at night. This is like a night owl
thing. I think we share, but it has to be like, you know, midnight, 2 AM kind of time window.
And I have a very specific like physical position I'll sit in, which is, I used to be,
I was homeschooled growing up. And so I did most of my work, like on the floor,
just like in my bedroom or whatever. And so I have a very specific situation on the floor,
on the floor that I sit and play. And then you have to make sure like,
there's not a lot of weight on your elbow when you're playing so that you can move quickly.
And then I turned the gain of the cursor. So the speed of the cursor way, way up.
So it's like small motions that actually move the cursor.
Are you moving with your wrist or you, you're never.
I move with my fingers. So I, my wrist is almost completely still. I'm just moving my fingers.
Yeah. You know, those just on a small tangent, the, which I've been meaning to go down this
rabbit hole of people that set the world record in Tetris, those folks, they're playing.
There's a, there's a way to, did you see this?
It seems like the three, like all the fingers are moving.
Yeah. You could, you could find a way to do it where like, it's using a loophole,
like a bug that you can do some incredibly fast stuff. So it's, it's along that line,
but not quite, but you do realize there'll be like a few programmers right now,
listening to this cool fast and eat peanut butter.
Yeah, please, please try my record. I mean, the reason I did this literally was just
because I wanted the bar to be high team. Like I wanted the, the number that we aim for should not
be like the median performance. It should be like, it should be able to beat all of us,
at least like that should be the minimum bar.
What do you think is possible? Like 20?
Yeah. I don't know what the limit, I mean, the limits you can calculate just in terms of
like screen refresh rate and like cursor immediately jumping to the next target.
But there's, I mean, I'm sure there's limits before that with just sort of reaction time and
visual perception and things like this. I would guess it's in the below 40, but above 20,
somewhere in there, it's probably the right there I'd never be thinking about. It also matters like how
difficult the task is. You can imagine like some people might be able to do like 10,000 targets
on the screen and maybe they can do better that way. So there's some like task optimizations you
could do to try to boost your performance as well.
What do you think it takes for Nolan to be able to do above 8.5 to keep increasing that number? You
said like every increase in the number might require different, different improvements in the system.
Yeah. I think the nature of this work is, I mean, the first, the first answer that's important
is I don't know. This is, you know, edge of the research. So again, nobody's gotten to that number
before. So what's next is going to be, you know, a heuristic, a guess from my part.
What we've seen historically is that different parts of the stack become bottlenecks at different
time points. So, you know, when I first joined Neuralink like three years ago or so, one of the
major problems was just the latency of the Bluetooth connection. It was just like the radio on the
device wasn't super good. It was an early revision of the implant. And it just like, no matter how good
your decoder was, if your thing is updating every 30 milliseconds or 50 milliseconds, it's just going
to be choppy. And no matter how good you are, that's going to be frustrating and lead to challenges.
So, you know, at that point, it was very clear that the main challenge is just get the data off
the device in a very reliable way, such that you can enable the next challenge to be tackled.
And then, you know, at some point it was, you know, actually the modeling challenge of how do you
just build a good mapping, like the supervised learning problem of you have a bunch of data
and you have a label you're trying to predict, just what is the right like
neural decoder architecture and hyper parameters to optimize that. That was a problem for a bit.
And once you saw that, it became a different bottleneck. I think the next bottleneck after
that was actually just sort of software stability and reliability. You know, if you have widely
varying sort of inference latency
in your system or your, you know, your app just lags out every once in a while, it decreases your
ability to maintain and get in a state of flow. And it basically just disrupts your control experience.
And so there's a variety of different software bugs and improvements we made that basically
increased the performance of the system, made it much more reliable, much more stable,
and led to a state where we could reliably collect data to build better models with.
So that was a bottleneck for a while. It's just sort of like the software stack itself.
If I were to guess right now, there's sort of two major directions you could think about for
improving BPS further. The first major direction is labeling. So labeling is, again, this fundamental
challenge of given a window of time where the user is expressing some behavioral intent,
what are they really trying to do at the granularity of every millisecond? And that,
again, is a task design problem. It's a UX problem. It's a machine learning problem. It's a software
problem. Sort of touches all those different domains. The second thing you can think about
to improve BPS further is either completely changing the thing you're decoding or just
extending the number of things that you're decoding. So this is sort of in the direction
of functionality. Basically, you can imagine giving more clicks, for example, a left click,
a right click, a middle click, different actions like click and drag, for example. And that can improve
the effective bit rate of your communication processes. If you're trying to allow the user to
express themselves through any given communication channel, you can measure that with bits per second. But what
actually matters at the end of the day is how effective are they at navigating their computer.
And so from the perspective of the downstream tasks that you care about,
functionality and extending functionality is something we're very interested in. Because not
only can it improve the sort of number of BPS, but it can also improve the downstream sort of
independence that the user has and the skill and efficiency with which they can operate their computer.
Would the number of threads increasing also potentially help?
Yes. Short answer is yes. It's a bit nuanced how that curve, or how that manifests
in the numbers. So what you'll see is that if you sort of plot a curve of number of channels
that you're using for decode, versus either the offline metric of how good you are at decoding,
or the online metric of sort of, in practice, how good is the user at using this device,
you see roughly a log curve. So as you move further out in number of channels,
you get a corresponding sort of logarithmic improvement in control quality
and offline validation metrics. The important nuance here is that each channel corresponds with
a specific, you know, represented intention in the brain. So for example, if you have a channel 254,
it might correspond with moving to the right. Channel 256 might mean move to the left.
If you want to expand the number of functions you want to control, you really want to have a broader
set of channels that covers a broader set of imagined movements. You can think of it like,
uh, kind of like Mr. Potato Man, actually. Like if you had a bunch of different imagined movements
you could do, how would you map those imagined movements to input to a computer?
Uh, you can imagine, you know, handwriting to output characters on the screen. You can imagine
just typing with your fingers and have that output text on the screen. You can imagine different
finger modulations for different clicks. You can imagine wiggling your big nose for
opening some, some menu or wiggling your, you know, your big toe to have like a command tab occur or
something like this. So it's really, uh, the amount of different actions you can take in the world
depends on how many channels you have and the information content that they carry.
Right. So that's more about the number of actions. So actually, as you increase the
number of threads, that's more about increasing, uh, the number of actions you're able to perform.
One other nuance there that is worth mentioning. So again, our goal is really to enable a user with
paralysis to control the computer as fast as I can. So that's BPS, uh, with all the same functionality
I have, which is what we just talked about, but then also as reliably as I can. Yeah.
And that last point is very related to channel count discussion. So as you scale out number of
channels, the relative importance of any particular feature of your model input to the output control
of the user diminishes, which means that if the sort of neural non-stationarity effect is per channel,
or if the noise is independent, such that more channels means on average, less output effect,
then your reliability of your system will improve. So one sort of core thesis, um, that at least I have
is that scaling channel count should improve the reliability system without any work on the decoder
itself. Can you linger on the reliability here? So first of all, when you say non-stationarity
of the signal, which aspect are you referring to? Yeah. So maybe let's talk briefly what the
actual underlying signal looks like. So again, I spoke very briefly at the beginning about how
when you imagine moving to the right or imagine moving to the left, neurons might fire more or less.
And their frequency content of that signal, at least in the motor cortex,
it's very correlated with the output intention or the behavioral, uh, task that the user is doing.
You could imagine, actually, this is not obvious that rate coding, which is the name of that, um,
phenomenon is like the only way the brain could represent information. You can imagine many
different ways in which the brain could encode, uh, intention. And there's actually evidence like
in baths, for example, that there's temporal codes. So timing codes of like exactly when particular
neurons fire is the mechanism of information, uh, representation. But at least in the motor cortex,
there's substantial evidence that it's, uh, rate coding or at least one like first order effect
is that it's rate coding. So then if the brain is representing information by changing the sort of
frequency of a neuron firing, what really matters is sort of the delta between sort of the baseline
state of the neuron and what it looks like when it's modulated. And what we've observed and what
has also been observed in academic work is that that baseline rate, sort of the, if you're to target
the scale, if you imagine, uh, that analogy for like measuring, you know, flour or something when
you're baking, that baseline state of how much the pot weighs is actually different day to day.
And so if what you're trying to measure is how much rice is in the pot, you're going to get a
different measurement different days because you're measuring with different pots. So that baseline
rate shifting is really the thing that, uh, at least from a first order description of the problem
is what's causing this downstream bias. There can be other effects, not linear effects on top of that,
but at least at a very first order description of the problem, that's what we observe day to day,
is that the baseline firing rate of any particular neuron or observed on a particular channel is
changing. So can you just adjust to the baseline to make it relative to the baseline nonstop?
Yeah, this is a great question. So, um, with monkeys, we have found various ways to do this.
Um, one example way to do this is you ask them to do some behavioral task, like play the game with
a joystick. You measure what's going on in the brain, you compute some mean of what's going on across
all the input features, and you subtract that in the input when you're doing your
BCI session works super well. For whatever reason, that doesn't work super well with Nolan.
I actually don't know the full reason why, but I can imagine several, several explanations.
Um, one such explanation could be that the context effect difference between some open
loop task and some closed loop task is much more significant with, um, Nolan than it is with monkey.
Maybe in this open loop task, he's, you know, watching the Lex Freeman podcast while he's doing the task,
or he's whistling and listening to music and talking with his friend and
ask his mom what's for dinner while he's doing this task. And so the, the exact sort of difference
in context between those two states may be much larger and thus lead to a bigger sort of generalization
gap between the features that you're normalizing at sort of open loop time and what you're trying
to use at close loop time. That's interesting. Just on that point, it's kind of incredible to watch
Nolan be able to do, uh, to multitask to do multiple tasks at the same time to be able to move the mouse
cursor effectively while talking and while being nervous because he's talking in front of my ass and
chest to kicking your ass and now we're and talk trash while doing it. So all at the same time. And yes,
if you're trying to normalize to the baseline, that might throw everything off.
Boy, is that interesting. Maybe one comment on that too, for folks that aren't familiar with
assistive technology, I think there's a common belief that, you know, well, why can't you just
use an eye tracker or something like this for helping somebody move a mouse on the screen?
And it's, it's a really a fair question. And one that I actually did was not confident before
sir Nolan, that this was going to be a profoundly transformative technology for people like him.
And, uh, I'm very confident now that it will be, but the reasons are subtle. It really has to do
with ergonomically how it fits into their life. Even if you can just offer the same level of control
as what they would have with an eye tracker or with a mouse stick,
but you don't need to have that thing in your face. You don't need to be positioned a certain
way. You don't need your caretaker to be around to set it up for you. You can activate it when you
want, how you want, wherever you want. That level of independence is so game-changing for people.
It means that they can text a friend at night privately without their mom needing to be in the
loop. It means that they can like open up, you know, and browse the internet at 2am when nobody's
around to set their iPad up for them. This is like profoundly game-changing thing for folks in that
situation. And this is even before we start talking about folks that, you know, may not be able to
communicate at all or ask for help when they want to. This can be the potentially the only link that
they have to the outside world. And, uh, yeah, that one doesn't, I think, need explanation of why that's
so impactful.
You mentioned neural decoder. How much machine learning is in the decoder? How much magic,
how much science, how much art, how difficult is it to come up with a decoder that figures out
what these, uh, sequence of spikes mean?
Yeah. Good question. Uh, there's a couple of different ways to answer this. So maybe I'll zoom
out briefly first and then I'll go down one of the rabbit holes. So the zoomed out view is that
building the decoder is really the process of building the data set, plus compiling it into
the weights. And, uh, each of those steps is important. Uh, the direction I think of further
improvement is primarily going to be in the data set side of how do you construct the optimal labels
for the model. But there's an entirely separate challenge of then how do you compile the best
model? And so I'll go briefly down the second one, down the second rabbit hole. One of the main
challenges with designing the optimal model for BCI is that offline metrics don't necessarily
correspond to online metrics. Uh, it's fundamentally a control problem. The user is
trying to control something on the screen and the exact sort of user experience of how
you output the intention, uh, impacts ability to control. So for example, if you just look at
validation loss as predicted by your model, there can be multiple ways to achieve the same validation
loss. Not all of them are equally controllable by the end user. And so the, you know, it might be as
simple as saying, oh, you could just add auxiliary loss terms that like help you capture the thing
that actually matters, but this is a very complex nuanced process. So how you turn the labels into
the model is, uh, more of a nuanced process than just like a standard supervised learning problem.
One very fascinating, uh, anecdote here, we've tried many different sort of neural network
architectures that translate brain data to, uh, velocity outputs, for example. And one, uh, example
that's stuck in my brain from a couple of years ago now, uh, is we, at one point we were using just
fully connected networks to decode the brain activity. We tried, uh, a B test where we were
measuring, uh, the relative performance and online control sessions of, uh, sort of one
D convolution over the input signal. So if you imagine per channel, you have a sliding window that's
producing some, uh, convolved feature for each of those input sequences for every single channel
simultaneously, you can actually get better validation metrics, meaning you're fitting the data better
and it's generalizing better in offline data. If you use this convolutional architecture, you're
reducing parameters. It's sort of a standard, uh, standard procedure when you're dealing with time
series data. Now it turns out that when using that model online, the controllability was, was worse,
was far worse, even though the offline metrics were better. And, uh, there can be many ways to
interpret that, but what that taught me at least was that, hey, it's at least the case right now that
if you were to just throw a bunch of compute at this problem and you were trying to sort of
hyperparameter optimize or, you know, let some GPT model hard code or come up with or invent many
different solutions, if you were just optimizing for loss, it would not be sufficient, which means
that there's still some inherent modeling gap here. There's still some artistry left to be uncovered
here of how to get your model to scale with more compute. And that may be fundamentally labeling
problem, but there may be other components to this as well. Is it a data constraint at this time?
Like the, which is what it sounds like, like how do you get a lot of good labels?
Yeah. I think it's data quality constrained, not necessarily data quantity constrained.
But even like, even just the quantity, I mean, because it has to be trained on the, on the
interactions. I guess there's not that many interactions.
Yeah. So it depends what version of this you're talking about. So if you're talking about like,
let's say the simplest example of just 2d velocity, then I think, yeah, data quality is the main thing.
If you're talking about how to build a sort of multifunction output that lets you do all the
inputs to the computer that you and I can do, then it's actually a much more sophisticated,
nuanced modeling challenge, because now you need to think about not just when the user is left
clicking, but when you're building the left click model, you also need to be thinking about how to
make sure it doesn't fire when they're trying to right click or when they're trying to move the
mouse. So one example of an interesting bug from like sort of week one of BCI with Nolan was
when he moved the mouse, the click signal sort of dropped off a cliff. And when he stopped,
the click signal went up. So again, there's a contamination between the two inputs. Another
good example was at one point he was trying to do sort of a left click in drag. And the
minute he started moving, the left click signal dropped off a cliff. So again, because there's
some contamination between the two signals, you need to come up with some way to either in the
dataset or in the model, build robustness against this kind of, you can think of it like overfitting,
but really it's just that the model has not seen this kind of variability before. So you need to
find some way to help the model with that. This is super cool because it feels like all
of this is very solvable, but it's hard. Yes, it is fundamentally an engineering
challenge. This is important to emphasize. And it's also important to emphasize that it may
not need fundamentally new techniques, which means that people who work on, let's say,
unsupervised speech classification using CTC loss, for example, with internal to Siri,
they could potentially have very applicable skills to this. So what things are you excited about in
the future development of the software stack on Neuralink? So everything we've been talking about,
the decoding, the UX. I think there's some I'm excited about, like something I'm excited about
from the technology side, and some I'm excited about for understanding how this technology is going
to be best situated for entering the world. So I'll work backwards. On the technology entering the world
side of things, I'm really excited to understand how this device works for folks that, you know,
cannot speak at all, that have no ability to sort of bootstrap themselves into useful control by voice
command, for example, and are extremely limited in their current capabilities. I think that will be
an incredibly useful signal for us to understand, I mean, really what is an existential threat for
all startups, which is product market fit. Does this device have the capacity and potential to
transform people's lives in the current state? And if not, what are the gaps? And if there are gaps,
how do we solve them most efficiently? So that's what I'm very excited about for the next year or so of
clinical trial operations. On the technology side, I'm quite excited about basically everything we're
doing. I think it's going to be awesome. The most prominent one, I would say, is scaling channel
count. So right now we have a thousand channel device. The next version will have between three
and six thousand channels, and I would expect that curve to continue in the future. And it's unclear
what set of problems will just disappear completely at that scale, and what set of problems will remain
and require further focus. And so I'm excited about the clarity of gradient that that gives us
in terms of the user experience that we choose to focus our time and resources on.
And also in terms of the, you know, even things as simple as non-stationarity, like,
does that problem just completely go away at that scale? Or do we need to come up with new
creative UXs still even at that point? And also, when we get to that time point,
when we start expanding out dramatically the set of functions that you can output from one brain,
how to deal with all the nuances of both the user experience of not being able to feel the
different keys under your fingertips, but still needing to be able to modulate all of them
in synchrony to achieve the thing you want. And again, you don't have that properly set
to feedback loops, so how can you make that intuitive for a user to control a high dimensional
control surface without feeling the thing physically? I think that's going to be a super
interesting problem. I'm also quite excited to understand, you know, do these scaling laws
continue? Like, as you scale channel count, how much further out do you go before that
saturation point is truly hit? And it's not obvious today. I think we only know what's in the sort of
interpolation space. We only know what's between 0 and 1024, but we don't know what's beyond that.
And then there's a whole sort of like range of interesting sort of neuroscience and brain
questions, which is when you stick more stuff in the brain in more places, you get to learn much
more quickly about what those brain regions represent. And so I'm excited about that fundamental
neuroscience learning, which is also important for figuring out how to most efficiently insert
electrodes in the future. So yeah, I think all those dimensions I'm really, really excited about,
and that doesn't even get close to touching the sort of software stack that we work on every
single day and what we're working on right now. Yeah. It seems virtually impossible to me that
a thousand electrodes is where it saturates. It feels like this would be one of those
silly notions in the future where obviously you should have millions of electrodes. And this is where
like the true breakthroughs happen. You tweeted, some thoughts are most precisely described in poetry.
Why do you think that is? I think it's because the information bottleneck of language is
pretty steep. And yet you're like, you're able to reconstruct on the other person's,
in the other person's brain more effectively without being literal. Like if you, if you can
express the sentiment such that in their brain, they can reconstruct the, the actual true underlying
meaning and beauty of the thing that you're trying to get across, the generator function in their brain
is more powerful than what language can express. And so the, the mechanism poetry is really just to
feed or seed that generator function.
So being literal sometimes is a suboptimal compression for the, for the thing you're trying to convey.
And it's actually in the process of the user going through that generation that they understand
what you mean. Like that's the, that's the beautiful part. It's also like when you look at a beautiful
painting, like it's not the, the pixels of the painting that are beautiful. It's the thought process
that occurs when you see that, the, the experience of that, that actually is the thing that matters.
Yeah. It's resonating with some deep thing within you that the artist also experienced and was able
to convey that through the pixels. And that's actually going to be relevant for, for, for full
on telepathy. You know, it's like, if you just read the poetry, literally that doesn't say much of
anything interesting. It requires a human to interpret it. So it's the combination of the human mind and all
the experiences that human being has within the context of the collective intelligence of the
human species that makes that poem make sense. And they, they load that in. And so in that same way,
the signal that carries from human to human, uh, meaning might not may seem trivial, but may actually
carry a lot of power, uh, because of the complexity of the human mind and the receiving end.
Yeah. That's interesting. I approach. It still doesn't, who was it? I think, uh,
Yoshi Bach of first wash all I said, uh, uh, something about
all the people that think we've achieved a GI explain why humans like music.
Oh yeah. And I, and until, until the HGI likes music, you haven't achieved a GI or something like
that. Do you not think that's like some next token entropy surprise kind of thing going on there?
I don't know. I don't know either. I, I, I listen to a lot of classical music and also read a lot of
poetry. And, uh, yeah, I do wonder if like, there is some element of the next token surprise factor going
on there. Yeah, maybe. Cause I mean, like a lot of the tricks in both poetry and music are like,
basically you have some repeated structure and then you do like a twist. Yeah. Like, it's like,
okay, verse or like clause one, two, three is one thing. And then clause four is like,
okay, now we're onto the next theme. Yeah. And they kind of play with exactly when the surprise
happens and the expectation of the user. And that's even true. Like through history, as musicians
evolve music, they take like some known structure that people are familiar with and they just tweak it a
little bit. Like they tweak it and add a surprising element. This is especially true in like
classical music heritage. But that's what I'm wondering, like, is it all just entropy? Like the,
the, uh. So, so break, so breaking structure or breaking symmetry is something that humans seem
to like. Maybe as simple as that. Yeah. And I mean, great artists copy, uh, and they also,
you know, knowing which rules to break is the important part. And, uh, fundamentally it must be
about the, the listener of the piece. Like which rule is, is the right one to break? It's about the user,
or the audience member perceiving that as interesting. Uh, what do you think is the meaning of human existence?
There's a TV show I really like called the West Wing. And in, uh, in the West Wing, there's a
character. He's the president of the United States, who's, uh, having a discussion about the Bible with
one of their colleagues. And, uh, what the colleague says something about, you know, the Bible says X, Y,
and Z. And, uh, the president says, yeah, but it also says ABC. And, uh, person says, well,
do you believe the Bible to be literally true? And the president says, yes, but I also think that
neither of us are smart enough to understand it. I think to like the analogy here for the
meaning of life is that largely we don't know the right question to ask. And so I'm, I think
I'm very aligned with, uh, sort of the hitchhikers guide, the galaxy version of this question,
which is basically, if we can ask the right questions, it's much more likely we find the
meaning of human existence. And so in the short term, as a heuristic in the sort of search policy
space, we should try to increase the diversity of, uh, people asking such questions or generally
of consciousness and conscious beings asking such questions. Um, so again, I think I'll take the,
I don't know card here, but say, I do think there are meaningful things we can do that improve the
likelihood of answering that question. It's interesting how much value you assign
to the task of asking the right questions. That's the, that's the main thing is not the answers,
it's the questions. This point, by the way, is driven home, uh, in a very painful way when you try
to communicate with someone who cannot speak because a lot of the time, the last thing to go is they have
the ability to somehow, you know, wiggle a lip or move something that allows them to say yes or no.
And in that situation, it's very obvious that what matters is, are you asking them the right
question to be able to say yes or no to? Wow. That's powerful. Well, Bliss, thank you for everything
you do and thank you for being you and thank you for talking today. Thank you. Thanks for listening to
this conversation with Bliss Chapman. And now, dear friends, here's Nolan Arbaugh, the first human
being to have a neural link device implanted in his brain. You had a diving accident in 2016 that left
you paralyzed with no feeling from the shoulders down. How did that accident change your life?
It was sort of a freak thing that happened. Imagine you're running into the ocean, although this is a
lake, but you're running into the ocean and you get to about waist high and then you kind of like dive in,
take the rest of the plunge under the wave or something. That's what I did. And then I just
never came back up. Not sure what happened. I did it running into the water with a couple of guys.
And so my idea of what happened is really just that I took like a stray fist, elbow, knee, foot,
something to the side of my head. The left side of my head was sore for about a month afterwards. So
must've taken a pretty big knock. And then they both came up and I didn't. And so I was face down in the water
for a while. I was conscious. Um, and then eventually just, you know, realized I couldn't
hold my breath any longer. And I keep saying, took a big drink. Um, people, I don't know if they like
that. I say that it seems like I'm making light of it all, but, um, this is kind of how I am. And I
I don't know, like I'm a very relaxed sort of stress-free person. I rolled with the punches
for a lot of this. I kind of took it in stride. It's like, all right, well, what can I do next?
How can I improve my life even a little bit, um, on a day-to-day basis at first, just trying to
find some way to heal as much of my body as possible, um, to try to get healed, to try to
get off a ventilator, um, learn as much as I could. So I could somehow survive, um, once I left the
hospital. Um, and then thank God I had like my family around me. If I didn't have my parents, uh,
my siblings, then I would have never made it this far. They've done so much for me, um, more
than like I can ever thank them for, honestly. And a lot of people don't have that. A lot of people
in my situation, their families either aren't capable of providing for them or honestly just
don't want to. And so they get placed somewhere and, you know, in some sort of home. Uh, so thankfully,
I had my family, I have a great group of friends, a great group of buddies from college who have all
rallied around me and we're all, um, still incredibly close. People always say, you know,
if you're lucky, you'll end up with one or two friends from high school that you keep throughout
your life. I have, uh, about 10, 10 or 12 from high school that have all stuck around and we still get
together all of us twice a year. Um, we call it the spring series on the fall series. Um,
this last one we all did, uh, we dressed up like X-Men. So I did a professor Xavier and it was
freaking awesome. It was so good. So yeah, I have such a great support system around me. And so,
you know, being a quadriplegic, isn't that bad. I get weighted on, um, all the time. People bring me
food and drinks and I get to sit around and watch as much TV and movies and anime as I want. I get to
read as much as I want. Um, I mean, it's, it's great.
It's beautiful to see that you see the silver lining in all of this, uh, was just going back.
Do you remember the moment when you first realized you're paralyzed from the neck down?
Yep. I was face down in the water. Um, right when I, whatever, something had my head, I, um,
tried to get up and I realized I couldn't move and it just sort of clicked. I'm like, all right,
I'm paralyzed. Can't move. What do I do? Um, if I can't get up, I can't flip over, can't do anything,
then I'm going to drown eventually. Um, and I knew I couldn't hold my breath forever.
So I just held my breath and thought about it for maybe 10, 15 seconds. Um, I've heard from other
people that like look on liquors, I guess the two girls that pulled me out of the water were two of my
best friends. They're lifeguards. Um, and one of them said that, um, it looked like my body was
sort of shaking in the water. Like I was trying to flip over and stuff. Um, but I knew, I knew
immediately and I just kind of, I realized that that's like what my situation was from here on out.
Maybe if I got to the hospital, they'd be able to do something. When I was in the hospital,
like right before surgery, I was trying to calm, uh, one of my friends down. I had like brought her
with me from college to camp and she was just bawling over me. And I was like, Hey, it's going
to be fine. Like, don't worry. Um, I was cracking some jokes to try to lighten the mood. Um, the nurse
had called my mom and I was like, don't tell my mom. Um, she's just going to be stressed out. Call her
after I'm out of surgery. Cause at least she'll have some answers then like whether I live or not really.
Um, and I didn't want her to be stressed through the whole thing, but I knew. And then when I first
woke up after surgery, um, I was super drugged up. Uh, they had me on fentanyl like three ways,
which was awesome. Um, I don't, I don't recommend it, but, um, I saw, I saw some crazy stuff, uh,
on that fentanyl and it was still the best I've ever felt, uh, on drugs, um, medication,
sorry on medication. Um, and, uh, I remember the first time I saw my mom in the hospital,
I was just bawling. I had like ventilator in, um, like I couldn't talk or anything. And, uh, I just
started crying because it was more like seeing her, not that, I mean, the whole situation obviously was
pretty rough, but, uh, it was just like seeing her face for the first time was pretty hard.
But, um, yeah, I just, I never had like a moment of, you know, man, I'm paralyzed.
This sucks. I don't want to like be around anymore. It was always just, I hate that I have to do this,
but like sitting here and wallowing, isn't going to help.
So immediate acceptance.
Yeah. Yeah.
Has there been low points along the way?
Yeah. Yeah, sure. Um, I mean, there are days when I don't really feel like doing anything,
not so much anymore, like not for the last couple of years, I don't really feel that way.
I've, um, more so just wanted to try to do anything possible to make my life better
at this point. Um, but at the beginning, there were some ups and downs. There were some really
hard things to adjust to. Um, first off, just like the first couple of months, the amount of pain I was
in was really, really hard. I mean, I remember screaming at the top of my lungs in the hospital
because I thought my legs were on fire and obviously I can't feel anything, but it's all
nerve pain. And so that was a really hard night. I asked them to give me as much pain meds as
possible. They're like, you've had as much as you can have. So just kind of deal with it,
go to a happy place sort of thing. So that was a pretty low point. Um, and then every now and again,
it's hard, like realizing things that I wanted to do in my life that I won't be able to do anymore.
Um, you know, I always wanted to be a husband and father, and I just don't think that I could
do it now as a quadriplegic. Maybe it's possible, but I'm not sure I would ever, um, put, you know,
someone I love through that. Um, like having to take care of me and stuff, um, not being able to,
you know, go out and play sports. I was a huge athlete growing up. So that was pretty hard. Um,
little things too, when I realized I can't do them anymore, like there's something really special
about being able to hold a book and smell a book, like the feel, uh, the texture, the smell,
like, as you turn the pages, like, I just love it. I can't do it anymore. And it's little things
like that. Um, the two year mark was pretty rough. Two years is when they say you will, um,
get back basically as much as you're ever going to get back as far as movement and sensation goes.
And so for the first two years, that was the only thing on my mind was like, try as much as I can to
move my fingers, my hands, my feet, everything possible to try to get sensation and movement
back. And then when the two year mark hit, so, um, June 30th, 2018, I was, I was really sad that
that's kind of where I was. Um, and then just randomly here and there, but I was never like
depressed for long periods of time. Just, it never seemed worthwhile to me.
What gave you strength?
My faith, my faith in God, uh, was a big one. My understanding that it was all for a purpose.
And even if that purpose wasn't anything involving Neuralink, even if that purpose was, you know,
there's, there's a story in the Bible about Job. And I think it's a really, really popular story
about how Job, you know, has all of these terrible things happen to him and he praises God throughout
the whole situation. I thought, and I think a lot of people think for most of their lives that
they are Job, that they're the ones going through something terrible and they just need to, you know,
praise God through the whole thing and everything will work out. At some point after my accident,
I realized that I might not be Job, that I might be, you know, one of his children that gets killed
or kidnapped or taken from him. And so it's about terrible things that happen to those around you,
who you love. So maybe, you know, in this case, my mom would be Job and she has to get through
something extraordinarily hard. And I just need to try and make it as best as possible for her
because she's the one that's really going through this massive trial. And that gave me a lot of
strength. And obviously my family, my family and my friends, they, they give me all the strength that
I need on a day-to-day basis. So it makes things a lot easier having that great support system around me.
From everything I've seen of you online, your streams and the way you are today,
I really admire, let's say your unwavering positive outlook on life. Has that always been this way?
Yeah. Yeah. I've, I mean, I've just always
thought I could do anything I ever wanted to do. There was never anything too big, like whatever I set
my mind to, I felt like I could do it. Um, I didn't want to do a lot. I wanted to like travel
around and be sort of like a gypsy and like go work odd jobs. I had this dream of traveling around
Europe and being like, I don't know, a shepherd in like Wales or Ireland, and then going and being a
fisherman in Italy, uh, doing all of these things for like a year. Like it's such like cliche things,
but I just thought it would be so much fun to go and travel and do different things. And so, um,
I've always just seen the best in people around me too. And I've always tried to be good to people.
And growing up with my mom too, she's like the most positive, energetic person in the world.
And we're all just people, people like, uh, I just get along great with people. Um, I really enjoy
meeting new people. And so, um, I just wanted to do everything. Um, this is just kind of just how
I've been. It's just great to see that cynicism didn't take over given everything you've been
through. Yeah. That's, uh, was that like a deliberate choice you made that you're not going to
let this keep you down. Yeah. A bit also like, I just, it's just kind of how I am. I just,
like I said, I roll with the punches with everything. I always used to tell people like,
I don't stress about things much. Um, and whenever I'd see people getting stressed,
I would just say, you know, like, it's not hard, just don't stress about it. And like,
that's all you need to do. Uh, and they're like, that's not how that works. Like it works for me.
Like just don't stress and everything will be fine. Like everything will work out.
Obviously not everything always goes well, and it's not like it all works out for the
best all the time, but I just don't think stress has had, uh, any place in my life since I was a kid.
What was the experience like of you being selected to be the first human being to have a
neural link device implants in your brain? Were you scared? Excited? No, no, it was cool.
Um, like I was, I was never afraid of it. I had to think through a lot. Should I,
should I do this? Um, like be the first person I could wait until number two or three and get a
better version of the neural link. Like the first one might not work. Maybe, um, it's actually going
to kind of suck. Um, it's going to be the worst version ever in a person. So why would I do the
first one? Like I've already kind of been selected. I could just tell them, you know,
like, okay, find someone else and then I'll do number two or three. Like, I'm sure they would
let me, they're looking for a few people anyways. But ultimately I was like, I don't know. There's
something about being the first one to do something. It's pretty cool. I always thought that
if I had the chance that I would like to do something for the first time, um, this seemed
like a pretty good opportunity. Um, and I was, I was never scared. I think my like faith had a huge,
uh, part in that. I always felt like God was preparing me for something. Um, I almost wish it
wasn't this because I had many conversations with God about not wanting to do any of this as a
quadriplegic. I told him, you know, I'll go out and talk to people. I'll go out and travel the world
and talk to, you know, stadiums, thousands of people give my testimony. I'll do all of it,
but like heal me first. Don't make me do all this in a chair that sucks. Um, and I guess he won that
argument. I didn't really have much of a choice. I always felt like there was something going on and
to see how, I guess easily I made it through the interview process and how quickly everything
happened, um, how the star sort of aligned with all of this. It, it just told me like,
as the surgery was getting closer, it just told me that, you know, it, it was all meant to happen.
It was all meant to be. And so I shouldn't be afraid of anything that's to come. And so I wasn't,
I kept telling myself like, you know, you say that now, but as soon as the surgery comes,
you're probably going to be freaking out. Like you're about to have brain surgery and brain surgery
is a big deal for a lot of people, but it's a even bigger deal for me. Like it's all I have left.
The amount of times I've been like, thank you, God, that you didn't take my brain and my personality
and my ability to think, um, my like love of learning, like my character, everything,
like, thank you so much. Like, as long as you left me that, then I think I can get by.
And I was about to let people go like root around in there. Like, Hey, we're going to go
like put some stuff in your brain. Like, hopefully it works out. Um, and so it was,
it was something that gave me pause, but like I said, how smoothly everything went,
I never expected for a second that anything would go wrong. Plus the more people I met
on the borrows side and on the knurling side, they're just the most impressive people in the
world. Like I can't speak enough to how much I trust these people with my life and how impressed
I am with all of them. And to see the excitement on their faces to like walk into a room and roll
into a room and see all of these people looking at me, like, we're just, we're so excited. Like
we've been working so hard on this and it's finally happening. It's super infectious. And, um, it just
makes me want to do it even more and to help them achieve their dreams. Like, I don't know. It's so,
it's so rewarding and I'm so happy for all of them, honestly. What was the, uh, day of surgery?
Like what's, uh, when'd you wake up? What'd you feel? Yeah. Minute by minute. Yeah. Were you freaking
out? No, no. I thought I was going to, but the surgery approached the night before the morning of,
I was just excited. Like, I was like, let's make this happen. I think I said that, uh, something like
that to Elon on the phone. Uh, beforehand we were like, uh, FaceTiming and I was like, let's rock and
roll. And he's like, let's do it. Uh, I don't know. I just, I wasn't scared. So we woke up. I
think we had to be at the hospital at like five 30 AM. I think surgery was at like seven AM. So we
woke up pretty early. I'm not sure much of us slept that night. Um, um, got to the hospital,
five 30 went through like all the pre-op stuff. Everyone was super nice. Uh, Elon was supposed to
be there in the morning. Um, but something went wrong with his plane. So we ended up FaceTiming.
Uh, that was cool. Had one of the greatest one-liners of my life after that phone call,
um, hung up with him. There were like 20 people around me and I was like, I just hope he wasn't
too starstruck talking to me. Nice. Yeah. It was good. Well done. Yeah. Yeah.
Did you write that ahead of time? No, it just came to me. I was like, this is,
this seems right. You know, went into surgery. Um, I asked if I could pray right beforehand.
So I like prayed over the room. I asked God, if you like be with my mom in case anything happened
to me and, uh, just like calm her nerves out there, uh, woke up and played a bit of a prank
on my mom. Uh, I don't know if you've heard about it. Yeah. I read about it. Yeah. Uh, she wasn't,
she was not happy. Uh, can you take me through the prank? And yeah, this is something you regret
doing that now. No, no, not one bit. Um, it was something, it was something I,
I had talked about ahead of time with my buddy Bane. I was like, I would really like to play a
prank on my mom. Um, uh, very specifically my mom, she's very gullible. Um, I think she had knee
surgery once even, and, um, after she came out of knee surgery, um, uh, she was super groggy.
She's like, I can't feel my legs. And my dad looked at her. He was like, you don't have any
legs. Like they had, they had to amputate both your legs and we just do very mean things to her
all the time. Um, I'm so surprised that she still loves us. Um, but right after surgery,
I was really worried that I was going to be too like groggy, like not all there. I've had anesthesia
once before and it, it messed me up. Like I could not function, um, for a while afterwards. And I,
um, I like said a lot of things that I was like, I was really worried that I was going to start,
I don't know, like dropping, dropping some bombs and I wouldn't even know, I wouldn't remember.
Um, so I was like, I was like, please God, don't let that happen. And please let me be there enough
to do this to my mom. Um, and so she walked in, uh, after surgery, it was like the first time they
had been able to see me after surgery. And she just looked at me, she said, hi, like, how are you?
How are you doing? How do you feel? And I looked at her and this very, I think the anesthesia helped,
very like groggy sort of confused look on my face. It's like, who, who are you? And she just
started looking around the room, like at the surgeons or the doctors, like, what did you do
to my son? Like, you need to fix this right now. Tears started streaming. I saw how much she was
freaking out. I was like, I can't let this go on. And so I was like, mom, mom, I'm fine. Like,
like, uh, it's all right. And, uh, still, she was not happy about it. She, uh, still says she's
going to get me back someday, but I mean, I don't, I don't know. I don't know what that's
going to look like. It's a lifelong battle. Yeah. Yeah. But it was good. In some sense,
it was a demonstration that you still got. That's, that's all I wanted it to be. That's all
I wanted it to be. And I knew that doing something super mean to her like that would show her to show
that you're still there, that you love her. Yeah, exactly. Exactly. It's a dark way to do it,
but I love it. Yeah. Uh, what was the first time you were able to feel that you can use
the Neuralink device to affect the world around you? Yeah. Um, the first little taste I got of it
was, uh, actually not too long after surgery. Um, some of the Neuralink team had brought in, um,
like a little iPad, uh, a little tablet screen and they had put up eight different, um, channels,
um, and that were recording some of my neuron spikes. Um, and they put it in front of me and
they're like, this is like real time, your brain firing. It's like, that's super cool. Um,
my first thought was, I mean, if they're firing now, let's see if I can affect them in some way.
So I started trying to like wiggle my fingers and I just started like scanning through the channels.
And one of the things I was doing was like moving my index finger up and down. And I just saw this
yellow spike on like top row, like third box over or something. I saw this yellow spike every
time I did it. And I was like, Oh, that's cool. And everyone around me was just like, what,
what are you seeing? I was like, look, look at this one. Look at like this top row,
third box over this yellow spike. Like that's me right there, there, there. And everyone was
freaking out. They started like clapping. I was like, that's super unnecessary.
Like this is what's supposed to happen. Right? Like, so you're imagining yourself moving each
individual finger one at a time and then seeing like that you can notice something. And then when
you did the index finger, you're like, Oh, yeah, I was, I was wiggling kind of all of my fingers
to see if anything would happen. There was a lot of other things going on, but that big yellow spike
was the one that stood out to me. Like, I'm sure that if I would have stared at it long enough,
I could have mapped out maybe a hundred different things. But the big yellow spike was the one that
I noticed.
Maybe you can speak to what it's like to sort of wiggle your fingers to like to imagine that
the mental, the cognitive effort required to sort of wiggle your index finger, for example,
how easy is that to do?
Pretty easy for me. It's something that at the very beginning, after my accident,
they told me to try and move my body as much as possible, even if, you know, you can't just keep
trying because that's going to create new like neural pathways or pathways in my spinal cord to like
reconnect these things to hopefully regain some movement someday.
That's fascinating.
Yeah, I know. It's bizarre, but I-
So that's part of the recovery process is to keep trying to move your body.
Yep.
Just have a day as much as you can.
And the nervous system does its thing.
Yeah.
It starts reconnecting.
It'll start reconnecting for some people. Some people, it never works. Some people,
they'll do it. Like for me, I got some bicep control back and that's about it. I can, if I
try enough, I can wiggle some of my fingers, not like on command. It's more like if I try to move,
say my right pinky and I just keep trying to move it after a few seconds, it'll wiggle. So I know
there's stuff there. Like I know, like, and that happens with, you know, a few different of my fingers
and stuff. But yeah, that's what they tell you to do. One of the people at the time when I was in
the hospital came in and told me for one guy who had recovered most of his control, what he thought
about every day was actually walking like the act of walking just over and over again. So I tried that
for years. I tried just imagining walking, which is, it's hard. It's hard to imagine like all of the
steps that go into, well, taking a step, like all of the things that have to move, like all the
activations that have to happen along your leg in order for one step to occur.
But you're not just imagining, you're like doing it, right?
I'm trying, yeah. So it's like, it's imagining,
over again, what I had to do to take a step, because it's not something any of us think about.
We just, you want to walk and you take a step. You don't think about all of the different things
that are going on in your body. So I had to recreate that in my head as much as I could.
And then I practice it over and over and over.
So it's not like a third person perspective as a first person perspective. It's not like you're
imagining yourself walking. You're like literally doing this, everything, all the same stuff as
you're walking. Yeah. Which was hard. It was hard at the beginning.
Like frustrating hard or like actually cognitively hard?
It was both.
There's a scene in one of the Kill Bill movies, actually, oddly enough, where she is like
paralyzed. I don't know, from like a drug that was in her system. And then she like,
find some way to get into the back of a truck or something. And she stares at her toe and she says,
move, like move your big toe. And after, you know, a few seconds on screen, she does it.
And she did that with every one of her like body parts until she can move again. I did that for
years, just stared at my body and said, move your index finger, move your big toe. Sometimes vocalizing
it like out loud, but sometimes just thinking it, I tried every different way to do this,
to try to get some movement back. And it's hard because it, it actually is like taxing,
like physically taxing on my body, which is something I would have never expected because
it's not like I'm moving, but it feels like there's a buildup of, I don't know. The only way
I can describe it is there are like signals that aren't getting through from my brain down because
there's that gap in my spinal cord. So brain down and then from my hand back up to the brain. And so
it feels like those signals, um, get stuck in whatever body part that I'm trying to move
and they just build up and build up and build up until they burst. Um, and then once they burst,
I get like this really weird sensation of everything sort of like dissipating back out to level.
And then I do it again. Um, it's also just like a fatigue thing, like a muscle fatigue,
but without actually moving your muscles, it's very, very bizarre. And then, you know, uh, if you
try to stare at a body part or think about a body part and move for two, three, four, sometimes eight
hours, it's very taxing on your mind. It's takes a lot of focus. Um, it was a lot easier at the
beginning because I wasn't able to like control a TV in my room or anything. I wasn't able to, um,
control any of my environment. So for the first few years, a lot of what I was doing was staring at
walls. And so, um, obviously I did a lot of thinking and I tried to move a lot just over and over and over
again. So you never gave up sort of hope there just training hard essentially.
Yep. And I still do it. I do it like subconsciously. And I think that, uh, that helped a lot with
things with Neuralink, honestly, it's something that I talked about the other day at the all hands
that I did at Neuralink's Austin facility. Welcome to Austin. Yeah. Hey, thanks, man. I,
I went to school. Hey, thanks. Thanks, man. The, the gigafactory was super cool. I went to school at
Texas A&M. So I've been around for, um, so you should be saying, welcome to me. Yeah.
Welcome to Texas. Yeah. I get you. Um, but yeah, I was talking about how a lot of what they've had
me do, especially at the beginning. Um, well, I still do it now, um, is body mapping. So like,
there will be a visualization of a hand or an arm on the screen and I have to do that motion. And that's
how they sort of train, um, the algorithm to like understand what I'm trying to do. And so it made
things very, uh, seamless, um, for me, I think that's really, really cool. So it's, it's, it's
amazing to know. Cause I, I've learned a lot about the body mapping procedure. Yeah. Like with, uh,
with the interface and everything like that, it's cool to know that you've been a century, like
training to be like world-class at that task. Yeah. Yeah. I, I don't know if other quadriplegics,
like other paralyzed people give up. I hope they don't. Um, I hope they keep trying because I've
heard other paralyzed people say like, don't ever stop. They tell you two years, but, um, you, you just
never know. You're not, you're the human bodies capable of amazing things. So, um, I've heard
other people say, don't give up. Uh, like I think one girl had, um, spoken to me through some family
members and said that she had been paralyzed, you know, for 18 years and she'd been trying to like
wiggle her index finger for all that time. And she finally got a bat like 18 years later. So like,
I know that it's possible and I'll never give up doing it. I just, I do it when I'm lying down,
like watching TV, I'll find myself doing it kind of just almost like on its own. It's just something
I've gotten so used to doing that. I don't know. I, I don't think I'll ever stop.
That's really awesome to hear. Cause I think it's one of those things that can
really pay off in the longterm. Cause like it is training. You're not visibly seeing the results of
that training at the moment, but like, there's a, like Olympic level nervous system getting,
getting ready. Which honestly was like something that I think Neuralink gave me that, um, I can't,
I can't thank them enough for like, I can't show my appreciation for it enough was being able to
visually see that what I'm doing is actually having some effect. Um, it's a huge part of the reason why
like, I know now that I'm going to keep doing it forever because before Neuralink, I was doing it
every day and I was just assuming that things were happening. Like, it's not like I knew I wasn't
getting back any mobility or, um, sensation or anything. So I could have been running up against
a brick wall for all I knew. And with Neuralink, I get to see like all the signals happening real time.
And I get to see that, you know, what I'm doing can actually be mapped. You know, when we started
doing like click calibrations and stuff, when I go to click my index finger for a left click,
that it actually recognizes that like it, it changed how I think about what's possible with
like retraining my body to move. And so, yeah, I'll, I'll never give up.
And also just the signal that there's still a powerhouse of a brain there. That's like,
that's, uh, and as the technology develops, that brain is, I mean, that's the most important thing
about the human body is the brain and it can do a lot of the control. So what did it feel like when
you first could wiggle the index finger and saw the environment respond like that little thing,
wherever we're just being way too dramatic, according to you.
Yeah. It was very cool. I mean, it was cool, but it, I keep telling this to people,
it made sense to me. Like it made sense that, you know, like there are signals still happening in my
brain. And that as long as you had something near it, that could measure those that could record those,
then you should be able to like visualize it in some way, like see it happen. And so that was not
very surprising to me. I was just like, Oh cool. Like we, we found one, like we found something that
works. Um, it was cool to see that their technology worked, um, and that everything that they'd worked
so hard for was like going to pay off. Um, but I hadn't like moved a cursor or anything at that point.
I had like interact with a computer or anything at that point. Um, so it, it, it just made sense.
It was cool. Like I, I, I didn't really know much about BCI at that point either. So I didn't know
like what sort of step this was actually making. Um, like I didn't know if this was like a huge deal
or if this was just like, okay, this is, you know, it's cool that we got this far, but we're actually
hoping for something like much better down the road. It's like, okay. I just thought that they
knew that it turned on. So I was like, cool. Like this is, this is cool.
Well, did you like read up on the specs of the hardware you get installed?
Like the number of threads?
Yeah. Yeah. I knew all of that, but it's all like, it's all Greek to me. I was like, okay,
threads, 64 threads, 16 electrodes, thousand 24 channels. Okay. Like that, that, that math checks out.
Sounds right.
Yeah.
When was the first time you were able to move a mouse cursor?
I know it must've been within the first maybe week, a week or two weeks that I was able to
like first move the cursor. And again, like it kind of made sense to me. Like it, it didn't seem
like that big of a deal. Like it, it, it was like, okay, well, how do I explain this?
When everyone around you starts clapping for something that you've done, it's, it's easy to say,
okay, like I did something cool. Like that was, that was impressive in some way. What exactly that
meant, what it was hadn't really like set in for me. So again, I knew that me trying to move a body part
and then that being mapped in some sort of like machine learning algorithm to be able to
identify like my brain signals and then take that and give me cursor control. That all kind of made
sense to me. I don't know, like all the ins and outs of it, but I was like, there are still signals
in my brain firing. They just can't get through because there's like a gap in my spinal cord. And so
they just, they can't get all the way down and back up, but they're still there. So when I moved
the cursor for the first time, I was like, that's cool. But I expected that that should happen. Like
it, it made sense to me. Um, when I moved the cursor for the first time, um, with just my mind without
like physically trying to move. So I guess I can get into that just a little bit, like the difference
between attempted movement and imagined movement. Yeah. That's a fascinating difference.
Yeah. From one to the other. Yeah. Yeah. Yeah. Yeah. So like attempted movement is me physically
trying to attempt to move, say my hand. I try to attempt to move my hand to the right, to the left,
forward and back. Um, and that's all attempted attempt to, you know, like lift my finger up and
down attempt to kick or something. Um, I'm physically trying to do all of those things, even if you can't
see it, like I'm, this would be like me attempting to like shrug my shoulders or something. That's all
attempted movement. Um, that all, that's what I was doing for the first couple of weeks when they were
going to give me cursor control. When I was doing body mapping, it was attempt to do this attempt to
do that. Um, when, um, near was telling me, um, to, um, like imagine doing it, it like kind of made sense to
me, but it's not something that people practice. Like if you started school, um, as a child and they said,
okay, write your name with this pencil. And so you do that like, okay, now imagine writing your name
with that pencil. Kids would think, uh, like, I guess like that kind of makes sense and they would
do it. Um, but that's not something we're taught. It's all like how to do things physically. We think
about like thought experiments and things, but that's not like, that's not like a physical action
of doing things. It's more like what you would do in certain situations. So imagine movement. It never
really connected with me. Like, I guess you could maybe describe it as like a professional athlete,
like swinging a baseball bat or swinging like a golf club, like imagine what you're supposed to do,
but then you go right to that and physically do it. Like you, then you get a bat in your hand and then
you do what you've been imagining. And so I don't have that like connection. So telling me to imagine
something versus attempting it, it just, there wasn't a lot that I could do there. Um, mentally,
I just kind of had to accept what was going on and try. Um, but the attempted moving thing,
it all made sense to me. Like if I try to move, then there's a signal being sent in my brain. And
as long as they can pick that up, then they should be able to map it to what I'm trying to do. And so
when I first moved the cursor like that, it was, it was just like, yes, this should happen. Like,
I'm, I'm not surprised by that. But can you clarify, is there supposed to be a difference
between imagined movement and attempted movement? Yeah. Just that in imagined movement, you're not
attempting to move at all. So it's, you're like visualizing, visualizing, and then
theoretically, is that supposed to be a different part of the brain that lights up in those two
different situations? Yeah, not necessarily. I think all these signals can still be represented
in motor cortex, but the difference I think has to do with the naturalness of imagining something
worse. Got it. Attempting it and sort of the fatigue of that over time. And by the way,
on the mic is bliss. Uh, so like, this is just different ways to prompt you to kind of
get to the thing that you're around. Yeah. Yeah. Attempted movement does sound like the right
thing. Yeah. Try. Yeah. I mean, it makes sense to me. Cause imagine for me, I'll be, I would start
visualizing, like in my mind, visualizing attempted, I would actually start trying to like, yeah,
there's a, I mean, I, you know, I, you know, I did like comments my whole life like wrestling.
When I'm imagining a move, see, I'm like moving my muscle. Exactly. Like there's a,
there is a bit of an activation almost versus like visualizing yourself like a picture doing it.
Yeah. It's something that I feel like naturally anyone would do. If you try to tell someone to
imagine doing something, they might close their eyes and then start physically doing it. Um, but
it's just, it didn't click. Yeah. It's, it's hard. It was very hard at the beginning,
but attempted worked, attempted worked. It worked just like it should work like,
work like a charm. Um, I remember there was like one Tuesday we were messing around and I think,
I forget what swear word you used, but there's a swear word that came out of your mouth when you
figured out you could just do the direct cursor control. Yeah. That's it. It blew my mind,
like no pun intended, blew my mind when I first, um, moved the cursor just with my thoughts and not
attempting to move. It's something that I found, um, like over the couple of weeks, like building up to
that, um, that as I get better, cursor controls, like the model, uh, gets better. Um, then
it gets easier for me to like, um, like I don't have to attempt as much to move it.
And part of that is something that I'd even talked with them about. Um, when I was watching
the signals of my brain one day I was watching when I like attempted to move to the right.
And I watched the screen as like, I saw the spikes, like I was seeing the spike,
the signals being sent before I was actually attempting to move. Um, I imagine just because,
you know, when you go to say, move your hand or any body part, that signal gets sent before
you're actually moving has to make it all the way down and back up before you actually do any sort of
movement. So there's a delay there. And I noticed that there was something going on in my brain before
I was actually attempting to move that, um, my brain was like anticipating what I wanted to do.
And that all started sort of, um, I don't know, like percolating in my brain. Like it just,
it was just sort of there, like always in the back. Like that's so weird that it could do that.
It kind of makes sense, but I wonder what that means. Um, as far as like using the neural link and,
um, you know, and then as I was playing around with the attempted movement and playing around
with the cursor and I saw that like, as the cursor control got better, that it was anticipating my
movements, um, and what I wanted it to do, like cursor movements, what I wanted to do a bit better and
a bit better. And then one day I just randomly, as I was playing web grid, I, um, like looked at a
target before I had started like attempting to move. I was just trying to like get over, like train,
um, my eyes to start looking ahead. Like, okay, this is the target I'm on. But if I look over here
to this target, I know I can like maybe be a bit quicker getting there. And I looked over and the
cursor just shot over it. It was wild. Like I had to take a step back. I was like, this should not
be happening all day. I was just smiling. I was so giddy. I was like, guys, do you know that this
works? Like I can just think it and it happens, which like they'd all been saying this entire
time. Like, I can't believe like you're doing all this with your mind. I'm like, yeah, but is it
really with my mind? Like I'm attempting to move and it's just picking that up. So it doesn't feel like
it's with my mind. But when I moved it for the first time like that, it was, oh man, it like,
it made me think that this technology, that what I'm doing is actually way, way more impressive than
I ever thought. It was way cooler than I ever thought. And it just opened up a whole new world
of possibilities of like what could possibly happen with this technology and what I might be
able to be capable of with it. Because you had felt for the first time like this was digital
telepathy, like you're controlling a digital device with your mind. I mean, that's a real
moment of discovery. That's really cool. Like you've discovered something. I've seen like scientists
talk about like a big aha moment, you know, like Nobel Prize winning, they'll have this like,
holy crap. Yeah. Like, whoa.
That's what it felt like. I felt like I had discovered something, but for me,
maybe not necessarily for like the world at large or like this field at large, it just felt like an
aha moment for me. Like, oh, this works. Like, obviously it works. And so that's what I do like
all the time now. I kind of intermix the attempted movement and imagine movement. I do it all
like together because I've found that there is some interplay with it that maximizes efficiency with
the cursor. So it's not all like one or the other. It's not all just, I only use attempted or I only
use like imagine movements. It's more, I use them in parallel and I can do one or the other. I can just
completely think about whatever I'm doing, but I don't know. I like to play around with it. I also
like to just experiment with these things. Like every now and again, I'll get this idea in my head,
like, hmm, I wonder if this works and I'll just start doing it. And then afterwards I'll tell them,
by the way, I wasn't doing that. Like you guys wanted me to. I thought of something and I wanted
to try it. And so I did. It seems like it works. So maybe we should like explore that a little bit.
So I think that discovery is not just for you, at least from my perspective, that's a discovery for
everyone else, whoever uses in your link, that this is possible.
I don't think that's an obvious thing that this is even possible.
It's like, I was saying to Bliss earlier, it's like the four minute mile. People thought it was
impossible to run a mile in four minutes. And once the first person did it, then everyone just
started doing it. So like just to show that it's possible, that paves the way to like anyone can
not do it. That's the thing that's actually possible. You don't need to do the attempted
movement. You can just go direct. That's crazy.
It is crazy. It is crazy.
For people who don't know, can you explain how the Link app works? You have an amazing stream
on the topic. Your first stream, I think, on X, describing the app. Can you just describe how it
works?
Yeah. So it's just an app that Neuralink created to help me interact with the computer. So on the Link
app, there are a few different settings and different modes and things I can do on it. So
there's like the body mapping, which we kind of touched on. There's a calibration. Calibration is
how I actually get cursor control. So calibrating what's going on in my brain to translate that into
cursor control. So it will pop out models. What they use, I think, is like time. So it would be
five minutes and calibration would give me so good of a model. And then if I'm in it for 10 minutes and
15 minutes, the models will progressively get better. And so the longer I'm in it, generally, the
better the models will get.
That's really cool. Because you often refer to the models. The model is the thing that's
constructed once you go through the calibration step. And then you also talked about sometimes
you'll play like a really difficult game, like Snake, just to see how good the model is.
Yeah. Yeah. So Snake is kind of like my litmus test for models. If I can control Snake decently
well, then I know I have a pretty good model. So yeah, the Link app has all of those. It has
web grid in it now. It's also how I connect to the computer just in general. So they've given me a lot
of voice controls with it at this point. So I can say connect or implant disconnect. And as long as I
have that charger handy, then I can connect to it. So the charger is also how I connect to the Link app
to connect to the computer. I have to have the implant charger over my head when I want to connect
to have it wake up because the implants in hibernation mode, like always when I'm not using it.
I think there's a setting to like wake it up every, you know, so long. So we could set it to half an hour
or five hours or something if I just wanted to wake up periodically. So yeah, I'll like connect to the
Link app and then go through all sorts of things. Calibration for the day, maybe body mapping. I have
like, I made them give me like a little homework tab because I am very forgetful and I forget to do
things a lot. So I have like a lot of data collection things that they want me to do.
Is the body mapping part of the data collection or is that also part of the collection?
Yeah, it is. It's something that they want me to do daily, which I've been slacking on because I've
been doing so much media and traveling so much. So I've been super famous.
Yeah, I've been a terrible first candidate for how much I've been slacking on my homework.
But yeah, it's just something that they want me to do every day to, you know, track how
well the Neuralink is performing over time and have something to give. I imagine to give to the FDA to,
you know, create all sorts of fancy charts and stuff and show like, hey, this is what the Neuralink,
this is how it's performing, you know, day one versus day 90 versus day 180 and things like that.
What's the calibration step like? Is it, is it like move left, move right?
It's a bubble game. So there will be like yellow bubbles that pop up on the screen.
At first it is open loop. So open loop, this is something that I still don't fully understand the
open loop and closed loop thing.
And me and Blizz talked for a long time about the difference between the two from the,
on the technical side.
Okay.
So it'd be great to hear your, your side of the story.
Okay. So open loop is basically, um, I have no control over the cursor.
Um, the cursor will be moving on its own across the screen and I am following by intention,
um, the cursor to different bubbles. And then my, um, the algorithm is training off of what,
like the signals it's getting are as I'm doing this.
There are a couple of different ways that they've done it. They call it center out target.
So there will be a bubble in the middle and then eight bubbles around that. And the cursor will go
from, uh, the middle, uh, to one side. So say middle to left, back to middle to up to middle,
like up, right. And they'll do that all the way around the circle. And I will follow that cursor,
um, the whole time. And then it will train off of my intentions, what it is expecting my intentions to be,
um, throughout the whole process.
Can you actually speak to when you say follow, you don't mean with your eyes, you mean with your
intentions?
Yeah. So, uh, generally for calibration, I'm doing attempted movements, uh, cause I think it works
better. I think the better models as I progress through calibration, um, make it easier, um, to use,
imagine, uh, movements. Wait, wait, wait, wait. So calibrated on attempted movement
will create a model that makes it really effective for you to then use the force.
Yes. I've tried, um, doing calibration with imagined movement and it just doesn't work as well,
um, for some reason. So that was the center out targets. There's also one where, you know,
a random target will pop up on the screen and it's the same. I just like move, I follow along,
um, with wherever the cursor is to that target all across the screen.
Um, I've tried those with imagined movement and for some reason, the models just don't,
um, they don't give this high levels quality when we get into closed loop.
Um, I haven't played around with it a ton. So maybe like the different ways that we're doing
calibration now might make it a bit better. But what I've found is there will be a point
in calibration where I can use, uh, imagined movement before that point, it doesn't really work.
So if I do calibration for 45 minutes, the first 15 minutes, I can't use imagined movement.
It just like doesn't work for some reason. Um, and after a certain point, uh, I, I can just
sort of feel it. I can tell it moves different. Uh, that's the best way I can, I can describe it.
Like it's almost as if it is anticipating what I am going to do again before I go to do it. Um,
and so using attempted movement for 15 minutes at some point, I can kind of tell when I like move my
eyes to the next target that the cursor is starting to like pick up, like it's starting to understand
it's learning like what I'm going to do. So first of all, it's really cool that, I mean,
you are a true pioneer in all of this. You're like exploring how to do every aspect of this most
effectively. And there's just, uh, I imagine so many lessons learned from this. So thank you for
being a pioneer and all these kinds of different, like super technical ways. And it's also cool to
hear that there's like a different, like feeling to the experience when it's calibrated in different
ways. Like just cause I imagine your brain is doing something different and that's why there's a different
feeling to it and then trying to find the words and the measurements to those feelings would be also
interesting. But at the end of the day, you can also measure that your actual performance on whether
it's snake or web grid, you can see like what actually works well. And you're saying for the open
loop calibration, the attempted movement works best for now. Yep. Yep. So the, so the open loop,
you don't get the feedback. That's something that you did something. Yeah.
I'm just frustrating. No, no, it makes sense to me. Like, uh, we've done it with a cursor and
without a cursor in open loop. So sometimes it's just, um, say for like the center out the, um,
you'll start calibration with a bubble lighting up and I push towards that bubble. And then when that
bubble, you know, when it's pushed towards that bubble for say three seconds, a bubble will pop.
And then I come back to the middle. Um, so I'm doing it all just by my intentions. Like that's
what it's learning anyway. So it makes sense that as long as I follow what they want me to do, you
know, like follow the yellow brick road that it'll all work out. Um, you're full of great references.
Uh, is the, is the bubble game fun? Like, yeah, they always feel so bad making me do calibration.
Like, uh, we're about to do, you know, a 40 minute calibration. I'm like, all right,
would you guys want to do two of them? Um, like I'm always asking to like, whatever they need,
I'm more than happy to do. And it's not, it's not bad. Like I get to lie there and, um, or sit in my
chair and like do these things with some great people. I get to have great conversations. I can
give them feedback. Um, I can talk about all sorts of things. Uh, I could throw something on,
on my TV in the background and kind of like split my attention between them. Um, like it's not bad
at all. I don't, is there a score that you get? Like, can you do better on the bubble game?
No, I would love that. Um, I, I, I would love, yeah. Writing down, uh, suggestions from Nolan,
that's, uh, make it more fun gamified. Yeah. That's one thing that I really,
really enjoy about web grid is cause I'm so competitive. Um, like the higher, the BPS,
the higher the score, I know the better I'm doing. And so if I, I think I've asked at one point,
one of the guys, like if he could give me some sort of numerical feedback for calibration, like
I would like to know what they're looking at. Like, Oh, you know, it is, um, we see like this
number while you're doing calibration. And that means at least on our end that we think calibration
is going well. Um, and I would love that because I would like to know if what I'm doing is going well
or not. But then they've also told me like, yeah, not necessarily like one-to-one. It doesn't
actually mean that calibration is going well in some ways. Um, so it's not like a hundred percent
and they don't want to like skew what I'm experiencing or want me to change things based
on that. If that number isn't always accurate to like how the model will turn out or how like
the end result, that's at least what I got from it. Uh, one thing I do, uh, I have asked them and
something that I really enjoy, um, striving for is towards the end of calibration. There is
like a time between targets. Um, and so I like to keep like at the end, uh, that number as low
as possible. So at the beginning it can be, you know, four or five, six seconds between
me popping bubbles, but towards the end, I like to keep it below like 1.5 or if I could get it to
like one second between like bubbles, because in my mind that translates, um, really nicely to
something like web grid where I know if I can hit a target, uh, one, every second that I'm doing
real, real well. There you go. That's the way to get a score on the calibrations, like the speed,
how quickly can you get from bubble to bubble? Yeah. Uh, so there's the open loop and then it
goes to the closed loop and the closed loop can already start giving you a sense because you're
getting feedback of like how good the model is. Yeah. So closed loop is when I, um, first get
cursor control and how they've, uh, described it to me, someone who does not understand this stuff.
I am the dumbest person in the room every time I'm with any of the humility. Yeah. Um, is that I
am closing the loop. So I am actually now, um, the one that is like finishing the loop of whatever
this loop is. I don't even know what the loop is. They've never told me. They just say there is a loop
and at one point it's open and I can't control. And then I get control and it's closed. So I'm
finishing the loop. So how long the calibration usually take, you said like 10, 15 minutes.
Well, yeah, they're, they're trying to get that number down pretty low. Um, that's what we've
been working on a lot recently is getting that down as low as possible. So that way,
you know, if this is something that people need to do on a daily basis, or if some people need to do on
a, um, like every other day basis or once a week, they don't want people to be sitting in calibration
for long periods of time. I think they wanted to get it down seven minutes or below. Um, at least
where we're at right now, it'd be nice if they, you never had to do calibration. Um, so we'll get
there at some point. I'm sure the more we learn about the brain and, um, like, I think that's,
you know, the dream. Um, I think right now for me to get like really, really good models,
um, I'm in calibration 40 or 45 minutes. Um, and I don't mind, like I said, they always feel
really bad, but if it's going to get me a model that can like break these records on web grid,
I'll stay in it for flipping two hours. Let's talk business. So web grid,
um, I saw a presentation that where bliss said by March, you selected 89,000 targets in web grid.
Can you explain this game? Well, what, what is web grid and what does it take to be a world-class
performer and web grid as you continue to break world records? Yeah. Um,
it's like a gold medalist. Like, wow. Yeah. You know, I'd like to thank,
I'd like to thank everyone who's helped me get here. My coaches, my parents for driving me to
practice every day at five in the morning. Um, like, thank God, um, and just overall my dedication
to my craft. The interviews with athletes are always like that exact, it's like that template.
Yeah. So, so, um, so web grid, web grid is a grid itself. It's, it's literally just a grid.
They can make it as big or small as you can make a grid. A single box on that grid will light up and
you go and click it. And it is a way for them to benchmark how good a BCI is. So it's,
you know, pretty straightforward. You just click targets.
Only one blue cell appears and you're supposed to move the mouse to there and click on it.
So I like playing on like bigger grids cause it, the bigger, the grid, the like more BPS,
it's bits per second, um, that you get every time you click one. So I'll say I'll play on like a 35
by 35, um, grid. And then one of those little squares, a cell and call it target, whatever,
will light up and you move the cursor there and you click it. And then you do that, um, forever.
And you've been able to achieve at first eight bits per second. And you recently broke that.
Yeah. I'm, I'm at 8.5 right now. I would have beaten that literally the day before I came to
Austin. Um, but I had like a, I don't know, like a five second lag right at the end. And, um, I just
had to wait until the latency calmed down and then I kept clicking, but, um, I was at like 8.01 and
then five seconds of lag. And then the next like three targets I clicked all stayed at 8.01. So if
I would have been able to click, um, during that time of lag, I probably would have hit,
I don't know, I might've hit nine. So I'm there. I'm like, I'm really close. And then this whole
Austin trip has really gotten in the way of my web grid playing ability.
Yeah. So that's all you're thinking about right now. Yeah. I know. I just, I just want,
I want to do better at nine. I want to do better. I want to hit nine. I think, well,
I know nine is very, very achievable. I'm right there. Um, I think 10, I could hit maybe in the
next month. Like I could do it probably in the next few weeks. If I really push,
I think you and Elon are basically the same person. Cause last time I did a podcast with him,
he came in extremely frustrated that he can't beat Uber Lilith as a droid. That was like,
a year ago. I think I forget like solo. Yeah. And I could just tell there's some percentage of his
brain the entire time was thinking like, I wish I was right now attempting. I think he did it that night.
He did it that night. Yeah. He stayed up and did it that night. Yeah.
It's just crazy to me. I mean, in a, in a, in a fundamental way, it's really inspiring.
And what you're doing is inspiring in that way. Cause I mean, it's not just about the game,
everything you're doing there has impact by striving to do well on web grid. You're helping
everybody figure out how to create the system all along like the decoding, the software, the hardware,
the calibration, all of it, how to make all of that work so you can do everything else really well.
Yeah. It's just really fun.
Well, that's also, that's part of the thing is making it fun.
Yeah. It's addicting. I'm, I've joked about, um, like what they actually did when they went
in and put this thing in my brain, they must've flipped a switch to make me, uh, more susceptible
to these kinds of games to make me addicted to like web grid or something. Yeah. Do you know
bliss's high score? Yeah. He said like 14 or something.
17. Oh boy. 17.1 or something.
17 on the dot. 17.01. Yeah.
He told me he like does it on the floor with peanut butter and he like fasts. It's, it's, it's weird.
It sounds like cheating. Sounds like performance enhancing.
Uh,
Nolan's like the first time Nolan, uh, played this game, he asked, you know, how good are we at this
game? And I think you told me right then, you're gonna, you're gonna try to beat me.
I'm gonna get there someday. Yeah.
I think I can. I fully believe you.
I think I can. I'm excited for that.
Yeah. So I've been playing first off with the dwell cursor, which really hampers my web grid
playing ability. Basically I have to wait 0.3 seconds for every click.
Oh, so you can't do the clicks. Yeah.
So you have to, so you click by dwelling, you said 0.3, 0.3 seconds, which, which sucks.
It really slows down how much I'm able to like, how high I'm able to get.
Yeah.
I still hit like 50, I think I hit like 50 something trials, net trials per minute in
that. Um, which was pretty good. Um, cause I'm able to like, um, there's one of the settings is
also like how slow you need to be moving in order to initiate a click, to start a click.
So I can tell sort of when I'm on that, um, threshold to start initiating a click just a bit
early. So I'm not fully stopped over the target. When I go to click, I'm doing it like on my way to
the targets a little, um, to try to time it just right.
Wow. So you're slowing down.
Yeah. Just, just a hair right before the targets.
This is like a lead performance. Okay. But that's still, it's, it sucks that there's a ceiling of
the 0.3.
Well, there, I can get down to 0.2 and 0.1. 0.1. Yeah. And I've played with that a little
bit too. Um, I have to adjust a ton of different parameters in order to play with 0.1. And I don't
have control over all of that on my end yet. It also changes like how the models are trained.
Like if I train a model, like in web grid, I like a bootstrap on a model, which basically is them,
uh, training models as I'm playing web grid, um, based off of like the web grid data that I'm still
like, if I play web grid for 10 minutes, they can train off that data specifically, um, in order to
get me a better model. Um, if I do that with 0.3 versus 0.1, the models come out different.
Um, the way that they, um, interact is, it's just much, much different. So I have to be really
careful. I found that doing it with 0.3 is actually better in some ways, unless I can do it with 0.1
and change all of the different parameters, then that's more ideal. Cause obviously 0.3 is faster
than 0.1. So, uh, I could, I could get there. I can get there. Can you click using your brain
for right now? It's the hover clicking with the dwell cursor. Um, we, before all the thread
retraction stuff happened, we were calibrating clicks, left click, right click. That was, um,
my previous ceiling, um, before I broke the record again with the dwell cursor was I think on a 35
by 35 grid with left and right click. And you get more, um, BPS more bits per second using multiple
clicks. Cause it's more difficult. Oh, because what is it? The blue, you get,
you're supposed to do either a left click or like right click. Is it different colors?
Different colors. Yeah. Blue targets for left click, orange targets for right click is what
they had done. So, uh, my previous record of 7.5 was with the blue and the orange targets. Yeah.
Which, um, I think if I went back to that now, um, doing the click calibration, I would be able to,
and being able to like initiate clicks on my own. I think I would break that 10 ceiling like in a
couple of days, max. Like, yeah, you would start making bliss nervous about his 17. Why do you
think we haven't given him the, exactly. Uh, so what, what did it feel like with the retractions
that there is, uh, some of the threads are attracted? It sucked. It was really, really hard.
The day they told me was the day of my big Neuralink tour at their Fremont facility. And
they told me like right before we went over there, it was really hard to hear. My initial reaction
was, all right, go in, fix it. Like go and take it out and fix it. The first surgery was so easy.
Like, like I went to sleep a couple hours later, I woke up and here we are. Um, I didn't feel any
pain, didn't take like any, um, um, pain pills or anything. So I just knew that if they wanted to,
they could go in and put in a new one like next day, if that's what it took. Cause I just wanted,
I wanted it to be better and I wanted not to lose the capability. I had so much fun, um, playing with it
for a few weeks for a month. I had like, it had opened up so many doors for me and it opened up
so many more possibilities that I didn't want to lose it after a month. I thought it would have been
a cruel twist of fate if I had gotten to see the view from like the top of this mountain and then
have it all come crashing down after a month. And I knew like say the top of the mountain, but
like I, how I saw it was, I was just now starting to climb the mountain and I was like,
there was so much more that I knew was possible. And so to have all of that be taken away, it was
really, really hard. Um, but then on the drive over to the facility, I don't know, like five minute
drive, whatever it is. Um, I talked with my parents about it. I prayed about it. I was just like,
you know, I'm not going to let this ruin my day. I'm not going to let this, um, ruin this amazing
like tour that they have set up for me. Like, I want to go show everyone how much I appreciate
all the work they're doing. I want to go like meet all of the people who have made this possible.
And I want to go have one of the best days of my life. And I did, and it was amazing. And it
absolutely was one of the best days I've, uh, ever been privileged to experience.
And then for a few days, uh, I was pretty down in the dumps, but, uh, for like the first few days
afterwards, I was just like, I didn't know if it was ever going to work again. And then I just,
I made the decision that it, even if I lost the ability to use the neural link, even if I lost,
um, even if I like lost out on everything to come. Um, if I could keep giving them data in any way,
then I would do that. If I needed to just do, um, like some of the data collection every day or body
mapping every day for a year, then I would do it. Um, because I know that everything I'm doing
helps everyone to come after me. And that's all I wanted. I guess the whole reason that I did this
was to help people. And I knew that anything I could do to help, I would continue to do,
even if I never got to use the cursor again, then, you know, I was just happy to be a part of it.
And everything that I'd done was just a perk. It was something that I got to experience. And
I know how amazing it's going to be for everyone to come after me. So might as well just keep
trucking along, you know, that said you were able to get to work your way up to get the performance
back. So this is like go from Rocky one to Rocky two. So when did you first realize
that this is possible and what gave you sort of the strength of motivation, the determination to do it,
to increase back up and be your previous record? Uh, yeah, it was within a couple of weeks.
Again, this feels like I'm interviewing an athlete. This is great. I like to thank my parents.
The road back was long and hard from many difficulties. There were dark days.
It was a couple of weeks, I think. And then there was just a turning point. I think they had switched
how, um, they were measuring, um, the neuron spikes in my brain, like the bliss helped me out.
Uh, yeah, the way in which we were measuring, uh, the behavior of individual neurons.
Yeah. So we're switching from, uh, sort of individual spike detection to something called
spike band power, which, uh, if you watch the previous segments with either me or DJ,
you probably have some content. Yeah. Okay. So when they did that, it was kind of like,
uh, you know, light over the head, like light bulb moment, like, oh, this works. And
um, this seems like, like we can run with this. And I saw the, um,
uptick in performance immediately. Like I could feel it when they switched over. I was like,
this is better. Like, this is good. Like everything up till this point for the last few weeks,
last, like whatever, three or four weeks. Cause it was before they even told me like everything
before this sucked, like, let's keep doing what we're doing now. And at that point it was not like,
oh, I know I'm still only at like, say in web grid terms, like four or five BPS compared to
my 7.5 before. But I know that if we keep doing this, then like I can, I can get back there.
And then they gave me the dwell cursor and the dwell cursor sucked at first. It's not obviously
not what I want, but it gave me a path forward to be able to continue using it and, um, hopefully
to continue to help out. And so I just ran with it, never looked back. Like I said, I just kind
of person, I roll with the punches anyway. So what was the process? What was the feedback loop on the
figuring out how to do the spike detection in a way that would actually work well for Nolan?
Yeah, it's a great question. So maybe just to describe first, how the
actual update worked is basically an update to your implant. So we just did an over the air
software update to his implant, same way you'd update your Tesla or your iPhone. And, uh, that
firmware change enabled us to record sort of averages of populations of neurons nearby individual
electrodes. So we have, uh, less resolution about which individual neuron is doing what, but we have
a broader picture of what's going on nearby an electrode overall. And, uh, that feedback loop,
I mean, basically as Nolan described, it was immediate when we flipped that switch. Uh, I think
the first day we did that, you had three or four VPS right out of the box. And that was a light bulb
moment for, okay, this is the right path to go down. And from there, there's a lot of feedback around
like how to make this useful for independent use. So what we care about ultimately is that you can use
it independently to do whatever you want. And, uh, to get to that point, it required us to re-engineer the UX,
as you talked about with the dwell cursor, to make it something that you can use independently,
without us, need to be involved all the time. And, uh, yeah, this is obviously the start of this
journey still. Hopefully we get back to the places where you're doing multiple clicks and, uh, using
that to control much more fluidly everything and much more naturally the applications that you're
trying to interface with. And most importantly, get that web grid number up. Yeah. Yeah. So how is the,
on the hover click, do you accidentally click stuff sometimes? Yeah. Like what's, how hard is it to
avoid accidentally clicking? I have to continuously keep it moving basically. So like I said, there's
a threshold where it will initiate a click. So if I ever, um, drop below that, it'll start and I have
0.3 seconds to move it before it clicks anything. Um, and if I don't want it to ever get there, I just
keep it moving at a certain speed and like just constantly like doing circles on screen, moving it back
and forth to keep it from clicking stuff. Um, I actually noticed, uh, a couple of weeks back that
I was, when I was not using the implant, I was just moving my hand back and forth or in circles.
Like I was trying to keep the cursor from clicking and I was just doing it like while I was trying to
go to sleep and I was like, okay, this is a problem to avoid the clicking. I guess, does it, does that
create problems? Like when you're gaming, accidentally click a thing? Like, yeah, yeah. It happens in chess.
Um, I've lost, I've lost a number of games because I'll accidentally click something.
I think the first time I ever beat you was because of an, yeah, I missed clicking.
It's a nice excuse, right? Yeah.
You can always, anytime you lose, you could just say that was accidental.
Yeah. You said the app improved a lot from version one, when you first started using it,
it was very different. So can you just talk about the trial and error that you went through with the
team? Like 200 plus pages of notes, like what's that process like of work, going back and forth
and working together to improve the thing? It's a lot of me just using it like day in and day out
and saying like, Hey, can you guys do this for me? Like, give me this. I want to be able to do that.
Um, I need this. Um, I think a lot of it just doesn't occur to them maybe until someone is actually
using the app, using the implant. It's just something that you, they just never would have
thought of, or, um, it's very specific to even like me, maybe what I want. It's something I'm
a little worried about with the next people that come is, you know, um, maybe they will want things
much different than how I've set it up or what the advice I've given the team. And they're going to look
at some of the things I've, they've added for me. Like, that's a dumb idea. Like, why would he ask
for that? Um, and so I'm really looking forward to get the next people on because I guarantee that
they're going to think of things that I've never thought of. And they're going to think of
improvements. I'm like, wow, that's a really good idea. Like I wish I would have thought of that.
Um, and then they're also going to give me some pushback about like, yeah, what you are asking them
to do here. Um, that's a bad idea. Let's do it this way. And I'm more than happy, um, to have that
happen, but it's just a lot of like, you know, uh, different interactions with different games or
applications, um, the internet, just with the computer in general. Um, there's tons of bugs,
um, that end up popping up left, right, center. Um, so it's just me trying to use it as much as
possible and showing them what works and what doesn't work and what I would like to be
better. And, um, then they take that feedback and they usually create amazing things for me.
They solve these problems in ways I would have never imagined. Uh, they're so good at everything
they do. Um, and so I'm just really thankful that I'm able to give them feedback and they
can make something of it. Cause a lot of my feedback is like really dumb. It's just like,
I want this, please do something about it. And we'll come back and super well thought out.
And it's way better than anything I could have ever thought of or implemented myself.
So they're just great. They're really, really cool.
As the BCI community grows, would you like to hang out with the other folks with Neuralynx? Like
what, what relationship, if any, would you want to have with them? Cause you said like,
they might have a different set of like ideas of how to use the thing. Uh, would you be
intimidated by their web grid performance?
No, no, I hope compete. I hope day one, they like wipe the floor with me. I hope they beat it.
Um, and they crush it, you know, the double it if they can. Um, just because on one hand,
it's only going to push me to be better. Um, cause I'm super competitive. I want other people
to push me. Um, I think that is important for anyone trying to, um, achieve greatness is they need
other people around them who are going to push them to be better. And I even made a joke about
it on X once, like once the next people get chosen, like cue buddy cop music. Like I'm just
excited to have other people to do this with and to like share experiences with. I'm more than happy
to interact with them as much as they want more than happy to give them advice. I don't know what
kind of advice I could give them, but if they have questions, I'm more than happy.
What advice would you have for, uh, the next participant in the clinical trial?
That they should have fun with this, um, because it is a lot of fun. Um, and that I hope they work
really, really hard because it's not just for us. It's for everyone that comes after us. Um, and,
you know, come to me if they need anything and to go to Neuralink, if they need anything,
man, Neuralink moves mountains. Like they do absolutely anything for me that they can.
And it's an amazing support system to have. Um, it, it puts my mind at ease, um, for like so many
things that I, uh, I've had like questions about or so many things I want to do. Um, and they're always
there and that's really, really nice. Um, and so I just, I would tell them not to be afraid to go to
Neuralink with any questions that they have, any concerns, uh, anything that, you know,
they're looking to do with this and any help that Neuralink is capable of providing. I know they will.
Um, and I don't know, I don't know, just work your ass off because it's, it's really important that
we try to give our all to this.
So have fun and work hard.
Yeah. Yeah. There we go. Maybe that's what I'll just start saying to people. Have fun, work hard.
Now you're a real pro athlete. Just keep it short.
Um, maybe it's good to talk about what you've been able to do now that you have a Neuralink implant,
like the, the freedom you gain from this way of interacting with the outside world. Like you play
video games all night and you do that by yourself. Yeah. And that's a kind of freedom. Can you speak
to that freedom that you gain? Yeah. It's what all, I don't know, people in my position want,
they just want more independence. The more load that I can take away from people around me,
the better. If I'm able to interact with the world, uh, without using my family, without going
through any of my friends, um, like needing them to help me with things, the better. Um,
if I'm able to sit up on my computer all night and not need someone to like sit me up, uh, say like on
my iPad, like in a position where I can use it and then have to have them wait up for me all night
until I'm ready to be done using it. Um, like that, it takes a load off of all of us. And it's,
it's really like all I can ask for. Um, it's something that, you know, I could never think
Neuralink enough for, and I know my family feels the same way. Um, you know, just being able to
have the freedom to do things on my own, uh, at any hour of the day or night, it means the world to me.
And, um, I don't know.
When you're up at 2am playing web grid by yourself, I just imagine like it's darkness
and there's just a light glowing and you're just focused. What's going through your mind?
Are you like in a state of flow where it's like the mind is empty, like those like Zen masters?
Yeah. Generally it is me playing music of some sort. I have a massive playlist. And so I'm just
like rocking out to music. And then it's also just like a race against time because I'm constantly,
constantly looking at how much battery percentage I've left on my implant. Like, all right, I have
30%, which equates to, you know, X amount of time, which means I have to break this record in the next,
you know, hour and a half or else it's not happening tonight. Um, and so it's, it's a little
stressful when that happens when it's like, when it's above 50%, I'm like, okay, like I got time.
It starts getting down to 30 and then 20. It's like, all right, uh, 10%, a little pop-ups going
to pop up right here. And it's going to really screw my web grid flow. It's going to tell me that,
you know, like there's a, like a low battery, low battery pop-up comes up and I'm like,
it's really going to screw me over. So if I have to, if I'm going to break this record,
I have to do it in the next like 30 seconds or else that pop-up is going to get in the way,
like cover my web grid. Um, and then it, after that I go click on it, go back into web grid.
And I'm like, all right, that means I have, you know, 10 minutes left before this thing's dead.
That's what's going on in my head. Generally that and whatever song is playing. Um, and I just,
I just want, I want to break those records so bad. Like it's all I want when I'm playing web grid.
It, it has become less of like, oh, this is just a leisurely activity. Like I just enjoy doing this
because it just feels so nice and it puts me at ease. It is no, once I'm in web grid,
you better break this record or you're going to waste like five hours of your life right now. And,
um, I don't know. It's just fun. It's fun, man.
Have you ever tried web grid with like two targets and three targets? Can you get higher BPS with that?
Can you, can you do that? You mean like different color targets or are you being,
Oh, get multiple targets. Yeah. So change the thing.
Yeah. So BPS is a log of number of targets times correct minus incorrect divided by time.
And so you can think of like different clicks as basically doubling the number of active targets.
Got it. So, you know, you basically higher BPS,
the more options there are, the more difficult to task. And, uh, there's also like Zen mode you've
played in before, which is like infinite canvas. It covers, it covers the whole screen with a grid.
And, um, I don't know what, no, yeah. And so you can go like, that's, that's insane.
Yeah. He doesn't like it because it didn't show BPS. So like, you know, oh, yeah, I had them,
I had them put in a giant BPS in the background. So now it's like the opposite of Zen mode. It's like,
it's like super hard mode, like just metal mode. If it's just like a giant number in the back counter.
We should be named that metal mode isn't much better. So you also play civilization six.
I love Civ six. Yeah.
Uh, usually go with Korea.
I do. Yeah. So the great part about Korea is they, uh, focus on like science tech victories,
which was not planned. Like I've been playing Korea for years and then all of the knurling stuff happened.
Um, so it kind of aligns. Um, but what I've noticed with tech victories is if you can just rush tech,
rush science, um, then you can do anything like at one point in the game, you will be so far ahead
of everyone technologically that you will have like musket men, infantry men playing sometimes,
and people will still be fighting with like bows and arrows. And so if you want to win a domination
victory, you just get to a certain point with the science and then go and wipe out the rest of the
world. Or, um, you can just take science all the way and win that way. And you're going to be so far
ahead of everyone because you're producing so much science that it's not even close. Um, I've accidentally
won in different ways just by focusing on science accidentally one by focusing on science. I was,
yeah, I like, I, I was playing only science, obviously like just science all the way, just
tech. And I was trying to get like every tech in the tech tree and stuff. And then I accidentally won
through a diplomatic victory. And I was so mad. I was so mad. Uh, it, cause it just like ends the game
one turn. It was like, Oh, you won. You're so diplomatic. I'm like, I don't want to do this.
I should have declared war on more people or something. Um, it was terrible, but you don't
need like giant civilizations with tech, especially with Korea, you can keep it pretty small. So I
generally just, you know, get to a certain military unit and put them all around my border to keep
everyone out. And then I will just build up. So very isolationist. Um, nice. Yeah.
Just work on the science. Yeah. That's it.
You're making it sound so fun. It's so much fun.
And I also saw a civilization seven trailer.
Oh man. I'm so pumped.
And that's probably coming out.
Come on. Civ seven hit me up. All alpha beta tests, whatever.
Wait, when is it coming out?
In 2025.
Yeah. Yeah. Next year. Yeah.
What other stuff would you like to see improved, uh, about the Neuralink app and just the entire
experience?
I would like to, like I said, get back to the, um, like click on demand, like the regular clicks.
That would be great. Uh, I would like to be able to connect to more devices. Uh, right now,
it's just the computer. I'd like to be able to use it on my phone or use it on different consoles,
different, uh, platforms. Um, I'd like to be able to control as much stuff as possible. Honestly,
um, like an optimist robot would be pretty cool. That would be sick. If I could control an optimist
robot, uh, the link app itself. Um, it seems like we are getting pretty, um, dialed in to what,
um, it might look like down the road. It seems like we've gotten through a lot of what I want from it.
At least the only other thing I would say is like more control over all the parameters that I, um,
can tweak, uh, with my like cursor and stuff. There's a lot of things that, you know, go into
how the cursor moves in certain ways. Um, and I have, I don't know, like three or four of those
parameters and there might gain and friction, gain friction. Yeah. And there's maybe double the amount
of those with just like velocity and then with the actual dwell cursor. Um, so I would like all of it.
I want as much control over my environment as possible. Um, especially like advanced mode,
you know, like in like there's menus, usually there's basic mode and you're like one of those folks,
like the power user advanced. Yeah. That's, that's, that's what I want. I want as much control over
this is possible. Um, so yeah, that's, that's really all I can ask for. Just give me, give me
everything. Uh, has speech been useful? Like just being able to talk also in addition to everything
else? Yeah. You mean like while I'm using it while you're using it, like speech to text?
Oh yeah. Or do you type or look, cause there's also a keyboard.
Yeah. Yeah. So there's a virtual keyboard. That's another thing I would like to work
more on is finding some way to, um, type or text in a different way right now. It is, um,
like a dictation basically, and a virtual keyboard that I can use with the cursor, but we've played
around with, um, like finger spelling, like sign language, finger spelling. Um, and that seems really
promising. So I have this thought in my head that it's going to be a very similar learning curve
that I had with, um, the cursor where I went from attempted movement to imagine movement.
At one point I have a feeling, um, this is just my intuition that at some point I'm going to be
doing finger spelling and I won't need to actually attempt to finger spell anymore that I'll just be
able to think the like letter that I want and it'll pop up. That'll be epic. Yeah. And that's
challenging. That's hard. That's a lot of work for you to kind of take that leap, but that would be
awesome. And then like going from letters to words is another step. Like you would go from,
you know, right now it's finger spelling of like just the sign language alphabet, but if it's able
to pick that up, then it should be able to pick up like the whole sign language, like language. Um,
and so then if I could do something along those lines or just the sign language, um, spelled word,
if I can, you know, spell it at a reasonable speed and it can pick that up, then I would just be able
to think that through and it would do the same thing. I don't see why not after what I saw with
the, um, with the cursor control, I don't see why it wouldn't work, but we'd have to play around with
it more. What was the process in terms of like training yourself to go from attempted movement
to imagine movement? Yeah. How long did that take? So like, how long would this kind of process take?
Well, it was a couple of weeks before it just like happened upon me, but now that I know
that that was possible, I think I could make it happen with other things. I think it would be much,
much simpler. Would you get an upgraded implant device? Sure. Absolutely. Whenever, whenever
they'll let me. Uh, so you don't have any concerns for you with the surgery experience, all of it was,
um, like no regrets. No. So everything's been good so far. Yep. You just keep getting upgrades. Yeah.
I mean, why not? I've seen how much it's impacted my life already. And I know that everything from
here on out is just going to get better and better. So, um, I would love to, I would love to get the
upgrade. What, uh, future capabilities, uh, are you excited about sort of beyond this kind of, uh,
telepathy, uh, is vision interesting. So for folks who, for example, who are blind,
so you're like, uh, enabling people to see or, or for speech.
Yeah. There's a lot. That's very, very cool about this. I mean, we're talking about the brain. So
there's like, this is just motor cortex stuff. There's so much more that can be done.
The vision one is fascinating to me. I think that is going to be very, very cool to give someone the
ability to see for the first time in their life would just be, I mean, it, it might be more amazing
than even helping someone like me. Like that just sounds incredible. Um, the speech thing is
really interesting being able to have some sort of like real time translation and, um, cut away
that language barrier would be really cool. Um, any sort of like actual impairments, um, that it could
solve like with speech would be very, very cool. And then also there are a lot of different
disabilities that all originate in the brain and you would be able to hopefully be able to
solve a lot of those. Um, I know there's already stuff to help people with seizures, um, that can
be implanted in the brain. This would do, I imagine the same thing. And so you could do something like
that. I know that, you know, even someone like Joe Rogan has talked about, uh, the possibilities
with being able to, um, stimulate the brain in different ways. Um, I'm not sure.
I'm not sure. I'm not sure what, you know, like how ethical a lot of that would be. That's beyond
me, honestly, but I know that there's a lot that can be done when we're talking about the brain and
being able to go in and physically make changes to help people or to improve their lives. So I'm
really looking forward to everything that comes from this. And I don't think it's all that far off.
Um, I think a lot of this can be implemented within my lifetime. Um, assuming that I live a long life.
What you were referring to is things like people suffering from depression or things of that nature,
potentially getting help. Yeah. Flip a switch like that, make someone happy. Um, I know,
I think Joe has talked about it more in terms of like, you want to experience like what a drug trip
feels like, like you want to experience what you'd like to be on course. Oh yeah. Mushrooms or
something like that. DMT. Like you can just flip that switch in the brain. Uh, my buddy Bane has
talked about being able to like wipe parts of your memory and re-experience things that like for the
first time, like your favorite movie or your favorite book, like just wipe that out real quick and then
re fall in love with Harry Potter or something. Um, I told him, I was like, I don't know how I feel about
like people being able to just wipe parts of your memory. Um, that seems a little sketchy to me.
He's like, they're already doing it. So sounds legit. Uh, I would love memory replay just like
actually like high resolution replayable memories. Yeah. I saw an episode of black mirror about that
once. I don't think I want it. Yeah. So black mirror always kind of considers the worst case,
which is important. I think people don't consider the best case or the average case enough.
I don't know what it is about us humans. We want to think about the worst possible thing.
We love drama. It's like, how's this new technology going to kill everybody?
We just love that. Okay. Like, yes, let's watch. Hopefully people don't think about that too much
with me. It'll ruin a lot of my plans. Yeah. Yeah. I assume you're going to have to
take over the world. I mean, you're, I love your Twitter. You, uh, you tweeted, I'd like to make
jokes about hearing voices in my head since getting the neural link, but I feel like people would take it
the wrong way. Plus the voices in my head told me not to. Yeah. Yeah. Yeah. Please never stop.
So you were talking about Optimus. Um, is that something, uh, you would love to be able to do
to control the robotic arm or the entirety of Optimus? Oh yeah, for sure. For sure. Absolutely.
You think there's something like fundamentally different about just being able to physically
interact with the world. Yeah. Oh, a hundred percent. Um, um, this, I know another thing
with like being able to like give people the ability to like feel sensation and stuff too,
by going in with the brain and having the neural link, maybe do that. That could be something that,
um, could be translated through, transferred through the Optimus as well. Like there's all sorts of really
cool, um, interplay between that. And then also, like you said, just physically interacting. I mean,
99% of the things that I can't do myself, um, obviously need, I need a caretaker for someone to
physically do things for me. If an Optimus robot could do that, like I could live an incredibly
independent life and not be such a burden on those around me. Um, and that would, it would change the
way people like me live, um, at least until whatever this is gets cured. Um, but being able to
interact with the world physically like that would just be amazing. Um, and, and they're not just like
for being, for having to be a caretaker or something, but something like I talked about just
being able to read a book, uh, imagine an Optimus robot just being able to hold a book open in front
of me, like get that smell again. I might not be able to feel it at that point. Um, or maybe I could
again with the sensation and stuff, but being, there's something different about reading like a
physical book than staring at a screen or listening to an audio book. I actually don't like audio books.
I've listened to a ton of them at this point, but I don't really like them. Um, I would much rather
like read a physical copy. So one of the things you would love to be able to experience is
opening the book, bringing it up to you and to feel the touch of the paper.
Yeah. Oh man. The touch, the smell. I mean, it's just like, just something about the words on the
page and you know, they've, they've replicated, you know, that page color on like the Kindle and stuff.
Yeah. It's just not the same. Yeah. So just something as simple as that.
So one of the things you miss is touch. I do. Yeah. A lot of, a lot of things that
I interact with in the world, like clothes or literally any physical thing that I interact
with in the world. A lot of times what people around me will do is they'll just come like
rub it on my face. They'll like lay something on me so I can feel the weight. They will rub,
you know, a shirt on me so I can feel fabric. Like there's something very profound about touch.
And, uh, it is, it's something that I miss a lot. Um, and something I would love to
do again. Uh, we'll see.
What would be the first thing you do with a, with a hand that can touch your mama hug after that,
right? Yeah. Yeah. I know that's, it's one thing that I've, that I've asked, um, like God for
basically every day since, uh, my accident was just being able to like one day move, even if it was
only like my hand. So that way, like I could squeeze my mom's hand or something just to like show
her that, you know, like how much I care and how much I love her and everything.
Um, something along those lines, um, being able to just interact with the people around me,
handshake, give someone a hug. Um, I don't know, anything like that, being able to help me eat.
Like I'd probably get really fat, um, which would be a terrible, terrible thing.
Also beat bliss and chess on a physical chess board.
Yeah. Yeah. I mean, there are just so many upsides, you know,
and any, any way to find some way to feel like I'm bringing bliss down to my level.
Yeah. Because, um, he's just such an amazing guy and everything about him is just
so above and beyond, um, that anything I can do to take him down a notch.
Yeah. Yeah. Humble him a bit. He needs it.
Yeah. Okay. As he's sitting next to me, um, did you ever make sense of why God
puts good people through such hardship? Oh, man. Um,
I think it's all about understanding how much we need God. And I don't think that there's any
light without the dark. I think that if all of us were happy all the time, um, there would be,
you know, no reason to turn to God ever. I feel like there would be no concept of, you know, good
or bad. And I think that as much of like the darkness and the evil that's in the world, it
makes us all appreciate the good and the things we have so much more. And I think,
you know, like when I had my accident, the first, one of the first things I said to one of my best
friends was, and this was within like the first month or two after my accident, I said, you know,
everything about this accident has just made me understand and believe that like God is real and
that there really is a God basically. And that, um, like my interactions with him have all been,
you know, real and worthwhile. And he said, if anything, seeing me go through this accident,
he believes that there isn't a God and it's a very different reaction. Um, but I believe that it is,
it is a way for God to test us, to build our character, to, um, um, send us through trials and
tribulations to make sure that we understand, um, how precious, you know, he is and the things that
he's given us and the time that he's given us, and then, um, to hopefully grow from all of that.
Um, I think that's a huge part of being here is to, um, not just, you know, have an easy life and do
everything that's easy, but to step out of our comfort zones and really challenge ourselves,
uh, because I think that's how we grow. What gives you hope about this whole thing
we have going on human civilization? Oh man. Um, I think people are my biggest, uh, inspiration.
Even just being at Neuralink, um, for a few months, looking people in the eyes and hearing their
motivations for why they're doing this. It's, it's so inspiring. And I know that they could be
other places, um, at Kushier jobs, um, working somewhere else doing X, Y, or Z that doesn't
really mean that much. Um, but instead they're here and they want to better humanity and they
want to better just the people around them, the people that they've interacted with in their life.
They want to make better lives for their own family members who might have disabilities, or
they look at someone like me and they say, you know, I can do something about that. So I'm going
to, and it's always been what I've connected with most in the world are people. I'm, I've always been
a people person and I love learning about people and I love learning like how people developed and
where they came from and to see like how much people are willing to do for someone like me when
they don't have to, and they're going out of their way to make my life better. It gives me a lot of
hope for just humanity in general, how much, how much we care and how much we're capable of when we
all kind of get together and try to make a difference. And I know there's a lot of bad out there in the
world, but there always has been, and there always will be. Um, and I think that that is, it shows
human resiliency and it shows what we're able, what we're able to endure and how much,
how much we just want to be there and help each other and how much satisfaction we get from that.
Because I think that's one of the reasons that we're here is just to help each other. And, um,
I don't know that, that always gives me hope is just realizing that there are people out there who
still care and who want to help. And thank you for being one such human being
and continuing to be a great human being through everything you've been through and being an
inspiration to many people, to myself for many reasons, including your epic, unbelievably great
performance on web grid. I will be training all night tonight to try to catch up. You can do it.
And I believe in you that you can, uh, once you come back, so sorry to interrupt with the Austin trip.
Once you come back, uh, eventually beat bliss. Yeah. Yeah, for sure. Absolutely.
I'm rooting for you. The whole world is rooting for you. Thank you.
Thank you for everything you've done, man. Thanks. Thanks, man.
Thanks for listening to this conversation with Nolan Arbaugh. And before that with Elon Musk,
DJ saw Matthew McDougal and bliss Chapman to support this podcast. Please check out our sponsors in the
description. And now let me leave you with some words from all this Huxley in the doors of perception.
We live together. We act on and react to one another. But always, and in all circumstances,
we are by ourselves. The martyrs go hand in hand into the arena. They are crucified alone.
Embraced, the lovers desperately try to fuse their insulated ecstasies into a single self-transcendence
in vain. But its very nature, every embodied spirit is doomed to suffer and enjoy its solitude.
Sensations, feelings, insights, fancies, all these are private and except through symbols and,
at second hand, incommunicable. We can pull information about experiences, but never the experiences
themselves. From family to nation, every human group is a society of island universes.
Thank you for listening, and hope to see you next time.
Thank you.
Thank you.
Thank you.
Thank you.