The following is a conversation with Sertesh Karaman, a professor at MIT, co-founder of the
autonomous vehicle company Optimus Ride, and is one of the top roboticists in the world,
including robots that drive and robots that fly. To me, personally, he has been a mentor,
a colleague, and a friend. He's one of the smartest, most generous people I know,
so it was a pleasure and honor to finally sit down with him for this recorded conversation.
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advance robotics and STEM education for young people around the world. And now, here's my
conversation with Sirtash Karaman. Since you have worked extensively on both, what is the more
difficult task? Autonomous flying or autonomous driving? That's a good question. I think that
autonomous flying, just kind of doing it for consumer drones and so on, the kinds of applications
that we're looking at right now, is probably easier. And so I think that that's maybe one of the
reasons why it took off like literally a little earlier than the autonomous cars. But I think
if we look ahead, I would think that the real benefits of autonomous flying, unleashing them
in like transportation logistics and so on, I think it's a lot harder than autonomous driving. So
I think my guess is that we've seen a few kind of machines fly here and there, but we really
haven't yet seen any kind of machine like at massive scale, large scale being deployed and
flown and so on. And I think that's going to be after we kind of resolve some of the large scale
deployments of autonomous driving. So what's the hard part? What's your intuition behind why
at scale, when consumer facing drones are tough? So I think in general, at scale is tough. Like
for example, when you think about it, we have actually deployed a lot of robots in the, let's
say the past 50 years. We as academics or we business entrepreneur? I think we as humanity.
A lot of people working on it. So we humans deployed a lot of robots. And I think that
when you think about it, robots, they're autonomous. They work on their own, but they are either like
in isolated environments, or they are in sort of, they may be at scale, but they're really confined
to a certain environment that they don't interact so much with humans. And so they work in I don't
know factory floors, warehouses, they work on Mars. They are fully autonomous over there.
But I think that the real challenge of our time is to take these vehicles and put them into places
where humans are present. So now I know that there's a lot of human robot interaction type of
things that need to be done. And so that's one thing. But even just from the fundamental algorithms
and systems and the business cases or maybe the business models, even like architecture,
planning, societal issues, legal issues, there's a whole bunch of pack of things that are related to
us putting robotic vehicles into human present environments. And these humans,
you know, they will not potentially be even trained to interact with them. They may not even
be using the services that are provided by these vehicles. They may not even know that
they're autonomous. They're just doing their thing, living in environments that are designed for humans,
not for robots. And that I think is one of the biggest challenges, I think, of our time to put
vehicles there. And, you know, to go back to your question, I think doing that at scale, meaning,
you know, you go out in a city, and you have, you know, like thousands or tens of thousands of
autonomous vehicles that are going around. It is so dense to the point where if you see one of them,
you look around, you see another one. It is that dense. And that density, we've never done anything
like that before. And I would bet that that kind of density will first happen with autonomous cars,
because I think, you know, we can bend the environment a little bit. We can especially
kind of making them safe is a lot easier when they're like on the ground. When they're in the air,
it's a little bit more complicated. But I don't see that there's going to be a big separation. I
think that, you know, there will come a time that we're going to quickly see these things unfold.
Do you think there will be a time where there's tens of thousands of delivery drones that fill
the sky? You know, I think it's possible, to be honest. Delivery drones is one thing, but, you
know, you can imagine for transportation, like an important use case is, you know, we're in Boston,
you want to go from Boston to New York, and you want to do it from the top of this building
to the top of another building in Manhattan, and you're going to do it in one and a half hours.
And that's a big opportunity, I think.
Personal transport. So like you and me, be a friend, like almost like an Uber?
Yeah, or almost like a like an Uber. So like four people, six people, eight people.
In our work in autonomous vehicles, I see that. So there's kind of like a bit of a need for,
you know, one person transport, but also like a few people. So you and I could take that trip
together. We could have lunch. I think kind of sounds crazy, maybe even sounds a bit cheesy,
but I think that those kinds of things are some of the real opportunities. And I think,
you know, it's not like the typical airplane and the airport would disappear very quickly,
but I would think that, you know, many people would feel like they would spend an extra hundred
dollars on doing that and cutting that four hour travel down to one and a half hours.
So how feasible are flying cars? It's been the dream that's like when people imagine the future
for 50 plus years, they think flying cars. It's like all technologies, it's cheesy to think about
now because it seems so far away, but overnight it can change. But just technically speaking in
your view, how feasible is it to make that happen? I'll get to that question. But just one thing is
that I think, you know, sometimes we think about what's going to happen in the next 50 years. It's
just really hard to guess, right? Next 50 years, I don't know. I mean, we could get what's going
to happen in transportation in the next 50. We could get flying saucers. I could bet on that.
I think there's a 50-50 chance that, you know, like you can build machines that can ionize the air
around them and push it down with magnets and they would fly like a flying saucer. That is possible.
And it might happen in the next 50 years. So it's a bit hard to guess, like when you think
about 50 years before. But I would think that, you know, there's this kind of notion where
there's a certain type of airspace that we call the agile airspace. And there's good amount of
opportunities in the airspace. So that would be the space that is kind of a little bit higher than
the place where you can throw a stone. Because that's a tough thing when you think about it.
You know, it takes a kid and a stone to take an aircraft down and then what happens. But,
you know, imagine the airspace that's high enough so that you cannot throw a stone. But it is low
but it is low enough that you're not interacting with the very large aircraft that are, you know,
flying several thousand feet above. And that airspace is underutilized. Or it's actually kind
of not utilized at all. Yeah, that's right. So there's, you know, there's like recreational people
kind of fly every now and then. But it's very few. Like if you look up in the sky, you may not see
any of them at any given time. Every now and then you'll see one airplane kind of utilizing that
space and you'll be surprised. And the moment you're outside of an airport a little bit,
like it just kind of flies off and then it goes out. And I think utilizing that airspace,
the technical challenge is there is, you know, building an autonomy and ensuring that that
kind of autonomy is safe. Ultimately, I think it is going to be building in complex software,
complicated so that it's maybe a few orders of magnitude more complicated than what we have
on aircraft today. And at the same time, ensuring just like we ensure on aircraft, ensuring that
it's safe. And so that becomes like building that kind of complicated hardware and software
becomes a challenge, especially when, you know, you build that hardware, I mean, you build that
software with data. And so, you know, it's, of course, there's some rule based software in there
that kind of do a certain set of things. But, but then, you know, there's a lot of training there
to machine learning will be key to these kinds of delivering safe vehicles in the future,
especially flight, not maybe the safe part, but I think the intelligent part. I mean,
there are certain things that we do it with machine learning. And it's just there's like
right now, no other way. And I don't know how else they could be done. And, you know, there's
always this conundrum, I mean, we could like could be like, we could maybe gather billions of programmers,
humans who program perception algorithms that detect things in the sky and whatever, or, you
know, we, I don't know, we maybe even have robots like learning the simulation environment and
transfer. And they might be learning a lot better in a simulation environment than
a billion humans put their brains together and try to program humans pretty limited.
So what's, what's the role of simulations with drones? If you've done quite a bit of work there,
how promising just the very thing you said just now, how promising is the possibility of
training and developing a safe flying robot in simulation and deploying it and having that
work pretty well in the real world. I think that, you know, a lot of people when they hear
simulation, they will focus on training immediately. But I think one thing that you said, which was
interesting, it's developing. I think simulation environments are actually could be key and great
for development. And that's not new. Like for example, you know, there's people in the automotive
industry have been using dynamic simulation for like decades now. And, and it's pretty standard
that, you know, you would build and you would simulate. If you want to build an embedded
controller, you plug that kind of embedded computer into another computer, that other
computer would simulate tiny and so on. And I think, you know, fast forward, these things,
you can create pretty crazy simulation environments. Like for instance, one of the things that has
happened recently, and that, you know, we can do now is that we can simulate cameras a lot better
than we used to simulate them, we were able to simulate them before. And that's, I think we
just hit the elbow on that kind of improvement. I would imagine that with improvements in hardware
especially, and with improvements in machine learning, I think that we would get to a point
where we can simulate cameras very, very well. Simulate cameras means simulate how a real camera
would see the real world. Therefore, you can explore the limitations of that. You can train
perception algorithms on that in simulation, all that kind of stuff.
Exactly. So, you know, it's, it's, it has been easier to simulate what we would call
intraceptive sensors, like internal sensors. So for example, inertial sensing has been easy
to simulate. It has also been easy to simulate dynamics, like, like physics that are governed
by ordinary differential equations. I mean, like how a car goes around, maybe how it rolls on the
road, how it interacts with the road, or even an aircraft flying around, like the dynamic,
the physics of that. What has been really hard has been to simulate extraceptive sensors,
sensors that kind of like look out from the vehicle. And that's a new thing that's coming,
like laser rangefinders that are a little bit easier, cameras, radars are a little bit tougher.
I think once we nail that down, the next challenge, I think, in simulation will be
to simulate human behavior. That's also extremely hard. Even when you imagine, like, how a human
driven car would act around, even that is hard. But imagine trying to simulate, you know, a model
of a human, just doing a bunch of gestures and so on. And, you know, it's, it's actually simulated.
It's not captured like with motion capture, but it is simulated. That's, that's very hard. In fact,
today, I get involved a lot with like sort of this kind of very high end rendering projects.
And I have like this test that I pass it to my friends or my mom, you know, I send like two photos,
two kind of pictures, and I say, rendered, which one is rendered, which one is real.
And it's pretty hard to distinguish, except I realized, except when we put humans in there.
It's possible that our brains are trained in a way that we recognize humans extremely well.
But we don't so much recognize the built environments, because built environments sort of
came after, per se, we evolved into sort of being humans, but, but humans were always there.
Same thing happens, for example, you look at like monkeys and you can't distinguish one from another,
but they sort of do. And it's very possible that they look at humans. It's kind of pretty hard
to distinguish one from another, but we do. And so our eyes are pretty well trained to look at
humans and understand if something is off, we will get it. We may not be able to pinpoint it.
So in my typical friend test or mom test, what would happen is that we'd put like a human walking
in a, in a, in a thing and they, they say, you know, this is not right. Something is off in this
video. I don't know what, but I can tell you it's the human I can take the human and I can show
you like inside of a building or like an apartment and it will look like if we had time to render
it, it will look great. And this should be no surprise. A lot of movies that people are watching,
it's all computer generated, you know, even nowadays, even when you watch a drama movie,
and like there's nothing going on action wise, but it turns out it's kind of like cheaper,
I guess to render the background. And so they would. But how do, how do we get there? How do
we get a, a human that's would pass the mom slash friend test, a simulation of a human walking?
So do you think that's something we can creep up to by just doing kind of a comparison learning
where you have humans annotate what's more realistic and not just by watching? Like what's
the path? Because it seems totally mysterious how we simulate human behavior. It's, it's,
it's hard because a lot of the other things that I mentioned to you, including simulating cameras,
right? It is the, the thing there is that, you know, we know the physics, we know how it works,
like in the real world, and we can write some rules and we can do that. Like for example,
simulating cameras, there's this thing called ray tracing. I mean, you literally just kind of
imagine it's very similar to, it's not exactly the same, but it's very similar to tracing photon
by photon, they're going around, bouncing on things and coming to your eye. But human behavior,
developing a dynamic, like, like, like a model of that, that is mathematical so that you can put
it into a processor that would go through that, that's going to be hard. And so, so what else
do you got? You can collect data, right? And you can try to match the data. Or another thing that
you can do is that, you know, you can show the front tests, you know, you can say this or that,
and this or that, and that would be labeling. Anything that requires human labeling, ultimately,
we're limited by the number of humans that, you know, we have available at our disposal,
and the things that they can do, you know, they have to do a lot of other things than
also labeling this data. So, so that modeling human behavior part is, is I think going,
we're going to realize it's very tough. And I think that also affects, you know,
our development of autonomous vehicles. I see them self driving as well, like, you want to use,
so you're building self driving, you know, at the first time, like right after urban challenge,
I think, everybody focused on localization, mapping and localization, you know, slam
algorithms came in, Google was just doing that. And so building these HD maps, basically, that's
about knowing where you are. And then five years later in 2012 2013 came the kind of
coding code AI revolution. And that started telling us about everybody else's,
but we're still missing what everybody else is going to do next. And so you want to know where
you are, you want to know what everybody else is, hopefully, you know that what you're going to do
next. And then you want to predict what other people are going to do. And that last bit has,
has been a real, real challenge. What do you think is the role your own of your, of your,
your, the ego vehicle, the robot, you, the, the you, the robotic you in controlling and having
some control of how the future enrolls of what's going to happen in the future. That seems to be
a little bit ignored in trying to predict the future is how you yourself can affect that future
by being either aggressive or less aggressive or signaling in some kind of way, sort of this kind
of game to erratic dance seems to be ignored for the moment. It's, yeah, it's, it's totally
ignored. I mean, it's, it's quite interesting, actually, like how we, how we interact with
things versus we interact with humans. Like, so if, if you see a vehicle that's completely empty
and it's trying to do something, all of a sudden it becomes a thing. So interact it with, like,
you interact with this table. And so you can throw your backpack or you can kick your, kick it,
put your feet on it and things like that. But when it's a human, there's all kinds of ways
of interacting with a human. So if, you know, like you and I are face to face, we're very civil,
you know, we talk and understand each other for the most part. We'll see you just
never know what's going to happen. But, but the thing is that, like, for example, you and I might
interact through YouTube comments. And, you know, the conversation may go a totally different angle.
And so I think people kind of abusing as autonomous vehicles is a real issue in some sense. And so
when you're an ego vehicle, you're trying to, you know, coordinate your way, make your way,
it's actually kind of harder than being a human. You know, it's like, it's you, you, you not only
need to be as smart as kind of humans are, but you also, you're a thing. So they're going to abuse
you a little bit. So you need to make sure that you can get around and do something. So I in
general believe in that sort of game theoretic aspects, I've actually personally have done,
you know, quite a few papers, both on that kind of game theory, and also like this, this kind of
understanding people's social value orientation, for example, you know, some people are aggressive,
some people not so much. And, and, you know, like a robot could understand that by just looking at
how people drive. And as they kind of come an approach, you can actually understand, like,
if someone is going to be aggressive or, or not as a robot, and you can make certain decisions.
Well, in terms of predicting what they're going to do, the hard question is, you as a robot,
should you be aggressive or not? When faced with an aggressive robot, right now, it seems like
aggressive is a very dangerous thing to do, because it's costly from a societal perspective,
how you're perceived. People are not very accepting of aggressive robots in modern society.
I think that's accurate. So that it really is. And so I'm not entirely sure like how to,
how to go about, but I know, I know for a fact that how these robots interact with other people
in there is going to be, and then interaction is always going to be there. I mean, you could be
interacting with other vehicles, or other just people kind of like walking around. And like I
said, the movement, there's like nobody in the seat. It's like an empty thing just rolling off the
street. It becomes like no different than like any other thing that's not human. And so, so people,
and maybe abuse is the wrong word, but you know, people may be rightfully even they feel like,
you know, this is a human present environments designed for humans to be, and, and they kind
of they want to own it. And then, you know, the robots, they would, they would need to understand
it and they would need to respond in a certain way. And I think that, you know, this actually opens
up like quite a few interesting societal questions for us as we deploy, like we talk robots at large
scale. So what would happen when we try to deploy robots at large scale, I think is that we can
design systems in a way that they're very efficient, or we can design them that they're very
sustainable. But ultimately, the sustainability efficiency tradeoffs, like they're going to be
right in there. And we're going to have to make some choices, like we're not going to be able to
just kind of put it aside. So for example, we can be very aggressive. And we can reduce transportation
delays, increase capacity of transportation. Or, you know, we can, we can be a lot nicer and allow
other people to kind of code and code on the environment and live in a nice place. And then
efficiency will drop. So when you think about it, I think sustainability gets attached to
energy consumption or the impact immediately. And those are those are there. But like livability
is another sustainability impact. So you create an environment that people want to live in. And if
robots are going around being aggressive, you don't want to live in that environment, maybe.
However, you should note that if you're not being aggressive, then, you know, you're probably taking
up some some delays in transportation and this and that. So you're always balancing that. And I
think this this choice has always been there in transportation. But I think the more autonomy
comes in, the more explicit the choice becomes. Yeah, and when it becomes explicit, then we can
start to optimize it. And then we'll get to ask the very difficult societal questions of what do
we value more efficiency or sustainability? It's kind of interesting. That will happen. I think
we're going to have to like, I think that the interesting thing about like the whole autonomous
vehicles question, I think, is also kind of, I think a lot of times, you know, we have, we have
focused on technology development, like hundreds of years, and, you know, the products somehow
followed. And then, you know, we got to make these choices and things like that. But this is,
this is a good time that, you know, we even think about, you know, autonomous taxi type of
deployments, and the systems that would evolve from there. And you realize the business models
are different, the impact on architecture is different, urban planning, you get into like
regulations. And then you get into like these issues that you didn't think about before,
but like sustainability and ethics is like right in the middle of it. I mean, even testing
autonomous vehicles, like think about it, you're testing autonomous vehicles in human present
environments. I mean, the risk may be very small, but still, you know, it's, it's, it's, it's, it's,
it's a, you know, strictly greater than zero risk that you're putting people into. And so then
you have that innovation, you know, risk tradeoff that you're, you're in that somewhere.
And we understand that pretty now that it pretty well now is that if we don't test,
the, at least the, the development will be slower. I mean, it doesn't mean that we're not going to
be able to develop. I think it's going to be pretty hard, actually. Maybe we can, we don't,
we don't, I don't know, but the thing is that those kinds of tradeoffs we already are making.
And as these systems become more ubiquitous, I think those tradeoffs will just really hit.
So you are one of the founders of Optimus Ride, an autonomous vehicle company. We'll talk about
it. But let me, on that point, ask maybe good examples, keeping Optimus Ride out of this question,
sort of exemplars of different strategies on the spectrum of innovation and safety or caution.
So the Waymo, Google self-driving car, Waymo represents maybe a more cautious approach.
And then you have Tesla on the other side, headed by Elon Musk, that represents a more,
however, which adjective you want to use, aggressive, innovative, I don't know. But what,
what do you think about the difference in strategies? In your view, what's more likely,
what's needed and is more likely to succeed in the short term and in the long term?
Definitely some sort of a balance is, is kind of the right way to go. But I do think that
I do think that the thing that is the most important is actually like an informed public. So I don't,
I don't mind, you know, I personally, like if I were in some place, I wouldn't mind so much,
like taking a certain amount of risk. Some other people might. And so I think the key
is for people to be informed. And so that they can, ideally, they can make a choice.
In some cases, that kind of choice, making that unanimously is of course very hard. But I don't
think it's actually that hard to inform people. So I think in one case, like for example, even the
Tesla approach, I don't know, it's hard to judge how informed it is, but it is somewhat informed.
I mean, you know, things kind of come out, I think people know what they're taking and things
like that and so on. But I think the underlying, I do think that these two companies are a little
bit kind of representing like, of course, they, you know, one of them seems a bit safer, the other
one, or, you know, whatever the objective for that is, and the other one seems more aggressive,
or whatever the objective for that is. But, but I think, you know, when you turn the tables,
they're actually there are two other orthogonal dimensions that these two are focusing on.
On the one hand, for Vamo, I can see that, you know, they're, I mean, they, I think they a little
bit see it as research as well. So they kind of, they don't, I'm not sure if they're like really
interested in like an immediate product. You know, they talk about it. Sometimes there's some pressure
to talk about it. So they kind of go for it. But I think, I think that they're thinking maybe in
the back of their mind, maybe they don't put it this way. But I think they realize that we're
building like a new engine. It's kind of like call it the AI engine or whatever that is. And, and,
you know, an autonomous vehicles is a very interesting embodiment of that engine that allows
you to understand where the ego vehicle is, the ego thing is, where everything else is what
everything else is going to do, and how do you react? How do you actually, you know, interact
with humans the right way? How do you build these systems? And I think they want to know that they
want to understand that. And so they keep going and doing that. And so on the other dimension,
Tesla is doing something interesting. I mean, I think that they have a good product, people use
it. I think that, you know, like, it's not for me. But I can totally see people, people like it.
And people, I think they have a good product outside of automation. But I was just referring to the
the automation itself. I mean, you know, like, it kind of drives itself, you still have to be kind
of, you still have to pay attention to it, right? But, you know, people seem to use it. So it works
for something. And so people, I think people are willing to pay for it. People are willing to buy
it. I think it's one of the other reasons why people buy a Tesla car. Maybe one of those reasons
is Elon Musk is the CEO. And, you know, he seems like a visionary person. That's what people think,
you know, he seems like a visionary person. And so it adds like 5k to the value of the car.
And then maybe another 5k is the autopilot. And, you know, it's useful. I mean, it's useful in
the sense that like, people are using it. And so I can see Tesla sure, of course, they want to be
visionary, they want to kind of put out a certain approach, and they may actually get there. But I
think that there's also a primary benefit of doing all these updates and rolling it out because,
you know, people pay for it. And it's, you know, it's basic, you know, demand supply market. And
people like it, they're happy to pay another 5k 10k for that novelty or whatever that is.
And they use it. It's not like they get it and they try it a couple times. It's a novelty, but
they use it a lot of the time. And so I think that's what Tesla is doing. It's actually pretty
different. Like they are on pretty orthogonal dimensions of what kind of things that they're
building. They are using the same AI engine. So it's very possible that, you know, they're both
going to be sort of one day kind of using a similar almost like an internal combustion engine.
It's a very bad metaphor, but similar internal combustion engine. And maybe one of them is
building like a car, the other one is building a truck or something. So ultimately, the use
case is very different. So you, like I said, are one of the founders of Optimus Ride. Let's take
a step back. It's one of the success stories in autonomous vehicle space. It's a great autonomous
vehicle company. Let's go from the very beginning. What does it take to start an autonomous vehicle
company? How do you go from idea to deploying vehicles like you are in a bunch of places,
including New York? I would say that I think that, you know, what happened to us was the
following. I think we've realized a lot of kind of talk in the autonomous vehicle industry back in
like 2014 even when we wanted to kind of get started. And I don't know, like I kind of,
I would hear things like fully autonomous vehicles two years from now, three years from now. I kind
of never bought it. You know, I was a part of MIT's urban challenge entry. Kind of like it has
an interesting history. So I did in college and in high school, sort of a lot of mathematically
oriented work. And I think I kind of, you know, at some point, it kind of hit me. I wanted to
build something. And so I came to MIT's mechanical engineering program. And I now realize, I think
my advisor hired me because I could do like really good math. But I told him that no, no, no, I want
to work on that urban challenge car. You know, I want to build the autonomous car. And I think
that was that was kind of like a process where we really learned, I mean, what the challenges are
and what kind of limitations are we up against, you know, like having the limitations of computers
or understanding human behavior, there's so many of these things. And I think it's just kind of
didn't. And so, so we said, hey, you know, like, why don't we take a more like a market based
approach? So we focus on a certain kind of market. And we build a system for that. What we're building
is not so much of like an autonomous vehicle only, I would say. So we build full autonomy into the
vehicles. But you know, the way we kind of see it is that rethink that the approach should actually
involve humans operating them, not just just not sitting in the vehicle. And I think today,
what we have is today, we have one person operate one vehicle, no matter what that vehicle,
it could be a forklift, it could be a truck, it could be a car, whatever that is. And we want
to go from that to 10 people operate 50 vehicles. How do we do that? You're referring to a world of
maybe perhaps teleoperation. So can you just say what it means for 10 might be confusing for people
listening? What does it mean for 10 people to control 50 vehicles? That's a good point. So I
think it's a very deliberately didn't call it teleoperation because people what people think
then is that people think away from the vehicle sits a person sees like maybe puts on goggles or
something VR and drives the car. So that's not at all what we mean. But we mean the kind of
intelligence whereby humans are in control, except in certain places, the vehicles can execute on
their own. And so imagine like, like a room where people can see what the other vehicles are doing
and everything. And, you know, there will be some people who are more like, more like air traffic
controllers call them like AV controllers. And so these AV controllers would actually see
kind of like like a whole map. And they would understand where vehicles are really confident
and where they kind of, you know, need a little bit more help. And the help shouldn't be for safety.
Help should be for efficiency. Vehicles should be safe. No matter what, if you had zero people,
they could be very safe, but they'd be going five miles an hour. And so if you want them to go around
25 miles an hour, then you need people to come in. And, and for example, you know, the vehicle
come to an intersection. And the vehicle can say, you know, I can wait, I can inch forward a little
bit, show my intent, or I can turn left. And right now it's clear, I can turn, I know that.
But before you give me the go, I won't. And so that's one example. This doesn't mean necessarily
we're doing that, actually. I think, I think if you go down all the, all that much detail that
every intersection, you're kind of expecting a person to press a button, then I don't think you'll
get the efficiency benefits you want. You need to be able to kind of go around and be able to do
these things. But, but I think you need people to be able to set high level behavior to vehicles.
That's the other thing with autonomous vehicles, you know, I think a lot of people kind of think
about it as follows. I mean, this happens with technology a lot, you know, you think, all right,
so I know about cars, and I heard robots. So I think how this is going to work out is that
I'm going to buy a car, press a button, and it's going to drive itself. And when is that going
to happen? You know, and people kind of tend to think about it that way. But when you think about
what really happens is that something comes in in a way that you didn't even expect. If asked,
you might have said, I don't think I need that, or I don't think it should be that and so on.
And then, and then that that becomes the next big thing, coding code. And so I think that this kind
of different ways of humans operating vehicles could be really powerful. I think that sooner than
later, we might open our eyes up to a world in which you go around walking a mall, and there's a
bunch of security robots that are exactly operated in this way. You go into a factory or a warehouse,
there's a whole bunch of robots that are probably exactly in this way. You go to a, you go to the
Brooklyn Navy Yard, you see a whole bunch of autonomous vehicles, Optimus Ride, and they're
operated maybe in this way. But I think people kind of don't see that. I sincerely think that
it's there's a possibility that we may almost see like like a whole mushrooming of this technology
in all kinds of places that we didn't expect before. And then maybe the real surprise.
And then one day when your car actually drives itself, it may not be all that much of a surprise
at all. Because you see it all the time, you interact with them, you take the Optimus Ride,
hopefully that's your choice. And then, you know, you hear a bunch of things, you go around,
you interact with them. I don't know, like you have a little delivery vehicle that goes around
the sidewalks and delivers your things. And then you take it, it says thank you. And then you get
used to that. And one day your car actually drives itself and the regulation goes by and,
you know, you can hit the button asleep. And it wouldn't be a surprise at all. I think that may
be the real reality. So there's going to be a bunch of applications that pop up around
autonomous vehicles, some of which maybe many of which we don't expect at all.
So if we look at Optimus Ride, what do you think, you know, the viral application,
that the one that like really works for people in mobility, what do you think Optimus Ride will
connect with in the near future first? I think that the first places that I like to target,
honestly, is like these places where transportation is required within an environment, like people
typically call it geofenced. So you can imagine like roughly two mile by two mile could be bigger,
could be smaller type of an environment. And there's a lot of these kinds of environments
that are typically transportation deprived. The Brooklyn Navy Art that you know, we're in today,
we're in a few different places, but that's that was the one that was last publicized.
That's a good example. So there's not a lot of transportation there. And you wouldn't expect,
like, I don't know, I think maybe operating an Uber there ends up being sort of a little too
expensive. Or when you compare it with operating Uber elsewhere, that becomes the elsewhere becomes
the priority and these people's places become totally transportation deprived. And then what
happens is that, you know, people drive into these places and to go from point A to point B
inside this place within that day, they use their cars. And so we end up building more parking
for them to, for example, take their cars and go to the launch place. And I think that one of the
things that can be done is that, you know, you can put in efficient, safe, sustainable transportation
systems into these types of places first. And I think that, you know, you could deliver mobility
in an affordable way, affordable, accessible, you know, sustainable way. But I think what also
enables is that this kind of effort, money, area, land that we spend on parking, you could reclaim
some of that. And that is on the order of like, even for a small environment, like two mile by
two mile, it doesn't have to be smack in the middle of New York. I mean, anywhere else, you're
talking tens of millions of dollars. If you're smack in the middle of New York, you're looking at
billions of dollars of savings just by doing that. And that's the economic part of it. And there's a
societal part, right? I mean, just look around. I mean, the places that we live are like built
for cars. It didn't look like this just like 100 years ago. Like today, no one walks in the middle
of the street. It's for cars. We, no one tells you that growing up, but you grow into that reality.
And so sometimes they close the road, it happens here, you know, like the celebration, they close
the road, still people don't walk in the middle of the road, like just walking and people don't.
But I think it has so much impact, the car in the space that we have. And I think we talked about
sustainability, livability, I mean, ultimately, these kinds of places that parking spots at the
very least could change into something more useful, or maybe just like park areas recreational.
And so I think that's the first thing that we're targeting. And I think that we're getting like
a really good response, both from an economic societal point of view, especially places that
are a little bit forward looking. And like, for example, Brooklyn Navy Art, they have tenants,
there's distinct, they're called like new lab. It's kind of like an innovation center. There's
a bunch of startups there. And so, you know, you get those kinds of people and, you know,
they're really interested in sort of making that environment more livable. And these kinds of
solutions that Optimus Ride provides almost kind of comes in and becomes that. And many of these
places that are transportation deprived, you know, they have, they actually rent shuttles.
And so, you know, you can ask anybody, the shuttle experience is like terrible. People hate shuttles.
And I can tell you why it's because, you know, like the driver is very expensive in a shuttle
business. So what makes sense is to attach 2030 seats to a driver. And a lot of people
have this misconception, they think that shuttles should be big. Sometimes we get that
our Optimus Ride. We tell them we're going to give you like four-seaters, six-seaters.
And we get asked like, how about like 20-seaters? Like, you know, you don't need 20-seaters.
You want to split up those seats so that they can travel faster and the transportation delays
would go down. That's what you want. If you make it big, not only you will get delays in
transportation, but you won't have an agile vehicle. It will take a long time to speed up,
slow down, and so on. You need to climb up to the thing. So it's kind of like really hard to
interact with. And scheduling too, perhaps, when you have more smaller vehicles, it becomes closer
to Uber, where you can actually get a personal, I mean, just the logistics of getting the vehicle
to you becomes easier when you have a giant shuttle. There's fewer of them. And it probably
goes on a route, a specific route that is supposed to hit. And when you go on a specific route and
all seats travel together versus, you know, you have a whole bunch of them, you can imagine,
the route you can still have, but you can imagine you split up the seats. And instead of, you know,
them traveling like, I don't know, a mile apart, they could be like, you know, half a mile apart
if you split them into two. That basically would mean that your delays, when you go out,
you won't wait for them for a long time. And that's one of the main reasons, or you don't have to
climb up. The other thing is that I think if you split them up in a nice way, and if you can actually
know where people are going to be somehow, you don't even need the app. A lot of people ask us
the app, we say, why don't you just walk into the vehicle? How about you just walk into the vehicle,
it recognizes who you are, and it gives you a bunch of options of places that you go and you just
kind of go there. I mean, people kind of also internalize the apps. Everybody needs an app.
It's like, you don't need an app, you just walk into the thing. But I think one of the things
that we really try to do is to take that shuttle experience that no one likes and tilt it into
something that everybody loves. And so I think that's another important thing. I would like to
say that carefully, just like to have an operation like we don't do shuttles. We're really kind of
thinking of this as a system or a network that we're designing. But ultimately, we go to places
that would normally rent a shuttle service that people wouldn't like as much. And we want to
tilt it into something that people love. So you've mentioned this earlier, but how many
Optimus Ride vehicles do you think would be needed for any person in Boston or New York?
If they step outside, there will be, this is like a mathematical question,
there'll be two Optimus Ride vehicles within line of sight. Is that the right number?
Like for example, that's the density. So meaning that if you see one vehicle, you look around,
you see another one too. Imagine Tesla would tell you they collect a lot of data. Do you see
that with Tesla? Like you just walk around and you look around and you see Tesla? Probably not.
Very specific areas of California, maybe. Maybe. You're right. Like there's a couple zip codes.
But I think that's kind of important because maybe the couple zip codes,
the one thing that we kind of depend on, I'll get to your question in a second,
but now we're taking a lot of tangents today. So I think that this is actually important.
People call this data density or data velocity. So it's very good to collect data in a way that
you see the same place so many times. Like you can drive 10,000 miles around the country
or you drive 10,000 miles in a confined environment. You'll see the same intersection
hundreds of times. And when it comes to predicting what people are going to do in that specific
intersection, you become really good at it. Versus if you're drawing like 10,000 miles around
the country, you've seen that only once. And so trying to predict what people do becomes hard.
And I think that you said what is needed. It's tens of thousands of vehicles. You really need
to be like a specific fraction of vehicle. Like for example, in good times in Singapore,
you can go and you can just grab a cab. And they are like 10%, 20% of traffic, those taxis.
Ultimately, that's why you need to get to so that you get to a certain place where you really,
the benefits really kick off and like orders of magnitude type of a point.
But once you get there, you actually get the benefits. And you can certainly carry people.
I think that's one of the things people really don't like to wait for themselves. But for example,
they can wait a lot more for the goods if they order something. Like if you're sitting at home
and you want to wait half an hour, that sounds great. People will say it's great. You're going
to take a cab, you're waiting half an hour, like that's crazy. You don't want to wait that much.
But I think you can, I think, really get to a point where the system at peak times really focuses
on kind of transporting humans around. And then it's a good fraction of the traffic to the point
where you go, you look around, there's something there, and you just kind of basically get in there.
And it's already waiting for you or something like that. And then you take it. If you do it at that
scale, like today, for instance, Uber, if you talk to a driver, Uber takes a certain cut,
it's a small cut, or drivers would argue that it's a large cut. But when you look at the grand
scheme of things, most of that money that you pay Uber kind of goes to the driver. And if you talk
to the driver, the driver will claim that most of it is their time. It's not spent on gas,
they think it's not spent on the car, per se, as much, it's like their time. And if you didn't have
a person driving, or if you're in a scenario where, you know, like, 0.1 person is driving the car,
a fraction of a person is kind of operating the car, because, you know, one operates several.
If you're in that situation, you realize that the internal combustion engine type of cars are
very inefficient. You know, we build them to go on highways, they pass crash tests, they're like
really heavy, they really don't need to be like 25 times the weight of its passengers or, you know,
like area wise and so on. But if you get through those inefficiencies, and if you really build like
urban cars and things like that, I think the economics really starts to check out. Like to the
point where, I mean, I don't know, you may be able to get into a car and it may be less than a dollar
to go from A to B. As long as you don't change your destination, you just pay 99 cents and go
there. If you share it, if you take another stop somewhere, it becomes a lot better. You know,
these kinds of things, at least for models, at least for mathematics and theory, they start to
really check out. So I think it's really exciting what OptimusRider is doing in terms of, it feels
the most reachable, like it'll actually be here and have an impact. Yeah, that is the idea.
And if we contrast that, again, we'll go back to our old friends Waymo and Tesla. So Waymo seems to
have sort of technically similar approaches as OptimusRide, but a different, they're not as
interested as having an impact today. They have a longer term sort of investment, it's almost
more of a research project still, meaning they're trying to solve as far as I understand it. Maybe
you can differentiate, but they seem to want to do more unrestricted movement, meaning move from A
to B where A to B is all over the place versus OptimusRide is really nicely GFS and really
sort of establish mobility in a particular environment before you expand it. And then Tesla
is like the complete opposite, which is the entirety of the world actually is going to be
automated. Highway driving, urban driving, every kind of driving, you kind of creep up to it by
incrementally improving the capabilities of the autopilot system. So when you contrast all of these,
and on top of that, let me throw a question that nobody likes, but is a timeline.
When do you think each of these approaches, loosely speaking, nobody can predict the future,
will see mass deployment? So Elon Musk predicts the craziest approach is at the,
I've heard figures like at the end of this year, right? So that's probably wildly inaccurate,
but how wildly inaccurate is it? I mean, first thing to lay out like everybody else,
it's really hard to guess. I mean, I don't know where Tesla can look at or Elon Musk can look at
and say, Hey, you know, it's the end of this year. I mean, I don't know what you can look at.
Even the data that you know, I mean, if you look at the data, even kind of trying to extrapolate
the end state without knowing what exactly is going to go, especially for like a machine
learning approach. I mean, it's just kind of very hard to predict. But I do think the following
does happen. I think a lot of people, you know, what they do is that there's something that I
called a couple times time dilation in technology prediction happens. Let me try to describe a
little bit. There's a lot of things that are so far ahead, people think they're close. And there's
a lot of things that are actually close, people think it's far ahead. People try to kind of look
at a whole landscape of technology development. Admittedly, it's chaos. Anything can happen in
any order at any time. And there's a whole bunch of things in there. People take it, clamp it,
and put it into the next three years. And so then what happens is that there's some things that
maybe can happen by the end of the year or next year and so on. And they push that into like
a few years ahead, because it's just hard to explain. And there are things that are like,
we're looking at 20 years more, maybe, hopefully in my lifetime type of things. Because we don't
know. I mean, we don't know how hard it is even. That's a problem. We don't know if some of these
problems are actually AI complete. We have no idea what's going on. And we take all of that,
and then we clump it, and then we say three years from now. And then some of us are more
optimistic. So they're shooting at the end of the year. And some of us are more realistic,
they say like five years. But we all, I think, it's just hard to know. And I think
trying to predict products ahead two, three years, it's hard to know in the following sense.
Like we typically say, okay, this is a technology company. But sometimes,
sometimes really, you're trying to build something where the technology does that.
Like there's a technology gap. And Tesla had that with electric vehicles. When they first started,
they would look at a chart, much like a Moore's law type of chart, and they would just kind of
extrapolate that out. And they'd say, we want to be here. What's the technology to get that?
We don't know. It goes like this, so it's probably just going to keep going. With AI
that goes into the cars, we don't even have that. I mean, what can you quantify?
Like what kind of chart are you looking at? But so I think when there's that technology
gap, it's just kind of really hard to predict. So now I realize I talked like five minutes
and avoid your question. I didn't tell you anything about that. It was very skillfully done.
That was very well done. And I don't think you, I think you've actually argued that it's not a
use, even any answer you provide now is not that useful to me.
It's going to be very hard. There's one thing that I really believe in. And this is not my idea.
And it's been discussed several times, but this kind of like something like a startup
or a kind of an innovative company, including definitely Waymo Tesla, maybe even some of the
other big companies that are kind of trying things. This kind of like iterated learning
is very important. The fact that we're over there and we're trying things and so on, I think that's
important. We try to understand. And I think that the code and code Silicon Valley has done that
with business models pretty well. And now I think we're trying to get to do it where there's a
literal technology gap. I mean, before, like, you know, you're trying to build, I'm not trying to,
you know, I think these companies are building great technology to, for example, enable internet
search to do it so quickly. And that kind of didn't, wasn't there so much. But at least,
like it was a kind of a technology that you could predict to some degree and so on. And now
we're just kind of trying to build, you know, things that it's kind of hard to quantify. What
kind of a metric are we looking at? So, psychologically, as a sort of as a leader of
graduate students and at Optimus Ride, a bunch of brilliant engineers, just curiosity,
psychologically, do you think it's good to think that, you know, whatever technology gap
we're talking about can be closed by the end of the year? Or do you, you know, because we don't know.
So the way, do you want to say that everything is going to improve exponentially to yourself and
to others around you as a leader? Or do you want to be more sort of maybe not cynical, but I don't
want to use realistic because it's hard to predict. But yeah, maybe more cynical pessimistic about the
ability to close that gap. Yeah, I think that, you know, going back, I think that iterated learning
is like key, that, you know, you're out there, you're running experiments to learn. And that
doesn't mean sort of like, you know, like, like you're Optimus Ride, you're kind of doing something,
but like in an environment. But like what Tesla is doing, I think is also kind of like this kind
of notion. And, you know, people can go around and say like, you know, this year, next year,
the other year, and so on. But I think that the nice thing about it is that they're out there,
they're pushing this technology in. I think what they should do more of, I think that kind of
informed people about what kind of technology that they're providing, you know, the good and the bad.
And then, you know, not just sort of, you know, it works very well. But I think,
you know, I'm not saying they're not doing bad and informing, I think they're kind of trying,
they, you know, they put up certain things, or at the very least, YouTube videos comes out on
how the summon function works every now and then. And, you know, people get informed. And so that
kind of cycle continues. But, you know, I admire it. I think they're kind of go out there and they
do great things. They do their own kind of experiment. I think we do our own. And I think
we're closing some similar technology gaps, but some also some are orthogonal as well.
You know, I think like we talked about, you know, people being remote, like it's something
or in the kind of environments that we're in, or think about a Tesla car, maybe, maybe you can
enable it one day, like there's, you know, low traffic, like you're kind of the stop and go
motion, you just hit the button, and you can release, or maybe there's another, you know,
lane that you can pass into, you go in that, I think they can enable these kinds of products,
I believe it. And so I think that that part, that is really important, and that is really key.
And beyond that, I think, you know, when is it exactly going to happen? And so on. I mean,
it's like I said, it's very hard to predict. And I would imagine that it would be good to do
some sort of like a one or two year plan, when it's a little bit more predictable,
that, you know, the technology gaps you close and the kind of sort of product that would ensue.
So I know that from Optimus Ride, or you know, other companies that I get involved in, I mean,
at some point, you find yourself in a situation where you're trying to build a product,
and people are investing in that, you know, building effort. And those investors that they
do want to know, as they compare the investments they want to make, they do want to know what
happens in the next one or two years. And I think that's good to communicate that. But I think beyond
that, it becomes a vision that we want to get to someday. And saying five years, 10 years,
I don't think it means anything. But iterated learning is key, though, to do and learn.
Oh, I think that is key. You know, I've got to sort of throw back right at you criticism
in terms of, you know, like Tesla or somebody communicating, you know, how someone works and
so on. I got the chance to visit Optimus Ride, and you guys are doing some awesome stuff.
And yet the internet doesn't know about it. So you should also communicate more showing off,
you know, showing off some of the awesome stuff, the stuff that works and stuff that doesn't work.
I mean, it's just the stuff I saw with the tracking of different objects and pedestrians. So I'm
incredible stuff going on there. Just maybe it's just the neuro to me. But I think the world would
love to see that kind of stuff. Yeah, that's that's well taken. I think, you know, I should
say that it's not like, you know, we weren't able to, I think we made a decision at some point.
That decision did involve me quite a bit on kind of sort of doing this in kind of coding code
stealth mode for a bit. But I think that, you know, we'll open it up quite a lot more. And I
think that we are also at Optimus Ride kind of hitting a new era. You know, we're big now,
we're doing a lot of interesting things. And, and I think, you know, some of the deployments
that we kind of announced were some of the first bits, bits of information that we kind of put
out into the world, we'll also put out our technology. A lot of the things that we've been
developing is really amazing. And then, you know, we're going to, we're going to start putting that
out. We're especially interested in sort of like being able to work with the best people. And I
think, and I think it's good to not just kind of show them when they come to our office for an
interview, but just put it out there in terms of like, you know, get people excited about what we're
doing. So on the autonomous vehicle space, let me ask one last question. So Elon Musk famously
said that lighter is a crutch. So I've talked to a bunch of people about it, gotta ask you.
You use that crutch quite a bit in the DARPA days. So, you know, and his idea in general,
sort of, you know, more provocative and fun, I think, than a technical discussion. But
the idea is that camera based, primarily camera based systems is going to be what defines the
future of autonomous vehicles. So what do you think of this idea? Ladders are crutch versus
primarily camera based systems? First things first, I think, you know, I'm a big believer in
just camera based autonomous vehicle systems. Like, I think that, you know, you can put in a lot of
autonomy, and then you can do great things. And it's very possible that at the time scales,
like I said, we can't predict 20 years from now, like you may be able to do things that we're doing
today only with LIDAR, and then you may be able to do them just with cameras. And I think that,
you know, you can just, I think that I will put my name on it too, like, you know, that will be a
time when you can only use cameras and you'll be fine. At that time, though, it's very possible that,
you know, you find the LIDAR system as another robustifier, or it's so affordable that it's
stupid not to, you know, just kind of put it there. And I think we may be looking at
a future like that. Do you think we're over relying on LIDAR right now? Because we understand
the better, it's more reliable in many ways, in terms from a safety perspective.
It's easier to build with. That's the other thing. I think, to be very frank with you,
I mean, you know, we've seen a lot of sort of autonomous vehicles, companies come and go,
and the approach has been, you know, you slap a LIDAR on a car, and it's kind of easy to build with
when you have a LIDAR, you know, you just kind of code it up, and you hit the button, and you do a
demo. So I think there's, admittedly, there's a lot of people that you focus on the LIDAR,
because it's easier to build with. That doesn't mean that, you know, without the camera, just
cameras, you can, you cannot do what they're doing, but it's just kind of a lot harder.
And so you need to have certain kind of expertise to exploit that. What we believe in, and you know,
you've maybe seen some of it, is that we believe in computer vision. We certainly work on computer
vision and Optimus Ride by a lot, like, and we've been doing that from day one. And we also believe
in sensor fusion. So, you know, we do, we have a relatively minimal use of LIDARs, but we do use
them. And I think, you know, in the future, I really believe that the following sequence of events
may happen. First things first, number one, there may be a future in which, you know, there's like
cars with LIDARs and everything and the cameras. But, you know, in this 50-year-ahead future,
they can just drive with cameras as well, especially in some isolated environments and
cameras, they go and they do the thing. In the same future, it's very possible that, you know,
the LIDARs are so cheap, and frankly, make the software maybe a little less compute-intensive
at the very least, or maybe less complicated so that they can be certified or
or insured of their safety and things like that, that it's kind of stupid not to put the LIDAR.
Like, imagine this, you either put, pay money for the LIDAR or you pay money for the compute.
And if you don't put the LIDAR, it's a more expensive system because you have to put in a lot
of compute. Like, this is another possibility. I do think that a lot of the sort of initial
deployments of self-driving vehicles, I think they will involve LIDARs. And especially,
either low-range or short, either short-range or low-resolution LIDARs are actually not that
hard to build in solid state. They're still scanning, but like MAMS type of scanning LIDARs
and things like that, they're like, they're actually not that hard. I think they will maybe
kind of playing with the spectrum and the phase arrays that are a little bit harder, but I think,
like, you know, putting a MAMS mirror in there that kind of scans the environment.
It's not hard. The only thing is that, you know, just like with a lot of the things that we do
nowadays in developing technology, you hit fundamental limits of the universe. The speed
of light becomes a problem in when you're trying to scan the environment so you don't get either
good resolution or you don't get range. But, you know, it's still, it's something that you can
put in there affordably. So let me jump back to drones. You have a role in the Lockheed Martin
Alpha Pilot Innovation Challenge, where teams compete in drone racing. It's super cool, super
intense, interesting application of AI. So can you tell me about the very basics of the challenge
and where you fit in, what your thoughts are on this problem, and its set of echoes of the early
DARPA challenge through the desert that we're seeing now, now with drone racing?
Yeah. I mean, one interesting thing about it is that, you know, people, the drone racing
exists as an eSport. And so it's much like you're playing a game, but there's a real drone going
in an environment. A human being is controlling it with goggles on. So there's no, it is a robot,
but there's no AI. There's no AI. Yeah. Human being is controlling it. And so that's already there.
And I've been interested in this problem for quite a while, actually,
from a roboticist's point of view. And that's what's happening in Alpha Pilot.
Which problem? Of aggressive flight?
Of aggressive flight. Fully autonomous aggressive flight. The problem that I'm interested in,
you asked about Alpha Pilot, and I'll get there in a second, but the problem that I'm interested in,
I'd love to build autonomous vehicles like drones that can go far faster than any human
possibly can. I think we should recognize that we as humans have limitations in how fast we can
process information. And those are some biological limitations. Like we think about this AI this
way too. I mean, this has been discussed a lot, and this is not sort of my idea per se, but a lot
of people kind of think about human level AI. And they think that AI is not human level, one day
it'll be human level, and humans and AI's, they kind of interact. Versus I think that the situation
really is that humans are at a certain place, and AI keeps improving, and at some point just
crosses off, and then you know, it gets smarter and smarter and smarter. And so drone racing,
the same issue. Humans play this game. And you know, you have to like react the milliseconds,
and there's really, you know, you see something with your eyes, and then that information just
flows through your brain into your hands so that you can command it. And there's some also delays
on you know, getting information back and forth. But suppose those delays that don't exist, you
just, just a delay between your eye and your fingers is a delay that a robot doesn't have to have.
So we end up building in my research group, like systems that, you know, see things at a kilohertz,
like a human eye would barely hit 100 hertz. So imagine things that see stuff in slow motion,
like 10x slow motion. It will be very useful, like we talked a lot about autonomous cars, so
you know, we don't get to see it, but 100 lives are lost every day, just in the United States on
traffic accidents. And many of them are like known cases, you know, like the, you're coming through
like, like a ramp, going into a highway, you hit somebody and you're off, or you know, like you
kind of get confused, you try to like swerve into the next lane, you go off the road and you crash
whatever. And I think if you had enough compute in a car, and a very fast camera, right at the
time of an accident, you could use all compute you have, like you could shut down the infotainment
system, and use that kind of computing resources instead of rendering, you use it for the kind
of artificial intelligence that goes in the autonomy. And you can, you can either take control
of the car and bring it to a full stop. But even, even if you can't do that, you can deliver what
the human is trying to do. Human is trying to change the lane, but goes off the road, not being
able to do that with motor skills and the eyes. And you know, you can get in there. And I was,
there's so many other things that you can enable with what I would call high throughput computing.
You know, data is coming in extremely fast. And in real time, you have to process it. And the
current CPUs, however fast you clock it, are typically not enough. You need to build those
computers from the ground up so that they can ingest all that data. That I'm really interested in.
Just on that point, just really quick, is the currently what's the bottom like you mentioned,
the delays in humans? Is it the hardware? Do you work a lot with NVIDIA hardware? Is it the hardware
or is it the software? I think it's both. I think it's both. In fact, they need to be co-developed,
I think, in the future. I mean, that's a little bit what NVIDIA does. Sort of like they almost
like build the hardware and then they build the neural networks and then they build the hardware
back and the neural networks back and it goes back and forth. But it's that co-design. And I think
that, you know, like, we try to weigh back, we try to build a fast drone that could use a camera
image to like track what's moving in order to find where it is in the world. This typical sort of,
you know, visual inertial state estimation problems that we would solve. And, you know,
we just kind of realized that we're at the limit sometimes of, you know, doing simple tasks. We're
at the limit of the camera frame rate. Because, you know, if you really want to track things,
you want the camera image to be 90% kind of like or some somewhat the same from one frame to the
next. And why are we at the limit of the camera frame rate? It's because camera captures data,
it puts into some serial connection. It could be USB or like there's something called camera
serial interface that we use a lot. It puts into some serial connection and copper wires can only
transmit so much data. And you hit the channel limit on copper wires. And, you know, you hit
yet another kind of universal limit that you can transfer the data. So you have to be much more
intelligent on how you capture those pixels. You can take compute and put it right next to the pixels.
How hard is it to do? How hard is it to get past the bottleneck of the copper wire?
Yeah, you need to do a lot of parallel processing, as you can imagine. The same thing happens in
the GPUs. You know, like the data is transferred in parallel somehow. It gets into some parallel
processing. I think that, you know, like now we're really kind of diverted off into so many
different dimensions. But great. So it's aggressive flight. How do we make drones see many more frames
a second in order to enable aggressive flight? That's a super interesting problem.
That's an interesting problem. But think about it. You have CPUs. You clock them at several
gigahertz. We don't clock them faster largely because we run into some heating issues and
things like that. But another thing is that three gigahertz clock, light travels kind of like on
the order of a few inches or an inch. That's the size of a chip. And so you pass a clock cycle.
And as the clock signal is going around in the chip, you pass another one. And so trying to
coordinate that, the design of the complexity of the chip becomes so hard. I mean, we have hit
the fundamental limits of the universe in so many things that we're designing. I don't know
if people realize that. It's great. But like we can't make transistors smaller because like quantum
effects, the electrons start to tunnel around. We can't clock it faster. One of the reasons why
is because like information doesn't travel faster in the universe. And we're limited by that. Same
thing with the laser scanner. But so then it becomes clear that the way you organize the chip
into a CPU or even a GPU, you now need to look at how to redesign that if you're going to stick
with silicon. You could go do other things too. I mean, there's that too. But you really almost
need to take those transistors, put them in a different way so that the information travels on
those transistors in a different way, in a much more way that is specific to the high speed cameras
coming in. And so that's one of the things that we talk about quite a bit. So drone racing kind of
really makes that embodies that and that's why it's exciting. It's exciting for people,
you know, students like it, it embodies all those problems. But going back, we're building
code and code and other engine. And that engine, I hope one day will be just like how impactful
seat belts were in driving. I hope so. Or it could enable, you know, next generation autonomous
air taxis and things like that. I mean, it sounds crazy, but one day we may need to perchland
these things. If you really want to go from Boston to New York in one and a half hours,
you may want to fix big aircraft. Most of these companies that are kind of doing
code flying cars, they're focusing on that. But then how do you land it on top of a building?
You may need to pull off like kind of fast maneuvers for a robot like perchland, it's going
to go into a building. If you want to do that, like you need these kinds of systems. And so
drone racing, you know, it's being able to go way faster than any human can comprehend.
Take an aircraft, forget the quadcopter, you take a fixed wing. While you're at it, you might as well
put some like rocket engines in the back and you just light it. You go through the gate,
and a human looks at it and just said, what just happened? And they would say it's impossible for
me to do that. And that's closing the same technology gap that would, you know, one day
steer cars out of accidents. So, but then let's get back to the practical, which is
sort of just getting the thing to work in a race environment, which is kind of what the,
it's another kind of exciting thing, which the DARPA challenge to the desert did. You know,
theoretically, we had autonomous vehicles, but making them successfully finish a race,
first of all, which nobody finished the first year. And then the second year, just to get,
you know, to finish and go at a reasonable time is really difficult engineering, practically
speaking challenge. So that, let me ask about the, the, the Alpha pilot challenge is a, I guess,
a big prize potentially associated with it. But let me ask reminiscent of the DARPA days,
predictions, you think anybody will finish?
Well, not, not soon. I think that depends on how you set up the race course. And so if the race
course is a slow on course, I think people will kind of do it. But can you set up some course,
like literally some core, you get to design it is the algorithm developer. Can you set up some
course so that you can beat the best human? When is that going to happen? Like, that's not very
easy. Even just setting up some course, if you let the human that you're competing with set up the
course, it becomes a lot easier, a lot harder. So how many in the space of all possible courses
are would humans win and would machines win? Great question. Let's get to that. I want to
answer your other question, which is like the DARPA challenge days, right? What was really hard? I
think, I think we understand, we understood what we wanted to build, but still building things,
that experimentation, that iterated learning that takes up a lot of time actually. And, and so in
my group, for example, in order for us to be able to develop fast, we build like VR environments,
we'll take an aircraft, we'll put it in a motion capture room, big, huge motion capture room,
and we'll fly it in real time, we'll render other images and beam it back to the drone. That sounds
kind of notionally simple, but it's actually hard because now you're trying to fit all that data
through the air into the drone. And so you need to do a few crazy things to make that happen. But
once you do that, then at least you can try things. If you crash into something, you didn't
actually crash. So it's like the whole drone is in VR, we can do augmented reality and so on.
And so I think at some point, testing becomes very important. One of the nice things about Alpha
Pilot is that they build the drone, and they build a lot of drones, and it's okay to crash. In fact,
I think maybe, you know, the viewers may kind of like to see things that crash.
That potentially could be the most exciting part. It could be the exciting part. And I think, you
know, as an engineer, it's a very different situation to be in. Like in academia, a lot of my
colleagues who are actually in this race, and they're really great researchers, but I've seen
them trying to do similar things whereby they built this one drone and, you know, somebody with
like a face mask and a glows are going, you know, right behind the drone, trying to hold it if it
falls down. Imagine you don't have to do that. I think that's one of the nice things about Alpha
Pilot Challenge, where, you know, we have these drones, and we're going to design the courses in
a way that will keep pushing people up until the crashes start to happen.
And, you know, we'll hopefully sort of, I don't think you want to tell people crashing is okay.
Like, we want to be careful here, but because, you know, we don't want people to crash a lot.
But certainly, you know, we want them to push it so that, you know, everybody crashes once or twice.
And, you know, they're really pushing it to their limits.
That's where iterated learning comes in. Every crash is a lesson.
It's a lesson, exactly.
So in terms of the space of possible courses, how do you think about it?
How do you think about it in the war of human versus machines?
Where do machines win?
We look at that quite a bit. I mean, I think that, you know, you will see quickly that,
like, you can design a course. And, you know, in certain courses, like in the middle somewhere,
if you kind of run through the course once, you know, the machine gets beaten pretty much
consistently by slightly. But if you go through the course like 10 times, humans get beaten
very slightly but consistently. So humans at some point, you know, you get confused,
you get tired and things like that versus this machine is just executing the same line of code
tirelessly, just going back to the beginning and doing the same thing exactly.
I think that kind of thing happens. And as I realize sort of as humans,
there's the classical things, you know, that everybody has realized. Like, if you put in
some sort of strategic thinking that's a little bit harder for machines that I think sort of
comprehend, precision is easy to do. So that's what they excel in. And also sort of repeatability
is easy to do. That's what they excel in. You can build machines that excel in strategy as well
and beat humans that way too. But that's a lot harder to build. I have a million more questions,
but in the interest of time, last question. Yeah. What is the most beautiful idea you've come across
in robotics? Whether it's simple equation, experiment, a demo, simulation, pieces of software,
what just gives you pause? That's an interesting question. I have done a lot of work myself in
decision making. So I've been interested in that area. So you know, in robotics, you have somehow
the field has split into like, you know, there's people who would work on like perception, how
robots perceive the environment, then how do you actually make like decisions? And there's people
also like how to interact, people interact with robots, there's a whole bunch of different fields.
And, and you know, I have admittedly worked a lot on the more control and decision making
than the others. And I think that, you know, the one equation that has always kind of baffled
me is Bellman's equation. And so it's this person who have realized like way back, you know, more
than half a century ago on like, how do you actually sit down? And if you have several variables
that you're kind of jointly trying to determine, how do you determine that? And there's one
beautiful equation that, you know, like today, people do reinforcement, we still use it. And,
and it's, it's baffling to me because it both kind of tells you the simplicity, because it's a single
equation that anyone can write down, you can teach it in the first course on decision making. At
the same time, it tells you how computationally how hard the problem is. I feel like my, like a lot
of the things that I've done at MIT for research has been kind of just this fight against the
computational efficiency things, like how can we get it faster to the point where we now got to,
like, let's just redesign this chip, like maybe that's the way. But I think it talks about how
computationally hard certain problems can be by nowadays what people call curse of dimensionality.
And so as the number of variables kind of grow, the number of decisions you can make grows
rapidly. Like if you have, you know, 100 variables, each one of them take 10 values,
all possible assignments is more than the number of atoms in the universe. It's just crazy.
And, and that kind of thinking is just embodied in that one equation that I really like.
And the beautiful balance between it being theoretically optimal, and somehow, practically
speaking, given the curse of dimensionality, nevertheless, in practice, works among, you
know, despite all those challenges, which is quite incredible, which is quite incredible. So,
you know, I would say that it's kind of like quite baffling, actually, you know,
in a lot of fields that we we think about how little we know, you know, like, and so I think
here too, you know, we know that in the worst case, things are pretty hard. But, you know,
in practice, generally things work. So it's just kind of it's kind of baffling and decision making
how little we know, just like how little we know about the beginning of time, how little we know
about, you know, our own future. Like if you actually go into like from Bellman's equation all
the way down, I mean, there's also how little we know about like mathematics, I mean, we don't
even know if the axioms are like consistent, it's just crazy. Yeah, I think a good lesson,
a lesson there just like as you said, we tend to focus on the worst case or the boundaries of
everything we're studying. And then the average case seems to somehow work out, if you think about
life in general, we mess it up a bunch, you know, we freak out about a bunch of the traumatic stuff,
but in the end, it seems to work out okay. Yeah, it seems like a good metaphor.
Sir Tash, thank you so much for being a friend, a colleague, a mentor. I really appreciate it.
It's an honor to talk to you. Likewise. Thank you, Lex. Thanks for listening to this conversation
with Sir Tash Karaman. And thank you to our presenting sponsor, Cash App. Please consider
supporting the podcast by downloading Cash App and using code Lex Podcast. If you enjoy this
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from Hal 9000 from the movie 2001, A Space Odyssey. I'm putting myself to the fullest
possible use, which is all I think that any conscious entity can ever hope to do.
Thank you for listening and hope to see you next time.