The following is a conversation with Greg Brockman.
He's the co-founder and CTO of OpenAI,
a world-class research organization
developing ideas in AI with a goal of eventually
creating a safe and friendly artificial general
intelligence, one that benefits and empowers humanity.
OpenAI is not only a source of publications, algorithms,
tools, and data sets.
Their mission is a catalyst for an important public discourse
about our future with both narrow and general intelligence
systems.
This conversation is part of the Artificial Intelligence
Podcast at MIT and beyond.
If you enjoy it, subscribe on YouTube, iTunes,
or simply connect with me on Twitter
at Lex Friedman, spelled F-R-I-D.
And now here's my conversation with Greg Brockman.
So in high school and right after you wrote a draft
of a chemistry textbook, I saw that.
That covers everything from basic structure of the atom
to quantum mechanics.
So it's clear you have an intuition and a passion
for both the physical world with chemistry and non-robotics
to the digital world with AI, deep learning,
reinforcement learning, and so on.
Do you see the physical world and the digital world as different?
And what do you think is the gap?
A lot of it actually boils down to iteration speed,
that I think that a lot of what really motivates me
is building things.
Is the, think about mathematics, for example,
where you think really hard about a problem,
you understand it.
You write down in this very obscure form
that we call a proof.
But then this is in humanity's library, right?
It's there forever.
This is some truth that we've discovered.
And maybe only five people in your field will ever read it,
but somehow you've kind of moved humanity forward.
And so I actually used to really think
that I was going to be a mathematician.
And then I actually started writing this chemistry textbook.
One of my friends told me, you'll never publish it
because you don't have a PhD.
So instead I decided to build a website
and try to promote my ideas that way.
And then I discovered programming.
And I, you know, that in programming
you think hard about a problem,
you understand it, you write down
in a very obscure form that we call a program.
But then once again, it's in humanity's library, right?
And anyone can get the benefit from it.
And the scalability is massive.
And so I think that the thing that really appeals to me
about the digital world
is that you can have this insane leverage, right?
A single individual with an idea
is able to affect the entire planet.
And that's something I think is really hard to do
if you're moving around physical atoms.
But you said mathematics.
So if you look at the wet thing over here, our mind,
do you ultimately see it as just math,
as just information processing?
Or is there some other magic
as you've seen if you've seen
through biology and chemistry and so on?
I think it's really interesting to think about humans
as just information processing systems.
And that it seems like it's actually a pretty good way
of describing a lot of kind of how the world works
or a lot of what we're capable of to think that...
Again, if you just look at technological innovations
over time, that in some ways,
the most transformative innovation that we've had
has been the computer, right?
In some ways, the internet,
what has the internet done, right?
The internet is not about these physical cables.
It's about the fact that I am suddenly able
to instantly communicate with any other human on the planet.
I'm able to retrieve any piece of knowledge
that in some ways the human race has ever had.
And that those are these insane transformations.
Do you see our society as a whole, the collective,
as another extension of the intelligence of the human being?
So if you look at the human being
as an information processing system,
you mentioned the internet, the networking.
Do you see us all together as a civilization
as a kind of intelligent system?
Yeah, I think this is actually a really interesting perspective
to take and to think about
that you sort of have this collective intelligence
of all of society.
The economy itself is this super human machine
that is optimizing something, right?
And in some ways, a company has a will of its own, right?
That you have all these individuals
who are all pursuing their own individual goals
and thinking really hard
and thinking about the right things to do.
But somehow the company does something
that is this emergent thing
and that it's a really useful abstraction.
And so I think that in some ways,
we think of ourselves as the most intelligent things
on the planet and the most powerful things on the planet,
but there are things that are bigger than us.
There are these systems that we all contribute to.
And so I think actually, it's interesting to think about
if you've read Isaac Isamov's Foundation, right,
that there's this concept of psycho history in there,
which is effectively this,
that if you have trillions or quadrillions of beings,
then maybe you could actually predict what that being,
that huge macro being will do
and almost independent of what the individuals want.
And I actually have a second angle on this
that I think is interesting,
which is thinking about technological determinism.
One thing that I actually think a lot about
with OpenAI is that we're kind of coming onto
this insanely transformational technology
of general intelligence that will happen at some point.
And there's a question of how can you take actions
that will actually steer it to go better rather than worse?
And that I think one question you need to ask
is as a scientist, as an inventor, as a creator,
what impact can you have in general?
You look at things like the telephone
invented by two people on the same day.
Like what does that mean?
Like what does that mean about the shape of innovation?
And I think that what's going on is everyone's building
on the shoulders of the same giants.
And so you can kind of, you can't really hope
to create something no one else ever would.
If Einstein wasn't born,
someone else would have come up with relativity.
You know, he changed the timeline a bit, right?
That maybe it would have taken another 20 years,
but it wouldn't be that fundamentally humanity
would never discover these fundamental truths.
There's some kind of invisible momentum
that some people like Einstein or OpenAI is plugging into
that anybody else can also plug into, and ultimately
that wave takes us into a certain direction.
That's what you mean by digitalism.
That's right, that's right.
And you know, this kind of seems to play out
in a bunch of different ways,
that there's some exponential that is being written
and that the exponential itself, which one it is,
changes, think about Moore's law,
an entire industry set, it's clocked to it for 50 years.
Like how can that be, right?
How is that possible?
And yet somehow it happened.
And so I think you can't hope to ever invent something
that no one else will.
Maybe you can change the timeline a little bit,
but if you really want to make a difference,
I think that the thing that you really have to do,
the only real degree of freedom you have
is to set the initial conditions
under which a technology is born.
And so you think about the internet, right?
That there are lots of other competitors
trying to build similar things.
And the internet one, and that the initial conditions
where it was created by this group
that really valued people being able to be,
you know, anyone being able to plug in
this very academic mindset of being open and connected.
And I think that the internet for the next 40 years
really played out that way.
You know, maybe today,
things are starting to shift in a different direction,
but I think that those initial conditions
were really important to determine
the next 40 years worth of progress.
That's really beautifully put.
So another example of that I think about,
you know, I recently looked at it.
I looked at Wikipedia, the formation of Wikipedia.
And I wonder what the internet would be like
if Wikipedia had ads.
You know, there's an interesting argument
that why they chose not to make it,
put advertisement on Wikipedia.
I think Wikipedia is one of the greatest resources
we have on the internet.
It's extremely surprising how well it works
and how well it was able to aggregate
all this kind of good information.
And essentially the creator of Wikipedia,
I don't know, there's probably some debates there,
but set the initial conditions
and now it carried itself forward.
That's really interesting.
So the way you're thinking about AGI
or artificial intelligence is you're focused on
setting the initial conditions for the progress.
That's right.
That's powerful.
Okay, so look into the future.
If you create an AGI system,
like one that can ace the Turing test, natural language,
what do you think would be the interactions
you would have with it?
What do you think are the questions you would ask?
Like what would be the first question you would ask?
It, her, him.
That's right.
I think that at that point,
if you've really built a powerful system
that is capable of shaping the future of humanity,
the first question that you really should ask
is how do we make sure that this plays out well?
And so that's actually the first question
that I would ask a powerful AGI system is.
So you wouldn't ask your colleague,
you wouldn't ask like Ilya, you would ask the AGI system.
Oh, we've already had the conversation with Ilya, right?
And everyone here.
And so you want as many perspectives
and a piece of wisdom as you can
for answering this question.
So I don't think you necessarily defer to
whatever your powerful system tells you,
but you use it as one input
to try to figure out what to do.
But I guess fundamentally what it really comes down to
is if you built something really powerful
and you think about, think about, for example,
the creation of, of shortly after
the creation of nuclear weapons, right?
The most important question in the world
was what's the world we're going to be like?
How do we set ourselves up in a place
where we're going to be able to survive as a species?
With AGI, I think the question is slightly different, right?
That there is a question of how do we make sure
that we don't get the negative effects?
But there's also the positive side, right?
You imagine that, you know, like,
like what won't AGI be like?
Like what will it be capable of?
And I think that one of the core reasons
that an AGI can be powerful and transformative
is actually due to technological development, right?
If you have something that's capable as capable as a human
and that it's much more scalable,
that you absolutely want that thing
to go read the whole scientific literature
and think about how to create cures for all the diseases, right?
You want it to think about how to go
and build technologies to help us create material abundance
and to figure out societal problems
that we have trouble with.
Like how are we supposed to clean up the environment?
And, you know, maybe you want this
to go and invent a bunch of little robots that will go out
and be biodegradable and turn ocean debris
into harmless molecules.
And I think that that positive side
is something that I think people miss sometimes
when thinking about what an AGI will be like.
And so I think that if you have a system that's capable
of all of that, you absolutely want its advice
about how do I make sure that we're using your capabilities
in a positive way for humanity.
So what do you think about that psychology
that looks at all the different possible trajectories
of an AGI system, many of which,
perhaps the majority of which are positive
and nevertheless focuses on the negative trajectories?
I mean, you get to interact with folks,
you get to think about this, maybe within yourself as well.
You look at Sam Harris and so on.
It seems to be, sorry to put it this way,
but almost more fun to think about the negative possibilities.
Whatever that's deep in our psychology,
what do you think about that?
And how do we deal with it?
Because we want AI to help us.
So I think there's kind of two problems
entailed in that question.
The first is more of the question of,
how can you even picture what a world
with a new technology will be like?
Now imagine we're in 1950
and I'm trying to describe Uber to someone.
Apps and the internet.
Yeah, I mean, that's going to be extremely complicated,
but it's imaginable.
It's imaginable, right?
But now imagine being in 1950 and predicting Uber, right?
And you need to describe the internet,
you need to describe GPS,
you need to describe the fact
that everyone's going to have this phone in their pocket.
And so I think that just the first truth
is that it is hard to picture
how a transformative technology
will play out in the world.
We've seen that before with technologies
that are far less transformative than AGI will be.
And so I think that one piece is that it's just
even hard to imagine and to really put yourself
in a world where you can predict
what that positive vision would be like.
And I think the second thing is that it is,
I think it is always easier
to support the negative side than the positive side.
It's always easier to destroy than create.
And less in a physical sense
or more just in an intellectual sense, right?
Because I think that with creating something,
you need to just get a bunch of things right
and to destroy, you just need to get one thing wrong.
And so I think that what that means
is that I think a lot of people is thinking dead ends
as soon as they see the negative story.
But that being said, I actually have some hope, right?
I think that the positive vision is something
that I think can be, is something that we can talk about.
And I think that just simply saying this fact of,
yeah, like there's positive, there's negatives.
Everyone likes to dwell on the negative.
People actually respond well to that message and say,
huh, you're right, there's a part of this
that we're not talking about, not thinking about.
And that's actually something that's,
I think really been a key part
of how we think about AGI at OpenAI, right?
You can kind of look at it as like, okay,
like OpenAI talks about the fact that there are risks
and yet they're trying to build this system.
Like, how do you square those two facts?
So do you share the intuition that some people have?
I mean, from Sam Harris to even Elon Musk himself,
that it's tricky as you develop AGI
to keep it from slipping into the existential threats,
into the negative.
What's your intuition about,
how hard is it to keep AI development
on the positive track?
What's your intuition there?
To answer that question,
you can really look at how we structure OpenAI.
So we really have three main arms.
So we have capabilities,
which is actually doing the technical work
and pushing forward what these systems can do.
There's safety, which is working on technical mechanisms
to ensure that the systems we build
are aligned with human values.
And then there's policy,
which is making sure that we have governance mechanisms,
answering that question of, well, whose values?
And so I think that the technical safety one
is the one that people kind of talk about the most, right?
You talk about, like think about,
you know, all of the dystopic AI movies,
a lot of that is about not having good technical safety
in place, and what we've been finding is that, you know,
I think that actually a lot of people
look at the technical safety problem
and think it's just intractable, right?
This question of what do humans want?
How am I supposed to write that down?
Can I even write down what I want?
No way.
And then they stop there.
But the thing is we've already built systems
that are able to learn things that humans can't specify.
You know, even the rules for how to recognize
if there's a cat or a dog in an image.
Turns out it's intractable to write that down.
And yet we're able to learn it.
And that what we're seeing with systems we built at OpenAI,
and they're still in early proof of concept stage,
is that you are able to learn human preferences.
You're able to learn what humans want from data.
And so that's kind of the core focus
for our technical safety team.
And I think that they're actually,
we've had some pretty encouraging updates
in terms of what we've been able to make work.
So you have an intuition and a hope that from data,
you know, looking at the value alignment problem,
from data we can build systems that align
with the collective better angels of our nature.
So align with the ethics and the morals of human beings.
To even say this in a different way,
I mean, think about how do we align humans, right?
Think about like a human baby can grow up
to be an evil person or a great person.
And a lot of that is from learning from data, right?
That you have some feedback as a child is growing up,
they get to see positive examples.
And so I think that just like the only example
we have of a general intelligence
that is able to learn from data
to align with human values and to learn values,
I think we shouldn't be surprised
that we can do the same sorts of techniques
or whether the same sort of techniques end up being
how we solve value alignment for AGI's.
So let's go even higher.
I don't know if you've read the book, Sapiens,
but there's an idea that, you know,
that as a collective as us human beings,
we kind of develop together an ideas that we hold.
There's no, in that context, objective truth.
We just kind of all agree to certain ideas
and hold them as a collective.
Did you have a sense that there is
in the world of good and evil,
do you have a sense that to the first approximation,
there are some things that are good
and that you could teach systems to behave to be good?
So I think that this actually blends into our third team,
right, which is the policy team.
And this is the one, the aspect that I think people
really talk about way less than they should, right?
Because imagine that we build super powerful systems
that we've managed to figure out all the mechanisms
for these things to do whatever the operator wants.
The most important question becomes,
who's the operator, what do they want,
and how is that going to affect everyone else, right?
And I think that this question of what is good,
what are those values?
I mean, I think you don't even have to go to those,
those very grand existential places to start,
to realize how hard this problem is.
You just look at different countries
and cultures across the world
and that there's a very different conception
of how the world works
and what kinds of ways that society wants to operate.
And so I think that the really core question
is actually very concrete.
And I think it's not a question
that we have ready answers to, right?
It's how do you have a world
where all the different countries that we have,
United States, China, Russia,
and the hundreds of other countries out there
are able to continue to not just operate
in the way that they see fit,
but in the world that emerges in these,
where you have these very powerful systems
operating alongside humans,
ends up being something that empowers humans more,
that makes human existence be a more meaningful thing
and that people are happier and wealthier
and able to live more fulfilling lives.
It's not an obvious thing for how to design that world
once you have that very powerful system.
So if we take a little step back,
and we're having like a fascinating conversation
and opening as in many ways a tech leader in the world,
and yet we're thinking about these big existential questions
which is fascinating, really important.
I think you're a leader in that space
and that's a really important space
of just thinking how AI affects society
in a big picture view.
So Oscar Wilde said, we're all in the gutter,
but some of us are looking at the stars
and I think OpenAI has a charter
that looks to the stars, I would say,
to create intelligence, to create general intelligence,
make it beneficial, safe, and collaborative.
So can you tell me how that came about?
How a mission like that and the path
to creating a mission like that at OpenAI was founded?
Yeah, so I think that in some ways
it really boils down to taking a look at the landscape.
So if you think about the history of AI
that basically for the past 60 or 70 years,
people have thought about this goal
of what could happen if you could automate
human intellectual labor.
Imagine you can build a computer system
that could do that, what becomes possible?
Maybe a lot of sci-fi that tells stories
of various dystopias and increasingly you have movies
like Her that tell you a little bit about
maybe more of a little bit utopic vision.
You think about the impacts that we've seen
from being able to have bicycles for our minds
and computers and that I think that the impact
of computers and the internet has just far outstripped
what anyone really could have predicted.
And so I think that it's very clear
that if you can build an AGI,
it will be the most transformative technology
that humans will ever create.
And so what it boils down to then is a question of,
well, is there a path, is there hope,
is there a way to build such a system?
And I think that for 60 or 70 years
that people got excited and that ended up
not being able to deliver on the hopes
that people had pinned on them.
And I think that then that after two winters
of AI development that people,
I think kind of almost stopped daring to dream, right?
That really talking about AGI or thinking about AGI
became almost this taboo in the community.
But I actually think that people took the wrong lesson
from AI history.
And if you look back, starting in 1959
is when the Perceptron was released.
And this is basically one of the earliest neural networks.
It was released to what was perceived
as this massive overhype.
So in the New York Times in 1959,
you have this article saying that the Perceptron
will one day recognize people,
call out their names,
instantly translate speech between languages.
And people at the time looked at this and said,
this is your system can't do any of that.
And basically spent 10 years trying to discredit
the whole Perceptron direction and succeeded.
And all the funding dried up
and people kind of went in other directions.
And in the 80s, there was a resurgence.
And I'd always heard that the resurgence in the 80s
was due to the invention of back propagation
and these algorithms that got people excited.
But actually the causality was due
to people building larger computers.
That you can find these articles from the 80s
saying that the democratization of computing power
suddenly meant that you could run these larger neural networks
and then people started to do all these amazing things,
the back propagation algorithm was invented.
And the neural nets people were running
were these tiny little like 20 neuron neural nets.
Like what are you supposed to learn with 20 neurons?
And so of course they weren't able to get great results.
And it really wasn't until 2012 that this approach
that's almost the most simple natural approach
that people had come up with in the 50s.
In some ways even in the 40s before there were computers
with the Pits McCullin neuron,
suddenly this became the best way of solving problems, right?
And I think there are three core properties
that deep learning has that I think
are very worth paying attention to.
The first is generality.
We have a very small number of deep learning tools,
SGD, deep neural net, maybe some, you know, RL
and it solves this huge variety of problems.
Speech recognition, machine translation, game playing,
all of these problems, small set of tools.
So there's the generality.
There's a second piece, which is the competence.
You want to solve any of those problems?
Throughout 40 years worth of normal computer vision research
replaced with a deep neural net, it's kind of worked better.
And there's a third piece, which is the scalability, right?
That one thing that has been shown time and time again
is that you, if you have a larger neural network,
throw more compute, more data at it, it will work better.
Those three properties together feel like essential parts
of building a general intelligence.
Now, it doesn't just mean that if we scale up
what we have that we will have an AGI, right?
There are clearly missing pieces.
There are missing ideas.
We need to have answers for reasoning.
But I think that the core here is that for the first time
it feels that we have a paradigm that gives us hope
that general intelligence can be achievable.
And so as soon as you believe that,
everything else becomes, comes into focus, right?
If you imagine that you may be able to,
and that the timeline I think remains uncertain,
but I think that certainly within our lifetimes
and possibly within a much shorter period of time
than people would expect,
if you can really build the most transformative technology
that will ever exist,
you stop thinking about yourself so much, right?
You start thinking about just like,
how do you have a world where this goes well?
And that you need to think about the practicalities
of how do you build an organization
and get together a bunch of people and resources
and to make sure that people feel motivated
and ready to do it?
But I think that then you start thinking about,
well, what if we succeed?
And how do we make sure that when we succeed,
that the world is actually the place
that we want ourselves to exist in?
And almost in the Rawlsian Vale sense of the word.
And so that's kind of the broader landscape.
And Open AI was really formed in 2015
with that high level picture of AGI might be possible
sooner than people think,
and that we need to try to do our best
to make sure it's going to go well.
And then we spent the next couple of years
really trying to figure out what does that mean?
How do we do it?
And I think that typically with a company,
you start out very small,
so you're a co-founder and you build a product,
you get some users, you get a product market fit,
then at some point you raise some money,
you hire people, you scale and then down the road,
then the big companies realize you exist
and try to kill you.
And for Open AI,
it was basically everything in exactly the opposite order.
Ah.
Let me just pause for a second.
He said a lot of things.
And let me just admire the jarring aspect
of what Open AI stands for, which is daring to dream.
I mean, you said it, it's pretty powerful.
It caught me off guard because I think that's very true.
The step of just daring to dream
about the possibilities of creating intelligence
in a positive and a safe way,
but just even creating intelligence
is a much needed, refreshing catalyst for the AI community.
So that's the starting point.
Okay, so then formation of Open AI.
What's the second?
I would just say that when we were starting Open AI,
that kind of the first question that we had is,
is it too late to start a lab with a bunch of the best people?
Right?
Is that even possible?
That was an actual question.
That was the core question of,
we had this dinner in July of 2015,
and that was really what we spent the whole time talking about.
And you think about kind of where AI was,
is that it transitioned from being an academic pursuit
to an industrial pursuit.
And so a lot of the best people
were in these big research labs,
and that we wanted to start our own one
that no matter how much resources we could accumulate
would be pale in comparison to the big tech companies.
And we knew that.
And there's a question of,
are we going to be actually able
to get this thing off the ground?
You need critical mass.
You can't just do you and a co-founder build a product, right?
You really need to have a group of five to 10 people.
And we kind of concluded it wasn't obviously impossible.
So it seemed worth trying.
Well, you're also a dreamer, so who knows, right?
That's right.
Okay, so speaking of that, competing with the big players,
let's talk about some of the tricky things
as you think through this process of growing,
of seeing how you can develop these systems
at scale that competes.
So you recently formed OpenAI LP,
and you cap profit company
that now carries the name OpenAI.
So OpenAI is now this official company.
The original nonprofit company still exists
and carries the OpenAI nonprofit name.
So can you explain what this company is,
what the purpose of its creation is,
and how did you arrive at the decision to create it?
OpenAI, the whole entity and OpenAI LP as a vehicle
is trying to accomplish the mission
of ensuring that artificial general intelligence
benefits everyone.
And the main way that we're trying to do that
is by actually trying to build general intelligence
to ourselves and make sure the benefits
are distributed to the world.
That's the primary way.
We're also fine if someone else does this, right?
It doesn't have to be us.
If someone else is going to build an AGI
and make sure that the benefits don't get locked up
in one company or with one set of people,
like, we're actually fine with that.
And so those ideas are baked into our charter,
which is kind of the foundational document
that describes kind of our values and how we operate.
But it's also really baked into the structure of OpenAI LP.
And so the way that we've set up OpenAI LP
is that in the case where we succeed, right?
If we actually build what we're trying to build,
then investors are able to get a return.
And, but that return is something that is capped.
And so if you think of AGI in terms of the value
that you could really create,
you're talking about the most transformative technology
ever created, it's gonna create,
or does the magnitude more value
than any existing company?
And that all of that value will be owned by the world,
like legally titled to the nonprofit
to fulfill that mission.
And so that's the structure.
So the mission is a powerful one.
And it's one that I think most people would agree with.
It's how we would hope AI progresses.
And so how do you tie yourself to that mission?
How do you make sure you do not deviate from that mission
that other incentives that are profit-driven
wouldn't don't interfere with the mission?
So this was actually a really core question for us
for the past couple of years,
because I'd say that the way that our history went
was that for the first year we were getting off the ground.
We had this high level picture,
but we didn't know exactly how we wanted to accomplish it.
And really two years ago is when we first started realizing
in order to build AGI,
we're just going to need to raise way more money
than we can as a nonprofit.
We're talking many billions of dollars.
And so the first question is how are you supposed to do that
and stay true to this mission?
And we looked at every legal structure out there
and included none of them were quite right
for what we wanted to do.
And I guess it shouldn't be too surprising
if you're gonna do some like crazy unprecedented technology
that you're gonna have to come
with some crazy unprecedented structure to do it in.
And a lot of our conversation was with people at OpenAI,
right, the people who really joined
because they believe so much in this mission
and thinking about how do we actually raise the resources
to do it and also stay true to what we stand for.
And the place you got to start is to really align
on what is it that we stand for, right?
What are those values?
What's really important to us?
And so I'd say that we spent about a year
really compiling the OpenAI charter and that determines,
and if you even look at the first line item in there,
it says that, look, we expect we're gonna have to marshal
huge amounts of resources,
but we're going to make sure that we minimize conflict
of interest with the mission.
And that kind of aligning on all of those pieces
was the most important step towards figuring out
how do we structure a company
that can actually raise the resources
to do what we need to do.
I imagine OpenAI, the decision to create OpenAI LP
was a really difficult one.
And there was a lot of discussions
as you mentioned for a year
and there was different ideas,
perhaps detractors within OpenAI,
sort of different paths that you could have taken.
What were those concerns?
What were the different paths considered?
What was that process of making that decision like?
Yep.
But so if you look actually at the OpenAI charter,
that there's almost two paths embedded within it.
There is, we are primarily trying to build AGI ourselves,
but we're also okay if someone else does it.
And this is a weird thing for a company.
It's really interesting, actually.
Yeah.
There is an element of competition
that you do want to be the one that does it,
but at the same time, you're okay if somebody else doesn't.
We'll talk about that a little bit, that trade-off,
that dance that's really interesting.
And I think this was the core tension
as we were designing OpenAI LP
and really the OpenAI strategy
is how do you make sure that both you have a shot
at being a primary actor,
which really requires building an organization,
raising massive resources,
and really having the will to go
and execute on some really, really hard vision, right?
You need to really sign up for a long period to go
and take on a lot of pain and a lot of risk.
And to do that, normally you just import
the startup mindset, right?
And that you think about, okay,
like how do we out-execute everyone?
You have this very competitive angle.
But you also have the second angle of saying that,
well, the true mission isn't for OpenAI to build AGI.
The true mission is for AGI to go well for humanity.
And so how do you take all of those first actions
and make sure you don't close the door on outcomes
that would actually be positive and fulfill the mission?
And so I think it's a very delicate balance, right?
And I think that going 100% one direction or the other
is clearly not the correct answer.
And so I think that even in terms of just
how we talk about OpenAI and think about it,
there's just like one thing that's always
in the back of my mind is to make sure
that we're not just saying OpenAI's goal
is to build AGI, right?
That it's actually much broader than that, right?
That first of all, it's not just AGI,
it's safe AGI that's very important.
But secondly, our goal isn't to be the ones to build it,
our goal is to make sure it goes well for the world.
And so I think that figuring out
how do you balance all of those
and to get people to really come to the table
and compile a single document that encompasses all of that
wasn't trivial.
So part of the challenge here is your mission is,
I would say, beautiful, empowering,
and a beacon of hope for people in the research community
and just people thinking about AI.
So your decisions are scrutinized
more than I think a regular profit-driven company.
Do you feel the burden of this
in the creation of the charter
and just in the way you operate?
Yes.
So why do you lean into the burden
by creating such a charter?
Why not to keep it quiet?
I mean, it just boils down to the mission, right?
Like, I'm here and everyone else is here
because we think this is the most important mission, right?
Dare to dream.
All right, so do you think you can be good for the world
or create an AGI system that's good
when you're a for-profit company?
From my perspective, I don't understand why profit interferes
with positive impact on society.
I don't understand why Google,
that makes most of its money from ads,
can't also do good for the world
or other companies, Facebook, anything.
I don't understand why those have to interfere.
You know, profit isn't the thing, in my view,
that affects the impact of a company.
What affects the impact of the company is the charter,
is the culture, is the people inside,
and profit is the thing that just fuels those people.
What are your views there?
Yeah, so I think that's a really good question
and there's some real longstanding debates
in human society that are wrapped up in it.
The way that I think about it is just think about
what are the most impactful non-profits in the world?
What are the most impactful for-profits in the world?
Right, it's much easier to list the for-profits.
That's right, and I think that there's some real truth here
that the system that we set up,
the system for kind of how today's world is organized,
is one that really allows for huge impact
and that kind of part of that is that you need to be,
that for-profits are self-sustaining
and able to kind of build on their own momentum.
And I think that's a really powerful thing.
It's something that when it turns out
that we haven't set the guardrails correctly,
causes problems, right?
Think about logging companies that go into the rainforest,
that's really bad, we don't want that.
And it's actually really interesting to me
that kind of this question of how do you get positive
benefits out of a for-profit company?
It's actually very similar to how do you get positive
benefits out of an AGI, right?
That you have this very powerful system
that's more powerful than any human
and is kind of autonomous in some ways.
It's super human in a lot of axes
and somehow you have to set the guardrails
to get good things to happen.
But when you do, the benefits are massive.
And so I think that when I think about
non-profit versus for-profit,
I think it's just not enough happens in non-profits,
they're very pure, but it's just kind of,
it's just hard to do things there.
And for-profits in some ways, like too much happens,
but if kind of shaped in the right way,
it can actually be very positive.
And so with OpenILP, we're picking a road in between.
Now, the thing that I think is really important
to recognize is that the way that we think about OpenILP
is that in the world where AGI actually happens, right?
In a world where we are successful,
we build the most transformative technology ever,
the amount of value we're gonna create
will be astronomical.
And so then in that case that the cap that we have
will be a small fraction of the value we create
and the amount of value that goes back
to investors and employees looks pretty similar
to what would happen in a pretty successful startup.
And that's really the case that we're optimizing for, right?
That we're thinking about in the success case,
making sure that the value we create doesn't get locked up.
And I expect that in other for-profit companies
that it's possible to do something like that.
I think it's not obvious how to do it, right?
And I think that as a for-profit company,
you have a lot of fiduciary duty to your shareholders
and that there are certain decisions that you just cannot make.
In our structure, we've set it up
so that we have a fiduciary duty to the charter.
That we always get to make the decision
that is right for the charter rather than
even if it comes at the expense of our own stakeholders.
And so I think that when I think about
what's really important,
it's not really about nonprofit versus for-profit.
It's really a question of if you build AGI
and you kind of, you know,
commanding is now at this new age, who benefits?
Whose lives are better?
And I think that what's really important
is to have an answer that is everyone.
Yeah, which is one of the core aspects of the charter.
So one concern people have, not just with OpenAI,
but with Google, Facebook, Amazon,
anybody really that's creating impact at scale
is how do we avoid, as your charter says,
avoid enabling the use of AI or AGI to unduly concentrate power?
Why would not a company like OpenAI
keep all the power of an AGI system to itself?
The charter?
The charter.
So, you know, how does the charter
actualize itself in day to day?
So I think that the first to zoom out, right,
that the way that we structure the company
is so that the power for sort of, you know,
dictating the actions that OpenAI takes
ultimately rests with the board, right?
The board of the nonprofit, and the board is set up
in certain ways, certain restrictions
that you can read about in the OpenAI LP blog post.
But effectively, the board is the governing body
for OpenAI LP, and the board has a duty
to fulfill the mission of the nonprofit.
And so that's kind of how we tie,
how we thread all these things together.
Now, there's a question of so day to day,
how do people, the individuals,
who in some ways are the most empowered ones, right?
You know, the board sort of gets to call the shots
at the high level, but the people who are actually executing
are the employees, right?
The people here on a day to day basis who have the,
you know, the keys to the technical kingdom.
And there, I think that the answer looks a lot like,
well, how does any company's values get actualized, right?
I think that a lot of that comes down to
the unique people who are here
because they really believe in that mission,
and they believe in the charter
and that they are willing to take actions
that maybe are worse for them,
but are better for the charter.
And that's something that's really baked into the culture.
And honestly, I think it's, you know,
I think that that's one of the things
that we really have to work to preserve as time goes on.
And that's a really important part of how we think
about hiring people and bringing people into OpenAI.
So there's people here, there's people here
who could speak up and say, like, hold on a second,
this is totally against what we stand for, culture-wise.
Yeah, yeah, for sure.
I mean, I think that we actually have,
I think that's like a pretty important part
of how we operate and how we have,
even again, with designing the charter
and designing OpenAI in the first place,
that there has been a lot of conversation with employees here
and a lot of times where employees said, wait a second,
this seems like it's going in the wrong direction
and let's talk about it.
And so I think one thing that's, I think are really,
and you know, here's actually one thing
that I think is very unique about us as a small company
is that if you're at a massive tech giant,
that's a little bit hard for someone
who's a line employee to go and talk to the CEO
and say, I think that we're doing this wrong.
And you know, you'll get companies like Google
that have had some collective action from employees
to, you know, make ethical change around things like Maven.
And so maybe there are mechanisms
that other companies that work.
But here, super easy for anyone to pull me aside,
to pull Sam aside, to pull Eli aside.
And people do it all the time.
One of the interesting things in the charter
is this idea that it'd be great
if you could try to describe or untangle
switching from competition to collaboration
and late stage AGI development.
It's really interesting, this dance
between competition and collaboration.
How do you think about that?
Yeah, assuming that you can actually do
the technical side of AGI development,
I think there's going to be two key problems
with figuring out how do you actually deploy it
and make it go well.
The first one of these is the run up
to building the first AGI.
You look at how self-driving cars are being developed
and it's a competitive race.
And the thing that always happens in competitive race
is that you have huge amounts of pressure
to get rid of safety.
And so that's one thing we're very concerned about, right?
Is that people, multiple teams figuring out,
we can actually get there.
But, you know, if we took the slower path
that is more guaranteed to be safe, we will lose.
And so we're going to take the fast path.
And so the more that we can, both ourselves,
be in a position where we don't generate
that competitive race.
Where we say, if the race is being run
and that someone else is further ahead than we are,
we're not going to try to leapfrog.
We're going to actually work with them, right?
We will help them succeed.
As long as what they're trying to do
is to fulfill our mission, then we're good.
We don't have to build AGI ourselves.
And I think that's a really important commitment from us.
But it can't just be unilateral, right?
I think that it's really important
that other players who are serious about building AGI
make similar commitments, right?
And I think that, you know, again,
to the extent that everyone believes
that AGI should be something to benefit everyone,
then it actually really shouldn't matter
which company builds it.
And we should all be concerned about the case
where we just race so hard to get there
that something goes wrong.
So what role do you think government,
our favorite entity, has in setting policy
and rules about this domain?
From research to the development
to early stage to late stage AI and AGI development.
So I think that, first of all,
is really important that government's in there, right?
In some way, shape or form, you know,
at the end of the day, we're talking about
building technology that will shape how the world operates
and that there needs to be government
as part of that answer.
And so that's why we've done
a number of different congressional testimonies.
We interact with a number of different lawmakers
and that right now, a lot of our message to them
is that it's not the time for regulation,
it is the time for measurement, right?
That our main policy recommendation is that people,
and you know, the government does this all the time
with bodies like NIST, spend time trying to figure out
just where the technology is, how fast it's moving,
and can really become literate and up to speed
with respect to what to expect.
So I think that today, the answer really is about measurement
and I think that there will be a time and place
where that will change.
And I think it's a little bit hard to predict
exactly what exactly that trajectory should look like.
So there will be a point which regulation,
federal and the United States, the government steps in
and helps be the, I don't wanna say the adult in the room
to make sure that there is strict rules,
maybe conservative rules that nobody can cross.
Well, I think there's kind of maybe two angles to it.
So today with narrow AI applications,
that I think there are already existing bodies
that are responsible and should be responsible for regulation.
You think about, for example, with self-driving cars
that you want the national highway.
Exactly to be regulated in that, that makes sense, right?
That basically what we're saying is
that we're going to have these technological systems
that are going to be performing applications
that humans already do.
Great, we already have ways of thinking about standards
and safety for those.
So I think actually empowering those regulators today
is also pretty important.
And then I think for AGI, that there's going to be a point
where we'll have better answers.
And I think that maybe a similar approach
of first measurement and start thinking
about what the rules should be.
I think it's really important
that we don't prematurely squash progress.
I think it's very easy to kind of smother a budding field.
And I think that's something to really avoid.
But I don't think that the right way of doing it
is to say, let's just try to blaze ahead
and not involve all these other stakeholders.
So you've recently released a paper on GPT-2
language modeling, but did not release the full model
because you had concerns about the possible negative effects
of the availability of such model.
It's outside of just that decision,
it's super interesting because of the discussion
at a societal level, the discourse it creates.
So it's fascinating in that aspect.
But if you think that's the specifics here at first,
what are some negative effects that you envisioned?
And of course, what are some of the positive effects?
Yeah, so again, I think to zoom out,
like the way that we thought about GPT-2
is that with language modeling,
we are clearly on a trajectory right now
where we scale up our models
and we get qualitatively better performance.
GPT-2 itself was actually just a scale up
of a model that we've released in the previous June.
We just ran it at much larger scale
and we got these results where suddenly starting
to write coherent pros,
which was not something we'd seen previously.
And what are we doing now?
Well, we're gonna scale up GPT-2 by 10x by 100x
by 1000x and we don't know what we're gonna get.
And so it's very clear that the model
that we released last June,
I think it's kind of like, it's a good academic toy.
It's not something that we think is something
that can really have negative applications
or to the extent that it can,
that the positive of people being able to play with it
is far outweighs the possible harms.
You fast forward to not GPT-2, but GPT-20,
and you think about what that's gonna be like.
And I think that the capabilities are going to be substantive.
And so there needs to be a point in between the two
where you say, this is something
where we are drawing the line
and that we need to start thinking about the safety aspects.
And I think for GPT-2, we could have gone either way.
And in fact, when we had conversations internally
that we had a bunch of pros and cons
and it wasn't clear which one outweighed the other.
And I think that when we announced
that, hey, we decide not to release this model,
then there was a bunch of conversation
where various people said it's so obvious
that you should have just released it.
There are other people said it's so obvious
you should not have released it.
And I think that that almost definitionally means
that holding it back was the correct decision.
If it's not obvious whether something is beneficial or not,
you should probably default to caution.
And so I think that the overall landscape
for how we think about it
is that this decision could have gone either way.
There are great arguments in both directions.
But for future models down the road,
and possibly sooner than you'd expect,
because scaling these things up doesn't actually
take that long, those ones,
you're definitely not going to want to release into the wild.
And so I think that we almost view this as a test case
and to see, can we even design,
how do you have a system that goes
from having no concept of responsible disclosure
where the mere idea of not releasing something
for safety reasons is unfamiliar,
to a world where you say, okay,
we have a powerful model,
let's at least think about it,
let's go through some process.
And you think about the security community,
it took them a long time
to design responsible disclosure.
You think about this question of,
well, I have a security exploit,
I send it to the company,
the company tries to prosecute me,
or just ignores it, what do I do?
And so the alternatives of,
oh, I just always publish your exploits,
that doesn't seem good either.
And so it really took a long time
and it was bigger than any individual.
It's really about building a whole community
that believe that, okay,
we'll have this process where you send it to the company,
if they don't act at a certain time,
then you can go public,
and you're not a bad person,
you've done the right thing.
And I think that in AI,
part of the response to GPT-2 just proves
that we don't have any concept of this.
So that's the high level picture.
And so I think that,
I think this was a really important move to make,
and we could have maybe delayed it for GPT-3,
but I'm really glad we did it for GPT-2.
And so now you look at GPT-2 itself,
and you think about the substance of, okay,
what are potential negative applications?
So you have this model that's been trained on the internet,
which is also going to be a bunch of very biased data,
a bunch of very offensive content in there,
and you can ask it to generate content for you
on basically any topic, right?
You just give it a prompt
and it'll just start writing,
and it writes content like you see on the internet,
you know, even down to like saying advertisement
in the middle of some of its generations.
And you think about the possibilities
for generating fake news or abusive content.
And you know, it's interesting
seeing what people have done with,
you know, we released a smaller version of GPT-2,
and the people have done things like try to generate,
you know, take my own Facebook message history,
and generate more Facebook messages like me,
and people generating fake politician content,
or you know, there's a bunch of things there
where you at least have to think,
is this going to be good for the world?
There's the flip side,
which is I think that there's a lot of awesome applications
that we really want to see like creative applications
in terms of if you have sci-fi authors
that can work with this tool and come with cool ideas,
like that seems awesome if we can write better sci-fi
through the use of these tools.
And we've actually had a bunch of people right into us
asking, hey, can we use it for, you know,
a variety of different creative applications?
So the positive are actually pretty easy to imagine.
There are, you know, the usual NLP applications
that are really interesting, but let's go there.
It's kind of interesting to think about a world
where, look at Twitter, where not just fake news,
but smarter and smarter bots being able to spread
in an interesting complex networking way in information
that just floods out us regular human beings
with our original thoughts.
So what are your views of this world with GPT-20?
Right, how do we think about it?
Again, it's like one of those things about in the 50s
trying to describe the internet or the smartphone.
What do you think about that world,
the nature of information?
One possibility is that we'll always try to design systems
that identify a robot versus human
and we'll do so successfully.
And so we'll authenticate that we're still human.
And the other world is that we just accept the fact
that we're swimming in a sea of fake news
and just learn to swim there.
Well, have you ever seen the, you know,
popular meme of a robot with a physical arm and pen
clicking the I'm not a robot button?
Yeah.
I think the truth is that really trying to distinguish
between robot and human is a losing battle.
Ultimately, you think it's a losing battle?
I think it's a losing battle ultimately, right?
I think that that is that in terms of the content,
in terms of the actions that you can take.
I mean, think about how captures have gone, right?
The captures used to be a very nice, simple.
You just have this image, all of our OCR is terrible.
You put a couple of artifacts in it, you know,
humans are gonna be able to tell what it is
an AI system wouldn't be able to today.
Like I could barely do captures.
And I think that this is just kind of where we're going.
I think captures were a moment in time thing.
And as AI systems become more powerful,
that they're being human capabilities
that can be measured in a very easy automated way
that the AIs will not be capable of.
I think that's just like,
it's just an increasingly hard technical battle.
But it's not that all hope is lost, right?
And you think about how do we already authenticate
ourselves, right?
That, you know, we have systems,
we have social security numbers.
If you're in the U.S. or, you know, you have,
you know, ways of identifying individual people
and having real world identity tied to digital identity
seems like a step towards, you know,
authenticating the source of content
rather than the content itself.
Now, there are problems with that.
How can you have privacy and anonymity in a world
where the only content you can really trust
is where the only way you can trust content
is by looking at where it comes from.
And so I think that building out good reputation networks
may be one possible solution.
But yeah, I think that this question is not an obvious one.
And I think that we, you know,
maybe sooner than we think we'll be in a world
where, you know, today, I often will read a tweet
and be like, do I feel like a real human wrote this?
Or, you know, do I feel like this is like genuine?
I feel like I can kind of judge the content a little bit.
And I think in the future, it just won't be the case.
You look at, for example, the FCC comments on net neutrality.
It came out later that millions of those
were auto-generated and that the researchers
were able to do various statistical techniques to do that.
What do you do in a world where
those statistical techniques don't exist?
It's just impossible to tell the difference
between humans and AIs.
And in fact, the most persuasive arguments
are written by AI, all that stuff.
It's not sci-fi anymore.
You look at GPT-2 making a great argument for why recycling
is bad for the world.
You gotta read that and be like, huh, you're right.
We are addressing just the symptoms.
Yeah, that's quite interesting.
I mean, ultimately, it boils down to the physical world
being the last frontier of proving,
so you said basically networks of people, humans,
vouching for humans in the physical world.
And somehow the authentication ends there.
I mean, if I had to ask you, I mean,
you're way too eloquent for a human.
So if I had to ask you to authenticate,
like prove how do I know you're not a robot
and how do you know I'm not a robot?
I think that's so far in this space,
this conversation we just had, the physical movements
you did is the biggest gap between us and AI systems
is the physical manipulation.
So maybe that's the last frontier.
Well, here's another question, is why is solving
this problem important?
What aspects are really important to us?
And I think that probably where we'll end up
is we'll hone in on what do we really want
out of knowing if we're talking to a human.
And I think that, again, this comes down to identity.
And so I think that the internet of the future,
I expect to be one that will have lots of agents out there
that will interact with you.
But I think that the question of,
is this real flesh and blood human
or is this an automated system?
May actually just be less important.
Let's actually go there.
It's GPT-2 is impressive and let's look at GPT-20.
Why is it so bad that all my friends are GPT-20?
Why is it so important on the internet?
Do you think to interact with only human beings?
Why can't we live in a world where ideas can come
from models trained on human data?
Yeah, I think this is actually a really interesting question.
This comes back to the,
how do you even picture a world with some new technology?
And I think that one thing that I think is important
is, let's say honesty.
And I think that if you have,
almost in the Turing test style sense of technology,
you have AIs that are pretending to be humans
and deceiving you.
I think that is, that feels like a bad thing.
I think that it's really important
that we feel like we're in control of our environment,
that we understand who we're interacting with.
And if it's an AI or a human,
that that's not something that we're being deceived about.
But I think that the flip side of,
can I have as meaningful of an interaction with an AI
as I can with a human?
Well, I actually think here, you can turn to sci-fi.
And her, I think is a great example
of asking this very question.
One thing I really love about her
is it really starts out almost by asking,
how meaningful are human virtual relationships?
And then you have a human who has a relationship with an AI
and that you really start to be drawn into that,
that all of your emotional buttons get triggered
in the same way as if there was a real human
that was on the other side of that phone.
And so I think that this is one way of thinking about it,
is that I think that we can have meaningful interactions
and that if there's a funny joke,
some sense, it doesn't really matter
if it was written by a human or an AI.
But what you don't want in a way
where I think we should really draw hard lines,
is deception.
And I think that as long as we're in a world
where why do we build AI systems at all?
The reason we want to build them
is to enhance human lives,
to make humans be able to do more things,
to have humans feel more fulfilled.
And if we can build AI systems that do that,
sign me up.
So the process of language modeling,
how far do you think it take us?
Let's look at movie HER.
Do you think a dialogue, natural language conversation
is formulated by the Turing test, for example?
Do you think that process could be achieved
through this kind of unsupervised language modeling?
So I think the Turing test in its real form
isn't just about language, right?
It's really about reasoning too, right?
That to really pass the Turing test,
I should be able to teach calculus
to whoever's on the other side
and have it really understand calculus
and be able to go and solve new calculus problems.
And so I think that to really solve the Turing test,
we need more than what we're seeing with language models.
We need some way of plugging in reasoning.
Now, how different will that be from what we already do?
That's an open question, right?
It might be that we need some sequence
of totally radical new ideas,
or it might be that we just need to kind of shape
our existing systems in a slightly different way.
But I think that in terms of how far language modeling will go,
it's already gone way further
than many people would have expected, right?
I think that things like,
and I think there's a lot of really interesting angles
to poke in terms of how much does GPT-2 understand
physical world?
Like, you know, you read a little bit
about fire underwater in GPT-2.
So it's like, okay, maybe it doesn't quite understand
what these things are.
But at the same time, I think that you also see
various things like smoke coming from flame,
and you know, a bunch of these things that GPT-2,
it has no body, it has no physical experience,
it's just statically read data.
And I think that if the answer is like,
we don't know yet, and these questions though,
we're starting to be able to actually ask them
to physical systems, to real systems that exist,
and that's very exciting.
Do you think, what's your intuition?
Do you think if you just scale language modeling,
like significantly scale, that reasoning can emerge
from the same exact mechanisms?
I think it's unlikely that if we just scale GPT-2,
that we'll have reasoning in the full-fledged way.
And I think that there's like,
the type signature is a little bit wrong, right?
That like, there's something we do with,
that we call thinking, right?
Where we spend a lot of compute,
like a variable amount of compute
to get to better answers, right?
I think a little bit harder, I get a better answer.
And that that kind of type signature
isn't quite encoded in a GPT, right?
GPT will kind of like, it's spent a long time
in it's like evolutionary history,
baking and all this information,
getting very, very good at this predictive process.
And then at runtime, I just kind of do one forward pass
and am able to generate stuff.
And so, there might be small tweaks
to what we do in order to get the type signature, right?
For example, well, it's not really one forward pass, right?
You generate symbol by symbol.
And so, maybe you generate like a whole sequence of thoughts
and you only keep like the last bit or something.
But I think that at the very least,
I would expect you have to make changes like that.
Yeah, just exactly how we, you said think
is the process of generating thought by thought
in the same kind of way, like you said,
keep the last bit, the thing that we converge towards.
And I think there's another piece which is interesting,
which is this out of distribution generalization, right?
That like thinking somehow lets us do that, right?
That we have an experience of thing
and yet somehow we just kind of keep refining
our mental model of it.
This is again, something that feels tied to
whatever reasoning is.
And maybe it's a small tweak to what we do.
Maybe it's many ideas and we'll take as many decades.
Yeah, so the assumption there, generalization
out of distribution is that it's possible
to create new ideas.
It's possible that nobody's ever created any new ideas.
And then we're scaling GPT-2 to GPT-20,
you would essentially generalize to all possible thoughts
as humans can have, just to play devil's advocate.
Right, I mean, how many new story ideas
have we come up with since Shakespeare, right?
Yeah, exactly.
It's just all different forms of love and drama and so on.
Okay.
Not sure if you read Biddle Lesson,
a recent blog post by Rich Sutton.
Yep, I have.
He basically says something that echoes
some of the ideas that you've been talking about,
which is he says the biggest lesson
that can be read from 70 years of AI research
is that general methods that leverage computation
are ultimately going to ultimately win out.
Do you agree with this?
So basically open AI in general about the ideas
you're exploring about coming up with methods,
whether it's GPT-2 modeling
or whether it's open AI-5, playing Dota,
where a general method is better than a more fine-tuned,
expert-tuned method.
Yeah, so I think that, well, one thing that I think
was really interesting about the reaction
to that blog post was that a lot of people have read this
as saying that compute is all that matters.
And that's a very threatening idea, right?
And I don't think it's a true idea either, right?
It's very clear that we have algorithmic ideas
that have been very important for making progress
and to really build AGI,
you wanna push as far as you can on the computational scale
and you wanna push as far as you can on human ingenuity.
And so I think you need both.
But I think the way that you phrase the question
is actually very good, right?
That it's really about what kind of ideas
should we be striving for?
And absolutely, if you can find a scalable idea,
you pour more compute into it,
you pour more data into it, it gets better.
Like that's the real Holy Grail.
And so I think that the answer to the question I think
is yes, that's really how we think about it
and that part of why we're excited about the power
of deep learning and the potential for building AGI
is because we look at the systems that exist
in the most successful AI systems
and we realize that you scale those up,
they're gonna work better.
And I think that that scalability
is something that really gives us hope
for being able to build transformative systems.
So I'll tell you, this is partially an emotional,
you know, a thing that a response that people often have,
if compute is so important for state-of-the-art performance,
you know, individual developers,
maybe a 13-year-old sitting somewhere in Kansas
or something like that, you know, they're sitting,
they might not even have a GPU
or may have a single GPU, a 1080 or something like that.
And there's this feeling like, well,
how can I possibly compete or contribute
to this world of AI if scale is so important?
So if you can comment on that,
and in general, do you think we need to also
in the future focus on democratizing compute resources
more or as much as we democratize the algorithms?
Well, so the way that I think about it
is that there's this space of possible progress, right?
There's a space of ideas and sort of systems
that will work, that will move us forward.
And there's a portion of that space,
and to some extent,
an increasingly significant portion of that space
that does just require massive compute resources.
And for that, I think that the answer is kind of clear
and that part of why we have the structure that we do
is because we think it's really important
to be pushing the scale
and to be building these large clusters and systems.
But there's another portion of the space
that isn't about the large-scale compute,
that are these ideas that, and again,
I think that for the ideas to really be impactful
and really shine, that they should be ideas
that if you scale them up,
would work way better than they do at small scale.
But you can discover them without massive
computational resources.
And if you look at the history of recent developments,
you think about things like the GAN or the VAE,
that these are ones that I think you could come up
with them without having,
and in practice, people did come up with them
without having massive, massive computational resources.
Right, I just talked to Ian Goodfellow,
but the thing is the initial GAN
produced pretty terrible results, right?
So only because it was in a very specific,
only because they're smart enough to know
that this is quite surprising,
it can generate anything that they know.
Do you see a world,
or is that too optimistic and dreamer-like,
to imagine that the compute resources
are something that's owned by governments
and provided as utility?
Actually, to some extent, this question reminds me
of a blog post from one of my former professors
at Harvard, this guy, Matt Welch,
who was a systems professor.
I remember sitting in his tenure talk, right,
and that he had literally just gotten tenure,
he went to Google for the summer,
and then decided he wasn't going back to academia, right?
And that kind of in his blog post,
he makes this point that, look, as a systems researcher,
that I come up with these cool system ideas,
right, and kind of build a little proof of concept,
and the best thing I could hope for
is that the people at Google or Yahoo,
which was around at the time,
will implement it and actually make it work at scale,
right, that's like the dream for me, right?
I built the little thing,
and they turned it into the big thing
that's actually working.
And for him, he said, I'm done with that.
I wanna be the person who's actually doing building
and deploying, and I think that there's
a similar dichotomy here, right?
I think that there are people who really actually
find value, and I think it is a valuable thing to do,
to be the person who produces those ideas, right,
who builds the proof of concept.
And yeah, you don't get to generate
the coolest possible GAN images,
but you invented the GAN, right?
And so, there's a real trade-off there.
And I think that that's a very personal choice,
but I think there's value in both sides.
So do you think creating AGI, something,
or some new models, we would see echoes of the brilliance
even at the prototype level.
So you would be able to develop those ideas
without scale, the initial seeds.
So take a look at, you know,
I always like to look at examples that exist, right?
Look at real precedent.
And so, take a look at the June 2018 model
that we released that we scaled up to turn to GPT-2.
And you can see that at small scale,
it set some records, right?
This was, you know, the original GPT.
We actually had some cool generations.
They weren't nearly as amazing and really stunning
as the GPT-2 ones, but it was promising.
It was interesting.
And so, I think it is the case that with a lot
of these ideas that you see promise at small scale.
But there is an asterisk here, a very big asterisk,
which is sometimes we see behaviors that emerge
that are qualitatively different
from anything we saw at small scale.
And that the original inventor of whatever algorithm
looks at and says, I didn't think I could do that.
This is what we saw in Dota, right?
So PPO was created by John Shulman,
who's a researcher here.
And with Dota, we basically just ran PPO
at massive, massive scale.
And you know, there's some tweaks
in order to make it work,
but fundamentally, it's PPO at the core.
And we were able to get this long-term planning,
these behaviors to really play out on a time scale
that we just thought was not possible.
And John looked at that and was like,
I didn't think he could do that.
That's what happens when you're at three orders
of magnitude more scale than you tested at.
Yeah, but it still has the same flavors
of at least echoes of the expected billions.
Although I suspect with GPT, it's scaled more and more.
You might get surprising things.
So yeah, you're right.
It's interesting that it's difficult to see
how far an idea will go when it's scaled.
It's an open question.
Well, so to that point with Dota and PPO,
like, I mean, here's a very concrete one, right?
It's like, it's actually,
one thing that's very surprising about Dota
that I think people don't really pay that much attention to
is the decree of generalization
out of distribution that happens, right?
That you have this AI that's trained
against other bots for its entirety,
the entirety of its existence.
Sorry to take a step back.
Can you talk through a story of Dota,
a story of leading up to opening AI5 and that past,
and what was the process of self-play
and so on of training?
Yeah, yeah, yeah.
So with Dota.
What is Dota?
Yeah, Dota is a complex video game.
And we started training, we started trying to solve Dota
because we felt like this was a step towards the real world
relative to other games like Chess or Go, right?
Those very cerebral games
where you just kind of have this board
of very discreet moves.
Dota starts to be much more continuous time.
So you have this huge variety of different actions
that you have a 45-minute game
with all these different units
and it's got a lot of messiness to it
that really hasn't been captured by previous games.
And famously, all of the hard-coded bots for Dota
were terrible, right?
It's just impossible to write anything good for it
because it's so complex.
And so this seemed like a really good place
to push what's the state of the art
in reinforcement learning.
And so we started by focusing on the one versus one
version of the game and we're able to solve that.
We're able to beat the world champions
and the learning, you know, the skill curve
was this crazy exponential, right?
It was like constantly we were just scaling up,
that we were fixing bugs and, you know,
that you look at the skill curve
and it was really a very, very smooth one.
So it's actually really interesting to see
how that like human iteration loop
yielded very steady exponential progress.
And to one side note, first of all,
it's an exceptionally popular video game.
The side effect is that there's a lot
of incredible human experts at that video game.
So the benchmark that you're trying to reach is very high.
And the other, can you talk about the approach
that was used initially
and throughout training these agents to play this game?
Yep.
And so the approach that we used is self-play.
And so you have two agents that don't know anything.
They battle each other.
They discover something a little bit good
and now they both know it.
And they just get better and better and better without bound.
And that's a really powerful idea, right?
That we then went from the one versus one version
of the game and scaled up to five versus five, right?
So you think about kind of like with basketball
where you have this like team sport
and you have to do all this coordination
and we were able to push the same idea,
the same self-play to really get to the professional level
at the full five versus five version of the game.
And the things I think are really interesting here
is that these agents in some ways
they're almost like an insect-like intelligence, right?
Where they have a lot in common with
how an insect is trained, right?
Insect kind of lives in this environment for very long time
or the ancestors of this insect have been around
for a long time and had a lot of experience
that gets baked into this agent.
And it's not really smart in the sense of a human, right?
It's not able to go and learn calculus
but it's able to navigate its environment extremely well
and it's able to handle unexpected things
in the environment that's never seen before pretty well.
And we see the same sort of thing with our Dota bots, right?
That they're able to, within this game,
they're able to play against humans,
which is something that never existed
in its evolutionary environment,
totally different play styles from humans versus the bots.
And yet it's able to handle it extremely well.
And that's something that I think was very surprising to us
was something that doesn't really emerge
from what we've seen with PPO at smaller scale, right?
And the kind of scale we're running this stuff at was,
you know, I could take 100,000 CPU cores,
running with like hundreds of GPUs.
It was probably about, you know, like, you know,
something like hundreds of years of experience
going into this bot every single real day.
And so that scale is massive.
And we start to see very different kinds of behaviors
out of the algorithms that we all know and love.
Dota, you mentioned beat the world expert 1v1.
And then you weren't able to win 5v5 this year
at the best players in the world.
So what's the comeback story?
What's, first of all, talk through that,
that's an exceptionally exciting event.
And what's the following months in this year look like?
Yeah, yeah.
So one thing that's interesting is that, you know,
we lose all the time because we play...
We lose we here.
So the Dota team at OpenAI,
we play the bot against better players
than our system all the time, or at least we used to, right?
Like, you know, the first time we lost publicly,
was we went up on stage at the international
and we played against some of the best teams in the world
and we ended up losing both games,
but we gave them a run for their money, right?
That both games were kind of 30 minutes, 25 minutes,
and they went back and forth, back and forth, back and forth.
And so I think that really shows
that we're at the professional level
and that kind of looking at those games,
we think that the coin could have gone a different direction
and we could have had some wins.
That was actually very encouraging for us.
And you know, it's interesting
because the international was at a fixed time
all the time, right?
So we knew exactly what day we were going to be playing
and we pushed as far as we could, as fast as we could.
Two weeks later, we had a bot that had an 80% win rate
versus the one that played at TI.
So the March of Progress, you know,
that you should think of as a snapshot
rather than as an end state.
And so in fact, we'll be announcing our finals pretty soon.
I actually think that we'll announce our final match
prior to this podcast being released.
So there should be, we'll be playing against
the world champions and you know, for us,
it's really less about like the way that we think
about what's upcoming is the final milestone,
the final competitive milestone for the project, right?
That our goal in all of this
isn't really about beating humans at Dota.
Our goal is to push the state of the art
in reinforcement learning and we've done that, right?
And we've actually learned a lot from our system
and that we have, you know,
I think a lot of exciting next steps that we want to take.
And so, you know, kind of the final showcase
of what we built, we're going to do this match.
But for us, it's not really the successor failure
to see, you know, do we have the coin flip go
in our direction or against?
Where do you see the field of deep learning
heading in the next few years?
Where do you see the work in reinforcement learning
perhaps heading and more specifically with OpenAI,
all the exciting projects that you're working on,
what does 2019 hold for you?
Massive scale.
Scale.
I will put an actress on that and just say, you know,
I think that it's about ideas plus scale.
You need both.
So that's a really good point.
So the question, in terms of ideas,
you have a lot of projects
that are exploring different areas of intelligence.
And the question is, when you think of scale,
do you think about growing the scale
of those individual projects
or do you think about adding new projects?
And sorry, if you were thinking about adding new projects
or if you look at the past,
what's the process of coming up with new projects
and new ideas?
Yep.
So we really have a life cycle of project here.
So we start with a few people
just working on a small scale idea
and language is actually a very good example of this.
That it was really, you know, one person here
who was pushing on language for a long time.
I mean, then you get signs of life, right?
And so this is like, let's say, you know,
with the original GPT, we had something that was interesting.
And we said, okay, it's time to scale this, right?
It's time to put more people on it,
put more computational resources behind it.
And then we just kind of keep pushing and keep pushing.
And the end state is something that looks like
Dota or Robotics, where you have a large team of, you know,
10 or 15 people that are running things
at very large scale and that you're able to really
have material engineering and, you know,
sort of machine learning science coming together
to make systems that work and get material results
that just would have been impossible otherwise.
So we do that whole life cycle.
We've done it a number of times, you know,
typically end to end.
It's probably two years or so to do it.
You know, the organization's been around for three years.
So maybe we'll find that we also have
longer life cycle projects.
But, you know, we'll work up to those.
We have, so one team that we were actually just starting,
Ili and I are kicking off a new team
called the reasoning team.
And this is to really try to tackle
how do you get neural networks to reason?
And we think that this will be a long-term project.
It's one that we're very excited about.
In terms of reasoning, super exciting topic.
What kind of benchmarks?
What kind of tests of reasoning do you envision?
What would, if you set back with whatever drink
and you would be impressed that this system
is able to do something, what would that look like?
Theorem-proving.
Theorem-proving.
So some kind of logic and especially mathematical logic.
I think so, right?
And I think that there's kind of other problems
that are dual to theorem-proving in particular.
You know, you think about programming.
I think about even like security analysis of code.
That these all kind of capture the same sorts of core
reasoning and being able to do some
out-of-distribution generalization.
It would be quite exciting if open AI reasoning team
was able to prove that P equals NP.
That would be very nice.
It would be very, very exciting especially.
If it turns out the P equals NP,
that'll be interesting too.
It would be ironic and humorous.
So what problem stands out to you
is the most exciting and challenging and impactful
to the work for us as a community in general
and for open AI this year.
You mentioned reasoning.
I think that's a heck of a problem.
Yeah, so I think reasoning is an important one.
I think it's gonna be hard to get good results in 2019.
You know, again, just like we think about the life cycle,
takes time.
I think for 2019, language modeling seems to be
kind of on that ramp, right?
It's at the point that we have a technique that works.
We wanna scale 100x, 1000x see what happens.
Awesome.
Do you think we're living in a simulation?
I think it's hard to have a real opinion about it.
You know, it's actually interesting.
I separate out things that I think can have like,
you know, yield materially different predictions
about the world from ones that are just kind of,
you know, fun to speculate about.
And I kind of view simulation as more like,
is there a flying teapot between Mars and Jupiter?
Like, maybe, but it's a little bit hard to know
what that would mean for my life.
So there is something actionable.
So some of the best work opening has done
is in the field of reinforcement learning.
And some of the success of reinforcement learning
come from being able to simulate
the problem you're trying to solve.
So do you have a hope for reinforcement,
for the future of reinforcement learning,
and for the future of simulation?
Like, whether we're talking about autonomous vehicles
or any kind of system, do you see that scaling?
So we'll be able to simulate systems
and hence be able to create a simulator
that echoes our real world
and proving once and for all,
even though you're denying it
that we're living in a simulation.
I feel like I've used that for questions, right?
So, you know, kind of for the core there of like,
can we use simulation for self-driving cars?
Take a look at our robotic system, DACTL, right?
That was trained in simulation using the Dota system, in fact,
and it transfers to a physical robot.
And I think everyone looks at our Dota system,
they're like, okay, it's just a game.
How are you ever gonna escape to the real world?
And the answer is, well, we did it with the physical robot,
the no-wink program.
And so I think the answer is simulation goes a lot further
than you think if you apply the right techniques to it.
Now, there's a question of, you know,
are the beings in that simulation gonna wake up
and have consciousness?
I think that one seems a lot harder to, again, reason about.
I think that, you know, you really should think about,
like, where exactly does human consciousness come from
in our own self-awareness?
And, you know, is it just that, like,
once you have, like, a complicated enough neural net,
do you have to worry about the agent's feeling pain?
And, you know, I think there's, like, interesting speculation
to do there, but, you know, again,
I think it's a little bit hard to know for sure.
Well, let me just keep with the speculation.
Do you think to create intelligence,
general intelligence, you need one consciousness
and two a body?
Do you think any of those elements are needed
or is intelligence something that's
sort of orthogonal to those?
I'll stick to the kind of, like,
the non-grand answer first, right?
So the non-grand answer is just to look at, you know,
what are we already making work?
You look at GPT2, a lot of people would have said
that to even get these kinds of results,
you need real world experience.
You need a body, you need grounding.
How are you supposed to reason about any of these things?
How are you supposed to, like, even kind of know
about smoke and fire and those things
if you've never experienced them?
And GPT2 shows that you can actually go way further
than that kind of reasoning would predict.
So I think that in terms of do we need consciousness?
Do we need a body?
It seems the answer is probably not, right?
That we could probably just continue to push
kind of the systems we have.
They already feel general.
They're not as competent or as general
or able to learn as quickly as an AGI would,
but, you know, they're at least like kind of proto AGI in some way
and they don't need any of those things.
Now, let's move to the grand answer,
which is, you know, if our neural nets conscious already
would we ever know, how can we tell, right?
And, you know, here's where the speculation starts
to become, you know, at least interesting or fun
and maybe a little bit disturbing,
depending on where you take it.
But it certainly seems that when we think about animals,
that there's some continuum of consciousness.
You know, my cat, I think, is conscious in some way, right?
You know, not as conscious as a human.
And you could imagine that you could build
a little consciousness meter, right?
You pointed at a cat, it gives you a little reading,
pointed at a human, it gives you much bigger reading.
What would happen if you pointed one of those
at a Dota neural net?
And if you're training in this massive simulation,
do the neural nets feel pain?
You know, it becomes pretty hard to know
that the answer is no, and it becomes pretty hard
to really think about what that would mean
if the answer were yes.
And it's very possible, you know, for example,
you could imagine that maybe the reason
that humans have consciousness is because
it's a convenient computational shortcut, right?
If you think about it, if you have a being
that wants to avoid pain, which seems pretty important
to survive in this environment, and wants to, like,
you know, eat food, then that maybe the best way
of doing it is to have a being that's conscious, right?
That, you know, in order to succeed in the environment,
you need to have those properties,
and how are you supposed to implement them,
and maybe this consciousness is a way of doing that.
If that's true, then actually maybe we should expect
that really competent reinforcement learning agents
will also have consciousness.
But, you know, it's a big if, and I think there are
a lot of other arguments that you can make
in other directions.
I think it's a really interesting idea
that even GPT2 has some degree of consciousness.
That something is actually not as crazy
to think about, it's useful to think about
as we think about what it means
to create intelligence of a dog,
intelligence of a cat, and the intelligence of a human.
So, last question, do you think we will ever fall in love
like in the movie, Her, with an artificial intelligence system,
or an artificial intelligence system falling out
in love with a human?
I hope so.
If there's any better way to end it, is on love.
So, Greg, thanks so much for talking today.
Thank you for having me.