The following is a conversation with Greg Brockman.

He’s the cofounder 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,

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 now robotics

to the digital world with AI, deep learning, reinforcement

learning, 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.

I think that a lot of what really motivates me

is building things.

I think about mathematics, for example,

where you think really hard about a problem.

You understand it.

You write it down in this very obscure form

that we call a proof.

But then, this is in humanity’s library.

It’s there forever.

This is some truth that we’ve discovered.

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 in programming, you think hard about a problem.

You understand it.

You write it down in a very obscure form

that we call a program.

But then once again, it’s in humanity’s library.

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.

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?

Yeah, I think it’s really interesting to think about

humans as just information processing systems.

And that seems like it’s actually

a pretty good way of describing a lot 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.

In some ways, the internet, that what has the internet done?

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 superhuman 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 is 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

that are the systems that we all contribute to.

And so I think actually, it’s interesting to think about

if you’ve read Isaac Asimov’s foundation, right?

That there’s this concept of psychohistory 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,

right, is that we’re kind of coming on

to this insanely transformational technology

of general intelligence, right,

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, right?

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.

You know, 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.

So 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 he means by digital.

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 its clock 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 won.

And that the initial conditions

were that it was created by this group

that really valued people being able to be,

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 that I think about,

you know, I recently looked at it.

I looked at Wikipedia, the formation of Wikipedia.

And I wondered 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’s 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 looking to 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, and I guess fundamentally what it really comes down to

is if you built something really powerful

and you think about, for example,

the creation of shortly after

the creation of nuclear weapons, right?

The most important question in the world was

what’s the world order 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, 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 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 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?

And 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

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

and 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’s 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, 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.

You can kind of look at it as like, okay,

OpenAI talks about the fact that there are risks

and yet they’re trying to build this system.

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.

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 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 build 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 there 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,

that 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 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 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 of 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

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 a fascinating conversation,

and OpenAI is 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 could build a computer system

that could do that, what becomes possible?

We have 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 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 this resurgence.

And I’d always heard that the resurgence in the 80s

was due to the invention of backpropagation

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.

Backpropagation algorithm was invented.

And the neural nets people were running

were these tiny little 20 neuron neural nets.

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 Pitts–McCullough neuron,

suddenly this became the best way of solving problems.

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 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?

Throw up 40 years worth of normal computer vision research,

replace it with a deep neural net,

it’s going to work better.

And there’s a third piece, which is the scalability.

One thing that has been shown time and time again

is that 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 comes into focus, right?

If you imagine that you may be able to,

and you know 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 Veil sense of the word.

And so that’s kind of the broader landscape.

And OpenAI 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, see you in 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 OpenAI, it was basically everything

in exactly the opposite order.

Let me just pause for a second, you said a lot of things.

And let me just admire the jarring aspect

of what OpenAI stands for, which is daring to dream.

I mean, you said 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, in a safe way,

but just even creating intelligence is a very powerful

is a much needed refreshing catalyst for the AI community.

So that’s the starting point.

Okay, so then formation of OpenAI, what’s that?

I would just say that when we were starting OpenAI,

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? Wow, okay.

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 know, because you think about kind of where AI was

is that it had 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 it was 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 cofounder build a product.

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 a scale that competes.

So you recently formed OpenAI LP,

a new 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 this 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

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?

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,

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 going to create orders of 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

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 like the way that our history went

was that for the first year,

we were getting off the ground, right?

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.

And 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 concluded 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,

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 gotta 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, so if you look actually at the OpenAI charter,

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.

There is an element of competition

that you do wanna 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 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.

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.

So 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 forest,

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 like very powerful system,

it’s more powerful than any human,

and is kind of autonomous in some ways,

it’s superhuman 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

nonprofit versus for profit,

I think just not enough happens in nonprofits,

they’re very pure, but it’s just kind of,

it’s just hard to do things there.

In 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 OpenAI LP, 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 OpenAI LP

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?

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, humanity’s now in 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.

So how does the charter

actualize itself in day to day?

So I think that first, to zoom out,

that the way that we structure the company

is so that the power for sort of dictating the actions

that OpenAI takes ultimately rests with the board,

the board of the nonprofit.

And the board is set up in certain ways

with 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?

Now the board sort of gets to call the shots

at the high level, but the people

who are actually executing are the employees, right?

People here on a day to day basis

who have the keys to the technical whole kingdom.

And there I think that the answer looks a lot like,

well, how does any company’s values get actualized, right?

And I think that a lot of that comes down to

that you need 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 LP 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 a 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 make ethical change around things like Maven.

And so maybe there are mechanisms

at other companies that work.

But here, super easy for anyone to pull me aside,

to pull Sam aside, to pull Ilya 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

in 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,

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,

is that people, multiple teams figuring out

we can actually get there,

but 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?

I think that, 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,

it’s really important that government’s in there, right?

In some way, shape, or form.

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 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 at which regulation,

federal in 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.

Netsa.

Yeah, exactly, to be regulating 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 recently released a paper on GPT2 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,

the way that we thought about GPT2

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.

GPT2 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 GPT2 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 GPT2, but GPT20,

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 GPT2, 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.

Right, 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,

you know, how do you have a society

or 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, right?

You know, you think about this question of,

well, I have a security exploit,

I send it to the company,

the company is like, tries to prosecute me

or just sit, just ignores it, what do I do, right?

And so, you know, the alternatives of,

oh, I just always publish your exploits,

that doesn’t seem good either, right?

And so it really took a long time

and took this, it was bigger than any individual, right?

It’s really about building a whole community

that believe that, okay, we’ll have this process

where you send it to the company, you know,

if they don’t act in 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 at GPT2 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 GPT3,

but I’m really glad we did it for GPT2.

And so now you look at GPT2 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, you know, it’s also going to be

a bunch of very biased data,

a bunch of, you know, 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 GPT2

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 up 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 write 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.

They’re, you know, the usual NLP applications

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 information

that just floods out us regular human beings

with our original thoughts.

So what are your views of this world with GPT20, 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 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 popular meme of 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, 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,

humans are gonna be able to tell what it is.

An AI system wouldn’t be able to.

Today, 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 there being human capabilities that can be measured

in a very easy, automated way that 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?

You think about how do we already authenticate ourselves,

right, that we have systems, we have social security numbers

if you’re in the US or you have ways of identifying

individual people and having real world identity

tied to digital identity seems like a step

towards 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,

or 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, maybe sooner than we think,

will be in a world where today I often will read a tweet

and be like, hmm, do I feel like a real human wrote this?

Or do I feel like this is 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 GPT2 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 like 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?

Yeah.

I think that’s so far where in this space,

this conversation we just had,

the physical movements we 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,

why is solving this problem important, right?

Like 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

flesh, real flesh and blood human

or is this an automated system,

may actually just be less important.

Let’s actually go there.

It’s GPT2 is impressive and let’s look at GPT20.

Why is it so bad that all my friends are GPT20?

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, you know, 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 feels like a bad thing, right?

I think that it’s really important that we feel like

we’re in control of our environment, right?

That we understand who we’re interacting with.

And if it’s an AI or a human, 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, right?

One thing I really love about her is it really starts out

almost by asking how meaningful

are human virtual relationships, right?

And then you have a human who has a relationship with an AI

and that you really start to be drawn into that, right?

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 and why 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, right?

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’d 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?

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?

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 GPT2

understand physical world?

Like, you read a little bit about fire underwater in GPT2.

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 a bunch of these things that GPT2,

it has no body, it has no physical experience,

it’s just statically read data.

And I think that the answer is like, we don’t know yet.

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 GPT2

that we’ll have reasoning in the full fledged way.

And I think that there’s like,

the type signature’s 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 been a long time,

and it’s like evolutionary history,

baking in all this information,

getting very, very good at this predictive process.

And then at runtime, I just kind of do one forward pass,

and I’m able to generate stuff.

And so, you know, there might be small tweaks

to what we do in order to get the type signature, right?

For example, well, you know,

it’s not really one forward pass, right?

You know, 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.

Yep.

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 haven’t experienced a 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.

Mm hmm.

You know, it’s possible that nobody’s ever created

any new ideas, and then with scaling GPT2 to GPT20,

you would essentially generalize to all possible thoughts

that us humans could have.

I mean.

Just to play devil’s advocate.

Right, right, 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 Bitter 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, and OpenAI in general,

but the ideas you’re exploring about coming up with methods,

whether it’s GPT2 modeling or whether it’s OpenAI 5

playing Dota, or 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 phrased 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 that’s really how we think about it

and that part of why we’re excited about the power

of deep learning, 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,

a response that people often have,

if compute is so important for state of the art performance,

individual developers, maybe a 13 year old

sitting somewhere in Kansas or something like that,

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 that 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,

it was 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 Welsh, 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 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 I kind of build a little proof of concept,

and the best thing I can 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 build the little thing,

and they turn it into the big thing that’s actually working.

And for him, he said, I’m done with that.

I want to 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 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 into GPT2.

And you can see that at small scale,

it set some records, right?

This was the original GPT.

We actually had some cool generations.

They weren’t nearly as amazing and really stunning

as the GPT2 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 it 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 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 it 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,

you know, at least echoes of the expected billions.

Although I suspect with GPT scaled more and more,

you might get surprising things.

So yeah, you’re right, it’s interesting.

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, you know, a story of Dota,

a story of leading up to opening I5 and that past,

and what was the process of self play

and so on of training on this?

Yeah, yeah, yeah.

So with Dota.

What is Dota?

Yeah, Dota is a complex video game

and 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,

very discreet moves.

Dota starts to be much more continuous time

that 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 skill curve was this crazy exponential, right?

And it was like constantly we were just scaling up

that we were fixing bugs

and that you look at the skill curve

and it was really a very, very smooth one.

This is actually really interesting

to see how that 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 need 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?

An insect kind of lives in this environment

for a 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 it’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,

I could say like 100,000 CPU cores

running with like hundreds of GPUs.

It was probably about 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 one v one.

And then you weren’t able to win five v five this year.

Yeah.

At the best players in the world.

So what’s the comeback story?

First of all, talk through that.

That was an exceptionally exciting event.

And what’s the following months and this year look like?

Yeah, yeah, so one thing that’s interesting

is that we lose all the time.

Because we play.

Who’s we here?

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 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 it’s interesting because the international

was at a fixed 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 should think of it

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 we’ll be playing against the world champions.

And 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, I think, a lot of exciting next steps

that we want to take.

And so kind of as a final showcase of what we built,

we’re going to do this match.

But for us, it’s not really the success or failure

to see 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 and 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 asterisk on that and just say,

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 to, and if you’re 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 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,

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 10 or 15 people

that are running things at very large scale

and that you’re able to really have material engineering

and 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, typically end to end.

It’s probably two years or so to do it.

The organization has been around for three years,

so maybe we’ll find that we also have

longer life cycle projects, but we’ll work up to those.

So one team that we were actually just starting,

Ilya and I are kicking off a new team

called the Reasoning Team,

and that 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 sat back with whatever drink

and you would be impressed that this system

is able to do something, what would that look like?

Theorem proving.

So some kind of logic, and especially mathematical logic.

I think so.

I think that there’s other problems that are dual

to theorem proving in particular.

You think about programming, you think about

even 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.

So it would be quite exciting if OpenAI Reasoning Team

was able to prove that P equals NP.

That would be very nice.

It would be very, very, very exciting, especially.

If it turns out that P equals NP,

that’ll be interesting too.

It would be ironic and humorous.

So what problem stands out to you

as the most exciting and challenging and impactful

to the work for us as a community in general

and for OpenAI this year?

You mentioned reasoning.

I think that’s a heck of a problem.

Yeah, so I think reasoning’s an important one.

I think it’s gonna be hard to get good results in 2019.

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.

It’s at the point that we have a technique that works.

We wanna scale 100x, 1,000x, 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.

It’s actually interesting.

I separate out things that I think can have like,

yield materially different predictions about the world

from ones that are just kind of fun to speculate about.

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 OpenAI 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 it’s, we’re talking about autonomous vehicles

or any kind of system.

Do you see that scaling to where 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 it’s two separate questions, right?

So kind of at the core there of like,

can we use simulation for self driving cars?

Take a look at our robotic system, Dactyl, 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 a physical robot

that no one could 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,

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 really should think about

where exactly does human consciousness come from

in our own self awareness?

And is it just that once you have a complicated enough

neural net, you have to worry about

the agents feeling pain?

And I think there’s interesting speculation to do there,

but 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 orthogonal to those?

I’ll stick to the non grand answer first, right?

So the non grand answer is just to look at,

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 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, are our neural nets conscious already?

Would we ever know?

How can we tell, right?

And here’s where the speculation starts to become

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?

Not as conscious as a human.

And you could imagine that you could build

a little consciousness meter, right?

You point at a cat, it gives you a little reading.

Point at a human, it gives you much bigger reading.

What would happen if you pointed one of those

at a donor 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’s 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, that’s a big if.

And I think there are a lot of other arguments

they can make in other directions.

I think that’s a really interesting idea

that even GPT2 has some degree of consciousness.

That’s something, it’s 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 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.