vtt/episode_017_small.vtt

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 The following is a conversation with Greg Brockman.

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 He's the cofounder and CTO of OpenAI,

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 a world class research organization

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 developing ideas in AI with a goal of eventually

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 creating a safe and friendly artificial general

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 intelligence, one that benefits and empowers humanity.

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 OpenAI is not only a source of publications, algorithms,

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 tools, and data sets.

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 Their mission is a catalyst for an important public discourse

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 about our future with both narrow and general intelligence

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 systems.

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 This conversation is part of the Artificial Intelligence

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 Podcast at MIT and beyond.

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 If you enjoy it, subscribe on YouTube, iTunes,

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 or simply connect with me on Twitter

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 at Lex Friedman, spelled F R I D.

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 And now here's my conversation with Greg Brockman.

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 So in high school and right after you wrote

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 a draft of a chemistry textbook,

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 I saw that that covers everything

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 from basic structure of the atom to quantum mechanics.

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 So it's clear you have an intuition and a passion

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 for both the physical world with chemistry and non robotics

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 to the digital world with AI, deep learning,

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 reinforcement learning, so on.

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 Do you see the physical world and the digital world

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 as different?

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 And what do you think is the gap?

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 A lot of it actually boils down to iteration speed,

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 that I think that a lot of what really motivates me

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 is building things, right?

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 Think about mathematics, for example,

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 where you think really hard about a problem.

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 You understand it.

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 You're right down in this very obscure form

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 that we call a proof.

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 But then this is in humanity's library, right?

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 It's there forever.

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 This is some truth that we've discovered.

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 And maybe only five people in your field will ever read it.

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 But somehow you've kind of moved humanity forward.

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 And so I actually used to really think

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 that I was going to be a mathematician.

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 And then I actually started writing this chemistry textbook.

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 One of my friends told me, you'll never publish it

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 because you don't have a PhD.

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 So instead, I decided to build a website

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 and try to promote my ideas that way.

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 And then I discovered programming.

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 And in programming, you think hard about a problem.

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 You understand it.

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 You're right down in a very obscure form

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 that we call a program.

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 But then once again, it's in humanity's library, right?

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 And anyone can get the benefit from it.

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 And the scalability is massive.

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 And so I think that the thing that really appeals to me

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 about the digital world is that you

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 can have this insane leverage, right?

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 A single individual with an idea is able to affect

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 the entire planet.

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 And that's something I think is really hard to do

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 if you're moving around physical atoms.

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 But you said mathematics.

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 So if you look at the wet thing over here, our mind,

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 do you ultimately see it as just math,

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 as just information processing?

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 Or is there some other magic if you've

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 seen through biology and chemistry and so on?

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 I think it's really interesting to think about humans

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 as just information processing systems.

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 And it seems like it's actually a pretty good way

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 of describing a lot of how the world works or a lot of what

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 we're capable of to think that, again, if you just

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 look at technological innovations over time,

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 that in some ways, the most transformative innovation

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 that we've had has been the computer, right?

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

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 The internet is not about these physical cables.

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 It's about the fact that I am suddenly

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 able to instantly communicate with any other human

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 on the planet.

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 I'm able to retrieve any piece of knowledge

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 that, in some ways, the human race has ever had,

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 and that those are these insane transformations.

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 Do you see our society as a whole the collective

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 as another extension of the intelligence

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 of the human being?

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 So if you look at the human being as an information processing

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 system, you mentioned the internet, the networking.

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 Do you see us all together as a civilization

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 as a kind of intelligent system?

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 Yeah, I think this is actually a really interesting

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 perspective to take and to think about

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 that you sort of have this collective intelligence

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 of all of society.

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 The economy itself is this superhuman machine

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 that is optimizing something, right?

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 And it's almost, in some ways, a company

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 has a will of its own, right?

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 That you have all these individuals who are all

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 pursuing their own individual goals

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 and thinking really hard and thinking

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 about the right things to do, but somehow the company does

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 something that is this emergent thing

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 and that it's a really useful abstraction.

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 And so I think that in some ways,

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 we think of ourselves as the most intelligent things

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 on the planet and the most powerful things on the planet.

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 But there are things that are bigger than us,

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 that are these systems that we all contribute to.

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

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 if you've read Asa Geismov's foundation, right,

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 that there's this concept of psycho history in there,

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 which is effectively this, that if you have trillions

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 or quadrillions of beings, then maybe you could actually

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 predict what that huge macro being will do

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 and almost independent of what the individuals want.

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 And I actually have a second angle on this

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 that I think is interesting, which is thinking about

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 technological determinism.

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 One thing that I actually think a lot about with OpenAI

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 is that we're kind of coming onto this insanely

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 transformational technology of general intelligence

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 that will happen at some point.

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 And there's a question of how can you take actions

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 that will actually steer it to go better rather than worse?

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 And that I think one question you need to ask is,

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 as a scientist, as an event or as a creator,

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 what impact can you have in general?

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 You look at things like the telephone

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 invented by two people on the same day.

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 Like what does that mean, like what does that mean

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 about the shape of innovation?

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 And I think that what's going on is everyone's building

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 on the shoulders of the same giants.

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 And so you can kind of, you can't really hope

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 to create something no one else ever would.

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 You know, if Einstein wasn't born,

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 someone else would have come up with relativity.

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 You know, he changed the timeline a bit, right?

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 That maybe it would have taken another 20 years,

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 but it wouldn't be that fundamentally humanity

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 would never discover these fundamental truths.

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 So there's some kind of invisible momentum

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 that some people like Einstein or OpenAI is plugging into

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 that anybody else can also plug into.

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 And ultimately, that wave takes us into a certain direction.

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 That's what you mean by digitalism?

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 That's right, that's right.

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 And you know, this kind of seems to play out

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 in a bunch of different ways.

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 That there's some exponential that is being written

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 and that the exponential itself, which one it is,

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 changes, think about Moore's Law,

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 an entire industry set, it's clocked to it for 50 years.

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 Like how can that be, right?

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 How is that possible?

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 And yet somehow it happened.

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 And so I think you can't hope to ever invent something

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 that no one else will.

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 Maybe you can change the timeline a little bit.

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 But if you really want to make a difference,

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 I think that the thing that you really have to do,

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 the only real degree of freedom you have

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 is to set the initial conditions

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 under which a technology is born.

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 And so you think about the internet, right?

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 That there are lots of other competitors

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 trying to build similar things.

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 And the internet one, and that the initial conditions

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 where it was created by this group

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 that really valued people being able to be,

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 you know, anyone being able to plug in

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 this very academic mindset of being open and connected.

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 And I think that the internet for the next 40 years

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 really played out that way.

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 You know, maybe today,

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 things are starting to shift in a different direction,

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 but I think that those initial conditions

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 were really important to determine

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 the next 40 years worth of progress.

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 That's really beautifully put.

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 So another example of that I think about,

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 you know, I recently looked at it.

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 I looked at Wikipedia, the formation of Wikipedia.

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 And I wonder what the internet would be like

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 if Wikipedia had ads.

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 You know, there's an interesting argument

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 that why they chose not to put advertisement on Wikipedia.

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 I think Wikipedia is one of the greatest resources

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 we have on the internet.

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 It's extremely surprising how well it works

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 and how well it was able to aggregate

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 all this kind of good information.

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 And essentially the creator of Wikipedia,

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 I don't know, there's probably some debates there,

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 but set the initial conditions

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 and now it carried itself forward.

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 That's really interesting.

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 So the way you're thinking about AGI

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 or artificial intelligence is you're focused on

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 setting the initial conditions for the progress.

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 That's right.

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 That's powerful.

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 Okay, so look into the future.

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 If you create an AGI system,

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 like one that can ace the Turing test, natural language,

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 what do you think would be the interactions

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 you would have with it?

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 What do you think are the questions you would ask?

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 Like what would be the first question you would ask?

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 It, her, him.

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 That's right.

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 I think that at that point,

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 if you've really built a powerful system

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 that is capable of shaping the future of humanity,

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 the first question that you really should ask

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 is how do we make sure that this plays out well?

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 And so that's actually the first question

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 that I would ask a powerful AGI system is.

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 So you wouldn't ask your colleague,

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 you wouldn't ask like Ilya, you would ask the AGI system.

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 Oh, we've already had the conversation with Ilya, right?

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 And everyone here.

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 And so you want as many perspectives

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 and a piece of wisdom as you can

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 for answering this question.

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 So I don't think you necessarily defer to

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 whatever your powerful system tells you,

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 but you use it as one input

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 to try to figure out what to do.

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 But, and I guess fundamentally,

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 what it really comes down to is

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 if you built something really powerful

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 and you think about, think about, for example,

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 the creation of, of shortly after

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 the creation of nuclear weapons, right?

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 The most important question in the world

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 was what's the world we're going to be like?

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 How do we set ourselves up in a place

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 where we're going to be able to survive as a species?

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 With AGI, I think the question is slightly different, right?

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 That there is a question of how do we make sure

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 that we don't get the negative effects?

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 But there's also the positive side, right?

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 You imagine that, you know, like,

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 like what will AGI be like?

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 Like what will it be capable of?

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 And I think that one of the core reasons

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 that an AGI can be powerful and transformative

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 is actually due to technological development, right?

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 If you have something that's capable,

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 that's capable as a human and that it's much more scalable,

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 that you absolutely want that thing

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 to go read the whole scientific literature

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 and think about how to create cures for all the diseases, right?

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 You want it to think about how to go

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 and build technologies to help us

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 create material abundance and to figure out societal problems

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 that we have trouble with,

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 like how are we supposed to clean up the environment?

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 And, you know, maybe you want this

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 to go and invent a bunch of little robots that will go out

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 and be biodegradable and turn ocean debris

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 into harmless molecules.

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 And I think that that positive side

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 is something that I think people miss

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 sometimes when thinking about what an AGI will be like.

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 And so I think that if you have a system

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 that's capable of all of that,

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 you absolutely want its advice about how do I make sure

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 that we're using your capabilities

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 in a positive way for humanity.

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 So what do you think about that psychology

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 that looks at all the different possible trajectories

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 of an AGI system, many of which,

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 perhaps the majority of which are positive

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 and nevertheless focuses on the negative trajectories?

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 I mean, you get to interact with folks,

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 you get to think about this maybe within yourself as well.

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 You look at Sam Harris and so on.

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 It seems to be, sorry to put it this way,

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 but almost more fun to think about the negative possibilities.

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 Whatever that's deep in our psychology,

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 what do you think about that?

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 And how do we deal with it?

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 Because we want AI to help us.

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 So I think there's kind of two problems

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 entailed in that question.

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 The first is more of the question of,

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 how can you even picture what a world

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 with a new technology will be like?

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 Now imagine we're in 1950

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 and I'm trying to describe Uber to someone.

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 Aps and the internet.

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 Yeah, I mean, that's going to be extremely complicated,

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 but it's imaginable.

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 It's imaginable, right?

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 But, and now imagine being in 1950

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 and predicting Uber, right?

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 And you need to describe the internet,

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 you need to describe GPS,

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 you need to describe the fact

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 that everyone's going to have this phone in their pocket.

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 And so I think that just the first truth

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 is that it is hard to picture

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 how a transformative technology will play out in the world.

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 We've seen that before with technologies

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 that are far less transformative than AGI will be.

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 And so I think that one piece

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 is that it's just even hard to imagine

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 and to really put yourself in a world

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 where you can predict what that positive vision

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 would be like.

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 And I think the second thing is that it is,

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 I think it is always easier to support

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 the negative side than the positive side.

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 It's always easier to destroy than create.

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 And, you know, less in a physical sense

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 and more just in an intellectual sense, right?

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 Because, you know, I think that with creating something,

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 you need to just get a bunch of things right

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 and to destroy, you just need to get one thing wrong.

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 And so I think that what that means

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 is that I think a lot of people's thinking dead ends

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 as soon as they see the negative story.

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 But that being said, I actually have some hope, right?

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 I think that the positive vision

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 is something that I think can be,

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 is something that we can talk about.

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 I think that just simply saying this fact of,

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 yeah, like there's positive, there's negatives,

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 everyone likes to dwell on the negative,

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 people actually respond well to that message and say,

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 huh, you're right, there's a part of this

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 that we're not talking about, not thinking about.

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 And that's actually something that's,

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 I think really been a key part

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 of how we think about AGI at OpenAI, right?

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

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 like OpenAI talks about the fact that there are risks

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 and yet they're trying to build this system.

12:53.160 --> 12:56.080
 Like how do you square those two facts?

12:56.080 --> 12:59.120
 So do you share the intuition that some people have,

12:59.120 --> 13:02.680
 I mean, from Sam Harris to even Elon Musk himself,

13:02.680 --> 13:06.600
 that it's tricky as you develop AGI

13:06.600 --> 13:10.400
 to keep it from slipping into the existential threats,

13:10.400 --> 13:11.760
 into the negative.

13:11.760 --> 13:13.640
 What's your intuition about,

13:13.640 --> 13:17.720
 how hard is it to keep AI development

13:17.720 --> 13:19.640
 on the positive track?

13:19.640 --> 13:20.680
 What's your intuition there?

13:20.680 --> 13:21.560
 To answer that question,

13:21.560 --> 13:23.960
 you can really look at how we structure OpenAI.

13:23.960 --> 13:25.840
 So we really have three main arms.

13:25.840 --> 13:26.960
 So we have capabilities,

13:26.960 --> 13:29.040
 which is actually doing the technical work

13:29.040 --> 13:31.160
 and pushing forward what these systems can do.

13:31.160 --> 13:35.120
 There's safety, which is working on technical mechanisms

13:35.120 --> 13:36.920
 to ensure that the systems we build

13:36.920 --> 13:38.480
 are aligned with human values.

13:38.480 --> 13:39.640
 And then there's policy,

13:39.640 --> 13:42.040
 which is making sure that we have governance mechanisms,

13:42.040 --> 13:45.280
 answering that question of, well, whose values?

13:45.280 --> 13:47.360
 And so I think that the technical safety one

13:47.360 --> 13:50.480
 is the one that people kind of talk about the most, right?

13:50.480 --> 13:52.080
 You talk about, like think about,

13:52.080 --> 13:54.200
 you know, all of the dystopic AI movies,

13:54.200 --> 13:55.960
 a lot of that is about not having good

13:55.960 --> 13:57.520
 technical safety in place.

13:57.520 --> 13:59.960
 And what we've been finding is that, you know,

13:59.960 --> 14:01.360
 I think that actually a lot of people

14:01.360 --> 14:02.680
 look at the technical safety problem

14:02.680 --> 14:05.400
 and think it's just intractable, right?

14:05.400 --> 14:07.840
 This question of what do humans want?

14:07.840 --> 14:09.160
 How am I supposed to write that down?

14:09.160 --> 14:11.240
 Can I even write down what I want?

14:11.240 --> 14:12.080
 No way.

14:13.040 --> 14:14.800
 And then they stop there.

14:14.800 --> 14:16.880
 But the thing is we've already built systems

14:16.880 --> 14:20.920
 that are able to learn things that humans can't specify.

14:20.920 --> 14:22.920
 You know, even the rules for how to recognize

14:22.920 --> 14:25.000
 if there's a cat or a dog in an image.

14:25.000 --> 14:26.520
 Turns out it's intractable to write that down

14:26.520 --> 14:28.400
 and yet we're able to learn it.

14:28.400 --> 14:31.040
 And that what we're seeing with systems we build at OpenAI

14:31.040 --> 14:33.800
 and they're still in early proof of concept stage

14:33.800 --> 14:36.320
 is that you are able to learn human preferences.

14:36.320 --> 14:38.920
 You're able to learn what humans want from data.

14:38.920 --> 14:40.400
 And so that's kind of the core focus

14:40.400 --> 14:41.760
 for our technical safety team.

14:41.760 --> 14:43.800
 And I think that they're actually,

14:43.800 --> 14:45.640
 we've had some pretty encouraging updates

14:45.640 --> 14:48.040
 in terms of what we've been able to make work.

14:48.040 --> 14:51.680
 So you have an intuition and a hope that from data,

14:51.680 --> 14:53.640
 you know, looking at the value alignment problem,

14:53.640 --> 14:57.040
 from data we can build systems that align

14:57.040 --> 15:00.600
 with the collective better angels of our nature.

15:00.600 --> 15:04.600
 So align with the ethics and the morals of human beings.

15:04.600 --> 15:05.880
 To even say this in a different way,

15:05.880 --> 15:08.560
 I mean, think about how do we align humans, right?

15:08.560 --> 15:10.400
 Think about like a human baby can grow up

15:10.400 --> 15:12.880
 to be an evil person or a great person.

15:12.880 --> 15:15.200
 And a lot of that is from learning from data, right?

15:15.200 --> 15:17.720
 That you have some feedback as a child is growing up.

15:17.720 --> 15:19.160
 They get to see positive examples.

15:19.160 --> 15:23.120
 And so I think that just like the only example

15:23.120 --> 15:25.400
 we have of a general intelligence

15:25.400 --> 15:28.040
 that is able to learn from data

15:28.040 --> 15:31.440
 to align with human values and to learn values,

15:31.440 --> 15:32.880
 I think we shouldn't be surprised

15:32.880 --> 15:36.040
 that we can do the same sorts of techniques

15:36.040 --> 15:37.440
 or whether the same sort of techniques

15:37.440 --> 15:41.080
 end up being how we solve value alignment for AGI's.

15:41.080 --> 15:42.680
 So let's go even higher.

15:42.680 --> 15:44.800
 I don't know if you've read the book, Sapiens.

15:44.800 --> 15:48.320
 But there's an idea that, you know,

15:48.320 --> 15:50.000
 that as a collective, as us human beings,

15:50.000 --> 15:54.720
 we kind of develop together ideas that we hold.

15:54.720 --> 15:57.920
 There's no, in that context, objective truth.

15:57.920 --> 16:00.000
 We just kind of all agree to certain ideas

16:00.000 --> 16:01.440
 and hold them as a collective.

16:01.440 --> 16:03.480
 Did you have a sense that there is

16:03.480 --> 16:05.360
 in the world of good and evil,

16:05.360 --> 16:07.560
 do you have a sense that to the first approximation,

16:07.560 --> 16:10.280
 there are some things that are good

16:10.280 --> 16:14.520
 and that you could teach systems to behave to be good?

16:14.520 --> 16:18.440
 So I think that this actually blends into our third team,

16:18.440 --> 16:19.880
 which is the policy team.

16:19.880 --> 16:22.320
 And this is the one, the aspect that I think people

16:22.320 --> 16:25.280
 really talk about way less than they should.

16:25.280 --> 16:27.640
 Because imagine that we build super powerful systems

16:27.640 --> 16:29.720
 that we've managed to figure out all the mechanisms

16:29.720 --> 16:32.800
 for these things to do whatever the operator wants.

16:32.800 --> 16:34.480
 The most important question becomes,

16:34.480 --> 16:36.720
 who's the operator, what do they want,

16:36.720 --> 16:39.400
 and how is that going to affect everyone else?

16:39.400 --> 16:43.080
 And I think that this question of what is good,

16:43.080 --> 16:44.720
 what are those values, I mean,

16:44.720 --> 16:45.960
 I think you don't even have to go

16:45.960 --> 16:48.400
 to those very grand existential places

16:48.400 --> 16:50.920
 to realize how hard this problem is.

16:50.920 --> 16:52.880
 You just look at different countries

16:52.880 --> 16:54.520
 and cultures across the world.

16:54.520 --> 16:57.120
 And that there's a very different conception

16:57.120 --> 17:01.920
 of how the world works and what kinds of ways

17:01.920 --> 17:03.400
 that society wants to operate.

17:03.400 --> 17:07.000
 And so I think that the really core question

17:07.000 --> 17:09.560
 is actually very concrete.

17:09.560 --> 17:10.960
 And I think it's not a question

17:10.960 --> 17:12.880
 that we have ready answers to,

17:12.880 --> 17:16.560
 how do you have a world where all the different countries

17:16.560 --> 17:19.720
 that we have, United States, China, Russia,

17:19.720 --> 17:22.720
 and the hundreds of other countries out there

17:22.720 --> 17:26.600
 are able to continue to not just operate

17:26.600 --> 17:28.440
 in the way that they see fit,

17:28.440 --> 17:32.520
 but in the world that emerges in these,

17:32.520 --> 17:34.680
 where you have these very powerful systems,

17:36.040 --> 17:37.800
 operating alongside humans,

17:37.800 --> 17:39.800
 ends up being something that empowers humans more,

17:39.800 --> 17:44.120
 that makes human existence be a more meaningful thing

17:44.120 --> 17:46.400
 and that people are happier and wealthier

17:46.400 --> 17:48.960
 and able to live more fulfilling lives.

17:48.960 --> 17:51.560
 It's not an obvious thing for how to design that world

17:51.560 --> 17:53.600
 once you have that very powerful system.

17:53.600 --> 17:55.800
 So if we take a little step back,

17:55.800 --> 17:58.200
 and we're having like a fascinating conversation

17:58.200 --> 18:01.880
 and open as in many ways a tech leader in the world,

18:01.880 --> 18:05.440
 and yet we're thinking about these big existential questions

18:05.440 --> 18:07.000
 which is fascinating, really important.

18:07.000 --> 18:09.160
 I think you're a leader in that space

18:09.160 --> 18:10.840
 and that's a really important space

18:10.840 --> 18:13.080
 of just thinking how AI affects society

18:13.080 --> 18:14.360
 in a big picture view.

18:14.360 --> 18:17.320
 So Oscar Wilde said, we're all in the gutter,

18:17.320 --> 18:19.000
 but some of us are looking at the stars

18:19.000 --> 18:22.320
 and I think OpenAI has a charter

18:22.320 --> 18:24.600
 that looks to the stars, I would say,

18:24.600 --> 18:26.880
 to create intelligence, to create general intelligence,

18:26.880 --> 18:29.440
 make it beneficial, safe, and collaborative.

18:29.440 --> 18:33.680
 So can you tell me how that came about?

18:33.680 --> 18:36.320
 How a mission like that and the path

18:36.320 --> 18:39.120
 to creating a mission like that at OpenAI was founded?

18:39.120 --> 18:41.640
 Yeah, so I think that in some ways

18:41.640 --> 18:45.040
 it really boils down to taking a look at the landscape.

18:45.040 --> 18:47.040
 So if you think about the history of AI

18:47.040 --> 18:49.920
 that basically for the past 60 or 70 years,

18:49.920 --> 18:51.640
 people have thought about this goal

18:51.640 --> 18:53.960
 of what could happen if you could automate

18:53.960 --> 18:55.640
 human intellectual labor.

18:56.680 --> 18:58.280
 Imagine you can build a computer system

18:58.280 --> 19:00.560
 that could do that, what becomes possible?

19:00.560 --> 19:02.400
 We have a lot of sci fi that tells stories

19:02.400 --> 19:04.920
 of various dystopias and increasingly you have movies

19:04.920 --> 19:06.480
 like Her that tell you a little bit about

19:06.480 --> 19:09.440
 maybe more of a little bit utopic vision.

19:09.440 --> 19:12.560
 You think about the impacts that we've seen

19:12.560 --> 19:16.280
 from being able to have bicycles for our minds

19:16.280 --> 19:20.360
 and computers and that I think that the impact

19:20.360 --> 19:23.480
 of computers and the internet has just far outstripped

19:23.480 --> 19:26.200
 what anyone really could have predicted.

19:26.200 --> 19:27.400
 And so I think that it's very clear

19:27.400 --> 19:29.360
 that if you can build an AGI,

19:29.360 --> 19:31.600
 it will be the most transformative technology

19:31.600 --> 19:33.040
 that humans will ever create.

19:33.040 --> 19:36.840
 And so what it boils down to then is a question of,

19:36.840 --> 19:38.680
 well, is there a path?

19:38.680 --> 19:39.520
 Is there hope?

19:39.520 --> 19:41.680
 Is there a way to build such a system?

19:41.680 --> 19:43.640
 And I think that for 60 or 70 years

19:43.640 --> 19:48.040
 that people got excited and that ended up not being able

19:48.040 --> 19:51.480
 to deliver on the hopes that people had pinned on them.

19:51.480 --> 19:54.880
 And I think that then, that after two winters

19:54.880 --> 19:57.600
 of AI development, that people,

19:57.600 --> 20:00.560
 I think kind of almost stopped daring to dream, right?

20:00.560 --> 20:03.280
 That really talking about AGI or thinking about AGI

20:03.280 --> 20:05.640
 became almost this taboo in the community.

20:06.640 --> 20:08.720
 But I actually think that people took the wrong lesson

20:08.720 --> 20:10.080
 from AI history.

20:10.080 --> 20:12.400
 And if you look back, starting in 1959

20:12.400 --> 20:14.240
 is when the Perceptron was released.

20:14.240 --> 20:17.720
 And this is basically one of the earliest neural networks.

20:17.720 --> 20:19.280
 It was released to what was perceived

20:19.280 --> 20:20.840
 as this massive overhype.

20:20.840 --> 20:22.360
 So in the New York Times in 1959,

20:22.360 --> 20:26.400
 you have this article saying that the Perceptron

20:26.400 --> 20:29.160
 will one day recognize people, call out their names,

20:29.160 --> 20:31.480
 instantly translate speech between languages.

20:31.480 --> 20:33.800
 And people at the time looked at this and said,

20:33.800 --> 20:36.120
 this is, your system can't do any of that.

20:36.120 --> 20:38.080
 And basically spent 10 years trying to discredit

20:38.080 --> 20:40.640
 the whole Perceptron direction and succeeded.

20:40.640 --> 20:41.840
 And all the funding dried up.

20:41.840 --> 20:44.960
 And people kind of went in other directions.

20:44.960 --> 20:46.920
 And in the 80s, there was this resurgence.

20:46.920 --> 20:49.320
 And I'd always heard that the resurgence in the 80s

20:49.320 --> 20:51.520
 was due to the invention of back propagation

20:51.520 --> 20:53.720
 and these algorithms that got people excited.

20:53.720 --> 20:55.760
 But actually the causality was due to people

20:55.760 --> 20:57.200
 building larger computers.

20:57.200 --> 20:59.280
 That you can find these articles from the 80s saying

20:59.280 --> 21:01.760
 that the democratization of computing power

21:01.760 --> 21:04.040
 suddenly meant that you could run these larger neural networks.

21:04.040 --> 21:06.280
 And then people started to do all these amazing things,

21:06.280 --> 21:08.000
 back propagation algorithm was invented.

21:08.000 --> 21:10.120
 And the neural nets people were running

21:10.120 --> 21:13.000
 were these tiny little like 20 neuron neural nets.

21:13.000 --> 21:15.160
 What are you supposed to learn with 20 neurons?

21:15.160 --> 21:18.640
 And so of course they weren't able to get great results.

21:18.640 --> 21:21.960
 And it really wasn't until 2012 that this approach,

21:21.960 --> 21:24.680
 that's almost the most simple, natural approach

21:24.680 --> 21:27.720
 that people had come up with in the 50s, right?

21:27.720 --> 21:30.360
 In some ways, even in the 40s before there were computers

21:30.360 --> 21:32.000
 with the Pits McCullin neuron,

21:33.040 --> 21:37.480
 suddenly this became the best way of solving problems, right?

21:37.480 --> 21:39.280
 And I think there are three core properties

21:39.280 --> 21:42.120
 that deep learning has that I think

21:42.120 --> 21:44.120
 are very worth paying attention to.

21:44.120 --> 21:45.920
 The first is generality.

21:45.920 --> 21:48.760
 We have a very small number of deep learning tools,

21:48.760 --> 21:52.360
 SGD, deep neural net, maybe some, you know, RL.

21:52.360 --> 21:55.600
 And it solves this huge variety of problems,

21:55.600 --> 21:57.240
 speech recognition, machine translation,

21:57.240 --> 22:00.200
 game playing, all of these problems,

22:00.200 --> 22:01.040
 small set of tools.

22:01.040 --> 22:02.760
 So there's the generality.

22:02.760 --> 22:05.000
 There's a second piece, which is the competence.

22:05.000 --> 22:07.040
 You wanna solve any of those problems?

22:07.040 --> 22:10.640
 Throughout 40 years worth of normal computer vision research

22:10.640 --> 22:13.640
 replaced with a deep neural net, it's gonna work better.

22:13.640 --> 22:16.320
 And there's a third piece, which is the scalability, right?

22:16.320 --> 22:18.720
 That one thing that has been shown time and time again

22:18.720 --> 22:21.760
 is that you, if you have a larger neural network,

22:21.760 --> 22:25.120
 throw more compute, more data at it, it will work better.

22:25.120 --> 22:28.880
 Those three properties together feel like essential parts

22:28.880 --> 22:30.800
 of building a general intelligence.

22:30.800 --> 22:33.000
 Now, it doesn't just mean that if we scale up

22:33.000 --> 22:35.200
 what we have, that we will have an AGI, right?

22:35.200 --> 22:36.800
 There are clearly missing pieces.

22:36.800 --> 22:38.000
 There are missing ideas.

22:38.000 --> 22:40.000
 We need to have answers for reasoning.

22:40.000 --> 22:44.800
 But I think that the core here is that for the first time,

22:44.800 --> 22:46.880
 it feels that we have a paradigm

22:46.880 --> 22:48.960
 that gives us hope that general intelligence

22:48.960 --> 22:50.560
 can be achievable.

22:50.560 --> 22:52.160
 And so as soon as you believe that,

22:52.160 --> 22:54.480
 everything else becomes into focus, right?

22:54.480 --> 22:56.560
 If you imagine that you may be able to,

22:56.560 --> 22:59.920
 and that the timeline I think remains uncertain,

22:59.920 --> 23:02.200
 but I think that certainly within our lifetimes

23:02.200 --> 23:04.640
 and possibly within a much shorter period of time

23:04.640 --> 23:06.560
 than people would expect,

23:06.560 --> 23:09.360
 if you can really build the most transformative technology

23:09.360 --> 23:11.720
 that will ever exist, you stop thinking about yourself

23:11.720 --> 23:12.560
 so much, right?

23:12.560 --> 23:14.240
 And you start thinking about just like,

23:14.240 --> 23:16.440
 how do you have a world where this goes well?

23:16.440 --> 23:18.160
 And that you need to think about the practicalities

23:18.160 --> 23:19.560
 of how do you build an organization

23:19.560 --> 23:22.000
 and get together a bunch of people and resources

23:22.000 --> 23:25.160
 and to make sure that people feel motivated

23:25.160 --> 23:26.800
 and ready to do it.

23:28.080 --> 23:30.720
 But I think that then you start thinking about,

23:30.720 --> 23:32.080
 well, what if we succeed?

23:32.080 --> 23:34.280
 And how do we make sure that when we succeed,

23:34.280 --> 23:35.600
 that the world is actually the place

23:35.600 --> 23:38.200
 that we want ourselves to exist in?

23:38.200 --> 23:41.080
 And almost in the Rawlsian Vale sense of the word.

23:41.080 --> 23:43.880
 And so that's kind of the broader landscape.

23:43.880 --> 23:46.680
 And Open AI was really formed in 2015

23:46.680 --> 23:51.480
 with that high level picture of AGI might be possible

23:51.480 --> 23:52.880
 sooner than people think

23:52.880 --> 23:55.840
 and that we need to try to do our best

23:55.840 --> 23:57.480
 to make sure it's going to go well.

23:57.480 --> 23:59.360
 And then we spent the next couple of years

23:59.360 --> 24:00.840
 really trying to figure out what does that mean?

24:00.840 --> 24:01.960
 How do we do it?

24:01.960 --> 24:04.800
 And I think that typically with a company,

24:04.800 --> 24:07.320
 you start out very small.

24:07.320 --> 24:09.000
 So you want a cofounder and you build a product,

24:09.000 --> 24:11.360
 you get some users, you get a product market fit,

24:11.360 --> 24:13.320
 then at some point you raise some money,

24:13.320 --> 24:14.840
 you hire people, you scale,

24:14.840 --> 24:17.440
 and then down the road, then the big companies

24:17.440 --> 24:19.080
 realize you exist and try to kill you.

24:19.080 --> 24:21.520
 And for Open AI, it was basically everything

24:21.520 --> 24:22.960
 in exactly the opposite order.

24:25.480 --> 24:26.760
 Let me just pause for a second.

24:26.760 --> 24:27.520
 He said a lot of things.

24:27.520 --> 24:31.240
 And let me just admire the jarring aspect

24:31.240 --> 24:35.160
 of what Open AI stands for, which is daring to dream.

24:35.160 --> 24:37.120
 I mean, you said it's pretty powerful.

24:37.120 --> 24:40.080
 You caught me off guard because I think that's very true.

24:40.080 --> 24:44.040
 The step of just daring to dream

24:44.040 --> 24:46.720
 about the possibilities of creating intelligence

24:46.720 --> 24:48.760
 in a positive and a safe way,

24:48.760 --> 24:50.640
 but just even creating intelligence

24:50.640 --> 24:55.640
 is a much needed, refreshing catalyst

24:56.280 --> 24:57.360
 for the AI community.

24:57.360 --> 24:58.800
 So that's the starting point.

24:58.800 --> 25:02.840
 Okay, so then formation of Open AI, what's your point?

25:02.840 --> 25:05.640
 I would just say that when we were starting Open AI,

25:05.640 --> 25:07.760
 that kind of the first question that we had is,

25:07.760 --> 25:12.000
 is it too late to start a lab with a bunch of the best people?

25:12.000 --> 25:13.160
 Right, is that even possible?

25:13.160 --> 25:14.320
 That was an actual question.

25:14.320 --> 25:17.280
 That was the core question of,

25:17.280 --> 25:19.320
 we had this dinner in July of 2015,

25:19.320 --> 25:21.240
 and that was really what we spent the whole time

25:21.240 --> 25:22.320
 talking about.

25:22.320 --> 25:26.800
 And because you think about kind of where AI was,

25:26.800 --> 25:30.200
 is that it transitioned from being an academic pursuit

25:30.200 --> 25:32.240
 to an industrial pursuit.

25:32.240 --> 25:34.240
 And so a lot of the best people were in these big

25:34.240 --> 25:37.000
 research labs and that we wanted to start our own one

25:37.000 --> 25:40.560
 that no matter how much resources we could accumulate

25:40.560 --> 25:43.520
 would be pale in comparison to the big tech companies.

25:43.520 --> 25:44.720
 And we knew that.

25:44.720 --> 25:45.800
 And there's a question of,

25:45.800 --> 25:47.720
 are we going to be actually able to get this thing

25:47.720 --> 25:48.720
 off the ground?

25:48.720 --> 25:49.760
 You need critical mass.

25:49.760 --> 25:52.120
 You can't just do you and a cofounder build a product, right?

25:52.120 --> 25:55.600
 You really need to have a group of five to 10 people.

25:55.600 --> 25:59.480
 And we kind of concluded it wasn't obviously impossible.

25:59.480 --> 26:00.840
 So it seemed worth trying.

26:02.240 --> 26:04.800
 Well, you're also a dreamer, so who knows, right?

26:04.800 --> 26:05.640
 That's right.

26:05.640 --> 26:07.720
 Okay, so speaking of that,

26:07.720 --> 26:10.520
 competing with the big players,

26:11.520 --> 26:14.080
 let's talk about some of the tricky things

26:14.080 --> 26:17.480
 as you think through this process of growing,

26:17.480 --> 26:20.080
 of seeing how you can develop these systems

26:20.080 --> 26:22.640
 at a scale that competes.

26:22.640 --> 26:25.720
 So you recently formed OpenAI LP,

26:26.560 --> 26:30.800
 a new cap profit company that now carries the name OpenAI.

26:30.800 --> 26:33.280
 So OpenAI is now this official company.

26:33.280 --> 26:36.520
 The original nonprofit company still exists

26:36.520 --> 26:39.800
 and carries the OpenAI nonprofit name.

26:39.800 --> 26:42.000
 So can you explain what this company is,

26:42.000 --> 26:44.280
 what the purpose of its creation is,

26:44.280 --> 26:48.800
 and how did you arrive at the decision to create it?

26:48.800 --> 26:53.280
 OpenAI, the whole entity and OpenAI LP as a vehicle

26:53.280 --> 26:55.560
 is trying to accomplish the mission

26:55.560 --> 26:57.520
 of ensuring that artificial general intelligence

26:57.520 --> 26:58.800
 benefits everyone.

26:58.800 --> 27:00.240
 And the main way that we're trying to do that

27:00.240 --> 27:01.840
 is by actually trying to build

27:01.840 --> 27:03.240
 general intelligence to ourselves

27:03.240 --> 27:05.920
 and make sure the benefits are distributed to the world.

27:05.920 --> 27:07.200
 That's the primary way.

27:07.200 --> 27:09.600
 We're also fine if someone else does this, right?

27:09.600 --> 27:10.640
 It doesn't have to be us.

27:10.640 --> 27:12.640
 If someone else is going to build an AGI

27:12.640 --> 27:14.840
 and make sure that the benefits don't get locked up

27:14.840 --> 27:18.160
 in one company or with one set of people,

27:19.280 --> 27:21.160
 like we're actually fine with that.

27:21.160 --> 27:25.400
 And so those ideas are baked into our charter,

27:25.400 --> 27:28.400
 which is kind of the foundational document

27:28.400 --> 27:31.920
 that describes kind of our values and how we operate.

27:31.920 --> 27:36.360
 And it's also really baked into the structure of OpenAI LP.

27:36.360 --> 27:37.960
 And so the way that we've set up OpenAI LP

27:37.960 --> 27:42.160
 is that in the case where we succeed, right?

27:42.160 --> 27:45.320
 If we actually build what we're trying to build,

27:45.320 --> 27:47.800
 then investors are able to get a return,

27:47.800 --> 27:50.400
 and but that return is something that is capped.

27:50.400 --> 27:53.000
 And so if you think of AGI in terms of the value

27:53.000 --> 27:54.160
 that you could really create,

27:54.160 --> 27:56.320
 you're talking about the most transformative technology

27:56.320 --> 27:58.000
 ever created, it's gonna create,

27:58.000 --> 28:01.880
 or does the magnitude more value than any existing company?

28:01.880 --> 28:05.960
 And that all of that value will be owned by the world,

28:05.960 --> 28:07.880
 like legally titled to the nonprofit

28:07.880 --> 28:09.560
 to fulfill that mission.

28:09.560 --> 28:12.800
 And so that's the structure.

28:12.800 --> 28:15.200
 So the mission is a powerful one,

28:15.200 --> 28:18.920
 and it's one that I think most people would agree with.

28:18.920 --> 28:22.960
 It's how we would hope AI progresses.

28:22.960 --> 28:25.440
 And so how do you tie yourself to that mission?

28:25.440 --> 28:29.240
 How do you make sure you do not deviate from that mission

28:29.240 --> 28:34.240
 that other incentives that are profit driven

28:34.560 --> 28:36.800
 wouldn't don't interfere with the mission?

28:36.800 --> 28:39.560
 So this was actually a really core question for us

28:39.560 --> 28:40.920
 for the past couple of years,

28:40.920 --> 28:43.560
 because I'd say that the way that our history went

28:43.560 --> 28:44.960
 was that for the first year,

28:44.960 --> 28:46.240
 we were getting off the ground, right?

28:46.240 --> 28:47.960
 We had this high level picture,

28:47.960 --> 28:51.880
 but we didn't know exactly how we wanted to accomplish it.

28:51.880 --> 28:53.440
 And really two years ago,

28:53.440 --> 28:55.040
 it's when we first started realizing

28:55.040 --> 28:56.160
 in order to build AGI,

28:56.160 --> 28:58.720
 we're just gonna need to raise way more money

28:58.720 --> 29:00.680
 than we can as a nonprofit.

29:00.680 --> 29:02.800
 We're talking many billions of dollars.

29:02.800 --> 29:05.440
 And so the first question is,

29:05.440 --> 29:06.840
 how are you supposed to do that

29:06.840 --> 29:08.680
 and stay true to this mission?

29:08.680 --> 29:10.560
 And we looked at every legal structure out there

29:10.560 --> 29:11.960
 and included none of them were quite right

29:11.960 --> 29:13.400
 for what we wanted to do.

29:13.400 --> 29:14.600
 And I guess it shouldn't be too surprising

29:14.600 --> 29:16.920
 if you're gonna do some crazy unprecedented technology

29:16.920 --> 29:17.920
 that you're gonna have to come

29:17.920 --> 29:20.320
 with some crazy unprecedented structure to do it in.

29:20.320 --> 29:25.320
 And a lot of our conversation was with people at OpenAI,

29:26.080 --> 29:27.240
 the people who really joined

29:27.240 --> 29:29.160
 because they believe so much in this mission

29:29.160 --> 29:32.120
 and thinking about how do we actually raise the resources

29:32.120 --> 29:35.920
 to do it and also stay true to what we stand for.

29:35.920 --> 29:38.000
 And the place you gotta start is to really align

29:38.000 --> 29:39.560
 on what is it that we stand for, right?

29:39.560 --> 29:40.560
 What are those values?

29:40.560 --> 29:41.840
 What's really important to us?

29:41.840 --> 29:43.760
 And so I'd say that we spent about a year

29:43.760 --> 29:46.240
 really compiling the OpenAI charter.

29:46.240 --> 29:47.560
 And that determines,

29:47.560 --> 29:50.240
 and if you even look at the first line item in there,

29:50.240 --> 29:52.360
 it says that, look, we expect we're gonna have to marshal

29:52.360 --> 29:53.760
 huge amounts of resources,

29:53.760 --> 29:55.160
 but we're going to make sure

29:55.160 --> 29:57.920
 that we minimize conflict of interest with the mission.

29:57.920 --> 30:00.720
 And that kind of aligning on all of those pieces

30:00.720 --> 30:04.240
 was the most important step towards figuring out

30:04.240 --> 30:06.040
 how do we structure a company

30:06.040 --> 30:08.240
 that can actually raise the resources

30:08.240 --> 30:10.360
 to do what we need to do.

30:10.360 --> 30:14.760
 I imagine OpenAI, the decision to create OpenAI LP

30:14.760 --> 30:16.360
 was a really difficult one.

30:16.360 --> 30:17.920
 And there was a lot of discussions

30:17.920 --> 30:19.640
 as you mentioned for a year.

30:19.640 --> 30:22.760
 And there was different ideas,

30:22.760 --> 30:25.120
 perhaps detractors within OpenAI,

30:26.120 --> 30:28.920
 sort of different paths that you could have taken.

30:28.920 --> 30:30.240
 What were those concerns?

30:30.240 --> 30:32.040
 What were the different paths considered?

30:32.040 --> 30:34.080
 What was that process of making that decision like?

30:34.080 --> 30:35.000
 Yep.

30:35.000 --> 30:37.200
 But so if you look actually at the OpenAI charter,

30:37.200 --> 30:40.880
 that there's almost two paths embedded within it.

30:40.880 --> 30:44.880
 There is, we are primarily trying to build AGI ourselves,

30:44.880 --> 30:47.360
 but we're also okay if someone else does it.

30:47.360 --> 30:49.040
 And this is a weird thing for a company.

30:49.040 --> 30:50.480
 It's really interesting, actually.

30:50.480 --> 30:51.320
 Yeah.

30:51.320 --> 30:53.280
 But there is an element of competition

30:53.280 --> 30:56.680
 that you do want to be the one that does it,

30:56.680 --> 30:59.040
 but at the same time, you're okay if somebody else doesn't.

30:59.040 --> 31:01.000
 We'll talk about that a little bit, that trade off,

31:01.000 --> 31:02.960
 that dance that's really interesting.

31:02.960 --> 31:04.600
 And I think this was the core tension

31:04.600 --> 31:06.360
 as we were designing OpenAI LP

31:06.360 --> 31:08.240
 and really the OpenAI strategy,

31:08.240 --> 31:11.080
 is how do you make sure that both you have a shot

31:11.080 --> 31:12.640
 at being a primary actor,

31:12.640 --> 31:15.840
 which really requires building an organization,

31:15.840 --> 31:17.720
 raising massive resources,

31:17.720 --> 31:19.440
 and really having the will to go

31:19.440 --> 31:22.000
 and execute on some really, really hard vision, right?

31:22.000 --> 31:23.760
 You need to really sign up for a long period

31:23.760 --> 31:27.120
 to go and take on a lot of pain and a lot of risk.

31:27.120 --> 31:29.000
 And to do that,

31:29.000 --> 31:31.720
 normally you just import the startup mindset, right?

31:31.720 --> 31:32.760
 And that you think about, okay,

31:32.760 --> 31:34.240
 like how do we out execute everyone?

31:34.240 --> 31:36.160
 You have this very competitive angle.

31:36.160 --> 31:38.120
 But you also have the second angle of saying that,

31:38.120 --> 31:41.600
 well, the true mission isn't for OpenAI to build AGI.

31:41.600 --> 31:45.080
 The true mission is for AGI to go well for humanity.

31:45.080 --> 31:48.080
 And so how do you take all of those first actions

31:48.080 --> 31:51.320
 and make sure you don't close the door on outcomes

31:51.320 --> 31:54.480
 that would actually be positive and fulfill the mission?

31:54.480 --> 31:56.680
 And so I think it's a very delicate balance, right?

31:56.680 --> 31:59.560
 And I think that going 100% one direction or the other

31:59.560 --> 32:01.320
 is clearly not the correct answer.

32:01.320 --> 32:03.920
 And so I think that even in terms of just how we talk about

32:03.920 --> 32:05.400
 OpenAI and think about it,

32:05.400 --> 32:07.600
 there's just like one thing that's always

32:07.600 --> 32:09.680
 in the back of my mind is to make sure

32:09.680 --> 32:12.120
 that we're not just saying OpenAI's goal

32:12.120 --> 32:14.000
 is to build AGI, right?

32:14.000 --> 32:15.560
 That it's actually much broader than that, right?

32:15.560 --> 32:19.360
 That first of all, it's not just AGI, it's safe AGI

32:19.360 --> 32:20.320
 that's very important.

32:20.320 --> 32:23.120
 But secondly, our goal isn't to be the ones to build it,

32:23.120 --> 32:24.720
 our goal is to make sure it goes well for the world.

32:24.720 --> 32:26.120
 And so I think that figuring out,

32:26.120 --> 32:27.960
 how do you balance all of those

32:27.960 --> 32:30.280
 and to get people to really come to the table

32:30.280 --> 32:35.280
 and compile a single document that encompasses all of that

32:36.360 --> 32:37.560
 wasn't trivial.

32:37.560 --> 32:41.680
 So part of the challenge here is your mission is,

32:41.680 --> 32:44.240
 I would say, beautiful, empowering,

32:44.240 --> 32:47.520
 and a beacon of hope for people in the research community

32:47.520 --> 32:49.200
 and just people thinking about AI.

32:49.200 --> 32:51.880
 So your decisions are scrutinized

32:51.880 --> 32:55.920
 more than, I think, a regular profit driven company.

32:55.920 --> 32:57.400
 Do you feel the burden of this

32:57.400 --> 32:58.560
 in the creation of the charter

32:58.560 --> 33:00.200
 and just in the way you operate?

33:00.200 --> 33:01.040
 Yes.

33:03.040 --> 33:05.920
 So why do you lean into the burden

33:07.040 --> 33:08.640
 by creating such a charter?

33:08.640 --> 33:10.440
 Why not keep it quiet?

33:10.440 --> 33:12.920
 I mean, it just boils down to the mission, right?

33:12.920 --> 33:15.200
 Like, I'm here and everyone else is here

33:15.200 --> 33:17.880
 because we think this is the most important mission, right?

33:17.880 --> 33:19.000
 Dare to dream.

33:19.000 --> 33:23.360
 All right, so do you think you can be good for the world

33:23.360 --> 33:26.000
 or create an AGI system that's good

33:26.000 --> 33:28.320
 when you're a for profit company?

33:28.320 --> 33:32.920
 From my perspective, I don't understand why profit

33:32.920 --> 33:37.640
 interferes with positive impact on society.

33:37.640 --> 33:40.760
 I don't understand why Google

33:40.760 --> 33:42.920
 that makes most of its money from ads

33:42.920 --> 33:45.040
 can't also do good for the world

33:45.040 --> 33:47.520
 or other companies, Facebook, anything.

33:47.520 --> 33:50.240
 I don't understand why those have to interfere.

33:50.240 --> 33:55.120
 You know, you can, profit isn't the thing in my view

33:55.120 --> 33:57.240
 that affects the impact of a company.

33:57.240 --> 34:00.360
 What affects the impact of the company is the charter,

34:00.360 --> 34:04.160
 is the culture, is the people inside

34:04.160 --> 34:07.360
 and profit is the thing that just fuels those people.

34:07.360 --> 34:08.760
 What are your views there?

34:08.760 --> 34:10.920
 Yeah, so I think that's a really good question

34:10.920 --> 34:14.200
 and there's some real like longstanding debates

34:14.200 --> 34:16.520
 in human society that are wrapped up in it.

34:16.520 --> 34:18.680
 The way that I think about it is just think about

34:18.680 --> 34:21.520
 what are the most impactful nonprofits in the world?

34:24.000 --> 34:26.760
 What are the most impactful for profits in the world?

34:26.760 --> 34:29.280
 Right, it's much easier to list the for profits.

34:29.280 --> 34:30.120
 That's right.

34:30.120 --> 34:32.400
 And I think that there's some real truth here

34:32.400 --> 34:34.600
 that the system that we set up,

34:34.600 --> 34:38.320
 the system for kind of how today's world is organized

34:38.320 --> 34:41.760
 is one that really allows for huge impact

34:41.760 --> 34:45.400
 and that kind of part of that is that you need to be,

34:45.400 --> 34:48.080
 that for profits are self sustaining

34:48.080 --> 34:51.200
 and able to kind of build on their own momentum.

34:51.200 --> 34:53.080
 And I think that's a really powerful thing.

34:53.080 --> 34:55.880
 It's something that when it turns out

34:55.880 --> 34:57.920
 that we haven't set the guardrails correctly,

34:57.920 --> 34:58.840
 causes problems, right?

34:58.840 --> 35:02.720
 Think about logging companies that go into the rainforest,

35:02.720 --> 35:04.680
 that's really bad, we don't want that.

35:04.680 --> 35:06.520
 And it's actually really interesting to me

35:06.520 --> 35:08.480
 that kind of this question of

35:08.480 --> 35:11.400
 how do you get positive benefits out of a for profit company?

35:11.400 --> 35:12.600
 It's actually very similar to

35:12.600 --> 35:15.800
 how do you get positive benefits out of an AGI, right?

35:15.800 --> 35:18.000
 That you have this like very powerful system,

35:18.000 --> 35:19.680
 it's more powerful than any human

35:19.680 --> 35:21.760
 and it's kind of autonomous in some ways.

35:21.760 --> 35:23.800
 You know, it's super human in a lot of axes

35:23.800 --> 35:25.400
 and somehow you have to set the guardrails

35:25.400 --> 35:26.800
 to get good things to happen.

35:26.800 --> 35:29.360
 But when you do, the benefits are massive.

35:29.360 --> 35:32.920
 And so I think that when I think about nonprofit

35:32.920 --> 35:36.120
 versus for profit, I think just not enough happens

35:36.120 --> 35:37.800
 in nonprofits, they're very pure,

35:37.800 --> 35:39.200
 but it's just kind of, you know,

35:39.200 --> 35:40.840
 it's just hard to do things there.

35:40.840 --> 35:44.000
 And for profits in some ways, like too much happens,

35:44.000 --> 35:46.440
 but if kind of shaped in the right way,

35:46.440 --> 35:47.840
 it can actually be very positive.

35:47.840 --> 35:52.160
 And so with OpenILP, we're picking a road in between.

35:52.160 --> 35:54.880
 Now, the thing that I think is really important to recognize

35:54.880 --> 35:57.160
 is that the way that we think about OpenILP

35:57.160 --> 36:00.440
 is that in the world where AGI actually happens, right?

36:00.440 --> 36:01.720
 In a world where we are successful,

36:01.720 --> 36:03.800
 we build the most transformative technology ever,

36:03.800 --> 36:06.600
 the amount of value we're going to create will be astronomical.

36:07.600 --> 36:12.600
 And so then in that case, that the cap that we have

36:12.760 --> 36:15.520
 will be a small fraction of the value we create.

36:15.520 --> 36:17.800
 And the amount of value that goes back to investors

36:17.800 --> 36:20.000
 and employees looks pretty similar to what would happen

36:20.000 --> 36:21.680
 in a pretty successful startup.

36:23.760 --> 36:26.520
 And that's really the case that we're optimizing for, right?

36:26.520 --> 36:28.560
 That we're thinking about in the success case,

36:28.560 --> 36:32.120
 making sure that the value we create doesn't get locked up.

36:32.120 --> 36:34.920
 And I expect that in other for profit companies

36:34.920 --> 36:37.800
 that it's possible to do something like that.

36:37.800 --> 36:39.720
 I think it's not obvious how to do it, right?

36:39.720 --> 36:41.440
 And I think that as a for profit company,

36:41.440 --> 36:44.240
 you have a lot of fiduciary duty to your shareholders

36:44.240 --> 36:45.640
 and that there are certain decisions

36:45.640 --> 36:47.520
 that you just cannot make.

36:47.520 --> 36:49.080
 In our structure, we've set it up

36:49.080 --> 36:52.440
 so that we have a fiduciary duty to the charter,

36:52.440 --> 36:54.400
 that we always get to make the decision

36:54.400 --> 36:56.720
 that is right for the charter,

36:56.720 --> 36:58.800
 rather than even if it comes at the expense

36:58.800 --> 37:00.680
 of our own stakeholders.

37:00.680 --> 37:03.400
 And so I think that when I think about

37:03.400 --> 37:04.360
 what's really important,

37:04.360 --> 37:06.280
 it's not really about nonprofit versus for profit.

37:06.280 --> 37:09.600
 It's really a question of if you build a GI

37:09.600 --> 37:10.600
 and you kind of, you know,

37:10.600 --> 37:13.080
 humanity is now at this new age,

37:13.080 --> 37:15.760
 who benefits, whose lives are better?

37:15.760 --> 37:17.120
 And I think that what's really important

37:17.120 --> 37:20.320
 is to have an answer that is everyone.

37:20.320 --> 37:23.400
 Yeah, which is one of the core aspects of the charter.

37:23.400 --> 37:26.520
 So one concern people have, not just with OpenAI,

37:26.520 --> 37:28.400
 but with Google, Facebook, Amazon,

37:28.400 --> 37:33.400
 anybody really that's creating impact at scale

37:35.000 --> 37:37.680
 is how do we avoid, as your charter says,

37:37.680 --> 37:40.080
 avoid enabling the use of AI or AGI

37:40.080 --> 37:43.640
 to unduly concentrate power?

37:43.640 --> 37:45.920
 Why would not a company like OpenAI

37:45.920 --> 37:48.640
 keep all the power of an AGI system to itself?

37:48.640 --> 37:49.520
 The charter.

37:49.520 --> 37:50.360
 The charter.

37:50.360 --> 37:51.960
 So, you know, how does the charter

37:53.120 --> 37:57.240
 actualize itself in day to day?

37:57.240 --> 38:00.480
 So I think that first to zoom out, right,

38:00.480 --> 38:01.880
 that the way that we structure the company

38:01.880 --> 38:04.560
 is so that the power for sort of, you know,

38:04.560 --> 38:06.720
 dictating the actions that OpenAI takes

38:06.720 --> 38:08.600
 ultimately rests with the board, right?

38:08.600 --> 38:11.720
 The board of the nonprofit and the board is set up

38:11.720 --> 38:13.480
 in certain ways, with certain restrictions

38:13.480 --> 38:16.280
 that you can read about in the OpenAI LP blog post.

38:16.280 --> 38:19.200
 But effectively the board is the governing body

38:19.200 --> 38:21.200
 for OpenAI LP.

38:21.200 --> 38:24.400
 And the board has a duty to fulfill the mission

38:24.400 --> 38:26.360
 of the nonprofit.

38:26.360 --> 38:28.800
 And so that's kind of how we tie,

38:28.800 --> 38:30.960
 how we thread all these things together.

38:30.960 --> 38:32.880
 Now there's a question of so day to day,

38:32.880 --> 38:34.800
 how do people, the individuals,

38:34.800 --> 38:36.960
 who in some ways are the most empowered ones, right?

38:36.960 --> 38:38.800
 You know, the board sort of gets to call the shots

38:38.800 --> 38:41.920
 at the high level, but the people who are actually executing

38:41.920 --> 38:43.120
 are the employees, right?

38:43.120 --> 38:45.480
 The people here on a day to day basis who have the,

38:45.480 --> 38:47.720
 you know, the keys to the technical kingdom.

38:48.960 --> 38:51.720
 And there I think that the answer looks a lot like,

38:51.720 --> 38:55.120
 well, how does any company's values get actualized, right?

38:55.120 --> 38:56.720
 And I think that a lot of that comes down to

38:56.720 --> 38:58.160
 that you need people who are here

38:58.160 --> 39:01.320
 because they really believe in that mission

39:01.320 --> 39:02.800
 and they believe in the charter

39:02.800 --> 39:05.440
 and that they are willing to take actions

39:05.440 --> 39:08.600
 that maybe are worse for them, but are better for the charter.

39:08.600 --> 39:11.440
 And that's something that's really baked into the culture.

39:11.440 --> 39:13.200
 And honestly, I think it's, you know,

39:13.200 --> 39:14.560
 I think that that's one of the things

39:14.560 --> 39:18.200
 that we really have to work to preserve as time goes on.

39:18.200 --> 39:20.760
 And that's a really important part of how we think

39:20.760 --> 39:23.040
 about hiring people and bringing people into OpenAI.

39:23.040 --> 39:25.320
 So there's people here, there's people here

39:25.320 --> 39:30.320
 who could speak up and say, like, hold on a second,

39:30.840 --> 39:34.600
 this is totally against what we stand for, culture wise.

39:34.600 --> 39:35.440
 Yeah, yeah, for sure.

39:35.440 --> 39:37.120
 I mean, I think that we actually have,

39:37.120 --> 39:38.760
 I think that's like a pretty important part

39:38.760 --> 39:41.920
 of how we operate and how we have,

39:41.920 --> 39:44.160
 even again with designing the charter

39:44.160 --> 39:46.680
 and designing OpenAI in the first place,

39:46.680 --> 39:48.760
 that there has been a lot of conversation

39:48.760 --> 39:50.480
 with employees here and a lot of times

39:50.480 --> 39:52.400
 where employees said, wait a second,

39:52.400 --> 39:53.920
 this seems like it's going in the wrong direction

39:53.920 --> 39:55.120
 and let's talk about it.

39:55.120 --> 39:57.360
 And so I think one thing that's, I think are really,

39:57.360 --> 39:58.880
 and you know, here's actually one thing

39:58.880 --> 40:02.080
 that I think is very unique about us as a small company,

40:02.080 --> 40:04.360
 is that if you're at a massive tech giant,

40:04.360 --> 40:05.680
 that's a little bit hard for someone

40:05.680 --> 40:08.120
 who's a line employee to go and talk to the CEO

40:08.120 --> 40:10.520
 and say, I think that we're doing this wrong.

40:10.520 --> 40:13.040
 And you know, you'll get companies like Google

40:13.040 --> 40:15.720
 that have had some collective action from employees

40:15.720 --> 40:19.400
 to make ethical change around things like Maven.

40:19.400 --> 40:20.680
 And so maybe there are mechanisms

40:20.680 --> 40:22.240
 that other companies that work,

40:22.240 --> 40:24.480
 but here, super easy for anyone to pull me aside,

40:24.480 --> 40:26.320
 to pull Sam aside, to pull Eli aside,

40:26.320 --> 40:27.800
 and people do it all the time.

40:27.800 --> 40:29.800
 One of the interesting things in the charter

40:29.800 --> 40:31.640
 is this idea that it'd be great

40:31.640 --> 40:34.240
 if you could try to describe or untangle

40:34.240 --> 40:36.440
 switching from competition to collaboration

40:36.440 --> 40:38.920
 and late stage AGI development.

40:38.920 --> 40:39.760
 It's really interesting,

40:39.760 --> 40:42.160
 this dance between competition and collaboration,

40:42.160 --> 40:43.400
 how do you think about that?

40:43.400 --> 40:45.000
 Yeah, assuming that you can actually do

40:45.000 --> 40:47.040
 the technical side of AGI development,

40:47.040 --> 40:48.960
 I think there's going to be two key problems

40:48.960 --> 40:50.400
 with figuring out how do you actually deploy it

40:50.400 --> 40:51.520
 and make it go well.

40:51.520 --> 40:53.160
 The first one of these is the run up

40:53.160 --> 40:56.360
 to building the first AGI.

40:56.360 --> 40:58.920
 You look at how self driving cars are being developed,

40:58.920 --> 41:00.680
 and it's a competitive race.

41:00.680 --> 41:02.560
 And the thing that always happens in competitive race

41:02.560 --> 41:04.160
 is that you have huge amounts of pressure

41:04.160 --> 41:05.600
 to get rid of safety.

41:06.800 --> 41:08.920
 And so that's one thing we're very concerned about, right?

41:08.920 --> 41:12.000
 Is that people, multiple teams figuring out,

41:12.000 --> 41:13.600
 we can actually get there,

41:13.600 --> 41:16.680
 but you know, if we took the slower path

41:16.680 --> 41:20.240
 that is more guaranteed to be safe, we will lose.

41:20.240 --> 41:22.360
 And so we're going to take the fast path.

41:22.360 --> 41:25.480
 And so the more that we can, both ourselves,

41:25.480 --> 41:27.280
 be in a position where we don't generate

41:27.280 --> 41:29.000
 that competitive race, where we say,

41:29.000 --> 41:31.520
 if the race is being run and that someone else

41:31.520 --> 41:33.280
 is further ahead than we are,

41:33.280 --> 41:35.600
 we're not going to try to leapfrog.

41:35.600 --> 41:37.200
 We're going to actually work with them, right?

41:37.200 --> 41:38.800
 We will help them succeed.

41:38.800 --> 41:40.440
 As long as what they're trying to do

41:40.440 --> 41:42.920
 is to fulfill our mission, then we're good.

41:42.920 --> 41:44.800
 We don't have to build AGI ourselves.

41:44.800 --> 41:47.080
 And I think that's a really important commitment from us,

41:47.080 --> 41:49.080
 but it can't just be unilateral, right?

41:49.080 --> 41:50.400
 I think that it's really important

41:50.400 --> 41:53.120
 that other players who are serious about building AGI

41:53.120 --> 41:54.680
 make similar commitments, right?

41:54.680 --> 41:56.640
 And I think that, you know, again,

41:56.640 --> 41:57.840
 to the extent that everyone believes

41:57.840 --> 42:00.080
 that AGI should be something to benefit everyone,

42:00.080 --> 42:01.240
 then it actually really shouldn't matter

42:01.240 --> 42:02.440
 which company builds it.

42:02.440 --> 42:04.160
 And we should all be concerned about the case

42:04.160 --> 42:06.080
 where we just race so hard to get there

42:06.080 --> 42:07.640
 that something goes wrong.

42:07.640 --> 42:09.600
 So what role do you think government,

42:10.560 --> 42:13.840
 our favorite entity has in setting policy and rules

42:13.840 --> 42:18.320
 about this domain, from research to the development

42:18.320 --> 42:22.880
 to early stage, to late stage AI and AGI development?

42:22.880 --> 42:25.640
 So I think that, first of all,

42:25.640 --> 42:28.080
 it's really important that government's in there, right?

42:28.080 --> 42:29.800
 In some way, shape, or form, you know,

42:29.800 --> 42:30.920
 at the end of the day, we're talking about

42:30.920 --> 42:35.080
 building technology that will shape how the world operates

42:35.080 --> 42:39.040
 and that there needs to be government as part of that answer.

42:39.040 --> 42:42.160
 And so that's why we've done a number

42:42.160 --> 42:43.600
 of different congressional testimonies.

42:43.600 --> 42:46.440
 We interact with a number of different lawmakers

42:46.440 --> 42:50.040
 and that right now, a lot of our message to them

42:50.040 --> 42:54.360
 is that it's not the time for regulation,

42:54.360 --> 42:56.400
 it is the time for measurement, right?

42:56.400 --> 42:59.080
 That our main policy recommendation is that people,

42:59.080 --> 43:00.680
 and you know, the government does this all the time

43:00.680 --> 43:04.880
 with bodies like NIST, spend time trying to figure out

43:04.880 --> 43:07.920
 just where the technology is, how fast it's moving,

43:07.920 --> 43:11.200
 and can really become literate and up to speed

43:11.200 --> 43:13.520
 with respect to what to expect.

43:13.520 --> 43:15.240
 So I think that today, the answer really

43:15.240 --> 43:17.320
 is about measurement.

43:17.320 --> 43:20.160
 And I think that there will be a time and place

43:20.160 --> 43:21.720
 where that will change.

43:21.720 --> 43:24.840
 And I think it's a little bit hard to predict exactly

43:24.840 --> 43:27.120
 what exactly that trajectory should look like.

43:27.120 --> 43:31.080
 So there will be a point at which regulation,

43:31.080 --> 43:34.200
 federal in the United States, the government steps in

43:34.200 --> 43:39.200
 and helps be the, I don't wanna say the adult in the room,

43:39.520 --> 43:42.400
 to make sure that there is strict rules,

43:42.400 --> 43:45.200
 maybe conservative rules that nobody can cross.

43:45.200 --> 43:47.400
 Well, I think there's kind of maybe two angles to it.

43:47.400 --> 43:49.800
 So today with narrow AI applications,

43:49.800 --> 43:51.960
 that I think there are already existing bodies

43:51.960 --> 43:54.880
 that are responsible and should be responsible for regulation.

43:54.880 --> 43:57.040
 You think about, for example, with self driving cars,

43:57.040 --> 43:59.440
 that you want the national highway.

44:00.720 --> 44:02.920
 Yeah, exactly to be regulated in that.

44:02.920 --> 44:04.040
 That makes sense, right?

44:04.040 --> 44:04.960
 That basically what we're saying

44:04.960 --> 44:08.120
 is that we're going to have these technological systems

44:08.120 --> 44:10.600
 that are going to be performing applications

44:10.600 --> 44:12.280
 that humans already do.

44:12.280 --> 44:14.800
 Great, we already have ways of thinking about standards

44:14.800 --> 44:16.160
 and safety for those.

44:16.160 --> 44:18.880
 So I think actually empowering those regulators today

44:18.880 --> 44:20.040
 is also pretty important.

44:20.040 --> 44:24.760
 And then I think for AGI, that there's going to be a point

44:24.760 --> 44:26.040
 where we'll have better answers.

44:26.040 --> 44:27.640
 And I think that maybe a similar approach

44:27.640 --> 44:30.520
 of first measurement and start thinking about

44:30.520 --> 44:31.640
 what the rules should be.

44:31.640 --> 44:32.640
 I think it's really important

44:32.640 --> 44:36.280
 that we don't prematurely squash progress.

44:36.280 --> 44:40.160
 I think it's very easy to kind of smother a budding field.

44:40.160 --> 44:42.160
 And I think that's something to really avoid.

44:42.160 --> 44:43.760
 But I don't think that the right way of doing it

44:43.760 --> 44:46.920
 is to say, let's just try to blaze ahead

44:46.920 --> 44:50.280
 and not involve all these other stakeholders.

44:51.480 --> 44:56.240
 So you've recently released a paper on GPT2

44:56.240 --> 45:01.240
 language modeling, but did not release the full model

45:02.040 --> 45:05.280
 because you had concerns about the possible negative effects

45:05.280 --> 45:07.480
 of the availability of such model.

45:07.480 --> 45:10.680
 It's outside of just that decision,

45:10.680 --> 45:14.360
 and it's super interesting because of the discussion

45:14.360 --> 45:17.000
 at a societal level, the discourse it creates.

45:17.000 --> 45:19.320
 So it's fascinating in that aspect.

45:19.320 --> 45:22.880
 But if you think that's the specifics here at first,

45:22.880 --> 45:25.920
 what are some negative effects that you envisioned?

45:25.920 --> 45:28.600
 And of course, what are some of the positive effects?

45:28.600 --> 45:30.640
 Yeah, so again, I think to zoom out,

45:30.640 --> 45:34.040
 like the way that we thought about GPT2

45:34.040 --> 45:35.800
 is that with language modeling,

45:35.800 --> 45:38.560
 we are clearly on a trajectory right now

45:38.560 --> 45:40.880
 where we scale up our models

45:40.880 --> 45:44.480
 and we get qualitatively better performance, right?

45:44.480 --> 45:47.360
 GPT2 itself was actually just a scale up

45:47.360 --> 45:50.680
 of a model that we've released in the previous June, right?

45:50.680 --> 45:52.880
 And we just ran it at much larger scale

45:52.880 --> 45:53.880
 and we got these results

45:53.880 --> 45:57.240
 where suddenly starting to write coherent pros,

45:57.240 --> 46:00.040
 which was not something we'd seen previously.

46:00.040 --> 46:01.320
 And what are we doing now?

46:01.320 --> 46:05.760
 Well, we're gonna scale up GPT2 by 10x by 100x by 1000x

46:05.760 --> 46:07.840
 and we don't know what we're gonna get.

46:07.840 --> 46:10.120
 And so it's very clear that the model

46:10.120 --> 46:12.840
 that we released last June,

46:12.840 --> 46:16.440
 I think it's kind of like, it's a good academic toy.

46:16.440 --> 46:18.920
 It's not something that we think is something

46:18.920 --> 46:20.440
 that can really have negative applications

46:20.440 --> 46:21.680
 or to the extent that it can,

46:21.680 --> 46:24.360
 that the positive of people being able to play with it

46:24.360 --> 46:28.280
 is far outweighs the possible harms.

46:28.280 --> 46:32.600
 You fast forward to not GPT2, but GPT20,

46:32.600 --> 46:34.720
 and you think about what that's gonna be like.

46:34.720 --> 46:38.200
 And I think that the capabilities are going to be substantive.

46:38.200 --> 46:41.120
 And so there needs to be a point in between the two

46:41.120 --> 46:43.480
 where you say, this is something

46:43.480 --> 46:45.200
 where we are drawing the line

46:45.200 --> 46:48.000
 and that we need to start thinking about the safety aspects.

46:48.000 --> 46:50.160
 And I think for GPT2, we could have gone either way.

46:50.160 --> 46:52.720
 And in fact, when we had conversations internally

46:52.720 --> 46:54.760
 that we had a bunch of pros and cons

46:54.760 --> 46:58.160
 and it wasn't clear which one outweighed the other.

46:58.160 --> 46:59.840
 And I think that when we announced

46:59.840 --> 47:02.160
 that, hey, we decide not to release this model,

47:02.160 --> 47:03.600
 then there was a bunch of conversation

47:03.600 --> 47:05.200
 where various people said it's so obvious

47:05.200 --> 47:06.360
 that you should have just released it.

47:06.360 --> 47:07.520
 There are other people that said it's so obvious

47:07.520 --> 47:08.840
 you should not have released it.

47:08.840 --> 47:10.960
 And I think that that almost definitionally means

47:10.960 --> 47:13.800
 that holding it back was the correct decision.

47:13.800 --> 47:17.000
 If it's not obvious whether something is beneficial

47:17.000 --> 47:19.720
 or not, you should probably default to caution.

47:19.720 --> 47:22.440
 And so I think that the overall landscape

47:22.440 --> 47:23.760
 for how we think about it

47:23.760 --> 47:25.920
 is that this decision could have gone either way.

47:25.920 --> 47:27.960
 There are great arguments in both directions.

47:27.960 --> 47:30.080
 But for future models down the road,

47:30.080 --> 47:32.320
 and possibly sooner than you'd expect,

47:32.320 --> 47:33.880
 because scaling these things up doesn't actually

47:33.880 --> 47:36.800
 take that long, those ones,

47:36.800 --> 47:39.600
 you're definitely not going to want to release into the wild.

47:39.600 --> 47:42.640
 And so I think that we almost view this as a test case

47:42.640 --> 47:45.360
 and to see, can we even design,

47:45.360 --> 47:47.960
 how do you have a society or how do you have a system

47:47.960 --> 47:50.520
 that goes from having no concept of responsible disclosure

47:50.520 --> 47:53.440
 where the mere idea of not releasing something

47:53.440 --> 47:55.960
 for safety reasons is unfamiliar

47:55.960 --> 47:57.440
 to a world where you say, okay,

47:57.440 --> 47:58.720
 we have a powerful model.

47:58.720 --> 47:59.720
 Let's at least think about it.

47:59.720 --> 48:01.280
 Let's go through some process.

48:01.280 --> 48:02.680
 And you think about the security community.

48:02.680 --> 48:03.880
 It took them a long time

48:03.880 --> 48:05.960
 to design responsible disclosure.

48:05.960 --> 48:07.200
 You think about this question of,

48:07.200 --> 48:08.800
 well, I have a security exploit.

48:08.800 --> 48:09.760
 I send it to the company.

48:09.760 --> 48:12.000
 The company is like, tries to prosecute me

48:12.000 --> 48:14.760
 or just ignores it.

48:14.760 --> 48:16.080
 What do I do?

48:16.080 --> 48:17.320
 And so the alternatives of,

48:17.320 --> 48:19.120
 oh, I just always publish your exploits.

48:19.120 --> 48:20.200
 That doesn't seem good either.

48:20.200 --> 48:21.600
 And so it really took a long time

48:21.600 --> 48:25.320
 and it was bigger than any individual.

48:25.320 --> 48:27.080
 It's really about building a whole community

48:27.080 --> 48:28.760
 that believe that, okay, we'll have this process

48:28.760 --> 48:30.160
 where you send it to the company

48:30.160 --> 48:31.680
 if they don't act at a certain time,

48:31.680 --> 48:33.120
 then you can go public

48:33.120 --> 48:34.440
 and you're not a bad person.

48:34.440 --> 48:36.240
 You've done the right thing.

48:36.240 --> 48:38.680
 And I think that in AI,

48:38.680 --> 48:41.400
 part of the response to GPT2 just proves

48:41.400 --> 48:44.200
 that we don't have any concept of this.

48:44.200 --> 48:47.080
 So that's the high level picture.

48:47.080 --> 48:48.720
 And so I think that,

48:48.720 --> 48:51.240
 I think this was a really important move to make.

48:51.240 --> 48:54.000
 And we could have maybe delayed it for GPT3,

48:54.000 --> 48:56.080
 but I'm really glad we did it for GPT2.

48:56.080 --> 48:57.760
 And so now you look at GPT2 itself

48:57.760 --> 48:59.440
 and you think about the substance of, okay,

48:59.440 --> 49:01.320
 what are potential negative applications?

49:01.320 --> 49:04.120
 So you have this model that's been trained on the internet,

49:04.120 --> 49:06.520
 which is also going to be a bunch of very biased data,

49:06.520 --> 49:09.600
 a bunch of very offensive content in there.

49:09.600 --> 49:13.240
 And you can ask it to generate content for you

49:13.240 --> 49:14.600
 on basically any topic, right?

49:14.600 --> 49:15.440
 You just give it a prompt

49:15.440 --> 49:16.800
 and it'll just start writing

49:16.800 --> 49:19.120
 and it writes content like you see on the internet,

49:19.120 --> 49:21.960
 you know, even down to like saying advertisement

49:21.960 --> 49:24.200
 in the middle of some of its generations.

49:24.200 --> 49:26.200
 And you think about the possibilities

49:26.200 --> 49:29.280
 for generating fake news or abusive content.

49:29.280 --> 49:30.120
 And, you know, it's interesting

49:30.120 --> 49:31.880
 seeing what people have done with, you know,

49:31.880 --> 49:34.400
 we released a smaller version of GPT2

49:34.400 --> 49:37.480
 and the people have done things like try to generate,

49:37.480 --> 49:40.760
 you know, take my own Facebook message history

49:40.760 --> 49:43.360
 and generate more Facebook messages like me

49:43.360 --> 49:47.360
 and people generating fake politician content

49:47.360 --> 49:49.520
 or, you know, there's a bunch of things there

49:49.520 --> 49:51.920
 where you at least have to think,

49:51.920 --> 49:54.720
 is this going to be good for the world?

49:54.720 --> 49:56.320
 There's the flip side, which is I think

49:56.320 --> 49:57.840
 that there's a lot of awesome applications

49:57.840 --> 50:01.640
 that we really want to see like creative applications

50:01.640 --> 50:04.000
 in terms of if you have sci fi authors

50:04.000 --> 50:06.760
 that can work with this tool and come with cool ideas,

50:06.760 --> 50:09.720
 like that seems awesome if we can write better sci fi

50:09.720 --> 50:11.360
 through the use of these tools.

50:11.360 --> 50:13.080
 And we've actually had a bunch of people right into us

50:13.080 --> 50:16.160
 asking, hey, can we use it for, you know,

50:16.160 --> 50:18.360
 a variety of different creative applications?

50:18.360 --> 50:21.880
 So the positive are actually pretty easy to imagine.

50:21.880 --> 50:26.880
 There are, you know, the usual NLP applications

50:26.880 --> 50:30.960
 that are really interesting, but let's go there.

50:30.960 --> 50:32.960
 It's kind of interesting to think about a world

50:32.960 --> 50:37.960
 where, look at Twitter, where not just fake news

50:37.960 --> 50:42.960
 but smarter and smarter bots being able to spread

50:43.040 --> 50:47.400
 in an interesting complex networking way in information

50:47.400 --> 50:50.800
 that just floods out us regular human beings

50:50.800 --> 50:52.880
 with our original thoughts.

50:52.880 --> 50:57.880
 So what are your views of this world with GPT 20?

50:58.760 --> 51:01.600
 Right, how do we think about, again,

51:01.600 --> 51:03.560
 it's like one of those things about in the 50s

51:03.560 --> 51:08.560
 trying to describe the internet or the smartphone.

51:08.720 --> 51:09.960
 What do you think about that world,

51:09.960 --> 51:11.400
 the nature of information?

51:12.920 --> 51:16.760
 One possibility is that we'll always try to design systems

51:16.760 --> 51:19.680
 that identify a robot versus human

51:19.680 --> 51:21.280
 and we'll do so successfully.

51:21.280 --> 51:24.600
 And so we'll authenticate that we're still human.

51:24.600 --> 51:27.520
 And the other world is that we just accept the fact

51:27.520 --> 51:30.360
 that we're swimming in a sea of fake news

51:30.360 --> 51:32.120
 and just learn to swim there.

51:32.120 --> 51:34.800
 Well, have you ever seen the, there's a, you know,

51:34.800 --> 51:39.800
 popular meme of a robot with a physical arm and pen

51:41.520 --> 51:43.440
 clicking the I'm not a robot button?

51:43.440 --> 51:44.280
 Yeah.

51:44.280 --> 51:48.560
 I think the truth is that really trying to distinguish

51:48.560 --> 51:52.160
 between robot and human is a losing battle.

51:52.160 --> 51:53.800
 Ultimately, you think it's a losing battle?

51:53.800 --> 51:55.520
 I think it's a losing battle ultimately, right?

51:55.520 --> 51:57.800
 I think that that is that in terms of the content,

51:57.800 --> 51:59.360
 in terms of the actions that you can take.

51:59.360 --> 52:01.200
 I mean, think about how captures have gone, right?

52:01.200 --> 52:02.920
 The captures used to be a very nice, simple.

52:02.920 --> 52:06.320
 You just have this image, all of our OCR is terrible.

52:06.320 --> 52:08.880
 You put a couple of artifacts in it, you know,

52:08.880 --> 52:11.040
 humans are gonna be able to tell what it is

52:11.040 --> 52:13.840
 an AI system wouldn't be able to today.

52:13.840 --> 52:15.720
 Like I could barely do captures.

52:15.720 --> 52:18.360
 And I think that this is just kind of where we're going.

52:18.360 --> 52:20.400
 I think captures where we're a moment in time thing.

52:20.400 --> 52:22.520
 And as AI systems become more powerful,

52:22.520 --> 52:25.520
 that there being human capabilities that can be measured

52:25.520 --> 52:29.360
 in a very easy automated way that the AIs will not be

52:29.360 --> 52:31.120
 capable of, I think that's just like,

52:31.120 --> 52:34.160
 it's just an increasingly hard technical battle.

52:34.160 --> 52:36.240
 But it's not that all hope is lost, right?

52:36.240 --> 52:39.760
 And you think about how do we already authenticate

52:39.760 --> 52:40.600
 ourselves, right?

52:40.600 --> 52:41.760
 That, you know, we have systems.

52:41.760 --> 52:43.440
 We have social security numbers.

52:43.440 --> 52:46.560
 If you're in the U S or, you know, you have, you have,

52:46.560 --> 52:48.920
 you know, ways of identifying individual people

52:48.920 --> 52:51.880
 and having real world identity tied to digital identity

52:51.880 --> 52:54.880
 seems like a step towards, you know,

52:54.880 --> 52:56.200
 authenticating the source of content

52:56.200 --> 52:58.240
 rather than the content itself.

52:58.240 --> 53:00.000
 Now, there are problems with that.

53:00.000 --> 53:03.000
 How can you have privacy and anonymity in a world

53:03.000 --> 53:05.440
 where the only content you can really trust is,

53:05.440 --> 53:06.560
 or the only way you can trust content

53:06.560 --> 53:08.560
 is by looking at where it comes from.

53:08.560 --> 53:11.400
 And so I think that building out good reputation networks

53:11.400 --> 53:14.080
 maybe one possible solution.

53:14.080 --> 53:16.280
 But yeah, I think that this question is not

53:16.280 --> 53:17.720
 an obvious one.

53:17.720 --> 53:19.320
 And I think that we, you know,

53:19.320 --> 53:20.880
 maybe sooner than we think we'll be in a world

53:20.880 --> 53:23.800
 where, you know, today I often will read a tweet

53:23.800 --> 53:25.960
 and be like, do I feel like a real human wrote this?

53:25.960 --> 53:27.560
 Or, you know, do I feel like this was like genuine?

53:27.560 --> 53:30.160
 I feel like I can kind of judge the content a little bit.

53:30.160 --> 53:32.640
 And I think in the future, it just won't be the case.

53:32.640 --> 53:36.880
 You look at, for example, the FCC comments on net neutrality.

53:36.880 --> 53:39.880
 It came out later that millions of those were auto generated

53:39.880 --> 53:41.960
 and that the researchers were able to do various

53:41.960 --> 53:44.040
 statistical techniques to do that.

53:44.040 --> 53:47.160
 What do you do in a world where those statistical techniques

53:47.160 --> 53:48.000
 don't exist?

53:48.000 --> 53:49.120
 It's just impossible to tell the difference

53:49.120 --> 53:50.640
 between humans and AI's.

53:50.640 --> 53:53.960
 And in fact, the most persuasive arguments

53:53.960 --> 53:57.200
 are written by AI, all that stuff.

53:57.200 --> 53:58.600
 It's not sci fi anymore.

53:58.600 --> 54:01.320
 You look at GPT2 making a great argument for why recycling

54:01.320 --> 54:02.560
 is bad for the world.

54:02.560 --> 54:04.440
 You got to read that and be like, huh, you're right.

54:04.440 --> 54:06.520
 We are addressing just the symptoms.

54:06.520 --> 54:08.120
 Yeah, that's quite interesting.

54:08.120 --> 54:11.320
 I mean, ultimately it boils down to the physical world

54:11.320 --> 54:13.680
 being the last frontier of proving.

54:13.680 --> 54:16.080
 So you said like basically networks of people,

54:16.080 --> 54:19.400
 humans vouching for humans in the physical world.

54:19.400 --> 54:22.960
 And somehow the authentication ends there.

54:22.960 --> 54:24.560
 I mean, if I had to ask you,

54:25.520 --> 54:28.160
 I mean, you're way too eloquent for a human.

54:28.160 --> 54:31.240
 So if I had to ask you to authenticate,

54:31.240 --> 54:33.120
 like prove how do I know you're not a robot

54:33.120 --> 54:34.920
 and how do you know I'm not a robot?

54:34.920 --> 54:35.760
 Yeah.

54:35.760 --> 54:40.520
 I think that's so far were in this space,

54:40.520 --> 54:42.120
 this conversation we just had,

54:42.120 --> 54:44.000
 the physical movements we did

54:44.000 --> 54:47.040
 is the biggest gap between us and AI systems

54:47.040 --> 54:49.360
 is the physical manipulation.

54:49.360 --> 54:51.280
 So maybe that's the last frontier.

54:51.280 --> 54:53.040
 Well, here's another question is,

54:53.040 --> 54:57.320
 why is solving this problem important, right?

54:57.320 --> 54:59.080
 Like what aspects are really important to us?

54:59.080 --> 55:01.200
 And I think that probably where we'll end up

55:01.200 --> 55:03.600
 is we'll hone in on what do we really want

55:03.600 --> 55:06.400
 out of knowing if we're talking to a human.

55:06.400 --> 55:09.480
 And I think that again, this comes down to identity.

55:09.480 --> 55:11.760
 And so I think that the internet of the future,

55:11.760 --> 55:14.840
 I expect to be one that will have lots of agents out there

55:14.840 --> 55:16.320
 that will interact with you.

55:16.320 --> 55:17.880
 But I think that the question of,

55:17.880 --> 55:21.520
 is this real flesh and blood human

55:21.520 --> 55:23.800
 or is this an automated system?

55:23.800 --> 55:25.800
 May actually just be less important.

55:25.800 --> 55:27.360
 Let's actually go there.

55:27.360 --> 55:32.360
 It's GPT2 is impressive and let's look at GPT20.

55:32.440 --> 55:37.440
 Why is it so bad that all my friends are GPT20?

55:37.440 --> 55:42.440
 Why is it so important on the internet?

55:43.320 --> 55:47.360
 Do you think to interact with only human beings?

55:47.360 --> 55:50.640
 Why can't we live in a world where ideas can come

55:50.640 --> 55:52.960
 from models trained on human data?

55:52.960 --> 55:55.720
 Yeah, I think this is actually a really interesting question.

55:55.720 --> 55:56.560
 This comes back to the,

55:56.560 --> 55:59.560
 how do you even picture a world with some new technology?

55:59.560 --> 56:02.080
 And I think that one thing that I think is important

56:02.080 --> 56:04.760
 is, you know, let's say honesty.

56:04.760 --> 56:07.520
 And I think that if you have, you know, almost in the

56:07.520 --> 56:11.120
 Turing test style sense of technology,

56:11.120 --> 56:13.200
 you have AIs that are pretending to be humans

56:13.200 --> 56:15.800
 and deceiving you, I think that is, you know,

56:15.800 --> 56:17.560
 that feels like a bad thing, right?

56:17.560 --> 56:19.720
 I think that it's really important that we feel like

56:19.720 --> 56:21.280
 we're in control of our environment, right?

56:21.280 --> 56:23.400
 That we understand who we're interacting with.

56:23.400 --> 56:25.880
 And if it's an AI or a human,

56:25.880 --> 56:28.680
 that that's not something that we're being deceived about.

56:28.680 --> 56:30.240
 But I think that the flip side of,

56:30.240 --> 56:32.680
 can I have as meaningful of an interaction with an AI

56:32.680 --> 56:34.240
 as I can with a human?

56:34.240 --> 56:36.880
 Well, I actually think here you can turn to sci fi.

56:36.880 --> 56:40.040
 And her, I think is a great example of asking this very

56:40.040 --> 56:40.880
 question, right?

56:40.880 --> 56:42.800
 And one thing I really love about her is it really starts

56:42.800 --> 56:45.800
 out almost by asking how meaningful are human

56:45.800 --> 56:47.280
 virtual relationships, right?

56:47.280 --> 56:51.200
 And then you have a human who has a relationship with an AI

56:51.200 --> 56:54.320
 and that you really start to be drawn into that, right?

56:54.320 --> 56:56.960
 And that all of your emotional buttons get triggered

56:56.960 --> 56:59.000
 in the same way as if there was a real human that was on

56:59.000 --> 57:00.400
 the other side of that phone.

57:00.400 --> 57:03.800
 And so I think that this is one way of thinking about it,

57:03.800 --> 57:07.160
 is that I think that we can have meaningful interactions

57:07.160 --> 57:09.720
 and that if there's a funny joke,

57:09.720 --> 57:11.320
 some sense it doesn't really matter if it was written

57:11.320 --> 57:14.600
 by a human or an AI, but what you don't want in a way

57:14.600 --> 57:17.360
 where I think we should really draw hard lines is deception.

57:17.360 --> 57:20.200
 And I think that as long as we're in a world where,

57:20.200 --> 57:22.640
 you know, why do we build AI systems at all, right?

57:22.640 --> 57:25.000
 The reason we want to build them is to enhance human lives,

57:25.000 --> 57:26.680
 to make humans be able to do more things,

57:26.680 --> 57:29.040
 to have humans feel more fulfilled.

57:29.040 --> 57:32.040
 And if we can build AI systems that do that,

57:32.040 --> 57:33.200
 you know, sign me up.

57:33.200 --> 57:35.160
 So the process of language modeling,

57:37.120 --> 57:38.760
 how far do you think it take us?

57:38.760 --> 57:40.680
 Let's look at movie HER.

57:40.680 --> 57:45.040
 Do you think a dialogue, natural language conversation

57:45.040 --> 57:47.840
 is formulated by the Turing test, for example,

57:47.840 --> 57:50.760
 do you think that process could be achieved through

57:50.760 --> 57:53.160
 this kind of unsupervised language modeling?

57:53.160 --> 57:56.960
 So I think the Turing test in its real form

57:56.960 --> 57:58.680
 isn't just about language, right?

57:58.680 --> 58:00.560
 It's really about reasoning too, right?

58:00.560 --> 58:01.920
 That to really pass the Turing test,

58:01.920 --> 58:03.880
 I should be able to teach calculus

58:03.880 --> 58:05.520
 to whoever's on the other side

58:05.520 --> 58:07.480
 and have it really understand calculus

58:07.480 --> 58:09.320
 and be able to, you know, go and solve

58:09.320 --> 58:11.280
 new calculus problems.

58:11.280 --> 58:13.960
 And so I think that to really solve the Turing test,

58:13.960 --> 58:16.440
 we need more than what we're seeing with language models.

58:16.440 --> 58:18.720
 We need some way of plugging in reasoning.

58:18.720 --> 58:22.400
 Now, how different will that be from what we already do?

58:22.400 --> 58:23.880
 That's an open question, right?

58:23.880 --> 58:25.480
 It might be that we need some sequence

58:25.480 --> 58:27.200
 of totally radical new ideas,

58:27.200 --> 58:29.560
 or it might be that we just need to kind of shape

58:29.560 --> 58:31.920
 our existing systems in a slightly different way.

58:33.040 --> 58:34.640
 But I think that in terms of how far

58:34.640 --> 58:35.920
 language modeling will go,

58:35.920 --> 58:37.520
 it's already gone way further

58:37.520 --> 58:39.760
 than many people would have expected, right?

58:39.760 --> 58:40.960
 I think that things like,

58:40.960 --> 58:42.720
 and I think there's a lot of really interesting angles

58:42.720 --> 58:45.920
 to poke in terms of how much does GPT2

58:45.920 --> 58:47.880
 understand physical world?

58:47.880 --> 58:49.360
 Like, you know, you read a little bit

58:49.360 --> 58:52.360
 about fire underwater in GPT2.

58:52.360 --> 58:54.200
 So it's like, okay, maybe it doesn't quite understand

58:54.200 --> 58:55.680
 what these things are.

58:55.680 --> 58:58.560
 But at the same time, I think that you also see

58:58.560 --> 59:00.640
 various things like smoke coming from flame,

59:00.640 --> 59:02.680
 and you know, a bunch of these things that GPT2,

59:02.680 --> 59:04.880
 it has no body, it has no physical experience,

59:04.880 --> 59:07.280
 it's just statically read data.

59:07.280 --> 59:11.680
 And I think that if the answer is like,

59:11.680 --> 59:14.600
 we don't know yet, and these questions though,

59:14.600 --> 59:16.240
 we're starting to be able to actually ask them

59:16.240 --> 59:18.720
 to physical systems, to real systems that exist,

59:18.720 --> 59:19.880
 and that's very exciting.

59:19.880 --> 59:21.160
 Do you think, what's your intuition?

59:21.160 --> 59:24.040
 Do you think if you just scale language modeling,

59:24.040 --> 59:29.040
 like significantly scale, that reasoning can emerge

59:29.320 --> 59:31.320
 from the same exact mechanisms?

59:31.320 --> 59:34.960
 I think it's unlikely that if we just scale GPT2,

59:34.960 --> 59:38.600
 that we'll have reasoning in the full fledged way.

59:38.600 --> 59:39.760
 And I think that there's like,

59:39.760 --> 59:41.520
 the type signature is a little bit wrong, right?

59:41.520 --> 59:44.560
 That like, there's something we do with,

59:44.560 --> 59:45.800
 that we call thinking, right?

59:45.800 --> 59:47.640
 Where we spend a lot of compute,

59:47.640 --> 59:49.160
 like a variable amount of compute

59:49.160 --> 59:50.680
 to get to better answers, right?

59:50.680 --> 59:53.040
 I think a little bit harder, I get a better answer.

59:53.040 --> 59:55.160
 And that that kind of type signature

59:55.160 --> 59:58.880
 isn't quite encoded in a GPT, right?

59:58.880 --> 1:00:01.880
 GPT will kind of like, it's spent a long time

1:00:01.880 --> 1:00:03.640
 in it's like evolutionary history,

1:00:03.640 --> 1:00:04.680
 baking and all this information,

1:00:04.680 --> 1:00:07.000
 getting very, very good at this predictive process.

1:00:07.000 --> 1:00:10.320
 And then at runtime, I just kind of do one forward pass

1:00:10.320 --> 1:00:13.240
 and am able to generate stuff.

1:00:13.240 --> 1:00:15.560
 And so, there might be small tweaks

1:00:15.560 --> 1:00:18.040
 to what we do in order to get the type signature, right?

1:00:18.040 --> 1:00:21.040
 For example, well, it's not really one forward pass, right?

1:00:21.040 --> 1:00:22.640
 You generate symbol by symbol.

1:00:22.640 --> 1:00:25.560
 And so, maybe you generate like a whole sequence of thoughts

1:00:25.560 --> 1:00:28.200
 and you only keep like the last bit or something.

1:00:28.200 --> 1:00:29.840
 But I think that at the very least,

1:00:29.840 --> 1:00:32.160
 I would expect you have to make changes like that.

1:00:32.160 --> 1:00:35.520
 Yeah, just exactly how we, you said think

1:00:35.520 --> 1:00:38.400
 is the process of generating thought by thought

1:00:38.400 --> 1:00:40.360
 in the same kind of way, like you said,

1:00:40.360 --> 1:00:43.640
 keep the last bit, the thing that we converge towards.

1:00:45.000 --> 1:00:47.280
 And I think there's another piece which is interesting,

1:00:47.280 --> 1:00:50.240
 which is this out of distribution generalization, right?

1:00:50.240 --> 1:00:52.600
 That like thinking somehow lets us do that, right?

1:00:52.600 --> 1:00:54.400
 That we have an experience of thing

1:00:54.400 --> 1:00:56.080
 and yet somehow we just kind of keep refining

1:00:56.080 --> 1:00:58.040
 our mental model of it.

1:00:58.040 --> 1:01:01.160
 This is again, something that feels tied to

1:01:01.160 --> 1:01:03.360
 whatever reasoning is.

1:01:03.360 --> 1:01:05.720
 And maybe it's a small tweak to what we do.

1:01:05.720 --> 1:01:08.080
 Maybe it's many ideas and we'll take as many decades.

1:01:08.080 --> 1:01:11.920
 Yeah, so the assumption there, generalization

1:01:11.920 --> 1:01:14.160
 out of distribution is that it's possible

1:01:14.160 --> 1:01:16.880
 to create new ideas.

1:01:18.160 --> 1:01:20.840
 It's possible that nobody's ever created any new ideas.

1:01:20.840 --> 1:01:25.360
 And then with scaling GPT2 to GPT20,

1:01:25.360 --> 1:01:30.360
 you would essentially generalize to all possible thoughts

1:01:30.520 --> 1:01:34.200
 as humans can have, just to play devil's advocate.

1:01:34.200 --> 1:01:37.280
 Right, I mean, how many new story ideas

1:01:37.280 --> 1:01:39.120
 have we come up with since Shakespeare, right?

1:01:39.120 --> 1:01:40.160
 Yeah, exactly.

1:01:41.600 --> 1:01:44.680
 It's just all different forms of love and drama and so on.

1:01:44.680 --> 1:01:45.800
 Okay.

1:01:45.800 --> 1:01:47.520
 Not sure if you read Biddle Lesson,

1:01:47.520 --> 1:01:49.400
 a recent blog post by Rich Sutton.

1:01:49.400 --> 1:01:50.880
 Yep, I have.

1:01:50.880 --> 1:01:53.720
 He basically says something that echoes

1:01:53.720 --> 1:01:55.480
 some of the ideas that you've been talking about,

1:01:55.480 --> 1:01:58.320
 which is, he says the biggest lesson

1:01:58.320 --> 1:02:00.680
 that can be read from 70 years of AI research

1:02:00.680 --> 1:02:03.880
 is that general methods that leverage computation

1:02:03.880 --> 1:02:07.920
 are ultimately going to ultimately win out.

1:02:07.920 --> 1:02:08.960
 Do you agree with this?

1:02:08.960 --> 1:02:13.520
 So basically open AI in general about the ideas

1:02:13.520 --> 1:02:15.880
 you're exploring about coming up with methods,

1:02:15.880 --> 1:02:20.120
 whether it's GPT2 modeling or whether it's open AI5,

1:02:20.120 --> 1:02:23.160
 playing Dota, where a general method

1:02:23.160 --> 1:02:27.160
 is better than a more fine tuned, expert tuned method.

1:02:29.760 --> 1:02:32.200
 Yeah, so I think that, well, one thing that I think

1:02:32.200 --> 1:02:33.800
 was really interesting about the reaction

1:02:33.800 --> 1:02:36.480
 to that blog post was that a lot of people have read this

1:02:36.480 --> 1:02:39.440
 as saying that compute is all that matters.

1:02:39.440 --> 1:02:41.360
 And that's a very threatening idea, right?

1:02:41.360 --> 1:02:43.720
 And I don't think it's a true idea either, right?

1:02:43.720 --> 1:02:45.800
 It's very clear that we have algorithmic ideas

1:02:45.800 --> 1:02:47.920
 that have been very important for making progress.

1:02:47.920 --> 1:02:50.720
 And to really build AI, you wanna push as far as you can

1:02:50.720 --> 1:02:52.760
 on the computational scale, and you wanna push

1:02:52.760 --> 1:02:55.520
 as far as you can on human ingenuity.

1:02:55.520 --> 1:02:57.040
 And so I think you need both.

1:02:57.040 --> 1:02:58.320
 But I think the way that you phrase the question

1:02:58.320 --> 1:02:59.640
 is actually very good, right?

1:02:59.640 --> 1:03:02.200
 That it's really about what kind of ideas

1:03:02.200 --> 1:03:04.040
 should we be striving for?

1:03:04.040 --> 1:03:07.600
 And absolutely, if you can find a scalable idea,

1:03:07.600 --> 1:03:08.640
 you pour more compute into it,

1:03:08.640 --> 1:03:11.400
 you pour more data into it, it gets better.

1:03:11.400 --> 1:03:13.800
 Like that's the real Holy Grail.

1:03:13.800 --> 1:03:16.600
 And so I think that the answer to the question,

1:03:16.600 --> 1:03:19.920
 I think is yes, that's really how we think about it.

1:03:19.920 --> 1:03:22.760
 And that part of why we're excited about the power

1:03:22.760 --> 1:03:25.320
 of deep learning and the potential for building AGI

1:03:25.320 --> 1:03:27.600
 is because we look at the systems that exist

1:03:27.600 --> 1:03:29.720
 in the most successful AI systems,

1:03:29.720 --> 1:03:32.680
 and we realize that you scale those up,

1:03:32.680 --> 1:03:34.000
 they're gonna work better.

1:03:34.000 --> 1:03:36.320
 And I think that that scalability is something

1:03:36.320 --> 1:03:37.160
 that really gives us hope

1:03:37.160 --> 1:03:39.600
 for being able to build transformative systems.

1:03:39.600 --> 1:03:43.240
 So I'll tell you, this is partially an emotional,

1:03:43.240 --> 1:03:45.760
 you know, a thing that a response that people often have,

1:03:45.760 --> 1:03:49.280
 if compute is so important for state of the art performance,

1:03:49.280 --> 1:03:50.760
 you know, individual developers,

1:03:50.760 --> 1:03:52.960
 maybe a 13 year old sitting somewhere in Kansas

1:03:52.960 --> 1:03:55.040
 or something like that, you know, they're sitting,

1:03:55.040 --> 1:03:56.760
 they might not even have a GPU

1:03:56.760 --> 1:04:00.080
 and or may have a single GPU, a 1080 or something like that.

1:04:00.080 --> 1:04:02.640
 And there's this feeling like, well,

1:04:02.640 --> 1:04:07.280
 how can I possibly compete or contribute to this world of AI

1:04:07.280 --> 1:04:09.840
 if scale is so important?

1:04:09.840 --> 1:04:11.920
 So if you can comment on that,

1:04:11.920 --> 1:04:14.320
 and in general, do you think we need to also

1:04:14.320 --> 1:04:18.800
 in the future focus on democratizing compute resources

1:04:18.800 --> 1:04:22.680
 more or as much as we democratize the algorithms?

1:04:22.680 --> 1:04:23.960
 Well, so the way that I think about it

1:04:23.960 --> 1:04:28.880
 is that there's this space of possible progress, right?

1:04:28.880 --> 1:04:30.920
 There's a space of ideas and sort of systems

1:04:30.920 --> 1:04:32.960
 that will work, that will move us forward.

1:04:32.960 --> 1:04:34.840
 And there's a portion of that space,

1:04:34.840 --> 1:04:35.760
 and to some extent,

1:04:35.760 --> 1:04:37.960
 an increasingly significant portion of that space

1:04:37.960 --> 1:04:41.080
 that does just require massive compute resources.

1:04:41.080 --> 1:04:44.760
 And for that, I think that the answer is kind of clear

1:04:44.760 --> 1:04:47.960
 and that part of why we have the structure that we do

1:04:47.960 --> 1:04:49.640
 is because we think it's really important

1:04:49.640 --> 1:04:50.600
 to be pushing the scale

1:04:50.600 --> 1:04:53.840
 and to be building these large clusters and systems.

1:04:53.840 --> 1:04:55.920
 But there's another portion of the space

1:04:55.920 --> 1:04:57.880
 that isn't about the large scale compute,

1:04:57.880 --> 1:04:59.960
 that are these ideas that, and again,

1:04:59.960 --> 1:05:02.200
 I think that for the ideas to really be impactful

1:05:02.200 --> 1:05:04.200
 and really shine, that they should be ideas

1:05:04.200 --> 1:05:05.840
 that if you scale them up,

1:05:05.840 --> 1:05:08.840
 would work way better than they do at small scale.

1:05:08.840 --> 1:05:11.160
 But you can discover them without massive

1:05:11.160 --> 1:05:12.760
 computational resources.

1:05:12.760 --> 1:05:15.200
 And if you look at the history of recent developments,

1:05:15.200 --> 1:05:17.680
 you think about things like the GAN or the VAE,

1:05:17.680 --> 1:05:20.920
 that these are ones that I think you could come up with them

1:05:20.920 --> 1:05:22.720
 without having, and in practice,

1:05:22.720 --> 1:05:24.520
 people did come up with them without having

1:05:24.520 --> 1:05:26.560
 massive, massive computational resources.

1:05:26.560 --> 1:05:28.000
 Right, I just talked to Ian Goodfellow,

1:05:28.000 --> 1:05:31.600
 but the thing is the initial GAN

1:05:31.600 --> 1:05:34.200
 produced pretty terrible results, right?

1:05:34.200 --> 1:05:36.880
 So only because it was in a very specific,

1:05:36.880 --> 1:05:38.640
 only because they're smart enough to know

1:05:38.640 --> 1:05:41.520
 that this is quite surprising to generate anything

1:05:41.520 --> 1:05:43.160
 that they know.

1:05:43.160 --> 1:05:46.040
 Do you see a world, or is that too optimistic and dreamer,

1:05:46.040 --> 1:05:49.760
 like, to imagine that the compute resources

1:05:49.760 --> 1:05:52.200
 are something that's owned by governments

1:05:52.200 --> 1:05:55.040
 and provided as a utility?

1:05:55.040 --> 1:05:57.120
 Actually, to some extent, this question reminds me

1:05:57.120 --> 1:06:00.280
 of a blog post from one of my former professors

1:06:00.280 --> 1:06:02.440
 at Harvard, this guy, Matt Welch,

1:06:02.440 --> 1:06:03.760
 who was a systems professor.

1:06:03.760 --> 1:06:05.280
 I remember sitting in his tenure talk, right,

1:06:05.280 --> 1:06:08.800
 and that he had literally just gotten tenure.

1:06:08.800 --> 1:06:10.960
 He went to Google for the summer,

1:06:10.960 --> 1:06:15.680
 and then decided he wasn't going back to academia, right?

1:06:15.680 --> 1:06:17.760
 And kind of in his blog post, he makes this point

1:06:17.760 --> 1:06:20.800
 that, look, as a systems researcher,

1:06:20.800 --> 1:06:23.040
 that I come up with these cool system ideas,

1:06:23.040 --> 1:06:25.080
 right, and kind of build a little proof of concept,

1:06:25.080 --> 1:06:27.080
 and the best thing I could hope for

1:06:27.080 --> 1:06:30.120
 is that the people at Google or Yahoo,

1:06:30.120 --> 1:06:32.600
 which was around at the time,

1:06:32.600 --> 1:06:35.400
 will implement it and actually make it work at scale, right?

1:06:35.400 --> 1:06:36.640
 That's like the dream for me, right?

1:06:36.640 --> 1:06:38.000
 I build the little thing, and they turn it into

1:06:38.000 --> 1:06:40.000
 the big thing that's actually working.

1:06:40.000 --> 1:06:43.360
 And for him, he said, I'm done with that.

1:06:43.360 --> 1:06:45.320
 I want to be the person who's actually doing

1:06:45.320 --> 1:06:47.200
 building and deploying.

1:06:47.200 --> 1:06:49.560
 And I think that there's a similar dichotomy here, right?

1:06:49.560 --> 1:06:52.400
 I think that there are people who really actually

1:06:52.400 --> 1:06:55.240
 find value, and I think it is a valuable thing to do,

1:06:55.240 --> 1:06:57.440
 to be the person who produces those ideas, right,

1:06:57.440 --> 1:06:58.840
 who builds the proof of concept.

1:06:58.840 --> 1:07:00.600
 And yeah, you don't get to generate

1:07:00.600 --> 1:07:02.760
 the coolest possible GAN images,

1:07:02.760 --> 1:07:04.480
 but you invented the GAN, right?

1:07:04.480 --> 1:07:07.560
 And so there's a real trade off there.

1:07:07.560 --> 1:07:09.040
 And I think that that's a very personal choice,

1:07:09.040 --> 1:07:10.840
 but I think there's value in both sides.

1:07:10.840 --> 1:07:14.600
 So do you think creating AGI, something,

1:07:14.600 --> 1:07:19.600
 or some new models, we would see echoes of the brilliance

1:07:20.440 --> 1:07:22.240
 even at the prototype level.

1:07:22.240 --> 1:07:24.080
 So you would be able to develop those ideas

1:07:24.080 --> 1:07:27.240
 without scale, the initial seeds.

1:07:27.240 --> 1:07:30.680
 So take a look at, I always like to look at examples

1:07:30.680 --> 1:07:32.680
 that exist, right, look at real precedent.

1:07:32.680 --> 1:07:36.240
 And so take a look at the June 2018 model

1:07:36.240 --> 1:07:39.200
 that we released that we scaled up to turn to GPT2.

1:07:39.200 --> 1:07:41.280
 And you can see that at small scale,

1:07:41.280 --> 1:07:42.800
 it set some records, right?

1:07:42.800 --> 1:07:44.800
 This was the original GPT.

1:07:44.800 --> 1:07:46.840
 We actually had some cool generations.

1:07:46.840 --> 1:07:49.840
 They weren't nearly as amazing and really stunning

1:07:49.840 --> 1:07:52.000
 as the GPT2 ones, but it was promising.

1:07:52.000 --> 1:07:53.040
 It was interesting.

1:07:53.040 --> 1:07:55.280
 And so I think it is the case that with a lot

1:07:55.280 --> 1:07:58.280
 of these ideas that you see promise at small scale,

1:07:58.280 --> 1:08:00.800
 but there isn't an asterisk here, a very big asterisk,

1:08:00.800 --> 1:08:05.240
 which is sometimes we see behaviors that emerge

1:08:05.240 --> 1:08:07.280
 that are qualitatively different

1:08:07.280 --> 1:08:09.080
 from anything we saw at small scale.

1:08:09.080 --> 1:08:12.600
 And that the original inventor of whatever algorithm

1:08:12.600 --> 1:08:15.520
 looks at and says, I didn't think it could do that.

1:08:15.520 --> 1:08:17.400
 This is what we saw in Dota, right?

1:08:17.400 --> 1:08:19.320
 So PPO was created by John Shulman,

1:08:19.320 --> 1:08:20.560
 who's a researcher here.

1:08:20.560 --> 1:08:24.680
 And with Dota, we basically just ran PPO

1:08:24.680 --> 1:08:26.520
 at massive, massive scale.

1:08:26.520 --> 1:08:29.120
 And there's some tweaks in order to make it work,

1:08:29.120 --> 1:08:31.520
 but fundamentally it's PPO at the core.

1:08:31.520 --> 1:08:35.280
 And we were able to get this longterm planning,

1:08:35.280 --> 1:08:38.680
 these behaviors to really play out on a time scale

1:08:38.680 --> 1:08:40.760
 that we just thought was not possible.

1:08:40.760 --> 1:08:42.680
 And John looked at that and was like,

1:08:42.680 --> 1:08:44.240
 I didn't think it could do that.

1:08:44.240 --> 1:08:45.480
 That's what happens when you're at three orders

1:08:45.480 --> 1:08:48.400
 of magnitude more scale than you tested at.

1:08:48.400 --> 1:08:50.600
 Yeah, but it still has the same flavors of,

1:08:50.600 --> 1:08:55.600
 you know, at least echoes of the expected billions.

1:08:56.000 --> 1:08:57.880
 Although I suspect with GPT,

1:08:57.880 --> 1:09:01.800
 it's scaled more and more, you might get surprising things.

1:09:01.800 --> 1:09:03.200
 So yeah, you're right.

1:09:03.200 --> 1:09:06.360
 It's interesting that it's difficult to see

1:09:06.360 --> 1:09:09.320
 how far an idea will go when it's scaled.

1:09:09.320 --> 1:09:11.080
 It's an open question.

1:09:11.080 --> 1:09:13.080
 Well, so to that point with Dota and PPO,

1:09:13.080 --> 1:09:15.040
 like I mean, here's a very concrete one, right?

1:09:15.040 --> 1:09:16.680
 It's like, it's actually one thing

1:09:16.680 --> 1:09:17.720
 that's very surprising about Dota

1:09:17.720 --> 1:09:20.400
 that I think people don't really pay that much attention to.

1:09:20.400 --> 1:09:22.360
 Is the decree of generalization

1:09:22.360 --> 1:09:24.560
 out of distribution that happens, right?

1:09:24.560 --> 1:09:26.320
 That you have this AI that's trained

1:09:26.320 --> 1:09:28.880
 against other bots for its entirety,

1:09:28.880 --> 1:09:30.360
 the entirety of its existence.

1:09:30.360 --> 1:09:31.440
 Sorry to take a step back.

1:09:31.440 --> 1:09:36.440
 Can you talk through, you know, a story of Dota,

1:09:37.240 --> 1:09:42.040
 a story of leading up to opening I5 and that past,

1:09:42.040 --> 1:09:43.920
 and what was the process of self playing

1:09:43.920 --> 1:09:45.440
 and so on of training on this?

1:09:45.440 --> 1:09:46.280
 Yeah, yeah, yeah.

1:09:46.280 --> 1:09:47.120
 So with Dota.

1:09:47.120 --> 1:09:47.960
 What is Dota?

1:09:47.960 --> 1:09:50.000
 Dota is a complex video game

1:09:50.000 --> 1:09:51.320
 and we started training,

1:09:51.320 --> 1:09:52.720
 we started trying to solve Dota

1:09:52.720 --> 1:09:55.680
 because we felt like this was a step towards the real world

1:09:55.680 --> 1:09:58.040
 relative to other games like Chess or Go, right?

1:09:58.040 --> 1:09:59.160
 Those very cerebral games

1:09:59.160 --> 1:10:00.480
 where you just kind of have this board

1:10:00.480 --> 1:10:01.880
 of very discreet moves.

1:10:01.880 --> 1:10:04.040
 Dota starts to be much more continuous time.

1:10:04.040 --> 1:10:06.200
 So you have this huge variety of different actions

1:10:06.200 --> 1:10:07.680
 that you have a 45 minute game

1:10:07.680 --> 1:10:09.360
 with all these different units

1:10:09.360 --> 1:10:11.840
 and it's got a lot of messiness to it

1:10:11.840 --> 1:10:14.480
 that really hasn't been captured by previous games.

1:10:14.480 --> 1:10:17.320
 And famously all of the hard coded bots for Dota

1:10:17.320 --> 1:10:18.400
 were terrible, right?

1:10:18.400 --> 1:10:19.920
 It's just impossible to write anything good for it

1:10:19.920 --> 1:10:21.240
 because it's so complex.

1:10:21.240 --> 1:10:23.280
 And so this seemed like a really good place

1:10:23.280 --> 1:10:25.240
 to push what's the state of the art

1:10:25.240 --> 1:10:26.800
 in reinforcement learning.

1:10:26.800 --> 1:10:29.000
 And so we started by focusing on the one versus one

1:10:29.000 --> 1:10:32.360
 version of the game and we're able to solve that.

1:10:32.360 --> 1:10:33.880
 We're able to beat the world champions

1:10:33.880 --> 1:10:37.240
 and the learning, the skill curve

1:10:37.240 --> 1:10:38.960
 was this crazy exponential, right?

1:10:38.960 --> 1:10:41.000
 It was like constantly we were just scaling up,

1:10:41.000 --> 1:10:43.240
 that we were fixing bugs and that you look

1:10:43.240 --> 1:10:46.600
 at the skill curve and it was really a very, very smooth one.

1:10:46.600 --> 1:10:47.440
 So it's actually really interesting

1:10:47.440 --> 1:10:50.000
 to see how that like human iteration loop

1:10:50.000 --> 1:10:52.680
 yielded very steady exponential progress.

1:10:52.680 --> 1:10:55.160
 And to one side note, first of all,

1:10:55.160 --> 1:10:57.080
 it's an exceptionally popular video game.

1:10:57.080 --> 1:10:59.400
 The side effect is that there's a lot

1:10:59.400 --> 1:11:01.920
 of incredible human experts at that video game.

1:11:01.920 --> 1:11:05.200
 So the benchmark that you're trying to reach is very high.

1:11:05.200 --> 1:11:07.840
 And the other, can you talk about the approach

1:11:07.840 --> 1:11:10.600
 that was used initially and throughout training

1:11:10.600 --> 1:11:12.040
 these agents to play this game?

1:11:12.040 --> 1:11:12.880
 Yep.

1:11:12.880 --> 1:11:14.400
 And so the approach that we used is self play.

1:11:14.400 --> 1:11:17.320
 And so you have two agents that don't know anything.

1:11:17.320 --> 1:11:18.640
 They battle each other,

1:11:18.640 --> 1:11:20.760
 they discover something a little bit good

1:11:20.760 --> 1:11:22.000
 and now they both know it.

1:11:22.000 --> 1:11:24.520
 And they just get better and better and better without bound.

1:11:24.520 --> 1:11:27.040
 And that's a really powerful idea, right?

1:11:27.040 --> 1:11:30.160
 That we then went from the one versus one version

1:11:30.160 --> 1:11:32.400
 of the game and scaled up to five versus five, right?

1:11:32.400 --> 1:11:34.280
 So you think about kind of like with basketball

1:11:34.280 --> 1:11:35.440
 where you have this like team sport

1:11:35.440 --> 1:11:37.640
 and you need to do all this coordination

1:11:37.640 --> 1:11:40.920
 and we were able to push the same idea,

1:11:40.920 --> 1:11:45.920
 the same self play to really get to the professional level

1:11:45.920 --> 1:11:48.880
 at the full five versus five version of the game.

1:11:48.880 --> 1:11:52.400
 And the things that I think are really interesting here

1:11:52.400 --> 1:11:54.760
 is that these agents in some ways

1:11:54.760 --> 1:11:56.760
 they're almost like an insect like intelligence, right?

1:11:56.760 --> 1:11:59.920
 Where they have a lot in common with how an insect is trained,

1:11:59.920 --> 1:12:00.760
 right?

1:12:00.760 --> 1:12:02.640
 An insect kind of lives in this environment for a very long time

1:12:02.640 --> 1:12:05.280
 or the ancestors of this insect have been around

1:12:05.280 --> 1:12:07.000
 for a long time and had a lot of experience.

1:12:07.000 --> 1:12:09.680
 I think it's baked into this agent.

1:12:09.680 --> 1:12:12.720
 And it's not really smart in the sense of a human, right?

1:12:12.720 --> 1:12:14.560
 It's not able to go and learn calculus,

1:12:14.560 --> 1:12:17.000
 but it's able to navigate its environment extremely well.

1:12:17.000 --> 1:12:18.480
 And it's able to handle unexpected things

1:12:18.480 --> 1:12:22.080
 in the environment that's never seen before, pretty well.

1:12:22.080 --> 1:12:24.800
 And we see the same sort of thing with our Dota bots, right?

1:12:24.800 --> 1:12:26.720
 That they're able to, within this game,

1:12:26.720 --> 1:12:28.440
 they're able to play against humans,

1:12:28.440 --> 1:12:30.000
 which is something that never existed

1:12:30.000 --> 1:12:31.360
 in its evolutionary environment.

1:12:31.360 --> 1:12:34.400
 Totally different play styles from humans versus the bots.

1:12:34.400 --> 1:12:37.200
 And yet it's able to handle it extremely well.

1:12:37.200 --> 1:12:40.400
 And that's something that I think was very surprising to us

1:12:40.400 --> 1:12:43.440
 was something that doesn't really emerge

1:12:43.440 --> 1:12:47.200
 from what we've seen with PPO at smaller scale, right?

1:12:47.200 --> 1:12:48.560
 And the kind of scale we're running this stuff at

1:12:48.560 --> 1:12:51.920
 was I could take 100,000 CPU cores,

1:12:51.920 --> 1:12:54.040
 running with like hundreds of GPUs.

1:12:54.040 --> 1:12:59.040
 It was probably about something like hundreds of years

1:12:59.040 --> 1:13:03.800
 of experience going into this bot every single real day.

1:13:03.800 --> 1:13:06.200
 And so that scale is massive.

1:13:06.200 --> 1:13:08.400
 And we start to see very different kinds of behaviors

1:13:08.400 --> 1:13:10.760
 out of the algorithms that we all know and love.

1:13:10.760 --> 1:13:15.160
 Dota, you mentioned, beat the world expert 1v1.

1:13:15.160 --> 1:13:21.160
 And then you weren't able to win 5v5 this year

1:13:21.160 --> 1:13:24.080
 at the best players in the world.

1:13:24.080 --> 1:13:26.640
 So what's the comeback story?

1:13:26.640 --> 1:13:27.680
 First of all, talk through that.

1:13:27.680 --> 1:13:29.480
 That was an exceptionally exciting event.

1:13:29.480 --> 1:13:33.160
 And what's the following months in this year look like?

1:13:33.160 --> 1:13:33.760
 Yeah, yeah.

1:13:33.760 --> 1:13:38.640
 So one thing that's interesting is that we lose all the time.

1:13:38.640 --> 1:13:40.040
 Because we play here.

1:13:40.040 --> 1:13:42.840
 So the Dota team at OpenAI, we play the bot

1:13:42.840 --> 1:13:45.800
 against better players than our system all the time.

1:13:45.800 --> 1:13:47.400
 Or at least we used to, right?

1:13:47.400 --> 1:13:50.680
 Like the first time we lost publicly was we went up

1:13:50.680 --> 1:13:53.480
 on stage at the international and we played against some

1:13:53.480 --> 1:13:54.800
 of the best teams in the world.

1:13:54.800 --> 1:13:56.320
 And we ended up losing both games.

1:13:56.320 --> 1:13:58.520
 But we give them a run for their money, right?

1:13:58.520 --> 1:14:01.440
 That both games were kind of 30 minutes, 25 minutes.

1:14:01.440 --> 1:14:04.200
 And they went back and forth, back and forth, back and forth.

1:14:04.200 --> 1:14:06.360
 And so I think that really shows that we're

1:14:06.360 --> 1:14:08.280
 at the professional level.

1:14:08.280 --> 1:14:09.640
 And that kind of looking at those games,

1:14:09.640 --> 1:14:12.280
 we think that the coin could have gone a different direction

1:14:12.280 --> 1:14:13.560
 and we could have had some wins.

1:14:13.560 --> 1:14:16.200
 And so that was actually very encouraging for us.

1:14:16.200 --> 1:14:18.360
 And you know, it's interesting because the international was

1:14:18.360 --> 1:14:19.720
 at a fixed time, right?

1:14:19.720 --> 1:14:22.680
 So we knew exactly what day we were going to be playing.

1:14:22.680 --> 1:14:25.480
 And we pushed as far as we could, as fast as we could.

1:14:25.480 --> 1:14:28.040
 Two weeks later, we had a bot that had an 80% win rate

1:14:28.040 --> 1:14:30.120
 versus the one that played at TI.

1:14:30.120 --> 1:14:31.720
 So the March of Progress, you know,

1:14:31.720 --> 1:14:33.480
 that you should think of as a snapshot rather

1:14:33.480 --> 1:14:34.920
 than as an end state.

1:14:34.920 --> 1:14:39.000
 And so in fact, we'll be announcing our finals pretty soon.

1:14:39.000 --> 1:14:42.760
 I actually think that we'll announce our final match

1:14:42.760 --> 1:14:45.240
 prior to this podcast being released.

1:14:45.240 --> 1:14:49.240
 So there should be, we'll be playing against the world

1:14:49.240 --> 1:14:49.720
 champions.

1:14:49.720 --> 1:14:52.520
 And you know, for us, it's really less about,

1:14:52.520 --> 1:14:55.400
 like the way that we think about what's upcoming

1:14:55.400 --> 1:14:59.000
 is the final milestone, the final competitive milestone

1:14:59.000 --> 1:15:00.280
 for the project, right?

1:15:00.280 --> 1:15:02.760
 That our goal in all of this isn't really

1:15:02.760 --> 1:15:05.160
 about beating humans at Dota.

1:15:05.160 --> 1:15:06.760
 Our goal is to push the state of the art

1:15:06.760 --> 1:15:07.800
 in reinforcement learning.

1:15:07.800 --> 1:15:08.920
 And we've done that, right?

1:15:08.920 --> 1:15:10.680
 And we've actually learned a lot from our system

1:15:10.680 --> 1:15:13.320
 and that we have, you know, I think a lot of exciting

1:15:13.320 --> 1:15:14.680
 next steps that we want to take.

1:15:14.680 --> 1:15:16.440
 And so, you know, kind of the final showcase

1:15:16.440 --> 1:15:18.760
 of what we built, we're going to do this match.

1:15:18.760 --> 1:15:21.240
 But for us, it's not really the success or failure

1:15:21.240 --> 1:15:23.800
 to see, you know, do we have the coin flip go

1:15:23.800 --> 1:15:24.840
 in our direction or against.

1:15:25.880 --> 1:15:28.680
 Where do you see the field of deep learning

1:15:28.680 --> 1:15:30.680
 heading in the next few years?

1:15:31.720 --> 1:15:35.480
 Where do you see the work in reinforcement learning

1:15:35.480 --> 1:15:40.360
 perhaps heading and more specifically with OpenAI,

1:15:41.160 --> 1:15:43.480
 all the exciting projects that you're working on,

1:15:44.280 --> 1:15:46.360
 what does 2019 hold for you?

1:15:46.360 --> 1:15:47.400
 Massive scale.

1:15:47.400 --> 1:15:47.880
 Scale.

1:15:47.880 --> 1:15:49.480
 I will put an atrocious on that and just say,

1:15:49.480 --> 1:15:52.200
 you know, I think that it's about ideas plus scale.

1:15:52.200 --> 1:15:52.840
 You need both.

1:15:52.840 --> 1:15:54.920
 So that's a really good point.

1:15:54.920 --> 1:15:57.720
 So the question, in terms of ideas,

1:15:58.520 --> 1:16:02.200
 you have a lot of projects that are exploring

1:16:02.200 --> 1:16:04.280
 different areas of intelligence.

1:16:04.280 --> 1:16:07.480
 And the question is, when you think of scale,

1:16:07.480 --> 1:16:09.560
 do you think about growing the scale

1:16:09.560 --> 1:16:10.680
 of those individual projects,

1:16:10.680 --> 1:16:12.600
 or do you think about adding new projects?

1:16:13.160 --> 1:16:17.320
 And sorry, if you were thinking about adding new projects,

1:16:17.320 --> 1:16:19.800
 or if you look at the past, what's the process

1:16:19.800 --> 1:16:21.960
 of coming up with new projects and new ideas?

1:16:21.960 --> 1:16:22.680
 Yep.

1:16:22.680 --> 1:16:24.600
 So we really have a life cycle of project here.

1:16:25.240 --> 1:16:27.320
 So we start with a few people just working

1:16:27.320 --> 1:16:28.440
 on a small scale idea.

1:16:28.440 --> 1:16:30.520
 And language is actually a very good example of this,

1:16:30.520 --> 1:16:32.440
 that it was really, you know, one person here

1:16:32.440 --> 1:16:34.840
 who was pushing on language for a long time.

1:16:34.840 --> 1:16:36.680
 I mean, then you get signs of life, right?

1:16:36.680 --> 1:16:38.440
 And so this is like, let's say, you know,

1:16:38.440 --> 1:16:42.600
 with the original GPT, we had something that was interesting.

1:16:42.600 --> 1:16:44.760
 And we said, okay, it's time to scale this, right?

1:16:44.760 --> 1:16:45.960
 It's time to put more people on it,

1:16:45.960 --> 1:16:48.120
 put more computational resources behind it,

1:16:48.120 --> 1:16:51.560
 and then we just kind of keep pushing and keep pushing.

1:16:51.560 --> 1:16:52.920
 And the end state is something that looks like

1:16:52.920 --> 1:16:55.400
 Dota or Robotics, where you have a large team of,

1:16:55.400 --> 1:16:57.800
 you know, 10 or 15 people that are running things

1:16:57.800 --> 1:17:00.680
 at very large scale, and that you're able to really have

1:17:00.680 --> 1:17:04.280
 material engineering and, you know,

1:17:04.280 --> 1:17:06.520
 sort of machine learning science coming together

1:17:06.520 --> 1:17:10.200
 to make systems that work and get material results

1:17:10.200 --> 1:17:11.560
 that just would have been impossible otherwise.

1:17:12.200 --> 1:17:13.560
 So we do that whole life cycle.

1:17:13.560 --> 1:17:16.600
 We've done it a number of times, you know, typically end to end.

1:17:16.600 --> 1:17:19.960
 It's probably two years or so to do it.

1:17:19.960 --> 1:17:21.720
 You know, the organization's been around for three years,

1:17:21.720 --> 1:17:23.000
 so maybe we'll find that we also have

1:17:23.000 --> 1:17:24.760
 longer life cycle projects.

1:17:24.760 --> 1:17:27.480
 But, you know, we work up to those.

1:17:27.480 --> 1:17:30.280
 So one team that we're actually just starting,

1:17:30.280 --> 1:17:32.200
 Illy and I, are kicking off a new team

1:17:32.200 --> 1:17:35.080
 called the Reasoning Team, and this is to really try to tackle

1:17:35.080 --> 1:17:37.400
 how do you get neural networks to reason?

1:17:37.400 --> 1:17:41.400
 And we think that this will be a long term project.

1:17:41.400 --> 1:17:42.840
 It's one that we're very excited about.

1:17:42.840 --> 1:17:46.200
 In terms of reasoning, super exciting topic,

1:17:47.400 --> 1:17:52.200
 what kind of benchmarks, what kind of tests of reasoning

1:17:52.200 --> 1:17:53.800
 do you envision?

1:17:53.800 --> 1:17:55.880
 What would, if you set back,

1:17:55.880 --> 1:17:59.240
 whatever drink, and you would be impressed

1:17:59.240 --> 1:18:01.640
 that this system is able to do something,

1:18:01.640 --> 1:18:02.760
 what would that look like?

1:18:02.760 --> 1:18:03.800
 Theorem proving.

1:18:03.800 --> 1:18:04.840
 Theorem proving.

1:18:04.840 --> 1:18:09.480
 So some kind of logic, and especially mathematical logic.

1:18:09.480 --> 1:18:10.440
 I think so, right?

1:18:10.440 --> 1:18:12.440
 And I think that there's kind of other problems

1:18:12.440 --> 1:18:14.520
 that are dual to theorem proving in particular.

1:18:14.520 --> 1:18:16.840
 You know, you think about programming,

1:18:16.840 --> 1:18:19.960
 you think about even like security analysis of code,

1:18:19.960 --> 1:18:24.200
 that these all kind of capture the same sorts of core reasoning

1:18:24.200 --> 1:18:27.480
 and being able to do some out of distribution generalization.

1:18:28.440 --> 1:18:31.880
 It would be quite exciting if OpenAI Reasoning Team

1:18:31.880 --> 1:18:33.880
 was able to prove that P equals NP.

1:18:33.880 --> 1:18:35.080
 That would be very nice.

1:18:35.080 --> 1:18:37.720
 It would be very, very exciting especially.

1:18:37.720 --> 1:18:39.080
 If it turns out that P equals NP,

1:18:39.080 --> 1:18:40.120
 that'll be interesting too.

1:18:40.120 --> 1:18:45.160
 It would be ironic and humorous.

1:18:45.160 --> 1:18:51.800
 So what problem stands out to you as the most exciting

1:18:51.800 --> 1:18:55.720
 and challenging impactful to the work for us as a community

1:18:55.720 --> 1:18:58.440
 in general and for OpenAI this year?

1:18:58.440 --> 1:18:59.480
 You mentioned reasoning.

1:18:59.480 --> 1:19:01.320
 I think that's a heck of a problem.

1:19:01.320 --> 1:19:01.480
 Yeah.

1:19:01.480 --> 1:19:02.760
 So I think reasoning is an important one.

1:19:02.760 --> 1:19:04.840
 I think it's going to be hard to get good results in 2019.

1:19:05.480 --> 1:19:07.480
 You know, again, just like we think about the lifecycle,

1:19:07.480 --> 1:19:07.960
 takes time.

1:19:08.600 --> 1:19:11.320
 I think for 2019, language modeling seems to be kind of

1:19:11.320 --> 1:19:12.520
 on that ramp, right?

1:19:12.520 --> 1:19:14.760
 It's at the point that we have a technique that works.

1:19:14.760 --> 1:19:17.000
 We want to scale 100x, 1000x, see what happens.

1:19:18.040 --> 1:19:18.360
 Awesome.

1:19:18.360 --> 1:19:21.800
 Do you think we're living in a simulation?

1:19:21.800 --> 1:19:24.520
 I think it's hard to have a real opinion about it.

1:19:25.560 --> 1:19:26.200
 It's actually interesting.

1:19:26.200 --> 1:19:29.960
 I separate out things that I think can have yield

1:19:29.960 --> 1:19:31.880
 materially different predictions about the world

1:19:32.520 --> 1:19:35.640
 from ones that are just kind of fun to speculate about.

1:19:35.640 --> 1:19:37.800
 And I kind of view simulation as more like,

1:19:37.800 --> 1:19:40.200
 is there a flying teapot between Mars and Jupiter?

1:19:40.200 --> 1:19:43.800
 Like, maybe, but it's a little bit hard to know

1:19:43.800 --> 1:19:45.000
 what that would mean for my life.

1:19:45.000 --> 1:19:46.360
 So there is something actionable.

1:19:46.360 --> 1:19:50.680
 So some of the best work opening as done

1:19:50.680 --> 1:19:52.200
 is in the field of reinforcement learning.

1:19:52.760 --> 1:19:56.520
 And some of the success of reinforcement learning

1:19:56.520 --> 1:19:59.080
 come from being able to simulate the problem you're trying

1:19:59.080 --> 1:20:00.040
 to solve.

1:20:00.040 --> 1:20:03.560
 So do you have a hope for reinforcement,

1:20:03.560 --> 1:20:05.160
 for the future of reinforcement learning

1:20:05.160 --> 1:20:06.920
 and for the future of simulation?

1:20:06.920 --> 1:20:09.000
 Like, whether we're talking about autonomous vehicles

1:20:09.000 --> 1:20:12.760
 or any kind of system, do you see that scaling?

1:20:12.760 --> 1:20:16.280
 So we'll be able to simulate systems and, hence,

1:20:16.280 --> 1:20:19.400
 be able to create a simulator that echoes our real world

1:20:19.400 --> 1:20:22.520
 and proving once and for all, even though you're denying it

1:20:22.520 --> 1:20:23.800
 that we're living in a simulation.

1:20:24.840 --> 1:20:26.360
 I feel like I've used that for questions, right?

1:20:26.360 --> 1:20:28.200
 So, you know, kind of at the core there of, like,

1:20:28.200 --> 1:20:30.280
 can we use simulation for self driving cars?

1:20:31.080 --> 1:20:33.720
 Take a look at our robotic system, DACTL, right?

1:20:33.720 --> 1:20:37.720
 That was trained in simulation using the Dota system, in fact.

1:20:37.720 --> 1:20:39.560
 And it transfers to a physical robot.

1:20:40.280 --> 1:20:42.120
 And I think everyone looks at our Dota system,

1:20:42.120 --> 1:20:43.400
 they're like, okay, it's just a game.

1:20:43.400 --> 1:20:45.080
 How are you ever going to escape to the real world?

1:20:45.080 --> 1:20:47.320
 And the answer is, well, we did it with the physical robot,

1:20:47.320 --> 1:20:48.600
 the no one could program.

1:20:48.600 --> 1:20:50.840
 And so I think the answer is simulation goes a lot further

1:20:50.840 --> 1:20:53.400
 than you think if you apply the right techniques to it.

1:20:54.040 --> 1:20:55.400
 Now, there's a question of, you know,

1:20:55.400 --> 1:20:57.400
 are the beings in that simulation going to wake up

1:20:57.400 --> 1:20:58.520
 and have consciousness?

1:20:59.480 --> 1:21:02.840
 I think that one seems a lot harder to, again, reason about.

1:21:02.840 --> 1:21:05.240
 I think that, you know, you really should think about, like,

1:21:05.240 --> 1:21:07.800
 where exactly does human consciousness come from

1:21:07.800 --> 1:21:09.000
 in our own self awareness?

1:21:09.000 --> 1:21:10.600
 And, you know, is it just that, like,

1:21:10.600 --> 1:21:12.280
 once you have, like, a complicated enough neural net,

1:21:12.280 --> 1:21:14.440
 do you have to worry about the agent's feeling pain?

1:21:15.720 --> 1:21:17.560
 And, you know, I think there's, like,

1:21:17.560 --> 1:21:19.320
 interesting speculation to do there.

1:21:19.320 --> 1:21:22.920
 But, you know, again, I think it's a little bit hard to know for sure.

1:21:22.920 --> 1:21:24.840
 Well, let me just keep with the speculation.

1:21:24.840 --> 1:21:28.040
 Do you think to create intelligence, general intelligence,

1:21:28.600 --> 1:21:33.000
 you need one consciousness and two a body?

1:21:33.000 --> 1:21:34.920
 Do you think any of those elements are needed,

1:21:34.920 --> 1:21:38.360
 or is intelligence something that's orthogonal to those?

1:21:38.360 --> 1:21:41.560
 I'll stick to the kind of, like, the non grand answer first,

1:21:41.560 --> 1:21:41.720
 right?

1:21:41.720 --> 1:21:43.960
 So the non grand answer is just to look at,

1:21:43.960 --> 1:21:45.560
 you know, what are we already making work?

1:21:45.560 --> 1:21:47.640
 You look at GPT2, a lot of people would have said

1:21:47.640 --> 1:21:49.320
 that to even get these kinds of results,

1:21:49.320 --> 1:21:50.920
 you need real world experience.

1:21:50.920 --> 1:21:52.440
 You need a body, you need grounding.

1:21:52.440 --> 1:21:54.920
 How are you supposed to reason about any of these things?

1:21:54.920 --> 1:21:56.360
 How are you supposed to, like, even kind of know

1:21:56.360 --> 1:21:57.960
 about smoke and fire and those things

1:21:57.960 --> 1:21:59.560
 if you've never experienced them?

1:21:59.560 --> 1:22:03.000
 And GPT2 shows that you can actually go way further

1:22:03.000 --> 1:22:05.640
 than that kind of reasoning would predict.

1:22:05.640 --> 1:22:09.240
 So I think that in terms of, do we need consciousness?

1:22:09.240 --> 1:22:10.360
 Do we need a body?

1:22:10.360 --> 1:22:11.880
 It seems the answer is probably not, right?

1:22:11.880 --> 1:22:13.640
 That we could probably just continue to push

1:22:13.640 --> 1:22:14.680
 kind of the systems we have.

1:22:14.680 --> 1:22:16.520
 They already feel general.

1:22:16.520 --> 1:22:19.080
 They're not as competent or as general

1:22:19.080 --> 1:22:21.640
 or able to learn as quickly as an AGI would,

1:22:21.640 --> 1:22:24.680
 but, you know, they're at least like kind of proto AGI

1:22:24.680 --> 1:22:28.040
 in some way, and they don't need any of those things.

1:22:28.040 --> 1:22:31.640
 Now, let's move to the grand answer, which is, you know,

1:22:31.640 --> 1:22:34.840
 if our neural nets consciousness,

1:22:34.840 --> 1:22:37.240
 nets conscious already, would we ever know?

1:22:37.240 --> 1:22:38.680
 How can we tell, right?

1:22:38.680 --> 1:22:40.920
 And, you know, here's where the speculation starts

1:22:40.920 --> 1:22:44.760
 to become, you know, at least interesting or fun

1:22:44.760 --> 1:22:46.200
 and maybe a little bit disturbing,

1:22:46.200 --> 1:22:47.880
 depending on where you take it.

1:22:47.880 --> 1:22:51.080
 But it certainly seems that when we think about animals,

1:22:51.080 --> 1:22:53.080
 that there's some continuum of consciousness.

1:22:53.080 --> 1:22:56.040
 You know, my cat, I think, is conscious in some way, right?

1:22:56.040 --> 1:22:58.040
 You know, not as conscious as a human.

1:22:58.040 --> 1:22:59.880
 And you could imagine that you could build

1:22:59.880 --> 1:23:01.000
 a little consciousness meter, right?

1:23:01.000 --> 1:23:02.840
 You point at a cat, it gives you a little reading,

1:23:02.840 --> 1:23:06.200
 you point at a human, it gives you much bigger reading.

1:23:06.200 --> 1:23:07.960
 What would happen if you pointed one of those

1:23:07.960 --> 1:23:09.800
 at a Dota neural net?

1:23:09.800 --> 1:23:11.960
 And if you're training this massive simulation,

1:23:11.960 --> 1:23:14.600
 do the neural nets feel pain?

1:23:14.600 --> 1:23:16.760
 You know, it becomes pretty hard to know

1:23:16.760 --> 1:23:20.040
 that the answer is no, and it becomes pretty hard

1:23:20.040 --> 1:23:22.360
 to really think about what that would mean

1:23:22.360 --> 1:23:25.160
 if the answer were yes.

1:23:25.160 --> 1:23:27.400
 And it's very possible, you know, for example,

1:23:27.400 --> 1:23:29.400
 you could imagine that maybe the reason

1:23:29.400 --> 1:23:31.400
 that humans have consciousness

1:23:31.400 --> 1:23:35.000
 is because it's a convenient computational shortcut, right?

1:23:35.000 --> 1:23:36.920
 If you think about it, if you have a being

1:23:36.920 --> 1:23:39.320
 that wants to avoid pain, which seems pretty important

1:23:39.320 --> 1:23:41.000
 to survive in this environment

1:23:41.000 --> 1:23:43.640
 and wants to, like, you know, eat food,

1:23:43.640 --> 1:23:45.400
 then maybe the best way of doing it

1:23:45.400 --> 1:23:47.080
 is to have a being that's conscious, right?

1:23:47.080 --> 1:23:49.480
 That, you know, in order to succeed in the environment,

1:23:49.480 --> 1:23:51.080
 you need to have those properties

1:23:51.080 --> 1:23:52.600
 and how are you supposed to implement them?

1:23:52.600 --> 1:23:55.240
 And maybe this consciousness is a way of doing that.

1:23:55.240 --> 1:23:57.720
 If that's true, then actually maybe we should expect

1:23:57.720 --> 1:23:59.880
 that really competent reinforcement learning agents

1:23:59.880 --> 1:24:01.960
 will also have consciousness.

1:24:01.960 --> 1:24:03.240
 But, you know, that's a big if.

1:24:03.240 --> 1:24:04.760
 And I think there are a lot of other arguments

1:24:04.760 --> 1:24:05.880
 that you can make in other directions.

1:24:06.680 --> 1:24:08.360
 I think that's a really interesting idea

1:24:08.360 --> 1:24:11.400
 that even GPT2 has some degree of consciousness.

1:24:11.400 --> 1:24:14.200
 That's something that's actually not as crazy

1:24:14.200 --> 1:24:14.760
 to think about.

1:24:14.760 --> 1:24:17.720
 It's useful to think about as we think about

1:24:17.720 --> 1:24:19.800
 what it means to create intelligence of a dog,

1:24:19.800 --> 1:24:24.360
 intelligence of a cat, and the intelligence of a human.

1:24:24.360 --> 1:24:30.760
 So, last question, do you think we will ever fall in love,

1:24:30.760 --> 1:24:33.560
 like in the movie, Her, with an artificial intelligence system

1:24:34.360 --> 1:24:36.200
 or an artificial intelligence system

1:24:36.200 --> 1:24:38.440
 falling in love with a human?

1:24:38.440 --> 1:24:38.920
 I hope so.

1:24:40.120 --> 1:24:43.640
 If there's any better way to end it is on love.

1:24:43.640 --> 1:24:45.560
 So, Greg, thanks so much for talking today.

1:24:45.560 --> 1:24:55.560
 Thank you for having me.