The following is a conversation with Ilya Satskeva, cofounder and chief scientist of Open AI,

one of the most cited computer scientists in history with over 165,000 citations,

and to me, one of the most brilliant and insightful minds ever in the field of deep learning.

There are very few people in this world who I would rather talk to and brainstorm with about

deep learning, intelligence, and life in general than Ilya, on and off the mic. This was an honor

and a pleasure. This conversation was recorded before the outbreak of the pandemic,

for everyone feeling the medical, psychological, and financial burden of this crisis,

I'm sending love your way. Stay strong, we're in this together, we'll beat this thing.

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for young people around the world. And now, here's my conversation with Ilya Setskever.

You were one of the three authors with Alex Kyshevsky, Jeff Hinton of the famed AlexNet paper

that is arguably the paper that marked the big catalytic moment that launched the deep learning

revolution. At that time, take us back to that time. What was your intuition about neural networks,

about the representational power of neural networks? And maybe you could mention how did

that evolve over the next few years, up to today, over the 10 years? Yeah, I can answer that question.

At some point in about 2010 or 2011, I connected two facts in my mind. Basically,

the realization was this. At some point, we realized that we can train very large, I shouldn't say very

you know, tiny by today's standards, but large and deep neural networks end to end with back

propagation. At some point, different people obtained this result. I obtained this result.

The first, the first moment in which I realized that deep neural networks are powerful was when

James Martens invented the Hessian free optimizer in 2010. And he trained a 10 layer neural network

end to end without pre training from scratch. And when that happened, I thought this is it.

Because if you can train a big neural network, a big neural network can represent very complicated

function. Because if you have a neural network with 10 layers, it says, though, you allow the

human brain to run for some number of milliseconds, neuron firings are slow. And so in maybe 100

milliseconds, your neurons only fire 10 times. So it's also kind of like 10 layers. And in 100

milliseconds, you can perfectly recognize any object. So I thought, so I already had the idea

then that we need to train a very big neural network on lots of supervised data. And then it

must succeed, because we can find the best neural network. And then there's also theory

that if you have more data than parameters, you won't overfit. Today, we know that actually,

this theory is very incomplete, and you won't overfit even if you have less data than parameters.

But definitely, if you have more data than parameters, you won't overfit.

So the fact that neural networks were heavily overparameterized, wasn't discouraging to you.

So you were thinking about the theory that the number of parameters,

the fact there's a huge number of parameters is okay. Is it going to be okay?

I mean, there was some evidence before that it was okay, but the theory was most,

the theory was that if you had a big data set and a big neural net, it was going to work.

The overparameterization just didn't really figure much as a problem. I thought, well,

with images, you're just going to add some data augmentation, and it's going to be okay.

So where was any doubt coming from? The main doubt was can we train a big,

really have enough compute to train a big enough neural net with back propagation.

Back propagation, I thought was would work. The thing which wasn't clear would was whether

there would be enough compute to get a very convincing result. And then at some point,

Alex Krzewski wrote these insanely fast UDA kernels for training convolutional neural nets,

and that was bam, let's do this. Let's get image net, and it's going to be the greatest thing.

Was your intuition, most of your intuition from empirical results by you and by others?

So like just actually demonstrating that a piece of program can train a 10 layer neural network?

Or was there some pen and paper or marker and whiteboard thinking intuition?

Because you just connected a 10 layer large neural network to the brain. So you just mentioned

the brain. So in your intuition about neural networks, does the human brain come into play

as an intuition builder? Definitely. I mean, you know, you got to be precise with these analogies

between neural artificial neural networks and the brain. But there's no question that the brain

is a huge source of intuition and inspiration for deep learning researchers since all the way

from Rosenblatt in the 60s. Like if you look at the whole idea of a neural network is directly

inspired by the brain. You had people like McCallum and Pitts who were saying, hey, you got these

neurons in the brain. And hey, we recently learned about the computer and automata.

Can we use some ideas from the computer and automata to design some kind of computational

object that's going to be simple, computational, and kind of like the brain, and they invented the

neuron. So they were inspired by it back then. Then you had the convolutional neural network

from Fukushima, and then later young Lacan, who said, hey, if you limit the receptive fields of

a neural network, it's going to be especially suitable for images as it turned out to be true.

So there was a very small number of examples where analogies to the brain were successful.

And then I thought, well, probably an artificial neuron is not that different from the brain if

it's quite hard enough. So let's just assume it is and roll with it. So we're now at a time where

deep learning is very successful. So let us squint less and say, let's open our eyes and say, what

to you is an interesting difference between the human brain? Now, I know you're probably not an

expert, neither in your scientists and your biologists, but loosely speaking, what's the

difference between the human brain and artificial neural networks? That's interesting to you for

the next decade or two. That's a good question to ask. What is an interesting difference between

the neural between the brain and our artificial neural networks? So I feel like today,

artificial neural networks, so we all agree that there are certain dimensions in which the human

brain vastly outperforms our models. But I also think that there are some ways in which artificial

neural networks have a number of very important advantages over the brain. Looking at the advantages

versus disadvantages is a good way to figure out what is the important difference. So the brain

uses spikes, which may or may not be important. Yes, that's a really interesting question. Do you

think it's important or not? That's one big architectural difference between artificial

neural networks. It's hard to tell, but my prior is not very high. And I can say why. There are

people who are interested in spiking neural networks. And basically, what they figured out is

that they need to simulate the non spiking neural networks in spikes. And that's how they're going

to make them work. If you don't simulate the non spiking neural networks in spikes, it's not going

to work because the question is, why should it work? And that connects to questions around back

propagation and questions around deep learning. You've got this giant neural network. Why should

it work at all? Why should that learning rule work at all? It's not a self evident question,

especially if you, let's say if you were just starting in the field and you read the very

early papers, you can say, Hey, people are saying, let's build neural networks. That's a great idea

because the brain is a neural network. So it would be useful to build neural networks. Now,

let's figure out how to train them. It should be possible to train them probably, but how?

And so the big idea is the cost function. That's the big idea. The cost function is a way of measuring

the performance of the system according to some measure. By the way, that is a big, actually,

let me think. Is that, is that a one, a difficult idea to arrive at? And how big of an idea is that

that there's a single cost function? Sorry, let me take a pause. Is supervised learning a difficult

concept to come to? I don't know. All concepts are very easy in retrospect. Yeah, that's what it

seems trivial now, but I, because the reason I asked that, and we'll talk about it, because is there

other things? Is there things that don't necessarily have a cost function, maybe have many cost

functions, or maybe have dynamic cost functions, or maybe a totally different kind of architectures?

Because we have to think like that in order to arrive at something new, right? So the only,

so the good examples of things which don't have clear cost functions are GANs.

Right. And again, you have a game. So instead of thinking of a cost function,

where you want to optimize, where you know that you have an algorithm gradient descent,

which will optimize the cost function. And then you can reason about the behavior of your system

in terms of what it optimizes. With GAN, you say, I have a game, and I'll reason about the behavior

of the system in terms of the equilibrium of the game. But it's all about coming up with these

mathematical objects that help us reason about the behavior of our system. Right. That's really

interesting. Yeah. So GAN is the only one. It's kind of a, the cost function is emergent from the

comparison. I don't know if it has a cost function. I don't know if it's meaningful to talk about the

cost function of a GAN. It's kind of like the cost function of biological evolution or the cost

function of the economy. It's, you can talk about regions to which it will go towards, but I don't

think, I don't think the cost function analogy is the most useful. So evolution doesn't,

that's really interesting. So if evolution doesn't really have a cost function, like a cost function

based on it's something akin to our mathematical conception of a cost function, then do you think

cost functions in deep learning are holding us back? Yeah. So you just kind of mentioned that

cost function is a nice first profound idea. Do you think that's a good idea? Do you think it's

an idea will go past? So self play starts to touch on that a little bit in reinforcement

learning systems. That's right. Self play and also ideas around exploration where you're trying to

take action. That's surprise a predictor. I'm a big fan of cost functions. I think cost functions

are great and they serve us really well. And I think that whenever we can do things with

cost functions, we should. And you know, maybe there is a chance that we will come up with some

yet another profound way of looking at things that will involve cost functions in a less central way.

But I don't know. I think cost functions are I mean,

I would not bet against against cost functions. Is there other things about the brain

that pop into your mind that might be different and interesting for us to consider in designing

artificial neural networks? So we talked about spiking a little bit. I mean, one thing which may

potentially be useful, I think people, neuroscientists figured out something about the learning rule of

the brain, or I'm talking about spike time independent plasticity. And it would be nice

if some people were to study that in simulation. Wait, sorry, spike time independent plasticity.

Yeah, that's that STD. It's a particular learning rule that uses spike timing to figure out how to

determine how to update the synapses. So it's kind of like, if a synapse fires into the neuron before

the neuron fires, then it's strengthened the synapse. And if the synapse fires into the

neuron shortly after the neuron fired, then it becomes the synapse something along this line.

I'm 90% sure it's right. So if I said something wrong here, don't don't get too angry.

But you saw me brilliant while saying it. But the timing, that's one thing that's missing.

The temporal dynamics is not captured. I think that's like a fundamental property of the brain,

is the timing of the signals. Well, you're recording neural networks.

But you think of that as, I mean, that's a very crude simplified, what's that called?

There's a clock, I guess, to recurrent neural networks. This seems like the brain is the

general, the continuous version of that, the generalization where all possible timings are

possible. And then within those timings is contained some information. You think recurrent neural

networks, the recurrence in recurrent neural networks can capture the same kind of phenomena

as the timing that seems to be important for the brain in the firing of neurons in the brain?

I mean, I think recurrent neural networks are amazing. And I think they can do anything we'd

want a system to do. Right now, recurrent neural networks have been superseded by

transformers, but maybe one day they'll make a comeback, maybe it'll be back. We'll see.

Let me, in a small tangent, say, do you think they'll be back? So so much of the breakthroughs

recently that we'll talk about on natural language processing and language modeling has been with

transformers that don't emphasize recurrence. Do you think recurrence will make a comeback?

Well, some kind of recurrence, I think, very likely. Recurrent neural networks, as they're

typically thought of for processing sequences, I think it's also possible.

What is, to you, a recurrent neural network? And generally speaking, I guess, what is a

recurrent neural network? You have a neural network which maintains a high dimensional

hidden state. And then when an observation arrives, it updates its high dimensional hidden state

through its connections in some way. So do you think, you know, that's what like expert systems

did, right? Symbolic AI, the knowledge based, growing a knowledge base is maintaining a

hidden state, which is its knowledge base and is growing it by sequentially processing. Do you

think of it more generally in that way? Or is it simply, is it the more constrained form of

a hidden state with certain kind of gating units that we think of as today with LSDMs and that?

I mean, the hidden state is technically what you described there, the hidden state that goes

inside the LSDM or the RNN or something like this. But then what should be contained, you know,

if you want to make the expert system analogy, I'm not, I mean, you could say that the knowledge

is stored in the connections and then the short term processing is done in the hidden state.

Yes. Could you say that? So sort of, do you think there's a future of building large scale

knowledge bases within the neural networks? Definitely.

So we're going to pause on that confidence because I want to explore that. But let me zoom back out

and ask back to the history of ImageNet. Neural networks have been around for many decades,

as you mentioned. What do you think were the key ideas that led to their success, that ImageNet

moment and beyond the success in the past 10 years? Okay, so the question is to make sure I

didn't miss anything. The key ideas that led to the success of deep learning over the past 10 years.

Exactly. Even though the fundamental thing behind deep learning has been around for much longer.

So the key idea about deep learning or rather the key fact about deep learning before deep learning

started to be successful is that it was underestimated. People who worked in machine learning

simply didn't think that neural networks could do much. People didn't believe that large neural

networks could be trained. People thought that, well, there was lots of, there was a lot of debate

going on in machine learning about what are the right methods and so on. And people were arguing

because there was no way to get hard facts. And by that, I mean, there were no benchmarks which

were truly hard, that if you do really well on them, then you can say, look, here's my system.

That's when you switch from, that's when this field becomes a little bit more of an engineering

field. So in terms of deep learning, to answer the question directly, the ideas were all there.

The thing that was missing was a lot of supervised data and a lot of compute.

Once you have a lot of supervised data and a lot of compute, then there is a third thing which is

needed as well. And that is conviction, conviction that if you take the right stuff, which already

exists, and apply and mixed with a lot of data and a lot of compute, that it will in fact work.

And so that was the missing piece. It was you had the, you needed the data, you needed the compute

which showed up in terms of GPUs, and you needed the conviction to realize that you need to mix

them together. So that's really interesting. So I guess the presence of compute and the presence

supervised data allowed the empirical evidence to do the convincing of the majority of the computer

science community. So I guess there's a key moment with Jitendra Malik and Alex Alyosha Efros,

who were very skeptical, right? And then there's a Jeffrey Hinton that was the opposite of skeptical.

And there was a convincing moment. And I think Emission had served as that moment.

That's right. And they represented this kind of, or the big pillars of computer vision community,

kind of the wizards got together. And then all of a sudden there was a shift. And

it's not enough for the ideas to all be there and the computer to be there. It's

for it to convince the cynicism that existed. That's interesting. That people just didn't

believe for a couple of decades. Yeah. Well, but it's more than that. It's kind of, when put this

way, it sounds like, well, you know, those silly people who didn't believe what were they, what

were they missing. But in reality, things were confusing because neural networks really did

not work on anything. And they were not the best method on pretty much anything as well.

Well, and it was pretty rational to say, yeah, this stuff doesn't have any traction.

And that's why you need to have these very hard tasks, which are, which produce undeniable evidence.

And that's how we make progress. And that's why the field is making progress today,

because we have these hard benchmarks, which represent true progress. And so, and this is

why we were able to avoid endless debate. So incredibly, you've contributed some of the

biggest recent ideas in AI in computer vision, language, natural language processing, reinforcement

learning, sort of everything in between, maybe not GANs. There may not be a topic you haven't

touched. And of course, the fundamental science of deep learning. What is the difference to you

between vision, language, and as in reinforcement learning, action, as learning problems,

and what are the commonalities? Do you see them as all interconnected? Are they fundamentally

different domains that require different approaches? Okay, that's a good question.

Machine learning is a field with a lot of unity, a huge amount of unity.

What do you mean by unity? Like overlap of ideas?

Overlap of ideas, overlap of principles. In fact, there's only one or two or three principles,

which are very, very simple. And then they apply in almost the same way, in almost the

same way to the different modalities to the different problems. And that's why today,

when someone writes a paper on improving optimization of deep learning and vision,

it improves the different NLP applications, and it improves the different reinforcement

learning applications. Reinforcement learning. So I would say that computer vision and NLP are

very similar to each other. Today, they differ in that they have slightly different architectures.

We use transformers in NLP, and we use convolutional neural networks in vision.

But it's also possible that one day this will change and everything will be unified with a

single architecture. Because if you go back a few years ago in natural language processing,

there were a huge number of architectures for every different tiny problem had its own architecture.

Today, there's just one transformer for all those different tasks. And if you go back in time even

more, you had even more and more fragmentation and every little problem in AI had its own

little subspecialization and sub, you know, little set of collection of skills, people who would

know how to engineer the features. Now it's all been subsumed by deep learning. We have this

unification. And so I expect vision to become unified with natural language as well. Or rather,

I just expect I think it's possible. I don't want to be too sure because I think on the

convolutional neural net is very computationally efficient. Arrel is different. Arrel does require

slightly different techniques because you really do need to take action. You really do need to do

something about exploration, your variance is much higher. But I think there is a lot of unity

even there. And I would expect, for example, that at some point, there will be some

broader unification between Arrel and supervised learning where somehow the Arrel will be making

decisions to make the supervised learning go better. And it will be, I imagine one big black

box and you just throw every, you know, you shovel, shovel things into it. And it just

figures out what to do with whatever you shovel in it. I mean, reinforcement learning has

some aspects of language and vision combined, almost. There's elements of a long term

memory that you should be utilizing. And there's elements of a really rich sensory space. So it

seems like the, it's like the union of the two or something like that. I'd say something

slightly differently. I'd say that reinforcement learning is neither, but it naturally interfaces

and integrates with the two of them. Do you think action is fundamentally different? So yeah,

what is interesting about what is unique about policy of learning to act? Well, so one example,

for instance, is that when you learn to act, you are fundamentally in a non stationary world.

Because as your actions change, the things you see start changing. You,

you experience the world in a different way. And this is not the case for

the more traditional static problem where you have some distribution and you just apply a model to

that distribution. Do you think it's a fundamentally different problem or is it just a more difficult

it's a generalization of the problem of understanding? I mean, it's a question of

definitions almost. There is a huge amount of commonality for sure. You take gradients,

you take gradients, we try to approximate gradients in both cases. In some case,

in the case of reinforcement learning, you have some tools to reduce the variance of the gradients.

You do that. There's lots of commonalities, the same neural net in both cases,

you compute the gradient, you apply Adam in both cases.

So I mean, there's lots in common for sure, but there are some small

differences which are not completely insignificant. It's really just a matter of your point of view,

what frame of reference you what how much do you want to zoom in or out as you look at these

problems? Which problem do you think is harder? So people like Noam Chomsky believe that language

is fundamental to everything. So it underlies everything. Do you think language understanding

is harder than visual scene understanding or vice versa? I think that asking if a problem is hard

is slightly wrong. I think the question is a little bit wrong and I want to explain why.

Okay. So what does it mean for a problem to be hard? Okay, the non interesting dumb answer to

that is there's a benchmark and there's a human level performance on that benchmark. And how

is the effort required to reach the human level? Okay, benchmark. So from the perspective of how

much until we get to human level on a very good benchmark. Yeah, like some I understand what you

mean by that. So what I was going to say that a lot of it depends on, you know, once you solve a

problem, it stops being hard. And that's, that's always true. And so but something is hard or not

depends on what our tools can do today. So you know, you say today, through human level, language

understanding and visual perception are hard in the sense that there is no way of solving the

problem completely in the next three months. Right. So I agree with that statement. Beyond

that, I'm just I'd be my guess would be as good as yours. I don't know. Okay, so you don't have a

fundamental intuition about how hard language understanding is. I think I not change my mind.

I'd say language is probably going to be hard. I mean, it depends on how you define it. Like if

you mean absolute top notch 100% language understanding, I'll go with language. And so

but then if I show you a piece of paper with letters on it, is that you see what I mean?

So you have a vision system, you say it's the best human level vision system. I show you I open

a book, and I show you letters. Will it understand how these letters form into word and sentences

and meaning is this part of the vision problem? Where does vision and the language begin?

Yeah, so Chomsky would say it starts at language. So vision is just a little example of the kind of

structure and, you know, fundamental hierarchy of ideas that's already represented in our brain

somehow that's represented through language. But where does vision stop and language begin?

That's a really interesting question.

So one possibility is that it's impossible to achieve really deep understanding

in either images or language without basically using the same kind of system.

So you're going to get the other for free. I think I think it's pretty likely that yes,

if we can get one we probably our machine learning is probably that good that we can get the other

but it's not 100 I'm not 100% sure. And also, I think a lot, a lot of it really does depend on

your definitions. Definitions of like perfect vision. Because, you know, reading is vision,

but should it count? Yeah, to me, so my definition is if a system looked at an image,

and then a system looked at a piece of text, and then told me something about that,

and I was really impressed. That's relative. You'll be impressed for half an hour and then

you're going to say, well, I mean, all the systems do that. But here's the thing they don't do.

Yeah, but I don't have that with humans. Humans continue to impress me.

Is that true?

Well, the ones, okay, so I'm a fan of monogamy. So I like the idea of marrying somebody being

with them for several decades. So I believe in the fact that yes, it's possible to have somebody

continuously giving you pleasurable, interesting, witty new ideas, friends. Yeah, I think so. They

continue to surprise you. The surprise, it's that injection of randomness seems to be a nice source

of, yeah, continued inspiration, like the wit, the humor. I think, yeah, that would be,

it's a very subjective test, but I think if you have enough humans in the room.

Yeah, I understand what you mean. Yeah, I feel like I misunderstood what you meant by

impressing you. I thought you meant to impress you with its intelligence, with how good,

valid understands an image. I thought you meant something like, I'm going to show you

a really complicated image and it's going to get it right and you're going to say, wow,

that's really cool. The systems of January 2020 have not been doing that.

Yeah, no, I think it all boils down to the reason people click like on stuff on the

internet, which is like it makes them laugh. So it's like humor or wit or insight.

I'm sure we'll get that as well. So forgive the romanticized question, but looking back to you,

what is the most beautiful or surprising idea in deep learning or AI in general you've come across?

So I think the most beautiful thing about deep learning is that it actually works.

And I mean it because you got these ideas, you got the little neural network, you got the back

propagation algorithm. And then you got some theories as to, you know, this is kind of like

the brain. So maybe if you make it large, if you make the neural network large and you're

trained on a lot of data, then it will do the same function that the brain does.

And it turns out to be true. That's crazy. And now we just train these neural networks and you

make them larger and they keep getting better. And I find it unbelievable. I find it unbelievable

that this whole AI stuff with neural networks works. Have you built up an intuition of why are

there a little bits and pieces of intuitions of insights of why this whole thing works?

I mean, some definitely, while we know that optimization, we now have good, you know,

we've had lots of empirical, huge amounts of empirical reasons to believe that optimization

should work on all most problems we care about. Do you have insights of what, so you just said

empirical evidence is most of your sort of empirical evidence kind of convinces you,

it's like evolution is empirical, it shows you that look, this evolutionary process seems to be

a good way to design organisms that survive in their environment. But it doesn't really get you

to the insides of how the whole thing works. I think it's a good analogy is physics. You know how

you say, Hey, let's do some physics calculation and come up with some new physics theory and make

some prediction. But then you got around the experiment. You know, you got around the experiment,

it's important. So it's a bit the same here, except that maybe sometimes the experiment came

before the theory. But it still is the case, you know, you have some data and you come up with

some prediction, you say, Yeah, let's make a big neural network, let's train it, and it's going to

work much better than anything before it. And it will in fact continue to get better as you make

it larger. And it turns out to be true. That's, that's amazing when a theory is validated like

this, you know, it's not a mathematical theory, it's more of a biological theory almost. So I

think there are not terrible analogies between deep learning and biology. I would say it's like

the geometric mean of biology and physics, that's deep learning. The geometric mean of biology and

physics, I think I'm going to need a few hours to wrap my head around that. Because just to find

the geometric, just to find the set of what biology represents. Well, biology, in biology,

things are really complicated. The theories are really, really, it's really hard to have good

predictive theory. And in physics, the theories are too good. In theory, in physics, people make

these super precise theories, which make these amazing predictions. And in machine learning,

they're kind of in between. Kind of in between. But it'd be nice if machine learning somehow

helped us discover the unification of the two as opposed to serve the in between.

But you're right, that's, you're kind of trying to juggle both. So do you think there are still

beautiful and mysterious properties in neural networks that are yet to be discovered? Definitely.

I think that we are still massively underestimating deep learning.

What do you think it will look like? Like what if I knew I would have done it?

So, but if you look at all the progress from the past 10 years, I would say most of it,

I would say there have been a few cases where some were things that felt like really new ideas

showed up. But by and large, it was every year, we thought, okay, deep learning goes this far.

Nope, it actually goes further. And then the next year, okay, now you know, this is this is

big deep learning. We are really done. Nope, it goes further. It just keeps going further each

year. So that means that we keep underestimating, we keep not understanding it as surprising properties

all the time. Do you think it's getting harder and harder to make progress, need to make progress?

It depends on what we mean. I think the field will continue to make very robust progress

for quite a while. I think for individual researchers, especially people who are doing

research, it can be harder because there is a very large number of researchers right now.

I think that if you have a lot of compute, then you can make a lot of very interesting discoveries,

but then you have to deal with the challenge of managing a huge computer, a huge class,

huge computer cluster to run your experiments. It's a little bit harder.

So I'm asking all these questions that nobody knows the answer to, but you're one of the smartest

people I know. So I'm going to keep asking the, so let's imagine all the breakthroughs that happen

in the next 30 years in deep learning. Do you think most of those breakthroughs can be done by

one person with one computer? Sort of in the space of breakthroughs, do you think compute

will be, compute and large efforts will be necessary? I mean, I can't be sure. When you say

one computer, you mean how large? You're clever. I mean, one GPU. I see. I think it's pretty unlikely.

I think it's pretty unlikely. I think that there are many, the stack of deep learning is starting

to be quite deep. If you look at it, you've got all the way from the ideas, the systems to build

the datasets, the distributed programming, the building the actual cluster, the GPU programming,

putting it all together. So the stack is getting really deep. And I think it becomes,

it can be quite hard for a single person to become, to be world class in every single layer of the

stack. What about what like Vladimir Vapnik really insists on is taking MNIST and trying to learn

from very few examples. So being able to learn more efficiently. Do you think that there'll be

breakthroughs in that space that would may not need this huge compute? I think there will be a

large number of breakthroughs in general that will not need a huge amount of compute. So maybe I

should clarify that. I think that some breakthroughs will require a lot of compute. And I think building

systems which actually do things will require a huge amount of compute. That one is pretty obvious.

If you want to do X, and X requires a huge neural net, you got to get a huge neural net.

But I think there will be lots of, I think there is lots of room for very important work being

done by small groups and individuals. Can you maybe sort of on the topic of the science of

deep learning, talk about one of the recent papers that you've released, the deep double descent,

where bigger models and more data hurt. I think it's a really interesting paper.

Can you can describe the main idea? And yeah, definitely. So what happened is that some over

the years, some small number of researchers noticed that it is kind of weird that when you make the

neural network larger, it works better. And it seems to go in contradiction with statistical

ideas. And then some people made an analysis showing that actually you got this double descent

bump. And what we've done was to show that double descent occurs for pretty much all practical

deep learning systems. And that it'll be also so can you step back? What's the X axis and the Y

axis of a double descent plot? Okay, great. So you can you can look you can do things like

you can take your neural network. And you can start increasing its size slowly,

while keeping your data set fixed. So if you increase the size of the neural network slowly,

and if you don't do early stopping, that's a pretty important detail. Then when the

neural network is really small, you make it larger, you get a very rapid increase in performance.

Then you continue to make it larger. And at some point performance will get worse.

And it gets and it gets the worst exactly at the point at which it achieves zero training

error precisely zero training loss. And then as you make it larger, it starts to get better again.

And it's kind of counterintuitive, because you'd expect deep learning phenomena to be

monotonic. And it's hard to be sure what it means. But it also occurs in the case of linear

classifiers. And the intuition basically boils down to the following. When you when you have a lot

when you have a large data set, and a small model, then small, tiny, random. So basically,

what is overfitting? Overfitting is when your model is somehow very sensitive to the small, random,

unimportant stuff in your data set in the training data in the training data set precisely. So if

you have a small model, and you have a big data set, and there may be some random thing, you know,

some training cases are randomly in the data set, and others may not be there. But the small model,

but the small model is kind of insensitive to this randomness, because it's the same you there is

pretty much no uncertainty about the model, when the data set is large. So okay, so at the very

basic level, to me, it is the most surprising thing that neural networks don't overfit every time,

very quickly, before ever being able to learn anything, the huge number of parameters. So here

is so there is one way okay, so maybe so let me try to give the explanation, maybe that will be

that will work. So you got a huge neural network, let's suppose you got them. You are you have a

huge neural network, you have a huge number of parameters. And now let's pretend everything is

linear, which is not let's just pretend. Then there is this big subspace, where your network

achieves zero error. And SGT is going to find approximately the point really that's right,

approximately the point with the smallest norm in that subspace. Okay, and that can also be proven

to be insensitive to the small randomness in the data, when the dimensionality is high. But when

the dimensionality of the data is equal to the dimensionality of the model, then there is a

one to one correspondence between all the data sets and the models. So small changes in the data

set actually lead to large changes in the model. And that's why performance gets worse. So this

is the best explanation more or less. So then it would be good for the model to have more parameters

so to be bigger than the data. That's right. But only if you don't really stop. If you introduce

early stop in your regularization, you can make a double as a descent pump almost completely

disappear. What is early stop early stopping is when you train your model, and you monitor your

test validation performance. And then if at some point validation performance starts to get worse,

you say, Okay, let's stop training. We are good. We are good. We are good enough.

So the magic happens after after that moment. So you don't want to do the early stopping.

Well, if you don't do the early stopping, you get this very, you get a very pronounced double

descent. Do you have any intuition why this happens? Double descent or sorry, are you stopping?

No, the double descent. So the Well, yeah, so I try it, let's see the intuition is basically

is this that when the data set has as many degrees of freedom as the model, then there is a one to

one correspondence between them. And so small changes to the data set lead to noticeable changes

in the model. So your model is very sensitive to all the randomness, it is unable to discard it.

Whereas, it turns out that when you have a lot more data than parameters, or a lot more parameters

than data, the resulting solution will be insensitive to small changes in the data set.

So it's able to nicely put discard the small changes, the randomness. Exactly. The spurious

correlation which you don't want. Jeff Hinton suggested we need to throw back propagation.

We already kind of talked about this a little bit, but he suggested we need to throw away

back propagation and start over. I mean, of course, some of that is a little bit

wit and humor. But what do you think? What could be an alternative method of training neural networks?

Well, the thing that he said precisely is that to the extent that you can't find back propagation

in the brain, it's worth seeing if we can learn something from how the brain learns. But back

propagation is very useful and we should keep using it. Oh, you're saying that once we discover the

mechanism of learning in the brain or any aspects of that mechanism, we should also try to implement

that in neural networks? If it turns out that you can't find back propagation in the brain?

If we can't find back propagation in the brain? Well, so I guess your answer to that is back

propagation is pretty damn useful. So why are we complaining? I mean, I personally am a big fan

of back propagation. I think it's a great algorithm because it solves an extremely fundamental problem

which is finding a neural circuit subject to some constraints. And I don't see that problem going

away. So that's why I really, I think it's pretty unlikely that you'll have anything which is going

to be dramatically different. It could happen. But I wouldn't bet on it right now.

So let me ask a sort of big picture question. Do you think neural networks can be made to reason?

Why not? Well, if you look, for example, at AlphaGo or AlphaZero, the neural network of AlphaZero

plays Go, which we all agree is a game that requires reasoning, better than 99.9% of all humans,

just the neural network without the search, just the neural network itself.

Doesn't that give us an existence proof that neural networks can reason?

To push back and disagree a little bit, we all agree that Go is reasoning. I think I agree. I

don't think it's at trivial. So obviously reasoning like intelligence is a loose gray area term

a little bit. Maybe you disagree with that. But yes, I think it has some of the same elements

of reasoning. Reasoning is almost akin to search. There's a sequential element of

stepwise consideration of possibilities and sort of building on top of those possibilities in a

sequential manner until you arrive at some insight. So yeah, I guess playing Go is kind of like that.

And when you have a single neural network doing that without search, that's kind of like that.

So there's an existence proof in a particular constrained environment that a process akin to

what many people call reasoning exists, but more general kind of reasoning. So off the board.

There is one other existence proof. Oh boy, which one? Us humans? Yes. Okay. All right. So

do you think the architecture that will allow neural networks to reason will look similar

to the neural network architectures we have today? I think it will. I think, well, I don't want to make

two overly definitive statements. I think it's definitely possible that the neural networks

that will produce the reasoning breakthroughs of the future will be very similar to the

architectures that exist today, maybe a little bit more recurrent, maybe a little bit deeper.

But these neural nets are so insanely powerful. Why wouldn't they be able to learn to reason?

Humans can reason. So why can't neural networks? So do you think the kind of stuff we've seen

neural networks do is a kind of just weak reasoning? So it's not a fundamentally different

process. Again, this is stuff we don't nobody knows the answer to. So when it comes to our

neural networks, I would think which I would say is that neural networks are capable of reasoning.

But if you train a neural network on a task which doesn't require reasoning,

it's not going to reason. This is a well known effect where the neural network will solve

exactly the it will solve the problem that you pose in front of it in the easiest way possible.

Right. That takes us to one of the brilliant ways you describe neural networks, which is

you've referred to neural networks as the search for small circuits,

and maybe general intelligence as the search for small programs,

which I found is a metaphor very compelling. Can you elaborate on that difference?

Yeah. So the thing which I said precisely was that if you can find the shortest program that

outputs the data at your disposal, then you will be able to use it to make the best prediction

possible. And that's a theoretical statement which can be proved mathematically. Now,

you can also prove mathematically that it is that finding the shortest program which generates

some data is not a computable operation. No finite amount of compute can do this.

So then with neural networks, neural networks are the next best thing that actually works in

practice. We are not able to find the best, the shortest program which generates our data,

but we are able to find a small, but now that statement should be amended. Even a large circuit

which fits our data in some way. Well, I think what you meant by the small

circuit is the smallest needed circuit. Well, the thing which I would change now,

back then I really haven't fully internalized the overparameterized results. The things we know

about overparameterized neural nets, now I would phrase it as a large circuit whose weights contain

a small amount of information, which I think is what's going on. If you imagine the training

process of a neural network as you slowly transmit entropy from the data set to the parameters,

then somehow the amount of information in the weights ends up being not very large,

which would explain why the general is so well. So the large circuit might be one that's

helpful for the generalization. Yeah, something like this. But do you see it important to be able

to try to learn something like programs? I mean, if we can, definitely. I think it's kind of,

the answer is kind of yes, if we can do it. We should do things that we can do it.

The reason we are pushing on deep learning, the fundamental reason, the root cause is that we

are able to train them. So in other words, training comes first. We've got our pillar,

which is the training pillar. And now if you're trying to contort our neural networks around

the training pillar, we got to stay trainable. This is an invariant we cannot violate. And so

being trainable means starting from scratch, knowing nothing, you can actually pretty quickly

converge towards knowing a lot or even slowly. But it means that given the resources at your

disposal, you can train the neural net and get it to achieve useful performance. Yeah, that's a

pillar we can't move away from. That's right. Because if you can, whereas if you say, Hey,

let's find the shortest program, we can't do that. So it doesn't matter how useful that would be.

We can do it. So we want. So do you think you kind of mentioned that the neural networks are

good at finding small circuits or large circuits? Do you think then the matter of finding small

programs is just the data? No, sorry, not the size or the type of data. Ask giving it programs.

Well, I think the thing is that right now, there are no good precedents of people successfully

finding programs really well. And so the way you'd find programs is you'd train a deep neural

network to do it basically. Right. Which is the right way to go about it. But there's not good

illustrations of that. Yes, hasn't been done yet. But in principle, it should be possible.

Can you elaborate a little bit? What's your insight in principle? And put another way,

you don't see why it's not possible. Well, it's kind of like more, it's more a statement of

I think that it's, I think that it's unwise to bet against deep learning. And

if it's a, if it's a cognitive function that humans seem to be able to do, then

it doesn't take too long for some deep neural net to pop up that can do it too.

Yeah, I'm there with you. I can, I've stopped betting against neural networks at this point

because they continue to surprise us. What about long term memory? Can neural networks have long

term memory or something like knowledge basis? So being able to aggregate important information

over long periods of time, that would then serve as useful sort of representations of

state that you can make decisions by. So have a long term context based on what you make in the

decision. So in some sense, the parameters already do that. The parameters are an aggregation of the

day of the neural of the entirety of the neural nets experience. And so they count as the long

as long form long term knowledge. And people have trained various neural nets to act as

knowledge basis and, you know, investigated with invest people have investigated language

models as knowledge basis. So there is work, there is work there. Yeah, but in some sense,

do you think in every sense, do you think there's a, it's all just a matter of coming up with a

better mechanism of forgetting the useless stuff and remembering the useful stuff? Because right

now, I mean, there's not been mechanisms that do remember really long term information.

What do you mean by that precisely?

Precisely. I like the word precisely. So

I'm thinking of the kind of compression of information the knowledge bases represent,

sort of creating a, now I apologize for my sort of human centric thinking about

what knowledge is because neural networks aren't interpretable necessarily with the

kind of knowledge they have discovered. But a good example for me is knowledge bases being

able to build up over time something like the knowledge that Wikipedia represents.

It's a really compressed structured

knowledge base, obviously not the actual Wikipedia or the language, but like a semantic web,

the dream that semantic web represented. So it's a really nice compressed knowledge base

or something akin to that in the noninterpretable sense as neural networks would have.

Well, the neural networks would be noninterpretable if you look at their weights, but their outputs

should be very interpretable. Okay, so yeah, how do you make very smart neural networks like

language models interpretable? Well, you ask them to generate some text and the text will

generally be interpretable. Do you find that the epitome of interpretability, like can you do better?

Because you can't, okay, I'd like to know what does it know and what doesn't know.

I would like the neural network to come up with examples where it's completely dumb

and examples where it's completely brilliant. And the only way I know how to do that now is to

generate a lot of examples and use my human judgment. But it would be nice if the neural

network had some self awareness about it. Yeah, 100%. I'm a big believer in self awareness. And I

think that I think, I think neural net self awareness will allow for things like the capabilities,

like the ones you described, like for them to know what they know and what they don't know,

and for them to know where to invest to increase their skills most optimally. And to your question

of interpretability, there are actually two answers to that question. One answer is, you know,

we have the neural net, so we can analyze the neurons and we can try to understand what the

different neurons and different layers mean. And you can actually do that and OpenAI has done

some work on that. But there is a different answer which is that I would say that's the human

centric answer where you say, you know, you look at a human being, you can't read, you know,

how do you know what a human being is thinking? You ask them, you say, Hey, what do you think about

this? What do you think about that? And you get some answers. The answers you get are sticky. In

the sense, you already have a mental model, you already have an, yeah, mental model of that human

being. You already have an understanding of like a big conception of what it of that human being,

how they think, what they know, how they see the world, and then everything you ask, you're

adding onto that. And that stickiness seems to be, that's one of the really interesting qualities

of the human being is that information is sticky. You don't, you seem to remember the useful stuff,

aggregate it well, and forget most of the information that's not useful. That process,

but that's also pretty similar to the process that neural networks do. It's just that neural

networks are much crappier at this time. It doesn't seem to be fundamentally that different.

But just to stick on reasoning for a little longer, you said, why not? Why can't that reason?

What's a good, impressive feat benchmark to you of reasoning that you'll be impressed by if

neural networks were able to do? Is that something you already have in mind? Well, I think writing,

writing really good code, I think, proving really hard theorems, solving open ended problems

without the box solutions. And sort of theorem type mathematical problems. Yeah, I think those

ones are a very natural example as well. You know, if you can prove an unproven theorem,

then it's hard to argue it on reason. And so by the way, and this comes back to the point about

the hard results, you know, if you've got a hard, if you have machine learning, deep learning as a

field is very fortunate, because we have the ability to sometimes produce these unambiguous

results. And when they happen, the debate changes, the conversation changes. It's a converse, you

have the ability to produce conversation changing results. Conversation. And then of course,

just like you said, people kind of take that for granted, say that wasn't actually a hard problem.

Well, I mean, at some point, we'll probably run out of hard problems.

Yeah, that whole mortality thing is kind of a sticky problem that we haven't quite figured

out. Maybe we'll solve that one. I think one of the fascinating things in your entire body of work,

but also the work at OpenAI recently, one of the conversation changes has been in the world of

language models. Can you briefly kind of try to describe the recent history of using neural

networks in the domain of language and text? Well, there's been lots of history. I think the

Elman network was a small, tiny recurrent neural network applied to language back in the 80s.

So the history is really, you know, fairly long, at least. And the thing that started the thing

that changed the trajectory of neural networks and language is the thing that changed the trajectory

of all deep learning and that's data and compute. So suddenly you move from small language models,

which learn a little bit. And with language models, in particular, you can, there's a very clear

explanation for why they need to be large, to be good. Because they're trying to predict the

next word. So when you don't know anything, you'll notice very, very broad strokes, surface level

patterns, like sometimes there are characters and there is space between those characters,

you'll notice this pattern. And you'll notice that sometimes there is a comma and then the next

character is a capital letter, you'll notice that pattern. Eventually, you may start to notice that

there are certain words occur often, you may notice that spellings are a thing, you may notice

syntax. And when you get really good at all these, you start to notice the semantics,

you start to notice the facts. But for that to happen, the language model needs to be larger.

So that's, let's linger on that, is that's where you and Noam Chomsky disagree.

See, you think we're actually taking incremental steps, sort of larger network, larger compute

will be able to get to the semantics, be able to understand language without what Noam likes to

sort of think of as a fundamental understandings of the structure of language, like imposing

your theory of language onto the learning mechanism. So you're saying the learning you can learn from

raw data, the mechanism that underlies language? Well, I think it's pretty likely. But I also

want to say that I don't really know precisely what Chomsky means when he talks about him.

You said something about imposing your structural language. I'm not 100% sure what he means, but

empirically, it seems that when you inspect those larger language models, they exhibit signs of

understanding the semantics, whereas the smaller language models do not. We've seen that a few

years ago when we did work on the sentiment neuron, we trained a small, you know, smallish LSTM

to predict the next character in Amazon reviews. And we noticed that when you increase the size

of the LSTM from 500 LSTM cells to 4,000 LSTM cells, then one of the neurons starts to represent

the sentiment of the article of sorry, of their view. Now, why is that sentiment is a pretty

semantic attribute? It's not a syntactic attribute. And for people who might not know, I don't know

if that's a standard term, but sentiment is whether it's a positive or negative review.

That's right. Like, is the person happy with something or is the person unhappy with something?

And so here we had very clear evidence that a small neural net does not capture sentiment

while a large neural net does. And why is that? Well, our theory is that at some point,

you run out of syntax to models, you start to got to focus on something else.

And besides, you quickly run out of syntax to model, and then you really start to focus on

the semantics. This would be the idea. That's right. And so I don't want to imply that our models

have complete semantic understanding, because that's not true. But they definitely are showing

signs of semantic understanding, partial semantic understanding, but the smaller models do not show

that those signs. Can you take a step back and say, what is GPT2, which is one of the big language

models that was the conversation changer in the past couple of years? Yeah, so GPT2 is a

transformer with one and a half billion parameters that was trained on about 40 billion tokens of

text, which were obtained from webpages that were linked to from Reddit articles with more than three

uploads. And what's the transformer? The transformer, it's the most important advance

in neural network architectures in recent history. What is the tension maybe two,

because I think that's an interesting idea, not necessarily sort of technically speaking, but

the idea of attention versus maybe what recurrent neural networks represent.

Yeah. So the thing is, the transformer is a combination of multiple ideas simultaneously

of which attention is one. Do you think attention is the key? No, it's a key, but it's not the key.

The transformer is successful because it is the simultaneous combination of multiple ideas. And

if you were to remove either idea, it would be much less successful. So the transformer uses a

lot of attention, but attention exists for a few years. So that can't be the main innovation.

The transformer is designed in such a way that it runs really fast on the GPU.

And that makes a huge amount of difference. This is one thing. The second thing is that

transformer is not recurrent. And that is really important too, because it is more shallow and

therefore much easier to optimize. So in other words, uses attention. It is, it is a really

great fit to the GPU. And it is not recurrent. So therefore, less deep and easier to optimize.

And the combination of those factors make it successful. So now it makes, it makes great

use of your GPU. It allows you to achieve better results for the same amount of compute.

And that's why it's successful. Were you surprised how well transformers worked?

And GPT2 worked? So you worked on language. You've had a lot of great ideas before

transformers came about in language. So you got to see the whole set of revolutions before and

after. Were you surprised? Yeah, a little. A little. Yeah. I mean, it's hard, it's hard to

remember because you adapt really quickly. But it definitely was surprising. It definitely was,

in fact, I'll, you know what, I'll, I'll retract my statement. It was, it was pretty amazing.

It was just amazing to see generate this text of this. And you know, I gotta keep in mind that

we've seen, at that time, we've seen all this progress in GANs and improving, you know, the

samples produced by GANs were just amazing. You have these realistic faces, but text hasn't really

moved that much. And suddenly we moved from, you know, whatever GANs were in 2015, to the best,

most amazing GANs in one step. And I was really stunning. Even though theory predicted, yeah,

you train a big language model, of course, you should get this, but then to see it with your

own eyes, it's something else. And yet we adapt really quickly. And now there's sort of

some cognitive scientists, right, articles saying that GPT two models don't really understand

language. So we adapt quickly to how amazing the fact that they're able to model the language so

well is. So what do you think is the bar for what for impressing us that I don't know, do you think

that bar will continuously be moved? Definitely. I think when you start to see really dramatic

economic impact, that's when I think that's in some sense the next barrier. Because right now,

if you think about the work in AI, it's really confusing. It's really hard to know what to make

of all these advances. It's kind of like, okay, you got an advance and now you can do more things

and you got another improvement and you got another cool demo. At some point, I think people who are

outside of AI, they can no longer distinguish this progress anymore. So we were talking offline about

translating Russian to English and how there's a lot of brilliant work in Russian that the rest

of the world doesn't know about. That's true for Chinese. It's true for a lot of scientists and

just artistic work in general. Do you think translation is the place where we're going

to see sort of economic big impact? I don't know. I think there is a huge number of

applications. First of all, I want to point out that translation already today is huge.

I think billions of people interact with big chunks of the internet primarily through translation.

So translation is already huge and it's hugely positive too. I think self driving is going to be

hugely impactful. It's unknown exactly when it happens, but again, I would not bet against

deep learning. So that's deep learning in general. Deep learning for self driving.

Yes, deep learning for self driving. But I was talking about sort of language models.

I see. Just to check. I beard off a little bit. Just to check. You're not seeing a connection

between driving and language. No, no. Okay. I'd rather both use neural nets.

That'd be a poetic connection. I think there might be some, like you said, there might be some kind

of unification towards a kind of multitask transformers that can take on both language

and vision tasks. That'd be an interesting unification. Let's see. What can I ask about

GPT2 more? It's simple. It's not much to ask. You take a transform, you make it bigger,

give it more data, and suddenly it does all those amazing things.

Yeah. One of the beautiful things is that GPT, the transformers are fundamentally simple to

explain, to train. Do you think bigger will continue to show better results in language?

Probably. Sort of like what are the next steps with GPT2? Do you think?

I mean, I think for sure seeing what larger versions can do is one direction. Also,

I mean, there are many questions. There's one question which I'm curious about and that's

the following. Right now, GPT2, so we feed it all this data from the internet, which means that

it needs to memorize all those random facts about everything in the internet. It would be nice if

the model could somehow use its own intelligence to decide what data it wants to start, accept,

and what data it wants to reject, just like people. People don't learn all data indiscriminately.

We are super selective about what we learn. I think this kind of active learning I think

would be very nice to have. Yeah. Listen, I love active learning. Let me ask,

does the selection of data, can you just elaborate that a little bit more? Do you think the selection

of data is, I have this kind of sense that the optimization of how you select data,

so the active learning process is going to be a place for a lot of breakthroughs,

even in the near future, because there hasn't been many breakthroughs there that are public.

I feel like there might be private breakthroughs that companies keep to themselves,

because the fundamental problem has to be solved if you want to solve self driving,

if you want to solve a particular task. What do you think about the space in general?

Yeah, so I think that for something like active learning, or in fact for any kind of capability,

like active learning, the thing that it really needs is the problem. It needs a problem that

requires it. It's very hard to do research about the capability if you don't have a task,

because then what's going to happen is you will come up with an artificial task,

get good results, but not really convince anyone. Right. We're now past the stage where

getting a result on MNIST, some clever formulation of MNIST will convince people.

That's right. In fact, you could quite easily come up with a simple active learning scheme

on MNIST and get a 10x speed up, but then so what? I think that active learning will naturally arise

as problems that require it to pop up. That's my take on it.

There's another interesting thing that OpenAS brought up with GPT2, which is when you create a

powerful artificial intelligence system, and it was unclear what kind of detrimental, once you

release GPT2, what kind of detrimental effect it will have. Because if you have a model that can

generate pretty realistic text, you can start to imagine that it would be used by bots in some

way that we can't even imagine. There's this nervousness about what it's possible to do.

So you did a really brave and profound thing, which just started a conversation about this.

How do we release powerful artificial intelligence models to the public? If we do it all, how do we

privately discuss with other even competitors about how we manage the use of the systems and so on?

So from this whole experience, you've released a report on it, but in general, are there any

insights that you've gathered from just thinking about this, about how you release models like this?

I mean, I think that my take on this is that the field of AI has been in a state of childhood,

and now it's exiting that state and it's entering a state of maturity.

What that means is that AI is very successful and also very impactful, and its impact is not only

large, but it's also growing. And so for that reason, it seems wise to start thinking about

the impact of our systems before releasing them, maybe a little bit too soon, rather than a little

bit too late. And with the case of GPT2, like I mentioned earlier, the results really were stunning,

and it seemed plausible. It didn't seem certain. It seemed plausible that something like GPT2 could

easily use to reduce the cost of disinformation. And so there was a question of what's the best

way to release it? And a staged release seemed logical. A small model was released, and there

was time to see the many people use these models in lots of cool ways. There have been lots of

really cool applications. There haven't been any negative applications we know of. And so eventually

it was released, but also other people replicated similar models. That's an interesting question,

though, that we know of. So in your view, staged release is at least part of the answer to the

question of what do we do once we create a system like this? It's part of the answer, yes.

Is there any other insights? Say you don't want to release the model at all, because it's useful

to you for whatever the business is. Well, there are plenty of people who don't release models

already, right? Of course. But is there some moral ethical responsibility when you have a

very powerful model to sort of communicate? Just as you said, when you had GPT2, it was

unclear how much it could be used for misinformation. It's an open question. And getting an answer to

that might require that you talk to other really smart people that are outside of your particular

group. Have you please tell me there's some optimistic pathway for people across the world

to collaborate on these kinds of cases? Or is it still really difficult from one company to

talk to another company? So it's definitely possible. It's definitely possible to discuss

these kind of models with colleagues elsewhere and to get their take on what to do.

How hard is it though? I mean,

do you see that happening? I think that's the place where it's important to gradually build

trust between companies. Because ultimately, all the AI developers are building technology,

which is going to be increasingly more powerful. And so it's

the way to think about it is that ultimately, we're only together.

Yeah, I tend to believe in the better angels of our nature, but I do hope that

when you build a really powerful AI system in a particular domain, that you also think about

the potential negative consequences of AI. It's an interesting and scary possibility

that there will be a race for AI development that would push people to close that development

and not share ideas with others. I don't love this. I've been in a pure academic for 10 years.

I really like sharing ideas and it's fun. It's exciting. Let's talk about AGI a little bit.

What do you think it takes to build a system of human level intelligence? We talked about reasoning,

we talked about long term memory, but in general, what does it take, do you think?

Well, I can't be sure. But I think the deep learning plus maybe another small idea.

Do you think self play will be involved? You've spoken about the powerful mechanism of self play,

where systems learn by exploring the world in a competitive setting against other entities that

are similarly skilled as them and so incrementally improve in this way. Do you think self play will

be a component of building an AGI system? Yeah. What I would say to build AGI, I think it's going

to be deep learning plus some ideas. I think self play will be one of those ideas. I think that

that is a very self play has this amazing property that it can surprise us in truly novel ways. For

example, like we, I mean, pretty much every self play system, both are dot a bot. I don't know if

openly I had a release about multi agents where you had two little agents who were playing hide and

seek. And of course, also alpha zero, they will all produce surprising behaviors. They all produce

behaviors that we didn't expect. They are creative solutions to problems. And that seems like an

important part of AGI that our systems don't exhibit routinely right now. And so that's why I

like this area, I like this direction because of its ability to surprises.

To surprises. And AGI system would surprise us fundamentally. Yes. But and to be precise, not

just a not just a random surprise, but to find the surprising solution to a problem that's also

useful. Right. Now, a lot of the self playing mechanisms have been used in the game context,

or at least in the simulation context. How much, how much, how far along the path to AGI do you

think will be done in simulation? How much faith promise do you have in simulation versus having

to have a system that operates in the real world, whether it's the real world of digital real world

data or real world, like actual physical world with robotics? I don't think it's in either war.

I think simulation is a tool. And it helps it has certain its strengths and certain weaknesses.

And we should use it. Yeah, but okay. I understand that that's

that's true. But one of the criticisms of self play, one of the criticisms and reinforcement

learning is one of the the its current power, its current results, while amazing have been

demonstrated in a simulated environments, or very constrained physical environments,

do you think it's possible to escape them, escape the simulator environments and be able to learn

in non simulated environments? Or do you think it's possible to also just simulate in a photo

realistic, and physics realistic way, the real world in a way that we can solve real problems

with self play in simulation. So I think that's transfer from simulation to the real world is

definitely possible, and has been exhibited many times in by many different groups. It's been

especially successful in vision. Also open AI in the summer has demonstrated a robot hand which

was trained entirely in simulation, in a certain way that allowed for seem to real transfer to occur.

This is for the Rubik's cube. That's right. And I wasn't aware that was trained in simulation

was trained in simulation entirely. Really? So what it wasn't in the physical that the hand

wasn't trained? No, 100% of the training was done in simulation. And the policy that was

learned in simulation was trained to be very adaptive. So adaptive that when you transfer it,

it could very quickly adapt to the physical to the physical world. So the kind of perturbations

with the giraffe or whatever the heck it was, those weren't were those part of the simulation?

Well, the simulation was generally so the simulation was trained to be robust to many

different things, but not the kind of perturbations we've had in the video. So it's never been

trained with a glove. It's never been trained with a stuff giraffe. So in theory, these are

novel perturbations? Correct. It's not a theory in practice. And that those are novel perturbations?

Well, that's okay. That's a clean, small scale, but clean example of a transfer from the simulated

world to the physical world. Yeah. And I will also say that I expect the transfer capabilities

of deep learning to increase in general. And the better the transfer capabilities are,

the more useful simulation will become. Because then you could take you could

experience something in simulation, and then learn a moral of the story, which you could then

carry with you to the real world, right? As humans do all the time in the computer games.

So let me ask sort of an embodied question, staying an AGI for a sec. Do you think AGI system

would need to have a body? We need to have some of those human elements of self awareness, consciousness,

sort of fear of mortality, sort of self preservation in the physical space, which comes with having

a body? I think having a body will be useful. I don't think it's necessary. But I think it's

very useful to have a body for sure, because you can learn a whole new you can learn things which

cannot be learned without a body. But at the same time, I think that you can go if you don't have

a body, you could compensate for it and still succeed. You think so? Yes. Well, there is evidence

for this. For example, there are many people who were born deaf and blind, and they were able to

compensate for the lack of modalities. I'm thinking about Helen Kahler specifically. So even if you're

not able to physically interact with the world, and if you're not able to, I mean, I actually was

getting at maybe let me ask on the more particular, I'm not sure if it's connected to having a body

or not, but the idea of consciousness and a more constrained version of that is self awareness.

Do you think an AGI system should have consciousness? It's what we can't define kind of whatever the

heck you think consciousness is. Yeah, hard question to answer, given how hard is to define it.

Do you think it's useful to think about? I mean, it's definitely interesting. It's fascinating.

I think it's definitely possible that our systems will be conscious.

Do you think that's an emergent thing that just comes from? Do you think consciousness could

emerge from the representation that's stored within your networks? So like that it naturally just

emerges when you become more and more able to represent more and more of the world?

Well, let's say I'd make the following argument, which is humans are conscious. And if you believe

that artificial neural nets are sufficiently similar to the brain, then there should at least

exist artificial neural nets you should be conscious to. You're leaning on that existence proof pretty

heavily. Okay. But that's the best answer I can give. No, I know. I know. I know. There's still

an open question if there's not some magic in the brain that we're not. I mean, I don't mean

a non materialistic magic, but that the brain might be a lot more complicated and interesting

than we give it credit for. If that's the case, then it should show up. And at some point,

at some point, we will find out that we can't continue to make progress. But I think it's

unlikely. So we talk about consciousness, but let me talk about another poorly defined concept of

intelligence. Again, we've talked about reasoning. We've talked about memory. What do you think is

a good test of intelligence for you? Are you impressed by the test that Alan Turing formulated

with the imitation game of natural language? Is there something in your mind that you will be

deeply impressed by if a system was able to do? I mean, lots of things. There's certain

frontier. There is a certain frontier of capabilities today. And there exists things

outside of that frontier. And I would be impressed by any such thing. For example, I would be

impressed by a deep learning system, which solves a very pedestrian, you know, pedestrian task

like machine translation or computer vision task or something, which never makes mistake a human

wouldn't make under any circumstances. I think that is something which have not yet been demonstrated.

And I would find it very impressive. Yes. So right now, they make mistakes in different,

they might be more accurate than human beings, but they still make a different set of mistakes.

So my, my, I would guess that a lot of the skepticism that some people have about deep learning

is when they look at their mistakes and they say, well, those mistakes,

they make no sense. Like if you understood the concept, you wouldn't make that mistake us.

And I think that changing that would be would would that would inspire me that would be,

yes, this is this is this is this is progress. Yeah, that's a really nice way to put it.

But I also just don't like that human instinct to criticize a model is not intelligent. That's

the same instinct as we do when we criticize any group of creatures as the other. Because

it's very possible that GPT two is much smarter than human beings at many things.

That's definitely true. It has a lot more breadth of knowledge.

Yes, breadth of knowledge and even, and even perhaps depth on certain topics. It's kind of

hard to judge what depth means, but there's definitely a sense in which humans don't make

mistakes that these models do. Yes, the same is applied to autonomous vehicles. The same is

probably going to continue being applied to a lot of artificial intelligence systems. We find

this is the annoying thing. This is the process of in the 21st century, the process of analyzing

the progress of AI is the search for one case where the system fails in a big way where humans

would not. And then many people writing articles about it. And then broadly, as the public

generally gets convinced that the system is not intelligent. And we like pacify ourselves by

thinking it's not intelligent because of this one anecdotal case. And this can seems to continue

happening. Yeah, I mean, there is truth to that. Although I'm sure that plenty of people are also

extremely impressed by the systems that exist today. But I think this connects to the earlier

point we discussed that it's just confusing to judge progress in AI. And you have a new robot

demonstrating something. How impressed should you be? And I think that people will start to be

impressed once AI starts to really move the needle on the GDP. So you're one of the people that

might be able to create an AGS system here, not you, but you and open AI. If you do create an

AGS system, and you get to spend sort of the evening with it, him, her, what would you talk

about do you think? The very first time? The first time? Well, the first time was just,

we would just ask all kinds of questions and try to make it to get it to make a mistake. And that

would be amazed that it doesn't make mistakes and just keep asking broad. What kind of questions do

you think? Would they be factual or would they be personal, emotional, psychological? What do you

think? All of the above. Would you ask for advice? Definitely. I mean, why would I limit myself

talking to a system like this? Now, again, let me emphasize the fact that you truly are one of

the people that might be in the room where this happens. So let me ask a sort of a profound

question about, I've just talked to Stalin historian, been talking to a lot of people who

are studying power. Abraham Lincoln said, nearly all men can stand adversity. But if you want to

test a man's character, give him power. I would say the power of the 21st century, maybe the 22nd,

but hopefully the 21st would be the creation of an AGI system and the people who have control,

direct possession and control of the AGI system. So what do you think after spending that evening

having a discussion with the AGI system? What do you think you would do?

Well, the ideal world I'd like to imagine is one where humanity are like the board,

the board members of a company where the AGI is the CEO. So it would be,

I would like the picture of which I would imagine is you have some kind of different

entities, different countries or cities and the people that leave their vote for what the AGI

that represents them should do and the AGI that represents them goes and does it. I think a picture

like that, I find very appealing. You could have multiple AGI, you would have an AGI for a city,

for a country and it would be trying to in effect take the democratic process to the next level.

And the board can almost fire the CEO. Essentially, press the reset button, say.

Press the reset. Rerandomize the parameters.

Well, let me sort of, that's actually, okay, that's a beautiful vision, I think,

as long as it's possible to press the reset button. Do you think it will always be possible to

press the reset button? So I think that it's definitely really possible to build.

So you're talking, so the question that I really understand from you is, will humans or humans

people have control over the AI systems that they build? Yes. And my answer is, it's definitely

possible to build AI systems which will want to be controlled by their humans. Wow, that's part of

their, so it's not that just they can't help but be controlled, but that's one of the objectives

of their existence is to be controlled. In the same way that human parents

generally want to help their children, they want their children to succeed. It's not a burden for

them. They are excited to help the children and to feed them and to dress them and to take care of

them. And I believe with high conviction that the same will be possible for an AGI. It will be

possible to program an AGI, to design it in such a way that it will have a similar deep drive that

it will be delighted to fulfill and the drive will be to help humans flourish. But let me take a step

back to that moment where you create the AGI system. I think this is a really crucial moment.

And between that moment and the Democratic Board members with the AGI at the head,

there has to be a relinquishing of power. So it's George Washington, despite all the bad

things he did, one of the big things he did is he relinquished power. He first of all didn't want

to be president. And even when he became president, he gave, he didn't keep just serving as most

dictators do for indefinitely. Do you see yourself being able to relinquish control over an AGI system

given how much power you can have over the world at first financial, just make a lot of money,

and then control by having possession as a AGI system? I'd find it trivial to do that. I'd find

it trivial to relinquish this kind of power. I mean, the kind of scenario you are describing

sounds terrifying to me. That's all. I would absolutely not want to be in that position.

Do you think you represent the majority or the minority of people in the AGI community?

Well, I mean, it's an open question, an important one. Are most people good is another way to ask it.

So I don't know if most people are good. But I think that when it really counts,

people can be better than we think. That's beautifully put. Yeah.

Are there specific mechanisms you can think of aligning AI gene values to human values?

Is that do you think about these problems of continued alignment as we develop the AI systems?

Yeah, definitely. In some sense, the kind of question which you are asking is,

so if you have to translate the question to today's terms, it would be a question about

how to get an RL agent that's optimizing a value function, which itself is learned.

And if you look at humans, humans are like that because the reward function,

the value function of humans is not external, it is internal.

That's right. And there are definite ideas of how to train a value function,

basically an objective, an as objective as possible perception system

that will be trained separately to recognize, to internalize human judgments on different

situations. And then that component wouldn't be integrated as the value as the base value

function for some more capable RL system. You could imagine a process like this.

I'm not saying this is the process. I'm saying this is an example of the kind of thing you could do.

So on that topic of the objective functions of human existence, what do you think is the

objective function that's implicit in human existence? What's the meaning of life? I think

the question is wrong in some way. I think that the question implies that there is an

objective answer, which is an external answer, you know, your meaning of life is X. I think

what's going on is that we exist and that's amazing. And we should try to make the most

of it and try to maximize our own value and enjoyment of a very short time while we do exist.

It's funny because action does require an objective function. It's definitely there

in some form, but it's difficult to make it explicit and maybe impossible to make it explicit.

I guess is what you're getting at. And that's an interesting fact of an RL environment.

Well, what I was making a slightly different point is that humans want things and their wants

create the drives that cause them to, you know, our wants are our objective functions,

our individual objective functions. We can later decide that we want to change,

that what we wanted before is no longer good and we want something else.

Yeah, but they're so dynamic. There's got to be some underlying sort of Freud.

There's things, there's like sexual stuff. There's people who think it's the fear of death.

And there's also the desire for knowledge and, you know, all these kinds of things,

procreation, sort of all the evolutionary arguments, it seems to be,

there might be some kind of fundamental objective function from which everything else emerges.

But it seems like it's very difficult to make it explicit.

I think that probably is an evolutionary objective function, which is to survive and

procreate and make your students succeed. That would be my guess. But it doesn't give an answer

to the question of what's the meaning of life. I think you can see how humans are part of this

big process, this ancient process we are, we are, we exist on a small planet. And that's it.

So given that we exist, try to make the most of it and try to

enjoy more and suffer less as much as we can. Let me ask two silly questions about life.

One, do you have regrets, moments that if you went back, you would do differently? And two,

are there moments that you're especially proud of that made you truly happy?

So I can answer both questions. Of course, there's a huge number of choices and decisions that

have made that with the benefit of hindsight, I wouldn't have made them. And I do experience

some regret, but, you know, I try to take solace in the knowledge that at the time I did the best

I could. And in terms of things that I'm proud of, I'm very fortunate to have done things I'm

proud of. And they made me happy for some time, but I don't think that that is the source of

happiness. So your academic accomplishments, all the papers, you're one of the most cited people

in the world, all the breakthroughs I mentioned in computer vision and language and so on is

what is the source of happiness and pride for you? I mean, all those things are a source of pride,

for sure. I'm very grateful for having done all those things. And it was very fun to do them.

But happiness comes, you know, you can, happiness, well, my current view is that happiness comes from

our to a lot to a very large degree from the way we look at things. You know, you can have a

simple meal and be quite happy as a result, or you can talk to someone and be happy as a result

as well. Or conversely, you can have a meal and be disappointed that the meal wasn't a better meal.

So I think a lot of happiness comes from that, but I'm not sure. I don't want to be too confident.

Being humble in the face of the uncertainty seems to be also a part of this whole happiness thing.

Well, I don't think there's a better way to end it than meaning of life and discussions of happiness.

So, Ilya, thank you so much. You've given me a few incredible ideas. You've given the world

many incredible ideas. I really appreciate it. And thanks for talking today.

Yeah, thanks for stopping by. I really enjoyed it.

Thanks for listening to this conversation with Ilya Setskever. And thank you to our

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on Twitter at Lex Freedman. And now let me leave you with some words from Alan Turing on Machine

Learning. Instead of trying to produce a program to simulate the adult mind, why not rather try

to produce one which simulates the child? If this were then subjected to an appropriate course of

education, one would obtain the adult brain. Thank you for listening and hope to see you next time.