Lex Fridman Podcast - #94 - Ilya Sutskever: Deep Learning

The following is a conversation with Ilya Sotskever,

cofounder and chief scientist of OpenAI,

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.

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And now here’s my conversation with Ilya Satsgever.

You were one of the three authors with Alex Kaszewski,

Geoff 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, tiny by today’s standards,

but large and deep neural networks

end to end with backpropagation.

At some point, different people obtained this result.

I obtained this result.

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’s as 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 that there’s a huge number of parameters is okay?

Is it gonna be okay?

I mean, there was some evidence before that it was okayish,

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 gonna add some data augmentation

and it’s gonna be okay.

So where was any doubt coming from?

The main doubt was, can we train a bigger,

will we have enough computer train

a big enough neural net?

With backpropagation.

Backpropagation I thought would work.

The thing which wasn’t clear

was whether there would be enough compute

to get a very convincing result.

And then at some point, Alex Kerchevsky wrote

these insanely fast CUDA kernels

for training convolutional neural nets.

Net was bam, let’s do this.

Let’s get image in it and it’s gonna 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?

Like, cause 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 a intuition builder?


I mean, you gotta be precise with these analogies

between artificial neural networks and the brain.

But there is 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 Yann LeCun 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 I thought, well, probably an artificial neuron

is not that different from the brain

if it’s cleaned hard enough.

So let’s just assume it is and roll with it.

So now we’re not at a time where deep learning

is very successful.

So let us squint less and say, let’s open our eyes

and say, what do you use 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 neurons

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 our 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.

Yeah, it’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 gonna 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 the 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 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, 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 a GAN, you have a game.

So instead of thinking of a cost function,

where you wanna 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 a 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.

It’s, 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 if evolution doesn’t, that’s really interesting.

So if evolution doesn’t really have a cost function,

like a cost function based on its,

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 we’ll 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 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 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 have 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

would just study that in simulation.

Wait, sorry, spike time independent plasticity?

Yeah, that’s right.

What’s that?


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 strengthens the synapse,

and if the synapse fires into the neurons

shortly after the neuron fired,

then it weakens the synapse.

Something along this line.

I’m 90% sure it’s right, so if I said something wrong here,

don’t get too angry.

But you sounded 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 timing of the timing

of the signals.

Well, you have recurrent neural networks.

But you think of that as this,

I mean, that’s a very crude, simplified,

what’s that called?

There’s a clock, I guess, to recurrent neural networks.

It’s, 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 they can do, I think they can do anything

we’d want them to, 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 they’ll be back, we’ll see.

Let me, on 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?

In 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, that’s what 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 sequential 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 LSTMs and that?

I mean, the hidden state is technically

what you described there, the hidden state

that goes inside the LSTM or the RNN or something like this.

But then what should be contained,

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?


So we’re gonna pause on that confidence,

because I want to explore that.

Well, 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 were no,

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 mix it

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 of 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 ImageNet had served as that moment.

That’s right.

And they represented this kind of,

were 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 compute to be there,

it’s for it to convince the cynicism that existed.

It’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, those silly people

who didn’t believe, 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.

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 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 are 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.

But 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.

In fact. 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 shouldn’t say expect, I think it’s possible.

I don’t wanna be too sure because

I think on the convolutional neural net

is very computationally efficient.

RL is different.

RL does require slightly different techniques

because you really do need to take action.

You really 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 RL and supervised learning

where somehow the RL 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, you know, you shovel things into it

and it just figures out what to do

with whatever you shovel at 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 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 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.

You think it’s a fundamentally different problem

or is it just a more difficult 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 try, you take gradients.

We try to approximate gradients in both cases.

In the case of reinforcement learning,

you have some tools to reduce the variance of the gradients.

You do that.

There’s lots of commonality.

Use 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,

how much do you wanna 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 wanna explain why.

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 benchmark.

So from the perspective of how much

until we get to human level on a very good benchmark.

Yeah, I understand what you mean by that.

So what I was going to say that a lot of it depends on,

once you solve a problem, it stops being hard

and that’s always true.

And so whether something is hard or not depends

on what our tools can do today.

So 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.

So I agree with that statement.

Beyond that, my guess would be as good as yours,

I don’t know.

Oh, okay, so you don’t have a fundamental intuition

about how hard language understanding is.

I think, I know I changed my mind.

I’d say language is probably going to be harder.

I mean, it depends on how you define it.

Like if you mean absolute top notch,

100% language understanding, I’ll go with language.

But then if I show you a piece of paper with letters on it,

is that, you see what I mean?

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 end and language begin?

Yeah, so Chomsky would say it starts at language.

So vision is just a little example of the kind

of a structure and fundamental hierarchy of ideas

that’s already represented in our brains 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 it’s pretty likely that yes,

if we can get one, our machine learning is probably

that good that we can get the other.

But I’m not 100% sure.

And also, I think a lot of it really does depend

on your definitions.

Definitions of?

Of like perfect vision.

Because 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 gonna 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.

It 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 well it understands an image.

I thought you meant something like,

I’m gonna show it a really complicated image

and it’s gonna get it right.

And you’re gonna say, wow, that’s really cool.

Our systems of January 2020 have not been doing that.

Yeah, no, I think it all boils down to like

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’ve got some theories as to,

this is kind of like the brain.

So maybe if you make it large,

if you make the neural network large

and you train it 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 lot of 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,

we’ve had lots of empirical,

huge amounts of empirical reasons

to believe that optimization should work

on most problems we care about.

Do you have insights of why?

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 insights

of how the whole thing works.

I think 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 amazing when a theory is validated like this.

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, in biology, things are really complicated.

Theories are really, really,

it’s really hard to have good predictive theory.

And in physics, the theories are too good.

In physics, people make these super precise theories

which make these amazing predictions.

And in machine learning, we’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 sort of 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?


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, you know?

So, but if you look at all the progress

from the past 10 years, I would say most of it,

I would say there’ve 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 this is peak 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.

It has 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 you 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 compute 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 gonna keep asking.

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 data sets,

the distributed programming,

the building the actual cluster,

the GPU programming, putting it all together.

So now 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 the, what like Vlad and Ravapnik

really insist on is taking MNIST

and trying to learn from very few examples.

So being able to learn more efficiently.

Do you think that’s, there’ll be breakthroughs in that space

that would, may not need the 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 gotta 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 released, the Deep Double Descent,

where bigger models and more data hurt.

I think it’s a really interesting paper.

Can you describe the main idea?

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 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 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 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,

some training cases are randomly in the data set

and others may not be there,

but the small model is kind of insensitive

to this randomness because it’s the same,

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

and maybe that will be, that will work.

So you’ve got a huge neural network.

Let’s suppose you’ve got, 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 neural network achieves zero error.

And SGD is going to find approximately the point.

That’s right.

Approximately the point with the smallest norm

in that subspace.


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 early stop.

If you introduce early stop in your regularization,

you can make the double descent bump

almost completely disappear.

What is early stop?

Early stopping is when you train your model

and you monitor your validation performance.

And then if at some point validation performance

starts to get worse, you say, okay, let’s stop training.

We are good enough.

So the magic happens after that moment.

So you don’t want to do the early stopping.

Well, if you don’t do the early stopping,

you get the very pronounced double descent.

Do you have any intuition why this happens?

Double descent?

Oh, sorry, early stopping?

No, the double descent.

So the…

Well, yeah, so I try…

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.

Oh, so it’s able to, let’s nicely put,

discard the small changes, the randomness.

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 that 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 we 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 we’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 a 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 like akin to search, right?

There’s a sequential element of reasoning

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, it’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?


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 wanna 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 nobody knows the answer to.

So when it comes to our neural networks,

the thing 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 the problem that you pose in front of it

in the easiest way possible.

Right, that takes us to one of the brilliant sort of ways

you’ve described 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 as 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 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 dataset to the parameters,

then somehow the amount of information in the weights

ends up being not very large,

which would explain why they generalize 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.

It’s 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 we’re trying to contort our neural networks

around the training pillar.

We gotta 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 say, hey, let’s find the shortest program,

well, we can’t do that.

So it doesn’t matter how useful that would be.

We can’t do it.

So we won’t.

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?


So the, sorry, not the size or the type of data.

Sort of ask, giving it programs.

Well, I think the thing is that right now,

finding, 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.


Which is the right way to go about it.

But there’s not good illustrations of that.

It hasn’t been done yet.

But in principle, it should be possible.

Can you elaborate a little bit,

what’s your answer in principle?

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 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’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?

Something like knowledge bases.

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 which you’re making the decision.

So in some sense, the parameters already do that.

The parameters are an aggregation of the neural,

of the entirety of the neural nets experience,

and so they count as long term knowledge.

And people have trained various neural nets

to act as knowledge bases and, you know,

investigated with, people have investigated

language models as knowledge bases.

So 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?

Like can you add, because you can’t, okay,

I’d like to know what does it know and what doesn’t it 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 a neural network

had some self awareness about it.

Yeah, 100%, I’m a big believer in self awareness

and I think that, 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,

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 a mental model of that human being.

You already have an understanding of like a big conception

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 I 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 really good code,

I think proving really hard theorems,

solving open ended problems with out of the box solutions.

And sort of theorem type, mathematical problems.

Yeah, I think those ones are a very natural example

as well.

If you can prove an unproven theorem,

then it’s hard to argue you don’t reason.

And so by the way, and this comes back to the point

about the hard results, 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, we have the ability

to produce conversation changing results.

Conversation, and then of course, just like you said,

people kind of take that for granted

and 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,

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 a 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,

because that’s where you and Noam Chomsky disagree.

So you think we’re actually taking incremental steps,

a sort of larger network, larger compute

will be able to get to the semantics,

to 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, sorry, of the review.

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 a negative review.

That’s right.

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 gotta focus on something else.

And with size, you quickly run out of syntax to model

and then you really start to focus on the semantics

would be the idea.

That’s right.

And so I don’t wanna 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 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 web pages

that were linked to from Reddit articles

with more than three outputs.

And what’s a transformer?

The transformer, it’s the most important advance

in neural network architectures in recent history.

What is attention maybe too?

Cause 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 existed 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, users attention,

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 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?

I mean, it’s hard to remember

because you adapt really quickly,

but it definitely was surprising.

It definitely was.

In fact, you know what?

I’ll retract my statement.

It was pretty amazing.

It was just amazing to see generate this text of this.

And you know, you gotta keep in mind

that at that time we’ve seen all this progress in GANs

in improving 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 that 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

write articles saying that GPT2 models

don’t truly 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 it…

I don’t know.

Do you think that bar will continuously be moved?


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’ve got another improvement

and you’ve 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…

I mean, first of all,

I wanna 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

and that’s, it’s unknown exactly when it happens,

but again, I would not bet against deep learning, so I…

So there’s deep learning in general,

but you think this…

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.

Beard off a little bit.

Just to check,

you’re not seeing a connection between driving and language.

No, no.


Or 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.

Now let’s see, what can I ask about GPT two more?

It’s simple.

There’s not much to ask.

It’s, you take a transform, you make it bigger,

you 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?


Sort of like what are the next steps

with GPT two, 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.

So right now GPT two,

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.

And it would be nice if the model could somehow

use its own intelligence to decide

what data it wants to 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.

And I think this kind of active learning,

I think would be very nice to have.

Yeah, listen, I love active learning.

So 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,

like 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 a 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 that you will come up with an artificial task,

get good results, but not really convince anyone.

Right, like 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?

And I think that with active learning,

the need, active learning will naturally arise

as problems that require it pop up.

That’s how I would, that’s my take on it.

There’s another interesting thing

that OpenAI has 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 a pretty realistic text,

you can start to imagine that it would be used by bots

in some way that we can’t even imagine.

So there’s this nervousness about what is possible to do.

So you did a really kind of brave

and I think profound thing,

which is start 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 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 this information.

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’ve been lots of really cool applications.

There haven’t been any negative application to be known 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 how do we,

what do we do once we create a system like this?

It’s part of the answer, yes.

Is there any other insights?

Like say you don’t wanna release the model at all,

because it’s useful to you for whatever the business is.

Well, plenty of people 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?

Like, 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 to be able to use this model

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 a 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 all in it 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, yeah.

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 a pure academic for 10 years.

I really like sharing ideas and it’s fun, it’s exciting.

What do you think it takes to,

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,

plus maybe another small idea.

Do you think self play will be involved?

So you’ve spoken about the powerful mechanism of self play

where systems learn by sort of 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, so what I would say, to build AGI,

I think it’s going to be deep learning plus some ideas.

And 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 Dota bot.

I don’t know if, OpenAI had a release about multi agent

where you had two little agents

who were playing hide and seek.

And of course, also alpha zero.

They were all produced 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 surprise us.

To surprise us.

And an AGI system would surprise us fundamentally.


And to be precise, not just a random surprise,

but to find the surprising solution to a problem

that’s also useful.


Now, a lot of the self play mechanisms

have been used in the game context

or at least in the simulation context.

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 of robotics.

I don’t think it’s an easy or.

I think simulation is a tool and it helps.

It has certain strengths and certain weaknesses

and we should use it.

Yeah, but okay, I understand that.

That’s true, but one of the criticisms of self play,

one of the criticisms of reinforcement learning

is one of 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 simulator 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 transfer from simulation to the real world

is definitely possible and has been exhibited many times

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.

Is this for the Rubik’s cube?

Yeah, that’s right.

I wasn’t aware that was trained in simulation.

It was trained in simulation entirely.

Really, so it wasn’t in the physical,

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 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 stuffed giraffe.

So in theory, these are novel perturbations.

Correct, it’s not in theory, in practice.

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.

As humans do all the time when they play computer games.

So let me ask sort of a embodied question,

staying on 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 if you don’t have a body,

you could compensate for it and still succeed.

You think so?


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 Keller 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?

We can’t define, whatever the heck you think consciousness is.

Yeah, hard question to answer,

given how hard it 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 neural networks?

So like that it naturally just emerges

when you become more and more,

you’re able to represent more and more of the world?

Well, I’d 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 too.

You’re leaning on that existence proof pretty heavily.

Okay, so 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 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 with 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 a certain frontier of capabilities today.

And there exist 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 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.

Yeah, so right now they make mistakes in different,

they might be more accurate than human beings,

but they still, they make a different set of mistakes.

So 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.

And I think that changing that would be,

that would inspire me.

That would be, yes, 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 GPT2

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 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.

The same is applied to autonomous vehicles.

The same is probably gonna 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, the public generally gets convinced

that the system is not intelligent.

And we pacify ourselves by thinking it’s not intelligent

because of this one anecdotal case.

And this 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 system that exists 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 AGI system here.

Not you, but you and OpenAI.

If you do create an AGI 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?

First time.

Well, the first time I would just ask all kinds of questions

and try to get it to make a mistake.

And I would be amazed that it doesn’t make mistakes

and just keep asking broad questions.

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?


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 sort of a profound question about,

I’ve just talked to a Stalin historian.

I’ve 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,

I like, the board members of a company

where the AGI is the CEO.

So it would be, I would like,

the picture 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 there would be multiple AGI’s,

for a city, for a country, and there would be,

it would be trying to, in effect,

take the democratic process to the next level.

And the board can always fire the CEO.

Essentially, press the reset button, say.

Press the reset button.

Rerandomize the parameters.

But 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 definitely will be 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?


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 the, they exist,

the 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 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 as 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, right?

And then control by having possession as 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 AI community?

Well, I mean.

Say 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 mechanism you can think of

of aligning AI 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 I were 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, you know,

and as objective as possible perception system

that will be trained separately to recognize,

to internalize human judgments on different situations.

And then that component would then be integrated

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

is 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, but 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 gotta 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 sure you make your children 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 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 that, I can answer both questions.

Of course, there’s a huge number of choices

and decisions that I’ve made

that with the benefit of hindsight,

I wouldn’t have made them.

And I do experience some regret,

but 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 of the breakthroughs I mentioned

in computer vision and language and so on,

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, but you know, happiness,

well, my current view is that happiness comes

from our, 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

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at Lex Friedman.

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.

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