The following is a conversation with Francois Chollet.

He’s the creator of Keras,

which is an open source deep learning library

that is designed to enable fast, user friendly experimentation

with deep neural networks.

It serves as an interface to several deep learning libraries,

most popular of which is TensorFlow,

and it was integrated into the TensorFlow main code base

a while ago.

Meaning, if you want to create, train,

and use neural networks,

probably the easiest and most popular option

is to use Keras inside TensorFlow.

Aside from creating an exceptionally useful

and popular library,

Francois is also a world class AI researcher

and software engineer at Google.

And he’s definitely an outspoken,

if not controversial personality in the AI world,

especially in the realm of ideas

around the future of artificial intelligence.

This is the Artificial Intelligence Podcast.

If you enjoy it, subscribe on YouTube,

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at Lex Friedman, spelled F R I D M A N.

And now, here’s my conversation with Francois Chollet.

You’re known for not sugarcoating your opinions

and speaking your mind about ideas in AI,

especially on Twitter.

It’s one of my favorite Twitter accounts.

So what’s one of the more controversial ideas

you’ve expressed online and gotten some heat for?

How do you pick?

How do I pick?

Yeah, no, I think if you go through the trouble

of maintaining a Twitter account,

you might as well speak your mind, you know?

Otherwise, what’s even the point of having a Twitter account?

It’s like having a nice car

and just leaving it in the garage.

Yeah, so what’s one thing for which I got

a lot of pushback?

Perhaps, you know, that time I wrote something

about the idea of intelligence explosion,

and I was questioning the idea

and the reasoning behind this idea.

And I got a lot of pushback on that.

I got a lot of flak for it.

So yeah, so intelligence explosion,

I’m sure you’re familiar with the idea,

but it’s the idea that if you were to build

general AI problem solving algorithms,

well, the problem of building such an AI,

that itself is a problem that could be solved by your AI,

and maybe it could be solved better

than what humans can do.

So your AI could start tweaking its own algorithm,

could start making a better version of itself,

and so on iteratively in a recursive fashion.

And so you would end up with an AI

with exponentially increasing intelligence.

That’s right.

And I was basically questioning this idea,

first of all, because the notion of intelligence explosion

uses an implicit definition of intelligence

that doesn’t sound quite right to me.

It considers intelligence as a property of a brain

that you can consider in isolation,

like the height of a building, for instance.

But that’s not really what intelligence is.

Intelligence emerges from the interaction

between a brain, a body,

like embodied intelligence, and an environment.

And if you’re missing one of these pieces,

then you cannot really define intelligence anymore.

So just tweaking a brain to make it smaller and smaller

doesn’t actually make any sense to me.

So first of all,

you’re crushing the dreams of many people, right?

So there’s a, let’s look at like Sam Harris.

Actually, a lot of physicists, Max Tegmark,

people who think the universe

is an information processing system,

our brain is kind of an information processing system.

So what’s the theoretical limit?

Like, it doesn’t make sense that there should be some,

it seems naive to think that our own brain

is somehow the limit of the capabilities

of this information system.

I’m playing devil’s advocate here.

This information processing system.

And then if you just scale it,

if you’re able to build something

that’s on par with the brain,

you just, the process that builds it just continues

and it’ll improve exponentially.

So that’s the logic that’s used actually

by almost everybody

that is worried about super human intelligence.

So you’re trying to make,

so most people who are skeptical of that

are kind of like, this doesn’t,

their thought process, this doesn’t feel right.

Like that’s for me as well.

So I’m more like, it doesn’t,

the whole thing is shrouded in mystery

where you can’t really say anything concrete,

but you could say this doesn’t feel right.

This doesn’t feel like that’s how the brain works.

And you’re trying to with your blog posts

and now making it a little more explicit.

So one idea is that the brain isn’t exist alone.

It exists within the environment.

So you can’t exponentially,

you would have to somehow exponentially improve

the environment and the brain together almost.

Yeah, in order to create something that’s much smarter

in some kind of,

of course we don’t have a definition of intelligence.

That’s correct, that’s correct.

I don’t think, you should look at very smart people today,

even humans, not even talking about AIs.

I don’t think their brain

and the performance of their brain is the bottleneck

to their expressed intelligence, to their achievements.

You cannot just tweak one part of this system,

like of this brain, body, environment system

and expect that capabilities like what emerges

out of this system to just explode exponentially.

Because anytime you improve one part of a system

with many interdependencies like this,

there’s a new bottleneck that arises, right?

And I don’t think even today for very smart people,

their brain is not the bottleneck

to the sort of problems they can solve, right?

In fact, many very smart people today,

you know, they are not actually solving

any big scientific problems, they’re not Einstein.

They’re like Einstein, but you know, the patent clerk days.

Like Einstein became Einstein

because this was a meeting of a genius

with a big problem at the right time, right?

But maybe this meeting could have never happened

and then Einstein would have just been a patent clerk, right?

And in fact, many people today are probably like

genius level smart, but you wouldn’t know

because they’re not really expressing any of that.

Wow, that’s brilliant.

So we can think of the world, Earth,

but also the universe as just as a space of problems.

So all these problems and tasks are roaming it

of various difficulty.

And there’s agents, creatures like ourselves

and animals and so on that are also roaming it.

And then you get coupled with a problem

and then you solve it.

But without that coupling,

you can’t demonstrate your quote unquote intelligence.

Exactly, intelligence is the meeting

of great problem solving capabilities

with a great problem.

And if you don’t have the problem,

you don’t really express any intelligence.

All you’re left with is potential intelligence,

like the performance of your brain

or how high your IQ is,

which in itself is just a number, right?

So you mentioned problem solving capacity.

Yeah.

What do you think of as problem solving capacity?

Can you try to define intelligence?

Like what does it mean to be more or less intelligent?

Is it completely coupled to a particular problem

or is there something a little bit more universal?

Yeah, I do believe all intelligence

is specialized intelligence.

Even human intelligence has some degree of generality.

Well, all intelligent systems have some degree of generality

but they’re always specialized in one category of problems.

So the human intelligence is specialized

in the human experience.

And that shows at various levels,

that shows in some prior knowledge that’s innate

that we have at birth.

Knowledge about things like agents,

goal driven behavior, visual priors

about what makes an object, priors about time and so on.

That shows also in the way we learn.

For instance, it’s very, very easy for us

to pick up language.

It’s very, very easy for us to learn certain things

because we are basically hard coded to learn them.

And we are specialized in solving certain kinds of problem

and we are quite useless

when it comes to other kinds of problems.

For instance, we are not really designed

to handle very long term problems.

We have no capability of seeing the very long term.

We don’t have very much working memory.

So how do you think about long term?

Do you think long term planning,

are we talking about scale of years, millennia?

What do you mean by long term?

We’re not very good.

Well, human intelligence is specialized

in the human experience.

And human experience is very short.

One lifetime is short.

Even within one lifetime,

we have a very hard time envisioning things

on a scale of years.

It’s very difficult to project yourself

at a scale of five years, at a scale of 10 years and so on.

We can solve only fairly narrowly scoped problems.

So when it comes to solving bigger problems,

larger scale problems,

we are not actually doing it on an individual level.

So it’s not actually our brain doing it.

We have this thing called civilization, right?

Which is itself a sort of problem solving system,

a sort of artificially intelligent system, right?

And it’s not running on one brain,

it’s running on a network of brains.

In fact, it’s running on much more

than a network of brains.

It’s running on a lot of infrastructure,

like books and computers and the internet

and human institutions and so on.

And that is capable of handling problems

on a much greater scale than any individual human.

If you look at computer science, for instance,

that’s an institution that solves problems

and it is superhuman, right?

It operates on a greater scale.

It can solve much bigger problems

than an individual human could.

And science itself, science as a system, as an institution,

is a kind of artificially intelligent problem solving

algorithm that is superhuman.

Yeah, it’s, at least computer science

is like a theorem prover at a scale of thousands,

maybe hundreds of thousands of human beings.

At that scale, what do you think is an intelligent agent?

So there’s us humans at the individual level,

there is millions, maybe billions of bacteria in our skin.

There is, that’s at the smaller scale.

You can even go to the particle level

as systems that behave,

you can say intelligently in some ways.

And then you can look at the earth as a single organism,

you can look at our galaxy

and even the universe as a single organism.

Do you think, how do you think about scale

in defining intelligent systems?

And we’re here at Google, there is millions of devices

doing computation just in a distributed way.

How do you think about intelligence versus scale?

You can always characterize anything as a system.

I think people who talk about things

like intelligence explosion,

tend to focus on one agent is basically one brain,

like one brain considered in isolation,

like a brain, a jaw that’s controlling a body

in a very like top to bottom kind of fashion.

And that body is pursuing goals into an environment.

So it’s a very hierarchical view.

You have the brain at the top of the pyramid,

then you have the body just plainly receiving orders.

And then the body is manipulating objects

in the environment and so on.

So everything is subordinate to this one thing,

this epicenter, which is the brain.

But in real life, intelligent agents

don’t really work like this, right?

There is no strong delimitation

between the brain and the body to start with.

You have to look not just at the brain,

but at the nervous system.

But then the nervous system and the body

are naturally two separate entities.

So you have to look at an entire animal as one agent.

But then you start realizing as you observe an animal

over any length of time,

that a lot of the intelligence of an animal

is actually externalized.

That’s especially true for humans.

A lot of our intelligence is externalized.

When you write down some notes,

that is externalized intelligence.

When you write a computer program,

you are externalizing cognition.

So it’s externalizing books, it’s externalized in computers,

the internet, in other humans.

It’s externalizing language and so on.

So there is no hard delimitation

of what makes an intelligent agent.

It’s all about context.

Okay, but AlphaGo is better at Go

than the best human player.

There’s levels of skill here.

So do you think there’s such a ability,

such a concept as intelligence explosion

in a specific task?

And then, well, yeah.

Do you think it’s possible to have a category of tasks

on which you do have something

like an exponential growth of ability

to solve that particular problem?

I think if you consider a specific vertical,

it’s probably possible to some extent.

I also don’t think we have to speculate about it

because we have real world examples

of recursively self improving intelligent systems, right?

So for instance, science is a problem solving system,

a knowledge generation system,

like a system that experiences the world in some sense

and then gradually understands it and can act on it.

And that system is superhuman

and it is clearly recursively self improving

because science feeds into technology.

Technology can be used to build better tools,

better computers, better instrumentation and so on,

which in turn can make science faster, right?

So science is probably the closest thing we have today

to a recursively self improving superhuman AI.

And you can just observe is science,

is scientific progress to the exploding,

which itself is an interesting question.

You can use that as a basis to try to understand

what will happen with a superhuman AI

that has a science like behavior.

Let me linger on it a little bit more.

What is your intuition why an intelligence explosion

is not possible?

Like taking the scientific,

all the semi scientific revolutions,

why can’t we slightly accelerate that process?

So you can absolutely accelerate

any problem solving process.

So a recursively self improvement

is absolutely a real thing.

But what happens with a recursively self improving system

is typically not explosion

because no system exists in isolation.

And so tweaking one part of the system

means that suddenly another part of the system

becomes a bottleneck.

And if you look at science, for instance,

which is clearly a recursively self improving,

clearly a problem solving system,

scientific progress is not actually exploding.

If you look at science,

what you see is the picture of a system

that is consuming an exponentially increasing

amount of resources,

but it’s having a linear output

in terms of scientific progress.

And maybe that will seem like a very strong claim.

Many people are actually saying that,

scientific progress is exponential,

but when they’re claiming this,

they’re actually looking at indicators

of resource consumption by science.

For instance, the number of papers being published,

the number of patents being filed and so on,

which are just completely correlated

with how many people are working on science today.

So it’s actually an indicator of resource consumption,

but what you should look at is the output,

is progress in terms of the knowledge

that science generates,

in terms of the scope and significance

of the problems that we solve.

And some people have actually been trying to measure that.

Like Michael Nielsen, for instance,

he had a very nice paper,

I think that was last year about it.

So his approach to measure scientific progress

was to look at the timeline of scientific discoveries

over the past, you know, 100, 150 years.

And for each major discovery,

ask a panel of experts to rate

the significance of the discovery.

And if the output of science as an institution

were exponential,

you would expect the temporal density of significance

to go up exponentially.

Maybe because there’s a faster rate of discoveries,

maybe because the discoveries are, you know,

increasingly more important.

And what actually happens

if you plot this temporal density of significance

measured in this way,

is that you see very much a flat graph.

You see a flat graph across all disciplines,

across physics, biology, medicine, and so on.

And it actually makes a lot of sense

if you think about it,

because think about the progress of physics

110 years ago, right?

It was a time of crazy change.

Think about the progress of technology,

you know, 170 years ago,

when we started having, you know,

replacing horses with cars,

when we started having electricity and so on.

It was a time of incredible change.

And today is also a time of very, very fast change,

but it would be an unfair characterization

to say that today technology and science

are moving way faster than they did 50 years ago

or 100 years ago.

And if you do try to rigorously plot

the temporal density of the significance,

yeah, of significance, sorry,

you do see very flat curves.

And you can check out the paper

that Michael Nielsen had about this idea.

And so the way I interpret it is,

as you make progress in a given field,

or in a given subfield of science,

it becomes exponentially more difficult

to make further progress.

Like the very first person to work on information theory.

If you enter a new field,

and it’s still the very early years,

there’s a lot of low hanging fruit you can pick.

That’s right, yeah.

But the next generation of researchers

is gonna have to dig much harder, actually,

to make smaller discoveries,

probably larger number of smaller discoveries,

and to achieve the same amount of impact,

you’re gonna need a much greater head count.

And that’s exactly the picture you’re seeing with science,

that the number of scientists and engineers

is in fact increasing exponentially.

The amount of computational resources

that are available to science

is increasing exponentially and so on.

So the resource consumption of science is exponential,

but the output in terms of progress,

in terms of significance, is linear.

And the reason why is because,

and even though science is regressively self improving,

meaning that scientific progress

turns into technological progress,

which in turn helps science.

If you look at computers, for instance,

our products of science and computers

are tremendously useful in speeding up science.

The internet, same thing, the internet is a technology

that’s made possible by very recent scientific advances.

And itself, because it enables scientists to network,

to communicate, to exchange papers and ideas much faster,

it is a way to speed up scientific progress.

So even though you’re looking

at a regressively self improving system,

it is consuming exponentially more resources

to produce the same amount of problem solving, very much.

So that’s a fascinating way to paint it,

and certainly that holds for the deep learning community.

If you look at the temporal, what did you call it,

the temporal density of significant ideas,

if you look at in deep learning,

I think, I’d have to think about that,

but if you really look at significant ideas

in deep learning, they might even be decreasing.

So I do believe the per paper significance is decreasing,

but the amount of papers

is still today exponentially increasing.

So I think if you look at an aggregate,

my guess is that you would see a linear progress.

If you were to sum the significance of all papers,

you would see roughly in your progress.

And in my opinion, it is not a coincidence

that you’re seeing linear progress in science

despite exponential resource consumption.

I think the resource consumption

is dynamically adjusting itself to maintain linear progress

because we as a community expect linear progress,

meaning that if we start investing less

and seeing less progress, it means that suddenly

there are some lower hanging fruits that become available

and someone’s gonna step up and pick them, right?

So it’s very much like a market for discoveries and ideas.

But there’s another fundamental part

which you’re highlighting, which as a hypothesis

as science or like the space of ideas,

any one path you travel down,

it gets exponentially more difficult

to get a new way to develop new ideas.

And your sense is that’s gonna hold

across our mysterious universe.

Yes, well, exponential progress

triggers exponential friction.

So that if you tweak one part of the system,

suddenly some other part becomes a bottleneck, right?

For instance, let’s say you develop some device

that measures its own acceleration

and then it has some engine

and it outputs even more acceleration

in proportion of its own acceleration

and you drop it somewhere,

it’s not gonna reach infinite speed

because it exists in a certain context.

So the air around it is gonna generate friction

and it’s gonna block it at some top speed.

And even if you were to consider the broader context

and lift the bottleneck there,

like the bottleneck of friction,

then some other part of the system

would start stepping in and creating exponential friction,

maybe the speed of flight or whatever.

And this definitely holds true

when you look at the problem solving algorithm

that is being run by science as an institution,

science as a system.

As you make more and more progress,

despite having this recursive self improvement component,

you are encountering exponential friction.

The more researchers you have working on different ideas,

the more overhead you have

in terms of communication across researchers.

If you look at, you were mentioning quantum mechanics, right?

Well, if you want to start making significant discoveries

today, significant progress in quantum mechanics,

there is an amount of knowledge you have to ingest,

which is huge.

So there’s a very large overhead

to even start to contribute.

There’s a large amount of overhead

to synchronize across researchers and so on.

And of course, the significant practical experiments

are going to require exponentially expensive equipment

because the easier ones have already been run, right?

So in your senses, there’s no way escaping,

there’s no way of escaping this kind of friction

with artificial intelligence systems.

Yeah, no, I think science is a very good way

to model what would happen with a superhuman

recursive research improving AI.

That’s your sense, I mean, the…

That’s my intuition.

It’s not like a mathematical proof of anything.

That’s not my point.

Like, I’m not trying to prove anything.

I’m just trying to make an argument

to question the narrative of intelligence explosion,

which is quite a dominant narrative.

And you do get a lot of pushback if you go against it.

Because, so for many people, right,

AI is not just a subfield of computer science.

It’s more like a belief system.

Like this belief that the world is headed towards an event,

the singularity, past which, you know, AI will become…

will go exponential very much,

and the world will be transformed,

and humans will become obsolete.

And if you go against this narrative,

because it is not really a scientific argument,

but more of a belief system,

it is part of the identity of many people.

If you go against this narrative,

it’s like you’re attacking the identity

of people who believe in it.

It’s almost like saying God doesn’t exist,

or something.

So you do get a lot of pushback

if you try to question these ideas.

First of all, I believe most people,

they might not be as eloquent or explicit as you’re being,

but most people in computer science

are most people who actually have built

anything that you could call AI, quote, unquote,

would agree with you.

They might not be describing in the same kind of way.

It’s more, so the pushback you’re getting

is from people who get attached to the narrative

from, not from a place of science,

but from a place of imagination.

That’s correct, that’s correct.

So why do you think that’s so appealing?

Because the usual dreams that people have

when you create a superintelligence system

past the singularity,

that what people imagine is somehow always destructive.

Do you have, if you were put on your psychology hat,

what’s, why is it so appealing to imagine

the ways that all of human civilization will be destroyed?

I think it’s a good story.

You know, it’s a good story.

And very interestingly, it mirrors a religious stories,

right, religious mythology.

If you look at the mythology of most civilizations,

it’s about the world being headed towards some final events

in which the world will be destroyed

and some new world order will arise

that will be mostly spiritual,

like the apocalypse followed by a paradise probably, right?

It’s a very appealing story on a fundamental level.

And we all need stories.

We all need stories to structure the way we see the world,

especially at timescales

that are beyond our ability to make predictions, right?

So on a more serious non exponential explosion,

question, do you think there will be a time

when we’ll create something like human level intelligence

or intelligent systems that will make you sit back

and be just surprised at damn how smart this thing is?

That doesn’t require exponential growth

or an exponential improvement,

but what’s your sense of the timeline and so on

that you’ll be really surprised at certain capabilities?

And we’ll talk about limitations and deep learning.

So do you think in your lifetime,

you’ll be really damn surprised?

Around 2013, 2014, I was many times surprised

by the capabilities of deep learning actually.

That was before we had assessed exactly

what deep learning could do and could not do.

And it felt like a time of immense potential.

And then we started narrowing it down,

but I was very surprised.

I would say it has already happened.

Was there a moment, there must’ve been a day in there

where your surprise was almost bordering

on the belief of the narrative that we just discussed.

Was there a moment,

because you’ve written quite eloquently

about the limits of deep learning,

was there a moment that you thought

that maybe deep learning is limitless?

No, I don’t think I’ve ever believed this.

What was really shocking is that it worked.

It worked at all, yeah.

But there’s a big jump between being able

to do really good computer vision

and human level intelligence.

So I don’t think at any point I wasn’t under the impression

that the results we got in computer vision

meant that we were very close to human level intelligence.

I don’t think we’re very close to human level intelligence.

I do believe that there’s no reason

why we won’t achieve it at some point.

I also believe that it’s the problem

with talking about human level intelligence

that implicitly you’re considering

like an axis of intelligence with different levels,

but that’s not really how intelligence works.

Intelligence is very multi dimensional.

And so there’s the question of capabilities,

but there’s also the question of being human like,

and it’s two very different things.

Like you can build potentially

very advanced intelligent agents

that are not human like at all.

And you can also build very human like agents.

And these are two very different things, right?

Right.

Let’s go from the philosophical to the practical.

Can you give me a history of Keras

and all the major deep learning frameworks

that you kind of remember in relation to Keras

and in general, TensorFlow, Theano, the old days.

Can you give a brief overview Wikipedia style history

and your role in it before we return to AGI discussions?

Yeah, that’s a broad topic.

So I started working on Keras.

It was the name Keras at the time.

I actually picked the name like

just the day I was going to release it.

So I started working on it in February, 2015.

And so at the time there weren’t too many people

working on deep learning, maybe like fewer than 10,000.

The software tooling was not really developed.

So the main deep learning library was Cafe,

which was mostly C++.

Why do you say Cafe was the main one?

Cafe was vastly more popular than Theano

in late 2014, early 2015.

Cafe was the one library that everyone was using

for computer vision.

And computer vision was the most popular problem

in deep learning at the time.

Absolutely.

Like ConvNets was like the subfield of deep learning

that everyone was working on.

So myself, so in late 2014,

I was actually interested in RNNs,

in recurrent neural networks,

which was a very niche topic at the time, right?

It really took off around 2016.

And so I was looking for good tools.

I had used Torch 7, I had used Theano,

used Theano a lot in Kaggle competitions.

I had used Cafe.

And there was no like good solution for RNNs at the time.

Like there was no reusable open source implementation

of an LSTM, for instance.

So I decided to build my own.

And at first, the pitch for that was,

it was gonna be mostly around LSTM recurrent neural networks.

It was gonna be in Python.

An important decision at the time

that was kind of not obvious

is that the models would be defined via Python code,

which was kind of like going against the mainstream

at the time because Cafe, Pylon 2, and so on,

like all the big libraries were actually going

with the approach of setting configuration files

in YAML to define models.

So some libraries were using code to define models,

like Torch 7, obviously, but that was not Python.

Lasagne was like a Theano based very early library

that was, I think, developed, I don’t remember exactly,

probably late 2014.

It’s Python as well.

It was like on top of Theano.

And so I started working on something

and the value proposition at the time was that

not only what I think was the first

reusable open source implementation of LSTM,

you could combine RNNs and covenants

with the same library,

which is not really possible before,

like Cafe was only doing covenants.

And it was kind of easy to use

because, so before I was using Theano,

I was actually using scikitlin

and I loved scikitlin for its usability.

So I drew a lot of inspiration from scikitlin

when I made Keras.

It’s almost like scikitlin for neural networks.

The fit function.

Exactly, the fit function,

like reducing a complex string loop

to a single function call, right?

And of course, some people will say,

this is hiding a lot of details,

but that’s exactly the point, right?

The magic is the point.

So it’s magical, but in a good way.

It’s magical in the sense that it’s delightful.

Yeah, yeah.

I’m actually quite surprised.

I didn’t know that it was born out of desire

to implement RNNs and LSTMs.

It was.

That’s fascinating.

So you were actually one of the first people

to really try to attempt

to get the major architectures together.

And it’s also interesting.

You made me realize that that was a design decision at all

is defining the model and code.

Just, I’m putting myself in your shoes,

whether the YAML, especially if cafe was the most popular.

It was the most popular by far.

If I was, if I were, yeah, I don’t,

I didn’t like the YAML thing,

but it makes more sense that you will put

in a configuration file, the definition of a model.

That’s an interesting gutsy move

to stick with defining it in code.

Just if you look back.

Other libraries were doing it as well,

but it was definitely the more niche option.

Yeah.

Okay, Keras and then.

So I released Keras in March, 2015,

and it got users pretty much from the start.

So the deep learning community was very, very small

at the time.

Lots of people were starting to be interested in LSTM.

So it was gonna release it at the right time

because it was offering an easy to use LSTM implementation.

Exactly at the time where lots of people started

to be intrigued by the capabilities of RNN, RNNs for NLP.

So it grew from there.

Then I joined Google about six months later,

and that was actually completely unrelated to Keras.

So I actually joined a research team

working on image classification,

mostly like computer vision.

So I was doing computer vision research

at Google initially.

And immediately when I joined Google,

I was exposed to the early internal version of TensorFlow.

And the way it appeared to me at the time,

and it was definitely the way it was at the time

is that this was an improved version of Theano.

So I immediately knew I had to port Keras

to this new TensorFlow thing.

And I was actually very busy as a noobler,

as a new Googler.

So I had not time to work on that.

But then in November, I think it was November, 2015,

TensorFlow got released.

And it was kind of like my wake up call

that, hey, I had to actually go and make it happen.

So in December, I ported Keras to run on top of TensorFlow,

but it was not exactly a port.

It was more like a refactoring

where I was abstracting away

all the backend functionality into one module

so that the same code base

could run on top of multiple backends.

So on top of TensorFlow or Theano.

And for the next year,

Theano stayed as the default option.

It was easier to use, somewhat less buggy.

It was much faster, especially when it came to audience.

But eventually, TensorFlow overtook it.

And TensorFlow, the early TensorFlow,

has similar architectural decisions as Theano, right?

So it was a natural transition.

Yeah, absolutely.

So what, I mean, that still Keras is a side,

almost fun project, right?

Yeah, so it was not my job assignment.

It was not.

I was doing it on the side.

And even though it grew to have a lot of users

for a deep learning library at the time, like Stroud 2016,

but I wasn’t doing it as my main job.

So things started changing in,

I think it must have been maybe October, 2016.

So one year later.

So Rajat, who was the lead on TensorFlow,

basically showed up one day in our building

where I was doing like,

so I was doing research and things like,

so I did a lot of computer vision research,

also collaborations with Christian Zighetti

and deep learning for theorem proving.

It was a really interesting research topic.

And so Rajat was saying,

hey, we saw Keras, we like it.

We saw that you’re at Google.

Why don’t you come over for like a quarter

and work with us?

And I was like, yeah, that sounds like a great opportunity.

Let’s do it.

And so I started working on integrating the Keras API

into TensorFlow more tightly.

So what followed up is a sort of like temporary

TensorFlow only version of Keras

that was in TensorFlow.com Trib for a while.

And finally moved to TensorFlow Core.

And I’ve never actually gotten back

to my old team doing research.

Well, it’s kind of funny that somebody like you

who dreams of, or at least sees the power of AI systems

that reason and theorem proving we’ll talk about

has also created a system that makes the most basic

kind of Lego building that is deep learning

super accessible, super easy.

So beautifully so.

It’s a funny irony that you’re both,

you’re responsible for both things,

but so TensorFlow 2.0 is kind of, there’s a sprint.

I don’t know how long it’ll take,

but there’s a sprint towards the finish.

What do you look, what are you working on these days?

What are you excited about?

What are you excited about in 2.0?

I mean, eager execution.

There’s so many things that just make it a lot easier

to work.

What are you excited about and what’s also really hard?

What are the problems you have to kind of solve?

So I’ve spent the past year and a half working on

TensorFlow 2.0 and it’s been a long journey.

I’m actually extremely excited about it.

I think it’s a great product.

It’s a delightful product compared to TensorFlow 1.0.

We’ve made huge progress.

So on the Keras side, what I’m really excited about is that,

so previously Keras has been this very easy to use

high level interface to do deep learning.

But if you wanted to,

if you wanted a lot of flexibility,

the Keras framework was probably not the optimal way

to do things compared to just writing everything

from scratch.

So in some way, the framework was getting in the way.

And in TensorFlow 2.0, you don’t have this at all, actually.

You have the usability of the high level interface,

but you have the flexibility of this lower level interface.

And you have this spectrum of workflows

where you can get more or less usability

and flexibility trade offs depending on your needs, right?

You can write everything from scratch

and you get a lot of help doing so

by subclassing models and writing some train loops

using ego execution.

It’s very flexible, it’s very easy to debug,

it’s very powerful.

But all of this integrates seamlessly

with higher level features up to the classic Keras workflows,

which are very scikit learn like

and are ideal for a data scientist,

machine learning engineer type of profile.

So now you can have the same framework

offering the same set of APIs

that enable a spectrum of workflows

that are more or less low level, more or less high level

that are suitable for profiles ranging from researchers

to data scientists and everything in between.

Yeah, so that’s super exciting.

I mean, it’s not just that,

it’s connected to all kinds of tooling.

You can go on mobile, you can go with TensorFlow Lite,

you can go in the cloud or serving and so on.

It all is connected together.

Now some of the best software written ever

is often done by one person, sometimes two.

So with a Google, you’re now seeing sort of Keras

having to be integrated in TensorFlow,

I’m sure has a ton of engineers working on.

And there’s, I’m sure a lot of tricky design decisions

to be made.

How does that process usually happen

from at least your perspective?

What are the debates like?

Is there a lot of thinking,

considering different options and so on?

Yes.

So a lot of the time I spend at Google

is actually discussing design discussions, right?

Writing design docs, participating in design review meetings

and so on.

This is as important as actually writing a code.

Right.

So there’s a lot of thought, there’s a lot of thought

and a lot of care that is taken

in coming up with these decisions

and taking into account all of our users

because TensorFlow has this extremely diverse user base,

right?

It’s not like just one user segment

where everyone has the same needs.

We have small scale production users,

large scale production users.

We have startups, we have researchers,

you know, it’s all over the place.

And we have to cater to all of their needs.

If I just look at the standard debates

of C++ or Python, there’s some heated debates.

Do you have those at Google?

I mean, they’re not heated in terms of emotionally,

but there’s probably multiple ways to do it, right?

So how do you arrive through those design meetings

at the best way to do it?

Especially in deep learning where the field is evolving

as you’re doing it.

Is there some magic to it?

Is there some magic to the process?

I don’t know if there’s magic to the process,

but there definitely is a process.

So making design decisions

is about satisfying a set of constraints,

but also trying to do so in the simplest way possible,

because this is what can be maintained,

this is what can be expanded in the future.

So you don’t want to naively satisfy the constraints

by just, you know, for each capability you need available,

you’re gonna come up with one argument in your API

and so on.

You want to design APIs that are modular and hierarchical

so that they have an API surface

that is as small as possible, right?

And you want this modular hierarchical architecture

to reflect the way that domain experts

think about the problem.

Because as a domain expert,

when you are reading about a new API,

you’re reading a tutorial or some docs pages,

you already have a way that you’re thinking about the problem.

You already have like certain concepts in mind

and you’re thinking about how they relate together.

And when you’re reading docs,

you’re trying to build as quickly as possible

a mapping between the concepts featured in your API

and the concepts in your mind.

So you’re trying to map your mental model

as a domain expert to the way things work in the API.

So you need an API and an underlying implementation

that are reflecting the way people think about these things.

So in minimizing the time it takes to do the mapping.

Yes, minimizing the time,

the cognitive load there is

in ingesting this new knowledge about your API.

An API should not be self referential

or referring to implementation details.

It should only be referring to domain specific concepts

that people already understand.

Brilliant.

So what’s the future of Keras and TensorFlow look like?

What does TensorFlow 3.0 look like?

So that’s kind of too far in the future for me to answer,

especially since I’m not even the one making these decisions.

Okay.

But so from my perspective,

which is just one perspective

among many different perspectives on the TensorFlow team,

I’m really excited by developing even higher level APIs,

higher level than Keras.

I’m really excited by hyperparameter tuning,

by automated machine learning, AutoML.

I think the future is not just, you know,

defining a model like you were assembling Lego blocks

and then collect fit on it.

It’s more like an automagical model

that would just look at your data

and optimize the objective you’re after, right?

So that’s what I’m looking into.

Yeah, so you put the baby into a room with the problem

and come back a few hours later

with a fully solved problem.

Exactly, it’s not like a box of Legos.

It’s more like the combination of a kid

that’s really good at Legos and a box of Legos.

It’s just building the thing on its own.

Very nice.

So that’s an exciting future.

I think there’s a huge amount of applications

and revolutions to be had

under the constraints of the discussion we previously had.

But what do you think of the current limits of deep learning?

If we look specifically at these function approximators

that tries to generalize from data.

You’ve talked about local versus extreme generalization.

You mentioned that neural networks don’t generalize well

and humans do.

So there’s this gap.

And you’ve also mentioned that extreme generalization

requires something like reasoning to fill those gaps.

So how can we start trying to build systems like that?

Right, yeah, so this is by design, right?

Deep learning models are like huge parametric models,

differentiable, so continuous,

that go from an input space to an output space.

And they’re trained with gradient descent.

So they’re trained pretty much point by point.

They are learning a continuous geometric morphing

from an input vector space to an output vector space.

And because this is done point by point,

a deep neural network can only make sense

of points in experience space that are very close

to things that it has already seen in string data.

At best, it can do interpolation across points.

But that means in order to train your network,

you need a dense sampling of the input cross output space,

almost a point by point sampling,

which can be very expensive if you’re dealing

with complex real world problems,

like autonomous driving, for instance, or robotics.

It’s doable if you’re looking at the subset

of the visual space.

But even then, it’s still fairly expensive.

You still need millions of examples.

And it’s only going to be able to make sense of things

that are very close to what it has seen before.

And in contrast to that, well, of course,

you have human intelligence.

But even if you’re not looking at human intelligence,

you can look at very simple rules, algorithms.

If you have a symbolic rule,

it can actually apply to a very, very large set of inputs

because it is abstract.

It is not obtained by doing a point by point mapping.

For instance, if you try to learn a sorting algorithm

using a deep neural network,

well, you’re very much limited to learning point by point

what the sorted representation of this specific list is like.

But instead, you could have a very, very simple

sorting algorithm written in a few lines.

Maybe it’s just two nested loops.

And it can process any list at all because it is abstract,

because it is a set of rules.

So deep learning is really like point by point

geometric morphings, train with good and decent.

And meanwhile, abstract rules can generalize much better.

And I think the future is we need to combine the two.

So how do we, do you think, combine the two?

How do we combine good point by point functions

with programs, which is what the symbolic AI type systems?

At which levels the combination happen?

I mean, obviously we’re jumping into the realm

of where there’s no good answers.

It’s just kind of ideas and intuitions and so on.

Well, if you look at the really successful AI systems

today, I think they are already hybrid systems

that are combining symbolic AI with deep learning.

For instance, successful robotics systems

are already mostly model based, rule based,

things like planning algorithms and so on.

At the same time, they’re using deep learning

as perception modules.

Sometimes they’re using deep learning as a way

to inject fuzzy intuition into a rule based process.

If you look at the system like in a self driving car,

it’s not just one big end to end neural network.

You know, that wouldn’t work at all.

Precisely because in order to train that,

you would need a dense sampling of experience base

when it comes to driving,

which is completely unrealistic, obviously.

Instead, the self driving car is mostly

symbolic, you know, it’s software, it’s programmed by hand.

So it’s mostly based on explicit models.

In this case, mostly 3D models of the environment

around the car, but it’s interfacing with the real world

using deep learning modules, right?

So the deep learning there serves as a way

to convert the raw sensory information

to something usable by symbolic systems.

Okay, well, let’s linger on that a little more.

So dense sampling from input to output.

You said it’s obviously very difficult.

Is it possible?

In the case of self driving, you mean?

Let’s say self driving, right?

Self driving for many people,

let’s not even talk about self driving,

let’s talk about steering, so staying inside the lane.

Lane following, yeah, it’s definitely a problem

you can solve with an end to end deep learning model,

but that’s like one small subset.

Hold on a second.

Yeah, I don’t know why you’re jumping

from the extreme so easily,

because I disagree with you on that.

I think, well, it’s not obvious to me

that you can solve lane following.

No, it’s not obvious, I think it’s doable.

I think in general, there is no hard limitations

to what you can learn with a deep neural network,

as long as the search space is rich enough,

is flexible enough, and as long as you have

this dense sampling of the input cross output space.

The problem is that this dense sampling

could mean anything from 10,000 examples

to like trillions and trillions.

So that’s my question.

So what’s your intuition?

And if you could just give it a chance

and think what kind of problems can be solved

by getting a huge amounts of data

and thereby creating a dense mapping.

So let’s think about natural language dialogue,

the Turing test.

Do you think the Turing test can be solved

with a neural network alone?

Well, the Turing test is all about tricking people

into believing they’re talking to a human.

And I don’t think that’s actually very difficult

because it’s more about exploiting human perception

and not so much about intelligence.

There’s a big difference between mimicking

intelligent behavior and actual intelligent behavior.

So, okay, let’s look at maybe the Alexa prize and so on.

The different formulations of the natural language

conversation that are less about mimicking

and more about maintaining a fun conversation

that lasts for 20 minutes.

That’s a little less about mimicking

and that’s more about, I mean, it’s still mimicking,

but it’s more about being able to carry forward

a conversation with all the tangents that happen

in dialogue and so on.

Do you think that problem is learnable

with a neural network that does the point to point mapping?

So I think it would be very, very challenging

to do this with deep learning.

I don’t think it’s out of the question either.

I wouldn’t rule it out.

The space of problems that can be solved

with a large neural network.

What’s your sense about the space of those problems?

So useful problems for us.

In theory, it’s infinite, right?

You can solve any problem.

In practice, well, deep learning is a great fit

for perception problems.

In general, any problem which is naturally amenable

to explicit handcrafted rules or rules that you can generate

by exhaustive search over some program space.

So perception, artificial intuition,

as long as you have a sufficient training dataset.

And that’s the question, I mean, perception,

there’s interpretation and understanding of the scene,

which seems to be outside the reach

of current perception systems.

So do you think larger networks will be able

to start to understand the physics

and the physics of the scene,

the three dimensional structure and relationships

of objects in the scene and so on?

Or really that’s where symbolic AI has to step in?

Well, it’s always possible to solve these problems

with deep learning.

It’s just extremely inefficient.

A model would be an explicit rule based abstract model

would be a far better, more compressed

representation of physics.

Then learning just this mapping between

in this situation, this thing happens.

If you change the situation slightly,

then this other thing happens and so on.

Do you think it’s possible to automatically generate

the programs that would require that kind of reasoning?

Or does it have to, so the way the expert systems fail,

there’s so many facts about the world

had to be hand coded in.

Do you think it’s possible to learn those logical statements

that are true about the world and their relationships?

Do you think, I mean, that’s kind of what theorem proving

at a basic level is trying to do, right?

Yeah, except it’s much harder to formulate statements

about the world compared to formulating

mathematical statements.

Statements about the world tend to be subjective.

So can you learn rule based models?

Yes, definitely.

That’s the field of program synthesis.

However, today we just don’t really know how to do it.

So it’s very much a grass search or tree search problem.

And so we are limited to the sort of tree session grass

search algorithms that we have today.

Personally, I think genetic algorithms are very promising.

So almost like genetic programming.

Genetic programming, exactly.

Can you discuss the field of program synthesis?

Like how many people are working and thinking about it?

Where we are in the history of program synthesis

and what are your hopes for it?

Well, if it were deep learning, this is like the 90s.

So meaning that we already have existing solutions.

We are starting to have some basic understanding

of what this is about.

But it’s still a field that is in its infancy.

There are very few people working on it.

There are very few real world applications.

So the one real world application I’m aware of

is Flash Fill in Excel.

It’s a way to automatically learn very simple programs

to format cells in an Excel spreadsheet

from a few examples.

For instance, learning a way to format a date, things like that.

Oh, that’s fascinating.

Yeah.

You know, OK, that’s a fascinating topic.

I always wonder when I provide a few samples to Excel,

what it’s able to figure out.

Like just giving it a few dates, what

are you able to figure out from the pattern I just gave you?

That’s a fascinating question.

And it’s fascinating whether that’s learnable patterns.

And you’re saying they’re working on that.

How big is the toolbox currently?

Are we completely in the dark?

So if you said the 90s.

In terms of program synthesis?

No.

So I would say, so maybe 90s is even too optimistic.

Because by the 90s, we already understood back prop.

We already understood the engine of deep learning,

even though we couldn’t really see its potential quite.

Today, I don’t think we have found

the engine of program synthesis.

So we’re in the winter before back prop.

Yeah.

In a way, yes.

So I do believe program synthesis and general discrete search

over rule based models is going to be

a cornerstone of AI research in the next century.

And that doesn’t mean we are going to drop deep learning.

Deep learning is immensely useful.

Like, being able to learn is a very flexible, adaptable,

parametric model.

So it’s got to understand that’s actually immensely useful.

All it’s doing is pattern cognition.

But being good at pattern cognition, given lots of data,

is just extremely powerful.

So we are still going to be working on deep learning.

We are going to be working on program synthesis.

We are going to be combining the two in increasingly automated

ways.

So let’s talk a little bit about data.

You’ve tweeted, about 10,000 deep learning papers

have been written about hard coding priors

about a specific task in a neural network architecture

works better than a lack of a prior.

Basically, summarizing all these efforts,

they put a name to an architecture.

But really, what they’re doing is hard coding some priors

that improve the performance of the system.

But which gets straight to the point is probably true.

So you say that you can always buy performance by,

in quotes, performance by either training on more data,

better data, or by injecting task information

to the architecture of the preprocessing.

However, this isn’t informative about the generalization power

the techniques use, the fundamental ability

to generalize.

Do you think we can go far by coming up

with better methods for this kind of cheating,

for better methods of large scale annotation of data?

So building better priors.

If you automate it, it’s not cheating anymore.

Right.

I’m joking about the cheating, but large scale.

So basically, I’m asking about something

that hasn’t, from my perspective,

been researched too much is exponential improvement

in annotation of data.

Do you often think about?

I think it’s actually been researched quite a bit.

You just don’t see publications about it.

Because people who publish papers

are going to publish about known benchmarks.

Sometimes they’re going to read a new benchmark.

People who actually have real world large scale

depending on problems, they’re going

to spend a lot of resources into data annotation

and good data annotation pipelines,

but you don’t see any papers about it.

That’s interesting.

So do you think, certainly resources,

but do you think there’s innovation happening?

Oh, yeah.

To clarify the point in the tweet.

So machine learning in general is

the science of generalization.

You want to generate knowledge that

can be reused across different data sets,

across different tasks.

And if instead you’re looking at one data set

and then you are hard coding knowledge about this task

into your architecture, this is no more useful

than training a network and then saying, oh, I

found these weight values perform well.

So David Ha, I don’t know if you know David,

he had a paper the other day about weight

agnostic neural networks.

And this is a very interesting paper

because it really illustrates the fact

that an architecture, even without weights,

an architecture is knowledge about a task.

It encodes knowledge.

And when it comes to architectures

that are uncrafted by researchers, in some cases,

it is very, very clear that all they are doing

is artificially reencoding the template that

corresponds to the proper way to solve the task encoding

a given data set.

For instance, I know if you looked

at the baby data set, which is about natural language

question answering, it is generated by an algorithm.

So this is a question answer pairs

that are generated by an algorithm.

The algorithm is solving a certain template.

Turns out, if you craft a network that

literally encodes this template, you

can solve this data set with nearly 100% accuracy.

But that doesn’t actually tell you

anything about how to solve question answering

in general, which is the point.

The question is just to linger on it,

whether it’s from the data side or from the size

of the network.

I don’t know if you’ve read the blog post by Rich Sutton,

The Bitter Lesson, where he says,

the biggest lesson that we can read from 70 years of AI

research is that general methods that leverage computation

are ultimately the most effective.

So as opposed to figuring out methods

that can generalize effectively, do you

think we can get pretty far by just having something

that leverages computation and the improvement of computation?

Yeah, so I think Rich is making a very good point, which

is that a lot of these papers, which are actually

all about manually hardcoding prior knowledge about a task

into some system, it doesn’t have

to be deep learning architecture, but into some system.

These papers are not actually making any impact.

Instead, what’s making really long term impact

is very simple, very general systems

that are really agnostic to all these tricks.

Because these tricks do not generalize.

And of course, the one general and simple thing

that you should focus on is that which leverages computation.

Because computation, the availability

of large scale computation has been increasing exponentially

following Moore’s law.

So if your algorithm is all about exploiting this,

then your algorithm is suddenly exponentially improving.

So I think Rich is definitely right.

However, he’s right about the past 70 years.

He’s like assessing the past 70 years.

I am not sure that this assessment will still

hold true for the next 70 years.

It might to some extent.

I suspect it will not.

Because the truth of his assessment

is a function of the context in which this research took place.

And the context is changing.

Moore’s law might not be applicable anymore,

for instance, in the future.

And I do believe that when you tweak one aspect of a system,

when you exploit one aspect of a system,

some other aspect starts becoming the bottleneck.

Let’s say you have unlimited computation.

Well, then data is the bottleneck.

And I think we are already starting

to be in a regime where our systems are

so large in scale and so data ingrained

that data today and the quality of data

and the scale of data is the bottleneck.

And in this environment, the bitter lesson from Rich

is not going to be true anymore.

So I think we are going to move from a focus

on a computation scale to focus on data efficiency.

Data efficiency.

So that’s getting to the question of symbolic AI.

But to linger on the deep learning approaches,

do you have hope for either unsupervised learning

or reinforcement learning, which are

ways of being more data efficient in terms

of the amount of data they need that required human annotation?

So unsupervised learning and reinforcement learning

are frameworks for learning, but they are not

like any specific technique.

So usually when people say reinforcement learning,

what they really mean is deep reinforcement learning,

which is like one approach which is actually very questionable.

The question I was asking was unsupervised learning

with deep neural networks and deep reinforcement learning.

Well, these are not really data efficient

because you’re still leveraging these huge parametric models

point by point with gradient descent.

It is more efficient in terms of the number of annotations,

the density of annotations you need.

So the idea being to learn the latent space around which

the data is organized and then map the sparse annotations

into it.

And sure, I mean, that’s clearly a very good idea.

It’s not really a topic I would be working on,

but it’s clearly a good idea.

So it would get us to solve some problems that?

It will get us to incremental improvements

in labeled data efficiency.

Do you have concerns about short term or long term threats

from AI, from artificial intelligence?

Yes, definitely to some extent.

And what’s the shape of those concerns?

This is actually something I’ve briefly written about.

But the capabilities of deep learning technology

can be used in many ways that are

concerning from mass surveillance with things

like facial recognition.

In general, tracking lots of data about everyone

and then being able to making sense of this data

to do identification, to do prediction.

That’s concerning.

That’s something that’s being very aggressively pursued

by totalitarian states like China.

One thing I am very much concerned about

is that our lives are increasingly online,

are increasingly digital, made of information,

made of information consumption and information production,

our digital footprint, I would say.

And if you absorb all of this data

and you are in control of where you consume information,

social networks and so on, recommendation engines,

then you can build a sort of reinforcement

loop for human behavior.

You can observe the state of your mind at time t.

You can predict how you would react

to different pieces of content, how

to get you to move your mind in a certain direction.

And then you can feed you the specific piece of content

that would move you in a specific direction.

And you can do this at scale in terms

of doing it continuously in real time.

You can also do it at scale in terms

of scaling this to many, many people, to entire populations.

So potentially, artificial intelligence,

even in its current state, if you combine it

with the internet, with the fact that all of our lives

are moving to digital devices and digital information

consumption and creation, what you get

is the possibility to achieve mass manipulation of behavior

and mass psychological control.

And this is a very real possibility.

Yeah, so you’re talking about any kind of recommender system.

Let’s look at the YouTube algorithm, Facebook,

anything that recommends content you should watch next.

And it’s fascinating to think that there’s

some aspects of human behavior that you can say a problem of,

is this person hold Republican beliefs or Democratic beliefs?

And this is a trivial, that’s an objective function.

And you can optimize, and you can measure,

and you can turn everybody into a Republican

or everybody into a Democrat.

I do believe it’s true.

So the human mind is very, if you look at the human mind

as a kind of computer program, it

has a very large exploit surface.

It has many, many vulnerabilities.

Exploit surfaces, yeah.

Ways you can control it.

For instance, when it comes to your political beliefs,

this is very much tied to your identity.

So for instance, if I’m in control of your news feed

on your favorite social media platforms,

this is actually where you’re getting your news from.

And of course, I can choose to only show you

news that will make you see the world in a specific way.

But I can also create incentives for you

to post about some political beliefs.

And then when I get you to express a statement,

if it’s a statement that me as the controller,

I want to reinforce.

I can just show it to people who will agree,

and they will like it.

And that will reinforce the statement in your mind.

If this is a statement I want you to,

this is a belief I want you to abandon,

I can, on the other hand, show it to opponents.

We’ll attack you.

And because they attack you, at the very least,

next time you will think twice about posting it.

But maybe you will even start believing this

because you got pushback.

So there are many ways in which social media platforms

can potentially control your opinions.

And today, so all of these things

are already being controlled by AI algorithms.

These algorithms do not have any explicit political goal

today.

Well, potentially they could, like if some totalitarian

government takes over social media platforms

and decides that now we are going to use this not just

for mass surveillance, but also for mass opinion control

and behavior control.

Very bad things could happen.

But what’s really fascinating and actually quite concerning

is that even without an explicit intent to manipulate,

you’re already seeing very dangerous dynamics

in terms of how these content recommendation

algorithms behave.

Because right now, the goal, the objective function

of these algorithms is to maximize engagement,

which seems fairly innocuous at first.

However, it is not because content

that will maximally engage people, get people to react

in an emotional way, get people to click on something.

It is very often content that is not

healthy to the public discourse.

For instance, fake news are far more

likely to get you to click on them than real news

simply because they are not constrained to reality.

So they can be as outrageous, as surprising,

as good stories as you want because they’re artificial.

To me, that’s an exciting world because so much good

can come.

So there’s an opportunity to educate people.

You can balance people’s worldview with other ideas.

So there’s so many objective functions.

The space of objective functions that

create better civilizations is large, arguably infinite.

But there’s also a large space that

creates division and destruction, civil war,

a lot of bad stuff.

And the worry is, naturally, probably that space

is bigger, first of all.

And if we don’t explicitly think about what kind of effects

are going to be observed from different objective functions,

then we’re going to get into trouble.

But the question is, how do we get into rooms

and have discussions, so inside Google, inside Facebook,

inside Twitter, and think about, OK,

how can we drive up engagement and, at the same time,

create a good society?

Is it even possible to have that kind

of philosophical discussion?

I think you can definitely try.

So from my perspective, I would feel rather uncomfortable

with companies that are uncomfortable with these new

student algorithms, with them making explicit decisions

to manipulate people’s opinions or behaviors,

even if the intent is good, because that’s

a very totalitarian mindset.

So instead, what I would like to see

is probably never going to happen,

because it’s not super realistic,

but that’s actually something I really care about.

I would like all these algorithms

to present configuration settings to their users,

so that the users can actually make the decision about how

they want to be impacted by these information

recommendation, content recommendation algorithms.

For instance, as a user of something

like YouTube or Twitter, maybe I want

to maximize learning about a specific topic.

So I want the algorithm to feed my curiosity,

which is in itself a very interesting problem.

So instead of maximizing my engagement,

it will maximize how fast and how much I’m learning.

And it will also take into account the accuracy,

hopefully, of the information I’m learning.

So yeah, the user should be able to determine exactly

how these algorithms are affecting their lives.

I don’t want actually any entity making decisions

about in which direction they’re going to try to manipulate me.

I want technology.

So AI, these algorithms are increasingly

going to be our interface to a world that is increasingly

made of information.

And I want everyone to be in control of this interface,

to interface with the world on their own terms.

So if someone wants these algorithms

to serve their own personal growth goals,

they should be able to configure these algorithms

in such a way.

Yeah, but so I know it’s painful to have explicit decisions.

But there is underlying explicit decisions,

which is some of the most beautiful fundamental

philosophy that we have before us,

which is personal growth.

If I want to watch videos from which I can learn,

what does that mean?

So if I have a checkbox that wants to emphasize learning,

there’s still an algorithm with explicit decisions in it

that would promote learning.

What does that mean for me?

For example, I’ve watched a documentary on flat Earth

theory, I guess.

I learned a lot.

I’m really glad I watched it.

It was a friend recommended it to me.

Because I don’t have such an allergic reaction to crazy

people, as my fellow colleagues do.

But it was very eye opening.

And for others, it might not be.

From others, they might just get turned off from that, same

with Republican and Democrat.

And it’s a non trivial problem.

And first of all, if it’s done well,

I don’t think it’s something that wouldn’t happen,

that YouTube wouldn’t be promoting,

or Twitter wouldn’t be.

It’s just a really difficult problem,

how to give people control.

Well, it’s mostly an interface design problem.

The way I see it, you want to create technology

that’s like a mentor, or a coach, or an assistant,

so that it’s not your boss.

You are in control of it.

You are telling it what to do for you.

And if you feel like it’s manipulating you,

it’s not actually doing what you want.

You should be able to switch to a different algorithm.

So that’s fine tune control.

You kind of learn that you’re trusting

the human collaboration.

I mean, that’s how I see autonomous vehicles too,

is giving as much information as possible,

and you learn that dance yourself.

Yeah, Adobe, I don’t know if you use Adobe product

for like Photoshop.

They’re trying to see if they can inject YouTube

into their interface, but basically allow you

to show you all these videos,

that everybody’s confused about what to do with features.

So basically teach people by linking to,

in that way, it’s an assistant that uses videos

as a basic element of information.

Okay, so what practically should people do

to try to fight against abuses of these algorithms,

or algorithms that manipulate us?

Honestly, it’s a very, very difficult problem,

because to start with, there is very little public awareness

of these issues.

Very few people would think there’s anything wrong

with the unused algorithm,

even though there is actually something wrong already,

which is that it’s trying to maximize engagement

most of the time, which has very negative side effects.

So ideally, so the very first thing is to stop

trying to purely maximize engagement,

try to propagate content based on popularity, right?

Instead, take into account the goals

and the profiles of each user.

So you will be, one example is, for instance,

when I look at topic recommendations on Twitter,

it’s like, you know, they have this news tab

with switch recommendations.

It’s always the worst coverage,

because it’s content that appeals

to the smallest common denominator

to all Twitter users, because they’re trying to optimize.

They’re purely trying to optimize popularity.

They’re purely trying to optimize engagement.

But that’s not what I want.

So they should put me in control of some setting

so that I define what’s the objective function

that Twitter is going to be following

to show me this content.

And honestly, so this is all about interface design.

And we are not, it’s not realistic

to give users control of a bunch of knobs

that define algorithm.

Instead, we should purely put them in charge

of defining the objective function.

Like, let the user tell us what they want to achieve,

how they want this algorithm to impact their lives.

So do you think it is that,

or do they provide individual article by article

reward structure where you give a signal,

I’m glad I saw this, or I’m glad I didn’t?

So like a Spotify type feedback mechanism,

it works to some extent.

I’m kind of skeptical about it

because the only way the algorithm,

the algorithm will attempt to relate your choices

with the choices of everyone else,

which might, you know, if you have an average profile

that works fine, I’m sure Spotify accommodations work fine

if you just like mainstream stuff.

If you don’t, it can be, it’s not optimal at all actually.

It’ll be in an efficient search

for the part of the Spotify world that represents you.

So it’s a tough problem,

but do note that even a feedback system

like what Spotify has does not give me control

over what the algorithm is trying to optimize for.

Well, public awareness, which is what we’re doing now,

is a good place to start.

Do you have concerns about longterm existential threats

of artificial intelligence?

Well, as I was saying,

our world is increasingly made of information.

AI algorithms are increasingly going to be our interface

to this world of information,

and somebody will be in control of these algorithms.

And that puts us in any kind of a bad situation, right?

It has risks.

It has risks coming from potentially large companies

wanting to optimize their own goals,

maybe profit, maybe something else.

Also from governments who might want to use these algorithms

as a means of control of the population.

Do you think there’s existential threat

that could arise from that?

So existential threat.

So maybe you’re referring to the singularity narrative

where robots just take over.

Well, I don’t, I’m not terminating robots,

and I don’t believe it has to be a singularity.

We’re just talking to, just like you said,

the algorithm controlling masses of populations.

The existential threat being,

hurt ourselves much like a nuclear war would hurt ourselves.

That kind of thing.

I don’t think that requires a singularity.

That requires a loss of control over AI algorithm.

Yes.

So I do agree there are concerning trends.

Honestly, I wouldn’t want to make any longterm predictions.

I don’t think today we really have the capability

to see what the dangers of AI

are going to be in 50 years, in 100 years.

I do see that we are already faced

with concrete and present dangers

surrounding the negative side effects

of content recombination systems, of newsfeed algorithms

concerning algorithmic bias as well.

So we are delegating more and more

decision processes to algorithms.

Some of these algorithms are uncrafted,

some are learned from data,

but we are delegating control.

Sometimes it’s a good thing, sometimes not so much.

And there is in general very little supervision

of this process, right?

So we are still in this period of very fast change,

even chaos, where society is restructuring itself,

turning into an information society,

which itself is turning into

an increasingly automated information passing society.

And well, yeah, I think the best we can do today

is try to raise awareness around some of these issues.

And I think we’re actually making good progress.

If you look at algorithmic bias, for instance,

three years ago, even two years ago,

very, very few people were talking about it.

And now all the big companies are talking about it.

They are often not in a very serious way,

but at least it is part of the public discourse.

You see people in Congress talking about it.

And it all started from raising awareness.

Right.

So in terms of alignment problem,

trying to teach as we allow algorithms,

just even recommender systems on Twitter,

encoding human values and morals,

decisions that touch on ethics,

how hard do you think that problem is?

How do we have lost functions in neural networks

that have some component,

some fuzzy components of human morals?

Well, I think this is really all about objective function engineering,

which is probably going to be increasingly a topic of concern in the future.

Like for now, we’re just using very naive loss functions

because the hard part is not actually what you’re trying to minimize.

It’s everything else.

But as the everything else is going to be increasingly automated,

we’re going to be focusing our human attention

on increasingly high level components,

like what’s actually driving the whole learning system,

like the objective function.

So loss function engineering is going to be,

loss function engineer is probably going to be a job title in the future.

And then the tooling you’re creating with Keras essentially

takes care of all the details underneath.

And basically the human expert is needed for exactly that.

That’s the idea.

Keras is the interface between the data you’re collecting

and the business goals.

And your job as an engineer is going to be to express your business goals

and your understanding of your business or your product,

your system as a kind of loss function or a kind of set of constraints.

Does the possibility of creating an AGI system excite you or scare you or bore you?

So intelligence can never really be general.

You know, at best it can have some degree of generality like human intelligence.

It also always has some specialization in the same way that human intelligence

is specialized in a certain category of problems,

is specialized in the human experience.

And when people talk about AGI,

I’m never quite sure if they’re talking about very, very smart AI,

so smart that it’s even smarter than humans,

or they’re talking about human like intelligence,

because these are different things.

Let’s say, presumably I’m oppressing you today with my humanness.

So imagine that I was in fact a robot.

So what does that mean?

That I’m impressing you with natural language processing.

Maybe if you weren’t able to see me, maybe this is a phone call.

So that kind of system.

Companion.

So that’s very much about building human like AI.

And you’re asking me, you know, is this an exciting perspective?

Yes.

I think so, yes.

Not so much because of what artificial human like intelligence could do,

but, you know, from an intellectual perspective,

I think if you could build truly human like intelligence,

that means you could actually understand human intelligence,

which is fascinating, right?

Human like intelligence is going to require emotions.

It’s going to require consciousness,

which is not things that would normally be required by an intelligent system.

If you look at, you know, we were mentioning earlier like science

as a superhuman problem solving agent or system,

it does not have consciousness, it doesn’t have emotions.

In general, so emotions,

I see consciousness as being on the same spectrum as emotions.

It is a component of the subjective experience

that is meant very much to guide behavior generation, right?

It’s meant to guide your behavior.

In general, human intelligence and animal intelligence

has evolved for the purpose of behavior generation, right?

Including in a social context.

So that’s why we actually need emotions.

That’s why we need consciousness.

An artificial intelligence system developed in a different context

may well never need them, may well never be conscious like science.

Well, on that point, I would argue it’s possible to imagine

that there’s echoes of consciousness in science

when viewed as an organism, that science is consciousness.

So, I mean, how would you go about testing this hypothesis?

How do you probe the subjective experience of an abstract system like science?

Well, the point of probing any subjective experience is impossible

because I’m not science, I’m Lex.

So I can’t probe another entity, it’s no more than bacteria on my skin.

You’re Lex, I can ask you questions about your subjective experience

and you can answer me, and that’s how I know you’re conscious.

Yes, but that’s because we speak the same language.

You perhaps, we have to speak the language of science in order to ask it.

Honestly, I don’t think consciousness, just like emotions of pain and pleasure,

is not something that inevitably arises

from any sort of sufficiently intelligent information processing.

It is a feature of the mind, and if you’ve not implemented it explicitly, it is not there.

So you think it’s an emergent feature of a particular architecture.

So do you think…

It’s a feature in the same sense.

So, again, the subjective experience is all about guiding behavior.

If the problems you’re trying to solve don’t really involve an embodied agent,

maybe in a social context, generating behavior and pursuing goals like this.

And if you look at science, that’s not really what’s happening.

Even though it is, it is a form of artificial AI, artificial intelligence,

in the sense that it is solving problems, it is accumulating knowledge,

accumulating solutions and so on.

So if you’re not explicitly implementing a subjective experience,

implementing certain emotions and implementing consciousness,

it’s not going to just spontaneously emerge.

Yeah.

But so for a system like, human like intelligence system that has consciousness,

do you think it needs to have a body?

Yes, definitely.

I mean, it doesn’t have to be a physical body, right?

And there’s not that much difference between a realistic simulation in the real world.

So there has to be something you have to preserve kind of thing.

Yes, but human like intelligence can only arise in a human like context.

Intelligence needs other humans in order for you to demonstrate

that you have human like intelligence, essentially.

Yes.

So what kind of tests and demonstration would be sufficient for you

to demonstrate human like intelligence?

Yeah.

Just out of curiosity, you’ve talked about in terms of theorem proving

and program synthesis, I think you’ve written about

that there’s no good benchmarks for this.

Yeah.

That’s one of the problems.

So let’s talk program synthesis.

So what do you imagine is a good…

I think it’s related questions for human like intelligence

and for program synthesis.

What’s a good benchmark for either or both?

Right.

So I mean, you’re actually asking two questions,

which is one is about quantifying intelligence

and comparing the intelligence of an artificial system

to the intelligence for human.

And the other is about the degree to which this intelligence is human like.

It’s actually two different questions.

So you mentioned earlier the Turing test.

Well, I actually don’t like the Turing test because it’s very lazy.

It’s all about completely bypassing the problem of defining and measuring intelligence

and instead delegating to a human judge or a panel of human judges.

So it’s a total copout, right?

If you want to measure how human like an agent is,

I think you have to make it interact with other humans.

Maybe it’s not necessarily a good idea to have these other humans be the judges.

Maybe you should just observe behavior and compare it to what a human would actually have done.

When it comes to measuring how smart, how clever an agent is

and comparing that to the degree of human intelligence.

So we’re already talking about two things, right?

The degree, kind of like the magnitude of an intelligence and its direction, right?

Like the norm of a vector and its direction.

And the direction is like human likeness and the magnitude, the norm is intelligence.

You could call it intelligence, right?

So the direction, your sense, the space of directions that are human like is very narrow.

Yeah.

So the way you would measure the magnitude of intelligence in a system

in a way that also enables you to compare it to that of a human.

Well, if you look at different benchmarks for intelligence today,

they’re all too focused on skill at a given task.

Like skill at playing chess, skill at playing Go, skill at playing Dota.

And I think that’s not the right way to go about it because you can always

beat a human at one specific task.

The reason why our skill at playing Go or juggling or anything is impressive

is because we are expressing this skill within a certain set of constraints.

If you remove the constraints, the constraints that we have one lifetime,

that we have this body and so on, if you remove the context,

if you have unlimited string data, if you can have access to, you know,

for instance, if you look at juggling, if you have no restriction on the hardware,

then achieving arbitrary levels of skill is not very interesting

and says nothing about the amount of intelligence you’ve achieved.

So if you want to measure intelligence, you need to rigorously define what

intelligence is, which in itself, you know, it’s a very challenging problem.

And do you think that’s possible?

To define intelligence? Yes, absolutely.

I mean, you can provide, many people have provided, you know, some definition.

I have my own definition.

Where does your definition begin?

Where does your definition begin if it doesn’t end?

Well, I think intelligence is essentially the efficiency

with which you turn experience into generalizable programs.

So what that means is it’s the efficiency with which

you turn a sampling of experience space into

the ability to process a larger chunk of experience space.

So measuring skill can be one proxy across many different tasks,

can be one proxy for measuring intelligence.

But if you want to only measure skill, you should control for two things.

You should control for the amount of experience that your system has

and the priors that your system has.

But if you look at two agents and you give them the same priors

and you give them the same amount of experience,

there is one of the agents that is going to learn programs,

representations, something, a model that will perform well

on the larger chunk of experience space than the other.

And that is the smaller agent.

Yeah. So if you fix the experience, which generate better programs,

better meaning more generalizable.

That’s really interesting.

That’s a very nice, clean definition of…

Oh, by the way, in this definition, it is already very obvious

that intelligence has to be specialized

because you’re talking about experience space

and you’re talking about segments of experience space.

You’re talking about priors and you’re talking about experience.

All of these things define the context in which intelligence emerges.

And you can never look at the totality of experience space, right?

So intelligence has to be specialized.

But it can be sufficiently large, the experience space,

even though it’s specialized.

There’s a certain point when the experience space is large enough

to where it might as well be general.

It feels general. It looks general.

Sure. I mean, it’s very relative.

Like, for instance, many people would say human intelligence is general.

In fact, it is quite specialized.

We can definitely build systems that start from the same innate priors

as what humans have at birth.

Because we already understand fairly well

what sort of priors we have as humans.

Like many people have worked on this problem.

Most notably, Elisabeth Spelke from Harvard.

I don’t know if you know her.

She’s worked a lot on what she calls core knowledge.

And it is very much about trying to determine and describe

what priors we are born with.

Like language skills and so on, all that kind of stuff.

Exactly.

So we have some pretty good understanding of what priors we are born with.

So we could…

So I’ve actually been working on a benchmark for the past couple years,

you know, on and off.

I hope to be able to release it at some point.

That’s exciting.

The idea is to measure the intelligence of systems

by countering for priors,

countering for amount of experience,

and by assuming the same priors as what humans are born with.

So that you can actually compare these scores to human intelligence.

You can actually have humans pass the same test in a way that’s fair.

Yeah. And so importantly, such a benchmark should be such that any amount

of practicing does not increase your score.

So try to picture a game where no matter how much you play this game,

that does not change your skill at the game.

Can you picture that?

As a person who deeply appreciates practice, I cannot actually.

There’s actually a very simple trick.

So in order to come up with a task,

so the only thing you can measure is skill at the task.

Yes.

All tasks are going to involve priors.

Yes.

The trick is to know what they are and to describe that.

And then you make sure that this is the same set of priors as what humans start with.

So you create a task that assumes these priors, that exactly documents these priors,

so that the priors are made explicit and there are no other priors involved.

And then you generate a certain number of samples in experience space for this task, right?

And this, for one task, assuming that the task is new for the agent passing it,

that’s one test of this definition of intelligence that we set up.

And now you can scale that to many different tasks,

that each task should be new to the agent passing it, right?

And also it should be human interpretable and understandable

so that you can actually have a human pass the same test.

And then you can compare the score of your machine and the score of your human.

Which could be a lot of stuff.

You could even start a task like MNIST.

Just as long as you start with the same set of priors.

So the problem with MNIST, humans are already trying to recognize digits, right?

But let’s say we’re considering objects that are not digits,

some completely arbitrary patterns.

Well, humans already come with visual priors about how to process that.

So in order to make the game fair, you would have to isolate these priors

and describe them and then express them as computational rules.

Having worked a lot with vision science people, that’s exceptionally difficult.

A lot of progress has been made.

There’s been a lot of good tests and basically reducing all of human vision into some good priors.

We’re still probably far away from that perfectly,

but as a start for a benchmark, that’s an exciting possibility.

Yeah, so Elisabeth Spelke actually lists objectness as one of the core knowledge priors.

Objectness, cool.

Objectness, yeah.

So we have priors about objectness, like about the visual space, about time,

about agents, about goal oriented behavior.

We have many different priors, but what’s interesting is that,

sure, we have this pretty diverse and rich set of priors,

but it’s also not that diverse, right?

We are not born into this world with a ton of knowledge about the world,

with only a small set of core knowledge.

Yeah, sorry, do you have a sense of how it feels to us humans that that set is not that large?

But just even the nature of time that we kind of integrate pretty effectively

through all of our perception, all of our reasoning,

maybe how, you know, do you have a sense of how easy it is to encode those priors?

Maybe it requires building a universe and then the human brain in order to encode those priors.

Or do you have a hope that it can be listed like an axiomatic?

I don’t think so.

So you have to keep in mind that any knowledge about the world that we are

born with is something that has to have been encoded into our DNA by evolution at some point.

Right.

And DNA is a very, very low bandwidth medium.

Like it’s extremely long and expensive to encode anything into DNA because first of all,

you need some sort of evolutionary pressure to guide this writing process.

And then, you know, the higher level of information you’re trying to write, the longer it’s going to take.

And the thing in the environment that you’re trying to encode knowledge about has to be stable

over this duration.

So you can only encode into DNA things that constitute an evolutionary advantage.

So this is actually a very small subset of all possible knowledge about the world.

You can only encode things that are stable, that are true, over very, very long periods of time,

typically millions of years.

For instance, we might have some visual prior about the shape of snakes, right?

But what makes a face, what’s the difference between a face and an art face?

But consider this interesting question.

Do we have any innate sense of the visual difference between a male face and a female face?

What do you think?

For a human, I mean.

I would have to look back into evolutionary history when the genders emerged.

But yeah, most…

I mean, the faces of humans are quite different from the faces of great apes.

Great apes, right?

Yeah.

That’s interesting.

Yeah, you couldn’t tell the face of a female chimpanzee from the face of a male chimpanzee,

probably.

Yeah, and I don’t think most humans have all that ability.

So we do have innate knowledge of what makes a face, but it’s actually impossible for us to

have any DNA encoded knowledge of the difference between a female human face and a male human face

because that knowledge, that information came up into the world actually very recently.

If you look at the slowness of the process of encoding knowledge into DNA.

Yeah, so that’s interesting.

That’s a really powerful argument that DNA is a low bandwidth and it takes a long time to encode.

That naturally creates a very efficient encoding.

But one important consequence of this is that, so yes, we are born into this world with a bunch of

knowledge, sometimes high level knowledge about the world, like the shape, the rough shape of a

snake, of the rough shape of a face.

But importantly, because this knowledge takes so long to write, almost all of this innate

knowledge is shared with our cousins, with great apes, right?

So it is not actually this innate knowledge that makes us special.

But to throw it right back at you from the earlier on in our discussion, it’s that encoding

might also include the entirety of the environment of Earth.

To some extent.

So it can include things that are important to survival and production, so for which there is

some evolutionary pressure, and things that are stable, constant over very, very, very long time

periods.

And honestly, it’s not that much information.

There’s also, besides the bandwidths constraint and the constraints of the writing process,

there’s also memory constraints, like DNA, the part of DNA that deals with the human brain,

it’s actually fairly small.

It’s like, you know, on the order of megabytes, right?

There’s not that much high level knowledge about the world you can encode.

That’s quite brilliant and hopeful for a benchmark that you’re referring to of encoding

priors.

I actually look forward to, I’m skeptical whether you can do it in the next couple of

years, but hopefully.

I’ve been working.

So honestly, it’s a very simple benchmark, and it’s not like a big breakthrough or anything.

It’s more like a fun side project, right?

But these fun, so is ImageNet.

These fun side projects could launch entire groups of efforts towards creating reasoning

systems and so on.

And I think…

Yeah, that’s the goal.

It’s trying to measure strong generalization, to measure the strength of abstraction in

our minds, well, in our minds and in artificial intelligence agencies.

And if there’s anything true about this science organism is its individual cells love competition.

So and benchmarks encourage competition.

So that’s an exciting possibility.

If you, do you think an AI winter is coming?

And how do we prevent it?

Not really.

So an AI winter is something that would occur when there’s a big mismatch between how we

are selling the capabilities of AI and the actual capabilities of AI.

And today, some deep learning is creating a lot of value.

And it will keep creating a lot of value in the sense that these models are applicable

to a very wide range of problems that are relevant today.

And we are only just getting started with applying these algorithms to every problem

they could be solving.

So deep learning will keep creating a lot of value for the time being.

What’s concerning, however, is that there’s a lot of hype around deep learning and around

AI.

There are lots of people are overselling the capabilities of these systems, not just

the capabilities, but also overselling the fact that they might be more or less, you

know, brain like, like given the kind of a mystical aspect, these technologies and also

overselling the pace of progress, which, you know, it might look fast in the sense that

we have this exponentially increasing number of papers.

But again, that’s just a simple consequence of the fact that we have ever more people

coming into the field.

It doesn’t mean the progress is actually exponentially fast.

Let’s say you’re trying to raise money for your startup or your research lab.

You might want to tell, you know, a grandiose story to investors about how deep learning

is just like the brain and how it can solve all these incredible problems like self driving

and robotics and so on.

And maybe you can tell them that the field is progressing so fast and we are going to

have AGI within 15 years or even 10 years.

And none of this is true.

And every time you’re like saying these things and an investor or, you know, a decision maker

believes them, well, this is like the equivalent of taking on credit card debt, but for trust,

right?

And maybe this will, you know, this will be what enables you to raise a lot of money,

but ultimately you are creating damage, you are damaging the field.

So that’s the concern is that that debt, that’s what happens with the other AI winters is

the concern is you actually tweeted about this with autonomous vehicles, right?

There’s almost every single company now have promised that they will have full autonomous

vehicles by 2021, 2022.

That’s a good example of the consequences of over hyping the capabilities of AI and

the pace of progress.

So because I work especially a lot recently in this area, I have a deep concern of what

happens when all of these companies after I’ve invested billions have a meeting and

say, how much do we actually, first of all, do we have an autonomous vehicle?

The answer will definitely be no.

And second will be, wait a minute, we’ve invested one, two, three, four billion dollars

into this and we made no profit.

And the reaction to that may be going very hard in other directions that might impact

even other industries.

And that’s what we call an AI winter is when there is backlash where no one believes any

of these promises anymore because they’ve turned that to be big lies the first time

around.

And this will definitely happen to some extent for autonomous vehicles because the public

and decision makers have been convinced that around 2015, they’ve been convinced by these

people who are trying to raise money for their startups and so on, that L5 driving was coming

in maybe 2016, maybe 2017, maybe 2018.

Now we’re in 2019, we’re still waiting for it.

And so I don’t believe we are going to have a full on AI winter because we have these

technologies that are producing a tremendous amount of real value.

But there is also too much hype.

So there will be some backlash, especially there will be backlash.

So some startups are trying to sell the dream of AGI and the fact that AGI is going to create

infinite value.

Like AGI is like a free lunch.

Like if you can develop an AI system that passes a certain threshold of IQ or something,

then suddenly you have infinite value.

And well, there are actually lots of investors buying into this idea and they will wait maybe

10, 15 years and nothing will happen.

And the next time around, well, maybe there will be a new generation of investors.

No one will care.

Human memory is fairly short after all.

I don’t know about you, but because I’ve spoken about AGI sometimes poetically, I get a lot

of emails from people giving me, they’re usually like a large manifestos of they’ve, they say

to me that they have created an AGI system or they know how to do it.

And there’s a long write up of how to do it.

I get a lot of these emails, yeah.

They’re a little bit feel like it’s generated by an AI system actually, but there’s usually

no diagram, you have a transformer generating crank papers about AGI.

So the question is about, because you’ve been such a good, you have a good radar for crank

papers, how do we know they’re not onto something?

How do I, so when you start to talk about AGI or anything like the reasoning benchmarks

and so on, so something that doesn’t have a benchmark, it’s really difficult to know.

I mean, I talked to Jeff Hawkins, who’s really looking at neuroscience approaches to how,

and there’s some, there’s echoes of really interesting ideas in at least Jeff’s case,

which he’s showing.

How do you usually think about this?

Like preventing yourself from being too narrow minded and elitist about deep learning, it

has to work on these particular benchmarks, otherwise it’s trash.

Well, you know, the thing is, intelligence does not exist in the abstract.

Intelligence has to be applied.

So if you don’t have a benchmark, if you have an improvement in some benchmark, maybe it’s

a new benchmark, right?

Maybe it’s not something we’ve been looking at before, but you do need a problem that

you’re trying to solve.

You’re not going to come up with a solution without a problem.

So you, general intelligence, I mean, you’ve clearly highlighted generalization.

If you want to claim that you have an intelligence system, it should come with a benchmark.

It should, yes, it should display capabilities of some kind.

It should show that it can create some form of value, even if it’s a very artificial form

of value.

And that’s also the reason why you don’t actually need to care about telling which papers have

actually some hidden potential and which do not.

Because if there is a new technique that’s actually creating value, this is going to

be brought to light very quickly because it’s actually making a difference.

So it’s the difference between something that is ineffectual and something that is actually

useful.

And ultimately usefulness is our guide, not just in this field, but if you look at science

in general, maybe there are many, many people over the years that have had some really interesting

theories of everything, but they were just completely useless.

And you don’t actually need to tell the interesting theories from the useless theories.

All you need is to see, is this actually having an effect on something else?

Is this actually useful?

Is this making an impact or not?

That’s beautifully put.

I mean, the same applies to quantum mechanics, to string theory, to the holographic principle.

We are doing deep learning because it works.

Before it started working, people considered people working on neural networks as cranks

very much.

No one was working on this anymore.

And now it’s working, which is what makes it valuable.

It’s not about being right.

It’s about being effective.

And nevertheless, the individual entities of this scientific mechanism, just like Yoshua

Banjo or Jan Lekun, they, while being called cranks, stuck with it.

Right?

Yeah.

And so us individual agents, even if everyone’s laughing at us, just stick with it.

If you believe you have something, you should stick with it and see it through.

That’s a beautiful inspirational message to end on.

Francois, thank you so much for talking today.

That was amazing.

Thank you.