The following is a conversation with Francois Chalet.
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 Freedman, spelled F R I D M A N.
And now, here's my conversation with Francois Chalet.
You're known for not sugarcoating your opinions
and speaking your mind about ideas in AI, especially
on Twitter.
That'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
Twitter accounts, you might as well speak your mind.
Otherwise, what's even the point of doing Twitter accounts,
like getting an eye scar and just leaving it in the garage?
Yeah, so that's one thing for which
I got a lot of pushback.
Perhaps 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 problems
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.
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.
So let's look at 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?
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 and this information.
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 will improve exponentially.
So that's the logic that's used actually
by almost everybody that is worried
about super human intelligence.
Yeah, 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 post
and now making it a little more explicit.
So one idea is that the brain isn't,
exists 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 yet 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
to the even humans, not even talking about AI's.
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 the capabilities, like what emerges
out of this system to just, you know,
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're 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, there's 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.
Well, that's brilliant. So we can think of the world, earth,
but also the universe as just, as a space of problems.
So all of 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.
Yeah, exactly. Intelligence is the meaning of
great problem solving capabilities with a great problem.
And if you don't have the problem,
you don't really express in intelligence.
All you're left with is potential intelligence,
like the performance of your brain or, you know,
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?
What, 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 intelligence 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 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 problems
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, you know?
So how do you think about long term?
Do you think long term planning,
we're 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, like one lifetime is short.
Even within one lifetime, we have a very hard time envisioning,
you know, things on a scale of years.
Like it's very difficult to project yourself at the scale of five,
at the scale of 10 years and so on.
Right. 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 artificial intelligence 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 super human, 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 super human.
Yeah, it's a 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 and defining intelligent systems?
And we're here at Google, there is millions of devices doing computation
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 jar
that's controlling a body in a very 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,
then the body is manipulating objects in an 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.
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, there is externalized intelligence.
When you write a computer program, you are externalizing cognition.
So it's externalized in books.
It's externalized in computers, the internet, in other humans.
It's externalized in language and so on.
So there is no hard delimitation of what makes an intelligent agent.
It's all about context.
OK, but AlphaGo is better at Go than the best human player.
There's levels of skill here.
So do you think there is such a concept as an 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 free classivity self improving
intelligent systems.
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 fits into technology.
Technology can be used to build better tools, better computers,
better instrumentation and so on, which in turn can make science faster.
So science is probably the closest thing we have today
to a real civility self improving superhuman AI.
And you can just observe, is science, is scientific progress today 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 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 recursively, recursive 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 patterns 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 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 sciences in the 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 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.
It was a time of crazy change.
Think about the progress of technology 170 years ago, when we started 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, 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.
But the next generation of researchers is going to have to dig much harder, actually,
to make smaller discoveries, probably larger numbers, smaller discoveries.
And to achieve the same amount of impact, you're going to need a much greater head count.
And that's exactly the picture you're seeing with science, is 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 rigorously 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 spinning 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 recursively self improving system, it is consuming exponentially
more resources to produce the same amount of problem solving, in fact.
So that's a fascinating way to paint it.
And certainly that holds for the deep learning community, right?
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 a roughly linear 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 going to step up and pick them.
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 the space of ideas, any one path you travel down, it gets exponentially
more difficult to develop new ideas.
And your sense is that's going to 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.
For instance, let's say we 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 going to reach infinite speed because it exists in a certain context.
So the error on this is going to generate friction and it's going to block it at some
top speed.
And even if you were to consider a 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, like 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.
But there is a very large overhead to even start to contribute, there is 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 is 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 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 his 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.
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?
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 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.
It's a very appealing story on a fundamental level.
And we all need stories.
We all need stories to structure in the way we see the world, especially at timescales
that are beyond our ability to make predictions.
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 intelligence 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, so I would say it has already happened.
Was there a moment, there must have 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 an 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 the problem with talking about human level intelligence is that implicitly
you're considering an axis of intelligence with different levels.
But that's not really how intelligence works.
Intelligence is very multidimensional.
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 a 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.
Absolutely.
Like, Covenant 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 going to be mostly around LSTM recurrent neural
networks.
So in Python, an important decision at the time that was kind of nonobvious 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 you, approaching static 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 that 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 Psykitlin.
And I loved Psykitlin for its usability.
So I drew a lot of inspiration from Psykitlin when I met Keras.
It's almost like Psykitlin for neural networks.
The fit function.
Exactly.
The fit function.
Like reducing a complex string loop to a single function call.
And of course, some people will say, this is hiding a lot of details, but that's exactly
the point.
The magic is the point.
So it's magical, but in a good way, it's magical in the sense that it's delightful.
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
architecture together.
And it's also interesting, I mean, you 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, we're doing it as well, but it was definitely the
more niche option.
Yeah.
Okay.
Keras and then Keras.
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 LSTMs.
So it was going to release at the right time because it was offering an easy to use LSTM
implementation.
Exactly at the time where lots of you started to be intrigued by the capabilities of RNN,
RNNs 1LP.
So it grew from there.
Then I joined Google about six months later, and that was actually completely unrelated
to Keras.
Keras 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 Tiano.
So I immediately knew I had to port Keras to this new TensorFlow thing.
And I was actually very busy 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 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 Tiano.
And for the next year, Tiano stayed as the default option, it was easier to use, it was
much faster, especially when it came to on it.
But eventually, TensorFlow overtook it.
And TensorFlow, the early TensorFlow has similar architectural decisions as Tiano.
So it was a natural transition.
So what, I mean, that still carries as a side, almost one 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 uses for 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 has the lead in 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 Zegedi and Deep Learning for Theraim
Proving, that was a really interesting research topic.
And so Rajat was saying, hey, we saw Keras, we like it, we saw that you had 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 temporary TensorFlow only version of Keras that was
in TensorFlow.contrib 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 Theraim Proving will 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.
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?
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, the tradeoffs, depending on your needs.
You can write everything from scratch and you get a lot of help doing so by subclassing
models and writing some train loops using eager 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 psychedelic and 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 lower 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.
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 it has a ton of engineers working on.
So I'm sure there are a lot of tricky design decisions to be made.
How does that process usually happen?
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, writing design
docs, participating in design review meetings and so on.
This is as important as actually writing a code.
So 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.
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, 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 going to 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're 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 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.
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 defining a model like you were assembling Lego blocks
and then colleague fit on it, it's more like an automagical model that would just look
at your data and optimize the objective you're after.
So that's what I'm looking into.
Yes.
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, and just building the thing on the song.
Very nice.
So that's an exciting feature.
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 are the current limits of deep learning?
If we look specifically at these function approximators that tries to generalize from
data?
If 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.
Yes.
So this is by design, right?
And deep learning models are 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're learning a continuous geometric morphing from an input vector space to an output vector
space, right?
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 ways that's
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, right?
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, morphings trained with
God and Descent.
And meanwhile, abstract rules can generalize much better.
And I think the future is really 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?
Yeah.
At which levels the combination happened.
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.
Yeah.
Well, if you look at the really successful AI systems today, I think there 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 a system like a self driving car, it's not just one big end to end neural
network that wouldn't work at all, precisely because in order to train that, you would
need a dense sampling of experience space when it comes to driving, which is completely
unrealistic, obviously.
Instead, the self driving car is mostly symbolic, it's software, it's programmed by hand.
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.
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.
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, I don't know how 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 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.
It'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 into Asian behavior and actually into Asian
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.
It'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 this kind of 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 read 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?
Useful problems for us.
In theory, it's infinite.
You can solve any problem.
In practice, while 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 data set.
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 at has to step in?
Well, it's always possible to solve these problems with deep learning is just extremely
inefficient.
A model would be an explicit rule based abstract model would be a far better, more compressed
representation of physics than 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 where 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?
I mean, that's kind of what they're improving at a basic level is trying to do, right?
Yeah, except it's much harder to formulate statements about the world compared to fermenting
mathematical statements.
Statements about the world tend to be subjective.
So can you learn rule based models?
Yes.
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 a tree session grass search algorithms that we have
today.
Personally, I think genetic algorithms are very promising.
So it's almost like genetic programming.
Genetic programming, exactly.
Can you discuss the field of program synthesis, like what, how many people are working and
thinking about it?
What, 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, okay, that's that's fascinating topic.
I was wondering 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.
It's fascinating whether that's learnable patterns and you're saying they're working
on that.
Yeah.
How big is the toolbox currently?
Yeah.
Are we completely in the dark?
So if you set the 90s.
In terms of program synthesis?
No.
So I would say, so maybe 90s is even too optimistic because by the 90s, you know, we already understood
backprop.
We already understood, you know, the engine of deep learning, even though we couldn't
really see its potential quite today, I don't think we found the engine of program synthesis.
So we're in the winter before backprop.
Yeah.
In a way, yes.
So I do believe program synthesis, in general, discrete search over rule based models is going
to be a cornerstone of AI research in the next century, right?
And that doesn't mean we're going to drop deep learning.
Deep learning is immensely useful.
Like being able to learn this is a very flexible, adaptable, parametric models, that's actually
immensely useful.
Like all it's doing, it's 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 and we're going to be working on
program synthesis.
We're 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, it works better than
a lack of a prior.
By 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 we get straight to the point, it's probably true.
So you say that you can always buy performance, buy in quotes performance by either training
on more data, better data, or by injecting task information to the architecture of the
preprocessing.
However, this is informative about the generalization power the techniques use, the fundamentals
of 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've made 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.
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 defining 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.
Do you think there are certain resources, but do you think there's innovation happening?
Oh, yeah.
To clarify at the point in the twist, machine learning in general is the science of generalization.
You want to generate knowledge that can be reused across different datasets, across different
tasks.
If instead you're looking at one dataset, 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.
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 very interesting paper because it really illustrates
the fact that an architecture, even without weight, an architecture is a knowledge about
a task.
It encodes knowledge.
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 and coding in a given dataset.
For instance, if you've looked at the baby dataset, which is about natural language
question answering, it is generated by an algorithm.
This is a question under 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
dataset 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 the 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 Ray 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.
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?
Yes.
I think Rich is making a very good point, which is that a lot of these papers, which
are actually all about manually hard coding prior knowledge about a task into some system,
doesn't have to be deeply architected into some system, right?
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 Morse law.
So if your algorithm is all about exploiting this, then your algorithm is suddenly exponentially
improving, right?
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, right, in which this research took place.
And the context is changing, like Morse 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, the data today, and the quality of data, and the scale of
data is the bottleneck.
And in this environment, the beta lesson from Rich is not going to be true anymore, right?
So I think we are going to move from a focus on a scale of a competition scale to focus
on data efficiency.
Data efficiency.
So that's getting to the question of symbolic AI.
But the 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 require 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 deeper
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.
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 or 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, then you can feed 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 we have 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 it's 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.
Absolutely, yeah.
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, right?
It has many, many vulnerabilities.
Exploit surfaces, yeah.
Where 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, right?
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 a 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, right, will 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, you know, stop believing this because you got pushed back, right?
So there are many ways in which social media platforms can potentially control your opinions.
And today, the, so all of these things are already being controlled by algorithms.
These algorithms do not have any explicit political goal today.
Well, potentially they could, like if some totalitarian government takes over, you know,
social media platforms and decides that, you know, now we're going to use this not just
for my surveillance, but also for my 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 this content recommendation algorithms behave.
Because right now, the goal, the objective function of these algorithms is to maximize
engagement, right, which seems fairly innocuous at first, right?
However, it is not because content that will maximally engage people, you know, get people
to react in an emotional way, get people to click on something.
It is very often content that, you know, 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
are artificial, right?
Yeah.
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.
Because the question is, how do we get into rooms and have discussions?
So inside Google, inside Facebook, inside Twitter, and think about, okay, 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 in control
of these new 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?
Like, for example, I've watched a documentary on Flat Earth theory, I guess.
It was very, like, 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 the same with the 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 the YouTube wouldn't be promoting or Twitter wouldn't be.
It's just a really difficult problem.
How do we do, how do 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, right, 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, it's not actually doing
what you want.
You should be able to switch to a different algorithm, you know.
So that fine tune control, you kind of learn, 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.
Yeah, they're trying to see if they can inject YouTube into their interface, but basically
allow you to show you all these videos that, because everybody's confused about what to
do with features.
So basically teach people by linking to, in that way, it's an assistant that shows, uses
videos as a basic element of information.
Okay, so what practically should people do to try to, 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 that, you know, anything wrong with their new 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, right?
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, 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.
That's always the worst garbage because it's content that appeals to the smallest command
denominator to all Twitter users because they're trying to optimize, they're purely
trying to obtain us 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 an 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.
But 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 why 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 long term 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 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 entire 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 not terminate a robot, 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 algorithms.
So I do agree there are concerning trends.
Honestly, I wouldn't want to make any long term 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 new seed 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.
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 processing 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 are 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, 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.
So in terms of alignment problem, trying to teach as we allow algorithms, just even recommend
their 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 are 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.
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.
And it's also always as 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?
I'm oppressing you with natural language processing.
Maybe if you weren't able to see me, maybe this is a phone call.
That kind of system.
Okay.
So 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.
But 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
a science.
So I can't probe another entity's, another, it's no more than bacteria on my skin.
Your legs, 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 and we have 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 embedded agents, 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 air in this artificial intelligence in the sense that it is solving
problems, it is committing knowledge, committing 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 intelligent 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 to be tired.
You need other humans in order for you to demonstrate that you have human like intelligence, essentially.
So what kind of tests and demonstration would be sufficient for you to demonstrate human
like intelligence?
Yeah.
And just out of curiosity, you 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 programs, 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 a degree to which this intelligence is human like.
It's actually two different questions.
So if you look, you mentioned earlier the Turing test.
Right.
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 cobalt, 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 BFU and compare it to what the 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.
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.
That's 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 be the human
at one specific task.
The reason why our skill at playing Go or at 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 train 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 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 because many, many different tasks can be one proxy
for measure 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 control, 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, representation, 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...
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.
So intelligence has to be specialized.
But it can be sufficiently large, the experience space, even though specialized is a certain
point when the experience space is large enough to where it might as well be general.
It feels general.
It looks general.
I mean, it's very relative.
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, Elzebeth Spelke from Harvard,
and if you know her, she's worked a lot on what she calls a 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 and 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 of years, on and off.
I hope to be able to release it at some point.
The idea is to measure the intelligence of systems by considering for priors, considering
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 and you can actually
have humans pass the same test in a way that's fair.
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, it 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 a task.
All tasks are going to involve priors.
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.
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.
And also 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.
It could even start a task like MNIST, just as long as you start with the same set of
priors.
Yeah, so the problem with MNIST, humans are already trained to recognize digits.
But let's say we're considering objects that are not digits, some complete 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 has 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 Belke 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.
There is only a small set of core knowledge, right?
Yeah.
So 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 XAMAT?
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.
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 be, 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 a nonface?
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.
But 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 evolve all that ability.
We do have innate knowledge of what makes a face, but it's actually impossible for us
to have any DNA encoding 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 very high level knowledge about the world like 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, that encoding
might also include the entirety of the environment of Earth.
To sum up, 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, constraints and 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 that you can do it in the next couple of years,
but hopefully...
I've been working on it.
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?
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 an artificially intelligent agency.
And if there's anything true about this science organism, it's individual cells love competition.
So 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, 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
even today.
And we are only just getting started with applying 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.
A lot 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 brain like, like given a kind
of a mystical aspect, these technologies, and also overselling the pace of progress,
which 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.
Like, let's say you're trying to raise money for your startup or your research lab.
You might want to tell, you know, a grand yos 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're going to have AI 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.
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.
That's the concern is 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 overhyping 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 every 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
you that even other industries.
And that's what we call in the air winter is when there is backlash where no one believes
any of these promises anymore because they've turned out 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, May 2018.
Now 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.
AGI is like a freelance, 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 very 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 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.
They're a little bit feel like it's generated by an AI system actually, but there's usually
no backup.
Maybe that's recursively self improving AI, it's you have a transformer generating crank
papers about a GI.
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, 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 don't have an improvement on some benchmark, maybe
it's a new benchmark.
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, it's actually creating value.
This is going to be brought to light very quickly because it's actually making a difference.
So it's a 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, you know, is this actually having an effect on something else?
You know, is this actually useful?
Is this making an impact or not?
As 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.
Like, you know, 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 Yoshio
Banjo or Yanlacun, they, while being called cranks, stuck with it, right?
And so, us individual agents, even if everyone's laughing at us, should 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.