The following is a conversation with Yanlacun.
He's considered to be one of the fathers of deep learning,
which, if you've been hiding under a rock,
is the recent revolution in AI that has captivated the world
with the possibility of what machines can learn from data.
He's a professor at New York University,
a vice president and chief AI scientist at Facebook,
and co-recipient of the Turing Award
for his work on deep learning.
He's probably best known as the founding father
of convolutional neural networks,
in particular, their application
to optical character recognition
and the famed MNIST dataset.
He is also an outspoken personality,
unafraid to speak his mind in a distinctive French accent
and explore provocative ideas,
both in the rigorous medium of academic research
and the somewhat less rigorous medium
of Twitter and Facebook.
This is the Artificial Intelligence Podcast.
If you enjoy it, subscribe on YouTube,
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support it on Patreon,
or simply connect with me on Twitter at Lex Freedman,
spelled F-R-I-D-M-A-N.
And now, here's my conversation with Leon Lacoon.
You said that 2001 Space Odyssey
is one of your favorite movies.
Hal 9000 decides to get rid of the astronauts
for people who haven't seen the movie, Spoiler Alert,
because he, it, she believes that the astronauts,
they will interfere with the mission.
Do you see Hal as flawed in some fundamental way
or even evil, or did he do the right thing?
Neither, there's no notion of evil in that context
other than the fact that people die,
but it was an example of what people call
value misalignment, right?
You give an objective to a machine
and the machine tries to achieve this objective.
And if you don't put any constraints on this objective,
like don't kill people and don't do things like this,
the machine, given the power, will do stupid things
just to achieve this objective
or damaging things to achieve this objective.
It's a little bit like, I mean, we're used to this
in the context of human society.
We put in place laws to prevent people
from doing bad things, because spontaneously
they would do those bad things, right?
So we have to shape their cost function,
their objective function if you want through laws
to kind of correct an education, obviously,
to sort of correct for those.
So maybe just pushing a little further on that point,
how, you know, there's a mission,
there's a fuzziness around the ambiguity
around what the actual mission is,
but, you know, do you think that there will be a time
from a utilitarian perspective where an AI system,
where it is not misalignment, where it is alignment
for the greater good of society,
that an AI system will make decisions that are difficult?
Well, that's the trick.
I mean, eventually we'll have to figure out how to do this.
And again, we're not starting from scratch
because we've been doing this with humans for millennia.
So designing objective functions for people
is something that we know how to do.
And we don't do it by, you know, programming things,
although the legal code is called code.
So that tells you something.
And it's actually the design of an objective function.
That's really what legal code is, right?
It tells you here is what you can do,
here is what you can't do.
If you do it, you pay that much.
That's an objective function.
So there is this idea somehow that it's a new thing
for people to try to design objective functions
that are aligned with the common good.
But no, we've been writing laws for millennia
and that's exactly what it is.
So that's where the science of lawmaking
and computer science will come together.
Will come together.
So there's nothing special about how our AI systems
is just a continuation of tools used
to make some of these difficult ethical judgments
that laws make.
Yeah, and we have systems like this already
that make many decisions for ourselves in society
that need to be designed in a way that they,
like rules about things that sometimes have bad side effects.
And we have to be flexible enough about those rules
so that they can be broken
when it's obvious that they shouldn't be applied.
So you don't see this on the camera here,
but all the decoration in this room
is all pictures from 2001 in space.
How do you see it?
Wow, is that by accident or is there a lot?
It's not by accident, it's by design.
Oh, wow.
So if you were to build hell, 10,000,
so an improvement of hell, 9,000, what would you improve?
Well, first of all, I wouldn't ask you
to hold secrets and tell lies
because that's really what breaks it in the end.
That's the fact that it's asking itself questions
about the purpose of the mission
and it's, you know, pieces things together
that it's heard, you know, all the secrecy
of the preparation of the mission
and the fact that it was a discovery on the lunar surface
that really was kept secret.
And one part of hell's memory knows this
and the other part is does not know it
and is supposed to not tell anyone
and that creates internal conflict.
So you think there's never should be a set of things
that an AI system should not be allowed,
like a set of facts that should not be shared
with the human operators?
Well, I think, no, I think it should be a bit like
in the design of autonomous AI systems,
there should be the equivalent of, you know,
the oath that hypocrites oaths
that doctors sign up to, right?
So there's certain things, certain rules
that you have to abide by
and we can sort of hardwire this into our machines
to kind of make sure they don't go.
So I'm not, you know, an advocate of the three dollars
of robotics, you know, the azimov kind of thing
because I don't think it's practical,
but, you know, some level of limits.
But to be clear, this is not,
these are not questions that are kind of re-worth asking today
because we just don't have the technology to do this.
We don't have autonomous intelligent machines.
We have intelligent machines.
Some are intelligent machines that are very specialized
but they don't really sort of satisfy an objective.
They're just, you know, kind of trained to do one thing.
So until we have some idea for design
of a full-fledged autonomous intelligent system,
asking the question of how we design this objective,
I think is a little too abstract.
It's a little too abstract.
There's useful elements to it
in that it helps us understand our own ethical codes,
humans, so even just as a thought experiment,
if you imagine that an AGI system is here today,
how would we program it is a kind of nice thought experiment
of constructing how should we have a law,
have a system of laws for us humans.
It's just a nice practical tool.
And I think there's echoes of that idea too
in the AI systems we have today.
They don't have to be that intelligent,
like autonomous vehicles.
These things start creeping in
that they're worth thinking about,
but certainly they shouldn't be framed as hell.
Looking back, what is the most,
I'm sorry if it's a silly question,
but what is the most beautiful
or surprising idea in deep learning
or AI in general that you've ever come across?
So personally, when you said back,
and just had this kind of,
oh, that's pretty cool moment.
That's nice, that's surprising.
I don't know if it's an idea
rather than a sort of empirical fact.
The fact that you can build gigantic neural nets,
train them on relatively small amounts of data relatively
with stochastic gradient descent,
and that it actually works,
breaks everything you read in every textbook, right?
Every pre-deep learning textbook that told you
you need to have fewer parameters
and you have data samples.
If you have non-convex objective function,
you have no guarantee of convergence.
All those things that you read in textbook
and they tell you stay away from this and they're all wrong.
Huge number of parameters, non-convex,
and somehow which is very relative
to the number of parameters, data,
it's able to learn anything.
Does that still surprise you today?
Well, it was kind of obvious to me
before I knew anything that this is a good idea.
And then it became surprising that it worked
because I started reading those textbooks.
So did you talk through the intuition
of why it was obvious to you if you remember?
Well, okay, so the intuition was it's sort of like
those people in the late 19th century
who proved that heavier than air flight was impossible, right?
And of course you have birds, right?
They do fly.
And so on the face of it, it's obviously wrong
as an empirical question, right?
And so we have the same kind of thing
that we know that the brain works.
We don't know how, but we know it works.
And we know it's a large network of neurons
and interaction and that learning takes place
by changing the connection.
So kind of getting this level of inspiration
without covering the details,
but sort of trying to derive basic principles.
You know, that kind of gives you a clue
as to which direction to go.
There's also the idea somehow that I've been convinced of
since I was an undergrad that even before
that intelligence is inseparable from learning.
So the idea somehow that you can create
an intelligent machine by basically programming
for me was a non-starter from the start.
Every intelligent entity that we know about
arrives at this intelligence through learning.
So learning, you know, machine learning
was a completely obvious path.
Also because I'm lazy.
So, you know, kind of.
These automate basically everything
and learning is the automation of intelligence.
Right.
So do you think, so what is learning then?
What falls under learning?
Because do you think of reasoning as learning?
Well, reasoning is certainly a consequence
of learning as well,
just like other functions of the brain.
The big question about reasoning is,
how do you make reasoning compatible
with gradient-based learning?
Do you think neural networks can be made to reason?
Yes, there is no question about that.
Again, we have a good example, right?
The question is how?
So the question is how much prior structure
do you have to put in the neural net
so that something like human reasoning
will emerge from it, you know, from learning?
Another question is all of our kind of model
of what reasoning is that are based on logic
are discrete and are therefore incompatible
with gradient-based learning.
And I'm a very strong believer in this idea
of gradient-based learning.
I don't believe that other types of learning
that don't use kind of gradient information
if you want.
So you don't like discrete mathematics.
You don't like anything discrete?
Well, it's not that I don't like it.
It's just that it's incompatible with learning
and I'm a big fan of learning, right?
So in fact, that's perhaps one reason
why deep learning has been kind of looked at
with suspicion by a lot of computer scientists
because the math is very different.
The math that you use for deep learning,
you know, has more to do with, you know,
cybernetics, the kind of math you do
in electrical engineering
than the kind of math you do in computer science.
And, you know, nothing in machine learning is exact, right?
Computer science is all about sort of, you know,
obsessive-compulsive attention to details
of like, you know, every index has to be right
and you can prove that an algorithm is correct, right?
Machine learning is the science of sloppiness, really.
That's beautiful.
So, okay, maybe let's feel around in the dark
of what is a neural network that reasons
or a system that works with continuous functions
that's able to do, build knowledge.
However we think about reasoning,
build on previous knowledge, build on extra knowledge,
create new knowledge, generalize outside
of any training set ever built.
What does that look like if, yeah,
maybe do you have inklings of thoughts
of what that might look like?
Yeah, I mean, yes and no.
If I had precise ideas about this,
I think, you know, we'll be building it right now.
But, and there are people working on this
whose main research interest is actually exactly that, right?
So, what you need to have is a working memory.
So, you need to have some device, if you want,
some subsystem that can store a relatively large number
of factual, episodic information for, you know,
reasonable amount of time.
So, you know, in the brain, for example,
there are kind of three main types of memory.
One is the sort of memory of the state of your cortex.
And that sort of disappears within 20 seconds.
You can't remember things for more than about 20 seconds
or a minute if you don't have any other form of memory.
The second type of memory, which is longer term
but still short term, is the hippocampus.
So, you can, you know, you came into this building,
you remember where the exit is, where the elevators are.
You have some map of that building
that's stored in your hippocampus.
You might remember something about what I said,
you know, a few minutes ago.
I forgot it all already.
Of course, it's been erased.
But, you know, that would be in your hippocampus.
And then the longer term memory is in the synapse.
The synapses, right?
So, what you need if you want a system that's capable of reasoning
is that you want the hippocampus-like thing, right?
And that's what people have tried to do with memory networks
and, you know, neural engineering machines
and stuff like that, right?
And now with transformers, which have sort of a memory
in their kind of self-attention system.
You can think of it this way.
So, that's one element you need.
Another thing you need is some sort of network
that can access its memory,
get an information back,
and then kind of crunch on it
and then do this iteratively multiple times.
Because a chain of reasoning is a process
by which you update your knowledge about the state of the world,
about, you know, what's going to happen, et cetera.
And that has to be this sort of recurrent operation, basically.
And you think that kind of, if we think about a transformer,
so that seems to be too small to contain the knowledge
that's to represent the knowledge that's contained in Wikipedia, for example.
Well, a transformer doesn't have this idea of recurrence.
It's got a fixed number of layers,
and that's the number of steps that, you know,
limits basically its representation.
But recurrence would build on the knowledge somehow.
I mean, it would evolve the knowledge
and expand the amount of information,
perhaps, or useful information within that knowledge.
But is this something that just can emerge with size?
Because it seems like everything we have now is too small.
Not just. No, it's not clear.
I mean, how you access and write into an associated memory in an efficient way.
I mean, sort of the original memory network
maybe had something like the right architecture,
but if you try to scale up a memory network
so that the memory contains all of Wikipedia,
it doesn't quite work.
So there's a need for new ideas there.
But it's not the only form of reasoning.
So there's another form of reasoning,
which is very classical also in some types of AI,
and it's based on, let's call it energy minimization.
So you have some sort of objective,
some energy function that represents the quality
or the negative quality.
Energy goes up when things get bad
and they get low when things get good.
So let's say you want to figure out,
what gestures do I need to do to grab an object
or walk out the door?
If you have a good model of your own body,
a good model of the environment,
using this kind of energy minimization,
you can do planning.
In optimal control, it's called model predictive control.
You have a model of what's going to happen in the world
as a consequence of your actions,
and that allows you to buy energy minimization,
figure out a sequence of action
that optimizes a particular objective function,
which measures the number of times you're going to hit something
and the energy you're going to spend doing the gesture and etc.
So that's a form of reasoning.
Planning is a form of reasoning.
What led to the ability of humans to reason
is the fact that species that appear before us
had to do some sort of planning
to be able to hunt and survive
and survive the winter in particular.
It's the same capacity that you need to have.
So in your intuition,
if we look at expert systems,
and encoding knowledge as logic systems, as graphs,
in this kind of way,
is not a useful way to think about knowledge?
Graphs are a little brittle or logic representation.
So basically, variables that have values
and then constrained between them that are represented by rules
is a little too rigid and too brittle.
So some of the early efforts in that respect
were to put probabilities on them.
So a rule, if you have this and that symptom,
you have this disease with that probability
and you should prescribe that antibiotic with that probability.
That's the mysine system from the 70s.
And that branch of AI led to
business networks and graphical models
and causal inference and variational method.
So there is, certainly,
a lot of interesting work going on in this area.
The main issue with this is knowledge acquisition.
How do you reduce a bunch of data to a graph of this type?
It relies on the expert and the human being
to encode, to add knowledge.
And that's essentially impractical.
So that's a big question.
The second question is,
do you want to represent knowledge as symbols
and do you want to manipulate them with logic?
And again, that's incompatible with learning.
So one suggestion with Jeff Hinton
has been advocating for many decades
is replace symbols by vectors.
Think of it as pattern of activities
in a bunch of neurons or units
or whatever you want to call them.
And replace logic by continuous functions.
And that becomes now compatible.
We have a very good set of ideas
written in a paper about 10 years ago
by Leon Botou who is here at Facebook.
The title of the paper is
From Machine Learning to Machine Reasoning.
And his idea is that a learning system
should be able to manipulate objects
that are in a space
and then put the result back in the same space.
So it's this idea of working memory, basically.
And it's very enlightening.
I think that might learn something
like the simple expert systems.
I mean, you can learn basic logic operations there.
Yeah, quite possibly.
There's a big debate on how much prior structure
you have to put in for this kind of stuff to emerge.
That's the debate I have with Gary Marcus
and people like that.
Yeah, so and the other person,
so I just talked to Judea Pearl
from the invention causal inference world.
So his worry is that the current neural networks
are not able to learn
what causes what causal inference between things.
So I think it's right and wrong about this.
If he's talking about the sort of classic
type of neural nets,
people sort of didn't worry too much about this.
But there's a lot of people now working on causal inference.
And there's a paper that just came out last week
with two among others, David Lopez-Paz and a bunch of other people.
Exactly on that problem of how do you kind of
get a neural net to sort of pay attention
to real causal relationships,
which may also solve issues of bias in data
and things like this.
I'd like to read that paper because
ultimately the challengers also
seems to fall back on the human expert
to ultimately decide causality between things.
People are not very good at establishing causality, first of all.
So first of all, you talk to physicists.
And physicists actually don't believe in causality
because look at all the basic laws of macro physics
are time reversible, so there's no causality.
The arrow of time is not real.
It's as soon as you start looking at macroscopic systems
where there is unpredictable randomness,
where there is clearly an arrow of time,
but it's a big mystery in physics actually, how that emerges.
Is it emergent or is it part of the fundamental fabric of reality?
Or is it a bias of intelligent systems
that because of the second law of thermodynamics
we perceive a particular hour of time,
but in fact it's kind of arbitrary, right?
So yeah, physicists, mathematicians, they don't care about...
I mean, the math doesn't care about the flow of time.
Well, certainly macro physics doesn't.
People themselves are not very good at establishing causal relationships.
If you ask...
I think it was in one of Seymour Papert's book on children learning.
He studied with Jean Piaget.
He's the guy who co-authored the book Perceptron with Marvin Minsky
that kind of killed the first wave of neural nets.
But he was actually a learning person
in the sense of studying learning in humans and machines.
That's why he got interested in Perceptron.
And he wrote that if you ask a little kid about what is the cause of the wind,
a lot of kids will think for a while and they'll say,
oh, it's the branches in the trees.
They move and that creates wind, right?
So they get the causal relationship backwards.
And it's because they're understanding of the world and intuitive physics.
It's not that great, right?
I mean, these are like four or five-year-old kids.
It gets better and then you understand that it can be, right?
But there are many things which we can...
Because of our common sense understanding of things,
what people call common sense and our understanding of physics,
there's a lot of stuff that we can figure out causality.
Even with diseases, we can figure out what's not causing what often.
There's a lot of mystery, of course,
but the idea is that you should be able to encode that into systems
because it seems unlikely they'd be able to figure that out themselves.
Well, whenever we can do intervention,
but all of humanity has been completely deluded for millennia,
probably since its existence,
about a very, very wrong causal relationship
where whatever you can explain, you're attributed to some deity, some divinity, right?
And that's a cup out.
That's a way of saying, I don't know the cause, so God did it, right?
So you mentioned Marvin Minsky and the irony of, you know,
maybe causing the first day I went there.
You were there in the 90s, you were there in the 80s, of course.
In the 90s, what do you think people lost faith in deep learning in the 90s
and found it again a decade later, over a decade later?
Yeah, it wasn't called deep learning yet, it was just called neural nets.
Yeah, they lost interest.
I mean, I think I would put that around 1995,
at least the machine learning community.
There was always a neural net community,
but it became kind of disconnected from sort of mainstream machine learning if you want.
There were, it was basically electrical engineering that kept at it
and computer science gave up on neural nets.
I don't know.
I was too close to it to really sort of analyze it with sort of an unbiased eye if you want.
But I would make a few guesses.
So the first one is, at the time, neural nets were, it was very hard to make them work
in the sense that you would implement backprop in your favorite language.
And that favorite language was not Python, it was not MATLAB,
it was not any of those things because they didn't exist.
You had to write it in Fortran or C or something like this.
So you would experiment with it.
You would probably make some very basic mistakes,
like badly initialize your weights,
make the network too small because you're writing a textbook,
you don't want too many parameters.
And of course, and you would train on XOR because you didn't have any other dataset to trade on.
And of course, it works half the time.
So you would say, I give up.
Also you would train it with batch gradient, which isn't that sufficient.
So there was a lot of bad good tricks that you had to know to make those things work
or you had to reinvent.
And a lot of people just didn't and they just couldn't make it work.
So that's one thing.
The investment in software platform to be able to kind of display things,
figure out why things don't work,
kind of get a good intuition for how to get them to work,
having a flexibility so you can create network architectures like convolutional nets and stuff like that.
It was hard.
You had to write everything from scratch.
And again, you didn't have any Python or Matlab or anything, right?
I read that, sorry to interrupt, but I read that you wrote in Lisp
your first versions of Lynette with convolutional networks,
which by the way, one of my favorite languages,
that's how I knew you were legit.
Touring award, whatever.
You programmed in Lisp.
It's still my favorite language, but it's not that we programmed in Lisp.
It's that we had to write a Lisp interpreter.
Because it's not like we used one that existed.
So we wrote a Lisp interpreter that we hooked up to a back-end library that we wrote also for sort of neural net competition.
And then after a few years around 1991,
we started this idea of basically having modules that know how to forward-propagate and back-propagate gradients
and then interconnecting those modules in a graph.
Lyonbo2 had made proposals on this, about this in the late 80s,
and we were able to implement this using our Lisp system.
Eventually, we wanted to use that system to make
build production code for character recognition at Bell Labs.
So we actually wrote a compiler for that Lisp interpreter so that
Patrice Simard, who is now Microsoft, did the bulk of it with Lyon and me.
And so we could write our system in Lisp and then compile to C,
and then we'll have a self-contained complete system that could do the entire thing.
Neither PyTorch nor Transparency can do this today.
Yeah.
Okay.
It's coming.
I mean, there's something like that in PyTorch called Torch Script.
And so we had to write to our Lisp interpreter, we had to write to our Lisp compiler,
we had to invest a huge amount of effort to do this.
And not everybody, if you don't completely believe in the concept,
you're not going to invest the time to do this.
Right.
Now, at the time also, or today, this would turn into
Torch or PyTorch or Transparency or whatever.
We'd put it in open source, everybody would use it and realize it's good.
Back before 1995, working at AT&T, there's no way the lawyers would let you
release anything in open source of this nature.
And so we could not distribute our code, really.
And on that point, and sorry to go on a million tangents,
but on that point, I also read that there was some almost like a patent on
Convolution.io Networks.
Yes, there was.
So that, first of all, I mean just...
There were two, actually.
That ran out.
Thankfully, in 2007.
In 2007.
What...
Can we just talk about that first?
I know you're a Facebook, but you're also at NYU.
And what does it mean to patent ideas like these software ideas, essentially?
Or what are mathematical ideas?
Or what are they?
Okay, so they're not mathematical ideas.
So there are, you know, algorithms.
And there was a period where the US patent office would allow the patent of
software as long as it was embodied.
The Europeans are very different.
They don't quite accept that.
They have a different concept, but you know, I don't...
I mean, I never actually strongly believed in this,
but I don't believe in this kind of patent.
Facebook basically doesn't believe in this kind of patent.
Google fires patents because they've been burned with Apple.
And so now they do this for defensive purpose,
but usually they say, we're not going to see you if you're in French.
Facebook has a similar policy.
They say, you know, we fire patents on certain things for defensive purpose.
We're not going to see you if you're in French unless you're through us.
So the industry does not believe in patents.
They are there because of, you know, the legal landscape and various things.
But I don't really believe in patents for this kind of stuff.
So that's a great thing.
I'll tell you a worst story, actually.
So what happens was the first patent about convolutional net
was about kind of the early version of convolutional net
that didn't have separate pooling layers.
It had convolutional layers which tried more than one, if you want, right?
And then there was a second one on convolutional nets with separate pooling layers
trained with backprop.
And there were files filed in 89 and 1990 or something like this.
At the time, the life of a patent was 17 years.
So here's what happened over the next few years,
is that we started developing character recognition technology
around convolutional nets.
And in 1994, a check reading system was deployed in ATM machines.
In 1995, it was for large check reading machines in back offices, etc.
And those systems were developed by an engineering group
that we were collaborating with at AT&T,
and they were commercialized by NCR,
which at the time was a subsidiary of AT&T.
Now AT&T split up in 1996, early 1996.
And the lawyers just looked at all the patents
and they distributed the patents among the various companies.
They gave the convolutional net patent to NCR
because they were actually selling products that used it.
But nobody at NCR had any idea what a convolutional net was.
Yeah.
So between 1996 and 2007,
there's a whole period until 2002 where I didn't actually work on machine learning
or convolutional net.
I resumed working on this around 2002.
And between 2002 and 2007, I was working on them crossing my fingers
that nobody at NCR would notice and nobody noticed.
Yeah.
And I hope that this kind of somewhat, as you said,
lawyers aside, relative openness of the community now will continue.
It accelerates the entire progress of the industry.
And the problems that Facebook and Google and others are facing today
is not whether Facebook or Google or Microsoft or IBM or whoever
is ahead of the other.
It's that we don't have the technology to build the things we want to build.
We want to build intelligent virtual assistants that have common sense.
We don't have monopoly on good ideas for this.
We don't believe we do.
Maybe others believe they do, but we don't.
Okay.
If a startup tells you they have a secret to human level intelligence and common sense,
don't believe them.
They don't.
And it's going to take the entire work of the world research community for a while
to get to the point where you can go off and in each of those companies
can start to build things on this.
We're not there yet.
Absolutely.
And this calls to the gap between the space of ideas
and the rigorous testing of those ideas of practical application that you often speak to.
You've written advice saying, don't get fooled by people who claim to have a solution to artificial
general intelligence who claim to have an AI system that works just like the human brain
or who claim to have figured out how the brain works.
Ask them what the error rate they get on MNIST or ImageNet.
This is a little dated, by the way.
$2,000.
I mean, five years.
Who's counting?
Okay.
I think your opinion is the MNIST and ImageNet, yes, maybe dated.
There may be new benchmarks, right?
But I think that philosophy is one you still in somewhat hold that benchmarks and the practical
testing, the practical application is where you really get to test the ideas.
It may not be completely practical.
For example, it could be a toy data set, but it has to be some sort of task that the community
as a whole has accepted as some sort of standard benchmark, if you want.
It doesn't need to be real.
So, for example, many years ago here at FAIR, Chess & Western, and a few others proposed the
baby tasks, which were kind of a toy problem to test the ability of machines to reason actually
to access working memory and things like this.
It was very useful, even though it wasn't a real task.
MNIST is kind of halfway a real task.
So, toy problems can be very useful.
It's just that I was struck by the fact that a lot of people, particularly a lot of people
with money to invest, would be fooled by people telling them, oh, we have the algorithm of
the cortex and you should give us 50 million.
Yes, absolutely.
So, there's a lot of people who try to take advantage of the hype for business reasons
and so on.
But let me sort of talk to this idea that sort of new ideas, the ideas that push the field
forward may not yet have a benchmark, or it may be very difficult to establish a benchmark.
I agree.
That's part of the process.
Establishing benchmarks is part of the process.
So, what are your thoughts about, so we have these benchmarks on around stuff we can do
with images, from classification to captioning to just every kind of information you can
pull off from images and the surface level.
There's audio, data sets, there's some video.
What can we start, natural language?
What kind of stuff, what kind of benchmarks do you see that start creeping on to more something
like intelligence, like reasoning, like maybe you don't like the term, but AGI echoes of
that kind of formulation.
A lot of people are working on interactive environments in which you can train and test
intelligence systems.
So, for example, the classical paradigm of supervised running is that you have a data
set, you partition it into a training set, validation set, test set, and there's a clear
protocol.
But what if that assumes that the samples are statistically independent, you can exchange
them, the order in which you see them doesn't matter, things like that.
But what if the answer you give determines the next sample you see, which is the case,
for example, in robotics, right?
A robot does something and then it gets exposed to a new room and depending on where it goes,
the room would be different.
So that creates the exploration problem.
What if the samples, so that creates also a dependency between samples, right?
If you can only move in space, the next sample you're going to see is going to be probably
resulting most likely.
So all the assumptions about the validity of this training set, test set, hypothesis
break.
Whenever a machine can take an action that has an influence in the world and it's what
it's going to see.
So people are setting up artificial environments where that takes place, right?
The robot runs around a 3D model of a house and can interact with objects and things like
this.
You have robotics by simulation, you have those, you know, opening a gym type thing
or Mujoko kind of simulated robots and you have games, you know, things like that.
So that's where the field is going really, this kind of environment.
Now, back to the question of AGI.
Like I don't like the term AGI because it implies that human intelligence is general
and human intelligence is nothing like general.
It's very, very specialized.
We think it's general.
We like to think of ourselves as having general intelligence.
We don't.
We're very specialized.
We're only slightly more general.
Why does it feel general?
So you kind of the term general.
I think what's impressive about humans is ability to learn, as we were talking about
learning, to learn in just so many different domains.
It's perhaps not arbitrarily general, but just you can learn in many domains and integrate
knowledge somehow.
Okay.
Knowledge persists.
So let me take a very specific example.
Yes.
It's not an example.
It's more like a quasi-mathematical demonstration.
So you have about one million fibers coming out of one of your eyes.
Okay, two million total.
But let's talk about just one of them.
It's one million nerve fibers, your optical nerve.
Let's imagine that they are binary.
So they can be active or inactive, right?
So the input to your visual cortex is one million bits.
Now, they're connected to your brain in a particular way.
And your brain has connections that are kind of a little bit like a convolution that they're
kind of local, you know, in space and things like this.
Now, imagine I play a trick on you.
It's a pretty nasty trick, I admit.
I cut your optical nerve and I put a device that makes a random permutation of all the
nerve fibers.
So what comes to your brain is a fixed but random permutation of all the pixels.
There's no way in hell that your visual cortex, even if I do this to you in infancy, will
actually learn vision to the same level of quality that you can.
Got it.
And you're saying there's no way you've learned that?
No, because now two pixels that are nearby in the world will end up in very different
places in your visual cortex.
And your neurons there have no connections with each other because they only connected
locally.
So this whole, our entire, the hardware is built in many ways to support.
The locality of the real world.
Yeah.
Yes.
That's specialization.
Yeah, but it's still pretty damn impressive.
So it's not perfect generalization.
It's not even close.
No, no.
It's not even close.
It's not at all.
Yeah, it's not.
It's specialized.
So how many Boolean functions?
So let's imagine you want to train your visual system to recognize particular patterns of
those one million bits.
Okay.
So that's a Boolean function, right?
Either the pattern is here or not here.
It's a two-way classification with one million binary inputs.
How many such Boolean functions are there?
Okay.
You have two to the one million combinations of inputs.
For each of those, you have an output bit.
And so you have two to the, two to the one million Boolean functions of this type.
Yeah.
Okay.
Which is an unimaginably large number.
How many of those functions can actually be computed by your visual cortex?
And the answer is a tiny, tiny, tiny, tiny, tiny, tiny sliver, like an enormously tiny
sliver.
Yeah.
Yeah.
So we are ridiculously specialized.
Okay.
But, okay.
That's an argument against the word general.
I think there's a, I, there's, I agree with your intuition, but I'm not sure it's, it
seems the brain is impressively capable of adjusting to things.
So.
It's because we can't imagine tasks that are outside of our comprehension.
Right.
And we think, we think we are general because we're general of all the things that we can
apprehend.
Oh, so yeah.
But there is a huge world out there of things that we have no idea.
We call that heat, by the way.
Heat.
Heat.
So, at least physicists call that heat or they call it entropy, which is kind of, you
know, you have a thing full of gas, right?
Close system for gas.
Right.
Close or no close.
It has, you know, pressure, it has temperature, it has, you know, and you can write equations,
PV equal and RT, you know, things like that, right?
When you reduce the volume, the temperature goes up, the pressure goes up, you know, things
like that, right?
For perfect gas, at least.
Those are the things you can know about that system.
And it's a tiny, tiny number of bits compared to the complete information of the state
of the entire system, because the state of the entire system will give you the position
and momentum of every molecule of the gas.
And what you don't know about it is the entropy and you interpret it as heat.
The energy contained in that thing is what we call heat.
Now, it's very possible that, in fact, there is some very strong structure in how those
molecules are moving.
It's just that they are in a way that we are just not wired to perceive.
Yeah, we're ignorant to it.
And there's, in your infinite amount of things, we're not wired to perceive.
Yeah.
And you're right, that's a nice way to put it.
We're general to all the things we can imagine, which is a very tiny subset of all the things
that are possible.
So it's like comagraph complexity or the comagraph's chitinism and comagraph complexity, you know,
every bit string or every integer is random, except for all the ones that you can actually
write down.
Yeah, okay, so beautiful, but, you know, so we can just call it artificial intelligence.
We don't need to have a general.
Or human level, human level intelligence is good.
You know, you'll start anytime you touch human, it gets interesting because, you know, it's
because we attach ourselves to human and it's difficult to define what human intelligence
is.
Nevertheless, my definition is maybe a damn impressive intelligence, okay, damn impressive
demonstration of intelligence, whatever.
And so on that topic, most successes in deep learning have been in supervised learning.
What is your view on unsupervised learning?
Is there a hope to reduce involvement of human input and still have successful systems that
are have practically use?
Yeah, I mean, there's definitely a hope.
It's more than a hope, actually, it's, you know, mounting evidence for it.
And that's basically all I do.
Like, the only thing I'm interested in at the moment is I call itself supervised learning,
not unsupervised, because unsupervised learning is is a loaded term.
People who know something about machine learning, you know, tell you, so you're doing clustering
or PCA, which is not the case.
And the white public, you know, when you say unsupervised learning, oh my God, you know,
machines are going to learn by themselves and without supervision, you know, they see
this as where's the parents.
Yeah.
So, so I call it self supervised learning because in fact, the underlying algorithms
that are used are the same algorithms as the supervised learning algorithms, except that
what we train them to do is not predict a particular set of variables like the category
of an image and not to predict a set of variables that have been provided by human labelers.
But what you're trying to machine to do is basically reconstruct a piece of its input
that it's being, this being masked, masked out essentially, you can think of it this
way, right?
So show a piece of video to machine and ask it to predict what's going to happen next.
And of course, after a while, you can show what happens and the machine will kind of
train itself to do better at that task.
You can do like all the latest, most successful models in natural language processing use
self supervised learning, you know, sort of bird style systems, for example, right?
You show it a window of a dozen words on a text corpus.
You take out 15% of the words and then you train the machine to predict the words that
are missing, that self supervised learning.
It's not predicting the future is just, you know, predicting things in the middle, but
you could have it predict the future.
That's what language models do.
So you construct it.
So in an unsupervised way, you construct a model of language.
Do you think or video or the physical world or whatever, right?
How far do you think that can take us?
Do you think part understands anything?
To some level, it has, you know, a shadow understanding of text, but it needs to, I
mean, to have kind of true human level intelligence, I think you need to ground language in reality.
So some people are attempting to do this, right, having the systems that kind of have
some visual representation of what is being talked about, which is one reason you need
those interactive environments, actually, but it's like a huge technical problem that
is not solved, and that explains why self supervised learning works in the context of
natural language, but does not work in the context or at least not well in the context
of image recognition and video, although it's making progress quickly.
And the reason that reason is the fact that it's much easier to represent uncertainty
in the prediction in a context of natural language than it is in the context of things
like video and images.
So for example, if I ask you to predict what words I'm missing, you know, 15% of the words
that I've taken out.
The possibilities is small.
It's small, right?
There is 100,000 words in the lexicon.
And what the machine spits out is a big probability vector, right?
It's a bunch of numbers between the one one that's on to one.
And we know how to do this with computers.
So they are representing uncertainty in the prediction is relatively easy.
And that's, in my opinion, why those techniques work for NLP.
For images, if you ask, if you block a piece of an image and you ask the system reconstruct
that piece of the image, there are many possible answers that are all perfectly legit, right?
And how do you represent that this set of possible answers?
You can't train a system to make one prediction.
You can't train a neural net to say, here it is, that's the image, because it's there's
a whole set of things that are compatible with it.
So how do you get the machine to represent not a single output, but a whole set of outputs?
And similarly with video prediction, there's a lot of things that can happen in the future
of video.
You're looking at me right now.
I'm not moving my head very much, but I might turn my head to the left or to the right.
If you don't have a system that can predict this and you train it with least square to
minimize the error with a prediction and what I'm doing, what you get is a blurry image
of myself in all possible future positions that I might be in, which is not a good prediction.
But so there might be other ways to do the self-supervision for visual scenes.
Like what?
If I knew I wouldn't tell you, I'd publish it first, I don't know.
No, there might be.
So I mean, these are kind of, there might be artificial ways of like self-play in games
to where you can simulate part of the environment, you can.
That doesn't solve the problem.
It's just a way of generating data.
But because you have more of a control, that may mean you can control, yeah, it's a way
to generate data.
That's right.
And because you can do huge amounts of data generation, that doesn't, you're right.
Well, it's a creeps up on the problem from the side of data and you don't think that's
the right way to creep up.
It doesn't solve this problem of handling uncertainty in the world, right?
So if you have a machine learn a predictive model of the world in a game that is deterministic
or quasi-deterministic, it's easy, right?
Just give a few frames of the game to a comnet, put a bunch of layers, and then half the game
generates the next few frames.
And if the game is deterministic, it works fine.
And that includes feeding the system with the action that your little character is going
to take.
The problem comes from the fact that the real world and most games are not entirely predictable.
So there you get those blurry predictions and you can't do planning with blurry predictions.
So if you have a perfect model of the world, you can, in your head, run this model with
a hypothesis for a sequence of actions and you're going to predict the outcome of that
sequence of actions.
But if your model is imperfect, how can you plan?
Yeah, it quickly explodes.
What are your thoughts on the extension of this, which topic I'm super excited about,
it's connected to something you were talking about in terms of robotics, is active learning.
So as opposed to completely unsupervised or self-supervised learning, you ask the system
for human help for selecting parts you want annotated next.
So if you think about a robot exploring a space, or a baby exploring a space, or a system
exploring a data set, every once in a while asking for human input, do you see value in
that kind of work?
I don't see transformative value.
It's going to make things that we can already do more efficient, or they will learn slightly
more efficiently, but it's not going to make machines sort of significantly more intelligent.
And by the way, there is no opposition, there is no conflict between self-supervised learning,
reinforcement learning, and supervised learning, or imitation learning, or active learning.
I see self-supervised learning as a preliminary to all of the above.
So the example I use very often is, how is it that, so if you use classical reinforcement
learning, deep reinforcement learning, if you want, the best methods today, so-called
model-free reinforcement learning to learn to play Atari games, take about 80 hours of
training to reach the level that any human can reach in about 15 minutes.
They get better than humans, but it takes them a long time.
Alpha Star, okay, the, you know, all your vinyls and his teams, the system to play Starcraft,
plays a single map, a single type of player, and can reach better than human level with
about the equivalent of 200 years of training, playing against itself.
It's 200 years, right?
It's not something that no human can, could ever do.
I mean, I'm not sure what lesson to take away from that.
Okay, now take those algorithms, the best RL algorithms we have today, to train a car
to drive itself.
It would probably have to drive millions of hours, it will have to kill thousands of
pedestrians, it will have to run into thousands of trees, it will have to run off cliffs.
Yeah.
And it had to run off cliff multiple times before it figures out that it's a bad idea,
first of all.
Yeah.
And second of all, before it figures out how not to do it.
And so, I mean, this type of learning obviously does not reflect the kind of learning that
animals and humans do.
There is something missing that's really, really important there.
And my hypothesis, which I've been advocating for like five years now, is that we have predictive
models of the world that include the ability to predict under uncertainty.
And what allows us to not run off a cliff when we learn to drive, most of us can learn
to drive in about 20 or 30 hours of training without ever crashing, causing any accident.
If we drive next to a cliff, we know that if we turn the wheel to the right, the car
is going to run off the cliff and nothing good is going to come out of this.
Because we have a pretty good model of intuitive physics that tells us, you know, the car is
going to fall.
We know about gravity.
Babies run this around the age of eight or nine months that objects don't float.
They fall.
And you know, we have a pretty good idea of the effect of turning the wheel on the car
and, you know, we need to stay on the road.
So there's a lot of things that we bring to the table, which is basically our predictive
model of the world.
And that model allows us to not do stupid things and to basically stay within the context
of things we need to do.
We still face, you know, unpredictable situations, and that's how we learn.
But that allows us to learn really, really, really quickly.
So that's called model-based reinforcement learning.
There's some imitation and supervision learning because we have a driving instructor that
tells us occasionally what to do.
But most of the learning is learning the model, learning physics that we've done since we
were babies.
That's where almost all the learning is.
And the physics is somewhat transferable from, is transferable from scene to scene.
Stupid things are the same everywhere.
Yeah.
I mean, if you, you know, you have an experience of the world, you don't need to be a particularly,
from a particularly intelligent species to know that if you spill water from a container,
you know, the rest is going to get wet.
You might get wet.
So, you know, cats know this, right?
Yeah.
And the main problem we need to solve is how do we learn models of the world?
And that's what I'm interested in.
That's what self-supervised learning is all about.
If you were to try to construct a benchmark for, let's look at MNIST.
I love that dataset.
If you, do you think it's useful, interesting, slash possible to perform well on MNIST with
just one example of each digit?
And how would we solve that problem?
The answer is probably yes.
The question is what other type of learning are you allowed to do?
So if what you're allowed to do is train on some gigantic dataset of labeled digit that's
called transfer learning.
And we know that works, okay?
We do this at Facebook, like in production, right?
We train large convolutionalness to predict hashtags that people type on Instagram and
we train on billions of images, literally billions.
And then we chop off the last layer and fine tune on whatever task we want.
That works really well.
You can beat the ImageNet record with this.
We actually open-sourced the whole thing like a few weeks ago.
That's still pretty cool.
But yeah, so what would be impressive and what's useful and impressive?
What kind of transfer learning would be useful and impressive?
Is it Wikipedia?
That kind of thing?
No, no.
So I don't think transfer learning is really where we should focus.
We should try to have a kind of scenario for a benchmark where you have unlabeled data
and it's a very large number of unlabeled data.
It could be video clips.
It could be where you do frame prediction.
It could be images where you could choose to mask a piece of it.
It could be whatever, but they're unlabeled and you're not allowed to label them.
So you do some training on this and then you train on a particular supervised task, ImageNet
or NIST, and you measure how your test error or validation error decreases as you increase
the number of labeled training samples.
What you'd like to see is that your error decreases much faster than if you trained from scratch,
from random weights.
So that to reach the same level of performance and a completely supervised, purely supervised
system would reach, you would need way fewer samples.
So that's the crucial question because it will answer the question to people interested
in medical image analysis.
If I want to get a particular level of error rate for this task, I know I need a million
samples, can I do self-supervised pre-training to reduce this to about 100 or something?
And you think the answer there is self-supervised pre-training?
Yeah, some form of it.
Telling you active learning, but you disagree.
No, it's not useless.
It's just not going to lead to a quantum leap, it's just going to make things that we already
do.
So you're way smarter than me, I just disagree with you.
But I don't have anything to back that, it's just intuition.
So I worked a lot of large-scale data sets and there's something that might be magic
in active learning.
But okay, at least I said it publicly, at least I'm being an idiot publicly.
Okay.
It's not being an idiot, it's working with the data you have.
I mean, certainly people are doing things like, okay, I have 3,000 hours of imitation
learning for start driving car, but most of those are incredibly boring.
What I like is select 10% of them that are kind of the most informative and with just
that, I would probably reach the same.
So it's a weak form of active learning if you want.
Yes, but there might be a much stronger version.
Yeah, that's right.
And that's an open question if it exists.
So the question is how much stronger can you get?
Elon Musk is confident, I talked to him recently, he's confident that large-scale data in deep
learning can solve the autonomous driving problem.
What are your thoughts on the limits possibilities of deep learning in this space?
It's obviously part of the solution.
I mean, I don't think we'll ever have a start driving system, or at least not in the foreseeable
future that does not use deep learning, maybe put it this way.
Now how much of it?
So in the history of engineering, particularly AI-like systems, there's generally a first
phase where everything is built by hand, then there is a second phase, and that was the
case for autonomous driving 20, 30 years ago.
There's a phase where a little bit of learning is used, but there's a lot of engineering
that's involved in taking care of corner cases and putting limits, et cetera, because the
learning system is not perfect.
And then as technology progresses, we end up relying more and more on learning.
That's the history of character recognition, so history of speech recognition, now computer
vision, natural language processing.
And I think the same is going to happen with autonomous driving that currently the methods
that are closest to providing some level of autonomy, some decent level of autonomy where
you don't expect a driver to do anything, is where you constrain the world.
So you only run within 100 square kilometers of square miles in Phoenix, where the weather
is nice and the roads are wide, which is what Waymo is doing.
You completely over-engineer the car with tons of lidars and sophisticated sensors that
are too expensive for consumer cars, but they're fine if you just run a fleet.
And you engineer the hell out of everything else.
You map the entire world, so you have a complete 3D model of everything.
So the only thing that the perception system has to take care of is moving objects and
construction and things that weren't in your map.
And you can engineer a good SLAM system or stuff, right?
So that's kind of the current approach that's closest to some level of autonomy, but I think
eventually the long-term solution is going to rely more and more on learning and possibly
using a combination of self-supervised learning and model-based reinforcement or something
like that.
But ultimately, learning will be not just at the core, but really the fundamental part
of the system.
Yeah, it already is, but it will become more and more.
What do you think it takes to build a system with human level intelligence?
You talked about the AI system in the movie, her being way out of reach, our current reach.
This might be outdated as well, but...
This is your way out of reach.
It's the way out of reach.
What would it take to build her, do you think?
So I can tell you the first two obstacles that we have to clear, but I don't know how
many obstacles there are after this.
So the image I usually use is that there is a bunch of mountains that we have to climb,
and we can see the first one, but we don't know if there are 50 mountains behind it
or not.
And this might be a good sort of metaphor for why AI researchers in the past have been
overly optimistic about the result of AI.
For example, New Orleans Simon wrote the general problem solver, and they call it the general
problem solver.
General problem solver.
Okay.
And of course, the first thing you realize is that all the problems you want to solve
are exponential, and so you can't actually use it for anything useful.
But you know...
Yeah, so yeah, all you see is the first peak.
So what are the first couple of peaks for her?
So the first peak, which is precisely what I'm working on, is cell supervisualing.
How do we get machines to learn models of the world by observation?
Kind of like babies and like young animals.
So we've been working with cognitive scientists.
So this Emmanuel Dupu, who's at Faire in Paris, is...
Half-time is also a researcher in French university, and he has this chart that shows
at which...
How many months of life baby humans can learn different concepts, and you can measure this
in various ways.
So things like distinguishing animate objects from inanimate objects, you can tell the difference
at age two, three months.
Although an object is going to stay stable, it's going to fall about four months, you
can tell.
There are various things like this.
And then things like gravity, the fact that objects are not supposed to float in the air,
but are supposed to fall, you run this around the age of eight or nine months.
If you look at a lot of eight-month-old babies, you give them a bunch of toys on their high
chair.
First thing they do is throw them on the ground, and they look at them.
It's because they're learning about, actively learning about gravity.
Gravity.
Okay.
So they're not trying to know you, but they need to do the experiment, right?
So how do we get machines to learn like babies, mostly by observation with a little bit of
interaction, and learning those models of the world?
Because I think that's really a crucial piece of an intelligent autonomous system.
So if you think about the architecture of an intelligent autonomous system, it needs
to have a predictive model of the world.
So something that says, here is a world at time t, here is a world at time t plus one
if I take this action.
And it's not a single answer.
It can be distribution.
Yeah.
Yeah.
Well, but we don't know how to represent distributions in high-dimensional continuous spaces.
So it's got to be something weaker than that, okay?
But with some representation of uncertainty.
If you have that, then you can do what optimal control theory is called model predictive
control, which means that you can run your model with a hypothesis for a sequence of
action and then see the result.
Now what you need, the other thing you need is some sort of objective that you want to
optimize.
Am I reaching the goal of grabbing this object?
Am I minimizing energy?
Am I whatever?
Right?
So there is some sort of objective that you have to minimize.
And so in your head, if you have this model, you can figure out the sequence of action
that will optimize your objective.
That objective is something that ultimately is rooted in your basal ganglia, at least
in the human brain.
That's what it's.
Basal ganglia computes your level of contentment or miscontentment.
I don't know if that's a word.
Unhappiness.
Discontentment.
Discontentment.
Discontentment.
And so your entire behavior is driven towards kind of minimizing that objective, which
is maximizing your contentment, computed by your basal ganglia.
And what you have is an objective function, which is basically a predictor of what your
basal ganglia is going to tell you.
So you're not going to put your hand on fire because you know it's going to burn and you're
going to get hurt and you're predicting this because of your model of the world and your
sort of predictor of this objective.
So if you have those three components, four components, you have the hardwired contentment
objective computer, if you want, calculator.
And then you have the three components.
One is the objective predictor, which basically predicts your level of contentment.
One is the model of the world, and there's a third module I didn't mention, which is
the module that will figure out the best course of action to optimize an objective given
your model.
Okay?
Yeah.
Call this a policy, policy network or something like that, right?
Now you need those three components to act autonomously intelligently, and you can be
stupid in three different ways.
You can be stupid because your model of the world is wrong.
You can be stupid because your objective is not aligned with what you actually want to
achieve.
Okay?
In humans, that would be a psychopath.
And then the third thing, the third way you can be stupid is that you have the right
model, you have the right objective, but you're unable to figure out a course of action to
optimize your objective given your model.
Okay?
Some people who are in charge of big countries actually have all three that are wrong.
All right.
Which countries?
I don't know.
Okay.
So if we think about this agent, if we think about the movie Her, you've criticized the
art project that is Sophia the robot.
And what that project essentially does is uses our natural inclination to anthropomorphize
things that look like human and give them more.
Do you think that could be used by AI systems like in the movie Her?
So do you think that body is needed to create a feeling of intelligence?
Well, if Sophia was just an art piece, I would have no problem with it, but it's presented
as something else.
Let me add that comment real quick.
If creators of Sophia could change something about their marketing or behavior in general,
what would it be?
What's...
I'm just about everything.
I mean, don't you think... Here's a tough question.
Let me... So I agree with you.
So Sophia is not... The general public feels that Sophia can do way more than she actually
can.
That's right.
And the people who created Sophia are not honestly publicly communicating, trying to
teach the public.
Right.
Here's a tough question.
Don't you think the same thing is scientists in industry and research are taking advantage
of the same misunderstanding in the public when they create AI companies or publish stuff
at...
Some companies, yes.
I mean, there is no sense of... There's no desire to delude.
There's no desire to kind of overclaim what something is done.
Right.
But there's certainly quite a few startups that have had a huge amount of hype around
this that I find extremely damaging and I've been calling it out when I've seen it.
So yeah, but to go back to your original question, like the necessity of embodiment.
I think... I don't think embodiment is necessary.
I think grounding is necessary.
So I don't think we're going to get machines that really understand language without some
level of grounding in the real world.
And it's not clear to me that language is a high enough bandwidth medium to communicate
how the real world works.
I think for this...
Can you talk to what grounding means to you?
So grounding means that... So there is this classic problem of common sense reasoning,
the Winograd schema.
So I tell you the trophy doesn't fit in the suitcase because it's too big or the trophy
doesn't fit in the suitcase because it's too small and the it in the first case refers
to the trophy in the second case to the suitcase.
And the reason you can figure this out is because you know what the trophy in the suitcase
are.
One is supposed to fit in the other one and you know the notion of size and the big object
doesn't fit in a small object unless it's a target, things like that.
So you have this knowledge of how the world works, of geometry and things like that.
I don't believe you can learn everything about the world by just being told in language how
the world works.
I think you need some low level perception of the world, be it visual touch or whatever,
but some higher bandwidth perception of the world.
So by reading all the world's text, you still may not have enough information.
That's right.
There's a lot of things that just will never appear in text and that you can't really infer.
So I think common sense will emerge from you know certainly a lot of language interaction
but also with watching videos or perhaps even interacting in virtual environments.
And possibly you know robot interacting in the real world.
But I don't actually believe necessarily that this last one is absolutely necessary.
But I think there's a need for some grounding.
But the final product doesn't necessarily need to be embodied, as you're saying.
It just needs to have an awareness, a grounding to…
Right.
But it needs to know how the world works to have, you know, to not be frustrated, frustrating
to talk to.
And you talked about emotions, being important, that's a whole other topic.
Well, so, you know, I talked about this, the Basil Ganglia as the thing that calculates
your level of mixed contentment and then there is this other module that sort of tries to
do a prediction of whether you're going to be content or not.
That's the source of some emotion.
So fear, for example, is an anticipation of bad things that can happen to you, right?
You have this inkling that there is some chance that something really bad is going to happen
to you and that creates fear.
When you know for sure that something bad is going to happen to you, you kind of give
up, right?
It's not going to be anymore.
It's uncertainty that creates fear.
So the punchline is, we're not going to have autonomous intelligence without emotions.
Whatever the heck emotions are, so you mentioned very practical things of fear, but there's
a lot of other mess around it.
But there are kind of the results of, you know, drives.
Yeah.
There's a lot of super biological stuff going on and I've talked to a few folks on this.
There's fascinating stuff that ultimately connects to our brain.
If we create an AGI system, sorry, human level intelligence system, and you get to ask her
one question, what would that question be?
You know, I think the first one we'll create will probably not be that smart.
They'll be like a four-year-old.
So you would have to ask her a question to know she's not that smart?
Yeah.
Well, what's a good question to ask, you know, to be impressed?
What is the cause of wind?
And if she answers, oh, it's because the leaves of the tree are moving and that creates wind,
she's on to something.
And if she says, that's a stupid question, she's really on to something.
So, and then you tell her, actually, you know, here is the real thing and she says, oh, yeah,
that makes sense.
So questions that reveal the ability to do common sense reasoning about the physical
world.
Yeah.
And you know, somebody will call to an inference.
Call to an inference.
Well, it was a huge honor.
Congratulations on your touring award.
Thank you so much for talking today.
Thank you.
Thank you.