Jay McClelland: Neural Networks and the Emergence of Cognition
嘉宾：Jay McClelland
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The following is a conversation with Jay McClelland,
a cognitive scientist at Stanford
and one of the seminal figures
in the history of artificial intelligence
and specifically neural networks.
Having written the parallel distributed processing book
with David Rommelhart,
who co-authored the back propagation paper
with Jeff Hinton.
In their collaborations,
they've paved the way for many of the ideas
at the center of the neural network based
machine learning revolution of the past 15 years.
To support this podcast,
please check out our sponsors in the description.
This is the Lex Friedman podcast
and here is my conversation with Jay McClelland.
You are one of the seminal figures
in the history of neural networks
at the intersection of cognitive psychology
and computer science.
What to you has over the decades emerged
as the most beautiful aspect about neural networks,
both artificial and biological?
- The fundamental thing I think about with neural networks
is how they allow us to link
biology with the mysteries of thought.
When I was first entering the field myself
in the late 60s, early 70s,
cognitive psychology had just become a field.
There was a book published in '67
called Cognitive Psychology.
And the author said that, you know,
the study of the nervous system
was only of peripheral interest.
It wasn't gonna tell us anything about the mind.
And I didn't agree with that.
I always felt, oh, look,
I'm a physical being.
From dust to dust, you know, ashes to ashes,
and somehow I emerged from that.
- So that's really interesting.
So there was a sense with cognitive psychology
that in understanding the sort of neuronal structure
of things, you're not going to be able
to understand the mind.
And then your sense is if we study these neural networks,
we might be able to get at least very close
to understanding the fundamentals of the human mind.
- Yeah.
I used to think, or I used to talk about the idea
of awakening from the Cartesian dream.
So Descartes, you know, thought about these things, right?
He was walking in the gardens of Versailles one day,
and he stepped on a stone, and a statue moved.
And he walked a little further, he stepped on another stone,
and another statue moved.
And he, like, why did the statue move
when I stepped on the stone?
And he went and talked to the gardeners,
and he found out that they had a hydraulic system
that allowed the physical contact with the stone
to cause water to flow in various directions,
which caused water to flow into the statue
and move the statue.
And he used this as the beginnings of a theory
about how animals act.
And he had this notion that these little fibers
that people had identified that weren't carrying the blood,
you know, were these little hydraulic tubes
that if you touch something, there would be pressure,
and it would send a signal of pressure
to the other parts of the system,
and that would cause action.
So he had a mechanistic theory of animal behavior.
And he thought that the human had this animal body,
but that some divine something else
had to have come down and been placed in him
to give him the ability to think, right?
So the physical world includes the body in action,
but it doesn't include thought, according to Descartes,
right? - Right.
- And so the study of physiology at that time
was the study of sensory systems and motor systems
and things that you could directly measure
when you stimulated neurons and stuff like that.
And the study of cognition was something that, you know,
was tied in with abstract computer algorithms
and things like that.
But when I was an undergraduate,
I learned about the physiological mechanisms
and so when I'm studying cognitive psychology
as a first year PhD student, I'm saying,
wait a minute, the whole thing is biological, right?
(laughs)
- You had that intuition right away.
That seemed obvious to you.
- Yeah, yeah.
- Isn't that magical, though, that from just
the little bit of biology can emerge
the full beauty of the human experience?
Why is that so obvious to you?
- Well, obvious and not obvious at the same time.
And I think about Darwin in this context, too,
because Darwin knew very early on that none of the ideas
that anybody had ever offered gave him a sense
of understanding how evolution could have worked.
But he wanted to figure out how it could have worked.
That was his goal.
And he spent a lot of time working on this idea
and reading about things that gave him hints
and thinking they were interesting but not knowing why,
and drawing more and more pictures of different birds
that differ slightly from each other and so on.
And then he figured it out.
But after he figured it out, he had nightmares about it.
He would dream about the complexity of the eye
and the arguments that people had given
about how ridiculous it was to imagine
that that could have ever emerged from some sort of
unguided process, that it hadn't been the product of design.
And so he didn't publish for a long time,
in part because he was scared of his own ideas.
He didn't think they could possibly be true.
Yeah.
But then by the time the 20th century rolls around,
we all, we understand that,
many people understand or believe that evolution produced
the entire range of animals that there are.
And Descartes' idea starts to seem a little wonky
after a while, right?
Like, well, wait a minute.
There's the apes and the chimpanzees and the bonobos,
and they're pretty smart in some ways.
Oh, somebody comes up,
oh, there's a certain part of the brain
that's still different.
They don't, there's no hippocampus in the monkey brain.
It's only in the human brain.
Huxley had to do a surgery in front of many, many people
in the late 19th century to show to them
there's actually a hippocampus in the chimpanzee's brain.
So their continuity of the species is another element
that contributes to this sort of idea
that we are ourselves a total product of nature.
And that to me is the magic and the mystery,
how nature could actually give rise to organisms
that have the capabilities that we have.
- So it's interesting because even the idea of evolution
is hard for me to keep all together in my mind.
So because we think of a human time scale,
it's hard to imagine that like the development
of the human eye would give me nightmares too,
because you have to think across many, many,
many generations.
And it's very tempting to think about kind of a growth
of a complicated object.
And it's like, how is it possible for that such a thing
to be built?
'Cause also me from a robotics engineering perspective,
it's very hard to build these systems.
How can through an undirected process,
can a complex thing be designed?
It seems wrong.
- Yeah, so that's absolutely right.
And a slightly different career path
that would have been equally interesting to me
would have been to actually study the process
of embryological development flowing on
into brain development and the exquisite sort of
laying down of pathways and so on that occurs in the brain.
And I know the slightest bit about that is not my field,
but there are fascinating aspects to this process
that eventually result in the complexity of various brains.
At least one thing where in the field,
I think people have felt for a long time,
in the study of vision, the continuity between humans
and non-human animals has been second nature
for a lot longer.
I had this conversation with somebody
who's a vision scientist and he was saying,
oh, we don't have any problem with this.
The monkey's visual system and the human visual system,
extremely similar up to certain levels, of course.
They diverge after a while, but the first,
the visual pathway from the eye to the brain
and the first few layers of cortex or cortical areas,
I guess one would say, are extremely similar.
- Yeah, so on the cognition side is where the leap
seems to happen with humans.
That it does seem we're kind of special.
And that's a really interesting question
when thinking about alien life
or if there's other intelligent alien civilizations
out there is how special is this leap?
So one special thing seems to be the origin of life itself.
However you define that, there's a gray area.
And the other leap, this is very biased perspective
of a human, is the origin of intelligence.
And again, from an engineering perspective,
§02Transcript · 11:22–23:34
it's a difficult question to ask.
An important one is how difficult does that leap?
How special were humans?
Did a monolith come down?
Did aliens bring down a monolith
and some apes had to touch a monolith to get it?
- That's a lot like Descartes' idea, right?
- Exactly, but it just seems one heck of a leap
to get to this level of intelligence.
- Yeah, and so Chomsky,
argued that some genetic fluke occurred 100,000 years ago.
And just happened that some human,
some hominin predecessor of current humans
had this one genetic tweak that resulted in language.
And language then provided this special thing
that separates us from all other animals.
I think there's a lot of truth
to the value and importance of language,
but I think it comes along with the evolution
of a lot of other related things related to sociality
and mutual engagement with others
and establishment of, I don't know,
rich mechanisms for organizing
and understanding of the world,
which language then plugs into.
- Right, so language is a tool that allows you
to do this kind of collective intelligence.
And whatever is at the core of the thing
that allows for this collective intelligence
is the main thing.
And it's interesting to think about that one fluke,
one mutation could lead to the first crack,
opening of the door to human intelligence.
Like all it takes is one.
Like evolution just kind of opens the door a little bit
and then time and selection takes care of the rest.
- You know, there's so many fascinating aspects
to these kinds of things.
So we think of evolution as continuous, right?
We think, oh yes, okay, over 500 million years,
there could have been this relatively continuous changes.
But that's not what anthropologists,
evolutionary biologists found from the fossil record.
They found hundreds of years,
hundreds of millions of years of stasis.
And then suddenly a change occurs.
Well, suddenly on that scale is a million years or something,
or even 10 million years.
But the concept of punctuated equilibrium
was a very important concept in evolutionary biology.
And that also feels somehow right
about the stages of our mental abilities.
We seem to have a certain kind of mindset at a certain age.
And then at another age,
we like look at that four-year-old and say,
oh my God, how could they have thought that way?
So Piaget was known for this kind of stage theory
of child development, right?
And you look at it closely
and suddenly those stages are so discreet, transitions.
But the difference between the four-year-old
and the seven-year-old is profound.
And that's another thing that's always interested me
is how we, something happens over the course
of several years of experience,
where at some point we reached the point
where something like an insight or a transition
or a new stage of development occurs.
And these kinds of things can be understood
in complex systems research.
And so evolutionary biology, developmental biology,
cognitive development are all things
that have been approached in this kind of way.
- Yeah, just like you said,
I find both fascinating those early years of human life,
but also the early minutes, days
of the embryonic development to how from embryos
you get the brain, that development.
Again, from the engineering perspective, it's fascinating.
When you deploy the brain to the human world
and it gets to explore that world and learn,
that's fascinating.
But just like the assembly of the mechanism
that is capable of learning, that's amazing.
The stuff they're doing with brain organoids
where you can build many brains and study
that self-assembly of a mechanism from the DNA material,
that's like, what the heck?
You have literally biological programs
that just generate a system, this mushy thing
that's able to be robust and learn
in a very unpredictable world
and learn seemingly arbitrary things
or like a very large number of things
that enable survival.
- Yeah, ultimately that is a very important part
of the whole process of understanding
this sort of emergence of mind from brain kind of thing.
- And the whole thing seems to be pretty continuous.
So let me step back to neural networks
for another brief minute.
You wrote parallel distributed processing books
that explored ideas of neural networks in the 1980s
together with a few folks.
But the books you wrote with David Rommelhart,
who is the first author on the back propagation paper
with Geoff Hinton.
So these are just some figures at the time
that were thinking about these big ideas.
What are some memorable moments of discovery
and beautiful ideas from those early days?
- I'm gonna start sort of with my own process
in the mid '70s and then into the late '70s
when I met Geoff Hinton and he came to San Diego
and we were all together.
In my time in graduate school,
as I've already described to you,
I had this sort of feeling of,
okay, I'm really interested in human cognition,
but this disembodied sort of way of thinking about it
that I'm getting from the current mode of thought about it
isn't working fully for me.
And when I got my assistant professorship,
I went to UCSD and that was in 1974.
Something amazing had just happened.
Dave Rommelhart had written a book together
with another man named Don Norman.
And the book was called "Explorations in Cognition."
And it was a series of chapters exploring
interesting questions about cognition,
but in a completely sort of abstract,
non-biological kind of way.
And I'm saying, gee, this is amazing.
I'm coming to this community where people can get together
and feel like they've collectively exploring ideas.
And it was a book that had a lot of, I don't know,
lightness to it.
And Don Norman, who was the more senior figure
at Rommelhart at that time, who led that project,
always created this spirit of playful exploration of ideas.
And so I'm like, wow, this is great.
But I was also still trying to get from the neurons
to the cognition.
And I realized at one point,
I got this opportunity to go to a conference
where I heard a talk by a man named James Anderson,
who was an engineer, but by then a professor
in a psychology department who had used linear algebra
to create neural network models of perception
and categorization and memory.
And it just blew me out of the water
that one could create a model that was simulating neurons,
not just kind of engaged in a stepwise algorithmic process
that was construed abstractly,
but it was simulating, remembering, and recalling,
and recognizing the prior occurrence of a stimulus
or something like that.
So for me, this was a bridge between the mind and the brain.
And I just like, and I remember I was walking
across campus one day in 1977,
and I almost felt like St. Paul on the road to Damascus.
I said to myself, you know, if I think about the mind
in terms of a neural network,
it will help me answer the questions about the mind
that I'm trying to answer.
And that really excited me.
So I think that a lot of people
were becoming excited about that.
And one of those people was Jim Anderson,
who I had mentioned.
Another one was Steve Grossberg,
who had been writing about neural networks since the '60s.
And Jeff Hinton was yet another.
And his PhD dissertation showed up in an applicant pool
to a postdoctoral training program
that Dave and Don, the two men I mentioned before,
Romelhart and Norman, were administering.
And Romelhart got really excited
about Hinton's PhD dissertation.
And so Hinton was one of the first people
who came and joined this group of postdoctoral scholars
that was funded by this wonderful grant that they got.
Another one who is also well-known
in neural network circles is Paul Smolensky.
He was another one of that group.
Anyway, Jeff and Jim Anderson
organized a conference at UCSD where we were.
And it was called Parallel Models of Associative Memory.
And it brought all the people together
who had been thinking about these kinds of ideas
in 1979 or 1980.
And this began to kind of really resonate
with some of Romelhart's own thinking,
some of his reasons for wanting something
other than the kinds of computation
he'd been doing so far.
So let me talk about Romelhart now for a minute,
okay, with that context.
- Well, let me also just pause
'cause he said so many interesting things
§03Transcript · 23:36–34:56
before we go to Romelhart.
So first of all, for people who are not familiar,
neural networks are at the core of the machine learning,
deep learning revolution of today.
Jeffrey Hinton that we mentioned is one of the figures
that were important in the history, like yourself,
in the development of these neural networks,
artificial neural networks that are then used
for the machine learning application.
Like I mentioned, the back propagation paper
is one of the optimization mechanisms
by which these networks can learn.
And the word parallel is really interesting.
So it's almost like synonymous
from a computational perspective
how you thought at the time about neural networks,
that it's parallel computation.
Would that be fair to say?
- Well, yeah, the parallel,
the word parallel in this comes from the idea
that each neuron is an independent computational unit.
It gathers data from other neurons,
it integrates it in a certain way,
and then it produces a result.
And it's a very simple little computational unit,
but it's autonomous in the sense that
it does its thing, right?
It's in a biological medium where it's getting nutrients
and various chemicals from that medium.
But you can think of it as almost like a little computer
in and of itself.
So the idea is that each, our brains have,
oh, look, 100 or hundreds,
almost a billion of these little neurons, right?
And they're all capable of doing their work
at the same time.
So it's like instead of just a single central processor
that's engaged in chug, chug, one step after another,
we have a billion of these little computational units
working at the same time.
- So at the time, that's, I don't know,
maybe you can comment, it seems to me, even still to me,
quite a revolutionary way to think about computation
relative to the development
of theoretical computer science alongside of that,
where it's very much like sequential computer.
You're analyzing algorithms
that are running on a single computer.
- That's right.
- You're saying, wait a minute,
why don't we take a really dumb, very simple computer
and just have a lot of them interconnected together?
And they're all operating in their own little world
and they're communicating with each other.
And thinking of computation in that way,
and from that kind of computation,
trying to understand how things like certain characteristics
of the human mind can emerge.
- Right.
- That's quite a revolutionary way of thinking, I would say.
- Well, yes, I agree with you.
And there's still this sort of sense of,
not sort of knowing how we kind of get all the way there,
I think, and this very much remains
at the core of the questions that everybody's asking
about the capabilities of deep learning
and all these kinds of things.
But if I could just play this out a little bit,
a convolutional neural network or a CNN,
which many people may have heard of,
is a set of, you could think of it biologically
as a set of collections of neurons.
Each one had, each collection has maybe 10,000 neurons in it
but there's many layers, right?
Some of these things are hundreds
or even a thousand layers deep,
but others are closer to the biological brain
and maybe they're like 20 layers deep
or something like that.
So we have, within each layer,
we have thousands of neurons or tens of thousands maybe.
Well, in the brain, we probably have millions in each layer,
but we're getting sort of similar in a certain way, right?
And then we think, okay, at the bottom level,
there's an array of things
that are like the photoreceptors in the eye.
They respond to the amount of light of a certain wavelength
at a certain location on the pixel array.
So that's like the biological eye.
And then there's several further stages going up,
layers of these neuron-like units.
And you go from that raw input,
array of pixels to a classification,
you've actually built a system
that could do the same kind of thing
that you and I do when we open our eyes
and we look around and we see there's a cup,
there's a cell phone, there's a water bottle.
And these systems are doing that now, right?
So they are, in terms of the parallel idea
that we were talking about before,
they are doing this massively parallel computation
in the sense that each of the neurons
in each of those layers is thought of as computing
its little bit of something about the input
simultaneously with all the other ones in the same layer.
We get to the point of abstracting that away
and thinking, oh, it's just one whole vector
that's being computed,
one activation pattern that's computed in a single step.
And that abstraction is useful,
but it's still that parallel and distributed processing.
Each one of these guys is just contributing
a tiny bit to that whole thing.
- And that's the excitement that you felt
that from these simple things,
you can emerge when you add these level
of abstractions on it.
You can start getting all the beautiful things
that we think about as cognition.
And so, okay, so you have this conference,
I forgot the name already,
but it's Parallel and Something Associative Memory
and so on, very exciting, technical and exciting title.
And you started talking about Dave Romerhart.
So who is this person that was so,
you've spoken very highly of him.
Can you tell me about him, his ideas, his mind,
who he was as a human being, as a scientist?
- So Dave came from a little tiny town
in Western South Dakota.
And his mother was the librarian
and his father was the editor of the newspaper.
And I know one of his brothers pretty well.
They grew up, there were four brothers
and they grew up together
and their father encouraged them
to compete with each other a lot.
They competed in sports and they competed in mind games.
I don't know, things like Sudoku and chess
and various things like that.
And Dave was a standout undergraduate.
He went at a younger age than most people do to college
at the University of South Dakota and majored in mathematics.
And I don't know how he got interested in psychology
but he applied to the mathematical psychology program
at Stanford and was accepted as a PhD student
to study mathematical psychology at Stanford.
So mathematical psychology is the use of mathematics
to model mental processes.
- So something that I think these days
might be called cognitive modeling,
that whole space.
- Yeah, it's mathematical in the sense that
you say if this is true and that is true
then I can derive that this should follow.
And so you say these are my stipulations
about the fundamental principles
and this is my prediction about behavior
and it's all done with equations.
It's not done with a computer simulation.
So you solve the equation and that tells you
what the probability that the subject will be correct
on the seventh trial of the experiment is
or something like that.
So it's a use of mathematics to descriptively characterize
aspects of behavior.
And Stanford at that time was the place where
there were several really, really strong
mathematical thinkers who were also connected
with three or four others around the country
who brought a lot of really exciting ideas onto the table.
And it was a very, very prestigious part
of the field of psychology at that time.
So Rumelhart comes into this.
He was a very strong student within that program.
And he got this job at this brand new university
in San Diego in 1967 where he's one of the first
assistant professors in the Department of Psychology at UCSD.
So I got there in '74, seven years later
and Rumelhart at that time was still doing
mathematical modeling, but he had gotten interested
in cognition, he'd gotten interested in understanding
and understanding I think remains,
what does it mean to understand anyway?
It's an interesting sort of curious,
like how would we know if we really understood something?
But he was interested in building machines
that would hear a couple of sentences
and have an insight about what was going on.
So for example, one of his favorite things at that time was,
Margie was sitting on the front step
when she heard the familiar jingle of the good humor man.
She remembered her birthday money and ran into the house.
What is Margie doing?
Why?
Well, there's a couple of ideas you could have,
but the most natural one is that the good humor man
brings ice cream, she likes ice cream,
she knows she needs money to buy ice cream
§04Transcript · 35:00–45:37
so she's gonna run into the house and get her money
so she can buy herself an ice cream.
It's a huge amount of inference that has to happen
to get those things to link up with each other.
And he was interested in how the hell that could happen.
And he was trying to build good old-fashioned AI style
models of representation of language
and content of things like has money.
- So like formal logic and knowledge basis,
like that kind of stuff.
So he was integrating that with his thinking
about cognition.
The mechanisms cognition,
how can they mechanistically be applied
to build these knowledge,
to actually build something that looks like a web
of knowledge and thereby from there emerges
something like understanding, whatever the heck that is.
- Yeah, he was grappling, this was something
that they grappled with at the end of that book
that I was describing, Explorations in Cognition.
But he was realizing that the paradigm
of good old-fashioned AI wasn't giving him
the answers to these questions.
- By the way, that's called good old-fashioned AI now.
It wasn't called that at the time.
- Well, it was.
It was beginning to be called that.
- Oh, 'cause it was from the '60s.
- Yeah, yeah, by the late '70s,
it was kind of old-fashioned
and it hadn't really panned out.
People were beginning to recognize that.
And Rumelhart was like, yeah, he was part
of the recognition that this wasn't all working.
Anyway, so he started thinking in terms of the idea
that we needed systems that allowed us
to integrate multiple simultaneous constraints
in a way that would be mutually influencing each other.
So he wrote a paper that just really,
first time I read it, I thought, oh, well, yeah,
but is this important?
But after a while, it just got under my skin.
And it was called an Interactive Model of Reading.
And in this paper, he laid out the idea
that every aspect of our brain's ability
to read, our interpretation of what's coming off the page
when we read, at every level of analysis you can think of
actually depends on all the other levels of analysis.
So what are the actual pixels making up each letter?
And what do those pixels signify about which letters
they are, and what do those letters tell us
about what words are there?
And what do those words tell us about what ideas
the author is trying to convey?
And so he had this model where we have these little tiny
elements that represent each of the pixels
of each of the letters, and then other ones
that represent the line segments in them,
and other ones that represent the letters,
and other ones that represent the words.
And at that time, his idea was there's this set of experts.
There's an expert about how to construct a line
out of pixels, and another expert about how,
which sets of lines go together to make which letters,
and another one about which letters go together
to make which words, and another one about
what the meanings of the words are,
and another one about how the meanings fit together,
and things like that.
And all these experts are looking at this data,
and they're updating hypotheses at other levels.
So the word expert can tell the letter expert,
oh, I think there should be a T there,
because I think there should be a word the here,
and the bottom-up sort of feature-to-letter expert
can say, I think there should be a T there too,
and if they agree, then you see a T, right?
And so there's a top-down, bottom-up, interactive process,
but it's going on at all layers simultaneously.
So everything can filter all the way down from the top,
as well as all the way up from the bottom,
and it's a completely interactive, bidirectional,
parallel, distributed process.
- That is somehow, because of the abstractions,
it's hierarchical, so there's different layers
of responsibilities, different levels of responsibilities.
First of all, it's fascinating to think about it
in this kind of mechanistic way.
So not thinking purely from the structure
of a neural network, or something like a neural network,
but thinking about these little guys
that work on letters, and then the letters become words,
and words become sentences,
and that's a very interesting hypothesis,
that from that kind of hierarchical structure
can emerge understanding.
- Yeah, but the thing is, though,
I wanna just sort of relate this
to the earlier part of the conversation.
When Romalhart was first thinking about it,
there were these experts on the side,
one for the features, and one for the letters,
and one for how the letters make the words, and so on,
and they would each be working,
sort of evaluating various propositions about,
is this combination of features here
going to be one that looks like the letter T, and so on?
And what he realized,
kind of after reading Hinton's dissertation,
and hearing about Jim Anderson's linear algebra book,
algebra-based neural network models
that I was telling you about before,
was that he could replace those experts
with neuron-like processing units,
which just would have their connection weights
that would do this job.
So what ended up happening was
that Romalhart and I got together,
and we created a model
called the Interactive Activation Model of Letter Perception,
which takes these little pixel-level,
inputs, constructs line segment features,
letters, and words,
but now we built it out of a set of neuron-like
processing units that are just connected to each other
with connection weights.
So the unit for the word time
has a connection to the unit for the letter T
in the first position,
and the letter I in the second position, so on,
and because these connections are bidirectional,
if you have prior knowledge that it might be the word time,
that starts to prime the letters and the features,
and if you don't, then it has to start bottom-up,
but the directionality just depends
on where the information comes in first,
and if you have context together
with features at the same time,
they can convergently result in an emergent perception,
and that was the piece of work that we did together
that sort of got us both completely convinced
that this neural network way of thinking
was going to be able to actually address the questions
that we were interested in as cognitive psychologists.
- So the algorithmic side, the optimization side,
those are all details, like when you first start,
the idea that you can get far
with this kind of way of thinking,
that in itself is a profound idea.
So do you like the term connectionism
to describe this kind of set of ideas?
- I think it's useful.
It highlights the notion that the knowledge
that the system exploits
is in the connections between the units, right?
There isn't a separate dictionary.
There's just the connections between the units.
So I already sort of laid that on the table
with the connections from the letter units
to the unit for the word time, right?
The unit for the word time isn't a unit for the word time
for any other reason than it's got the connections
to the letters that make up the word time.
Those are the units on the input that excite it
when it's excited that it in a sense represents
in the system that there's support for the hypothesis
that the word time is present in the input.
But it's not,
the word time isn't written anywhere inside the model.
It's only written there in the picture we drew of the model
to say that's the unit for the word time, right?
And if somebody wants to tell me,
well, how do you spell that word?
You have to use the connections from that out
to then get those letters, for example.
- That's such a, that's a counterintuitive idea.
We humans want to think in this logic way.
This idea of connectionism, it doesn't, it's weird.
It's weird that this is how it all works.
- Yeah, but let's go back to that CNN, right?
That CNN with all those layers of neuron-like processing
units that we were talking about before,
it's gonna come out and say, this is a cat, that's a dog.
But it has no idea why it said that.
It's just got all these connections
between all these layers of neurons,
like from the very first layer to the, you know,
like whatever these layers are,
they just get numbered after a while because they,
you know, they somehow further in you go,
the more abstract the features are,
but it's a graded and continuous sort of process
of abstraction anyway.
And, you know, it goes from very local,
very, very specific to much more sort of global,
but it's still, you know, another sort of pattern
of activation over an array of units.
And then at the output side, it says it's cat or it's a dog.
And when we, when I open my eyes and say, oh, that's Lex,
§05Transcript · 45:42–58:07
or, oh, you know, there's my own dog.
And I recognize my dog,
which is a member of the same species as many other dogs,
but I know this one because of some
slightly unique characteristics.
I don't know how to describe what it is that makes me know
that I'm looking at Lex or at my particular dog, right?
Or even that I'm looking at a particular brand of car.
Like I could say a few words about it,
but if I wrote you a paragraph about the car,
you would have trouble figuring out
which car is he talking about, right?
So the idea that we have propositional knowledge
of what it is that allows us to recognize
that this is an actual instance
of this particular natural kind
has always been, you know, something that,
it never worked, right?
You couldn't ever write down a set of propositions
for, you know, visual recognition.
And so in that space,
it sort of always seemed very natural
that something more implicit, you know,
you don't have access to what the details
of the computation were in between,
you just get the result.
So that's the other part of connectionism.
You cannot, you don't read the contents of the connections.
The connections only cause outputs to occur based on inputs.
- Yeah, and for us that like final layer
or some particular layer is very important.
The one that tells us that it's our dog
or like it's a cat or a dog.
But, you know, each layer is probably equally as important
in the grand scheme of things.
Like there's no reason why the cat versus dog
is more important than the lower level activations.
It doesn't really matter.
I mean, all of it is just this beautiful stacking
on top of each other.
And we humans live in this particular layers for us.
For us, it's useful to survive,
to use those cat versus dog, predator versus prey,
all those kinds of things.
It's fascinating that it's all continuous.
But then you then ask, you know,
the history of artificial intelligence,
you ask, are we able to introspect and convert
the very things that allow us to tell the difference
between cat and dog into logic, into formal logic.
That's been the dream.
I would say that's still part of the dream of symbolic AI.
And I've recently talked to Doug Lennard who created Psych.
And that's a project that lasted for many decades
and still carries a sort of dream in it, right?
- And we still don't know the answer, right?
It seems like connectionism is really powerful,
but it also seems like there's this building of knowledge.
And so how do we, how do you square those two?
Like, do you think the connections can contain
the depth of human knowledge and the depth
of what Dave Romahart was thinking about of understanding?
- Well, that remains the $64 question.
- With inflation, that number is higher.
- Okay, $64,000.
Maybe it's the $64 billion question now.
You know, I think that from the emergentist side,
which, you know, I place myself on.
So I used to sometimes tell people
I was a radical eliminative connectionist
because I didn't want them to think
that I wanted to build like anything into the machine.
But I don't like the word eliminative anymore
because it makes it seem like it's wrong to think
that there is this emergent level of understanding.
And I disagree with that.
So I think, you know, I would call myself
in a radical emergentist connectionist
rather than eliminative connectionist, right?
Because I want to acknowledge that these higher level
kinds of aspects of our cognition are real,
but they're not, they don't,
they don't exist as such.
And there was an example that Doug Hofstadter used to use
that I thought was helpful in this respect.
Just the idea that we can think about sand dunes
as entities and talk about like how many there are even,
but we also know that a sand dune is a very fluid thing.
It's a pile of sand that is capable of moving around
under the wind and, you know,
reforming itself in somewhat different ways.
And if we think about our thoughts as like sand dunes,
as being things that, you know, emerge from
just the way all the lower level elements
sort of work together and are constrained
by external forces, then we can say,
yes, they exist as such, but they also, you know,
we shouldn't treat them as completely monolithic entities
that we can understand without understanding
sort of all of the stuff that allows them to change
in the ways that they do.
And that's where I think the connectionist
feeds into the cognitive.
It's like, okay, so if the substrate
is parallel distributed connectionist,
then it doesn't mean that the contents of thought
isn't, you know, like abstract and symbolic,
but it's more fluid maybe than is easier to capture
with a set of logical expressions.
- Yeah, that's a heck of a sort of thing to put
at the top of a resume, radical emerginist connectionist.
So there is just like you said,
a beautiful dance between that,
between the machinery of intelligence,
like the neural network side of it
and the stuff that emerges.
I mean, the stuff that emerges seems to be,
I don't know, I don't know what that is.
It seems like maybe all of reality is emergent.
What I think about,
this is made most distinctly rich to me
when I look at cellular automata,
look at game of life,
that from very, very simple things,
very rich, complex things emerge
that start looking very quickly like organisms
that you forget how the actual thing operates.
They start looking like they're moving around,
they're eating each other.
Some of them are generating offspring.
You forget very quickly.
And it seems like maybe it's something about the human mind
that wants to operate in some layer of the emergent
and forget about the mechanism
of how that emergence happens.
So it just like you are in your radicalness,
also it seems like unfair to eliminate the magic
of that emergent.
Like eliminate the fact that that emergent is real.
- Yeah, no, I agree.
That's why I got rid of eliminative, right?
- Eliminative, yeah.
- Yeah, because it seemed like that was trying to say
that it's all completely like--
- An illusion of some kind that's not--
- Well, who knows whether there aren't
some illusory characteristics there.
And I think that philosophically,
many people have confronted that possibility over time,
but it's still important to accept it as magic, right?
So I think of Fellini in this context,
I think of others who have appreciated the role of magic,
of actual trickery in creating illusions that move us.
And Plato was onto this too.
It's like somehow or other these shadows
give rise to something much deeper than that.
So we won't try to figure out what it is,
we'll just accept it as given that that occurs.
But he was still onto the magic of it.
- Yeah, yeah.
We won't try to really, really, really deeply understand
how it works, we'll just enjoy the fact
that it's kind of fun.
Okay, but you worked closely with Dave Rommelhart.
He passed away as a human being.
What do you remember about him?
Do you miss the guy?
- Absolutely.
He passed away 15-ish years ago now,
and his demise was actually one of the most poignant
and relevant tragedies relevant to our conversation.
He started to undergo a progressive neurological condition
that isn't fully understood.
That is to say his particular course isn't fully understood
because brain scans weren't done at certain stages
and no autopsy was done or anything like that.
The wishes of the family.
So we don't know as much about the underlying pathology
as we might, but I had begun to get interested
in this neurological condition that might have been
the very one that he was succumbing to
as my own efforts to understand another aspect
of this mystery that we've been discussing
while he was beginning to get progressively
more and more affected.
So I'm gonna talk about the disorder
and not about Rommelhart for a second, okay?
The disorder is something my colleagues and collaborators
have chosen to call semantic dementia.
So it's a specific form of loss of mind related to meaning,
semantic dementia.
And it's progressive in the sense that the patient
loses the ability to appreciate the meaning
of the experiences that they have,
either from touch, from sight, from sound, from language.
I hear sounds, but I don't know what they mean
kind of thing.
So as this illness progresses, it starts with
the patient being unable to differentiate
like similar breeds of dog or remember
§06Transcript · 58:14–1:10:08
the lower frequency unfamiliar categories
that they used to be able to remember.
But as it progresses, it becomes more and more striking
and the patient loses the ability to recognize
things like pigs and goats and sheep
and calls all middle-sized animals dogs
and can't recognize rabbits and rodents anymore.
They call all the little ones cats
and they can't recognize hippopotamuses
and cows where they call them all horses.
So there was this one patient who went through
this progression where at a certain point,
any four-legged animal, he would call it either a horse
or a dog or a cat.
And if it was big, he would tend to call it a horse.
If it was small, he'd tend to call it a cat.
Middle-sized ones he called dogs.
This is just a part of the syndrome though.
The patient loses the ability to relate
concepts to each other.
So my collaborator in this work, Carolyn Patterson,
developed a test called the pyramids and palm trees test.
So you give the patient a picture of pyramids
and they have a choice, which goes with the pyramids?
Palm trees or pine trees?
And she showed that this wasn't just a matter of language
because the patient's loss of this ability shows up
whether you present the material with words
or with pictures.
The pictures, they can't put the pictures together
with each other properly anymore.
They can't relate the pictures to the words either.
They can't do word picture matching.
But they've lost the conceptual grounding
from either modality of input.
And so that's why it's called semantic dementia.
The very semantics is disintegrating.
And we understand this in terms of our idea
that distributed representation,
a pattern of activation represents the concepts.
Really similar ones, as you degrade them,
they start being, you lose the differences.
And then, so the difference between the dog and the goat
sort of is no longer part of the pattern anymore.
And since dog is really familiar,
that's the thing that remains.
And we understand that in the way the models work and learn.
But Rommel-Hart underwent this condition.
So on the one hand, it's a fascinating aspect
of parallel distributed processing to me.
And it reveals this sort of texture
of distributed representation in a very nice way,
I've always felt.
But at the same time, it was extremely poignant
because this is exactly the condition
that Rommel-Hart was undergoing.
And there was a period of time when he was,
this man who had been the most focused,
goal-directed, competitive, (laughs)
thoughtful person who was willing to work for years
to solve a hard problem, he starts to disappear.
And there was a period of time when it was like,
hard for any of us to really appreciate
that he was sort of, in some sense,
not fully there anymore.
- Do you know if he was able to introspect
this dissolution of the understanding mind?
Was he, I mean, this is one of the big scientists
that thinks about this.
Was he able to look at himself
and understand the fading mind?
- You know, we can contrast Hawking
and Rommel-Hart in this way.
And I like to do that to honor Rommel-Hart
because I think Rommel-Hart is sort of like
the Hawking of cognitive science to me in some ways.
Both of them suffered from a degenerative condition.
In Hawking's case, it affected the motor system.
In Rommel-Hart's case, it's affecting the semantics.
And not just the pure object semantics,
but maybe the self semantics as well.
And we don't understand that.
- Concepts broadly.
- So I would say he didn't.
And this was part of what, from the outside,
was a profound tragedy.
But on the other hand, at some level, he sort of did.
Because there was a period of time
when it finally was realized
that he had really become profoundly impaired.
This was clearly a biological condition
and it wasn't just like he was distracted that day
or something like that.
So he retired from his professorship at Stanford
and he lived with his brother for a couple years
and then he moved into a facility
for people with cognitive impairments.
One that many elderly people end up in
when they have cognitive impairments.
And I would spend time with him during that period.
This was in the late '90s, around 2000 even.
And we would go bowling and he could still bowl.
And after bowling, I took him to lunch
and I said, "Where would you like to go?
"You wanna go to Wendy's?"
And he said, "Nah."
And I said, "Okay, well, where do you wanna go?"
And he just pointed.
He said, "Turn here."
So he still had a certain amount of spatial cognition
and he could get me to the restaurant.
And then when we got to the restaurant,
I said, "What do you wanna order?"
And he couldn't come up with any of the words
but he knew where on the menu the thing was that he wanted.
- That's so fascinating.
- He couldn't say what it was
but he knew that that's what he wanted to eat.
It isn't monolithic at all.
Our cognition is, first of all,
graded in certain kinds of ways but also multi-partite.
There's many elements to it and things,
certain sort of partial competencies still exist
in the absence of other aspects of these competencies.
So this is what always fascinated me
about what used to be called cognitive neuropsychology,
the effects of brain damage on cognition.
But in particular, this gradual disintegration part.
I'm a big believer that the loss of a human being
that you value is as powerful as first falling in love
with that human being.
I think it's all a celebration of the human being.
So the disintegration itself too is a celebration in a way.
- Yeah, yeah.
But just to say something more about the scientist
and the backpropagation idea that you mentioned.
So in 1982,
Hinton had been there as a postdoc
and organized that conference.
He'd actually gone away
and gotten an assistant professorship
and then there was this opportunity to bring him back.
So Jeff Hinton was back on a sabbatical.
- San Diego. - In San Diego.
And Rumelhart and I had decided we wanted to do this
we thought it was really exciting.
And the papers on the interactive activation model
that I was telling you about had just been published.
And we both sort of saw a huge potential for this work
and Jeff was there.
And so the three of us started a research group
which we called the PDP Research Group.
And several other people came.
Francis Crick, who was at the Salk Institute
heard about it from Jeff.
'Cause Jeff was known among Brits to be brilliant
and Francis was well connected with his British friends.
So Francis Crick came.
- That's a heck of a group of people, wow.
- And several, Paul Spolensky was one of the other postdocs.
He was still there as a postdoc.
And a few other people.
But anyway,
Jeff
talked to us about learning
and how we should think about
how learning occurs in a neural network.
And he said, "The problem
"with the way you guys have been approaching this
"is that you've been looking for inspiration from biology
"to tell you what the rules should be
"for how the synapses should change
"the strengths of their connections,
"how the connections should form."
He said, "That's the wrong way to go about it.
"What you should do is you should think in terms of
"how you can adjust connection weights to solve a problem.
"So you define your problem, and then you figure out
"how the adjustment of the connection weights
"will solve the problem."
And Rommelhart heard that and said to himself,
"Okay, so I'm gonna start thinking about it that way.
"I'm going to essentially imagine
"that I have some objective function,
"some goal of the computation.
"I want my machine to correctly classify
"all of these images, and I can score that.
"I can measure how well they're doing on each image,
"and I get some measure of error or loss,
"it's typically called in deep learning.
"And I'm going to figure out
"how to adjust the connection weights
"so as to minimize my loss or reduce the error."
And that's called gradient descent.
And engineers were already familiar
with the concept of gradient descent.
And in fact, there was an algorithm called the Delta Rule
that had been invented by a professor
in the electrical engineering department at Stanford,
Woodrow, Bernie Woodrow, and a collaborator named Hoff.
§07Transcript · 1:10:11–1:22:01
I never met him.
Anyway, so gradient descent in continuous neural networks
with multiple neuron-like processing units
was already understood
for a single layer of connection weights.
We have some inputs over a set of neurons.
We want the output to produce a certain pattern.
We can define the difference between our target
and what the neural network is producing,
and we can figure out how to change the connection weights
to reduce that error.
So what Romalhard did was to generalize that
so as to be able to change the connections
from earlier layers of units to the ones at a hidden layer
between the input and the output.
And so he first called the algorithm
the Generalized Delta Rule,
because it's just an extension of the gradient descent idea.
And interestingly enough, Hinton was thinking
that this wasn't going to work very well.
So Hinton had his own alternative algorithm at the time
based on the concept of the Boltzmann machine
that he was pursuing.
So the paper on the Boltzmann machine came out in,
learning in Boltzmann machines came out in 1985,
but it turned out that backprop worked better
than the Boltzmann machine learning algorithm.
- So this generalized delta algorithm
ended up being called backpropagation,
as you say, backprop.
- Yeah.
And probably that name is opaque to many people.
What does that mean?
What it meant was that in order to figure out
what the changes you needed to make
to the connections from the input to the hidden layer,
you had to backpropagate the error signals
from the output layer through the connections
from the hidden layer to the output
to get the signals that would be the error signals
for the hidden layer.
And that's how Rumelhart formulated it.
It was like, well, we know what the error signals
are at the output layer.
Let's see if we can get a signal at the hidden layer
that tells each hidden unit
what its error signal is, essentially.
So it's backpropagating through the connections
from the hidden to the output to get the signals
to tell the hidden units how to change their weights
from the input, and that's why it's called backprop.
Yeah, so it came from Hinton having introduced the concept
of define your objective function,
figure out how to take the derivative
so that you can adjust the connections
so that they make progress towards your goal.
- So stop thinking about biology for a second,
and let's start to think about optimization
and computation a little bit more.
So what about Jeff Hinton?
You've gotten a chance to work with him in that little,
the set of people involved there, it's quite incredible,
the small set of people under the PDP flag.
It's just, given the amount of impact
those ideas have had over the years,
it's kind of incredible to think about.
But just like you said, like yourself,
Jeffrey Hinton is seen as one of the,
not just like a seminal figure in AI,
but just a brilliant person,
just like the horsepower of the mind
is pretty high up there for him
'cause he's just a great thinker.
So what kind of ideas have you learned from him?
Have you influenced each other on?
Have you debated over,
what stands out to you in the full space of ideas here
at the intersection of computation and cognition?
- Well, so Jeff has said many things to me
that had a profound impact on my thinking.
And he's written several articles
which were way ahead of their time.
He had two papers in 1981, just to give one example,
one of which was essentially the idea of transformers,
and another of which was a early paper
on semantic cognition, which inspired him and Rommelhart
and me throughout the '80s,
and still I think sort of grounds my own thinking
about the semantic aspects of cognition.
He also, in a small paper that was never published
that he wrote in 1977,
before he actually arrived at UCSD,
or maybe a couple of years even before that, I don't know,
when he was a PhD student,
he described how a neural network
could do recursive computation.
And it was a very clever idea
that he's continued to explore over time,
which was sort of the idea that
when you call a subroutine,
you need to save the state that you had when you called it
so you can get back to where you were
when you're finished with the subroutine.
And the idea was that you would save the state
of the calling routine
by making fast changes to connection weights,
and then when you finished with the subroutine call,
those fast changes in the connection weights
would allow you to go back to where you had been before
and reinstate the previous context
so that you could continue on
with the top level of the computation.
Anyway, that was part of the idea.
And I always thought, okay, that's really, you know,
he had extremely creative ideas
that were quite a lot ahead of his time,
and many of them in the 1970s and early 1980s.
So another thing about Geoff Hinton's way of thinking,
which has profoundly influenced my effort
to understand human mathematical cognition
is that he doesn't write too many equations.
And people tell stories like,
oh, in the Hinton lab meetings,
you don't get up at the board and write equations
like you do in everybody else's machine learning lab.
What you do is you draw a picture. (laughs)
And, you know, he explains aspects
of the way deep learning works
by putting his hands together
and showing you the shape of a ravine
and using that as a geometrical metaphor
for what's happening as this gradient descent process.
You're coming down the wall of a ravine.
If you take too big a jump,
you're gonna jump to the other side.
And so that's why we have to turn down
the learning rate, for example.
And it speaks to me of the fundamentally intuitive character
of deep insight together with the fact
that together with a commitment to really understanding
in a way that's absolutely, ultimately explicit and clear,
but also intuitive.
- Yeah, there's certain people like that.
He's an example, some kind of weird mix of visual
and intuitive and all those kinds of things.
Feynman is another example, different style of thinking,
but very unique.
And when you're around those people,
for me in the engineering realm,
there's a guy named Jim Keller,
who's a chip designer, engineer.
Every time I talk to him,
it doesn't matter what we're talking about.
Just having experienced that unique way of thinking
transforms you and makes your work much better.
And that's the magic.
You look at Daniel Kahneman,
you look at the great collaborations
throughout the history of science.
That's the magic of that.
It's not always the exact ideas that you talk about,
but it's the process of generating those ideas,
being around that, spending time with that human being,
you can come up with some brilliant work,
especially when it's cross-disciplinary
as it was a little bit in your case with Jeff.
- Yeah, Jeff is a descendant of the logician Boole.
He comes from a long line of English academics.
And together with the deeply intuitive thinking ability
that he has, he also has, it's been clear.
He's described this to me,
and I think he's mentioned it from time to time
in other interviews that he's had with people.
He's wanted to be able to sort of think of himself
as contributing to the understanding of reasoning itself,
not just human reasoning.
Like Boole is about logic, right?
It's about what can we conclude from what else
and how do we formalize that.
And as a computer scientist, logician,
philosopher, the goal is to understand
how we derive truths from givens and things like this.
And the work that Jeff was doing in the early to mid '80s
on something called the Bolton machine
was his way of connecting with that Boolean tradition
and bringing it into the more continuous,
probabilistic-graded constraint satisfaction realm.
And it was beautiful, a set of ideas
linked with theoretical physics,
as well as with logic.
And it's always been, I mean,
I've always been inspired by the Bolton machine too.
It's like, well, if the neurons are probabilistic
rather than deterministic in their computations,
then maybe this somehow is part of the serendipity
or adventitiousness of the moment of insight, right?
It might not have occurred at that particular instant.
It might be sort of partially the result
of a stochastic process.
And that too is part of the magic of the emergence
§08Transcript · 1:22:05–1:36:25
of some of these things.
- Well, you're right with the Boolean lineage
and the dream of computer science is somehow,
I mean, I certainly think of humans this way,
that humans are one particular manifestation of intelligence,
that there's something bigger going on,
and you're hoping to figure that out.
The mechanisms of intelligence, the mechanisms of cognition
are much bigger than just humans.
- Yeah, so I think of, I started using the phrase
computational intelligence at some point
as to characterize the field that I thought,
people like Geoff Hinton and many of the people
I know at DeepMind are working in,
and where I feel like I'm,
I'm a kind of a human-oriented
computational intelligence researcher
in that I'm actually kind of interested
in the human solution.
But at the same time, I feel like that's where
a huge amount of the excitement of deep learning
actually lies is in the idea that,
we may be able to even go beyond what we can achieve
with our own nervous systems when we build
computational intelligences that are
not limited in the ways that we are by our own biology.
- Perhaps allowing us to scale the very mechanisms
of human intelligence, just increase its power through scale.
- Yes, and I think that that, obviously that's the,
that's being played out massively at Google Brain,
at OpenAI, and to some extent at DeepMind as well.
I guess I shouldn't say to some extent.
This massive scale of the computations that
are used to succeed at games like Go
or to solve the protein folding problems
that they've been solving and so on.
- Still not as many synapses and neurons as the human brain.
So we still got, we're still beating them on that.
We humans are beating the AIs,
but they're catching up pretty quickly.
You write about modeling of mathematical cognition.
So let me first ask about mathematics in general.
There's a paper titled
Parallel Distributed Processing Approach
to Mathematical Cognition, where in the introduction,
there's some beautiful discussion of mathematics.
And you reference there Tristan Needham,
who criticizes a narrow form of view of mathematics
by liking the studying of mathematics
as simple manipulation to studying music
without ever hearing a note.
So from that perspective, what do you think is mathematics?
What is this world of mathematics like?
- Well, I think of mathematics as a set of tools
for exploring idealized worlds
that often turn out to be extremely relevant
to the real world, but need not.
But they're worlds in which
objects exist with idealized properties,
and in which the relationships among them
can be characterized with precision
so as to allow the implications of certain facts
to then allow you to derive other facts with certainty.
So, you know, if you have two triangles
and you know that there is an angle in the first one
that has the same measure as an angle in the second one,
and you know that the lengths of the sides
adjacent to that angle in each of the two triangles,
the corresponding sides adjacent to that angle
also have the same measure,
then you can then conclude that the triangles are congruent.
That is to say, they have all of their properties in common.
And that is something about triangles.
It's not a matter of formulas.
These are idealized objects.
In fact, you know, we built bridges out of triangles,
and we understand how to measure the height
of something we can't climb
by extending these ideas about triangles a little further.
And, you know,
all of the ability to get a tiny speck of matter
launched from the planet Earth
to intersect with some tiny, tiny little body
way out in, way beyond Pluto somewhere
at exactly a predicted time and date
is something that depends on these ideas, right?
So, but, and it's actually happening
in the real physical world
that these ideas make contact with it
in those kinds of instances.
And so, but you know,
there are these idealized objects,
these triangles or these distances or these points,
whatever they are, that allow for this set of tools
to be created that then gives human beings the,
it's this incredible leverage
that they didn't have without these concepts.
And I think this is actually already true
when we think about just, you know,
the natural numbers.
I always like to include zero,
so I'm gonna say the non-negative integers,
but that's a place where some people
prefer not to include zero, but--
- Oh, we like zero here.
Natural numbers, zero, one, two, three, four,
five, six, seven, and so on.
- Yeah, and you know, because they give you
the ability to be exact about the things
that you have, like how many sheep you have.
Like, you know, I sent you out this morning,
there were 23 sheep, you came back with only 22.
What happened, right?
- The fundamental problem of physics,
how many sheep you have, yeah.
- It's a fundamental problem of human society
that you damn well better bring back
the same number of sheep as you started with.
And you know, it allows commerce,
it allows contracts, it allows the establishment
of records and so on to have systems
that allow these things to be notated.
But they have an inherent aboutness to them
that's one at the, one in the same time
sort of abstract and idealized and generalizable
while on the other hand,
potentially very, very grounded and concrete.
And one of the things that makes
for the incredible achievements of the human mind
is the fact that humans invented these idealized systems
that leverage the power of human thought
in such a way as to allow all this kind of thing to happen.
And so that's what mathematics to me
is the development of systems for thinking about
the properties and relations among sets of idealized objects.
And you know, the mathematical notation system
that we unfortunately focus way too much on
is just our way of expressing propositions
about these properties.
- Right, it's just like we're talking
with Chomsky in language.
It's the thing we've invented
for the communication of those ideas.
They're not necessarily the deep representation
of those ideas.
- Yeah.
- What's a good way to model
such powerful mathematical reasoning, would you say?
What are some ideas you have for capturing this in a model?
- The insights that human mathematicians have had
is a combination of the kind of the intuitive
kind of connectionist-like knowledge
that makes it so that something is just like obviously true
so that you don't have to think about why it's true.
That then makes it possible to then take the next step
and ponder and reason and figure out something
that you previously didn't have that intuition about.
It then ultimately becomes a part of the intuition
that the next generation of mathematical thinkers
have to ground their own thinking on
so that they can extend the ideas even further.
I came across this quotation from Henri Poincaré
while I was walking in the woods with my wife
in a state park in Northern California late last summer.
And what it said on the bench was,
it is by logic that we prove,
but by intuition that we discover.
And so what for me the essence of the project
is to understand how to bring
the intuitive connectionist resources to bear on
letting the intuitive discovery arise
from engagement in thinking with this formal system.
So I think of the ability of somebody like Hinton
or Newton or Einstein or Romal Hart or Poincaré
to Archimedes is another example, right?
So suddenly a flash of insight occurs.
It's like the constellation of all of these
simultaneous constraints that somehow or other
causes the mind to settle into a novel state
that it never did before and give rise to a new idea
that then you can say, okay, well now,
how can I prove this?
How do I write down the steps of that theorem
that allow me to make it rigorous and certain?
And so I feel like the kinds of things
that we're beginning to see deep learning systems do
of their own accord kind of gives me this feeling
of, I don't know, hope or encouragement
that ultimately it'll all happen.
- So in particular, as many people now
have become really interested in thinking about,
neural networks that have been trained
with massive amounts of text can be given a prompt
and they can then sort of generate some really interesting,
fanciful, creative story from that prompt.
And there's kind of like a sense that they've somehow
synthesized something like novel out of the,
all of the particulars of all of the billions
and billions of experiences that went into the training data
that gives rise to something like this sort of
§09Transcript · 1:36:28–1:50:42
intuitive sense of what would be a fun
and interesting little story to tell
or something like that.
It just sort of wells up out of the,
letting the thing play out its own imagining
of what somebody might say given this prompt
as a input to get it to start to generate its own thoughts.
And to me that sort of represents the potential
of capturing the intuitive side of this.
- Yeah, and there's other examples.
I don't know if you find them as captivating as,
you know, on the deep mind side with AlphaZero,
if you study chess, the kind of solutions
that has come up in terms of chess,
there's novel ideas there.
It feels very like there's brilliant moments of insight.
And the mechanism they use,
if you think of search as maybe more
towards the good old fashioned AI,
and then there's the connectionist network
that has the intuition of looking at a board,
looking at a set of patterns and saying,
how good is this set of positions?
And the next few positions, how good are those?
And that's it.
That's just an intuition.
Grandmasters have this and understanding positionally,
tactically, how good the situation is,
how can it be improved without doing this full,
like deep search, and then maybe doing a little bit
of what human chess players call calculation,
which is the search, taking a particular set of steps
down the line to see how they unroll.
But there is moments of genius in those systems too.
So that's another hopeful illustration
that from neural networks can emerge
this novel creation of an idea.
Yes, and I think that, I think Demis Hassabis is,
he's spoken about those things.
I heard him describe a move that was made
in one of the Go matches against Lee Sedol
in a very similar way.
And it caused me to become really excited
to kind of collaborate with some of those guys at DeepMind.
So I think though that what I like to really emphasize here
is one part of what I like to emphasize
about mathematical cognition at least
is that philosophers and logicians going back
three or even a little more than 3,000 years ago
began to develop these formal systems.
And gradually the whole idea about thinking formally
got constructed.
And it's preceded Euclid, certainly President Euclid,
certainly present in the work of Thales and others.
And I'm not the world's leading expert
in all the details of that history.
But Euclid's elements were the kind of the touch point
of a coherent document that sort of laid out
this idea of an actual formal system
within which these objects were characterized
and the system of inference that allowed new truths
to be derived from others
was sort of like established as a paradigm.
And what I find interesting is the idea
that the ability to become a person
who is capable of thinking in this abstract formal way
is a result of the same kind of immersion
in experience thinking in that way
that we now begin to think of our understanding
of language as being.
So we immerse ourselves in a particular language,
in a particular world of objects and their relationships,
and we learn to talk about that.
And we develop intuitive understanding of the real world.
In a similar way, we can think that what academia
has created for us, what those early philosophers
and their academies in Athens and Alexandria
and other places allowed was the development
of these schools of thought, modes of thought
that then become deeply ingrained.
And it becomes what it is that makes it so
that somebody like Jerry Fodor would think
that systematic thought is the essential characteristic
of the human mind as opposed to a derived,
an acquired characteristic that results
from acculturation in a certain mode
that's been invented by humans.
- Would you say it's more fundamental than like language?
If we start dancing, if we bring Chomsky
back into the conversation, first of all,
is it unfair to draw a line between mathematical cognition
and language, linguistic cognition?
- I think that's a very interesting question.
And I think it's one of the ones
that I'm actually very interested in right now.
But I think the answer is, in important ways,
it is important to draw that line,
but then to come back and look at it again
and see some of the subtleties
and interesting aspects of the difference.
So if we think about Chomsky himself,
he was born into an academic family.
His father was a professor of rabbinical studies
at a small rabbinical college in Philadelphia.
And he was deeply enculturated in
a culture of thought and reason
and brought to the effort to understand natural language
this profound engagement with these formal systems.
And I think that there was tremendous power in that
and that Chomsky had some amazing insights
into the structure of natural language,
but that, and I'm gonna use the word but there,
the actual intuitive knowledge of these things
only goes so far and does not go as far
as it does in people like Chomsky himself.
And this was something that was discovered
in the PhD dissertation of Lila Gleitman,
who was actually trained in the same linguistics department
with Chomsky.
So what Lila discovered was that the intuitions
that linguists had about even the meaning of a phrase,
not just about its grammar,
but about what they thought a phrase must mean
were very different from the intuitions
of an ordinary person who wasn't a formally trained thinker.
And well, it recently has become much more salient.
I happen to have learned about this when I myself
was a PhD student at the University of Pennsylvania,
but I never knew how to put it together
with all of my other thinking about these things.
So I actually currently have the hypothesis
that formally trained linguists
and other formally trained academics,
whether it be linguistics, philosophy,
cognitive science, computer science,
machine learning, mathematics,
have a mode of engagement with experience
that is intuitively deeply structured
to be more organized around the systematicity
and ability to be conformant with the principles
of a system than is actually true
of the natural human mind without that immersion.
- That's fascinating.
And so the different fields and approaches
with which you start to study the mind
actually take you away from the natural operation
of the mind.
So it makes it very difficult for you
to be somebody who introspects.
- Yes.
And this is where things about human belief
and so-called knowledge that we consider private,
not our business to manipulate in others.
We are not entitled to tell somebody else
what to believe about certain kinds of things.
What are those beliefs?
Well, they are the product of this sort of immersion
and enculturation.
That is what I believe.
- So- - And that's limiting.
- It's something to be aware of.
- Does that limit you from having a good model
of some of cognition?
- It can.
- So when you look at mathematical or linguistics,
I mean, what is that line then?
So is Chomsky unable to sneak up
to the full picture of cognition?
Are you, when you're focusing on mathematical thinking,
are you also unable to do so?
- I think you're right.
I think that's a great way of characterizing it.
And I also think that it's related
to the concept of beginner's mind
and another concept called the expert blind spot.
So the expert blind spot is much more prosaic-seeming
than this point that you were just making.
But it's something that plagues experts
when they try to communicate their understanding
to non-experts.
And that is that things are self-evident to them
that they can't begin to even think about
how they could explain it to somebody else
because it's just so patently obvious
that it must be true.
And when Kronenker said,
God made the natural numbers, all else is the work of man,
he was expressing that intuition
that somehow or other, the basic fundamentals
of discrete quantities being countable and innumerable
and indefinite in number was not something
that had to be discovered, but he was wrong.
It turns out that many cognitive scientists
agreed with him for a time.
There was a long period of time where the natural numbers
were considered to be a part of the innate endowment
of core knowledge or to use the kind of phrases
that Spelke and Carey used to talk about
what they believe are the innate primitives
of the human mind.
§10Transcript · 1:50:44–2:02:08
And they no longer believe that.
It's actually been more or less accepted
by almost everyone that the natural numbers
are actually a cultural construction.
And it's so interesting to go back
and sort of like study those few people who still exist
who don't have those systems.
So this is just an example to me
and where a certain mode of thinking about language itself
or a certain mode of thinking about geometry
and those kinds of relations,
so it becomes so second nature that you don't know
what it is that you need to teach.
And in fact, we don't really teach it
all that explicitly anyway.
And it's, you take a math class,
the professor sort of teaches it to you
the way they understand it.
Some of the students in the class sort of like,
they get it, they start to get the way of thinking
and they can actually do the problems
that get put on the homework
that the professor thinks are interesting
and challenging ones.
But most of the students who don't kind of engage
as deeply don't ever get.
And we think, oh, that man must be brilliant.
He must have this special insight,
but he must have some biological sort of bit
that's different, right?
That makes him so that he or she could have that insight.
But I'm, although I don't wanna dismiss
biological individual differences completely,
I find it much more interesting to think
about the possibility that,
it was that difference in the dinner table conversation
at the Chomsky house when he was growing up
that made it so that he had that cast of mind.
- Yeah, and there's a few topics we talked about
that kind of interconnect.
'Cause I wonder, the better I get at certain things,
we humans, the deeper we understand something,
what are you starting to then miss
about the rest of the world?
We talked about David and his degenerative mind.
And when you look in the mirror and wonder,
how different am I cognitively
from the man I was a month ago,
from the man I was a year ago?
Like what, if I can, having thought about language
if I'm Chomsky for 10, 20 years,
what am I no longer able to see?
What is in my blind spot and how big is that?
And then to somehow be able to leap back
out of your deep structure that you formed for yourself
about thinking about the world,
leap back and look at the big picture again,
or jump out of your current way of thinking.
And to be able to introspect,
like what are the limitations of your mind?
How is your mind less powerful than it used to be
or more powerful or different, powerful in different ways?
So that seems to be a difficult thing to do
'cause we're living, we're looking at the world
through the lens of our mind, right?
To step outside and introspect is difficult,
but it seems necessary if you want to make progress.
- You know, one of the threads of psychological research
that's always been very, I don't know,
important to me to be aware of is the idea
that our explanations of our own behavior
aren't necessarily actually part of the process
that caused that behavior to occur,
or even valid observations of the set of constraints
that led to the outcome.
But they are post hoc rationalizations
that we can give based on information at our disposal
about what might have contributed to the result
that we came to when asked.
And so this is an idea that was introduced
in a very important paper by Nisbet and Wilson
about the limits on our ability to be aware of the factors
that cause us to make the choices that we make.
And I think it's something that's very important
and it's something that we really ought to be
much more cognizant of in general as human beings
is that our own insight into exactly why
we hold the beliefs that we do and we hold the attitudes
and make the choices and feel the feelings that we do
is not something that we totally control
or totally observe.
And it's subject to our culturally transmitted
understanding of what it is that is the mode
that we give to explain these things when asked to do so
as much as it is about anything else.
And so even our ability to introspect
and think we have access to our own thoughts
is a product of culture and belief, practice.
- So let me ask you the big question of advice.
So you've lived an incredible life in terms of the ideas
you've put out into the world,
in terms of the trajectory you've taken through your career,
through your life.
What advice would you give to young people today
in high school and college about how to be more open
how to have a career or how to have a life
they can be proud of?
- Finding the thing that you are intrinsically motivated
to engage with and then celebrating that discovery
is what it's all about.
When I was in college, I struggled with that.
I had thought I wanted to be a psychiatrist
because I think I was interested in human psychology
in high school.
And at that time, the only sort of information I had
that had anything to do with the psyche was,
you know, Freud and Eric Fromm
and sort of popular psychiatry kinds of things.
And so, well, they were psychiatrists, right?
So I had to be a psychiatrist.
And that meant I had to go to medical school.
And I got to college and I find myself taking, you know,
the first semester of a three-quarter physics class
and it was mechanics.
And this was so far from what it was I was interested in,
but it was also too early in the morning
in the winter court semester.
So I never made it to the physics class.
But I wandered about the rest of my freshman year
and most of my sophomore year
until I found myself in the midst of this situation
where around me,
there was this big revolution happening.
I was at Columbia University in 1968
and the Vietnam War is going on.
Columbia's building a gym in Morningside Heights,
which is part of Harlem.
And people are thinking, oh, the big, bad rich guys
are stealing the parkland
that belongs to the people of Harlem.
And, you know, they're part of the military
industrial complex, which is enslaving us
and sending us all off to war in Vietnam.
And so there was a big revolution
that involved a confluence of black activism
and, you know, SDS and social justice
and the whole university blew up and got shut down.
And I got a chance to sort of think about
why people were behaving the way they were in this context.
And I, you know, I happened to have taken
mathematical statistics.
I happened to have been taking psychology that quarter,
just psych one.
And somehow things in that space all ran together in my mind
and got me really excited about asking questions
about why people, what made certain people
go into the buildings and not others and things like that.
And so suddenly I had a path forward
and I had just been wandering around aimlessly.
And at the different points in my career, you know,
when I think, okay, well, should I take this class
or should I
just
read that book about
some idea that I wanna understand better, you know,
or should I pursue the thing that excites me
and interests me or should I, you know,
meet some requirement, you know, that's,
I always did the latter.
So I ended up, my professors in psychology
thought I was great.
They wanted me to go to graduate school.
They nominated me for Phi Beta Kappa
and I went to the Phi Beta Kappa ceremony
and this guy came up and he said,
"Oh, are you Magna or Summa?"
And I wasn't even getting honors based on my grades.
They just happened to have thought I was interested enough
in ideas to belong to Phi Beta Kappa.
So--
- I mean, would it be fair to say
you kind of stumbled around a little bit
through accidents of too early morning of classes
in physics and so on until you discovered
intrinsic motivation, as you mentioned,
and then that's it, it hooked you
and then you celebrate the fact that this happens
to human beings and it's like--
- Yeah, like, and what is it that made
what I did intrinsically motivating to me?
Well, that's interesting and I don't know
all the answers to it and I don't think I wanna,
I want anybody to think that you should be sort of
in any way, I don't know, sanctimonious
or anything about it.
You know, it's like, I really enjoyed
doing statistical analysis of data.
I really enjoyed running my own experiment,
which was what I got a chance to do
in the psychology department that chemistry
§11Transcript · 2:02:10–2:12:32
and physics had never, I never imagined
that mere mortals would ever do an experiment
in those sciences, except one that was in the textbook
that you were told to do in lab class.
But in psychology, we were already,
like even when I was taking Psych I,
it turned out we had our own rat
and we got to, after two set experiments,
we got to, okay, do something you think of,
you know, with your rat, you know?
So it's the opportunity to do it myself
and to bring together a certain set of things
that engaged me intrinsically.
And I think it has something to do
with why certain people turn out to be,
you know, profoundly amazing musical geniuses, right?
They get immersed in it at an early enough point
and it just sort of gets into the fabric.
So my little brother had intrinsic motivation for music
as we witnessed when he discovered how
to put records on the phonograph
when he was like 13 months old
and recognize which one he wanted to play,
not 'cause he could read the labels,
because he could sort of see which ones had which scratches,
which were the different, you know,
oh, that's rapid E Espanol and that's--
- Oh, wow.
- You know, and--
- And he enjoyed that, that connected with him somehow.
- Yeah, and there was something that it fed into
and you're extremely lucky if you have that
and if you can nurture it and can let it grow
and let it be an important part of your life.
- Yeah, those are the two things,
is like be attentive enough to feel it when it comes.
Like this is something special.
I mean, I don't know, for example,
I really like tabular data, like Excel sheets.
Like it brings me a deep joy.
I don't know how useful that is for anything.
- That's part of what I'm talking about.
- Exactly.
- So there's like a million, not a million,
but there's a lot of things like that for me
and you have to hear that for yourself.
Like realize this is really joyful,
but then the other part that you're mentioning,
which is the nurture, is take time and stay with it,
stay with it a while and see where that takes you in life.
- Yeah, and I think the motivational engagement
results in the immersion that then creates
the opportunity to obtain the expertise.
So we could call it the Mozart effect, right?
I mean, when I think about Mozart,
I think about the person who was born
as the fourth member of the Family String Quartet, right?
And they handed him the violin when he was six weeks old.
All right, start playing.
So the level of immersion there was amazingly profound,
but hopefully he also had something,
maybe this is where the more sort of the genetic part
comes in sometimes, I think.
Something in him resonated to the music
so that the synergy of the combination
of that was so powerful.
So that's what I really consider to be the Mozart effect.
It's sort of the synergy of something with experience
that then results in the unique flowering
of a particular mind.
So I know my siblings and I are all very different
from each other.
We've all gone in our own different directions.
And I mentioned my younger brother who was very musical.
I had my other younger brother
was like this amazing like intuitive engineer.
And one of my sisters was passionate about
water conservation well before it was
such a hugely important issue that it is today.
So we all sort of somehow find a different thing.
And I don't mean to say it isn't tied in
with something about us biologically,
but it's also when that happens, where you can find that,
then you can do your thing and you can be excited about it.
So people can be excited about fitting people on bicycles
as well as excited about making neural networks,
achieve insights into human cognition, right?
- Yeah, like for me personally,
I've always been excited about love
and friendship between humans.
And just like the actual experience of it
is since I was a child, just observing people around me
and also been excited about robots.
And there's something in me that thinks
I really would love to explore
how those two things combine.
And it doesn't make any sense.
A lot of it is also timing,
just to think of your own career and your own life.
You find yourself in certain pieces,
places that happen to involve
some of the greatest thinkers of our time.
And so it just worked out that like,
you guys developed those ideas
and there may be a lot of other people similar to you
and they were brilliant and they never found
that right connection and place
to where the ideas could flourish.
So it's timing, it's place, it's people.
And ultimately the whole ride, it's undirected.
Can I ask you about something you mentioned
in terms of psychiatry when you were younger?
Because I had a similar experience
of reading Freud and Carl Jung
and just those kind of popular psychiatry ideas.
And that was a dream for me early on in high school
to like, I hope to understand the human mind by,
somehow psychiatry felt like the right discipline for that.
Does that make you sad that psychiatry is not
the mechanism by which you want to,
are able to explore the human mind?
So for me, I was a little bit disillusioned
because of how much prescription medication
and biochemistry is involved in the discipline of psychiatry
as opposed to the dream of the Freud,
like use the mechanisms of language
to explore the human mind.
So that was a little disappointing.
And that's why I kind of went to computer science
and thinking like, maybe you can explore the human mind
by trying to build the thing.
- Yes, I wasn't exposed to the,
sort of the biomedical/pharmacological aspects
of psychiatry at that point because I didn't,
I dropped out of that whole idea of pre-med
that I never even found out about that until much later.
But you're absolutely right.
So I was actually a member of the
National Advisory Mental Health Council.
That is to say the board of scientists
who advised the director of the National Institute
of Mental Health.
And that was around the year 2000.
And in fact, at that time, the man who came in
as the new director, I had been on this board for a year
when he came in, said, okay,
schizophrenia is a biological illness.
It's a lot like cancer.
We've made huge strides in curing cancer
and that's what we're gonna do with schizophrenia.
We're gonna find the medications
that are gonna cure this disease.
And we're not gonna listen to anybody's grandmother anymore.
And good old behavioral psychology
is not something we're going to support any further.
And he completely alienated me from the institute
and from all of its prior policies,
which had been much more holistic,
I think really at some level.
And the other people on the board
were like psychiatrists, right?
Very biological psychiatrists.
It didn't pan out, right?
That nothing has changed in our ability
to help people with mental illness.
And so 20 years later, that particular path
was a dead end as far as I can tell.
- Well, there's some aspect to,
and sorry to romanticize the whole philosophical
conversation about the human mind,
but to me, psychiatrists for a time
held the flag of we're the deep thinkers.
In the same way that physicists are the deep thinkers
about the nature of reality,
psychiatrists are the deep thinkers
about the nature of the human mind.
And I think that flag has been taken from them
and carried by people like you.
It's more in the cognitive psychology,
especially when you have a foot
in the computational view of the world,
'cause you can both build it,
you can like intuit about the functioning of the mind
by building little models
and be able to say mathematical things
and then deploying those models,
especially in computers to say,
does this actually work?
And they do little experiments.
And then some combination of neuroscience
where you're starting to actually be able to observe,
you know, do certain experiments on human beings
and observe how the brain is actually functioning.
And there, using intuition,
you can start being the philosopher.
Like Richard Feynman is the philosopher,
a cognitive psychologist can become the philosopher
and psychiatrists become much more like doctors.
They're like very medical.
They help people with medication,
§12Transcript · 2:12:34–2:25:03
biochemistry and so on,
but they are no longer the book writers
and the philosophers,
which of course I admire.
I admire the Richard Feynman ability
to do great low level mathematics and physics
and the high level philosophy.
- Yeah, I think it was Fromm and Jung more than Freud
that was sort of initially kind of like made me feel like,
oh, this is really amazing and interesting
and I wanna explore it further.
I actually, when I got to college and I lost that thread,
I found more of it in sociology and literature
than I did in any place else.
So I took quite a lot of both of those disciplines
as an undergraduate.
And, you know, I was actually deeply ambivalent
about the psychology because I was doing experiments
after the initial flurry of interest
in why people would occupy buildings during an insurrection
and consider, you know, be sort of like so over committed
to their beliefs.
But I ended up in the psychology laboratory
running experiments on pigeons.
And so I had these profound sort of like dissonance
between, okay, the kinds of issues that would be explored
when I was thinking about what I read about
in modern British literature versus what I could study
with my pigeons in the laboratory.
That got resolved when I went to graduate school
and I discovered cognitive psychology.
And so for me, that was the path out of this sort of like,
extremely sort of ambivalent divergence
between the interest in the human condition
and the desire to do, you know,
actual mechanistically oriented thinking about it.
And I think we've come a long way in that regard
and that you're absolutely right that nowadays
this is something that's accessible to people
through the pathway in through computer science
or the pathway in through neuroscience.
You know, you can get derailed in neuroscience
down to the bottom of the system
where you might find the cures of various conditions,
but you don't get a chance to think
about the higher level stuff.
So it's in the systems and cognitive neuroscience
and computational intelligence,
miasma up there at the top
that I think these opportunities are most,
are richest right now.
And so yes, I am indeed blessed
by having had the opportunity to fall into that space.
- So you mentioned the human condition,
speaking of which, you happen to be a human being
who's unfortunately not immortal.
That seems to be a fundamental part of the human condition
that this ride ends.
Do you think about the fact that you're going to die one day?
Are you afraid of death?
- I would say that I am not as much afraid of death
as I am of degeneration.
And I say that in part for reasons of having, you know,
seen some tragic degenerative situations unfold.
It's exciting when you can continue to participate
and feel like you're near the place
where the wave is breaking on the shore, if you like.
And I think about, you know, my own future potential
if I were to undergo, begin to suffer from dementia,
Alzheimer's disease or semantic dementia
or some other condition, you know,
I would sort of gradually lose the thread of that ability.
And so one can live on for several,
for a decade after, you know, sort of having to retire
because one no longer has these kinds of abilities to engage.
And I think that's the thing that I fear the most.
- The losing of that, like the breaking of the wave,
the flourishing of the mind where you have these ideas
and they're swimming around, you're able to play with them.
- Yeah, and collaborate with other people who, you know,
are themselves really helping to push these ideas forward.
So, yeah.
- What about the edge of the cliff?
The end, I mean, the mystery of it, I mean.
- The migrated sort of conception of mind
and, you know, sort of continuous sort of way
of thinking about most things makes it so that,
to me, the discreteness of that transition
is less apparent than it seems to be to most people.
- I see, I see, yeah.
Yeah, I wonder, so I don't know if you know the work
of Ernest Becker and so on, I wonder what role mortality
and our ability to be cognizant of it,
anticipate it and perhaps be afraid of it,
what role that plays in our reasoning of the world.
- I think that it can be motivating to people
to think they have a limited period left.
I think in my own case, you know,
it's like seven or eight years ago now
that I was sitting around doing experiments
on decision making that were satisfying in a certain way
because I could really get closure
on whether the model fit the data perfectly or not.
And I could see how one could test, you know,
the predictions in monkeys as well as humans
and really see what the neurons were doing.
But I just realized, hey, wait a minute, you know,
I may only have about 10 or 15 years left here
and I don't feel like I'm getting towards the answers
to the really interesting questions
while I'm doing this particular level of work.
And that's when I said to myself,
okay, let's pick something that's hard, you know?
So that's when I started working on mathematical cognition.
And I think it was more in terms of,
well, I got 15 more years possibly of useful life left,
let's imagine that it's only 10.
I'm actually getting close to the end of that now,
maybe three or four more years.
But I'm beginning to feel like,
well, I probably have another five after that.
So, okay, I'll give myself another six or eight.
- But a deadline is looming and therefore--
- It's not gonna go on forever.
And so, yeah, I gotta keep thinking about the questions
that I think are the interesting
and important ones for sure.
- What do you hope your legacy is?
You've done some incredible work in your life
as a man, as a scientist.
When the aliens and the human civilization is long gone
and the aliens are reading the encyclopedia
about the human species,
what do you hope is the paragraph written about you?
- I would want it to sort of highlight
a couple things that I was able to see
one path that was more exciting to me
than the one that seemed already to be there
for a cognitive psychologist.
But not for any super special reason
other than that I'd had the right context prior to that,
but that I had gone ahead and followed that lead.
And then, I forget the exact wording,
but I said in this preface that the joy of science
is the moment in which a partially formed thought
in the mind of one person gets crystallized
a little better in the discourse
and becomes the foundation of some exciting,
concrete piece of actual scientific progress.
And I feel like that moment happened
when Rumelhart and I were doing
the interactive activation model
and when Rumelhart heard Hinton talk about gradient descent
and having the objective function
to guide the learning process.
And it happened a lot in that period
and I sort of seek that kind of thing
in my collaborations with my students.
So the idea that this is a person
who contributed to science
by finding exciting collaborative opportunities
to engage with other people through
is something that I certainly hope
is part of the paragraph.
- And like you said, taking a step
maybe in directions that are non-obvious.
So it's the old Robert Frost, road less taken.
So maybe, 'cause you said like this incomplete initial idea,
that step you take is a little bit off the beaten path.
- If I could just say one more thing here.
This was something that really contributed
to energizing me in a way
that I feel it would be useful to share.
My PhD dissertation project
was completely empirical experimental project.
And I wrote a paper based on the two main experiments
that were the core of my dissertation.
And I submitted it to a journal.
And at the end of the paper,
I had a little section where I laid out
the beginnings of my theory
about what I thought was going on
that would explain the data that I had collected.
And I had submitted the paper
to the Journal of Experimental Psychology.
So I got back a letter from the editor saying,
"Thank you very much, these are great experiments.
"We'd love to publish them in the journal.
"But what we'd like you to do
"is to leave the theorizing to the theorists
"and take that part out of the paper."
And so I did, I took that part out of the paper.
But I almost found myself labeled
as a non-theorist by this.
And I could have succumbed to that and said,
"Okay, well, I guess my job is to just go on
"and do experiments, right?"
But that's not what I wanted to do.
And so when I got to my assistant professorship,
although I continued to do experiments
§13Transcript · 2:25:06–2:31:51
because I knew I had to get some papers out,
I also, at the end of my first year,
submitted my first article to Psychological Review,
which was the theoretical journal,
where I took that section and elaborated it
and wrote it up and submitted it to them.
And they didn't accept that either,
but they said, "Oh, this is interesting.
"You should keep thinking about it this time."
And then that was what got me going to think,
"Okay, so it's not a superhuman thing
"to contribute to the development of theory.
"You don't have to be, you can do it as a mere mortal."
- And the broader, I think, lesson is
don't succumb to the labels of a particular reviewer.
- Yeah, that's for sure.
(laughing)
Or anybody labeling you, right?
- Exactly.
Especially as you become successful,
labels get assigned to you
for that you're successful for that thing.
- Yeah, I'm a connectionist or a cognitive scientist
and not a neuroscientist, whatever.
- And then you can completely,
that's just, that's the stories of the past.
You're, today, a new person
that can completely revolutionize in totally new areas.
So don't let those labels hold you back.
Well, let me ask the big question.
When you look into the,
you said it started with Columbia
trying to observe these humans
and they're doing weird stuff
and you wanna know why are they doing this stuff.
So let's zoom out even bigger.
At the hundred plus billion people
who've ever lived on Earth,
why do you think we're all doing what we're doing?
What do you think is the meaning of it all?
The big why question.
We seem to be very busy doing a bunch of stuff
and we seem to be kind of directed towards somewhere,
but why?
- Well, I myself think that we make meaning for ourselves
and that we find inspiration in the meaning
that other people have made in the past.
And the great religious thinkers
of the first millennium BC
and few that came in the early part
of the second millennium
laid down some important foundations for us.
But I do believe that we are an emergent result
of a process that happened naturally without guidance
and that meaning is what we make of it
and that the creation of efforts to reify meaning
in religious traditions and so on
is just a part of the expression of that goal
that we have to not find out what the meaning is
but to make it ourselves.
So to me, it's something that's very personal,
it's very individual, it's like meaning will come for you
through the particular combination of synergistic elements
that are your fabric and your experience
and your context and you should,
it's all made in a certain kind of a local context though.
Here I am at UCSD with this brilliant man, Rommelhart,
who's having these doubts about
symbolic artificial intelligence
that resonate with my desire
to see it grounded in the biology
and let's make the most of that.
- Yeah, and so from that little pocket,
there's some kind of peculiar little emergent process
that then, which is basically each one of us,
each one of us humans is a kind of,
you think cells and they come together
and it's an emergent process
that then tells fancy stories about itself
and then gets, just like you said,
just enjoys the beauty of the stories
we tell about ourselves.
It's an emergent process that lives for a time,
is defined by its local pocket and context in time and space
and then tells pretty stories
and we write those stories down
and then we celebrate how nice the stories are
and then it continues
'cause we build stories on top of each other
and eventually we'll colonize, hopefully, other planets,
other solar systems, other galaxies
and we'll tell even better stories.
But it all starts here on Earth.
Jay, you're speaking of peculiar emergent processes
that lived one heck of a story.
You're one of the great scientists
of cognitive science, of psychology, of computation.
It's a huge honor that you would talk to me today,
that you spend your very valuable time.
I really enjoy talking with you
and thank you for all the work you've done.
I can't wait to see what you do next.
- Well, thank you so much
and this has been an amazing opportunity for me
to let ideas that I've never fully expressed before come out
'cause you ask such a wide range of the deeper questions
that we've all been thinking about for so long.
So thank you very much for that.
- Thank you.
- Thanks for listening to this conversation
with Jay McClelland.
To support this podcast,
please check out our sponsors in the description.
And now let me leave you with some words
from Geoffrey Hinton.
"In the long run, curiosity-driven research works best.
"Real breakthroughs come from people focusing
"on what they're excited about."
Thanks for listening and hope to see you next time.
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