The following is a conversation with John Hopfield,
professor at Princeton, whose life’s work weaved beautifully
through biology, chemistry, neuroscience, and physics.
Most crucially, he saw the messy world of biology
through the piercing eyes of a physicist.
He’s perhaps best known for his work
on associative neural networks,
now known as Hopfield networks,
that were one of the early ideas that catalyzed
the development of the modern field of deep learning.
As his 2019 Franklin Medal in Physics Award states,
he applied concepts of theoretical physics
to provide new insights on important biological questions
in a variety of areas, including genetics and neuroscience
with significant impact on machine learning.
And as John says in his 2018 article titled,
Now What?, his accomplishments have often come about
by asking that very question, now what?
And often responding by a major change of direction.
This is the Artificial Intelligence Podcast.
If you enjoy it, subscribe on YouTube,
give it five stars on Apple Podcast,
support it on Patreon, or simply connect with me on Twitter,
and Lex Friedman, spelled F R I D M A M.
As usual, I’ll do one or two minutes of ads now
and never any ads in the middle
that can break the flow of the conversation.
I hope that works for you
and doesn’t hurt the listening experience.
This show is presented by Cash App,
the number one finance app in the App Store.
When you get it, use code LexPodcast.
Cash App lets you send money to friends, buy Bitcoin,
and invest in the stock market with as little as $1.
Since Cash App does fractional share trading,
let me mention that the order execution algorithm
that works behind the scenes
to create the abstraction of fractional orders
is to me an algorithmic marvel.
So big props to the Cash App engineers
for solving a hard problem
that in the end provides an easy interface
that takes a step up the next layer of abstraction
over the stock market,
making trading more accessible for new investors
and diversification much easier.
So again, if you get Cash App from the App Store,
Google Play, and use code LexPodcast,
you’ll get $10,
and Cash App will also donate $10 to First,
one of my favorite organizations
that is helping advance robotics and STEM education
for young people around the world.
And now here’s my conversation with John Hopfield.
What difference between biological neural networks
and artificial neural networks
is most captivating and profound to you?
At the higher philosophical level,
let’s not get technical just yet.
But one of the things that very much intrigues me
is the fact that neurons have all kinds of components,
properties to them.
And in evolutionary biology,
if you have some little quirk
in how a molecule works or how a cell works,
and it can be made use of,
evolution will sharpen it up
and make it into a useful feature rather than a glitch.
And so you expect in neurobiology for evolution
to have captured all kinds of possibilities
of getting neurons,
of how you get neurons to do things for you.
And that aspect has been completely suppressed
in artificial neural networks.
So the glitches become features
in the biological neural network.
Look, let me take one of the things
that I used to do research on.
If you take things which oscillate,
they have rhythms which are sort of close to each other.
Under some circumstances,
these things will have a phase transition
and suddenly the rhythm will,
everybody will fall into step.
There was a marvelous physical example of that
in the Millennium Bridge across the Thames River,
about, built about 2001.
And pedestrians walking across,
pedestrians don’t walk synchronized,
they don’t walk in lockstep.
But they’re all walking about the same frequency
and the bridge could sway at that frequency
and the slight sway made pedestrians tend a little bit
to lock into step and after a while,
the bridge was oscillating back and forth
and the pedestrians were walking in step to it.
And you could see it in the movies made out of the bridge.
And the engineers made a simple minor mistake.
They assume when you walk, it’s step, step, step
and it’s back and forth motion.
But when you walk, it’s also right foot left
with side to side motion.
And it’s the side to side motion
for which the bridge was strong enough,
but it wasn’t stiff enough.
And as a result, you would feel the motion
and you’d fall into step with it.
And people were very uncomfortable with it.
They closed the bridge for two years
while they built stiffening for it.
Now, nerve cells produce action potentials.
You have a bunch of cells which are loosely coupled together
producing action potentials at the same rate.
There’ll be some circumstances
under which these things can lock together.
Other circumstances in which they won’t.
Well, if they’re fired together,
you can be sure that other cells are gonna notice it.
So you can make a computational feature out of this
in an evolving brain.
Most artificial neural networks
don’t even have action potentials,
let alone have the possibility for synchronizing them.
And you mentioned the evolutionary process.
So the evolutionary process
that builds on top of biological systems
leverages the weird mess of it somehow.
So how do you make sense of that ability
to leverage all the different kinds of complexities
in the biological brain?
Well, look, in the biological molecule level,
you have a piece of DNA
which encodes for a particular protein.
You could duplicate that piece of DNA
and now one part of it can code for that protein,
but the other one could itself change a little bit
and thus start coding for a molecule
which is slightly different.
Now, if that molecule was just slightly different,
had a function which helped any old chemical reaction
which was important to the cell,
you would go ahead and let that try,
and evolution would slowly improve that function.
And so you have the possibility of duplicating
and then having things drift apart.
One of them retain the old function,
the other one do something new for you.
And there’s evolutionary pressure to improve.
Look, there isn’t in computers too,
but improvement has to do with closing some companies
and opening some others.
The evolutionary process looks a little different.
Yeah, similar timescale perhaps.
Much shorter in timescale.
Companies close, yeah, go bankrupt and are born,
yeah, shorter, but not much shorter.
Some companies last a century, but yeah, you’re right.
I mean, if you think of companies as a single organism
that builds and you all know, yeah,
it’s a fascinating dual correspondence there
between biological organisms.
And companies have difficulty having a new product
competing with an old product.
When IBM built its first PC, you probably read the book,
they made a little isolated internal unit to make the PC.
And for the first time in IBM’s history,
they didn’t insist that you build it out of IBM components.
But they understood that they could get into this market,
which is a very different thing
by completely changing their culture.
And biology finds other markets in a more adaptive way.
Yeah, it’s better at it.
It’s better at that kind of integration.
So maybe you’ve already said it,
but what to use the most beautiful aspect
or mechanism of the human mind?
Is it the adaptive, the ability to adapt
as you’ve described, or is there some other little quirk
that you particularly like?
Adaptation is everything when you get down to it.
But the difference, there are differences between adaptation
where your learning goes on only over generations
and over evolutionary time,
where your learning goes on at the time scale
of one individual who must learn from the environment
during that individual’s lifetime.
And biology has both kinds of learning in it.
And the thing which makes neurobiology hard
is that a mathematical system, as it were,
built on this other kind of evolutionary system.
What do you mean by mathematical system?
Where’s the math and the biology?
Well, when you talk to a computer scientist
about neural networks, it’s all math.
The fact that biology actually came about from evolution,
and the fact that biology is about a system
which you can build in three dimensions.
If you look at computer chips,
computer chips are basically two dimensional structures,
maybe 2.1 dimensions, but they really have difficulty
doing three dimensional wiring.
Biology is, the neocortex is actually also sheet like,
and it sits on top of the white matter,
which is about 10 times the volume of the gray matter
and contains all what you might call the wires.
But there’s a huge, the effect of computer structure
on what is easy and what is hard is immense.
And biology does, it makes some things easy
that are very difficult to understand
how to do computationally.
On the other hand, you can’t do simple floating point
arithmetic because it’s awfully stupid.
And you’re saying this kind of three dimensional
complicated structure makes, it’s still math.
It’s still doing math.
The kind of math it’s doing enables you to solve problems
of a very different kind.
That’s right, that’s right.
So you mentioned two kinds of adaptation,
the evolutionary adaptation and the adaptation
or learning at the scale of a single human life.
Which do you, which is particularly beautiful to you
and interesting from a research
and from just a human perspective?
And which is more powerful?
I find things most interesting that I begin to see
how to get into the edges of them
and tease them apart a little bit and see how they work.
And since I can’t see the evolutionary process going on,
I’m in awe of it.
But I find it just a black hole as far as trying
to understand what to do.
And so in a certain sense, I’m in awe of it,
but I couldn’t be interested in working on it.
The human life’s time scale is however thing
you can tease apart and study.
Yeah, you can do, there’s developmental neurobiology
which understands how the connections
and how the structure evolves from a combination
of what the genetics is like and the real,
the fact that you’re building a system in three dimensions.
In just days and months, those early days
of a human life are really interesting.
They are and of course, there are times
of immense cell multiplication.
There are also times of the greatest cell death
in the brain is during infancy.
So what is not effective, what is not wired well enough
to use at the moment, throw it out.
It’s a mysterious process.
From, let me ask, from what field do you think
the biggest breakthrough is in understanding
the mind will come in the next decades?
Is it neuroscience, computer science, neurobiology,
psychology, physics, maybe math, maybe literature?
Well, of course, I see the world always
through a lens of physics.
I grew up in physics and the way I pick problems
is very characteristic of physics
and of an intellectual background which is not psychology,
which is not chemistry and so on and so on.
Yeah, both of your parents are physicists.
Both of my parents were physicists
and the real thing I got out of that was a feeling
that the world is an understandable place
and if you do enough experiments and think about
what they mean and structure things
so you can do the mathematics of the,
relevant to the experiments, you ought to be able
to understand how things work.
But that was, that was a few years ago.
Did you change your mind at all through many decades
of trying to understand the mind,
of studying in different kinds of ways?
Not even the mind, just biological systems.
You still have hope that physics, that you can understand?
There’s a question of what do you mean by understand?
When I taught freshman physics, I used to say,
I wanted to get physics to understand the subject,
to understand Newton’s laws.
I didn’t want them simply to memorize a set of examples
to which they knew the equations to write down
to generate the answers.
I had this nebulous idea of understanding
so that if you looked at a situation,
you could say, oh, I expect the ball to make that trajectory
or I expect some intuitive notion of understanding
and I don’t know how to express that very well
and I’ve never known how to express it well.
And you run smack up against it when you do these,
look at these simple neural nets,
feed forward neural nets, which do amazing things
and yet, you know, contain nothing of the essence
of what I would have felt was understanding.
Understanding is more than just an enormous lookup table.
Let’s linger on that.
How sure you are of that?
What if the table gets really big?
So, I mean, asked another way,
these feed forward neural networks,
do you think they’ll ever understand?
Could answer that in two ways.
I think if you look at real systems,
feedback is an essential aspect
of how these real systems compute.
On the other hand, if I have a mathematical system
with feedback, I know I can unlayer this and do it,
but I have an exponential expansion
in the amount of stuff I have to build
if I can resolve the problem that way.
So feedback is essential.
So we can talk even about recurrent neural nets,
so recurrence, but do you think all the pieces are there
to achieve understanding through these simple mechanisms?
Like back to our original question,
what is the fundamental, is there a fundamental difference
between artificial neural networks and biological
or is it just a bunch of surface stuff?
Suppose you ask a neurosurgeon, when is somebody dead?
So we’ll probably go back to saying,
well, I can look at the brain rhythms
and tell you this is a brain
which has never could have functioned again.
This is one of the, this other one is one of the stuff
we treat it well is still recoverable.
And then just do that by some electrodes
looking at simple electrical patterns,
which don’t look in any detail at all
what individual neurons are doing.
These rhythms are utterly absent
from anything which goes on at Google.
Yeah, but the rhythms.
But the rhythms what?
So, well, that’s like comparing, okay, I’ll tell you,
it’s like you’re comparing the greatest classical musician
in the world to a child first learning to play.
The question I’m at, but they’re still both
playing the piano.
I’m asking, is there, will it ever go on at Google?
Do you have a hope?
Because you’re one of the seminal figures
in both launching both disciplines,
both sides of the river.
I think it’s going to go on generation after generation.
The way it has where what you might call
the AI computer science community says,
let’s take the following.
This is our model of neurobiology at the moment.
Let’s pretend it’s good enough
and do everything we can with it.
And it does interesting things.
And after a while it sort of grinds into the sand
and you say, ah, something else is needed for neurobiology.
And some other grand thing comes in
and enables you to go a lot further.
What will go into the sand again?
And I think it could be generations of this evolution.
I don’t know how many of them.
And each one is going to get you further
into what a brain does.
And in some sense, past the Turing test longer
and in more broad aspects.
And how many of these are going to have to be
before you say, I’ve made something,
I’ve made a human, I don’t know.
But your sense is it might be a couple.
My sense is it might be a couple more.
And going back to my brainwaves as it were.
Yes, from the AI point of view,
they would say, ah, maybe these are an epiphenomenon
and not important at all.
The first car I had, a real wreck of a 1936 Dodge,
go above about 45 miles an hour and the wheels would shimmy.
Good speedometer that.
Now, nobody designed the car that way.
The car is malfunctioning to have that.
But in biology, if it were useful to know
when are you going more than 45 miles an hour,
you just capture that.
And you wouldn’t worry about where it came from.
It’s going to be a long time before that kind of thing,
which can take place in large complex networks of things
is actually used in the computation.
Look, how many transistors are there
in your laptop these days?
Actually, I don’t know the number.
It’s on the scale of 10 to the 10.
I can’t remember the number either.
And all the transistors are somewhat similar.
And most physical systems with that many parts,
all of which are similar, have collective properties.
Sound waves in air, earthquakes,
what have you, have collective properties.
There are no collective properties used
in artificial neural networks, in AI.
Yeah, it’s very.
If biology uses them,
it’s going to take us to more generations of things
for people to actually dig in
and see how they are used and what they mean.
See, you’re very right.
We might have to return several times to neurobiology
and try to make our transistors more messy.
At the same time, the simple ones will conquer big aspects.
And I think one of the most, biggest surprises to me was
how well learning systems
because they’re manifestly nonbiological,
how important they can be actually,
and how important and how useful they can be in AI.
So if we can just take a stroll to some of your work.
If we can just take a stroll to some of your work
that is incredibly surprising,
that it works as well as it does,
that launched a lot of the recent work with neural networks.
If we go to what are now called Hopfield networks,
can you tell me what is associative memory in the mind
for the human side?
Let’s explore memory for a bit.
Okay, what do you mean by associative memory is,
ah, you have a memory of each of your friends.
Your friend has all kinds of properties
from what they look like, what their voice sounds like,
to where they went to college, where you met them,
go on and on, what science papers they’ve written.
And if I start talking about a 5 foot 10 wire,
cognitive scientist who’s got a very bad back,
it doesn’t take very long for you to say,
oh, he’s talking about Jeff Hinton.
I never mentioned the name or anything very particular.
But somehow a few facts that are associated
with a particular person enables you to get a hold
of the rest of the facts.
Or not the rest of them, another subset of them.
And it’s this ability to link things together,
link experiences together, which goes under
the general name of associative memory.
And a large part of intelligent behavior
is actually just large associative memories at work,
as far as I can see.
What do you think is the mechanism of how it works?
What do you think is the mechanism of how it works
in the mind?
Is it a mystery to you still?
Do you have inklings of how this essential thing
for cognition works?
What I made 35 years ago was, of course,
a crude physics model to actually enable you
to understand my old sense of understanding
as a physicist, because you could say,
ah, I understand why this goes to stable states.
It’s like things going downhill.
And that gives you something with which to think
in physical terms rather than only in mathematical terms.
So you’ve created these associative artificial networks.
Now, if you look at what I did,
I didn’t at all describe a system which gracefully learns.
I described a system in which you could understand
how learning could link things together,
how very crudely it might learn.
One of the things which intrigues me
as I reinvestigate that system now to some extent is,
look, I see you, I’ll see you every second
for the next hour or what have you.
Each look at you is a little bit different.
I don’t store all those second by second images.
I don’t store 3,000 images.
I somehow compact this information.
So I now have a view of you,
which I can use.
It doesn’t slavishly remember anything in particular,
but it compacts the information into useful chunks,
which are somehow these chunks,
which are not just activities of neurons,
bigger things than that,
which are the real entities which are useful to you.
Which are useful to you.
Useful to you to describe,
to compress this information coming at you.
And you have to compress it in such a way
that if the information comes in just like this again,
I don’t bother to rewrite it or efforts to rewrite it
simply do not yield anything
because those things are already written.
And that needs to be not,
look this up, have I stored it somewhere already?
There’ll be something which is much more automatic
in the machine hardware.
Right, so in the human mind,
how complicated is that process do you think?
So you’ve created,
feels weird to be sitting with John Hotfield
calling him Hotfield Networks, but.
It is weird.
Yeah, but nevertheless, that’s what everyone calls him.
So here we are.
So that’s a simplification.
That’s what a physicist would do.
You and Richard Feynman sat down
and talked about associative memory.
Now, if you look at the mind
where you can’t quite simplify it so perfectly,
do you think that?
Well, let me backtrack just a little bit.
Biology is about dynamical systems.
Computers are dynamical systems.
You can ask, if you want to model biology,
if you want to model neurobiology,
what is the time scale?
There’s a dynamical system in which,
of a fairly fast time scale in which you could say,
the synapses don’t change much during this computation,
so I’ll think of the synapses fixed
and just do the dynamics of the activity.
Or you can say, the synapses are changing fast enough
that I have to have the synaptic dynamics
working at the same time as the system dynamics
in order to understand the biology.
Most, if you look at the feedforward artificial neural nets,
they’re all done as learnings.
First of all, I spend some time learning, not performing,
and I turn off learning and I turn off learning,
and I turn off learning and I perform.
That’s not biology.
And so as I look more deeply at neurobiology,
even as associative memory,
I’ve got to face the fact that the dynamics
of the synapse change is going on all the time.
And I can’t just get by by saying,
I’ll do the dynamics of activity with fixed synapses.
So the synaptic, the dynamics of the synapses
is actually fundamental to the whole system.
And there’s nothing necessarily separating the time scales.
When the time scale’s gonna be separated,
it’s neat from the physicist’s
or the mathematician’s point of view,
but it’s not necessarily true in neurobiology.
So you’re kind of dancing beautifully
between showing a lot of respect to physics
and then also saying that physics
cannot quite reach the complexity of biology.
So where do you land?
Or do you continuously dance between the two points?
I continuously dance between them
because my whole notion of understanding
is that you can describe to somebody else
how something works in ways which are honest and believable
and still not describe all the nuts and bolts in detail.
I can describe weather
as 10 to the 32 molecules colliding in the atmosphere.
I can simulate weather that way if I have a big enough machine.
I’ll simulate it accurately.
It’s no good for understanding.
If I want to understand things, I want to understand things
in terms of wind patterns, hurricanes,
pressure differentials, and so on,
all things as they’re collective.
And the physicist in me always hopes
that biology will have some things
that can be said about it which are both true
and for which you don’t need all the molecular details
as the molecules colliding.
That’s what I mean from the roots of physics,
So what did, again, sorry,
but Hopfield Networks help you understand
what insight did give us about memory, about learning?
They didn’t give insights about learning.
They gave insights about how things having learned
could be expressed, how having learned a picture of you,
a picture of you reminds me of your name.
That would, but it didn’t describe a reasonable way
of actually doing the learning.
They only said if you had previously learned
the connections of this kind of pattern,
would now be able to,
behave in a physical way was to say,
ah, if I put the part of the pattern in here,
the other part of the pattern will complete over here.
I could understand that physics,
if the right learning stuff had already been put in.
And it could understand why then putting in a picture
of somebody else would generate something else over here.
But it did not have a reasonable description
of the learning that was going on.
It did not have a reasonable description
of the learning process.
But even, so forget learning.
I mean, that’s just a powerful concept
that sort of forming representations
that are useful to be robust,
you know, for error correction kind of thing.
So this is kind of what the biology does
we’re talking about.
Yeah, and what my paper did was simply enable you,
there are lots of ways of being robust.
If you think of a dynamical system,
you think of a system where a path is going on in time.
And if you think for a computer,
there’s a computational path,
which is going on in a huge dimensional space
of ones and zeros.
And an error correction system is a system,
which if you get a little bit off that trajectory,
will push you back onto that trajectory again.
So you get to the same answer in spite of the fact
that there were things,
so that the computation wasn’t being ideally done
all the way along the line.
And there are lots of models for error correction.
But one of the models for error correction is to say,
there’s a valley that you’re following, flowing down.
And if you push a little bit off the valley,
just like water being pushed a little bit by a rock,
it gets back and follows the course of the river.
And that basically the analog
in the physical system, which enables you to say,
oh yes, error free computation and an associative memory
are very much like things that I can understand
from the point of view of a physical system.
The physical system is, can be under some circumstances,
an accurate metaphor.
It’s not the only metaphor.
There are error correction schemes,
which don’t have a valley and energy behind them.
But those are error correction schemes,
which a mathematician may be able to understand,
but I don’t.
So there’s the physical metaphor that seems to work here.
That’s right, that’s right.
So these kinds of networks actually led to a lot of the work
that is going on now in neural networks,
artificial neural networks.
So the follow on work with restricted Boltzmann machines
and deep belief nets followed on from these ideas
of the Hopfield network.
So what do you think about this continued progress
of that work towards now re revigorated exploration
of feed forward neural networks
and recurrent neural networks
and convolutional neural networks
and kinds of networks that are helping solve
image recognition, natural language processing,
all that kind of stuff.
It always intrigued me that one of the most long lived
of the learning systems is the Boltzmann machine,
which is intrinsically a feedback network.
And with the brilliance of Hind and Sinowski
to understand how to do learning in that.
And it’s still a useful way to understand learning
and the learning that you understand in that
has something to do with the way
that feed forward systems work.
But it’s not always exactly simple
to express that intuition.
But it’s always amuses me to see Hinton
going back to the will yet again
on a form of the Boltzmann machine
because really that which has feedback
and interesting probabilities in it
is a lovely encapsulation of something in computational.
Something both computational and physical.
Computational and it’s very much related
to feed forward networks.
Physical in that Boltzmann machine learning
is really learning a set of parameters
for a physics Hamiltonian or energy function.
What do you think about learning in this whole domain?
Do you think the aforementioned guy,
Jeff Hinton, all the work there with backpropagation,
all the kind of learning that goes on in these networks,
if we compare it to learning in the brain, for example,
is there echoes of the same kind of power
that backpropagation reveals
about these kinds of recurrent networks?
Or is it something fundamentally different
going on in the brain?
I don’t think the brain is as deep
as the deepest networks go,
the deepest computer science networks.
And I do wonder whether part of that depth
of the computer science networks is necessitated
by the fact that the only learning
that’s easily done on a machine is feed forward.
And so there’s the question of to what extent
is the biology, which has some feed forward
and some feed back,
been captured by something which has got many more neurons
but much more depth than the neurons in it.
So part of you wonders if the feedback is actually
more essential than the number of neurons or the depth,
The dynamics of the feedback.
Look, if you don’t have feedback,
it’s a little bit like a building a big computer
and running it through one clock cycle.
And then you can’t do anything
until you reload something coming in.
How do you use the fact that there are multiple clock cycles?
How do I use the fact that you can close your eyes,
stop listening to me and think about a chessboard
for two minutes without any input whatsoever?
Yeah, that memory thing,
that’s fundamentally a feedback kind of mechanism.
You’re going back to something.
Yes, it’s hard to understand.
It’s hard to introspect,
let alone consciousness.
Oh, let alone consciousness, yes, yes.
Because that’s tied up in there too.
You can’t just put that on another shelf.
Every once in a while I get interested in consciousness
and then I go and I’ve done that for years
and ask one of my betters, as it were,
their view on consciousness.
It’s been interesting collecting them.
What is consciousness?
Let’s try to take a brief step into that room.
Well, ask Marvin Minsky,
his view on consciousness.
And Marvin said,
consciousness is basically overrated.
It may be an epiphenomenon.
After all, all the things your brain does,
but they’re actually hard computations
you do nonconsciously.
And there’s so much evidence
that even the simple things you do,
you can make decisions,
you can make committed decisions about them,
the neurobiologist can say,
he’s now committed, he’s going to move the hand left
before you know it.
So his view that consciousness is not,
that’s just like little icing on the cake.
The real cake is in the subconscious.
Nonconscious, what’s the better word, sir?
It’s only that Freud captured the other word.
Yeah, it’s a confusing word, subconscious.
Nicholas Chaiter wrote an interesting book.
I think the title of it is The Mind is Flat.
Flat in a neural net sense, might be flat
as something which is a very broad neural net
without any layers in depth,
whereas a deep brain would be many layers
and not so broad.
In the same sense that if you push Minsky hard enough,
he would probably have said,
consciousness is your effort to explain to yourself
that which you have already done.
Yeah, it’s the weaving of the narrative
around the things that have already been computed for you.
That’s right, and so much of what we do
for our memories of events, for example.
If there’s some traumatic event you witness,
you will have a few facts about it correctly done.
If somebody asks you about it, you will weave a narrative
which is actually much more rich in detail than that
based on some anchor points you have of correct things
and pulling together general knowledge on the other,
but you will have a narrative.
And once you generate that narrative,
you are very likely to repeat that narrative
and claim that all the things you have in it
are actually the correct things.
There was a marvelous example of that
in the Watergate slash impeachment era of John Dean.
John Dean, you’re too young to know,
had been the personal lawyer of Nixon.
And so John Dean was involved in the coverup
and John Dean ultimately realized
the only way to keep himself out of jail for a long time
was actually to tell some of the truths about Nixon.
And John Dean was a tremendous witness.
He would remember these conversations in great detail
and very convincing detail.
And long afterward, some of the tapes,
the secret tapes as it were from which these,
Don was, Gene was recalling these conversations
were published, and one found out that John Dean
had a good but not exceptional memory.
What he had was an ability to paint vividly
and in some sense accurately the tone of what was going on.
By the way, that’s a beautiful description of consciousness.
Do you, like where do you stand in your today?
So perhaps it changes day to day,
but where do you stand on the importance of consciousness
in our whole big mess of cognition?
Is it just a little narrative maker
or is it actually fundamental to intelligence?
That’s a very hard one.
When I asked Francis Crick about consciousness,
he launched forward in a long monologue
about Mendel and the peas and how Mendel knew
that there was something and how biologists understood
that there was something in inheritance,
which was just very, very different.
And the fact that inherited traits didn’t just wash out
into a gray, but this or this and propagated
that that was absolutely fundamental to the biology.
And it took generations of biologists to understand
that there was genetics and it took another generation
or two to understand that genetics came from DNA.
But very shortly after Mendel, thinking biologists
did realize that there was a deep problem about inheritance.
And Francis would have liked to have said,
and that’s why I’m working on consciousness.
But of course, he didn’t have any smoking gun
in the sense of Mendel.
And that’s the weakness of his position.
If you read his book, which he wrote with Koch, I think.
Yeah, Christoph Koch, yeah.
I find it unconvincing for the smoking gun reason.
So I’m going on collecting views without actually having taken
a very strong one myself,
because I haven’t seen the entry point.
Not seeing the smoking gun from the point of view
of physics, I don’t see the entry point.
Whereas in neurobiology, once I understood the idea
of a collective, an evolution of dynamics,
which could be described as a collective phenomenon,
I thought, ah, there’s a point where what I know
about physics is so different from any neurobiologist
that I have something that I might be able to contribute.
And right now, there’s no way to grasp at consciousness
from a physics perspective.
From my point of view, that’s correct.
And of course, people, physicists, like everybody else,
think very muddily about things.
You ask the closely related question about free will.
Do you believe you have free will?
Physicists will give an offhand answer,
and then backtrack, backtrack, backtrack,
where they realize that the answer they gave
must fundamentally contradict the laws of physics.
Natural, answering questions of free will
and consciousness naturally lead to contradictions
from a physics perspective.
Because it eventually ends up with quantum mechanics,
and then you get into that whole mess
of trying to understand how much,
from a physics perspective, how much is determined,
already predetermined, how much is already deterministic
about our universe, and there’s lots of different things.
And if you don’t push quite that far, you can say,
essentially, all of neurobiology, which is relevant,
can be captured by classical equations of motion.
Right, because in my view of the mysteries of the brain
are not the mysteries of quantum mechanics,
but the mysteries of what can happen
when you have a dynamical system, driven system,
with 10 to the 14 parts.
That that complexity is something which is,
that the physics of complex systems
is at least as badly understood
as the physics of phase coherence in quantum mechanics.
Can we go there for a second?
You’ve talked about attractor networks,
and just maybe you could say what are attractor networks,
and more broadly, what are interesting network dynamics
that emerge in these or other complex systems?
You have to be willing to think
in a huge number of dimensions,
because in a huge number of dimensions,
the behavior of a system can be thought
as just the motion of a point over time
in this huge number of dimensions.
And an attractor network is simply a network
where there is a line and other lines
converge on it in time.
That’s the essence of an attractor network.
That’s how you.
In a highly dimensional space.
And the easiest way to get that
is to do it in a highly dimensional space,
where some of the dimensions provide the dissipation,
which, if I have a physical system,
trajectories can’t contract everywhere.
They have to contract in some places and expand in others.
There’s a fundamental classical theorem
of statistical mechanics,
which goes under the name of Liouville’s theorem,
which says you can’t contract everywhere.
If you contract somewhere, you expand somewhere else.
In interesting physical systems,
you’ve got driven systems
where you have a small subsystem,
which is the interesting part.
And the rest of the contraction and expansion,
the physicists would say it’s entropy flow
in this other part of the system.
But basically, attractor networks are dynamics
that are funneling down so that you can’t be any,
so that if you start somewhere in the dynamical system,
you will soon find yourself
on a pretty well determined pathway, which goes somewhere.
If you start somewhere else,
you’ll wind up on a different pathway,
but I don’t have just all possible things.
You have some defined pathways which are allowed
and onto which you will converge.
And that’s the way you make a stable computer,
and that’s the way you make a stable behavior.
So in general, looking at the physics
of the emergent stability in networks,
what are some interesting characteristics that,
what are some interesting insights
from studying the dynamics of such high dimensional systems?
Most dynamical systems, most driven dynamical systems,
are driven, they’re coupled somehow to an energy source.
And so their dynamics keeps going
because it’s coupling to the energy source.
Most of them, it’s very difficult to understand at all
what the dynamical behavior is going to be.
You have to run it.
There’s a subset of systems which has
what is actually known to the mathematicians
as a Lyapunov function, and those systems,
you can understand convergent dynamics
by saying you’re going downhill on something or other.
And that’s what I found with ever knowing
what Lyapunov functions were in the simple model
I made in the early 80s, was an energy function
so you could understand how you could get this channeling
on the pathways without having to follow the dynamics
in infinite detail.
You started rolling a ball at the top of a mountain,
it’s gonna wind up at the bottom of a valley.
You know that’s true without actually watching
the ball roll down.
There’s certain properties of the system
that when you can know that.
And not all systems behave that way.
Most don’t, probably.
Most don’t, but it provides you with a metaphor
for thinking about systems which are stable
and who to have these attractors behave
even if you can’t find a Lyapunov function behind them
or an energy function behind them.
It gives you a metaphor for thought.
Yeah, speaking of thought,
if I had a glint in my eye with excitement
and said I’m really excited about this something
called deep learning and neural networks
and I would like to create an intelligent system
and came to you as an advisor, what would you recommend?
Is it a hopeless pursuit to use neural networks
to achieve thought?
Is it, what kind of mechanisms should we explore?
What kind of ideas should we explore?
Well, you look at the simple networks,
the one past networks.
They don’t support multiple hypotheses very well.
As I have tried to work with very simple systems
which do something which you might consider to be thinking,
thought has to do with the ability to do mental exploration
before you take a physical action.
Almost like we were mentioning, playing chess,
visualizing, simulating inside your head different outcomes.
And now you would do that in a feed forward network
because you’ve pre calculated all kinds of things.
But I think the way neurobiology does it
hasn’t pre calculated everything.
It actually has parts of a dynamical system
in which you’re doing exploration in a way which is.
There’s a creative element.
Like there’s an.
There’s a creative element.
And in a simple minded neural net,
you have a constellation of instances
of which you’ve learned.
And if you are within that space,
if a new question is a question within this space,
you can actually rely on that system pretty well
to come up with a good suggestion for what to do.
If on the other hand,
the query comes from outside the space,
you have no way of knowing how the system
is gonna behave.
There are no limitations on what can happen.
And so with the artificial neural net world
is always very much,
I have a population of examples.
The test set must be drawn from the equivalent population.
If the test set has examples,
which are from a population which is completely different,
there’s no way that you could expect
to get the answer right.
Yeah, what they call outside the distribution.
That’s right, that’s right.
And so if you see a ball rolling across the street at dusk,
if that wasn’t in your training set,
the idea that a child may be coming close behind that
is not going to occur to the neural net.
And it is to our,
there’s something in your biology that allows that.
Yeah, there’s something in the way
of what it means to be outside of the population
of the training set.
The population of the training set
isn’t just sort of this set of examples.
There’s more to it than that.
And it gets back to my question of,
what is it to understand something?
You know, in a small tangent,
you’ve talked about the value of thinking
of deductive reasoning in science
versus large data collection.
So sort of thinking about the problem.
I suppose it’s the physics side of you
of going back to first principles and thinking,
but what do you think is the value of deductive reasoning
in the scientific process?
Well, there are obviously scientific questions
in which the route to the answer to it
comes through the analysis of one hell of a lot of data.
Cosmology, that kind of stuff.
And that’s never been the kind of problem
in which I’ve had any particular insight.
Though I must say, if you look at,
cosmology is one of those.
If you look at the actual things that Jim Peebles,
one of this year’s Nobel Prize in physics,
ones from the local physics department,
the kinds of things he’s done,
he’s never crunched large data.
Never, never, never.
He’s used the encapsulation of the work of others
in this regard.
But it ultimately boiled down to thinking
through the problem.
Like what are the principles under which
a particular phenomenon operates?
And look, physics is always going to look
for ways in which you can describe the system
in a way which rises above the details.
And to the hard dyed, the wool biologist,
biology works because of the details.
In physics, to the physicists,
we want an explanation which is right
in spite of the details.
And there will be questions which we cannot answer
as physicists because the answer cannot be found that way.
There’s, I’m not sure if you’re familiar
with the entire field of brain computer interfaces
that’s become more and more intensely researched
and developed recently, especially with companies
like Neuralink with Elon Musk.
Yeah, I know there have always been the interests
both in things like getting the eyes
to be able to control things
or getting the thought patterns
to be able to move what had been a connected limb
which is now connected through a computer.
So in the case of Neuralink,
they’re doing 1,000 plus connections
where they’re able to do two way,
activate and read spikes, neural spikes.
Do you have hope for that kind of computer brain interaction
in the near or maybe even far future
of being able to expand the ability
of the mind of cognition or understand the mind?
It’s interesting watching things go.
When I first became interested in neurobiology,
most of the practitioners thought you would be able
to understand neurobiology by techniques
which allowed you to record only one cell at a time.
One cell, yeah.
People like David Hubel,
very strongly reflected that point of view.
And that’s been taken over by a generation,
a couple of generations later,
by a set of people who says not until we can record
from 10 to the four, 10 to the five at a time,
will we actually be able to understand
how the brain actually works.
And in a general sense, I think that’s right.
You have to begin to be able to look
for the collective modes, the collective operations of things.
It doesn’t rely on this action potential or that cell.
It relies on the collective properties of this set of cells
connected with this kind of patterns and so on.
And you’re not going to succeed in seeing
what those collective activities are
without recording many cells at once.
The question is how many at once?
What’s the threshold?
And that’s the…
Yeah, and look, it’s being pursued hard
in the motor cortex.
The motor cortex does something which is complex,
and yet the problem you’re trying to address
is fairly simple.
Now, neurobiology does it in ways that differ
from the way an engineer would do it.
An engineer would put in six highly accurate stepping motors
are controlling a limb rather than 100,000 muscle fibers,
each of which has to be individually controlled.
And so understanding how to do things in a way
which is much more forgiving and much more neural,
I think would benefit the engineering world.
The engineering world, a touch.
Let’s put in a pressure sensor or two,
rather than an array of a gazillion pressure sensors,
none of which are accurate,
all of which are perpetually recalibrating themselves.
So you’re saying your hope is,
your advice for the engineers of the future
is to embrace the large chaos of a messy, air prone system
like those of the biological systems.
Like that’s probably the way to solve some of these.
I think you’ll be able to make better computations
slash robotics that way than by trying to force things
into a robotics where joint motors are powerful
and stepping motors are accurate.
But then the physicists, the physicist in you
will be lost forever in such systems
because there’s no simple fundamentals to explore
in systems that are so large and messy.
Well, you say that, and yet there’s a lot of physics
in the Navier Stokes equations,
the equations of nonlinear hydrodynamics,
huge amount of physics in them.
All the physics of atoms and molecules has been lost,
but it’s been replaced by this other set of equations,
which is just as true as the equations at the bottom.
Now those equations are going to be harder to find
in general biology, but the physicist in me says
there are probably some equations of that sort.
They’re out there.
They’re out there, and if physics
is going to contribute anything,
it may contribute to trying to find out
what those equations are and how to capture them
from the biology.
Would you say that’s one of the main open problems
of our age is to discover those equations?
Yeah, if you look at, there’s molecules
and there’s psychological behavior,
and these two are somehow related.
They’re layers of detail, they’re layers of collectiveness,
and to capture that in some vague way,
several stages on the way up to see how these things
can actually be linked together.
So it seems in our universe, there’s a lot of elegant
equations that can describe the fundamental way
that things behave, which is a surprise.
I mean, it’s compressible into equations.
It’s simple and beautiful, but it’s still an open question
whether that link is equally between molecules
and the brain is equally compressible
into elegant equations.
But your sense, well, you’re both a physicist
and a dreamer, you have a sense that…
Yeah, but I can only dream physics dreams.
There was an interesting book called Einstein’s Dreams,
which alternates between chapters on his life
and descriptions of the way time might have been but isn’t.
The linking between these being important ideas
that Einstein might have had to think about
the essence of time as he was thinking about time.
So speaking of the essence of time in your biology,
you’re one human, famous, impactful human,
but just one human with a brain living the human condition.
But you’re ultimately mortal, just like all of us.
Has studying the mind as a mechanism
changed the way you think about your own mortality?
It has, really, because particularly as you get older
and the body comes apart in various ways,
I became much more aware of the fact
that what is somebody is contained in the brain
and not in the body that you worry about burying.
And it is to a certain extent true
that for people who write things down,
equations, dreams, notepads, diaries,
fractions of their thought does continue to live
after they’re dead and gone,
after their body is dead and gone.
And there’s a sea change in that going on in my lifetime
between when my father died, except for the things
which were actually written by him, as it were.
Very few facts about him will have ever been recorded.
And the number of facts which are recorded
about each and every one of us, forever now,
as far as I can see, in the digital world.
And so the whole question of what is death
may be different for people a generation ago
and a generation further ahead.
Maybe we have become immortal under some definitions.
Last easy question, what is the meaning of life?
Looking back, you’ve studied the mind,
us weird descendants of apes.
What’s the meaning of our existence on this little earth?
Oh, that word meaning is as slippery as the word understand.
Interconnected somehow, perhaps.
Is there, it’s slippery, but is there something
that you, despite being slippery,
can hold long enough to express?
I’ve been amazed at how hard it is
to define the things in a living system
in the sense that one hydrogen atom
is pretty much like another,
but one bacterium is not so much like another bacterium,
even of the same nominal species.
In fact, the whole notion of what is the species
gets a little bit fuzzy.
And do species exist in the absence
of certain classes of environments?
And pretty soon one winds up with a biology
which the whole thing is living,
but whether there’s actually any element of it
which by itself would be said to be living
becomes a little bit vague in my mind.
So in a sense, the idea of meaning
is something that’s possessed by an individual,
like a conscious creature.
And you’re saying that it’s all interconnected
in some kind of way that there might not even
be an individual.
We’re all kind of this complicated mess
of biological systems at all different levels
where the human starts and when the human ends is unclear.
Yeah, yeah, and we’re in neurobiology where the,
oh, you say the neocortex is the thinking,
but there’s lots of things that are done on the spinal cord.
And so where’s the essence of thought?
Is it just gonna be neocortex?
Can’t be, can’t be.
Yeah, maybe to understand and to build thought
you have to build the universe along with the neocortex.
It’s all interlinked through the spinal cord.
John, it’s a huge honor talking today.
Thank you so much for your time.
I really appreciate it.
Well, thank you for the challenge of talking with you.
And it’ll be interesting to see whether you can win
five minutes out of this with just coherence
to anyone or not.
Thanks for listening to this conversation
with John Hopfield and thank you
to our presenting sponsor, Cash App.
Download it, use code LexPodcast.
You’ll get $10 and $10 will go to FIRST,
an organization that inspires and educates young minds
to become science and technology innovators of tomorrow.
If you enjoy this podcast, subscribe on YouTube,
get five stars on Apple Podcast, support on Patreon,
or simply connect with me on Twitter at Lex Friedman.
And now let me leave you with some words of wisdom
from John Hopfield in his article titled, Now What?
Choosing problems is the primary determinant
of what one accomplishes in science.
I have generally had a relatively short attention span
in science problems.
Thus, I have always been on the lookout
for more interesting questions,
either as my present ones get worked out
or as they get classified by me as intractable,
given my particular talents.
He then goes on to say,
what I have done in science relies entirely
on experimental and theoretical studies by experts.
I have a great respect for them,
especially for those who are willing to attempt
communication with someone who is not an expert in the field.
I would only add that experts are good
at answering questions.
If you’re brash enough, ask your own.
Don’t worry too much about how you found them.
Thank you for listening and hope to see you next time.