John Hopfield: Physics View of the Mind and Neurobiology
Guests: John Hopfield
Full transcript · Read the full conversation in order, with its original context.
§01Transcript · 00:00–10:08
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,
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And Lex Friedman, spelled F-R-I-D-M-A-N.
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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.
One of the things that very much intrigues me
is the fact that neurons have all kinds of components,
properties to them.
And 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.
- Do the glitches become features
in the biological neural network?
- They can.
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
built about 2001.
And pedestrians walking across,
pedestrians don't walk, synchronize,
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.
You could see it in the movies made out of the bridge.
And the engineers made a simple, minor mistake.
They assumed 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 foot,
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, nerves, look, nerve cells produce action potentials.
You have a bunch of cells which are loosely coupled together
producing action potentials of the same rate.
There'll be some circumstances
under which these things can lock together.
Other circumstances in which they won't.
Well, they fire 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
that 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, at 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 that molecule was just slightly different,
had a function which helped any old chemical reaction
was as 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 is in computers too,
but it's improvement has to do with closing some companies
and opening some others.
(Lex laughing)
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 company lasts 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-
- And companies have difficulty having a new product
competing with an old product.
- Yeah.
And 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 you're learning goes on only over generations
and over evolutionary time,
where you're 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 it's 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
§02Transcript · 10:12–21:24
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.
- So--
- 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, so it's awfully stupid.
- Yeah, 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 the 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,
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.
- It's turnover.
- So what is not effective, what is not wired well enough
to use the moment, throw it out.
- It's a mysterious process.
- From, let me ask, from what field do you think
the biggest breakthroughs in understanding the mind
will come in the next decades?
Is it neuroscience, computer science, neurobiology,
psychology, physics, maybe math, maybe literature?
(laughs)
- 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.
- Now, 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,
or you can do the mathematics of the relevant
to the experiments, you also be able to understand
how things work.
- But that was a few years ago.
Did you change your mind at all through many decades
of trying to understand the mind,
of studying it 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?
- Of course.
- When I taught freshman physics, I used to say,
I wanted to give 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.
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, ask 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 solve the problem that way.
- So feedback is essential.
So we can talk even about recurrent neural networks,
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?
- Yeah.
- They'll probably go back to saying,
well, I can look at the brain rhythms
and tell you this is a brain
which is never gonna function again.
This one is, this other one is one
which if we treat it well, is still recoverable.
And then just do that by some electrodes
and looking at simple electrical patterns
which don't look in any detail at all
at 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 the 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.
But we'll go into the sand again.
And I think it's going to be generations of this evolution.
I don't know how many of them
and each one is going to get you further
into what our brain does.
And in some sense,
pass the Turing test longer and more broad aspects.
And how many of these are good
there 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.
- Yeah.
- And going back to my brain waves as it were.
- Yes.
- From the AI point of view,
they would say, ah, maybe these are an heavy phenomenon
and not important at all.
The first car I had, a real wreck of a 1936 Dodge
go above 45 miles an hour and the wheels was shimmy.
- Yeah.
- 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.
- Yeah.
- 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.
§03Transcript · 21:27–31:40
Look, how many transistors
are there in your laptop these days?
- Actually, I don't know the number.
It's--
- It's on the scale of 10 to the 10.
I can't remember the number either.
- Yeah.
- And all the transistors are somewhat similar.
And most physical systems with that many parts,
all of which are similar, have collective properties.
- Yes.
- Sound waves in air, earthquakes,
what have you have collective properties, weather.
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.
- Yeah, yeah.
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,
which are manifestly non-biological,
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,
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 you mean by associative memory is
how you have a memory of each of your friends.
Your friend has all kinds of properties
from what they look like,
to 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.
If I start talking about a
five foot 10 wire-aided cognitive scientist
that'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 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 show the kind,
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.
- Right.
- 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.
- That's right.
And 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'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 3000 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,
it's these chunks,
which are not just activities of neurons,
bigger things than that,
which are the real entities 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 written this,
have I stored it somewhere already?
It's gotta 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 Hopfield
calling him Hopfield Networks, but--
- It is weird.
(laughing)
- 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--
- Let me backtrack just a little bit.
- Yeah.
- 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,
fairly fast time scale,
in which you can say,
the synapses don't change much during this computation,
so I'll think of the synapses as 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 learning,
first of all, I spend some time learning,
not performing,
then I turn off learning and I perform.
- Right.
- That's not biology.
And so, as I look more deeply at neurobiology,
even as an associate of memory,
I've got to face the fact that the dynamics
of a 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.
- Yeah, yeah.
And there's nothing necessarily separating the time scales.
When the time scales can 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.
Weather.
I can describe weather
as 10 to the 32 molecules colliding in the atmosphere.
I can simulate weather that way,
I have a big enough machine, I'll simulate it accurately.
It's no good for understanding.
If I just 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
which can be said about it which are both true
and for which you don't need all the molecular details
of the molecules colliding.
§04Transcript · 31:42–42:50
That's what I mean from the roots of physics.
My understanding.
- So what did, again, sorry,
but Hopfield Networks help you understand,
what insight did it 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 reminds me of your name.
That would, it didn't describe a reasonable way
of actually doing the learning.
Or at least that if you had previously learned
the connections of this kind of pattern,
would now be able to behave in a physical way
which is a, ah, if I put 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 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,
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 of a computer,
there's a computational path,
which is going on in a huge dimensional space
of ones and zeros.
And an error correcting 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,
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,
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 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's 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 Hinton and Sanofsky
to understand how to do learning in that.
And it's still a useful way to understand learning
and understand, 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 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 computational.
- Something computational?
- Something both computational and physical.
Computational in the,
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.
- Mm-hmm.
What do you think about learning in this whole domain?
Do you think the aforementioned guy, Jeff Hinton,
all the work there with back propagation,
all the kind of learning that goes on in these networks,
how do you, if we compare it to learning in the brain,
for example, is there echoes of the same kind of power
that back propagation 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
has the biology, which has some feed forward
and some feed back,
been captured by something which has got many more neurons,
much more depth than neurons.
- 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.
- The dynamics of the feedback.
Look, if you don't have feedback,
it's a little bit like building a big computer
and running it through one clock cycle.
And then you can't do anything
'cause you'd reload something coming in.
How do you use the fact that there are multiple clocks?
How do I use the fact that you can close your eyes,
stop listening to me and think about a chess board
for a few 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.
'Cause that's all-
- Let alone consciousness, yes, yes.
- 'Cause 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 bettors, as it were,
their view on consciousness.
And it's been interesting collecting them.
- What is consciousness?
Let's try to take a brief step into that room.
- Well, I asked Marvin Minsky,
the view on consciousness.
And Marvin said,
consciousness is basically overrated.
It may be an epiphenomenon.
After all, all the things your brain does,
which are actually hard computations,
you do non-consciously.
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.
- Yeah, yeah.
Subconscious, non-conscious.
- Non-conscious, what's the better word, sir?
- It's only that Freud captured the other word.
- Yeah, it's a confusing word, subconscious.
- Nicholas Chater wrote an interesting book.
I think the title of it is "The Mind is Flat."
(chuckles)
Flat in a neural net sense,
might be flat is something which is a very broad neural net
without really any layers in depth,
or the 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.
(chuckles)
- Yeah, it's the weaving of the narrative
around the things that 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,
§05Transcript · 42:53–52:49
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/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 cover up,
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 John Dean 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.
(laughs)
Do you, like where do you stand in your, today,
(laughs)
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.
But I asked Francis Crick about consciousness.
He launched forward in a long monologue
about Mendel and the peas.
- Yeah.
- 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 were this or this,
and propagated,
that that was absolutely fundamental to 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.
(sighs)
So I've gone 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 a 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.
- Naturally, answering questions of free will
and consciousness naturally lead to contradictions
from a physics perspective.
'Cause 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,
much is already deterministic about our universe.
There's lots of different--
- 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.
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,
'cause in a huge number of dimensions,
the behavior of a system can be thought of
as just the motion of a point over time
in this huge number of dimensions.
- Right.
- 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 high dimensional space,
where some of the dimensions provide the dissipation,
which means, which,
look, 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.
You have to, if you contract somewhere,
you expand somewhere else.
And it's an interesting physical systems.
You get 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 is entropy flow
in this other part of the system.
But basically, attractor networks are dynamics
funneling down so 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.
You start somewhere else,
you'll wind up on a different pathway,
but you don't have just all possible things.
You have some defined pathways,
which are allowed and under 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 these 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,
by driven they're coupled somehow to an energy source.
And so if 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.
- 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 going to wind up at the bottom of a valley.
You know that's true
without actually watching the ball roll down.
- There are certain properties of the system
that when you can know that.
- That's right.
And not all systems behave that way.
§06Transcript · 52:51–1:03:38
- Most don't probably.
- Most don't, but it provides you with a metaphor
for thinking about systems,
which are stable and the good 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.
- Speaking of thought,
if I had a glint in my eye with excitement and said,
you know, 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,
one-pass 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 make it take a physical action.
- Almost a, like we were mentioning, playing chess,
visualizing, simulating inside your head,
different outcomes.
- Yeah, yeah.
And now you could do that in a feed-forward network
because you've pre-calculated all kinds of things.
But I think the way neurobiology does it,
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
from 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's gonna behave.
There are no limitations on what could happen.
And so 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 the neurobiology 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?
- Yeah.
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, look, 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.
- Right.
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,
one 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 ultimately boiled down to thinking through the problem.
Like what are the principles
under which a particular phenomenon operates?
- Yeah, yeah.
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-in-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.
(Peebles sniffs)
- 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 interest
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.
- That's right.
So in the case of Neuralink,
they're doing a thousand-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 Hubble
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
"or 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 look, you have to begin to be able to look
for the collective modes,
collective operations of things.
It doesn't rely on this action potential of 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.
Neurobiology does it in ways that are different
from the way an engineer would do it.
An engineer would put in six highly accurate
stepping motors 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, ah, 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,
error-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 physicists in you will be lost forever in such systems
'cause 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,
§07Transcript · 1:03:43–1:12:41
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 to 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.
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, you're both a physicist and a dreamer.
You have a sense that--
- Yeah, but I can only dream physics dreams.
- You 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, of course,
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 and 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,
when 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
than a generation further ahead.
- Maybe we have become immortal under some definitions.
- Yeah, yeah.
- 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?
- Well, 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 the species exists 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,
it 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, or 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 at the thinking,
but there's lots of things that are done
in the spinal cord.
And so we say, what is 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 coherent sense
to anyone or not.
- Beautiful.
Thanks for listening to this conversation
with John Hopfield, and thank you
to our presenting sponsor, Cash App.
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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.
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