Kind: captions
Language: en
the following is a conversation with
john hopfield professor Princeton whose
life's work weave 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 and
important biological questions in a
variety of areas including genetics and
neuroscience was 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
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Friedman spelled Fri D ma M as usual
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world and now here's my conversation
with john hopfield
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
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
and I even how a molecule works or how
it still works and it can may mean 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 them
in the biological neural network they
they can look let me take one of the
things that I used to do research on if
you take things which oscillate their
rhythms which are sort of close to each
other under some circumstances these
things will have a phase transition and
suddenly is the rhythm will everybody
will fall into step there was a
marvelous physical example of that in
the millenium bridge across the Thames
River about Bill about 2001 and
pedestrians walking across pedestrians
don't walk synchronized they don't walk
and lock lockstep but they're all
walking about the same frequency and the
bridge could say at that frequency in
the slight sway made pedestrians tend a
little bit to lock in the step master
well the bridge was oscillating back and
forth and the pedestrians were walking
in step to it you could see
we made it at the bridge and the
engineers made a simple-minded a mistake
they had a feeling when you walk it's
step step step and it's back at forth
motion but when you walk it's also right
foot left foot side to side motion and
the side to side motion for which the
bridge was strong enough but it wasn't
it wasn't stiff enough and as a result
you would feel the motion and you'd fall
under step with it and people were very
uncomfortable with it they closed the
bridge for two years really fully built
stiffening for it no nerves look nerve
cells loose action potentials you have a
bunch of cells which are loosely coupled
together perusing action potentials of
the same rate there'll be some
circumstances under which these things
can lock together other circumstances
which they won't
well they fire together you can be sure
the other cells are going to notice it
so you could make a computational
feature out of this and you're in an
evolving brain most artificial neural
networks don't even have action
potentials let alone have the Potala
bility for synchronizing them and you
mentioned the evolutionary process so
they're the evolutionary process that
builds on top of biological systems
leverage is that the 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 part
of the biological molecule level you
have a piece of DNA which included an
encode for a particular protein you
could duplicate that piece of DNA and
now one part of it
encode for that protein but the other
one could itself change a little bit and
the start coding for a molecule which is
just slightly different now is that
molecule was just slightly different had
it in a function which helped any old
chemical reaction was as important to
the cell
you would go ahead and let that e try an
evolution of slowly and 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 computers to
adjust improvement has to do with
closing some companies openings others
the evolutionary process looks a little
different yeah oh similar timescale
perhaps well or shorter in times to kill
companies close yeah go bankrupt and are
born yeah shorter but not much shorter
some some company lasts the century a
couple but yeah you're right I mean if
you think of companies 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 fraud large yeah
and when IBM built this first PC you
probably read the dread 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 he adds 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 there's 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 on the over
generation that over evolutionary time
or your learning goes on at the
timescale of one individual who must
learn from the environment during that
individuals lifetime and biology has
both kinds of learning in it and the
thing which makes neurobiology hard is
that a mathematical systems that were
built on this other kind of evolutionary
system what do you mean by mathematical
system where where's the math in 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 the thing 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 a 2.1 dimensions but they
really have difficulty doing
three-dimensional wiring biology biology
is the neocortex is actually also
sheet-like and it sits on top of the
white matter which is about ten times
the volume of the gray matter and
contains all what you might call the
wires but there's a huge the the effect
the effect of computer structure on what
is easy and what is hard is immense
so and biology does 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 though
it's awfully stupid yeah
and you're saying this kind of three
dimensional complicated structure makes
it's still math it still doing math the
kind of math is 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 at the end
the adaptation are learning at the scale
of a single human life which do you are
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
edges of them and tease them apart a
little bit see how they work and since I
can't see the evolutionary process going
on I am 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 a doll but I couldn't
be interested in working on it
the human life timescale is however
thing you can tease apart and study yeah
you can do there's developmental
neurobiology which understands all of
these connections and now the structure
evolves from a combination of what the
genetics is like and the real the fact
that this is you're building a system in
three dimensions in just days and months
those early early days of human life are
really interesting they are and of
course there are times of immense still
multiplication there are also times of
the craziest cell death in the brain is
during infancy it's turnover so what is
what what what is not effective which 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 neural science computer science
neurobiology psychology physics maybe
math maybe literature
[Laughter]
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
at both the air parents of physicists
both of our parents were physicists and
the real thing I gathered 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 that you
can do the mathematics of the relevant
of the experiments you also 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 minds just biological systems
you still have hope the physics that you
can understand there's the question of
what do you mean by understand of course
when I taught freshman physics I used to
say I wanted to get physics to
understand the subject to understand new
this 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 the if you
looked at a situation you can say oh I
expect the bowl to make that trajectory
all right I expect so I'm into a 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 well you choose 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 attending is more than
just an enormous lookup table let's
linger on that how sure you are of that
what if the table gets read
Liebig so i'm he asks another way these
feed-forward neural networks do you
think they'll ever understand
good 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 unlaid this and do it a
part of it but but I have an exponential
expansion and the amount of stuff I have
to build if I could resolve the problem
that way so feedback is essential so we
can talk even about recurrent 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 does somebody did yeah
it'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
another but this other one is one which
if we treat it well is still recoverable
and then just do that by so many
electrodes looking at simple like
electrical patterns just don't look in
any detail at all or what individual
neurons are doing these rhythms are
already absent from anything which goes
on in Google
yeah but the rhythms but the rhythms
would so well that's like comparing okay
I'll tell you it's like you're comparing
the the greatest classical musician in
the world to 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 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 sort of
grinds into the sand and you say Oh
something else is needed from
neurobiology and some other grand thing
comes in and enables you to go a lot
further according to the sandakan
everything 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 whatever
then in some sense passed 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 senses might be a couple more
yeah and going back to my brain waves of
the word yes
from the AI point of view if they would
say ah maybe these are heavy phenomena
and not important at all the first car I
had no record of 1936 dodge Kobo 45
miles an hour in the wheels was Jimmy
yeah good good speedometer that
now don't be designed at the car that
way the cars malfunctioning to have that
but in biology if you 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'll 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-the how many transistors are there
at your laptop these days actually I
don't know the number it's it's on a
scale of 10 to the 10 I can't remember
the number
yeah and all the transistors are
somewhat similar and most physical
systems with that many parts all of
which are selfs or have collective
properties yes soundly is an error
Earthquakes what have you have
collective properties wither there are
no collective properties used in
artificial neural networks in AI yeah
it's very
if biology uses them it's gonna take us
two more generations of things to be the
perfect people to actually dig in and
see how they are used what they mean see
you're very right might have to return
several times to neurobiology and try to
make our transistors more messy yo-yo at
the same time the simple ones whole
concert will conquer big aspects
and I think one of the most biggest
surprises to me was how well learning
systems buzzer manifesting
non-biological how important they can be
actually and you how important how
useful they can be in a high 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
do you mean by associative memory is oh
you have a memory of each of your
friends your friend has all kinds of
properties from what they look like is
whether voice sounds like to where they
went to college where you met them go on
and on what what science papers they've
written if I start talking about a five
foot ten wiry
cognitive scientist that's got a very
bad back it doesn't take very long for
you to say are you talking about geoff
hinton
I never been I never mentioned the name
or anything very particular but somehow
a few facts are associated with this
with a particular person enables you to
get a hold of the rest of the facts yeah
earned or not the dress that was another
subset of them and is this the ability
to link things together linked
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 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 kayak
Chua Li 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 down 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 well that warp those right
and now if you if you look at what I did
I didn't did all describe a system which
gracefully learns I described as a
system in which you could understand how
things 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 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 now I have a view of you
which can which I can use it doesn't
slavishly remember anything in
particular but it could PACs the
information in those useful chunks which
are somehow it's these chunks which are
not just activities of neurons from
bigger things than that which are the
real energies which are which are useful
which are useful to you
useful distal to you to describe to
compress this information present in
such a way that if I get the information
comes in just like this again I don't
bother about their 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 has ever started somewhere
already therapies not 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 them
hot field networks but it is weird
yeah but nevertheless that's what
everyone calls them so here we are so
that's a simplification that's what a
physicists would do you and richard
fineman sat down and talked about
associative memory now if you as a if
you look at the mind or you can't quite
simplify so perfectly do you let me
backtrack just a little bit yep biology
is about dynamical systems computers are
dynamical systems you can have if you
want to math the bottle biology bottle
neurobiology what is the time scale
there's a dynamical system in which you
have a fairly fast timescale in which
you goes eight the synopsis 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 artists if you look at the
feed-forward art revisional their own
ads they're all done as learning is
first of all I spent some time learning
and not performing and I turned off
learning and I perform right that's not
biology and so he is there look more
deeply at neurobiology even as
associative 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 thing I'll do the
academics of activity with fixed
synapses so the the synaptic the
dynamics of the synapses is actually
fundamental to the whole system yum yum
and there's no there's no there's
nothing necessarily separating the time
skills where the time skills can be
separate and it's neat for the
physicists of the mathematicians point
of view but it's not necessarily true in
neurobiology New York you're kind of
dancing beautifully between showing a
lot of respect to physics and then also
saying that physics cannot quite reach
the the complexity of biology so where
do you land or do you continuously dance
between the two I continuously dance
between them because my whole notion of
understanding is it you can describe to
somebody else how something works in
ways which are honest and believable and
still not described all the nuts and
bolts in detail whether I can describe
whether as ten to the 32 molecules
colliding in the atmosphere I can
stimulate whether that way or I pick
enough machine I'll simulate it
accurately it's no good for
understanding but I just want to
understand things I want to understand
things in terms of wind patterns
hurricanes pressure differentials and so
on all things is there
negative and the physicists physicists
in me always hopes that biology will
have some things which can be said about
it was are both true and for which you
don't need all the molecular details of
the molecules colliding that's what I
mean from the roots of physics by
understanding so what did again sorry
but hopfield networks help you
understand what insight to 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 a picture of
you reminds me of your name that would
put it describe a reasonable way of
actually doing the learning or only said
if he had previously learned the
connections of this kind of pattern
would now be able to behave in a
physical way with the day off I put the
part of the pattern in here the other
pattern of the pet part of the pattern
will complete over here I can understand
that physics if the right learning stuff
had already been put in and you couldn't
understand why then putting in a picture
of somebody else would generate
something else over here but it didn't
out under did not have a reasonable
description of the learning process but
even to 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 what my paper did was simply
enable you there long there are lots of
ways of being robust if you think of it
a dividend amical system here you think
of a system where a path is going on and
in time and if you think for a computer
is a computational path which is going
out 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 till you
get to the same answer in spite of the
fact that there were things though that
the computation wasn't being ideally
done all the way along a line and there
are lots of models for error correction
but one of the models for error
correction is to say there's a family
that you're following flowing down and
if you push a little bit off the valley
it's just like water being pushed a
little bit by a rock gets back and
follows the course of the river and then
basically the analog in the in the
physical system went to enable to just
say oh yes error free computation and an
associative memory are very much like
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 correction schemes such a
mathematician may be able to understand
but I don't so there's a the physical
metaphor that seems to a it 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
and you're on that works artificial
neural network so the follow-on work
with restrictive Boltzmann machines and
deep belief Nets
followed on from the from these ideas of
the hopfield network so what what do you
think about this continued progress of
that work towards now ree-ree vigor ated
exploration of feed-forward neural
networks and recurrent neural networks
in 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 one of the most
long-lived
all the learning systems is the
Boltzmann machine which is intrinsically
a feedback network and was the
brilliance of in
and Sadowski 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 and 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 as he 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 this is a lovely
encapsulation of something in
computational something computational
something both computational and
physical computational and they very
much related to feed-forward networks
physical in that Boltzmann machine
learning is really learning a set of
parameters for physics Hamiltonian or
energy function what do you think about
learning in this whole domain do you do
you think the aforementioned guy Geoff
Hinton all the work there with
backpropagation all the kind of learning
that goes on in these networks how do
you if we compared 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 where they're part of that depth
of the computer science networks is
necessitated by the fact that the only
learning is easily done on a machine is
his feed-forward and so there's the
question of to what extent as the
biology which has some feed-forward some
feedback been captured by something
which is it got many more neurons but
much more depth to the neurons
you know so party you wonders if the
feedback is actually more essential than
the number of neurons or the depth the
the dynamics of the feedback doesn't
ever have the feedback look if you don't
have if you don't have feedback it's a
little bit like a building a big
computer and having running up through
one clock cycle and then you can't do
anything do you put you reload something
coming in how do you use the fact that
there are multiple clocks like 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 it's
hard it's hard to understand so I don't
respect let alone consciousness oh is
that a little own consciousness yes
because that's tied up in there too you
can't just put that on another shelf
every once in a while like I 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 there's been interest in
collecting them what let's try to take a
brief step into that room well that's
Marvin Minsky does you want to
consciousness and Marvin said
consciousness is basically overrated
it may be an epiphenomenon after all all
the things your brain does but your
as they're actually hard computations
you do not consciously and there's so
much evidence that even the things the
simple things you do you can make
decisions you can make committed
decisions about them the neurobiology
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 yo-yo subconscious non
conscious non-conscious that's the
better word sir there's the it's only
the Freud captured the other word yeah
it's that's a confusing word
subconscious Nicholas Chater wrote an
interesting book I think the saw
delivers the mind is flat flat and in a
neural net sense you might have to be a
flat is something which is of very broad
they're all know without earlier than
the layers in depth or as a deep brain
would be many layers and not so broad in
the same sense that if you push Minsky
hard enough he would talk V of said
consciousness is your effort to explain
to yourself that would you have already
done yeah it's the weaving of the
narrative around the things that already
been computed for you that's right and
then 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
and pulling together general knowledge
on the other but you will have a
narrative and once you generate that
narrative if you are very likely to
repeat that
narrative and claim that all the things
you have hidden 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 Dickson
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 convincingly tail
and long afterward some of the some of
the tapes the secret cases were for
which these Don was Jean 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
[Music]
do you mean like where do you stand in
your today so perhaps has changed his
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 our
that's a very hard one but I asked
Francis Crick about consciousness he
launched forward a long monologue about
handling the peas
yeah and how Mendel knew that there was
something and how biologists understood
there was something in inherit
which was just very very different and
he is the effect that inherited traits
didn't just wash out into a grey but
it's this or this and propagated that
that was absolutely fundamentals of
biology and it took generations of
biologists to understand that there was
genetic and it took another generation
or two to understand that genetics came
from DNA but there but but but very
shortly after Mendel thinking biologists
did realize that there was a deep
problem about inheritance any Francis
blood of life would like to have said
and that's why I'm we're working
unconsciousness but of course he didn't
have any smoking gun in the sense of
Mendel and that's the weakness of his
physician that he read his his book but
you wrote with Corey bank
yeah Christophe go on I find it
unconvincing for this first poking gun
reason start going on and collecting
views without actually having taken a
very strong one myself because I haven't
seen the entry point not seeing the
smoking gun and the point of view of
physics I don't see the entry point
whereas whereas the neurobiology once
they understood the idea of a collective
a and evolution of dynamics which could
be described as a clock 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 this is
like everybody else do you think very
but early about things you have the
closely is related question about
freewill do you believe your freewill
physicists will give an offhand answer
and then backtrack backtrack backtrack
where they realized that the answer they
gave must fundamentally contradict the
laws of physics that natura answering
questions of freewill 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 much
is already deterministic about our
universe there's lots of difference and
if you don't push quite that far you can
say essentially all of Neurobiology
which is relevant it 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 for the mysteries of
what can happen when you have a
dynamical system driven system with 10
to the 14 parts the bare complexity is
something which is if the physical
complex systems is at least as badly
understood as the physics of phase
coherence and quantum mechanics can we
go there for a second you've talked
about attractor networks and just maybe
you could say what our 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 there's a
huge number of dimensions the behavior
of a system can be thought of as just
the motion of the point over time in
those huge number of adventures right
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 need a highly highly dimensional
space
and the easiest way to get that is to do
it in a high dimensional space where
some of these dimensions provide the
dissipation which base which a kind of a
physical system trajectories can dig our
contract everywhere they have to get
tracked in some places and expand in
others there was a fundamental classical
theorem most statistical mechanics which
goes under the name of liouville's
theorem which says you can't contract
everywhere after country if you contract
somewhere you were expand somewhere else
do you and is in interesting physical
systems you get driven systems where you
have a small subsystem which is the
interesting part and the rest of the
contraction of an expansion the
physicists say it's the entropy flow in
this other part of the system but but
basically attract your networks our
dynamics funneling downs of you can't be
any so 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
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
these not--when networks what are some
interesting characteristics that what
are some interesting insights from
studying the dynamics of such high
dimensional systems most dynamical
systems supposed I'm done driven
dynamical systems I driven there are
couples I'm out 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 as all to
understand it all with the devil
the dynamical behavior is going to be
you have to run it out you have to
running there's this there's a subset of
systems which has what was a clean tone
to the mathematicians as as the Alpen of
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 without
ever knowing what the alpha naught
functions were in the simple model I
made in the early eighties was an energy
function so you could understand how you
get this channeling I'm as under
pathways without having to follow the
dynamics in an infinite detail you
started rolling a ball as off of a
mountain that's gonna wind up at the
bottom of a valley you know that it's
true without actually watching the ball
fall roll down there's certain
properties of the system that when you
can know that that's right and not all
systems behave that way most don't
probably both don't but it provides you
with the metaphor for thinking about
systems which are stable in the whoo to
have these attractors behave even if you
can't find the idly up and a 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
intelligence system and came to you as
an adviser what would you recommend
is it a hopeless pursuit she's knew all
networks that she thought is it what
kind of mechanism should we explore what
kind of ideas should we explore well you
look at this as the simple net worth for
everyone
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 they like we were mentioning
playing chess visualizing simulating
inside your head different outcomes
yellow young and now you could do that
as 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 exactly
as 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 that there's there's
a creative element and in a
simple-minded neural net you ever a
constellation of instances from which
you've learned and if you are within
that space you know if a new fan new
question is the 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 going to behave there are
no limitations on what could happen and
so with the artificial neural network is
always very much I have a a population
of examples the test set must be drawn
from the equivalent population as the
test as examples which are from a
population which is completely different
there's no way that you could expect to
get the answer right
Meowth and so what they saw outside the
distribution that's right that's right
and so if you see a ball rolling across
the streets and dusk
if there wasn't in your your training
set the idea that a child may be coming
close behind that is not going to occur
with the neural lab and it is to our
there's something in your biology that
allows that yeah there's there's
something in the way of what it means to
be outside of the of the population of
the training said the probability is
that the training set isn't just sort of
the set of examples it's there's more to
it than that and it gets back to my own
question of where's is it to understand
something yeah you know is 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 but 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 in a
scientific process
well look there obviously scientific
questions in which the route to the
answer to it come through the analysis
of one hell of a lot of data right
cosmology at honest and that that's
never written the kind of problem in
which I've had any particular insight
though I would say if you look at
cosmology is was one of those if you
look at the actual things that Jim
Peebles one of this year's don't go
prized for the vision physics ones from
a 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 I
ultimately boil down to thinking through
the problem like what are the principles
under which a particular phenomena
operates yeah and look physics is always
going to look for ways in which you can
describe the system and which rises
above the rises above the details and to
the hard died the world biologists
biology works because of the details and
physics to the physicists we want an
explanation which is right in spite of
the details and they will leave
questions which we cannot answer as
physicists because the answer cannot be
found that way there's a met sure if
you're familiar with the entire field of
brain-computer interfaces has become
more and more intensely researched and
developed recently especially with
companies like neural link what Elon
Musk you know I know they've always been
the endres 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 they connected
limb which is now connected through a
computer that's right so in the case of
neural
they're doing thousand-plus connections
where they're able to do two-way
activate and read spikes in your annual
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 as this
was 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 once out yeah
people like David Hoople very strongly
reflected that point of view and that's
been taken over by a generation a couple
of generations later
a set of people who says not until we
can record from 10 to the 4 or 10 to the
5 at a time who 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 of the collective operations of
things it doesn't rely on this action
potential or death cell it relies on the
collective properties of this set of
cells connected to this kind of patterns
so on and you're not going to see did
the thing what those collective
activities are without recording many
cells at once and the question is how
many at once what's the threshold and
that's the that's the no and and because
we pursued hard in the motor cortex the
motor cortex does something which is
complex and yet with the problem you're
trying to address is very it's really
simple
now neurobiology does it in ways the
different from the way an engineer would
do it an engineer would put in six
highly accurate stepping motors are
controlling a limb rather than 2,000
muscle fibers each of which has to be
individually controlled 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 touch that's
where their pressure sensor or to let
very them an array of of a gazillion
pressure pressure sensors none of what
you're 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
the 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 compete
computations last robotics that way than
by trying to force things into a into a
robotics for joint motors are powerful
and stepping motors are accurate but
then the physicists the physicists in
you will be lost forever in such systems
because there's no simple fundamentals
to exploring systems that are so large
and we also you see that yep there's a
lot of physics and 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 or this other set of equations
which is just as true as the equations
of the bottle though those those
equations are going to be harder to find
in general biology but the physicist of
me says there are probably some
equations of there
sort they're out there there they're out
there and the physics is going to
contribute anything it may contribute to
trying to find out what those equations
are and how to capture them from them
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 theirs molecules
there's psychological behavior these two
are somehow related
there are layers of detail they're
layers of collectiveness and to capture
this to capture that at some vague wait
several stages on the way up to see how
these things that can actually be linked
together so it seems in our universe
there's a lot of 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 there isn't it's still an open
question whether that link is equally
between molecules and the brain is
equally compressible into elegant
equations but your ear sounds some well
you're both a physicist and a dreamer
you have a sense that yes I can although
I can only dream physics dreams physics
shapes there was an interesting book
called Einsteins dreams Forge alternates
between chapters on his life and
descriptions of the way time might have
been but isn't as linking between these
being of course the 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 neurobiology you're one human famous
impactful human but just one human with
a brain living the human condition
but you're ultimately mortal like all of
us has studying the mind as a mechanism
change the way you think about your own
mortality it has really because as
particularly as you get older in 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 out 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 there going
on in my lifetime between what if my
father died when except for the things
that you're actually ridden by hymns
that 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 to
go in a generation or through ahead
maybe we have become immortal under some
definitions yeah yeah last easy question
what is the meaning of life looking back
studied the mind a weird descendants of
apes what's the meaning of our existence
on this little earth Oh word meeting is
as slippery as the word understand
interconnected somehow perhaps is there
it's slippery but is ther