Kind: captions
Language: en
the following is a conversation with
Matt Botvinnik director of neuroscience
research deep mind he's a brilliant
cross-disciplinary mind navigating
effortlessly between cognitive
psychology computational neuroscience
and artificial intelligence quick
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you won't regret it and now here's my
conversation with Matt Botvinnik how
much of the human brain do you think we
understand I think we're at a weird
moment in the history of neuroscience in
the sense that there's a there I feel
like we understand a lot about the brain
at a very high level but a very very
coarse level when you say high level
what are you thinking you thinking
functional yeah structurally so in other
words what is what is the brain for you
know what what what kinds of computation
does the brain do you know what kinds of
behaviors would we have - would we have
to explain if we were going to look down
at the mechanistic level and at that
level I feel like we understand much
much more about the brain than we did
when I was in high school but what but
it's at a very it's almost like we're
seeing it
a fog it's only at a very coarse level
we don't really understand what the the
neuronal mechanisms are that underlie
these computations we've gotten better
at saying you know what are the
functions that the brain is computing
that we would have to understand you
know if we were going to get down to the
neuronal level and at the other end of
the spectrum we you know in the last few
years
incredible progress has been made in
terms of technologies that allow us to
see you know actually literally see in
some cases what's going on at the the
single unit level even the dendritic
level and then there's this yawning gap
in between oh that's interesting so it's
a high level so there's almost a
cognitive science yeah yeah and then at
the neuronal level that's neurobiology
and neuroscience
yeah just studying single neurons the
the the the synaptic connections and all
the dopamine all the kind of new
transmitters one blanket statement I
should probably make is that as I've
gotten older I have become more and more
reluctant to make a distinction between
psychology and neuroscience to me the
point of neuroscience is to study what
the brain is for if you if you if you're
if you're a nephrologist and you want to
learn about the kidney you start by at
by saying what is this thing for well it
seems to be for taking blood on one side
that has metabolites in it that are that
shouldn't be there
sucking them out of the blood while
leaving the good stuff behind and then
excreting that in the form of urine
that's what the kidney is for it's like
obvious so the rest of the work is
deciding how it does that and this it
seems to me is the right approach to
take to the brain you say well what is
the brain for the brain as far as I can
tell is for producing behavior it's from
going it's for going from perceptual
inputs to behavioral outputs and the
behavioral output should be adaptive
so that's what psychology is about it's
about understanding the structure of
that function and then the rest of
neuroscience is about figuring out how
those operations are actually carried
out at a mechanistic level it's really
interesting but so unlike the kidney the
the brain the the gap between the
electrical signal and behavior so you
truly see neuroscience as the science
oh that that touches behavior how the
brain generates behavior or how the
brain converts raw visual information
into understanding like and it's like
you you basically see cognitive science
psychology and neuroscience is all one
science yeah is that a personal
statement I said I'm hopeful is that is
that a hopeful or a realistic statement
so certainly you will be correct in your
feeling in some number of years but that
number of years could be two hundred
three hundred years from now oh well
there's a is that aspirational or is
that a pragmatic engineering feeling
that you have it's it's both in the
sense that this is what I hope and
expect will bear fruit over the coming
decades but it's also pragmatic in the
sense that I'm not sure what we're doing
in either in either psychology or
neuroscience if that's not the framing I
don't I don't I don't know what it means
to understand the brain if there's no if
part of the enterprise is not about
understanding the behavior that's being
produced I mean yeah but out I would
have compared to maybe astronomers
looking at the movement of the planets
and the stars and without any interest
of the underlying physics right and I
would argue that there at least in the
early days there are some valued is just
tracing the movement of the planets and
the stars without thinking about the
physics too much because it's such a
to start thinking about the physics
before you even understand even the
basic structural elements of oh I agree
with that
I agree what you're saying in the end
the goal should be yeah deeply
understand well right and I I think so I
thought about this a lot when I was in
grad school because a lot of what I
studied in grad school was psychology
and I found myself a little bit confused
about what it meant to it seems like
what we were talking about a lot of the
time were virtual causal mechanisms like
oh well you know attentional selection
then selects some object in the
environment and that is then passed on
to the motor you know information about
that is passed on to the motor system
but these are these are virtual
mechanisms these are you know they're
metaphors they're you know that there's
no they're not there's no reduction -
there's no reduction going on in that
conversation to some physical mechanism
that you know or which is really what it
would take to fully understand you know
how how behavior is arising but the
causal mechanisms are definitely neurons
interacting I'm willing to say that at
this point in history
so in psychology at least for me
personally there was this strange
insecurity about trafficking in these
metaphors you know which we're supposed
to explain the the function of the mind
if you can't ground them in physical
mechanisms then what you know you know
what is the what is the explanatory
validity of these explanations and I I
managed to I managed to soothe my own
nerves by thinking about the history of
genetics research so I'm very far from
being an expert on the history of this
field but I know enough to say that you
know Mendelian genetics preceded you
know Watson and Crick and so there was a
significant period of time during which
people were you know continued
productively investigating the structure
of inheritance using what was
essentially a metaphor
of gene you know and no genes do this
and genes do that but you know where the
genes they're they're sort of an
explanatory thing that we made up and we
we ascribed to them these causal
property so there's a dominant there's a
recessive and then then they recombine
and and and then later there was a kind
of blank there that was filled in with
it with a with a physical mechanism that
connection was made in but it was worth
having that metaphor because that's that
gave us a good sense of what kind of
cause what kind of causal mechanism we
were looking for and the fundamental
metaphor of cognition you said is the
interaction of neurons is that what is
the metaphor no no the metaphor the the
metaphors we use in in in cognitive
psychology are you know things like
attention way that memory works you know
I I retrieve something from memory right
you know a memory retrieval occurs what
is the Hat you know that's not that's
not a physical mechanism that I can
examine in its own right but if we if
but it's still worth having that that
metaphorical level yeah so yeah I
misunderstood actually so the higher
level abstractions is the metaphor
that's most useful yes but but what
about so how does that connect to the
the idea that that arises from
interaction of neurons well even it is
the interaction of neurons also not a
metaphor to you is or is it literally
like that's no longer a metaphor that's
that's already that's already the lowest
level of abstractions that could
actually be directly studied well I'm
hesitating because I think what I want
to say could end up being controversial
so what I want to say is yes the
interaction of the interactions of
neurons that's not metaphorical that's a
physical fact that's that's where that's
where the causal interactions actually
occur now I suppose you could say well
you know even
is metaphorical relative to the quantum
events that underlie yes you know I
don't want to go down that rabbit hole
so is turtles on top potatoes but there
is it there isn't there's a reduction
that you can do you can say these
psychological phenomena are can be
explained through a very different kind
of causal mechanism which has to do with
neurotransmitter release and and so what
we're really trying to do in
neuroscience writ large you know as I
say which for me includes psychology is
to take these psychological phenomena
and map them on to neural events
I think remaining forever at the level
of description that is natural for
psychology for me personally would be
disappointing I want to understand how
mental activity arises from neural
neural activity but the converse is also
true studying neural activity without
any sense of what you're trying to
explain to me feels like at best groping
around you know at random now you've
kind of talked about this bridging at
the gap between psychology in
neuroscience but do you think it's
possible like my love is like I fell in
love with psychology and psychiatry in
general with Freud and when I was really
young and I hope to understand the mind
and for me understanding the mind at
least at a young age before I discovered
AI and even neuroscience was to his
psychology and do you think it's
possible to understand the mind without
getting into all the messy details of
neuroscience like you kind of mentioned
to you it's appealing to try to
understand the mechanisms at the lowest
level but do you think that's needed
that's required to understand how the
mind works
that's an important part of the whole
picture but I would be the last person
on earth to suggest that that reality
renders psychology in its own right
unproductive I trained as a psychologist
I I am fond of saying that I have
learned much more from psychology than I
have from neuroscience to me psychology
is a hugely important discipline and and
one thing that worms in my heart is that
ways of ways of investigating behavior
that have been native to cognitive
psychology since its you know dawn in
the 60s are starting to become they're
starting to become interesting to AI
researchers for a variety of reasons and
that's been exciting for me to see can
you maybe talk a little bit about what's
what you see is beautiful aspects of
psychology maybe limiting aspects of
psychology
I mean maybe just started off as a
science as a field to me was when I
understood what psychology is analytical
psychology like the way it's actually
carried out is really disappointing to
see two aspects one is how few how small
the end is how many how small the number
of subject is in the studies and two was
disappointing to see how controlled the
entire how how much it was in the lab
how it wasn't studying humans in the
wild there's no mechanism for studying
humans in a while so that's where I
became a little bit disillusioned into
psychology and then the modern world of
the Internet is so exciting to me the
Twitter data or YouTube daily data of
human behavior on the Internet becomes
exciting because then the N grows and
then in the wild girls but that's just
my narrow sense they give us optimistic
or pessimistic cynical view of
psychology how do you see the field
broadly
when I was in graduate school it was
early enough that there was still a
thrill in seeing that there were ways of
doing there were ways of doing
experimental science that provided
insight to the structure of the mind one
thing that impressed me most when I was
at that stage in my education was
neuropsychology looking at looking at
the analyzing the behavior of
populations who had brain damage of
different kinds and trying to understand
what what the what the specific deficits
were that arose from a lesion in a
particular part of the brain and the the
kind of experimentation that was done
and that's still being done to get
answers in that context was so creative
and it was so deliberate you know the it
was good science an experiment answered
one question but raised another and
somebody would do an experiment that
answered that question and you really
felt like you were narrowing in on some
kind of approximate understanding of
what this part of the brain was for do
you have an example of the memory of
what kind of aspects of the mind could
be studied in this kind of way oh sure I
mean the very detailed
neuropsychological studies of language
language function looking at production
and reception and the relationship
between you know visual function you
know reading and auditory and semantic
and I mean there were these beauty and
still are these beautiful models that
came out of that kind of research that
really made you feel like you understood
something that you hadn't understood
stood before about how you know language
processing is organized in the brain but
having said all that you know I I think
you know I think you are I mean I agree
with you that the cost of doing highly
controlled experiments is that you
by construction miss out on the richness
and complexity of the real world one
thing that so I I was drawn into science
by what in those days was called
connectionism which is of course that
you know what we now called deep
learning and at that point in history
neural networks were primarily being
used in order to model human cognition
they weren't yet really useful for
industrial applications so you always
fall in neural networks in biological
form beautiful Oh neural networks were
very concretely the thing that drew me
into science I was handed are you
familiar with the the PDP books from
from the 80s some when I was in I went
to medical school before I went into
science and really yeah this thing Wow I
also I also did a graduate degree in art
history so I'm I kind of explored
well art history I understand there's
just a curious creative mind but medical
school with the dream of what if we take
that slight tangent what did you what
did you want to be a surgeon
I actually was quite interested in
surgery I was I was interested in
surgery and psychiatry and I thought
that must be I must be the only person
on the planet who had who was torn
between those two fields and III said
exactly that to my advisor in medical
school who turned out I found out later
to be a famous psychoanalyst and and he
said to me no no it's actually not so
uncommon to be interested in surgery and
psychiatry and he conjectured that the
reason that people develop these these
two interests is that both fields are
about going beneath the surface and kind
of getting into the kind of secret yeah
I mean maybe you understand this as
someone who was interested in
psychoanalysis and or the stage there's
sort of a this you know there's a cliche
phrase that people use now on you know
like an NPR The Secret Life of Bees like
right yeah you know and that was part of
the thrill of surgery was seeing you
know the secret
you know the secret activity that's
inside everybody is abdomen and thorax
it's a very poetic way to connect it to
disciplines that are very practically
speaking different
each other that's for sure that's for
sure yes so so how do we get on to
medical school so so I was in medical
school and I I was doing a psychiatry
rotation and my kind of advisor in that
rotation asked me what I was interested
in and I said well maybe psychiatry he
said why and I said well I've always
been interested in how the brain works
I'm pretty sure that nobody's doing
scientific research that addresses my
interests which are I didn't have a word
for it then but I would have said about
cognition and he said well you know I'm
not sure that's true
you might you might be interested in
these books and he pulled down the the
PDB books from his shelf and they were
still shrink-wrapped
he hadn't read them but he handed to me
a hint that inform you said he you can
you feel free to borrow these and that
was you know I went back to my dorm room
and I just you know read them cover to
cover and what's PDP parallel
distributed processing which was the one
of the original names for deep learning
and so I apologize for the romanticized
question but what what idea in the space
of neural size in the space of the human
brain is to use the most beautiful
mysterious surprising what what had
always fascinated me even when I was a
pretty young kid I think was the the the
paradox that lies in the fact that the
brain is so mysterious and so it seems
so distant but at the same time it's
responsible for the the the the full
transparency of everyday life it's the
brain is literally what makes everything
obvious and familiar and and and there's
always one in the room with you yeah I I
used to teach when I taught at Princeton
I used to teach a cognitive neuroscience
course and the very last thing I would
say to the students was you know
people often when people think of
scientific inspiration the the metaphor
is often we'll look to the stars you
know the stars will inspire you to
wonder at the universe and and you know
think about your place in it and how
things work and and I'm all for looking
at the stars but I've always been much
more inspired and my sense of wonder
comes from the not from the distant
mysterious stars but from the extremely
intimately close brain yeah there's
something just endlessly fascinating to
me about that the like just like you
said the the one is close and yet
distant in in terms of our understanding
of it do you are you all so captivated
by the the fact that this very
conversation is happening because two
brains are communicating the I guess
what I mean is the subjective nature of
the experience if can take a small
taejun into the the mystical of it the
unconsciousness or or when you are
saying you're captivated by the idea of
the brain you are you talking about
specifically the mechanism of cognition
or are you also just like at least for
me it's almost like paralyzing the
beauty and the mystery of the fact that
it creates the entirety of the
experience not just the reasoning
capability but the experience well I I
definitely resonate with that that
latter thought and I I often find
discussions of artificial intelligence
to be disappointingly narrow you know
speaking of someone who has always had
an interest in in in art great it was
just gonna go there cuz it sounds like
somebody who has an interest in art yeah
I mean I there there there
there are many layers to you know to
full-bore him and experience and and in
some ways it's not enough to say oh well
don't worry you know we're talking about
cognition but we'll add emotion you know
yeah there's there's there's an
incredible scope to what humans go
through in in every moment and and yes
so it's that's part of what fascinates
me is that is that our brains are
producing that but at the same time it's
so mysterious to us how we literally our
brains are literally in our heads
producing mystics and yet there and yet
there's there it's so mysterious to us
and so and in the scientific challenge
of getting at the the the actual
explanation for that is so overwhelming
it's not that's just i don't know that
certain people have fixations on
particular questions and that's always
that's just always been mine yeah I
would say the poetry that is fascinating
and I'm really interested in natural
language as well and when you look at
our personal intelligence community it
always saddens me how much when you try
to create a benchmark for the community
together around how much of the magic of
language is lost when you create that
benchmark that there's something would
we talk about experience the the music
of the language the wit the something
that makes a rich experience something
that would be required to pass the
spirit of the Turing test is lost in
these benchmarks and I wonder how to get
it back in because it's very difficult
the moment you tried to do like real
good rigorous science you lose some of
that magic when you try to study
cognition in a rigorous scientific way
it feels like you're losing some of the
magic mm-hmm-hmm the the seen cognition
in a mechanistic way that AI vote at
this stage in our history well okay I I
agree with you but at the same time one
one thing that I found really exciting
about that first wave of deep learning
models in cognition was
there was the the fact that the people
who were building these models were
focused on the richness and complexity
of human cognition so an early debate in
cognitive science which I sort of
witnessed as a grad student was about
something that sounds very dry which is
the formation of the past tense but
there were these two camps one said well
the the mind encodes certain rules and
it also has a list of exceptions because
of course you know the rule is a DB but
that's not always what you do so you
have to have a list of exceptions and
and then there were the connectionists
who you know evolved into the deep
learning people who said well well you
know if you look carefully at the data
if you look at actually look at corpora
like language corpora it's it turns out
to be very rich because yes there are
there are there's a you know the there
most verbs that and you know you just
tack on e d and then there are
exceptions but there are also there's
also there are there are rules that in
you know there's the exceptions aren't
just random they there are certain clues
to which which which verbs should be
exceptional and then there are some
exceptions to the exceptions and there
was a word that was kind of deployed in
order to capture this which was quasi
regular in other words there are rules
but it's it's messy and there there's
their structure even among the
exceptions and and it would be yeah you
could try to write down you could try to
write down the structure in some sort of
closed form but really the right way to
understand how the brain is handling all
this and by the way producing all of
this is to build a deep neural network
and trained it on this data and see how
it ends up representing all of this
richness so the way that deep learning
was deployed in cognitive psychology was
that was the spirit of it it was about
that richness and that's something that
I always found very very compelling
still do
is it is there something especially
interesting and profound to you in terms
of our current deep learning neural
network artificial neural network
approaches and the whatever we do
understand about the biological neural
networks in our brain is there there's
some there's quite a few differences are
some of them to you either
interesting or perhaps profound in terms
of in terms of the gap we might want to
try to close in trying to create a human
level intelligence what I would say here
is something that a lot of people are
saying which is that one seeming
limitation of the systems that we're
building now is that they lack the kind
of flexibility the readiness to sort of
turn on a dime when this when the
context calls for it
that is so characteristic of human
behavior
so is that connected to you to the like
which aspect of the neural networks in
our brain is that connected to is that
closer to the cognitive science level of
now again see like my natural
inclination is to separate into three
disciplines of neuroscience cognitive
science and psychology and you've
already kind of shut that down by saying
you you're kind of see them as separate
but just to look at those layers I guess
where is there something about the
lowest layer of the way the neural
neurons interact and that is profound to
you in terms of this difference to the
artificial neural networks or is all the
difference the key difference is at a
higher level of abstraction one thing I
often think about is that um
you know if you take an introductory
computer science course and they are
introducing you to the notion of Turing
machines one way of articulating what
the significance of a Turing machine is
is that it's a machine emulator it's it
can emulate any other machine and that
that to me you know that that and it was
that way of looking at a deterring
machine you know it really sticks with
me I think of humans as maybe sharing in
some of that character
we're capacity limited we're not Turing
machines obviously but we have the
ability to adapt behaviors that are very
much unlike anything we've done before
but there's some basic mechanism that's
implemented in our brain that allows us
to run run software but you're just in
that point you mentioned into a machine
but nevertheless it's fundamentally our
brains are just computational devices in
your view is that what you're getting
like is it I was a little bit unclear to
this line you drew mmm is is is there
any magic in there or is it just basic
computation I'm happy to think of it as
just basic computation but mind you I
won't be satisfied until somebody
explains to me how what the basic
computations are that are leading to the
full richness of human cognition yes I
mean it's not gonna be enough for me to
you know understand what the
computations are that allow people to
you know do arithmetic or play chess
I want I want the whole whole you know
the whole thing in a small tangent
because you kind of mentioned
coronavirus the this group behavior oh
sure I is that is there something
interesting to your search of
understanding the human mind where law
behavior of large groups of just
behavior of groups is interesting you
know seeing that as a collective mind as
a collective intelligence perhaps seeing
the groups of people as a single
intelligent organisms especially looking
at the reinforcement learning work mm-hm
even done recently well yeah I can't I
can't I mean I
I have the I have the the honor of
working with a lot of incredibly smart
people and I wouldn't want to take any
credit for for leading the way on the
the multi-agent work that's come out of
out of my group or deep mine lately but
I do find it fascinating and I mean I
think there you know I think it it can't
be debated you know the human behavior
arises within communities that just
seems to me self-evident but to me so it
is self-evident but that seems to be a
profound aspects of something that
created that was like if you look at
like 2001 Space Odyssey when that well
the monkeys touch the yeah like that's
the magical moment I think Eva Hari
argues that the ability of our large
numbers of humans to hold an idea to
converge towards idea together like you
said shaking and bumping elbows somehow
converge like without even like like
without you know without being in a room
all together just kind of this like
distributed convergence towards an idea
yeah over a particular period of time
seems to be fundamental to to just every
aspect of our cognition of our
intelligence because humans I will talk
about reward but it seems like we don't
really have a clear objective function
under which we operate but we all kind
of converge towards one somehow and that
that to me has always been a mystery
that I think is somehow productive for
also understanding AI systems but I
guess I guess that's the next step the
first step is trying to understand the
mind well I don't know I mean I think
there's something to the argument that
that kind of bottom like strictly
bottom-up approach is wrongheaded in
other words you know there are there are
basic phenomena that you know you know
basic aspects of human intelligence that
you know can only be understood in in
the context of groups I'm perfectly open
to that I've never been particularly
convinced by the notion that we should
be we should
consider intelligence to in here at the
level of communities I I don't know why
I just I'm sort of stuck on the notion
that the basic unit that we want to
understand is individual humans and if
if we have to understand that in the
context of other humans fine but for me
intelligence is just I'm stubbornly I
stubbornly defined it as something that
is you know an aspect of an individual
human
that's just my time with you with us
that could be the reduction is dream of
a scientist because you can understand a
single human it also is very possible
that intelligence can only arise when
there's multiple intelligences when
there's multiple sort of it's a sad
thing if that's true because it's very
difficult to study but if it's just one
human that one human will not be Homo
Sapien would not become that intelligent
that's a real that's a possibility I I'm
with you well one thing I will say along
these lines is that I think I think a
serious effort to understand human
intelligence and maybe to build a
human-like intelligence needs to pay
just as much attention to the structure
of the environment as to the structure
of the you know the the cognizing system
whether it's a brain or an AI system
that's one thing I took away actually
from my early studies with the pioneers
of neural network research people like
Jay McClelland and John Cohen you know
the the structure of cognition is really
it's only a only partly a function of
the the you know the the architecture of
the brain and the learning algorithms
that it implements what it's really a
function what what what really shapes it
is the interaction of those things with
the structure of the world in which
those things are embedded right and
that's especially important for this
made most clear and reinforcement
learning where I simulate an environment
as you can only learn as much as you can
simulate and that's what made well deep
mine made very clear well the other
aspect of the environment which is the
self play mechanism of the other agent
of the competitive behavior which the
other agent becomes the environment
essentially yeah and that's I mean one
of the most exciting ideas in AI is the
self play mechanism that's able to learn
successfully so there you go there's a
there's a thing where competition is
essential for yeah earning yeah at least
in that context so if we can step back
into another beautiful world which is
the actual mechanics the dirty mess of
it of the human brain is is there
something for people who might not know
is there something in common or describe
the key parts of the brain that are
important for intelligence or just in
general what are the different parts of
the brain that you're curious about that
you've studied and that are just good to
know about when you're thinking about
cognition well my area of expertise if I
have one is prefrontal cortex so what's
that or do we it depends on who you ask
the the the the the technical definition
is has is anatomical it there are there
are parts of your brain that are
responsible for motor behavior and
they're very easy to identify and the
region of your cerebral cortex they out
needs sort of outer crust of your brain
that lies in front of those is defined
as the prefrontal cortex and when you
say anatomical sorry to interrupt
so that's referring to sort of the
geographic region yeah as opposed to
some kind of functional definition
exactly so that it this is kind of the
coward's way out and I'm telling you
what the prefrontal cortex is just in
terms of like what part of the
real-estate it occupies the thing in the
front of them yeah exactly and and in
fact the early history of
you know the neuroscientific
investigation of what this like front
part of the brain does is sort of funny
to read because you know it was really
it was really World War one that started
people down this road of trying to
figure out what different parts of the
brain the human brain do in the sense
that there were a lot of people with
brain damage who came back from the war
with brain damage and it that provided
as tragic as that was it provided an
opportunity for scientists to try to
identify the functions of different
brain regions and it wasn't actually
incredibly productive but one of the
frustrations that neuropsychologist face
was they couldn't really identify
exactly what the deficit was that arose
from damage to this these most you know
kind of frontal parts of the brain it
was just a very difficult thing to you
know to you know to pin down there were
a couple of neuropsychologists who
identified through through a large
amount of clinical experience in close
observation they started to put their
finger on a syndrome that was associated
with frontal damage actually one of them
was a russian neuropsychologist named
Gloria who you know students of
cognitive psychology still read and and
what he started to figure out was that
the frontal cortex was somehow involved
in flexibility the in in in guiding
behaviors that required someone to
override a habit or to do something
unusual or to change what they were
doing in a very flexible way from one
moment to another so focused on like new
experiences and so the so the way your
brain processes and acts in new
experiences yeah what later helped bring
this function into better focus was a
distinction between controlled and
automatic behavior or - in in other
literature's this is referred to as
habitual behavior versus goal directed
behavior so it's very
very clear that the human brain has
pathways that are dedicated to habits to
things that you do all the time and they
need to be autumn at they don't require
you to concentrate too much so the that
leaves your cognitive capacity freed you
do other things just think about the
difference between driving when you're
learning to drive versus driving after
you're fairly expert there are brain
pathways that slowly absorb those
frequently performed behaviors so that
they can be habits so that they can be
automatic for that that's kind of like
the purest form of learning
I guess it's happening there which is
why I mean this is kind of jumping ahead
which is why that perhaps is the most
useful for us to focusing on and trying
to see how artificial intelligent
systems can learn is that the way it's
interesting I I do think about this
distinction between controlled and
automatic or goal directed and habitual
behavior a lot in thinking about where
we are in AI research but but just to
finish to finish the the kind of
dissertation here the the the role of
the front of the prefrontal cortex is
generally understood these days sort of
in in Contra distinction to that
habitual domain in other words the
prefrontal cortex is what helps you
override those habits it's what allows
you to say well what I usually do in
this situation is acts but given the
context I probably should do why I mean
the elbow bump is a great example right
if you know reaching out and shaking
hands is a probably habitual behavior
and it's the prefrontal cortex that
allows us to bear in mind that there's
something unusual going on right now
and in this situation I need to not do
the usual thing the kind of behaviors
that Luria reported and he built tests
for you know detect
these kinds of things we're exactly like
this so in other words when I stick out
my hand I want you instead to present
your elbow a patient with frontal damage
would have great deal of trouble with
that you know somebody preferring their
hand would elicit you know a handshake
the prefrontal cortex is what allows us
to say oh no hold on that's the usual
thing but I'm I have the ability to bear
in mind even very unusual contexts and
to reason about what behavior is
appropriate there just to get a sense is
our us humans special in the presence of
the prefrontal cortex do mice have a
prefrontal cortex do other mammals that
we can study if you if no then how do
they integrate new experiences yeah
that's a that's a really tricky question
and a very timely question because we
have revolutionary new technologies for
monitoring measuring and also causally
influencing neural behavior in mice and
fruit flies and these techniques are not
fully available even for studying brain
function in in monkeys let alone humans
and so it's a it's a very sort of for me
at least a very urgent question whether
the kinds of things that we want to
understand about human intelligence can
be pursued in these other organisms and
you know to put it briefly there's
disagreement
you know people who study fruit flies
will often tell you hey root flies are
smarter than you think
and they'll point to experiments where
fruit flies were able to learn new
behaviors we're able to generalize from
one stimulus to another in a way that
suggests that they have abstractions
that guide their generalization I've had
many conversations in which
I will start by observing you know
recounting some some observation about
Mouse behavior where it seemed like mice
were taking an awfully long time to
learn a task that for a human would be
profoundly trivial and I will conclude
from that that mice really don't have
the cognitive flexibility that we want
to explain and that a mouse researcher
will say to me well you know hold on
that experiment may not have worked
because you asked a mouse to deal with
stimuli and behaviors that were very
unnatural for the mouse if instead you
kept the logic of the experiment the
same but put you know kind of put it in
a you know presented it the information
in a way that aligns with what mice are
used to dealing with in their natural
habitats you might find that a mouse
actually has more intelligence than you
think and then they'll go on to show you
videos of mice doing things in their
natural habitat which seem strikingly
intelligent you know dealing with you
know physical problems you know I have
to drag this piece of food back to my
you know back to my lair but there's
something in my way and how do I get rid
of that thing so I think I think these
are open questions to put it you know to
sum that up and then taking a small step
back so related to that is you kind of
mentioned we're taking a little shortcut
by saying it's a geographic geographic
part of the the prefrontal cortex is a
region of the brain but if we what's
your sense in a bigger philosophical
view prefrontal cortex and the brain in
general do you have a sense that it's a
set of subsystems in the way we've kind
of implied that are they're pretty
distinct or to what degrees of that or
to what degree is it a giant
interconnected mess where everything
kind of does everything and is
impossible to disentangle them I think
there's overwhelming evidence that
there's functional differentiation that
it's clearly not the case that all parts
of the brain are doing the same thing
this follows immediately from the kinds
of studies of brain damage that we were
chatting about before it's obvious from
what you see if you stick an electrode
in the brain and measure what's going on
at the level of you neural activity
having said that there are two other
things to add which kind of I don't know
maybe tug in the other direction
one is that it's when you look carefully
at functional differentiation in the
brain what you usually end up concluding
at least this is my observation of the
literature is that the the differences
between regions are graded rather than
being discrete so it doesn't seem like
it's easy to divide the brain up into
true modules where you know that are you
know that have clear boundaries and that
have you know like like clear channels
of communication between them instead
lies to the prefrontal cortex yeah oh
yeah yeah the prefrontal cortex is made
up of a bunch of different sub regions
the you know the functions of which are
not clearly defined and which then the
borders of which seem to be quite vague
and then then there's another thing
that's popping up in very recent
research which you know which involves
application of these new techniques
which there are a number of studies that
suggest that parts of the brain that we
would have previously thought were quite
focused in their function are actually
carrying signals that we wouldn't have
thought would be there for example
looking in the primary visual cortex
which is classically thought of as
basically the first cortical way station
for processing visual information
basically what it should care about is
you know where are the edges in this
scene that I'm viewing
it turns out that if you have enough
data you can recover information from
primary visual cortex about all sorts of
things like you know what what behavior
the animal is engaged in right now and
what what how much reward is on offer in
the task that it's pursuing so it's
clear that even even regions whose
function is pretty well defined at a
course brain are nonetheless carrying
some information about information from
very different domains so you know the
history of neuroscience is sort of this
oscillation between the two views that
you articulated you know the kind of
modular view and then the big you know
mush view and you know I think I guess
we're gonna end up somewhere in the
middle which is which is unfortunate for
our understanding because the mod
there's something about our you know
conceptual system that finds it's easy
to think about a modular AI system and
easy to think about a completely
undifferentiated system but something
that kind of lies in between is
confusing but we're gonna have to get
used to it I think unless we can
understand deeply the lower-level
mechanism and you're all communicating
yeah so yeah on that on that topic you
kind of mention information just to get
a sense I imagine something that there's
still mystery and disagreement on is how
does the brain carry information and
signal like what in your sense is the
basic mechanism of communication in the
brain well I I guess I'm old-fashioned
in that I consider the networks that we
use in deep learning research to be a
reasonable approximation to you know the
the mechanisms that carry information in
the brain so the the the usual way of
articulating that is to say what really
matters is a rate code it what matters
is how how how quickly is an individual
neuron spiking how you know what's the
frequency at which it's spiking is the
timing of the spike yeah is it is it
firing fast or slow let's you know let's
put a number on that and that number is
enough to capture what what neurons are
doing there's you know there's
still uncertainty about whether that's
an an adequate description of how
information is is transmitted within the
brain there you know there are there are
studies that suggest that the precise
timing of spikes matters there are
studies that suggest that there are
computations that go on within the
dendritic tree within a neuron that are
quite rich and structured and that
really don't equate to anything that
we're doing in our artificial neural
networks having said that I feel like we
can get I feel like I feel like we're
getting somewhere
by sticking to this high level of
abstraction just the rate and by the way
we're talking about the electrical
signal that I remember reading some
vague paper somewhere recently where the
mechanical signal like the vibrations or
something of the of the neurons also
communicates and if I haven't seen that
but this is there somebody was arguing
that the the electrical signal this is
in nature paper something like that
where the electrical signal is actually
a side effect of the mechanical signal
but I don't think they changes the story
but it's almost the interesting idea
that there could be a deeper it's like
it's always like in physics with quantum
mechanics there's always a deeper story
that could be underlying the whole thing
but you think is basically the rate of
spiking that gets us that's like the
lowest hanging fruit that can get us
really far this is a this is a classical
view I mean this is this is this is not
the only way in which this stance would
be controversial is it you know in the
sense that there are there are members
of the neuroscience community who are
interested in alternatives but this is
really a very mainstream view the way
that neurons communicate is that
neurotransmitters arrive or you know at
a at you know they they wash up on a
neuron the neuron has receptors for
those transmitters the the the the the
meeting of the transmitter with these
receptors changes the voltage of the
neuro
and if enough voltage change occurs then
a spike occurs right one of these like
discrete events and it's that spike that
is conducted down the axon and leads to
neuroses this is just this is just like
neuroscience 101 this is like the way
the brain is supposed to work now what
we do when we build artificial neural
networks of the kind that are now
popular in the AI community is that we
don't worry about those individual
spikes we just worry about the frequency
at which those spikes are being
generated and the you know we consider
people talk about that as the activity
of a neuron and you know so the the
activity of units in a deep learning
system is you know broadly analogous to
the spike rate of a neuron there there
are people who who believe that there
are other forms of communication in the
brain in fact I've been involved in some
research recently that suggests that the
voltage the voltage fluctuations that
occur in populations of neurons that
aren't you know that are sort of below
the level of a spike production may be
important for for communication but I'm
still pretty old-school in the sense
that I think that the the things that
we're building in AI research constitute
reasonable models of how a brain would
work let me ask just for fun a crazy
question because I can do you think it's
possible were completely wrong about the
way this basic mechanism of your
neuronal communication that the
information is thought is some very
different kind of way in the brain oh
heck yes you know I would look I
wouldn't be a scientist if I didn't
think there was any chance we were wrong
but but I mean if you look if you look
at the history of deep learning research
as it's been applied to neuroscience of
course the vast majority of deep
learning research these days isn't about
neuroscience but you know if you go back
to the 1980s there's a you know sort of
an unbroken chain of research in in
which a particular strategy is taken
which is
hey let's train a deep a deep learning
system let's train a multi-layer neural
network on this task that we trained our
you know backbone or our monkey on or
this human being on and then let's look
at what the units deep in the system are
doing and let's ask whether what they're
doing resembles what we know about what
neurons deep in the brain are doing and
over and over and over and over that
strategy works in the sense that the
learning algorithms that we have access
to which typically send our own back
propagation they give rise to you know
patterns of activity patterns of
response patterns of like neuronal
behavior and these in these artificial
models that look haunting Lisa
hauntingly similar to what you see in
the brain and you know is that a commune
yes incidences at a certain point it
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