Kind: captions
Language: en
the following is a conversation with jay
mcclelland a cognitive scientist at
stanford and one of the seminal figures
in the history of artificial
intelligence and specifically neural
networks having written the parallel
distributed processing book with david
rommelhart who co-authored the
backpropagation paper with jeff hinton
in their collaborations they've paved
the way for many of the ideas at the
center of the neural network-based
machine learning revolution of the past
15 years
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this is the lex friedman podcast and
here is my conversation with jay
mcclelland
you are one of the seminal figures in
the history of neural networks
at the intersection of uh cognitive
psychology and computer science
what do you have over the decades
emerged as the most beautiful aspect
about neural networks
both artificial and biological
the fundamental thing i think about with
neural networks is how they allow us to
link
biology
with
the mysteries of thought and
um
you know in the
when i was first entering the field
myself in
the late 60s early 70s
cognitive psychology had just become a
field there was a book published in 67
called cognitive psychology
um
and the author said
that
you know the study of the nervous system
was only of peripheral interest it
wasn't going to tell us anything about
the mind
and i
didn't
agree with that i i always felt oh look
i'm i'm a physical
being i
from dust
to dust you know ashes to ashes and
somehow i emerged from that
um
so that's really interesting so there
was a sense with cognitive
psychology that
in understanding the sort of neuronal
structure of things you're not going to
be able to understand the mind
and then your senses if we study these
neural networks we might be able to get
at least very close to understanding the
fundamentals of the human mind yeah
i used to think um where i used to talk
about the idea of awakening from the
cartesian dream
so descartes
you know thought about these things
right
he
he was walking in the gardens of
versailles one day and he stepped on a
stone
and a statue moved
and he walked a little further stepped
on another stone and another statue
moved and he like
why did the statue move when i stepped
on the stone and he went and talked to
the gardeners and he found out that they
had a hydraulic system
that allowed
the physical contact with the stone to
cause water to flow in various
directions which caused water to flow
under the statue and move the statue
and he
used this as
the beginnings of a theory about how
animals
act
and
he had this notion that
these little fibers that people had
identified that weren't carrying the
blood
you know were these little hydraulic
tubes that if you touch something there
would be pressure and it would send a
signal of pressure to the
other parts of the system and that would
cause action
so he had a mechanistic theory of
animal behavior
and he thought that the human
had this animal
body but that some
divine something else had to have come
down and been placed in him
to give him the ability to think
right so
the physical world includes the body in
action but it doesn't include thought
according to descartes right
and so the study of physiology at that
time was the study of sensory systems
and motor systems and
things that you could
directly measure when you stimulated
neurons and stuff like that
and um
the study of cognition was something
that
you know was tied in with abstract
computer algorithms and things like that
but when i was an undergraduate i
learned about the physiological
mechanisms uh and so when i'm studying
cognitive psychology as a first year phd
student i'm saying
wait a minute the whole thing is
biological
you had that intuition right away that
was seemed obvious to you yeah yeah
it isn't that magical though that from
just
the little bit of biology can emerge the
full beauty of the human experience is
that
why is that so obvious to you well i
it's obvious
and not obvious at the same time um and
i think about darwin in this context too
because
darwin
knew very early on that none of the
ideas that anybody had ever offered
gave him a sense of understanding how
evolution
could have worked
but he wanted to figure out how it could
have worked that was his goal
and
he
spent a lot of time
working on this idea and coming you know
reading about things that gave him hints
and thinking they were interesting but
not knowing why and
drawing more and more pictures of
different birds that differ slightly
from each other and so on you know and
and then then he figured it out
but after he figured it out he had
nightmares about it
he would dream about the complexity of
the eye
and the arguments that people had given
about how ridiculous it was to imagine
that
that could have ever emerged from some
sort of you know
unguided process right that it hadn't
been the product of design
and and uh so he he didn't publish for a
long time in part because he was
scared of his own ideas he didn't think
they could probably possibly be true
yeah
um
but then you know by the time
the 20th century rolls around we all
uh
you know we understand that evolut or
many people understand or believe
that evolution uh produced you know the
entire uh range of uh animals that there
are
uh and uh you know descartes idea starts
to seem a little wonky after a while
right like well wait a minute
um
there's
the apes and the chimpanzees and the
bonobos and you know like they're pretty
smart in some ways you know so what
oh you know somebody comes oh there's a
certain part of the brain that's still
different they don't you know there's no
hippocampus in the
monkey brain it's only in the human
brain
uh huxley had to do a surgery in front
of many many people in the late 19th
century to show to them there's actually
a hippocampus
in the chimpanzees brain you know
so so their continuity of the species is
another
element uh that you know contributes
to um
this sort of you know
idea that we are
ourselves uh a total product of nature
um and uh
that to me is the is the magic in the
mystery how
how nature could actually um
you know give rise to
uh
organisms that have the capabilities
that we have
so it's interesting because even the
idea of evolution is hard for me to keep
all together in my mind so because we
think of a human time scale
it's hard to imagine that like like the
the development of the human eye would
give me nightmares too
because you have to think across many
many many generations
and it's very tempting to think about
kind of
a growth of a complicated object and
it's like how is it possible for that
such
such a thing to be built because also
me from a robotics engineering
perspective it's very hard to build
these systems how can
through an undirected process can a
complex thing be designed it seems not
it seems wrong yeah so that's absolutely
right and i you know
a slightly different career path that
would have been equally interesting to
me would have
would have been um to actually study the
process of
embryological development
flowing on into brain development and
the
um
exquisite sort of laying down of
pathways and so on that occurs in the
brain and uh i know the slightest bit
about that it's not my field but
um
there are
you know fascinating
aspects to this process that
eventually result in the
you know the complexity of of uh
various brains
at least you know one thing um we're
um
in in the field i think people have felt
for a long time
it
in the study of vision the continuity
between
humans and non-human animals has been
has been second nature for a lot longer
i was having i had this conversation
um with somebody who's a vision
scientist and you're saying oh we we
don't have any problem with this you
know the monkey's visual system and the
human visual system extremely similar
um
up to certain levels of course they they
diverge after a while but um the first
the the visual pathway from the eye to
the brain and the first few
um
layers of
cortex um or cortical areas i guess one
would say
uh are are extremely similar
yeah so on the cognition side is where
the leap seems to happen with humans
that it does seem we're kind of special
and that's a really interesting question
when thinking about alien life or if
there's other intelligent alien
civilizations out there is how special
is this leap so one special thing seems
to be the origin of life itself however
you define that there's a gray area and
the other leap this is very biased
perspective of a human is the
the
origin of intelligence
and again from an engineer perspective
it's a
difficult question to ask an important
one
is how difficult does that leap how
special were humans
did uh did uh a monolith come down did
aliens bring down a monolith and some
apes had to touch a monolith but
to get it it's a lot like dark descartes
uh you know idea right exactly i it's
but it just seems
that it seems one heck of a leap yeah to
get to this level of intelligence yeah
and you know so chomsky um
uh argued um
that you know some
uh
genetic fluke occurred a hundred
thousand years ago and
you know just happened that some
human some
homonym
of current humans
had this
one genetic
tweak that resulted in
language yeah and
language
then provided
this
special thing that separates us from all
other animals
um
i'm
i think there's a lot of truth to the
value and importance of language
but i think it comes along with
um the evolution of a lot of other
related things related to sociality
and mutual engagement with others
and um
establishment of
i don't know rich
mechanisms for
organizing an understanding of the world
which language then plugs into right so
it's uh
language is a tool
that allows you to do this kind of
collective intelligence and whatever is
at the core of
the thing that allows for this
collective intelligence is the main
thing
and it's interesting to think about that
one fluke
one mutation could lead to the like the
the first crack open opening of the door
to human intelligence like all it takes
is one like evolution just kind of opens
the door a little bit and then it time
and selection takes care of the rest
you know there's so many fascinating
aspects to these kinds of things so
we think of evolution as continuous
right we think oh yes okay over
500 million years there could have been
this
you know relatively continuous
uh changes and
um
but that's not what
anthropologists
evolutionary biologists found from the
fossil record they found
you know
hundreds of years of
hundreds of millions of years of stasis
and then you know suddenly a change
occurs well suddenly on that scale is a
million years or something
but but or even 10 million years but
but um
the concept of punctuated equilibrium
was a very important concept in
evolutionary biology
uh and
that
also feels
somehow right about
you know
the stages of our mental abilities we
we seem to have a certain kind of
mindset at a certain age and then
at another
another age we like look at that
four-year-old and say oh my god how
could they have thought that way
so piaget was known for this kind of
stage theory of child development right
and you look at it closely and suddenly
those stages are so discreet and the
transitions but the difference between
the four-year-old and the seven-year-old
is profound and
that's another thing that's always
interested me is how we
something happens over the course of
several years of experience where at
some point we reach the point where
something like an insight or a
transition or a new stage of development
occurs and uh
you know these kinds of things can be
understood
um
in complex systems uh research and so um
evolutionary biology developmental
biology
cognitive development are all things
that have been approached in this kind
of way yeah
just like you said i find both
fascinating
those early years of human life but also
the early like
minutes days of from the embryonic
development
to like how from embryos you get like
the brain that development
again from the engineering perspective
is fascinating so it's not so the early
when you deploy the brain to the human
world and it gets to explore that world
and learn that's fascinating but just
like the assembly of the mechanism that
is capable of learning that's like
amazing the stuff they're doing with
like brain organoids
where you can
build many brains and study that
um
self-assembly of a mechanism from like
the dna material that that's like what
the heck
you have literally like biological
programs
that just generate a system
this mushy thing
that's able to be robust and learn in a
very unpredictable world
and learn seemingly arbitrary things or
like
a very large number of things that
enable survival yeah
ultimately
um that is a very important part of the
whole process of you know understanding
this sort of
emergence of mind from brain kind of
kind of thing and the whole thing seems
to be pretty continuous so let me uh let
me step back to neural networks for for
another brief
minute you wrote parallel distributed
processing books that explored ideas of
neural networks
in the 1980s
together with a few folks but the books
you wrote with
david uh ronald hart who is the first
author on the back propagation paper
with jeff hinton
so these are just some figures at the
time that were thinking about these big
ideas
what are some memorable moments of
discovery and beautiful ideas from those
early days
i'm going to start
sort of with my own
process in the
mid 70s
and then into the late 70s
when
i met jeff hinson and
he came to san diego
and we were all together
in
my time in graduate school as i've
already described to you i had this sort
of feeling of
okay i'm really interested in human
cognition but
this disembodied sort of way of thinking
about it that i'm getting from the
current
mode of thought about it is isn't
working fully for me
and
when i
got my assistant professorship i went to
ucsd and um
that was in 1974.
something amazing had just happened dave
rummelhart had written a book together
with another man named don norman
and the book was called explorations in
cognition and it was
a series of chapters exploring
interesting questions about cognition
but in a completely sort of
abstract you know non-biological kind of
way and i'm saying gee this is amazing
i'm coming to this community where
people can get together and feel like
they've collectively exploring
you know ideas and um
it was a book that had a lot of
i don't know lightness to it and you
know
the the don norman who was
the the more senior figure the roman
heart at that time who led that project
um you know cr always created this
spirit of playful exploration of ideas
and so i'm like wow this is great
but
i was also
you know still trying to get from
the neurons to the
to the cognition
and
i realized at one point i i got this
opportunity to go to a conference where
i heard a talk by a man named james
anderson who is an engineer
but
by then a professor in a psychology
department who had used
linear algebra to create neural network
models of perception and categorization
and memory and i
just
blew me out of the water that one could
you know create a model
that was simulating neurons not
just
kind of
engaged in a stepwise
algorithmic process that was construed
abstractly but it was
simulating remembering and recalling and
um
recognizing the prior occurrence of a
stimulus or something like that so for
me this was
a bridge between the mind and the brain
and i just like stuck and i i remember i
was walking across campus one day in
1977 and i
almost felt like saint paul on the road
to damascus i said to myself you know if
i think about the mind
in terms of a neural network it will
help me answer the questions about the
mind that i'm trying to answer
and that really excited me
so
i think that a lot of people were
becoming excited about that and one of
those people
was jim anderson who i had mentioned
another one was steve grossberg who had
been
writing about neural networks since the
60s
and jeff hinton was yet another and
his phd dissertation showed up uh in an
applicant pool
to a postdoctoral training program
that dave
and don
the two men i mentioned before remember
heart and norman
were administering and rommelhardt got
really excited about hinton's phd
dissertation
um and so
uh hinton was one of the first um people
who came and joined this group of
postdoctoral scholars
uh that was funded by this this
wonderful grant that they got
another one who is also well known in
neural network circus
circles is pulse milenski he was another
one of that group
anyway
um
jeff and jim anderson organized a
conference at ucsd
uh where we we were and uh it was called
parallel models of associative memory
and it brought all the people together
who had been thinking about these kinds
of ideas
in 1979 or 1980 and
this
this began
to kind of
really resonate with some of rommel
hart's um
own thinking some of his reasons for
wanting something other than the kinds
of computation he'd been doing so far so
let me talk about ronald hart now for a
minute okay with that context well let
me also just pause because he said so
many interesting things before we go to
roma heart so first of all for people
who are not familiar
uh neural networks are at the core of
the machine learning deep learning
revolution of today uh jeffrey hidden
that we mentioned is one
of the figures that were important in
the history like yourself in the
development of these neural networks
artificial neural networks that are then
used for the machine learning
application like i mentioned the back
propagation paper is one of the
optimization
mechanisms by which these uh networks uh
can learn
and uh
the word parallel is really interesting
so it's it's almost like synonymous from
a computational perspective what how you
thought at the time about
neural networks that is parallel
computation
is that would that be fair to say well
yeah the the parallel the word parallel
in this
you know comes from the idea that each
neuron is
an independent computational unit right
it it gathers data from other neurons it
integrates it in a certain way and then
it produces a result and it's a very
simple little computational unit but it
it's autonomous in the sense that
you know it does its thing right it's
it's in a biological medium where it's
getting nutrients and various uh
chemicals from that medium
um but it's uh you know you can think of
it as almost like a little
little computer in and of itself
so the idea is that each you know our
brains have oh look you know a hundred
or
hundreds
almost a billion of these
little neurons right
um
and they're all capable of doing their
work at the same time so it's like
instead of just a single central
processor that's engaged in you know
chug chug one step after another
we have
a billion of these little computational
units working at the same time so at the
time that's i don't know maybe you can
comment it seems to me
even still to me uh quite a
revolutionary way to think about
computation
relative to
the development of theoretical computer
science alongside of that where it's
very much like sequential computer
you're analyzing algorithms that are
running on a single computer that's
right you're saying
wait a minute what what
why don't we take a really dumb very
simple computer and just have a lot of
them interconnected together and they're
all operating in their own little world
and they're communicating with each
other and thinking of computation in
that way and from that kind of
computation
on trying to understand how things like
certain characteristics of the human
mind can emerge
right that's quite a revolutionary way
of thinking i would say
well yes i agree with you and um
there's still this sort of sense
of
not sort of knowing how we
kind of get all the way there um i think
and this very much remains
at the core of the questions that
everybody's asking about the
capabilities of deep learning and all
these kinds of things but if i could
just play this out a little bit
a a convolutional neural network or a
cnn which
you know many people may have heard of
is
a set of
you could think of it biologically as
a set of
collections of neurons each one had
each collection has
maybe
10 000 neurons in it but there's many
layers right some of these things are
hundreds or even a thousand layers deep
but
others are closer to the biological
brain and maybe they're like 20 layers
deep or something like that
so we have
within each layer we have
thousands of neurons or tens of
thousands maybe well in the brain we
probably have
millions in each layer so but we're
getting sort of similar in a certain way
right
um
and then we think okay at the bottom
level
there's an array of things that are like
the photoreceptors in there in the eye
they respond to the amount of light of a
certain wavelength at a certain location
on the
on the pixel array
so that's like the biological eye
and then there's several further stages
going up layers of these neuron-like
units
and
you go from that raw
input array of pixels to
a classification
you've actually built a system that
could do the same kind of thing that you
and i do when we open our eyes and we
look around and we see there's a cup
there's a cell phone
there's a water bottle
and these systems are doing that now
right
so
they are
in in terms of the parallel idea that we
were talking about before
they are doing this massively parallel
computation in the sense that
each of the neurons in each of those
layers is
thought of as computing its little bit
of
something about the input uh
simultaneously with all the other ones
in the same layer
we get to the point of abstracting that
away and thinking oh it's just one whole
vector that's being computed one one
activation pattern is computed in a
single step and that
that that abstraction is useful
but it's still that parallel
and distributed processing right each
one of these guys is just contributing a
tiny bit to that whole thing and that's
the excitement that you felt that from
these simple
things you can emerge when you add these
level of abstractions on it yeah you can
start getting all the beautiful things
that we think about as cognition right
and so okay so you have this conference
i forgot the name already but it's
parallel and something associative
memory and so on
very exciting technical and exciting
title and you started talking about dave
romohart so who is this person
that was so
you've spoken very highly of him yeah
can you tell me about him his ideas his
mind
who he was as a human being as a
scientist
so
dave came from a little tiny town in
western south dakota
and
his
mother was the librarian and his father
was the editor of the newspaper
um
and uh i know one of his brothers pretty
well um
they grew up
there were four brothers uh and uh
they grew up
together
uh and their father encouraged them to
compete with each other a lot
they competed in sports and they
competed in mind games you know um
i don't know things like sudoku and
chess and various things like that
and uh
dave um
was
a standout undergraduate he went
as at a younger age than most people do
to college
at the university of south dakota and
majored in mathematics and i don't know
how he got interested in
psychology but he
applied to the mathematical psychology
program at stanford and was accepted as
a phd student to study mathematical
psychology at stanford so mathematical
psychology
is the
use of mathematics to model
mental processes right so something that
i think these days might be called
cognitive modeling that whole space yeah
it's mathematical in the sense that
um
you say
if
this is true and that is true then i can
derive that this should follow okay and
so you say these are my stipulations
about the fundamental principles and
this is my prediction about behavior and
it's all done with equations it's not
done with a computer simulation
right so the you you solve the equation
and that tells you what the
probability that the subject will be
correct on the seventh trial of the
experiment is or something like that
right so it's a it's a it's a
it's a
use of mathematics to descriptively
characterize uh aspects of of behavior
and uh stanford at that time was the
place where
uh there were several really really
strong mathematical thinkers who were
also connected with three or four others
around the country
who um you know brought a lot of really
exciting ideas uh onto the table
and it was a very very prestigious part
of the field of psychology at that time
so remember heart comes into this
um
he was a very strong student within that
program
uh and
uh
he got
this
job at this brand new university in san
diego in 1967
he's one of the first assistant
professors in the department of
psychology
at ucsd
so
i got there
in 74 seven years later
and
reunhard at that time
was
still doing mathematical modeling
um
but he had gotten interested
in cognition he'd gotten interested in
understanding
and you know understanding i think
remains
you know what does it mean to understand
anyway you know
uh it's it's an interesting sort of
curious you know like how would we know
if we really understood something but
but he was
interested in building machines that
would you know hear a couple of
sentences and have an insight about what
was going on so for example one of his
favorite things at that time was
marky was sitting on the front step when
she heard the familiar jingle
of the good humor man
she remembered her birthday money and
ran into the house
what is margie doing
why
well there's a couple of ideas you could
have but
the most natural one is that
the good humor man brings ice cream she
likes ice cream she's
she knows she needs money to buy ice
cream so she's gonna run into the house
and get her money so she can buy herself
an ice cream it's a huge amount of
inference that has to happen to get
those things to link up with each other
and and he was interested in how the
hell that could happen
and he was trying to build um
you know good old-fashioned ai style
models of
representation of language and
and content of
you know things like
has money
so like a lot or like formal logic and
like knowledge bases like that kind of
stuff yeah so he was integrating that
with his thinking about cognition yes
the mechanisms cognition
how can they like mechanistically be
applied to build these knowledge like to
actually build something that looks like
a web of knowledge and thereby from
from there emerges something like
understanding whatever the heck that is
yeah
he was grappling
this was something that they grappled
with at the end of that book that i was
describing explorations and cognition
but he was realizing that the paradigm
of
good old-fashioned ai wasn't giving him
the answers to these questions
yeah
and by the way that's called good
old-fashioned ai now it was called that
well it was it was beginning to be
called that because it was from the 60s
yeah by by the late 70s it was kind of
old-fashioned and it hadn't really
panned out you know and
people were beginning to recognize that
but
and and remember heart was you know like
yeah it was part of the recognition that
this wasn't all working
anyway so he
started thinking in terms of
uh
the idea that we needed systems that
allowed us to integrate multiple
simultaneous constraints
in a way that would be mutually
influencing each other
so
he wrote a paper that
just
really
first time i read it i said oh well you
know yeah
but is this important but after a while
it just got under my skin
and it was called an interactive model
of reading and in this paper he laid out
the idea that
every aspect
of
our
interpretation of
what what's coming off the page when we
read
at every level of analysis you can think
of
actually depends on all the other levels
of analysis
so
what are the actual
pixels
making up each letter and
what do those pixels signify about which
letters they are and what do those
letters tell us about
what words are there
and what do those words tell us about
what ideas the author is trying to
convey and
so he had this model where you know we
have these
little tiny
uh
elements that represent each of the
pixels of each of the letters and then
other ones that represent the line
segments in them and other ones that
represent the letters and other ones
that represent the words
and
um at that time his idea was there's
this set of experts
there's an expert about how to
construct a line out of pixels and
another expert about how
which sets of lines go together to make
which letters and another one about
which letters go together to make mitch
words and another one about what the
meanings of the words are and another
one about
how the meanings fit together and you
know things like that and all these
experts are looking at this data and
they're
they're um
updating
hypotheses at
at other levels so the word expert can
tell the letter expert oh i think there
should be a t there because i think
there should be a word the here and the
bottom up sort of feature to letter
expert could say i think there should be
a t there too and if they agree
then you see a t right and so there's a
top-down bottom-up interactive process
but it's going on at all layers
simultaneously so everything can filter
all the way down from the top as well as
all the way up from the bottom and it's
a completely interactive bi-directional
parallel distributed process that is
somehow because of the abstractions is
hierarchical so like yeah so there's
different layers of responsibilities
different levels of responsibilities
first of all it's fascinating to think
about it in this kind of mechanistic way
so not thinking purely
from the structure of a neural network
or something like a neural network but
thinking about these little little guys
that work on letters and then
the letters come words and words become
sentences
and uh that's a very interesting
hypothesis that from that
kind of hierarchical structure can
emerge
uh understanding yeah so but the thing
is though i want to just sort of
relate this to the earlier part of the
conversation
um
when rommelhart was first thinking about
it there were these experts on the side
one for the features and one for the
letters and one for how the letters make
the words and so on
and and they would each be working sort
of
evaluating various propositions about
you know is this combination of features
here going to be one that looks like the
letter t and so on
and
and what he
realized kind of after reading hinton's
dissertation and
hearing about jim anderson's
linear algebra-based neural network
models that i was telling you about
before was that he could replace those
experts with neuron-like processing
units which just would have their
connection weights that would do this
job
so there so
what ended up happening was that remote
heart and i got together and we created
a model called the interactive
activation model of letter perception
which is
takes these
little pixel level uh inputs
constructs
uh
line segment features
letters and words but now we built it
out of a set of neuron like processing
units that are just connected to each
other with connection weights so the
unit for the word time has a connection
to the unit for the letter t in the
first position and the letter i in the
second position so on
and
because these connections are
bi-directional
if you have prior knowledge that it
might be the word time that starts to
prime the feature to the letters and the
features and if you don't then it's it
has to start bottom up but the
directionality just depends on where the
information comes in first and
and if you have context together with
features at the same time they can
convergently result in an emergent
perception and that
um that was the
um
the piece of work that we did together
that uh
sort of got us both completely convinced
that you know this neural network way of
thinking
was going to be able to
actually address the questions that we
were interested in as cognitive cycle so
the algorithmic side the optimization
side those are all details like when you
first start
the idea that you can get far with this
kind of way of thinking that in itself
is a profound idea so do you like the
term uh connectionism
to describe this kind of set of ideas
i think it's useful
it highlights
the
notion that the knowledge
that the system exploits is
in the connections between the units
right there isn't a separate
dictionary
the connections between the units
so
i already sort of
laid that on the table with the
connections from the letter units to the
unit for the word time right the unit
for the word time isn't a unit for the
word time for any other reason then it's
got the connections to the letters that
make up the word time
those are the units on the input that
excite it when it's excited that
it in a sense represents in the system
that
there's support for the hypothesis that
the word time is present in the input
um
but it's not
there there's the word time isn't
written anywhere inside the bottle it's
only written there in the picture we
drew of the model to say that's the unit
for the word time right yeah and um if
if if somebody wants to tell me well
what are the how do you spell that word
you have to use the connections from
that out to
to then get those letters for example
that's such a
that's a counter-intuitive idea
we humans want to think in this logic
way
this this idea of connectionism
it doesn't it's weird it's weird that
this is how it all works yeah but let's
go back to that cnn right that cnn with
all those layers of neuron like
processing units that we were talking
about before
it's going to come out and say this is a
cat that's a dog
but it has no idea why it said that it's
just got all these connections between
all these
layers of neurons like from the
very first layer to the you know the
like whatever these layers are they just
get numbered after a while because they
you know they they
somehow further in you go the more
the more abstract the features are but
it's a graded and continuous sort of
process of abstraction anyway and
you know it goes from very local very
very specific to much more sort of
global
but it's still
you know another sort of pattern of
activation over an array of units and
then at the output side it says it's cat
or it's a dot and when when we when i
open my eyes and say oh that's lex
or
um
oh
you know there's my own dog and i
recognize my dog
which is a member of the same species as
many other dogs but
i know this one because of some slightly
unique characteristics i don't know how
to describe you know what it is that
makes me know that i'm looking at lex or
at my particular dog right yeah or even
that i'm looking at a particular brand
of car like i could say a few words
about it but if i wrote you a paragraph
about the car you you would have trouble
figuring out which car is he talking
about right so the idea that we have
propositional knowledge of what
it is that allows us to recognize that
this is an actual instance of this
particular natural kind is um has always
been you know something that
uh
it never worked right you couldn't ever
write down a set of propositions for you
know visual recognition
and and and so
in that space it sort of always seemed
very natural that something more
implicit um
you know
you don't have access to what the
details of the computation were in
between you just get the result so
that's the other part of connectionism
you cannot
you don't read the contents of the
connections the connections only
cause
outputs to occur based on inputs
yeah it's it's and for us that like
final layer or
some particular layer is very important
the one that tells us that it's our dog
or like it's a cat or a dog but
you know each layer is probably equally
as important in the grand scheme of
things
like
there's no reason why the cat versus dog
is more important than the lower level
activations it doesn't really matter i
mean all of it is just this beautiful
stacking on top of each other and we
humans live in this particular layers
for us for us it's useful to
to survive to to use those
cat versus dog predator versus prey all
those kinds of things it's fascinating
that it's all continuous but then you
then ask
you know the history of artificial
intelligence you ask are we able to
introspect and convert
the very things that allow us to tell
the difference to cat and dog
into
logic into formal logic that's been the
dream
i would say that's still part of the the
dream of symbolic ai and
i've recently
talked to uh doug
leonard who created psych
and that's that's a project that lasted
for many decades
and still carries a sort of dream in it
right
um
but we still don't know the answer right
it seems like connectionism is really
powerful
but it also seems like there's this
building of knowledge
and so how do we
how do you square those two like do you
think the connections can contain the
depth of human knowledge and the depth
of what uh dave romohart was thinking
about of understanding
well uh that remains the 64 question and
um
with inflation that number yeah
maybe it's the 64 billion dollar
question now
uh
you know i think that
um
from
the emergence side which you know
uh
i placed myself on um
so i i used to sometimes tell people i
was a radical eliminative connectionist
because
i didn't want them to
think
that i wanted to build like anything
into the machine
but um i
don't like the word eliminative
uh anymore because it makes it seem like
it's wrong to think that there is this
emergent level of
understanding and
um
i disagree with that so i think you know
i would call myself in a radical
emergentist
uh connectionist rather than eliminative
connectionist right because i want to
acknowledge
that
that these higher level kinds of
aspects of our cognition are
are real but they're not
they're they don't
they don't exist as
such and so there was an example that uh
doug hofstetter used to use that i
thought was helpful in this respect
just the idea that
we could think about sand dunes
as entities
and talk about like how many there are
even
um
but we also
know that a sand dune is a very fluid
thing it's it's it's a it's a
it's a pile of sand that is capable of
moving around under the wind and the and
and um
you know
reforming itself in somewhat different
ways and and if we think about our
thoughts it's like sand dunes as being
things that
you know emerge from
uh
just the the way all the lower level
elements sort of work together and and
are constrained by external forces
then we can we can say yes they exist as
such but they they also
you know
we shouldn't treat them as completely
monolithic
entities that we
we can understand without understanding
sort of all of the stuff that
allows them to change in the ways that
they do and that's where i think the
connectionist feeds into the
into the cognitive it's like okay so if
the under if the substrate is parallel
distributed connectionist
um then it doesn't mean that the
contents of thought isn't you know like
abstract and symbolic and um
but it's more fluid maybe then uh
is easier to capture with a set of
logical expressions yeah that's a heck
of a sort of thing to put
at the top of
a resume radical emergingist
connectionist
so i there is
just like you said a beautiful dance
between that between the machinery of
intelligence
like the neural network side of it and
the stuff that emerges
i mean the stuff that emerges
seems to be um
i don't know
i don't know what that is
that it seems like maybe all
of reality is emergent
what i what i think about
this is made most distinctly
rich to me when i look at cellular
automata look at game of life
they're from very very simple things
very rich complex things emerge that
start looking very quickly like
organisms
that you forget that the forget how the
actual thing operates they start looking
like they're moving around they're
eating each other some of them are
generating
offspring
it you forget very quickly and it seems
like maybe it's something about the
human mind that wants to operate in some
layer of the emergent
and forget about the the mechanism of
how that emerges happens so i it just
like you are in your radicalness
i'm uh
also it seems like unfair to
eliminate the magic of that emergent
like eliminate the
the fact that that the emergence is real
yeah no i agree i'm not
that's why i got rid of eliminative
right yeah yeah because it seemed like
that was trying to say that you know
it's all
completely like
an illusion of some kindness well it it
you know who knows whether there isn't
there aren't some illusory
characteristics there
um and and
i i think that uh philosophically um
many people have have confronted that
possibility over time but
but uh
it it's still
important to
um you know accept it as magic right so
you know i think of fellini in this
context i think of
um others who have
appreciated uh the role of magic uh of
actual trickery in creating illusions
that
that move
that move us
you know had plato was odd to this too
it's like somehow or other these shadows
you know
give rise to something
much deeper than that and and that's
that's
so you know we won't try to figure out
what it is we'll just accept it as given
that that that occurs and um
you know but he was still on to the
magic of it yeah yeah we won't try to
really really really deeply understand
how it works we just enjoy the fact that
it's kind of fun
okay but you uh worked closely with dave
around my heart
he passed away
as a human being what do you remember
about him
do you miss the guy
absolutely
you know he passed away um
15 ish years ago now
and um
his
his demise was actually one of the most
poignant and
um
you know like relevant
uh tragedies um
relevant to our conversation he
started to
undergo a progressive
neurological condition
that
isn't fully understood that is to say
his particular
course isn't fully understood um
because
certain you know brain scans weren't
done in certain stages
and no autopsy was done or anything like
that
the wishes of the family um
so we don't know as much about the
underlying pathology as we might but
um
i had begun to get interested in this
neurological condition that might have
been the very one that he was succumbing
to
as my own efforts to uh understand
another aspect of this mystery that
we've been discussing
while he was beginning to get
progressively more and more affected
so
i'm going to talk about the disorder and
not about remember heart for a second
okay sure the disorder is something my
colleagues and collaborators have chosen
to call
semantic dementia
so
it's a specific form of
loss of mind
related to meaning
semantic dementia
and it's progressive
in the sense that the patient
loses the ability
to
appreciate the meaning of the
experiences that they have either from
touch from sight from sound
from language
they i hear sounds but i don't know what
they mean kind of thing
um
the
so as as this illness progresses it
starts with
the patient being unable to
um
differentiate like similar breeds of dog
or
remember
you know the the lower frequency
unfamiliar categories that they used to
be able to remember
but as it progresses
it
it it becomes more and more striking and
and
you know the the patient loses the
ability to recognize
um
you know things like
pigs and goats and sheep and calls all
middle-sized animals dogs and all can't
recognize rabbits and
and rodents anymore they call all the
little ones cats and they can't
recognize
hippopotamuses and and cows anymore they
call them all horses you know so
there was this one patient who
went through this progression where uh
at a certain point
any four-legged animal he would call it
either a horse or a dog or a cat
and if it was big he would tend to call
it a horse if it was small he'd tend to
call it a cat middle-sized onesie called
dogs
this is just a part of the syndrome
though it
the the patient loses the ability to
relate
uh concepts to each other so my my
collaborator in this work carolyn
patterson developed
a test called the pyramids and palm
trees test
so
you give the patient a picture of
pyramids and they have a choice which
goes with the pyramids
palm trees or pine trees
and
you know she showed that this wasn't
just a matter of language because
the patient's
loss of this ability shows up whether
you present the material with words or
with pictures the pictures
they can't put the pictures together
with each other properly anymore they
can't relate the pictures to the words
either they can't do word picture
matching but they've lost the conceptual
grounding
from either modality of input and
um so it's that's why it's called
semantic dementia the very semantics is
disintegrating
and and we we understand this in terms
of our
idea that distributed representation a
pattern of activation represents the
concepts really similar ones as you
degrade them they start being
you lose the differences and
and then um so the difference between
the dog and the goat sort of is no
longer part of the pattern anymore and
since dog is really familiar that's the
thing that remains and and we understand
that in the way the models work and
learn but
but remember heart underwent this this
condition so on the one hand it's a
fascinating aspect of parallel
distributed processing to me
uh and it reveals this uh this sort of
texture of distributed representation
in a very nice way i've always felt but
at the same time it was extremely
poignant because
this is exactly the condition that romal
heart was undergoing and there was a
period of time when he was
this man who had been the most
focused
um
goal-directed
competitive
um
thoughtful
person who was willing to work for years
to solve a hard problem you know he
he he starts to disappear
and
there was a period of time when it was
like
hard for any of us to really appreciate
that he was sort of in some sense
not
fully there anymore do you know if he
was able to introspect
this um
the solution of this you know the the
understanding mind
was he i mean this is one of the big
scientists that thinks about this yeah
was he able to look at himself and
understand the fading mind
you know
um we can we can contrast um
hawking and normal heart in this way and
i i like to do that to honor rummelhart
because i think rummelhart is sort of
like the hawking of
you know cognitive science to me in some
ways um
but both of them
suffered from a degenerative
condition
and in hawking's case it affected the
motor system
in in romelhart's case it's it's
affecting the semantics
uh and
um
not
not just the pure uh object semantics
but maybe the self semantics as well and
we don't understand that
broadly but but but it's
so i would say uh he didn't and this was
part of what from the outside was a
profound tragedy
but
but on the other hand at some level he
sort of did because
you know there was a period of time when
it finally was realized that he had
really become
profoundly impaired this was clearly a
biological condition and he wasn't you
know it wasn't just like he was
distracted that day or something like
that
so he retired
uh you know from his professorship at
stanford and he became
um he he
uh lived with his brother for a couple
years and then he moved into a
a facility for people with um
cognitive impairments
um
a
one that
you know many elderly people end up in
when they have cognitive impairments and
i
would spend time with him during that
period this was like in the late 90s
around 2000 even
and
you know i would we would go
bowling
and he could still bowl
uh and um
i after bowling i took him to lunch and
i i said
where would you like to go you want to
go to wendy's and he said nah
and i said okay well where you want to
go and he he just pointed he's turn here
you know so
he still had a certain amount of spatial
cognition and he could get me to the
restaurant
and then when we got to the restaurant
i i said what do you want to order and
um
he couldn't
come up with any of the words but he
knew where on the menu the thing was
that he wanted so
so fascinating it's it you know and
he couldn't say what it was but he knew
that that's what he wanted to eat and
and so there was you know that
it's it's it's like it isn't monolithic
at all this the our cognition is
is
you know first of all graded in certain
kinds of ways but also
multipartite there's many elements to it
and things
uh
certain sort of
partial competencies still exist in the
absence of
of other aspects of these competencies
so this is what always fascinated me
about
what
uh
used to be called cognitive
neuropsychology
you know the effects of brain damage on
cognition but in particular this gradual
disintegration part you know i'm a big
believer that the loss of a
human being that you value is as
powerful as you know first falling in
love with that human being i think
it's all a celebration of the human
being so the disintegration itself too
is a celebration yeah
yeah yeah and
but just to say something more about
the scientists and and the back
propagation idea that you mentioned um
so
in in 1982
hinton had been there as a postdoc and
organized that conference he'd actually
gone away and gotten an assistant
professorship and then
um there was this opportunity to bring
him back so jeff hinton was back
on a sabbatical san diego in san diego
and uh remember heart and i had decided
we wanted to do this
you know we thought it was really
exciting and
um our the papers on the interactive
activation model that i was telling you
about had just been published and we
both sort of saw a huge potential for
this work and
and and jeff was there and so the three
of us
uh
started a research group which we called
the pdp research group
and
several other people
came um francis crick
who was at the salk institute heard
about it from jeff
um and because jeff was known among
brits to be brilliant and francis was
well connected with his british con
friends so
francis crick came and a heck of a group
of people wow and uh uh several as paul
spalensky um was one of the other
postdocs he was still there as a postdoc
and
a few other people but anyway
jeff
talk to us about
learning
and
how we should think about
how you know learning occurs in
a neural network and he said
the problem
with the way you guys have been
approaching this is that you've been
looking for inspiration from biology
to tell you how
what the rules should be for how the
synapses should change the strengths of
their connections how the connections
should form
he said that's the wrong way to go about
it what you should do is you should
think in terms of
how you can
adjust connection weights
to solve
a problem
so you define your problem
and then you figure out
how the adjustment of the connection
weights will solve the problem
and
removal heart heard that
and
said to himself okay so i'm going to
start thinking about it that way i'm
going to
essentially
imagine that i have some objective
function
some goal of the computation i want my
machine to correctly classify all of
these images
and i can score that i can measure how
well they're doing on each image and i
get some measure of law error or loss
it's typically called in in deep
learning
and um
i'm going to figure out how to adjust
the connection weights so as to minimize
my loss or reduce the error
uh and
that's called
you know gradient descent
and
engineers were already
familiar with the concept of gradient
descent
and in fact
there was an algorithm
called the delta rule
that had been invented
by
a professor in the engineering
electrical engineering department at
stanford
uh woodrow bernie woodrow and a
collaborator named hoff i don't never
met him anyway so
so gradient descent in continuous
neural networks with multiple
neuron-like processing units was already
understood
um
for a single layer of connection weights
we have some inputs over a set of
neurons we want the output to produce a
certain pattern
we can define the difference between our
target and what the narrow network is
producing and we can figure out how to
change the connection weights to reduce
that error
so what rommelhard did was to generalize
that
so as to be able to change the
connections from earlier layers of units
to the ones
at a hidden layer between the input and
the output
and
so he first called the algorithm the
generalized delta rule because it's just
an extension of the gradient descent
idea
and interestingly enough
hinton was thinking that this wasn't
going to work very well
so hinton had his own alternative
algorithm at the time
based on
the concept of the balsa machine that he
was pursuing so the paper on the balsa
machine came out in learning in bolster
machines came out in 1985
but it turned out that
backprop
worked better than the bolster machine
learning algorithm so this generalized
delta algorithm ended up being called
back propagation as you say back prop
yeah
and
the
you know probably that name is opaque to
maybe what what does that mean
what it what it meant was that in order
to figure out what the changes you
needed to make to the
connections from the input to the hidden
layer
you had to
back propagate the error signals
from the output layer through the
connections
from the hidden layer to the output
to get the signals
that would be the error signals for the
hidden layer
and that's how rimmel hard formulated it
was like well we know what the air
signals are at the output layer let's
see if we can get a signal at the hidden
layer that tells each hidden unit what
its error signal is essentially so it's
back propagating through the connections
from the hidden to the output to get the
signals to tell the hidden units how to
change their weights from the input and
that's why it's called back problems
yeah but
so it came from hinton having
introduced the concept of you know
define your objective function figure
out how to
take the derivative so that you can um
adjust the connections so that they make
progress towards your goal so stop
thinking about biology for a second and
let's start to think about optimization
and computation yeah a little bit more
so
what about
jeff hinton
what
you've gotten a chance to work with him
in that little
the set of people involved there is
quite incredible the small set of people
under the
pdp flag
it's just given the amount of impact
those ideas have had over the years it's
kind of incredible to think about but
you know
just like you said uh like yourself
jeffrey hinton is seen as one of the
not just like a seminal figure in ai but
just a brilliant person just a like the
horsepower of the mind is pretty high up
there for him because he's
just a great thinker so what kind of
ideas have you
learned from him have you influenced
each other on have you debated over what
stands out to you
in in in the full space of ideas here at
the intersection of computation and
cognition
well
so um
jeff has said many things to me that had
a profound impact on my thinking
um and he's written several articles
which um
uh were way ahead of their time
um
he uh
he had two papers in 1981 just to give
one example
uh one of which was essentially the idea
of transformers
and another of which
was a
early paper on semantic cognition which
inspired
uh him and rummelhart and me
uh
throughout
the 80s and uh
um
you know still uh i think sort of
grounds my own thinking about
um the semantic aspects of of cognition
he also
in a in a small paper that was never
published that he wrote in 1977 you know
before he actually arrived at ucsd or
maybe a couple of years even before that
i don't know
uh when he was a phd student he he um
described how a neural network could do
recursive computation
and um
it was a very clever idea that he's
continued to explore over time which was
sort of the idea that um
when you when you call a subroutine you
need to save the state that you had
when you called it so you can get back
to where you were when you're finished
with the subroutine and and the idea was
that you would save the state
of the calling routine by making fast
changes to connection weights
and then
when you
finished with the subroutine call those
fast changes and the connection weights
would allow you to go back to where you
had been before
and reinstate the previous context so
that you could continue on with the
the
top level of the computation
anyway that was part of the idea and um
i always thought okay that's really you
know he just
he had extremely creative ideas that
were uh quite a lot ahead of his time
and
many of them in the 1970s and early
early 1980s
so
another thing about jeff hinton's way of
thinking which
has profoundly influenced my
effort to understand human mathematical
cognition
is
that he doesn't write too many equations
and people tell stories like oh in
in the hints and lab meetings you don't
get up at the board and write equations
like you do in everybody else's machine
learning lab
what you do is you draw a picture
and and you know he he explains
aspects of the way deep learning works
by
putting his hands together and showing
you the shape of a ravine
and um
using that as a geometrical metaphor for
the
what's happening as this gradient
descent process you're coming down the
wall of a ravine if you take too big a
jump you're going to jump to the other
side
and um so that's why we have to turn
down the learning rate for example um
and
it it
speaks to me of the
fundamentally intuitive character of
uh deep insight
together with
a commitment to really understanding
um
in a way that's
absolutely ultimately explicit and clear
uh
but also intuitive
yeah the there's certain people like
that here's an example
some kind of weird mix of uh visual
and intuitive and all those kinds of
things feynman is another example
different style thinking but very unique
and when you when you're around those
people for me in the engineering realm
uh there's a guy named jim keller
who's a chip designer engineer
every time i talk to him
it doesn't matter what we're talking
about just
having experience that unique way of
thinking transforms you and makes your
work much better
and that's that's the magic you look at
daniel kahneman you look at the great
collaborations throughout the history of
science that's the magic of that it's
not always the exact ideas that you talk
about but it's the process of generating
those ideas being around that spending
time with that human being you can come
up with some brilliant work especially
when it's cross-disciplinary as it was a
little bit in your case yeah with jeff
yeah um
jeff is uh a descendant of the logician
bool
he comes from a long line of
english academics
and
together with the
um
deeply intuitive thinking ability that
he has he also
um
has uh
you know it's been clear he's he's
described this to me um and i think he's
mentioned it from time to time in other
interviews with that he's had with
people um you know he's
he's wanted to be able to sort of think
of himself as contributing to the
to the
understanding of
reasoning itself not just human
reasoning like bull like is about logic
right it's about
what can we conclude from what else and
how do we formalize that and
um as a computer scientist uh
logician
philosopher you know um
the goal is to
understand how we derive truths from
other
from givens and things like this and and
the work that jeff was doing in the
um
early to mid 80s
on something called the boltzmann
machine was
his way of
connecting with that boolean tradition
and bringing it
into the more continuous probabilistic
graded constraint satisfaction realm
um and it was it was um
beautiful uh
a set of ideas linked with theoretical
physics um and
um
as well as with logic and um
it it's always been i mean i've always
been inspired by the balsa machine too
it's it's like well if the neurons are
probabilistic rather than you know
deterministic in their computations then
you know that that maybe this somehow is
part of the
um
serendipity or you know
advantageousness of the moment of
insight right
it might not have occurred at that
particular instant it might be sort of
partially the result of a stochastic
process
and uh
and and that too is part of the magic of
the emergence of uh
some of these things well you're right
with the bullying lineage and the the
dream of computer science
is uh somehow
i mean i certainly think of humans this
way that humans are one particular
manifestation of intelligence
that there's something bigger going on
and you're trying to you're hoping to
figure that out
the mechanisms of intelligence the
mechanisms of cognition are much bigger
than just humans yeah
so i think of um i've i started using
the phrase computational intelligence at
some point as to characterize the
the field that i thought you know people
like jeff hinton
um and many of the
of the people i know at deepmind um
are are working in and where i i feel
like i'm
um
you know i'm a i'm a kind of a
human-oriented
computational intelligence researcher in
that i'm actually kind of interested in
the human solution
but at the same time i
i
i feel like
that's that's where um a huge amount of
the
the excitement of deep learning actually
lies is in the idea that
you know we may be able to even go
beyond what we can achieve with our own
nervous systems when we
build
computational intelligences that are
um
you know not limited in the ways that we
are by our own biology perhaps allowing
us to scale the very mechanisms of human
intelligence just increase its power
through scale
yes
and and i think that that you know
obviously that's the
that's being played out massively at
google brain at open ai and to some
extended deep mind as well um
i guess i shouldn't say to some extent
yeah uh the the massive scale of the um
computations that uh
are used to
succeed at games like go or to solve the
protein folding problems that they've
been solving and so on still not as many
uh synapses and neurons as the human
brain so we still got
we're still still beating them on that
we humans are beating the ais but uh
they're catching up qui pretty quickly
you write about modeling of uh
mathematical cognition so let me first
ask about mathematics in general
um
there's a paper uh titled parallel
distributed processing approach to
mathematical cognition where in the
introduction there's some beautiful
discussion of mathematics
and uh you reference there uh tristan
needham who criticizes a narrow formal
view of mathematics by
liking
the studying of mathematics as symbol
manipulation to studying music without
ever hearing a note
so from that perspective
what do you think is mathematics what is
this world of mathematics like well i
think of mathematics as
a set of tools for
exploring
idealized
worlds
that
often turn out to be
extremely relevant to the real world but
need not um
but there are worlds in which
objects exist with
idealized properties
and
in which
the relationships among them can be
characterized with precision
so as to allow
the implications of
certain facts to then allow you to
derive other facts with certainty
so
you know
if uh
you have two
triangles
and you know that there is um
uh
an angle
in the first one that has the same
measure as an angle in the second one
and you know that the lengths of the
sides
adjacent to that angle in each of the
two triangles
the corresponding sides adjacent to that
angle are
also have the same measure then you can
then conclude that the triangles
are
congruent that is to say they have all
of their properties in common
and and that
is something about
triangles it's not
a
matter of formulas these are idealized
objects
in fact
you know we built bridges out of
triangles and uh we understand
how to measure the height of something
we can't climb
by um extending these ideas about
triangles a little further
and um
uh
you know
all of the ability to um
get a tiny
speck of matter launched from
uh the planet earth to intersect with
some tiny tiny little body way out in
way beyond pluto somewhere
at exactly a predicted time and date is
is something that depends on these ideas
right so
but and it's actually uh
happening in the real physical world
that these
ideas
make contact with it uh in those kinds
of instances
um
and um
so
but you know there are these idealized
objects these triangles or these
distances or these points whatever they
are
that um
uh allow for this um set of tools to be
created that then
gives human beings the uh it's this
incredible leverage that they didn't
have without these concepts
and uh i think this is actually already
true
when we think about
just
you know
the
natural numbers
um i always like to include zero so i'm
going to say
the non-negative integers but
that's that's the place where some
people prefer not to include zero but uh
yeah we like zero here natural numbers
zero one two three four five six seven
and so on yeah and and you know because
they give you the ability to
um
be
exact
about
um
like how many sheep you have like you
know i sent you out this morning there
were 23 sheep you came back with only
22. what happened yeah right
the fundamental problem of physics how
many sheep you have yeah
it's a fundamental problem of life of
human uh society that you damn well
better bring back the same number of
sheep as you started with
uh and you know it allows commerce it
allows um
contracts it allows the establishment of
uh records and so on to have systems
that allow these things to be notated
but they they have um
an inherent aboutness to them that's at
this one
at the one in the same time sort of
abstract and idealized and generalizable
while at the other on the other hand um
potentially very very grounded and
concrete
and
one of the things that
makes
for the
incredible
achievements of the human mind is
the fact that humans invented these
idealized systems that
leverage
the power of human thought
in such a way as to allow all this kind
of thing to happen
and and so that's
what mathematics to me is the
development of systems for thinking
about
uh the properties and relations among uh
sets of
idealized objects and
um uh
you know the the mathematical notation
system that
we
unfortunately focus way too much on
is um
just our way of
expressing
uh propositions about these properties
right it's just just like we're talking
with chomsky and language
it's the thing we've invented for the
communication of those ideas they're not
necessarily
the deep representation of those ideas
yeah so what um
what's uh what's a good way to model
such powerful mathematical
reasoning would you say what what are
some ideas you have for capturing this
in a model
the insights that human mathematicians
have had is
a combination of the kind of the
intuitive
kind of
connectionist like knowledge that makes
it so that
something
is just
like obviously true
so that you don't have to think about
why it's true
that then makes it possible
to then
take the next step
and ponder and reason and figure out
something that you previously didn't
have that intuition about
it then ultimately becomes
a part of the intuition that
the next generation of
mathematical thinkers have to ground
their own thinking on so that they can
extend the ideas even further
i came across this quotation
from i'll replace while i was
um walking
in the
in the woods with my wife in a state
park in northern california uh late last
summer
and what it said on the bench was
it is by
logic that we prove but by intuition
that we discover and so what what for me
the the essence of the of the project
is to understand how to bring the
intuitive connectionist
resources to bear on
letting
the
intuitive discovery arise
uh
you know from
engagement in thinking with this formal
system
so
i i think of
you know
the
ability of somebody like hinton or
newton or einstein or
romal heart or
poincare
to um
archimedes is another example right so
suddenly a flash of insight occurs
it's it's like
the
constellation of all of these
simultaneous constraints that somehow or
other causes the mind to settle into a
novel state that it never did before and
and give rise to a new idea
um that
you know then
you can say okay well now how can i
prove this you know how do i write down
the steps of that theorem
that that'll allow me to make it
rigorous and certain
and so
i feel like the
the kinds of things that we're beginning
to see
um
deep learning systems do of their own
accord
kind of gives me
this feeling of
of um
i don't know hope or
encouragement that
ultimately
it'll all
happen
so in particular as
many people now have have
become really interested in thinking
about
you know neural networks that have been
trained with massive amounts of
text
can be given a prompt and they can then
sort of generate some
really interesting fanciful creative
story from that prompt
um and
there's there's kind of like a sense
that they've somehow synthesized
something
like novel out of the
you know all of the particulars of all
of the billions and billions of
experiences that went into the training
data
that that gives rise to something like
this sort of intuitive sense of what
would be a
a fun and interesting little story to
tell or something like that it just sort
of wells up out of the
out of the
letting the thing play out its own
imagining of what
somebody might say given this prompt as
a as a input to get it to start to
generate
its own thoughts
and and to me that that sort of
represents the potential of capturing
this the intuitive side of this
yeah and there's other examples i don't
know if you find them as captivating is
you know on the deep mind side with
alpha zero
if you study chess the kind of solutions
that has come up in terms of chess it is
it
there's novel ideas there it feels very
uh like there's brilliant moments of
insight and the
mechanism they use
if you think of search as
as maybe more towards good old-fashioned
ai and and then there's the connection
is the neural network that has the
intuition of
looking at a board looking at a set of
patterns and saying how good is this set
of positions and the next few positions
how good are those and that's it no
that's just an intuition uh yeah great
grandmasters have this and understanding
positionally tactically
how good the situation is how can it be
improved without doing this full
like deep search
um and then maybe doing a little bit of
the what uh human chess players call
calculation which is the search
taking a particular set of steps down
the line to see how they unroll but
there there is moments of genius in
those systems
too so that's another
hopeful illustration
that from neural networks can emerge
this novel
creation of an idea
yes and i think that
you know i think demas asabus is um
you know he's spoken about those things
he uh i heard him
describe a
move that was made in in one of the
go matches against lisa doll in this
very in a very similar way and
um it caused me to become really excited
to
kind of collaborate with some of those
guys at deepmind
um
so i think though that what
what i like to really emphasize
here is
one part of what i like to emphasize
about mathematical cognition at least is
that
philosophers
and logicians
going back
three or even
a little more than 3 000 years ago began
to develop
these formal systems
and
gradually
the
whole idea about
thinking formally
got constructed um
and you know it's preceded euclid
um
certainly present in the work of thales
and others and i'm not
the world's leading expert in all the
details of that history
but euclid's
elements were the
the kind of the touch point of a
of a coherent document that sort of laid
out this
idea of an actual formal system within
which
these objects were characterized and the
um
the system of uh inference
that
um allowed
new truths to be derived from others was
sort of like established as a paradigm
and
what
what i
find interesting is
the idea that
the ability to become
a person who is capable of thinking
in this abstract formal way
is
you know a result of the same kind of
immersion
uh in
in experience
thinking in that way that you know we
now begin to think of our understanding
of language as being right so
we immerse ourselves in
in a particular language
in a particular world of objects and
their relationships and we learn to talk
about that
and we develop intuitive understanding
of the real world
in in a similar way we can think that
what
academia
has created for us what you know those
early philosophers and their
academies in
athens and alexandria and others other
places
allowed was the
development of these
schools of thought modes of thought that
that then become deeply ingrained and
you know
it becomes what it is that makes it so
that somebody like jerry fodor would
think
that
um
systematic thought is
the essential characteristic of the
human mind as opposed to
a derived and an acquired characteristic
that results from acculturation in a
certain
mode
that's been invented by humans would you
say it's more fundamental than like
language if we start dancing if we if we
bring chomps get back into the
conversation
first of all is it unfair to draw a line
between mathematical
cognition and
language
linguistic cognition i think that's a
very interesting question
and i think um it's one of the ones that
i'm actually very interested in right
now
but i i think the answer is
in important ways
it is important to draw that line
but then to come back and look at it
again and see
some of the subtleties and interesting
aspects of the difference
so
if we think
about chomsky himself
he
was born into an academic family his
father was a professor of rabbinical
studies at a small rabbinical college in
philadelphia
and
he was deeply enculturated in
uh
you know a culture of thought and reason
and
brought
to the
effort to understand natural language
this
profound engagement with these formal
systems
and um
you know
i think that
there was tremendous power in that and
that chomsky had some amazing insights
into the structure of natural language
but
that
i'm going to use the word but there
the actual intuitive knowledge of these
things only goes so far and does not go
as far as it does in people like chomsky
himself
and this was something that was
discovered in the phd dissertation of
lila gleitman who was actually trained
in the same linguistics department with
chomsky
so what lila discovered
was that
the intuitions that linguists had
about
even the meaning of a phrase
not just about its grammar but about
what they thought a phrase
must mean
were very different from the intuitions
of
an ordinary person who wasn't a formally
trained thinker
and
well it recently has become much more
salient i happen to have learned about
this when i myself was a phd student at
the university of pennsylvania but
um
i never knew how to put it together with
all of my other thinking about these
things so
so i actually
currently have the hypothesis that
formally trained linguists and other
formally trained
academics
whether it be
linguistics philosophy
cognitive science computer science
machine learning mathematics
have a
mode of engagement with experience that
is intuitively
deeply
structured to be more
organized around
the
systematicity uh and
um
ability to be
conformed with
the principles of a system than um
then is actually true of the natural
human mind without that immersion that's
fascinating so the different fields and
approaches with which you start to study
the mind actually take you
away from the natural operation of the
mind so it makes it very difficult for
you to
to be somebody who introspects yes
and
you know this is where um
uh
things about
human
belief
and
so-called knowledge
that we
consider
private
not our business to
manipulate in others we are
not entitled to tell somebody else what
to believe about
certain kinds of things
um
what are those beliefs well they are
the product of this sort of immersion
and enculturation
uh that is what i believe
so and that's limiting
it's
it's something to be aware of
does that limit you from uh
having a good model
of some of cognition you can
so when you look at mathematical or
linguistics so i mean what what is that
line then what um
so is chomsky unable to sneak up to the
full picture of cognition are you when
you're focusing on mathematical
uh thinking are you also unable to do so
i think you're you're right i think
that's a great way of characterizing it
and um
i also think that
um it's related to
um the concept of beginner's mind uh
and um
another concept called the expert blind
spot so
the expert blind spot is much more
prosaic seeming than than this
point that you were just making but it's
it's something that plagues
experts
when they try to communicate their
understanding to non-experts and that is
that
things are self-evident to them
that
they they can't begin to even think
about how they could explain it to
somebody else because it's like well
it's just
like so patently obvious that it must be
true
and
um
you know like
um
when
kronecker said god made the natural
numbers all else is the work of man
he was expressing that that intuition
that um somehow or other
you know the basic fundamentals of
discrete quantities being countable and
innumerable and you know indefinite in
number
um
was was not something that
had to be
discovered um
but he was wrong it turns out that
many cognitive scientists agreed with
him for a time there was a long period
of time where there were
where um you know the natural numbers
were considered to be
a part of the innate endowment of
you know core knowledge or
you know to use the kind of phrases that
spelke and and kerry use to talk about
what they believe are the innate
primitives of the human mind and
um they no longer believe that they it's
actually
um
been more or less accepted by almost
everyone that the natural numbers are
actually a cultural construction
and it's it's so interesting to go back
and sort of like study those few people
who still exist who you know who don't
have those systems so so this is just an
example to me
and
where you know a certain mode of
thinking about language itself or a
certain mode of thinking about
geometry and those kinds of relations so
become so second nature that you don't
know what it is that you need to teach
[Music]
and um
and in fact we don't really teach it all
that explicitly anyway and it's it's you
know
you take a math class the professor sort
of teaches it to you the way they
understand it
some of the students in the class sort
of like you know they get it they start
to get the way of thinking and they can
actually do the problems that get
get put on the homework that the
professor thinks are interesting and
challenging ones but
but but
most of the students who don't
kind of engage as deeply don't ever get
you know and
we
we think oh that man must be brilliant
he must have this special insight but i
you know he must have some you know
biological sort of bit that's different
right that makes him so that he or she
could have that insight but i
i'm
i
although i don't want to dismiss
biological individual differences
completely i
i find it much more interesting to think
about the possibility that
um
you know it was that difference in the
dinner table conversation at the chomsky
house when he was growing up that made
it so that he had that cast of mind
yeah and uh there's there's a few topics
we talked about that kind of
interconnect
because because i wonder the better i
get at certain things
we humans
the deeper we understand something
what are you starting to then miss about
the rest of the world
we talked about
david and his uh degenerative
mind
and
you know when you look in the mirror and
wonder
how different am i am i cognitively from
the man i i was a month ago from the man
it was a year ago like what
you know
if i can um
having thought about language if i'm
chomsky for for 10 20 years
what am i no longer able to see what is
in my blind spot and how big is that
and then to somehow be able to leap back
out of your deep like structure that you
form for yourself about thinking about
the world leap back and look at the big
picture again
or jump out of the your current way of
thinking
um and to be able to introspect like
what are the limitations of your mind
are how is your mind less powerful than
you used to be or more powerful or
different powerful in different ways so
that seems to be a difficult thing to do
because we're living
we're looking at the world through the
lens of our mind right to step outside
and introspect is difficult but it seems
necessary if you want to make progress
you know one of the
threads of psychological research that's
always been very um
i don't know important to me to be aware
of is is is
the idea that
our explanations of our own behavior
aren't necessarily
um
actually
part of the causal process that caused
that behavior to occur
or even
valid observations of the set of
constraints that led to the outcome
but they are post-hoc rationalizations
that we can give based on
information at our disposal about what
might have contributed to
the result that we came to
when asked
and um so this this is an idea that
was introduced in a
very important paper
by nisbet and wilson about
you know the limits on our ability to to
uh be aware of
the factors
that cause us to make the choices that
we make
um and um
you know
i think it's
it's uh
it's something that we really ought to
be much more
um
cognizant of in general as human beings
is that
our own insight into exactly why we hold
the beliefs that we do and we hold the
attitudes and make the choices and
and and feel the feelings that we do is
not something that we
um
we totally control or totally observe
and
um
it's subject to
you know
our
culturally transmitted understanding of
what it is that is the mode that we give
to explain
uh these things uh when asked to do so
as much as it is about anything else and
so
even our ability to introspect and think
we have access to our own thoughts as a
product of of culture and uh belief you
know
practice
so let me ask you the big uh
question of advice so you've
lived an incredible life
in terms of the ideas you've put out
into the world in terms of the
trajectory you've taken through your
career through your life what advice
would you give to young people today
in high school and college
about um
how to have a career or how to have a
life they can be proud of
finding the thing that you are
intrinsically motivated to engage with
and then celebrating that discovery is
is what
uh
what it's all about
when when i was in college i struggled
with that i i um
i had thought i
wanted to be a psychiatrist
because i think i was interested in
human psychology in high school and it
it at that time the only
sort of information i had that had
anything to do with the psyche was you
know freud and eric from and sort of
popular psychiatry kinds of things
and so
well they were psychiatrists right so i
had to be a psychiatrist
and
that meant i had to go to medical school
and i got to college and i find myself
taking
you know
the first semester of a three-quarter
physics class and it was mechanics and
this was so far from what it was i was
interested in but it was also too early
in the morning in the winter court
semester so i i never made it to the
physics class
um but
i wondered about the rest of my freshman
year and um
most of my sophomore year
until
uh i found myself in the midst of this
situation where around
me um there was this big revolution
happening i was at columbia university
in 1968 and
the vietnam war is going on colombia's
building a gym in morningside heights
which is part of harlem and people are
thinking oh the big bad rich guys are
stealing the the
park land that belongs to the people of
harlem
and um
you know they're part of the
military-industrial complex which is
enslaving us and sending us all off to
war in vietnam
and um so there was a big revolution
that involved a confluence of black
activism and
you know sds and social justice and the
whole
university blew up and got shut down and
um i got a chance to sort of think about
why people were behaving the way they
were in this context
and
i you know i happen to have taken
mathematical statistics
i happened to have been taking
psychology that quarter
just psych one and somehow things in
that space all
ran together in my mind and got me
really excited about
about
asking questions about why people what
made certain people go into the
buildings and not others and things like
that
and so suddenly i had a path forward
that and i had just been wandering
around aimlessly and at the different
points in my career you know and i think
okay
well should i take this class or should
i
just
read that book about
some idea that i want to understand
better you know
or should i
should i pursue the thing that excites
me and interests me or should i
you know meet some requirement you know
that's
i always did the latter so i ended up my
my professors in psychology
were
thought i was great they wanted me to go
to graduate school
um they they nominated me for phi beta
kappa and i went to the phi beta kappa
in the ceremony and this guy came up now
he said oh are you magnar summa
i wasn't even getting honors based on my
grades they just happened to have
thought i was interested enough in ideas
to belong to phi beta kappa so
i mean would it be fair to say you kind
of stumbled around a little bit
through accidents of
too early morning of classes in physics
and so on until you discovered intrinsic
motivation as you mentioned and then
that's it it hooked you and then you
celebrate the fact that this happens to
you human beings
yeah like and what is it that made
what i did intrinsically motivating to
me
well that's interesting and i don't know
all the answers to it and i don't think
uh i wanna
i want anybody to think
that um you should be sort of in any way
i don't know sanctimonious or anything
about it you know it's like
i really enjoyed doing statistical
analysis of data i really enjoyed
running my own experiment which was what
i got a chance to do in the psychology
department that chemistry and physics
had never
i never imagined that mere mortals would
ever do an experiment in those sciences
except one that was in the textbook that
you were told to do in lab class but in
psychology we were already like even
when i was taking psych one it turned
out we had our own rat and we got to
after two set experiments we got to okay
do something you think of you know with
your rat you know so
it's the opportunity to do it myself
yeah and and to to bring together a
certain set of things that that engaged
me intrinsically
and and i think it it has something to
do with why certain people turn out to
be
you know profoundly
um
amazing
musical geniuses right they get immersed
in it at an early enough point
and it just sort of gets into the fabric
so my my little brother had intrinsic
motivation for music as we witnessed
when he discovered
how
to put records on the phonograph when he
was like 13 months old and recognize
which one he wanted to play not because
he could read the labels because he
could sort of see which ones had which
scratches which were the different you
know oh that's rapidly espanol and
that's oh wow you know and and and he
enjoyed that that connected with him
somehow yeah and and there was something
that it fed into and
you're extremely lucky if you have that
and if you
can nurture it and can let it grow and
let it be be a important part of your
life yeah those are those are the two
things is like
be attentive enough to
to feel it when it comes like this is
something special
i mean i don't know uh
for example i really
like
um
tabular data like excel sheets like it
it brings me deep joy i don't know how
useful that is for anything but there's
this i don't know what i'm talking about
exactly
so there's like a million
not a million but there's a lot of
things
like that for me you have to hear that
for yourself like be like realize this
is really joyful but then the other part
that you're mentioning which is the
nurture is take time and stay with it
stay with it a while and see where that
takes you
uh in life yeah and i think i think the
um
the the motivational engagement results
in the immersion that then creates the
opportunity to obtain the expertise so
you know that we could call it there the
mozart effect right i mean when i think
about mozart i think about
you know the person who was born
as the fourth member of the family's
dream quartet right and uh
and they handed him the violin when he
was six weeks old all right start
playing you know it's like
and um
so
the the level of immersion there was was
amazingly profound but
uh
hopefully he also had
you know some
something
maybe this is where the more
uh
sort of the genetic part comes in
sometimes i think uh
you know something in him resonated to
the music so that that the synergy of
the combination of that was so powerful
so so that's what i really consider to
be the mozart effect it's sort of the
the synergy of something
with with experience that that then
results in the unique flowering of a
particular you know mind
um
so i i know
my siblings and i are all very different
from each other we've all gone in our
own different directions and you know i
mentioned my younger brother who was
very musical
um i had my other younger brother was
like this amazing like intuitive
engineer
um
and um
my sister one of my sisters was
passionate about
uh
in
you know water conservation well before
it was a
you know such a hugely important issue
that it is today
so
we all sort of somehow these
find a different thing um
and uh i don't i don't mean to say it
isn't
uh
tied in with something about about us
biologically but but it's also
when that happens where you can find
that then you know you can do your thing
and you can be excited about it
so people can be excited about fitting
people on bicycles as well as excited
about making neural networks achieve
insights into human cognition right yeah
like for me personally i've always been
excited about
love and friendship between humans
and
just like the actual experience of it
since i was a child just observing
people around me and also been excited
about robots
and there's something in me that thinks
i really would love to explore how those
two things combine it doesn't make any
sense a lot of it is also timing just to
think of your own career in your own
life you found yourself in certain
pieces
places that happen to involve some of
the greatest thinkers of our time and so
it just worked out that like you guys
developed those ideas and there may be a
lot of other people similar to you and
they were brilliant and they never found
that right connection and place to where
they their ideas could flourish so it's
timing its place
it's people
and uh ultimately the whole ride you
know it's uh undirected
can ask you about something you
mentioned in terms of psychiatry when
you were younger
because i had a similar experience
of
you know
reading freud and uh called young and
just
you know those kind of popular
psychiatry ideas
and that was a dream for me early on in
high school to
uh like i hope to understand the human
mind by
i somehow psychiatry felt like
the right discipline for that
does that make you sad that psychiatry
is not
the the mechanism by which you want to
are able to explore the human mind so
for me i was a little bit disillusioned
because
of how much
prescription medication and biochemistry
is involved in the discipline of
psychiatry as opposed to the dream of
the the freud like
use the mechanisms of language to
explore the human mind so that was a
little disappointing
and and that's why i kind of went to
computer science and thinking like maybe
you can explore the human mind by trying
to build the thing
yes i wasn't exposed to the um
sort of the biomedical slash
pharmacological aspects of psychiatry at
that point because um i didn't
i dropped out of that whole
idea the physical pre-med that i never
even found out about that until much
later
but you're absolutely right that's uh so
i was actually a member of the um
national
advisory
mental health council that is to say the
board of scientists who advised the
director of the national institute of
mental health
and that was around the year 2000 and in
fact
um at that time the man who came in
as the new director i had been on this
board for a year when he came in
um okay
schizophrenia is a
biological illness it's a lot like
cancer we've made huge strides in curing
cancer and that's what we're going to do
with schizophrenia we're going to find
the medications
that are going to cure this disease
and we're not going to listen to
anybody's grandmother anymore and um
you know
good old behavioral psychology is not
something we're going to support any
further and um
you know he
he uh
completely alienated me from the
institute and from all of its prior
policies which had been much more
holistic i think really at some level
and and basically and the the other
people on the board were like
psychiatrists right
uh
very biological psychiatrist it didn't
pan out right that that that
nothing has changed in in our ability to
uh
to help people with mental illness uh
and um
so 20 years later that that that
particular path uh was a dead end as far
as i can tell
well there's some aspect to
and sorry to romanticize
the whole philosophical conversation
about the human mind but to me
psychiatrists for time
held the flag of
we're the deep thinkers
in the same way that physicists are the
deep thinkers about the nature of
reality psychiatrists are the deep
thinkers about the nature of the human
mind and i think that flag has been
taken from them and carried by people
like you
it's like it's more in the cognitive
psychology
especially when you have a foot in the
computational view of the world because
you can both build it you can like
intuit about the functioning of the mind
by building little models
and be able to say mathematical things
and then deploying those models
especially in computers to say does this
actually work
they do a little like
experiments and then some combination of
neuroscience where you're starting to
actually be able to
observe
you know do certain experiments on human
beings and observe how the
uh
the brain is actually functioning and
there using intuition you can start
being the philosopher like richard
feynman is the philosopher
a cognitive psychologist can become the
philosopher and psychiatrists become
much more like doctors they're like very
medical they help people with medication
by biochemistry and so on but they are
no longer the
the the the book writers and the
philosophers which of course i admire
the i admire the richard feynman ability
to do
great low-level
mathematics and physics and the
high-level philosophy
yeah i think it was uh
frohm and young more than freud that was
sort of initially kind of like made me
feel like
oh this is really amazing and
interesting and i want to explore it
further i actually
when i got to college and i lost that
thread i i found more of it in
sociology and literature than i did in
any place else so i took quite a lot of
both of those disciplines as an
undergraduate
and
you know i was actually deeply
ambivalent about
the psychology because i was doing
experiments
after the initial flurry of interest in
why people would occupy buildings during
a insurrection and consider
you know uh to be be sort of like so
over committed to their beliefs
but i ended up in in the psychology
laboratory running experiments on
pigeons and and so i had these profound
sort of like
dissonance between okay the kinds of
issues that would be explored when i was
thinking about
uh
what i read about in
in modern british literature
um versus what i could study with my
pigeons in the laboratory
that got resolved when i went to
graduate school and i discovered
cognitive psychology and and so for me
that was uh
that was the path out of this sort of
like
extremely sort of
um ambivalent divergence between the
interest in the human condition and and
uh the
desire to do
you know actual mechanistically oriented
thinking about it um
and i think we
we've come a long way in that regard and
that uh
is you're absolutely right that nowadays
this is something that's accessible to
people
through
the pathway in through computer science
or the pathway in through
uh neuroscience
you know you can get derailed in
neuroscience down to the bottom of
the
system where you might find the curious
of various
conditions
but you don't get a chance to think
about the higher level stuff so it's in
the systems in cognitive neuroscience
and
computational
intelligence miasma up there at the top
that i think these opportunities are
most
are richest uh right now and um so yes i
am indeed blessed by having had the
opportunity to
fall into that
space
so you mentioned the human condition
speaking which
you happen to be a human being who is
unfortunately
not immortal
that seems to be a fundamental part of
the human condition that this riot ends
do you think about
the fact that you're going to die one
day are you afraid of death
uh i i would say that i am
not as much afraid of death as i am of
um degeneration
uh and uh i say that
in part for
reasons of having
you know
seen some tragic degenerative situations
uh
unfold
it's exciting
when
you can
continue to
participate and uh
feel like you're you're near the
the place where the
the wave is breaking on the shore i feel
like you know
um
and
and i
i i think about
you know my own uh future potential
um if if i were to undergo a uh
begin to suffer from dementia uh
alzheimer's disease or semantic dementia
or some other condition
you know
i would sort of gradually lose the
thread of that ability and
so so
one can live on for several
for a decade after you know
sort of having to retire because one no
longer
has uh
these kinds of um abilities to engage
and uh i think that's the thing that i
feared the most
the losing of that like that that um
the the breaking of the way the
flourishing of the mind where you could
have these ideas and they're swimming
around you're able to play with them
yeah and and and and collaborate with
other people who you know are themselves
uh
um really helping to push these ideas
forward so
yeah what about the edge of the cliff
the end i mean the the mystery of it the
i mean
the migrated
sort of conception of mind and
you know sort of continuous sort of way
of thinking about most things makes it
so that
uh to to me the the the um
the discreteness of that transition is
less
less less apparent than it seems to be
to most people i see
i see yeah um
yeah i wonder so i don't know if you
know the work of ernest becker and so on
i wonder what what role mortality
and our ability to be cognizant of it
and anticipate it and perhaps be afraid
of it what role that plays in in our
reasoning of the world
i think that it it can be motivating to
people to think they have a limited
period left um
i think in in my own case you know it
it's like seven or eight years ago now
that i was
i was sitting around doing experiments
on
decision making that were
satisfying in a certain way because i
could really
get closure on what
whether the model fit the data perfectly
or not
and i could see how one could test you
know the predictions in monkeys as well
as humans and really see what the
neurons were doing
but i just
realized hey wait a minute you know i
may only have about 10 or 15 years left
here
and
i don't feel like i'm getting towards
the answers to the really interesting
questions while i'm doing this
this particular level of work and that's
when i said to myself
okay um let's pick something
that's hard
you know so that's when i started
working on mathematical cognition
and um
i i think it was more in terms of well i
got 15 more years possibly of useful
life left let's imagine that it's only
10.
i'm actually getting close to the end of
that now maybe three or four more years
um but i'm beginning to feel like well i
probably have another five after that so
okay i'll give myself another another
six or eight
um but a deadline is a little bit like
and that's not gonna go on forever yeah
and so um
so uh yeah i gotta keep um thinking
about the questions that i think are the
interesting and important ones for sure
what do you hope your legacy is
you've done some incredible work in your
life
as a man as a scientist
when the aliens and the human
civilization is long gone and the aliens
are reading the encyclopedia about the
human species
what do you hope is the paragraph
written about you
i would wanted to sort of highlight
a couple things
that i was
you know
able to see
um
one path
that was more exciting to me than the
one that seemed already to be there for
a cognitive psychologist you know but
not for any
super special reason other than that i'd
had the right context prior to that but
that i had gone ahead and
followed that lead you know and then i
forget the exact wording but i
i said
uh in this
preface that
the the joy of science is the moment in
which
you know
a partially formed thought in the mind
of one person
gets
crystallized a little better in the
discourse and becomes the foundation
of
some exciting concrete piece of actual
scientific progress and i feel like that
you know moment happened when romelu
heart and i were doing the interactive
activation model and when rommel heart
heard hinton talk about
gradient descent and
having the objective function to guide
the learning process and
um
it it happened a lot in that period and
i i sort of seek that kind of thing in
my
uh collaborations with my students right
so
um
you know the idea that this is a person
who
contributed to science by finding
exciting collaborative opportunities to
engage with other people
through
is something that
i certainly hope is part of the
paragraph and uh like you said taking a
step
maybe in directions that are not
not obvious so it's the the old robert
frost road less taken
so maybe because you said like this
incomplete initial idea
that step you take is a little bit
uh off the beaten path
if if i could just say one more thing
here
i uh
this was something that really
contributed to energizing me in a way
that i uh
that i feel it would be useful to share
i
uh my my phd
dissertation project was completely
empirical experimental project and i i
wrote
a paper based on the the two main
experiments that were the core of my
dissertation
and i submitted it to a journal
and
at the end of the paper
i had
a little
section where i laid out my
the beginnings of my theory about what i
thought was going on
that would explain the data that i had
collected
and i had submitted the paper to the
journal of experimental psychology so
i got back
a letter from the editor saying thank
you very much these are great
experiments we'd love to publish them in
the journal
but what we'd like you to do is to leave
the theorizing to the theorists and
take that part out of the paper
and so i did i took that part out of the
paper
but you know i almost found myself
labeled as a non-theorist right by this
uh and um i could have like succumbed to
that and said okay well i guess my job
is to just go on and do experiments
right
but
but uh
that's not what i wanted to do and and
so when i when i got to my assistant
professorship um
although i continued to do experiments
because i knew i had to get some papers
out
i also at the end of my first year
submitted my first article to
psychological review which was the
theoretical journal where i took that
section and elaborated it and wrote it
up and submitted it to them and they
didn't accept that either but they said
oh this is interesting you should keep
thinking about it this time and then
that was what got me going
to think okay you know
so it's not a superhuman thing to
contribute to the development of theory
you know you don't have to be
you can do it as a mere mortal
and
the broader i think lessons don't
succumb to the labels of a particular
or anybody labeling you right you know
exactly
i mean that yeah exactly and then you
especially as you become successful
you'll label labels get assigned to you
for that you're successful for that
connectionist cognitive scientist and
not a neuroscientist and then you can
you can completely that's just that's
the stories of the past you're today a
new person that can completely
revolutionize and totally new areas so
don't let those labels
um hold you back well let me ask the big
question
um
when you look at into
you said it started with colombia trying
to observe these humans and they're
doing weird stuff and you want to know
why are they doing this though so let's
zoom out even bigger
at the 100
plus billion people who've ever lived on
earth
why do you think we're all
doing what we're doing what do you think
is the meaning of it all the big why
question we seem to be very busy doing a
bunch of stuff
and we seem to be kind of directed
towards somewhere
but why
well um
i myself think that we make meaning for
ourselves and that um
we find inspiration in the meaning that
other people have made in the past uh
you know and the great
uh religious thinkers uh
of
the first millennium bc and
you know a few
few that came in the early part of the
second uh millennium uh
you know
laid down some important foundations for
us um
but i i i do believe that you know we
are
uh
an emergent
uh result of a process that happened
naturally without guidance and
um that
meaning
is what we make of it
and that the creation of
uh
efforts to refine meaning in
um
like religious traditions and so on
is just a part of the expression of that
of that goal that we have to
you know not
not find out what the meaning is but to
make it ourselves and um
so
to me
it's
something that's very
personal it's very
individual it's like
meaning will come
for you through
the particular combination of
synergistic elements that are your
fabric and your experience and your um
context and your
and um
you know you should
it's it's it it's all made in a in a
certain kind of a local context though
right it's what here i am at ucsd with
this brilliant man rommel heart
uh
who's
having
you know these doubts about
um
symbolic artificial intelligence that
resonate with my
desire to see it grounded in the biology
and
um
uh let's make the most of that you know
yeah and so and so from that like little
pocket there's some kind of uh peculiar
little emergent process
that then uh which is basically each one
of us
each one of us humans is a kind of
you know you think cells and they come
together and it's an emergent process
that then
tells fancy stories about itself
and then gets
just like you said just enjoys the
beauty of the stories we tell about
ourselves it's an emergent process
that lives for time
uh is defined by its local pocket and
context
uh in time and space
and then tells pretty stories and we
write those stories down and then we
celebrate how nice the stories are and
then it continues because we build
stories on top of each other
and
eventually we'll colonize hopefully
other planets
other
solar systems other galaxies and will
tell even better stories
but all starts uh
here on earth
jay year
speaking of
uh peculiar emerging processes
that lived one heck of a story you're
you're one of the the great scientists
of cognitive
uh science of psychology
of computation
it's a huge honor you would talk to me
today that you spend your very valuable
time i really enjoy talking with you and
thank you for all the work you've done i
can't wait to see what you do next
well thank you so much and i uh you know
this has been an amazing opportunity for
me to
let ideas that i've never fully
expressed before come out
because you asked such a wide range of
um you know the deeper questions that
we're all we've all been thinking about
for so long so thank you very much for
that thank you
thanks for listening to this
conversation with jay mcclelland to
support this podcast please check out
our sponsors in the description and now
let me leave you with some words from
jeffrey hinton
in the long run curiosity driven
research works best real breakthroughs
come from people focusing on what
they're excited about
thanks for listening and hope to see you
next time
you