EP20: Yann LeCun - part 14/15
December 15, 2025 • 1h 50m 6s
? (?)
00:00.150
do
you
think
about
vivec
coding
Yann LeCun (Chief AI Scientist)
00:03.310
i
mean
it's
cool
it
makes
is
going
to
cause
a
funny
kind
of
thing
where
a
lot
of
the
code
that
will
be
written
will
be
used
only
once
because
it's
going
to
become
so
cheap
to
write
code
right
you're
going
to
ask
your
kind
of
AI
assistant
like
you
know
produced
this
graph
or
like
Yann LeCun (Chief AI Scientist)
00:24.190
you
know
this
research
blah
blah
blah
he's
going
to
write
a
little
piece
of
code
to
do
this
or
maybe
it's
an
applet
that
you
need
to
play
with
for
you
know
a
little
simulator
and
you
can
use
it
once
and
throw
it
away
because
so
cheap
to
produce
right
so
the
idea
that
we're
not
Yann LeCun (Chief AI Scientist)
00:38.710
going
to
need
programmers
anymore
is
false
we're
going
to
the
cost
of
generating
software
you
know
has
been
going
down
continuously
for
decades
and
it's
that's
just
the
next
step
of
the
cars
going
down
but
it
doesn't
mean
computers
will
be
less
useful
they're
going
to
be
more
Yann LeCun (Chief AI Scientist)
00:57.030
useful
? (?)
01:00.930
OK
one
more
question
so
what
do
you
think
about
the
connection
between
neuroscience
and
machine
learning
there
are
some
ideas
a
lot
of
time
that
AI
borrows
from
neuroscience
and
the
other
way
right
predictive
coding
for
example
do
you
think
that
it's
useful
to
use
ideas
from
but
Yann LeCun (Chief AI Scientist)
01:22.030
there's
a
lot
of
inspiration
you
can
get
from
neuroscience
from
biology
in
general
but
neuroscience
in
particular
i
certainly
was
very
influenced
by
classic
work
in
neuroscience
like
you
know
hubble
and
riesel
's
work
on
the
architecture
of
the
visual
cortex
is
basically
what
Yann LeCun (Chief AI Scientist)
01:38.190
led
to
convolutional
nets
right
and
you
know
i
wasn't
the
first
one
to
use
those
ideas
in
in
artificial
neural
nets
right
there
were
people
in
the
sixties
trying
to
do
this
there
were
you
know
people
in
the
eighties
building
locally
connected
networks
with
multiple
layers
they
Yann LeCun (Chief AI Scientist)
01:52.470
didn't
have
ways
to
train
them
with
backdrop
you
know
there
was
the
the
cognitron
the
neo
cognitron
from
fukushima
which
had
a
lot
of
the
ingredients
just
not
proper
learning
algorithm
and
there
was
another
kind
of
aspect
of
the
cognitron
which
is
that
it
was
really
meant
to
be
Yann LeCun (Chief AI Scientist)
02:10.350
a
model
of
the
visual
cortex
so
it
tried
to
reproduce
every
quirks
of
biology
for
example
the
fact
that
in
the
brain
you
don't
have
positive
and
negative
weights
you
have
positive
and
negative
neurons
so
all
the
neurons
all
the
synapses
coming
out
of
an
inhibitor
neurons
neurons
Yann LeCun (Chief AI Scientist)
02:32.270
have
negative
weights
OK
and
all
the
synapses
coming
out
of
non
inhibitory
neurons
have
positive
weights
so
fukushima
implemented
this
in
his
model
right
he
implemented
the
fact
that
you
know
what
neurons
spike
so
we
didn't
have
a
spiking
neuron
model
but
but
you
cannot
have
a
Yann LeCun (Chief AI Scientist)
02:53.830
negative
number
of
spikes
and
so
so
his
function
was
basically
rectification
like
a
rail
you
except
it
had
a
saturation
and
then
he
knew
from
you
know
various
works
that
there
was
some
sort
of
normalization
and
he
had
to
use
this
because
otherwise
there
was
no
back
props
of
the
Yann LeCun (Chief AI Scientist)
03:12.080
activation
in
this
network
would
go
haywire
so
we
had
to
do
like
division
normalization
that
turns
out
to
actually
be
correspond
to
some
theoretical
models
of
the
visual
cortex
that
some
of
our
colleagues
at
the
center
for
neuroscience
said
NYU
have
been
pushing
like
david
heger
Yann LeCun (Chief AI Scientist)
03:29.230
and
people
like
that
so
yeah
i
mean
i
think
neuroscience
is
very
a
source
of
inspiration
you
know
more
more
recently
the
sort
of
macro
architecture
of
the
brain
in
terms
of
you
know
perhaps
the
world
model
and
planning
and
things
like
this
like
how
how
is
that
reproduced
like
Yann LeCun (Chief AI Scientist)
03:49.550
why
do
we
have
a
separate
module
in
the
brain
for
for
for
factual
memory
the
hippocampus
right
and
we
see
this
now
in
certain
neural
net
architectures
that
there
is
like
a
separate
memory
module
right
maybe
that's
a
good
idea
i
think
we're
going
to
have
i
think
what's
going
to
Yann LeCun (Chief AI Scientist)
04:09.310
happen
is
that
we're
going
to
come
up
with
sort
of
new
AI
neural
net
architectures
deep
learning
architectures
and
a
posterior
you
will
discover
that
the
characteristic
that
we
implemented
in
them
actually
exist
in
the
brain
and
in
fact
that's
a
lot
of
what's
happening
now
in
Yann LeCun (Chief AI Scientist)
04:26.870
neuroscience
which
is
that
there
is
a
lot
of
feedback
now
from
AI
to
neuroscience
where
the
best
models
of
human
perception
are
basically
conventional
nets
today
? (?)
04:38.600
yeah
OK
? (?)
04:41.440
do
you
have
anything
else
that
you
want
to
add
to
say
to
the
audience
whatever
Yann LeCun (Chief AI Scientist)
04:47.200
you
want
i
think
we
covered
a
lot
of
grounds
i
think
you
you
know
you
want
to
be
careful
who
you
listen
to
so
don't
listen
to
AI
scientists
talking
about
economics
OK
so
when
some
AI
person
or
even
a
business
person
tells
you
AI
is
going
to
put
everybody
out
of
work
talk
to
an
Yann LeCun (Chief AI Scientist)
05:11.030
economist
basically
none
of
them
is
saying
anything
anywhere
close
to
this
OK
and
you
know
the
effect
of
technological
revolutions
on
the
labor
market
is
something
that
a
few
people
have
devoted
their
career
on
doing
none
of
them
is
predicting
massive
unemployment
none
of
them
Yann LeCun (Chief AI Scientist)
05:34.650
is
predicting
that
radiologists
are
going
to
be
all
unemployed
you
know
et
cetera
right
also
realize
that
actually
fielding
practical
applications
of
AI
so
that
there
are
sufficiently
reliable
and
everything
is
super
difficult
and
it's
very
expensive
and
in
previous
waves
of
Yann LeCun (Chief AI Scientist)
05:53.400
interest
AI
the
techniques
that
people
have
put
a
big
hope
in
turned
out
to
be
overly
unwieldy
and
expensive
except
for
a
few
applications
so
there
was
a
big
wave
of
interest
in
expert
systems
back
in
the
nineteen
eighties
japan
started
a
huge
project
called
the
fifth
generation
Yann LeCun (Chief AI Scientist)
06:18.760
computer
project
which
was
like
computers
with
CPU
's
that
were
going
to
run
lists
and
you
know
inference
engines
and
stuff
right
and
the
hottest
job
in
the
late
eighties
was
going
to
be
knowledge
engineer
you
were
going
to
sit
next
to
an
expert
and
then
the
knowledge
of
the
Yann LeCun (Chief AI Scientist)
06:35.790
expert
into
rules
and
facts
right
and
then
the
computer
would
be
able
to
basically
do
what
the
expert
was
this
was
manual
behavior
clothing
OK
uh
and
it
kind
of
worked
but
only
for
a
few
domains
where
where
economically
it
made
sense
and
it
was
doable
at
the
level
of
reliability
Yann LeCun (Chief AI Scientist)
06:53.110
that
was
good
enough
but
it
was
not
a
pass
towards
kind
of
human
level
intelligence
the
idea
somehow
like
the
delusion
that
people
today
have
that
the
current
AI
mainstream
you
know
fashion
is
going
to
take
us
to
human
intelligence
has
happened
already
three
times
during
my
Yann LeCun (Chief AI Scientist)
07:16.990
career
and
probably
five
or
six
times
before
right
you
should
you
should
see
what
people
are
saying
about
the
perceptron
right
the
new
york
times
article
people
were
saying
oh
we're
going
to
have
like
super
intelligent
machines
within
ten
years
marvin
minsky
in
the
sixties
says
Yann LeCun (Chief AI Scientist)
07:29.470
oh
within
ten
years
the
best
chess
player
in
the
world
would
be
a
computer
it
took
a
bit
longer
than
that
and
you
know
and
you
know
this
you
know
this
happened
over
and
over
again
in
nineteen
fifty
six
or
something
when
newell
and
simon
produced
the
the
general
problem
solver
Yann LeCun (Chief AI Scientist)
07:49.360
very
modestly
called
the
general
problem
solver
OK
what
they
what
they
thought
was
really
cool
they
say
OK
the
way
we
think
is
very
simple
we
pose
a
problem
there
is
a
number
of
different
solutions
to
that
problem
different
proposals
for
solution
a
space
of
potential
solutions
Yann LeCun (Chief AI Scientist)
08:07.880
like
you
know
you
do
like
traveling
salesman
right
there
was
a
number
of
you
know
factorial
you
know
N
factorial
paths
possible
paths
you
just
have
to
look
for
the
one
that
is
the
best
right
and
they
say
like
every
problem
can
be
formulated
this
way
essentially
for
a
search
for
Yann LeCun (Chief AI Scientist)
08:24.360
the
best
solution
if
you
can
formulate
the
problem
as
an
objective
by
writing
a
program
that
checks
whether
it's
a
good
solution
or
not
or
gives
a
rating
to
it
and
then
you
have
a
search
algorithm
that
search
through
the
space
of
possible
solution
for
one
that
optimizes
that
Yann LeCun (Chief AI Scientist)
08:43.070
score
that
you
saw
the
AI
OK
now
what
they
didn't
know
at
the
time
is
order
complexity
theory
that
basically
every
problem
that
is
interesting
is
exponential
or
AP
complete
or
whatever
right
and
so
oh
we
have
to
use
heuristic
programming
you
know
kind
of
come
up
with
heuristics
Yann LeCun (Chief AI Scientist)
08:59.510
for
every
new
problem
and
basically
you
know
there
are
general
problem
solver
was
not
that
general
so
like
this
idea
somehow
that
the
latest
idea
is
going
to
take
you
to
you
know
AGI
or
whatever
you
want
to
call
it
is
very
dangerous
and
a
lot
of
very
smart
people
fell
into
that
Yann LeCun (Chief AI Scientist)
09:18.110
trap
many
times
over
the
last
seven
decades
do