EP20: Yann LeCun - part 4/11
December 15, 2025 • 1h 50m 6s
Ravid Shwartz-Ziv (Assistant Professor)
00:00.190
and
what
do
you
think
are
the
missing
party
like
do
you
think
like
more
compute
will
help
or
like
we
need
better
algorithms
like
it's
kind
of
like
do
you
believe
in
the
bitter
lessons
right
like
do
you
think
? (?)
00:13.310
well
and
and
furthermore
what
do
you
think
about
you
know
the
data
quality
problems
with
the
internet
post
twenty
twenty
two
right
i've
heard
people
compare
it
to
low
background
steal
now
to
refer
to
all
that
data
before
LLM's
came
out
like
low
background
tokens
i
mean
OK
Yann LeCun (Chief AI Scientist)
00:27.270
yeah
i
think
i'm
totally
escaping
that
problem
OK
here
is
the
thing
and
i've
i've
been
you
know
using
this
argument
publicly
over
the
last
couple
of
years
training
an
LLM
if
you
wanted
to
have
any
kind
of
you
know
decent
performance
requires
training
on
basically
all
the
Yann LeCun (Chief AI Scientist)
00:43.790
available
freely
available
text
on
the
internet
plus
some
you
know
synthetic
data
plus
licensed
data
et
cetera
so
a
typical
LLM
like
you
know
number
three
you
know
going
back
a
year
or
two
is
trained
on
thirty
trillion
tokens
the
token
is
typically
three
bytes
so
that's
ten
to
Yann LeCun (Chief AI Scientist)
01:01.510
the
fourteen
bytes
for
pre
training
OK
we're
not
talking
about
fine
tuning
ten
to
the
fourteen
bytes
and
for
the
LLM's
to
be
able
to
really
kind
of
exploit
this
they
need
to
have
a
lot
of
memory
storage
because
basically
those
are
isolated
facts
there
is
a
little
bit
of
Yann LeCun (Chief AI Scientist)
01:22.100
redundancy
in
text
but
a
lot
of
it
is
just
isolated
facts
right
and
so
you
need
a
lot
of
you
need
very
big
networks
because
you
need
a
lot
of
memory
to
store
all
those
facts
if
we
go
to
them
OK
now
compare
this
with
video
ten
to
the
fourteen
bytes
if
you
count
two
megabytes
per
Yann LeCun (Chief AI Scientist)
01:44.960
second
for
video
for
you
know
relatively
compressed
video
not
how
you
compress
but
a
bit
that
would
represent
fifteen
thousand
hours
of
video
ten
to
the
fourteen
bytes
if
fifteen
thousand
hours
of
video
you
have
the
same
amount
of
data
as
the
charity
of
all
the
texts
available
Yann LeCun (Chief AI Scientist)
02:03.710
on
the
internet
now
fifteen
thousand
hours
of
video
is
absolutely
nothing
it's
thirty
minutes
of
youtube
uploads
OK
it's
the
amount
of
visual
information
that
a
four
year
old
has
seen
in
his
or
her
life
the
entire
life
waking
time
is
about
sixteen
thousand
hours
in
four
years
so
Yann LeCun (Chief AI Scientist)
02:24.910
a
lot
of
information
we
have
video
models
now
vijay
vijaypat
two
actually
that
just
came
out
last
summer
that
was
trained
on
the
equivalent
of
a
century
of
video
data
and
it's
all
public
data
OK
much
more
data
but
much
less
than
the
biggest
LNM
actually
because
even
though
it's
Yann LeCun (Chief AI Scientist)
02:46.590
it's
more
bytes
it's
more
redundant
to
say
OK
it's
more
redundant
so
it's
less
useful
actually
when
you
use
self
supervised
learning
you
do
need
redundancy
you
cannot
learn
anything
in
self
supervise
or
anything
by
the
way
if
it's
completely
random
redundancy
is
what
you
can
Yann LeCun (Chief AI Scientist)
03:02.390
learn
and
so
so
this
just
much
richer
structure
in
you
know
real
world
data
like
video
than
there
is
in
text
which
kind
of
led
me
to
claim
that
we
absolutely
never
ever
going
to
get
to
human
level
AI
by
just
training
on
text
it's
just
never
going
to
happen
right
it's
a
big
Yann LeCun (Chief AI Scientist)
03:23.870
debate
in
philosophy
of
whether
AI
should
be
grounded
in
reality
or
whether
it
could
be
just
you
know
in
the
realm
of
symbolic
manipulation
and
things
like
this
? (?)
03:33.870
we
talk
about
for
world
models
and
grounding
i
think
you
know
there's
still
a
lot
of
people
who
don't
even
understand
what
the
idealized
world
model
is
in
a
sense
right
so
for
example
i'm
influenced
by
having
watched
star
trek
which
i
would
hope
you've
seen
a
little
bit
of
and
? (?)
03:48.670
thinking
of
the
holodecks
right
i
always
thought
that
the
holodeck
was
like
an
idealized
perfect
world
model
right
even
so
many
episodes
of
it
going
too
far
right
and
people
walking
out
of
it
right
but
you
know
it
even
simulates
things
like
smell
and
physical
touch
so
do
you
you
? (?)
04:04.110
think
that
something
like
that
is
like
the
idealized
world
model
or
do
you
think
like
a
different
model
or
like
way
of
defining
it
would
be
Yann LeCun (Chief AI Scientist)
04:12.110
OK
this
is
an
excellent
question
and
there
is
an
excellent
is
because
it
goes
to
the
core
of
really
what
you
know
what
i
think
we
should
be
doing
which
i'm
doing
and
how
wrong
a
secret
video
is
OK
so
so
people
think
you
know
think
that
a
world
model
is
something
that
reproduces
Yann LeCun (Chief AI Scientist)
04:33.790
all
details
of
what
the
world
does
they
think
of
it
as
a
simulator
yeah
right
and
of
course
because
you
know
deep
learning
is
the
thing
you're
going
to
use
some
deep
learning
system
as
a
simulator
a
lot
of
people
also
are
focused
on
video
generation
which
is
kind
of
a
cool
thing
Yann LeCun (Chief AI Scientist)
04:48.070
right
you
you
produce
those
cool
videos
and
they're
wow
you
know
people
are
sort
of
really
impressed
by
them
now
there's
no
guarantee
whatsoever
then
when
you
train
a
video
generation
system
it
actually
has
an
accurate
model
of
the
underlying
dynamics
of
the
world
and
it's
Yann LeCun (Chief AI Scientist)
05:03.070
learned
anything
you
know
particularly
abstract
about
it
and
so
so
the
idea
that
somehow
a
model
needs
to
reproduce
every
detail
of
the
reality
is
wrong
and
hurtful
and
i'm
going
to
tell
you
why
OK
a
good
example
of
simulation
is
CFD
computational
fluid
dynamics
it's
used
all
Yann LeCun (Chief AI Scientist)
05:29.550
the
time
people
use
supercomputers
for
that
right
so
you
want
to
simulate
the
flow
of
air
around
an
airplane
you
cut
up
the
space
into
little
cubes
and
within
each
cube
you
have
a
small
vector
that
represents
the
state
of
that
cube
which
is
you
know
velocity
density
or
mass
and
Yann LeCun (Chief AI Scientist)
05:51.670
temperature
and
maybe
a
couple
of
other
things
right
so
and
then
you
solve
navier
stokes
equations
which
are
which
is
a
differential
partial
differential
equation
and
you
can
see
related
flow
of
air
now
the
thing
is
this
does
not
actually
necessarily
solve
the
equations
very
Yann LeCun (Chief AI Scientist)
06:10.590
accurately
if
you
have
chaotic
behavior
like
turbulences
and
stuff
like
that
simulation
is
only
you
know
approximately
correct
but
in
fact
that's
already
an
abstract
representation
of
the
underlying
phenomenon
the
underlying
phenomenon
is
molecules
of
air
that
bump
into
each
Yann LeCun (Chief AI Scientist)
06:26.590
other
and
bump
on
the
wing
and
on
the
airplane
right
ever
goes
to
that
level
to
do
the
simulation
that
would
be
crazy
right
it
would
require
an
amount
of
computation
that's
just
insane
and
it
would
depend
on
the
initial
condition
i
mean
there's
all
kinds
of
reasons
we
don't
do
Yann LeCun (Chief AI Scientist)
06:43.990
this
and
maybe
it's
not
molecules
maybe
it's
you
know
at
a
lower
level
we
should
simulate
particles
and
like
you
know
do
the
feynman
diagrams
and
simulate
you
know
all
the
different
paths
that
those
particles
are
employing
because
they
don't
take
one
path
right
it's
not
Yann LeCun (Chief AI Scientist)
06:57.190
classical
it's
quantum
so
at
the
bottom
it's
like
quantum
field
theory
and
probably
already
that
that
is
an
abstract
representation
so
so
you
know
everything
that
takes
place
between
us
at
the
moment
in
principle
can
be
described
through
quantum
field
theory
OK
we
just
have
to
Yann LeCun (Chief AI Scientist)
07:17.200
measure
the
wave
function
of
the
universe
in
a
cube
that
contains
all
of
us
and
even
that
would
not
be
sufficient
because
they're
entering
all
particles
on
the
other
side
of
the
universe
that
you
know
we
have
so
it
wouldn't
be
sufficient
but
let's
imagine
OK
for
the
sake
of
of
Yann LeCun (Chief AI Scientist)
07:33.790
the
argument
first
of
all
we
would
not
be
able
to
measure
this
wave
function
and
second
of
all
the
amount
of
competition
we
would
need
to
devote
to
this
is
absolutely
gigantic
it
was
released
on
gigantic
quantum
computer
that
you
know
is
the
size
of
euros
or
something
so
no
way
Yann LeCun (Chief AI Scientist)
07:54.150
we
can
describe
anything
at
that
level
and
it's
very
likely
that
our
simulation
would
be
accurate
for
maybe
a
few
nanoseconds
you
know
beyond
that
we'll
diverge
from
reality
so
what
do
we
do
we
invent
abstractions
we
invent
abstractions
like
particles
atoms
molecules
in
the
Yann LeCun (Chief AI Scientist)
08:12.550
living
world
its
proteins
organelles
sales
organs
organisms
societies
ecosystems
etc
right
and
basically
every
level
in
this
hierarchy
ignores
a
lot
of
details
about
the
level
below
and
what
that
allows
us
to
do
is
make
longer
term
more
reliable
longer
term
predictions
OK
so
we
Yann LeCun (Chief AI Scientist)
08:38.830
can
describe
the
dynamics
between
us
now
in
terms
of
the
underlying
science
and
in
terms
of
psychology
OK
that's
a
much
much
higher
level
abstraction
than
particle
physics
right
and
in
fact
you
know
every
level
in
the
hierarchy
i
just
i
just
mentioned
is
a
different
field
of
Yann LeCun (Chief AI Scientist)
08:55.990
science
a
field
of
science
is
essentially
defined
by
the
level
of
abstraction
at
which
you
start
making
predictions
right
that
you
allow
yourself
to
use
to
make
predictions
in
fact
physicists
have
this
down
to
an
art
in
the
sense
that
you
know
if
i
give
you
a
box
full
of
gas
you
Yann LeCun (Chief AI Scientist)
09:18.190
could
in
principle
simulate
all
the
molecules
of
the
gas
but
nobody
ever
does
this
but
at
a
very
abstract
level
we
can
say
you
know
PV
equals
NRT
right
you
know
pressure
times
value
equals
the
number
of
particle
times
you
know
temperature
blah
blah
blah
and
so
you
know
that
you
Yann LeCun (Chief AI Scientist)
09:39.870
know
global
emergent
phenomenological
level
if
you
increase
the
pressure
the
temperature
will
go
up
or
if
you
increase
the
temperature
the
pressure
will
go
up
right
or
if
you
let
some
particles
out
then
the
pressure
will
go
down
and
blah
blah
blah
right
so
so
we
all
the
time
we
Yann LeCun (Chief AI Scientist)
09:57.710
build
phenomenological
models
of
something
complicated
by
ignoring
all
kinds
of
details
that
physicists
call
entropy
but
but
it's
really
systematic
that's
the
way
we
understand
the
world
we
do
not
memorize
every
detail
of
we
certainly
not
reconstruct
it
of
what
we
perceive
so
Yann LeCun (Chief AI Scientist)
10:20.120
world
models
don't
have
to
be
simulators
at
all
well
there
are
simulators
but
in
abstract
representation
space
and
what
they
simulate
is
only
the
relevant
part
of
reality
OK
if
i
ask
you
where
is
jupiter
going
to
be
one
hundred
years
from
now
i
mean
we
have
an
enormous
amount
of
Yann LeCun (Chief AI Scientist)
10:37.310
information
about
jupiter
right
but
within
this
whole
information
that
we
have
about
jupiter
to
be
able
to
make
that
prediction
where
jupyter
is
going
to
be
one
hundred
years
from
now
you
need
exactly
six
numbers
three
positions
and
three
velocities
and
the
rest
doesn't
matter
Yann LeCun (Chief AI Scientist)
10:52.350
so
you
don't
believe
in
a
synthetic
datasets
i
do
no
it's
useful
you
know
data
from
games
i
mean
there's
certainly
a
lot
of
things
that
you
learn
from
synthetic
data
from
you
know
from
games
and
things
like
that
i
mean
you
know
children
learn
a
huge
amount
from
from
play
which
Yann LeCun (Chief AI Scientist)
11:10.430
basically
are
kind
of
simulations
you
know
the
world
a
little
bit
right
but
but
in
conditions
where
they
can't
kill
themselves
but
i
worry
at
least
for
video
games
that
for
example
the
green
screen
like
actors
doing
the
animations
they're
doing
extremely
it's
designed
to
look
Yann LeCun (Chief AI Scientist)
11:27.840
good
you
know
for
like
an
often
badass
i
guess
for
an
action
game
but
these
often
don't
correspond
very
well
to
reality
and
so
i
i
worry
that
like
a
physical
system
that's
you
know
been
trained
or
through
or
with
the
assistance
of
world
models
might
get
similar
quirks
at
least
Yann LeCun (Chief AI Scientist)
11:46.030
in
the
very
short
term
is
this
something
that
worries
you
no
it
depends
on
what
level
you
train
them
so
for
example
i
mean
sure
if
you
use
a
very
accurate
robotic
simulator
for
example
right
it's
going
to
accurately
simulate
the
dynamics
of
an
arm
you
know
when
you
apply
torques
Yann LeCun (Chief AI Scientist)
12:00.550
to
it
it's
going
to
move
in
a
particular
way
dynamics
no
problem
now
simulating
the
friction
that
happens
you
know
when
you
grab
an
object
and
manipulate
it
that's
super
hard
to
do
it
accurately
friction
is
very
hard
to
simulate
OK
and
so
those
simulators
are
not
particularly
Yann LeCun (Chief AI Scientist)
12:15.950
accurate
for
manipulation
they're
good
enough
that
you
know
you
can
train
a
system
to
do
it
and
then
you
can
do
you
know
seem
to
real
with
a
little
bit
of
adaptation
so
that
can
work
but
it
does
not
i
mean
the
point
is
much
more
important
like
for
example
there's
a
lot
of
Yann LeCun (Chief AI Scientist)
12:32.240
completely
basic
things
about
the
world
that
we
completely
take
for
granted
which
we
can
learn
at
a
very
abstract
level
but
it's
not
language
related
OK
so
the
fact
for
example
and
i've
used
this
example
before
and
people
made
fun
of
me
for
it
but
it's
really
true
OK
i
have
Yann LeCun (Chief AI Scientist)
12:47.270
those
objects
on
the
table
and
the
fact
that
when
i
push
the
table
the
object
moves
with
it
like
this
is
something
we
learned
it's
not
something
that
you're
born
with
OK
the
fact
that
most
objects
will
fall
when
you
let
them
go
right
with
gravity
maybe
it's
run
this
around
the
Yann LeCun (Chief AI Scientist)
13:02.870
edge
of
and
the
reason
people
make
fun
of
me
with
this
is
because
i
said
you
know
LLM's
don't
understand
this
kind
of
stuff
right
and
and
they
absolutely
do
not
even
today
but
but
you
can
train
them
to
give
the
right
answer
when
you
ask
them
a
question
you
know
if
i
put
an
Yann LeCun (Chief AI Scientist)
13:20.990
object
on
the
table
then
i
push
the
table
what
will
happen
to
the
object
it
will
answer
the
object
moves
with
it
but
because
it's
been
fine
tuned
to
do
that
OK
so
it's
more
like
regurgitation
that
sort
of
real
understanding
of
the
underlying
dynamics
but
if
you
look
on
i
don't
Yann LeCun (Chief AI Scientist)
13:34.630
know
sura
like
nano
nano
banana
they
they
have
a
good
physics
of
the
world
right
they
are
not
perfect
they
have
some
physics
yeah
they
have
some
physics
right
so
do
you
think
like
we
can't
push
it
or
do
you
think
like
it's
a
one
way
to
learn
physics
actually
make
predictions
in
Yann LeCun (Chief AI Scientist)
13:54.630
our
presentation
space
they
use
diffusion
transformers
and
that
prediction
that
the
computation
of
the
video
snippet
at
an
abstract
level
is
done
in
representation
space
OK
not
always
auto
regressively
by
the
way
sometimes
it's
just
in
parallel
and
then
there's
a
second
Yann LeCun (Chief AI Scientist)
14:14.510
diffusion
model
that
turns
this
abstract
representations
into
a
nice
looking
video
and
that
might
be
more
collapse
we
don't
know
right
because
we
can't
really
measure
like
the
coverage
of
such
systems
with
reality
but
but
like
you
know
the
to
the
the
previous
point
i
can
train
Yann LeCun (Chief AI Scientist)
14:37.070
like
here
is
another
completely
obvious
concept
to
us
that
we
don't
even
imagine
that
we
learn
but
we
do
learn
it
a
person
cannot
be
in
two
places
at
the
same
time
OK
we
run
this
because
very
early
on
we
learn
object
permanence
the
fact
that
when
an
object
disappears
still
Yann LeCun (Chief AI Scientist)
14:56.990
exists
OK
and
reappears
it's
the
same
object
that
you
saw
before
how
can
we
train
an
AI
system
to
learn
this
concept
so
object
permanence
you
know
you
just
show
it
a
lot
of
videos
where
objects
you
know
go
behind
the
screen
and
then
reappear
on
the
other
side
or
what
they
go
Yann LeCun (Chief AI Scientist)
15:13.640
behind
the
screen
and
the
screen
goes
away
and
the
object
is
still
there
and
when
you
show
four
months
old
babies
scenarios
where
things
like
this
are
violated
their
eyes
open
like
super
big
and
they're
like
super
surprised
because
reality
just
you
know
violated
their
internal
Yann LeCun (Chief AI Scientist)
15:29.790
model
the
same
thing
when
you
show
a
scenario
of
like
a
little
car
on
the
platform
you
push
it
off
the
platform
and
it
appears
to
float
in
the
air
they
also
look
at
it
you
know
nine
months
ten
months
old
babies
look
at
it
like
really
surprised
six
months
old
baby
barely
pay
Yann LeCun (Chief AI Scientist)
15:45.870
attention
because
they
haven't
run
over
gravity
yet
so
they
haven't
been
able
to
like
you
know
incorporate
the
notion
every
object
is
supposed
to
fall
so
this
kind
of
learning
is
really
what's
what's
important
and
you
do
this
you
can
learn
this
from
very
abstract
things
you
know
Yann LeCun (Chief AI Scientist)
16:01.470
the
same
way
babies
learn
about
like
you
know
social
interactions
by
you
know
being
told
stories
with
like
simple
pictures
it's
a
simulation
an
abstract
simulation
of
the
world
but
it
sort
of
launched
them
you
know
particular
behavior
so
you
could
imagine
like
training
a
system
Yann LeCun (Chief AI Scientist)
16:18.830
from
let's
say
an
adventure