Yann LeCun: We Won't Reach AGI By Scaling Up LLMS

Channel: Alex Kantrowitz

Published at: 2025-05-30

YouTube video id: 4__gg83s_Do

Source: https://www.youtube.com/watch?v=4__gg83s_Do

We are not going to get to human level
AI by just scaling up MLMs. This is just
not going to happen. Okay, that's your
perspective. There's no way. Okay,
absolutely no way. Um and and whatever
you can hear from some of my uh more
adventurous colleagues, uh it's not
going to happen within the next two
years. There's absolutely no way in hell
to you know, pardon my French. um the
you know the idea that we're we're going
to have you know a country of genius in
a data center that's complete BS right
there's absolutely no way what we're
going to have maybe is systems that are
trained on sufficiently large amounts of
data that any question that any
reasonable person may ask will will find
an answer through those systems and it
would feel like you have you know a PhD
sitting next to you but it's not a PhD
you have next to you it's you know a
system with a gigantic uh memory and
retrieval ability
not not a system that can invent
solutions to to new problems. Um, which
is really what a PhD is. Okay, this is
actually it's it's you know connected to
this post that uh
tool made that uh um you you you
you inventing new things you know
requires
uh the the a type of skill and abilities
that uh you're not going to get from
from from Adams. So
um so there's a big question which is
the investment that is being done now is
not done for tomorrow. It's not is is
done for you know the next few years and
most of the investment at least for from
the meta side is investment in uh
infrastructure for inference. Okay. So
let's imagine that by the end of the
year, which is really the planet Ma, we
have 1 billion users of MAI through
smart glasses, you know, standalone app
and and whatever. Um, you got to serve
those people and that's a lot of
computation. So that's why you need, you
know, a lot of investment in
infrastructure to be able to scale this
up and, you know, build it up over
months or years.
Um, and so that, you know, that's where
most of the money is going. um um at
least on on you know on the side of
companies like like like Mai, Microsoft
and and and Google and potentially
Amazon
um then there is so this is just
operations essentially. Now is there
going to be the the market for um you
know one billion people using those
things regularly even if there is no
change of paradigms and the answer is
probably yes. So you know even if the
revolution of a new paradigm doesn't
come you know within 3 years this
infrastructure is going to be used is
there's very little question about that.
Okay. So, it's a good investment and it
takes so long to set up you know data
centers and all that stuff that you need
to to get started now and plan for you
know progress to be continuous uh so
that uh you know eventually the
investment is is justified but you can't
afford not to do it right because um
because that would be too much of a of a
risk to take if you have the cash. But
let's go back to what you said. The
stuff today is still deeply flawed and
there have been questions about whether
it's going to be used. Now Meta is
making this consumer bet, right? The
consumers want to use the AI. That makes
sense. OpenAI has 400 million users of
chat GPT. The Meta has three four
billion. I mean basically if you have a
phone 3 something billion users uh 600
million users of Meta,
right? Okay. So more than Chat GPT.
Yeah, but they but it's not used as much
as so the users are not as intense as
active. But basically the idea that that
Meta can get to a billion consumer users
that seems reasonable. But the thing is
a lot of this investment has been made
with the idea that this will be useful
to enterprises uh not just a consumer
app. And there's a problem because like
we've been talking about it's not good
enough yet. Uh you look at deep research
this is something Bendic Deans has
brought up. Deep research is pretty
good, but it might only get you 95% of
the way there and maybe 5% of it
hallucinates. So if you have a 100page
research report and 5% of it is wrong
and you don't know what 5% that's that's
a problem. And similarly in in
enterprises
today all every enterprise is trying to
figure out how to make uh AI useful to
them uh generative AI useful to them and
other types of AI. uh but only 10% or
20% maybe of proof of concepts make it
out the door into production because
there it's either too expensive or it's
fallible. So if this is if we are
getting to the top here uh what do you
anticipate is going to happen with with
everything that's that that has been
pushed in the anticipation that it is
going to get even better from here.
Well, so again it's a question of
timeline, right? When when are those
systems going to become sufficiently
reliable and intelligent so that the
deployment is made easier? Um but but
you know I mean this the situation
you're describing
that you know beyond the impressive
demos actually deploying systems that
are reliable is where things tend to
falter in in the use of computers and
technologies and particularly AI. This
is not new. Um it's it's
basically um you know why we we had
super
impressive you know autonomous driving
demos 10 years ago. Um but we still
don't have level five self-driving cars,
right? Um it's the last mile that's
really difficult uh so to speak for
cars, you know. It's you know the last
the last
few that was not deliberate the the you
know the last few few% of reliability
which makes a system uh practical um and
how you integrate it with sort of
existing systems and and and blah blah
blah and you know how it makes uh users
of it more efficient if you want or more
reliable or or whatever. Um that's where
that's where that's where it's
difficult. Um and you know this is why
if we take if we go back several several
years and we look what happened with IBM
Watson. Okay. So Watson was going to be
the thing that you know IBM was was
going to push and generate tons of
revenue by by having Watson uh you know
learn about medicine and then be
deployed in every um every
hospital. And it was basically a
complete failure and was sold for parts
right. um and cost a lot of money to to
IBM including the CEO and the what
happens is that actually deploying those
systems in in situations where they are
reliable and and actually help people
and don't like hurt the natural
conservatism of the of the labor force.
Um this is where things become
complicated. We're seeing the same you
know the process we're seeing now with
the difficulty of deploying AI system is
not new. It's it's it's happened
absolutely at at all times. This is also
why you know some some of your listeners
perhaps are too young to remember this
but there was a big wave of interest in
AI in the 1980s early 1980s um around
expert systems and you know the the
hottest job in the 1980s was going was
going to be knowledge engineer and your
job was going to be to sit next to a an
expert and then you know turn the
knowledge of the expert into rules and
facts that would then be fed to a um
inference engine that would be able to
kind of derive new facts and and answer
questions and blah blah blah. Um big
wave of interest. Uh the Japanese
government started a big program called
fifth generation computer. The hardware
was going to be designed to actually
take care of that and blah blah blah.
You know, mostly mostly a failure. There
was kind of
a you know the wave of interest kind of
died in the the mid90s about this and
and you know a few companies were
successful but basically for a narrow
set of applications for which you could
actually reduce human knowledge to a
bunch of rules and for which um uh it
was economy economically feasible to do
so. Um but the the the wide-ranging
impact on all of uh society and industry
was just not there. And so that's a
danger of uh of AI all the time. Um I
mean the the signals are clear that you
know still um LLMs with all the bells
and whistles actually play an important
role if nothing else for information
retrieval. uh you know most companies
want to have some sort of internal u
experts that know all the internal
documents so that any employee can ask
any question. We have one at Meta it's
called Metamate. It's pretty cool. It's
very useful. Yeah. Yeah, I'm I'm not
suggesting that AI is going to that
modern AI is not or modern generative AI
is not useful or uh I'm I'm asking
purely that there's been a lot of money
that's been invested into expecting this
stuff to effectively achieve godle
capabilities and we both are talking
about how like there's you know
potentially diminishing returns here and
then what happens if there's that
timeline mismatch like you mentioned and
um this is the last question I'll ask
about it because I feel like we have so
much else to cover. But I feel like
timeline mismatches uh that might be
personal to you. You and I first spoke 9
years ago, which is crazy now, 9 years
ago. Uh and you know about how in the
early days you had an idea for how AI
should be structured and you couldn't
even get a seat at the conferences. Um
and then eventually with the right
amount of when when the right amount of
compute came around, those ideas started
working and then the entire AI field
took off based off of your idea that you
you worked on with uh Benio and Hinton.
Um but and a bunch of others and many
others uh and but for the sake of
efficiency we'll say go look it up. Um
but just talking about those mismatched
timelines when there have been overhyped
moments uh in the AI field maybe with
expert systems that you were just
talking about and they don't pan out the
way that people expect the eye field
goes into what's called AI winter. Well,
there's a backlash. Yeah. Correct. And
so if we're going to if we are
potentially approaching this moment of
mismatched timelines, do you fear that
there could be another winter now given
the amount of investment? Uh given the
fact that there's going to be
potentially diminishing returns with the
main way of training these things and
maybe we'll add in the fact that the
market is is the stock market looks like
it's going through a bit of a downturn
right now. Now that's a variable uh
probably the third most important
variable of what we're talking about,
but it has to factor. So I yeah I I
think um I mean there's
certainly a question of timing there but
I think uh if we try to dig a little bit
deeper um as I said before if you think
that we're going to get to human level
AI by just training on more data and
scaling up LLMs you're making a mistake.
So if you're if you're an investor and
you invested in a company that told you
we're going to get to human level AI and
PhD level by just you know training on
more data and with a few tricks um I
don't know if you're going to use your
shirt but that was probably not a good
idea. Um however there are ideas about
how to uh go forward and have systems
that are capable of doing what what
every intelligent animal and and human
are capable of doing and that current AI
systems are not capable of doing. And
I'm I'm talking about understanding the
physical world um having persistent
memory and being able to reason and
plan. Those are the four characteristics
that that you know need to be there. Um
and that requires systems that you know
can acquire common sense that can learn
from uh natural sensors like video as
opposed to just text just human produced
uh data. Um and that's a big challenge.
I mean I've been talking about this for
many years now and uh and saying this is
this is where the challenge is. This is
what we have to uh to figure out and and
my group and I have or people working
with me and others who have listened to
me are making progress along along this
line uh of uh systems that can be
trained to understand how the world
works on video for example systems that
can use mental models of how the world
the physical world works to plan
sequences of actions to arrive at a
particular goal. So we we have kind of
early results of these kind of systems u
and there are people at deep mind
working on similar things and there you
know people in various universities
working on this. Uh so um the question
is you know when is this going to go
from uh interesting research papers uh
demonstrating a new capability with a
new architecture to you know
architectures at scale that you know are
practical for a lot of applications and
can find solutions to new problems
without being trained to do it um etc.
And you know it it's not going to happen
within the next three years but it may
happen with you know between three to
five years something like that and
that's kind of corresponds to you know
the sort of ramp up that we see in uh uh
in in investment. Now whether
other so so that that's the first thing.
Now the the second thing that's
important is that there's not going to
be one secret magic bullet that one
company or one group of people is going
to invent that is going to just solve
the problem. Um it's going to be a lot
of different ideas, a lot of effort,
some principles around which to base
this that that some people may may not
subscribe to and will will go um in a
direction that is you know will turn out
to be a dead end. Uh so there's not
going to be like a
day before which there is no AGI and
after which we we have AGI. This is not
going to be an event. Um it's going to
be continuous conceptual ideas that as
time goes by are going to be made bigger
and to scale and going to work better.
And it's not going to come from a single
entity. It's going to come from the
entire research community across the
world. And the people who share their
research are going to move faster than
the ones that don't. And so if you think
that there is some startup somewhere
with five people who has discovered the
secret of AGI and you should invest five
billion in them, you're making a huge
mistake.