Yann LeCun (Chief AI Scientist) 00:00.660
that's it. We're going to write a program that does this, and we're going to call it the general problem solver, GPS, 1950 seven, I think. Uh, they want a Turing award for for things like that, and It was It was great,
Yann LeCun (Chief AI Scientist) 00:13.420
but then they didn't realize that all the interesting problems actually have a complexity that grows exponentially with the size of the problem. So, in fact, you can't really use this uh technique to build uh intelligent machines.
Yann LeCun (Chief AI Scientist) 00:26.000
It can be a component of it, but it's really not not the thing. So Matthewsly, for it was on black came up with a perception on a machine that could learn. And he said, "If we can train a machine, then it can become infinitely
Yann LeCun (Chief AI Scientist) 00:36.240
smart." And so within 10 years, we'll have we just need to big, you know, to build bigger perceptrons, right? Not realizing that you need to train multiple layers and that turned out to be uh difficult to find a solution for this.
Yann LeCun (Chief AI Scientist) 00:49.360
Um, then in the 1980s, there was um expert systems. Okay. reasoning is is fine. Just write a bunch of facts and a bunch of rules and then just deduce all the facts from the original facts and the and the rules. And uh
Yann LeCun (Chief AI Scientist) 01:05.920
now we can reduce all the human knowledge into into this. The The coolest job is going to be knowledge engineer. You're going to sit down next to an expert and then write down all the rules and the facts and turn this into an expert system.
Yann LeCun (Chief AI Scientist) 01:19.200
And you know everybody was excited about this and there were you know billions that were invested the Japan started the fifth generation computer program pro uh project which was can which was going to revolutionize computer science, complete
Yann LeCun (Chief AI Scientist) 01:34.000
failure. Okay? It created an industry. It was useful for a few things, but basically the cost of reducing human knowledge to to rules uh was just too high for most problems and so the whole thing
Yann LeCun (Chief AI Scientist) 01:47.000
collapsed. Then there was neural nets, the the first the second wave of neural nets 1980s, deep you know, which we now call deep learning. a lot of interest, but then it was before the internet, we didn't have enough data, we didn't have powerful computers.
Yann LeCun (Chief AI Scientist) 02:01.280
And now we're we're going through the same cycle again and we're getting fooled again.
Janna Levin (Professor of Physics and Astronomy) 02:05.160
So just to be oh Adam please.
Adam Brown (Research Scientist) 02:07.240
In in technologies every dawn has before it false dawns, that doesn't mean we'll never we'll never hit the dawn. I I guess I would like um Jan, if you think that LLM's are going to saturate, what is a concrete task that they will never be able to do?
Adam Brown (Research Scientist) 02:24.160
That that an LLM them augmented by, you know, the the tools we give it today, will never be able to perform.
Yann LeCun (Chief AI Scientist) 02:32.360
Uh, clear up the dinner table, fill up the dishwasher.
Yann LeCun (Chief AI Scientist) 02:37.200
Okay. And that's easy I compared to I'm skeptical. That's super easy compared to fixing your toilets. Yeah. Okay, We build a plumber, right? You're never going to have a plumber with LLM's. You're never going to have a robot driven by LLM's. It just cannot understand the real
Yann LeCun (Chief AI Scientist) 02:49.480
world. It just
Janna Levin (Professor of Physics and Astronomy) 02:50.080
can't. So, I want to clarify for the audience that you're not saying that machines or robots won't be able to do this. That's not your position. You
Yann LeCun (Chief AI Scientist) 02:57.040
think they will. They will. They absolutely But just will not by this algorithmic approach or for this particular approach of the deep learning on the normal
Yann LeCun (Chief AI Scientist) 03:03.040
If the program I'm working on succeeds, which may take a while This is Jipa. Am I Jipa Jipa Jipa Jipa and and you know all the things world models and things that go with it.
Yann LeCun (Chief AI Scientist) 03:13.240
If it succeeds which may take you know several years then we we may have you know AI system there's no question at some point in the future we will have machines that are smarter than humans in all domains that you know, where humans have abilities.
Yann LeCun (Chief AI Scientist) 03:27.480
There's no question about that. They will happen, okay? You probably take longer than you know some of the people in Silicon Valley at the moment are saying it it it will. Uh
Yann LeCun (Chief AI Scientist) 03:37.000
and uh and it it will not be LLMs, it will not be generative models that predict discrete tokens. It will be models that learn abstract representations and make predictions in the abstract representations and can reason about what is going to be the effect of me taking
Yann LeCun (Chief AI Scientist) 03:53.080
this action, can I plan a sequence of actions to arrive at a particular goal.
Janna Levin (Professor of Physics and Astronomy) 03:57.200
You call the self-supervised learning.
Yann LeCun (Chief AI Scientist) 03:59.120
No, so self-supervised learning is used also by LLM's. Self-supervised learning is the idea that you train a system not for a particular task other than capturing the sort of underlying structure of the of the data you
Yann LeCun (Chief AI Scientist) 04:14.120
you show it. And one way to do this is to give it a piece of of data, corrupt it in some way by removing a piece of it, for example, masking a piece of it, and then training a bit more on that to predict the piece that is missing. So,
Yann LeCun (Chief AI Scientist) 04:30.760
LLMs do this, right? You take a text, you remove the last word, and you train the LLMs to predict the the word that is missing. You have other types of language models that actually fill up multiple words. They
Yann LeCun (Chief AI Scientist) 04:42.080
turn out to not work as well as the ones that just predict the last one. Um, at least for certain tasks. Um, you can do this with video. If you try to predict at the pixel level, it doesn't work, or it doesn't work very well. Um,
Yann LeCun (Chief AI Scientist) 04:55.000
My colleagues at Meta probably boiled a couple small lakes in the West Coast to, you know, trying to make this work. Um, to cool the GPUs. Uh, so it simply doesn't