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

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

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

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

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

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

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

chat GPT. The Meta has three four billion. I mean basically if you have a

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.

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

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

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

enough yet. Uh you look at deep research this is something Bendic Deans has brought up. Deep research is pretty

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

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

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

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

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

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

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

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

hospital. And it was basically a complete failure and was sold for parts right. um and cost a lot of money to to

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

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

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

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

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

any question. We have one at Meta it's called Metamate. It's pretty cool. 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

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

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

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

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.

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

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

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

entity. It's going to come from the entire research community across the world. And the people who share their

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.