Yann Lecun: Meta AI, Open Source, Limits of LLMs, AGI & the Future of AI | Lex Fridman Podcast #416

I see the danger of this concentration of power through, uh, through proprietary AI systems as a much bigger danger than everything else. What works against this is people who think that for reasons of security, we should keep AI systems under lock and key, because it's too dangerous to put it in the hands of- of everybody. That would lead to a very bad future (laughs) in which all of our information diet is controlled by a small number of, uh, uh, companies who proprietary systems.

I believe that people are fundamentally good, and so if AI, especially open source AI, can, um, make them smarter, it just empowers the goodness in humans.

So I sh- I share that feeling, okay? I think people are fundamentally good. (laughs) Uh, and in fact a lot of doomers are doomers because they don't think that people are fundamentally good.

The following is a conversation with Yann LeCun, his third time on the spot cast. He is the chief AI scientist at Meta, professor at NYU, Turing Award winner, and one of the seminal figures in the history of artificial intelligence. He and Meta AI have been big proponents of open sourcing AI development, and have been walking the walk by open sourcing many of their biggest models, including LLaMA 2 and eventually LLaMA 3. Also, Yann has been an outspoken critic of those people in the AI community who warn about the looming danger and existential threat of AGI. He believes the AGI will be created one day, but it will be good, it will not escape human control, nor will it dominate and kill all humans. At this moment of rapid AI development, this happens to be somewhat a controversial position. And so it's been fun seeing Yann get into a lot of intense and fascinating discussions online, as we do in this very conversation. This is the Lex Fridman Podcast. To support it, please check out our sponsors in the description. And now, dear friends, here's Yann LeCun. You've had some strong statements, technical statements, about the future of artificial intelligence recently, throughout your career actually, but recently as well. Uh, you've said that autoregressive LLMs are, uh, not the way we're going to make progress towards superhuman intelligence. These are the large language models like GPT-4, like LLaMA 2 and 3 soon, and so on. How do they work and why are they not going to take us all the way?

For a number of reasons. The first is that there is a number of characteristics of intelligent behavior. For example, the capacity to understand the world, understand the physical world. The ability to remember and retrieve things. Um, persistent memory. The ability to reason and the ability to plan. Those are four essential characteristic of intelligent, um, systems or entities. Humans, animals. LLMs can do none of those, or they can only do them in a very primitive way, and, uh, they don't really understand the physical world, they don't really have persistent memory, they can't really reason, and they certainly can't plan. And so, eh, you know, if- if- if you expect a system to become intelligent just, eh, you know, without having the possibility of doing those things, uh, you're making a mistake. That is not to say that autoregressive LLMs are not useful, they're certainly useful. Um, that they are not interesting, that we can't build a whole ecosystem of, uh, applications around them. Of course we can. But as a path towards human level intelligence, they're missing essential competence. And then there is another tidbit or- or fact that I think is very interesting. Those LLMs are trained on enormous amounts of text. Basically the entirety of all publicly available texts on the internet, right? That's typically on the order of, uh, 10 to the 13th tokens. Each token is typically two bytes, so that's two 10 to the 13th bytes as training data. It would take you or me 170,000 years to just read through this at eight hours a day. (laughs) Uh, so it seems like an enormous amount of knowledge, right, that those systems can accumulate. Um, but then you realize it's really not that much data. If you- you talk to developmental psychologists and they tell you a four-year-old has been awake for 16,000 hours in his or her life, um, and the amount of information that has, uh, reached the visual cortex of that child in four years, um, is about 10 to the 15th bytes, and you can compute this by estimating that the, uh, optical nerve carry about 20 megabit- megabytes per second roughly. And so 10 to the 15th bytes for a four-year-old versus two times 10 to the 13th bytes for 170,000 years worth of reading, what that tells you is that, uh, through sensory input we see a lot more information than we- than we do through language, and that despite our intuition, most of what we learn and most of our knowledge is through our observation and interaction with the real world, not through language. Everything that we learn in the first few years of life, and, uh, certainly everything that animals learn, has nothing to do with language.