Yann LeCun: Meta’s New AI Model LLaMA; Why Elon is Wrong about AI; Open-source AI Models | E1014

AI is going to bring a new renaissance for humanity, a new form of enlightenment, if you want. Because AI is going to amplify everybody's intelligence. It's like every one of us will have a staff of people who are smarter than us and know most things about most topics. So it's going to empower every one of us.

Yann, I am so excited for this. I heard so many great things from our mutual friends, obviously David Marcus and then Matthieu at PhotoRoom. So thank you so much for joining me today.

It's a pleasure.

Now, I would love to start, I heard some of the early stories, but I want to start with one from David Marcus. How did you first enter the world of AI and make that first foray?

I was still an undergraduate, uh, engineering student in France, and I stumbled on a philosophy book which was a debate between, uh, Jean Piaget, you know, the cognitive psychologist, and, uh, Noam Chomsky, the famous linguist. And they were arguing about nature versus nurture for lang- for language, whether language is acquired or innate. So Chomsky was on the side of innate and Piaget on the side of acquired with, you know, some innate structure. And on the side of Piaget was, uh, a guy called Seymour Papert, who was a professor at MIT. In his argument, he talked about something called a Perceptron, which was an early, uh, machine learning system. And I- I read this and discovered that people had been working on, uh, machines that could learn and I was fascinated, and I started digging the literature. Soon discovered that much of that literature was in the 1950s and '60s and basically stopped in the late '60s because of a book, that they killed it, and Seymour Papert was a co-author of that book. Um, so strangely enough. And- and here he was 10 years later actually, uh, praising the Perceptron as kind of a- a amazing concept. So I was hooked. I had s- you know, started getting interested in what was not yet called machine learning, but eventually became neural nets and now deep learning.

Can I ask you? David asked this as well. How long did it take to get... uh, in terms of, like, the major breakthroughs, how long did it take you to get to the major breakthroughs that you're at the origin of when you look back over that time to get to those major breakthroughs?

Well, so there's a- a few breakthroughs. So the first one was, uh, in- in the... when I was still an undergrad basically finishing my engineering studies, uh, I figured out that the- the way forward to kind of lift the limitations of the old systems that were abandoned in the '60s was to find learning algorithms that could train multi-layer neural nets essentially. And people had all but abandoned this, uh, type of research except for a handful of people in Japan.