Yann LeCun: commitment

Transcript context

…Yeah, and Hans Moravec comes to light once again. Just to linger on LLaMA, Marc announced that LLaMA 3 is coming out eventually. I don’t think there’s a release date, but what are you most excited about? First of all, LLaMA 2 that’s already out there and maybe the future a LLaMA 3, 4, 5, 6, 10, just the future of the open source under Meta? Well, a number of things. So there’s going to be various versions of LLaMA that are improvements of previous LLaMAs, bigger, better, multimodal, things like that. Then in future generations, systems that are capable of planning that really understand how the world works, maybe are trained from video, so they have some world model maybe capable of the type of reasoning and planning I was talking about earlier. How long is that going to take? When is the research that is going in that direction going to feed into the product line if you want of LLaMA? I don’t know. I can’t tell you. There’s a few breakthroughs that we have to basically go through before we can get there, but you’ll be able to monitor our progress because we publish our research. So last week we published the V-JEPA work, which is a first step towards training systems for video. Then the next step is going to be world models based on this type of idea training from video. There’s similar work at DeepMind also and taking place people, and also at UC Berkeley on world models and video. A lot of people are working on this. I think a lot of good ideas are appearing. My bet is that those systems are going to be JEPA light, they’re not going to be generative models, and we’ll see what the future will tell. There’s really good work, a gentleman called Danijar Hafner who is now DeepMind, who’s worked on models of this type that learn representations and then use them for planning or learning tasks by reinforcement training and a lot of work at Berkeley by Pieter Abbeel, Sergey Levine, a bunch of other people of that type I’m collaborating with actually in the context of some grants with my NYU hat. Then collaboration is also through Meta ’cause the lab at Berkeley is associated with Meta in some way, so with fair. So I think it is very exciting. I haven’t been that excited about the direction of machine learning and AI since 10 years ago when Fairway was started. Before that, 30 years ago, we were working, oh, sorry, 35 on combination nets and the early days of neural nets. So I’m super excited because I see a path towards potentially human-level intelligence with systems that can understand the world, remember, plan, reason. There is some set of ideas to make progress there that might have a chance of working, and I’m really excited about this. What I like is that somewhat we get on to a good direction and perhaps succeed before my brain turns to a white sauce or before I need to retire. Yeah. Yeah. Is it beautiful to you just the amount of GPUs involved, the whole training process on this much compute, just zooming out, just looking at earth and humans together have built these computing devices and are able to train this one brain, then we then open source, like giving birth to this open source brain trained on this gigantic compute system, there’s just the details of how to train on that, how to build the infrastructure and the hardware, the cooling, all of this kind of stuff, or are you just still that most of your excitement is in the theory aspect of it, meaning the software?…

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