Yann LeCun
speaker
384 appearances
5 recordings
4 series
first heard Mar 2024
last heard 20 Jun
Yann LeCun’s voice in public audio — every appearance, attributed to the second.
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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. And 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, you know, capable of the type of reasoning and planning I was talking about earlier. Like how long is that going to take? Like when is the research that is going in that direction going to sort of feed into the product line, if you want, of Lama? I don't know. I can't tell you. And there is, you know, 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, right? So, you know, last week we published the Vijepa work, which is sort of a first step towards training systems for video. And 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 from 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-like, they're not going to be generative models. And we'll see what the future will tell.
There's really good work at a gentleman called Danezhar Hafner, who is not DeepMind, who's worked on kind of 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 Peter Abbeel, Sergei Levine, a bunch of other people of that type.
I'm collaborating with, actually, in the context of some grants with my NYU hat. And then collaborations also through Meta, because the lab at Berkeley is associated with Meta in some way, with FAIR. So I think it's very exciting. I think... I'm super excited about... I haven't been that excited about the direction of machine learning and AI since 10 years ago when Fairway started.
Before that, 30 years ago, we were working on... 35 on convolutional 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 onto a good direction and perhaps succeed before my brain turns to a white sauce or before I need to retire.
Well, I used to be a hardware guy many years ago. Decades ago.
Changed a little bit.
I mean, certainly scale is necessary, but not sufficient. Absolutely. So we certainly need computation. I mean, we're still far in terms of computer power. from what we would need to match the compute power of the human brain. This may occur in the next couple of decades, but we're still some ways away. And certainly in terms of power efficiency, we're really far.
So a lot of progress to make in hardware. And right now, a lot of the progress is not, I mean, there's a bit coming from silicon technology, but a lot of it coming from architectural innovation. And quite a bit coming from more efficient ways of implementing the architectures that have become popular, basically a combination of transformers and convnets, right?
So there's still some ways to go until... we're going to saturate, we're going to have to come up with new principles, new fabrication technology, new basic components, perhaps based on different principles than classical digital CMOS.
Well, if you want to make it ubiquitous, yeah, certainly. Because we're going to have to reduce the power consumption. A GPU today is half a kilowatt to a kilowatt. Human brain is about 25 watts. And a GPU is way below the power of the human brain. You need something like 100,000 or a million to match it. So we are off by a huge factor here.
So first of all, it's not going to be an event. The idea somehow, which is popularized by science fiction and Hollywood, that somehow somebody is going to discover the secret to AGI or human-level AI or AMI, whatever you want to call it, and then turn on a machine and then we have AGI. That's just not going to happen. It's not going to be an event. It's going to be gradual progress.
Are we going to have systems that can learn from video how the world works and learn good representations? Yeah. Before we get them to the scale and performance that we observe in humans, it's going to take quite a while. It's not going to happen in one day. Are we going to get systems that can have large amount of associative memory so they can remember stuff?
Yeah, but same, it's not going to happen tomorrow. I mean, there is some basic techniques that need to be developed. We have a lot of them, but to get this to work together with a full system is another story. Are we going to have systems that can reason and plan, perhaps along the lines of objective-driven AI architectures that I described before?
yeah but like before we get this to work you know properly it's going to take a while so and before we get all those things to work together and then on top of this have systems that can learn like hierarchical planning hierarchical representations systems that can be configured for a lot of different situation at hands the way the human brain can you know all of this is going to take you know at least a decade and probably much more because there are
a lot of problems that we're not seeing right now, that we have not encountered. And so we don't know if there is an easy solution within this framework. So, you know, it's not just around the corner. I mean, I've been hearing people for the last 12, 15 years claiming that AGI is just around the corner and being systematically wrong. And I knew they were wrong when they were saying it.
I call their bullshit.
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