Yann LeCunn: Why AGI is a Dangerous Misnomer

jobs. I resisted the use of the phrase AGI. The reason is not that I don't believe in the concept that AI system will eventually become as intelligent as humans. I certainly have no doubt that at some point in the future, we will have machines that are as intelligent as humans in all the domains where humans are intelligent. There's no question this will happen. No doubt. It's a matter of time. But calling this AGI is complete nonsense because human intelligence is incredibly specialized. We have a hard time kind of accepting this concept that human intelligence is specialized, but it is very specialized. That's why I don't like the term. The term I've been using is either human-level AI or AMI. So that stands for advanced machine intelligence. This is kind of the term that we use internally at Meta. We pronounce it ABI because friend, there's a lot of French people. Okay. Also means friend, right? In French. But it's the same concept, right? So now how long is it going to take? Strangely enough, I get asked that question by people like Mark Zuckerberg. The reason is, it's an important thing to know if you want to invest tens of billions in infrastructure to train big AI systems. If you want to be able to tell people like within a few years, you're going to be able to wear those smart glasses that you were showing us initially. In those glasses, there will be an intelligent assistant that you can ask to be with you at all times. You can ask any question. It's going to be smarter than you possibly. You shouldn't feel threatened by that. It would be like having a smart colleague that you can talk to and ask any question. So how long is it going to take? So I think to have possibly a system that, at least to most people, feels like it has If all of the plans that, all of the things that we are imagining will work. So those JEPA architectures and some other ideas that we're playing with succeed. I don't see this happening in less than five or six years. But now, is it going to happen in five or six years? And I think there's a distribution with a tail that's very long. And the history of AI is that people just keep underestimating how hard it is. I'm probably making the same mistake right now, when I say five, six years. This is, if we don't run into a major obstacle that we didn't foresee, if all the things that we're planning to try out actually work, if things kind of scale, if computers accelerate and all that stuff. There's a lot of things, a lot of planets that need to line up for this to happen. So that's the best case. It's not going to happen next year. You might have heard from some other folks. Sam Altman. Yeah, right. Yeah, hopefully. Sam Altman, Elon Musk, various people, or, you know, Dario Amodi of, yeah, it's going to happen within the next two years or something. No, what may happen in the next two years is that it's going to be more and more difficult to find cases where common people will be able to ask questions to the latest chatbot that the chatbot would not be able to answer. But like, again, where is my cat? Where is my domestic robot? Where is my level five self-driving car? Where is the self-driving car that can learn to drive in 20 hours of practice without killing itself? I wonder how much, you know, not as an expert in this field, but just someone who's fascinated by it and has benefited, my life has just benefited so much because now, you know, I've got a bunch of kids and I don't read them, you know, stories. I ask, you know, Meta to read them stories. No, no, I don't do that. But I don't think there's anything wrong with it morally. I feel fine because if you're reading somebody's book, it's basically the same thing. But I think we're kind of arguing about stuff that's maybe the most, you know, analogous thing I can point to is like the Drake equation, like the Drake equation parameterizes, you know, are basically a statement about optimism for detecting aliens. And it's based on a whole bunch of parameters. And those parameters are always given to us without any uncertainty. And you as a scientist, and I know the most important thing are the systematic and statistical errors are simple. Systematic errors are hard. That's where the physics is. That's where the intuition comes in. That's where the craftsmanship comes in. So, you know, but in these questions, so you always get numbers like, oh, there's billions of, you know, civilizations in the universe, or there's none, depending on what you choose for your error bar. And likely too for AGI, it's such a nebulous thing. So, you know, people define in all different ways. I agree with you. I don't think it's true. But, but I think, you know, the Keating test, if I could be so, so bold, would be something like come up with a new law of physics, come up with a solution that that makes a prediction that can be testable and falsifiable that we can then say, this is never, this is truly new. It's not reproducing. It's not predicting. It doesn't have temperature dependent. So what would you say if you could have the Lacoon test instead of the, I think the Turing test was great for, you know, a hundred years ago. But the analogy with the Drake equation, the Drake equation is like, who is, who's talking to us? And the Turing test is like, who's listening to us? But I don't think that's sufficient. Either one of those. What would you say is a Lacoon test that you'd be comfortable with? Here's the bad news. I don't think there is any single test that would work. That's probably right. Because any, you know, area or sub-problems that you can formulate, there is probably a sort of specific solution towards, you know, solving that problem in sort of with superhuman performance. And we see this with computer. That's a history of computer science, right? Computers can, you know, calculate faster than humans, you know, now they can translate thousands of languages, right, in any direction. You know, can play chess, you know, a $30 gadget can beat you at chess, right? It can beat me at chess, definitely. You know, a lot of those tasks that we came up with, like games, we came up with them because they're hard for humans. And it turned out to not be that hard for machines, right? So like, you know, every search algorithm, like, you know, shortest path in a graph, things like that, that, you know, your GPS, your map software uses, your map application uses. Those are fairly simple algorithms and they have superhuman performance. So, you know, any particular application area you pick, there's going to be a specific specialized solution for it. And so no single test is going to test for intelligence. And what we're observing now is that people are being impressed by the fact that LLMs can manipulate language. And it turns out manipulating language is simple. It's much simpler than we thought. Okay. In fact, it has to be simple because it only popped up in evolution in the last, you know, a few hundred thousand years. And given the difference between the genomes of humans and chimpanzees or something like that, it only represents like a tiny portion of the genome, if anything, actually, maybe a tiny, tiny, tiny portion, maybe the equivalent of like a couple of megabytes of genomic information, which is really not that much. And in the brain, the whole language is handled by two tiny areas right here and right here, the Broca area and Verlique area, Broca area for producing language, Verlique area for more of our understanding. We get fooled into thinking those things are intelligent and generally intelligent because they behave a little bit like humans, but really they're very shallow. And we see this when, you know, we try to build systems that can accomplish very simple physical tasks, and it's just excruciatingly complicated. They really can't. I mean, I don't think we have a good solution, although, you know, there's progress being made in robotics and stuff like that because of machine learning, but we're still nowhere near where we need to be. Max has a second question. You can probably guess this, Jan. What is your plan for preventing loss of control over even smarter AGI? Okay. So Max and I disagree on this, right? And we've been on various panels discussing this issue. I also disagree with Geoff Hinton on this, right? We're friends, but we disagree on that question. So first of all, there is the sort of implied idea that if a system is intelligent, it wants to somehow take over or dominate, and that's just completely false. Not only is it false, it's not even true within the human species. You know, it is not the smartest among us who want to be the chief, right? We have examples on the political scene. That's right. We won't talk politics. The idea somehow that intelligence is necessarily associated with a desire to dominate is just false. To have a desire to dominate, or not a desire, but just even dominate by accident, there has to be some hardwire drive into the entity for competition, for resources, for example, or for influencing other entities to be able to profit from it or whatever. And this is a characteristic, the desire to dominate is a unique characteristic of social species within biology, right? So you have domination-submission behavior in baboons, you have them in chimpanzees, you have them in wolves and dogs. You have them in humans, although in humans there is another way to acquire status in human society, which is prestige, and you and I are both academics, so we're not influencing others by force, we're doing it by prestige, or at least we hope we do. But then take orang-utans, orang-utans are not social, they are solitary, they're territorial as a matter of fact, and they don't have any desire to dominate anybody, because they don't need to. So this idea that there is an intrinsic desire to dominate that is linked with intelligence is just false, it's just false. Okay, so now there is the question as to how do you make sure that the drives of an AI system, the objectives, are going to be aligned with human values, and those systems are not going to destroy us by deliberate action, but also not by accident. If you project, if you extrapolate the capabilities of LLM, then you might be entitled to be scared, because LLMs to some extent are intrinsically unsafe, now they're not very smart, so it doesn't matter, but they're not controllable in the sense that the way they produce an output is not by optimizing an objective, it's not by trying to figure out a sequence of actions that will satisfy an objective. They just produce one token after the other autoregressively without thinking very much. Right, but why do you say that's not safe? I mean, my toddler does the same thing, but has no power, right? There's no embodiment, there's no controllability network that could allow her to actually do something to me, even though she might be stochastically crazy, right? I mean, by an adult standard. She could be like, you know, sitting on your lap in front of your computer, and then sort of randomly slap on the keyboard and destroy your entire file system. Right, but not launch nuclear war, right? She's not going to... No, because she won't have that power, but people don't have that power either, and the AI system will not have that power either, unless we're very stupid with the way we build it. What your daughter has is a set of drives that were hardwired into her by evolution, some of which have to do with just exploring the world and things like that to learn how the world works, basically, but some of them are very specific, so there is a drive for kids around one year old to want to stand up, because that's the way you learn to walk, right? So they're happy when they stand up. That's hardwired. There are hardwired objectives. So I'm not talking about hardwired behavior. I'm talking about the fact that you touch the lips of a baby and the baby will start sucking, right? That's just hardwired behavior directly. That's just like a neural circuit that just does that. I'm talking about hardwired objectives. So things that you're driven to do, but nature doesn't tell you how to do that. Like eating, right? I mean, nature doesn't tell you how you find food.