Okay, we are joined by Dr. Yann LeCun, CEO of AMI Labs and former chief AI scientist at Meta. It's a huge honor to have you on the show.
It's a pleasure. Thanks for having me.
I appreciate you coming on the show. So, everyone has been discussing that AI could wipe out humanity or AI could be an existential risk to human beings. That idea has been discussed in Japan a lot as well. But you kind of deny the argument. So what's the biggest reason behind your view?
It's completely made up. There is no science behind it at all. You have people who claim that they can give some probability of a takeover of AI, and those numbers are completely pulled out of thin air. There is no argument behind it.
And it's based on an assumption that turns out to be false, which is that if an entity is intelligent, it will necessarily dominate less intelligent ones. That's not true.
It's not true in nature. A virus can take us down, and you know, it's not particularly intelligent. It's not even true within the human species. It's not the smartest people among us who want to be the leaders, right? We have examples of that on the international political scene.
That is clearly not the case. The smartest people I know, many of them want nothing to do with their fellow humans. They want to work on the problem that interests them. They're, you know, mathematicians or computer scientists or physicists. So this idea somehow that if an entity is intelligent it will necessarily dominate is just false.
This is fueled by science fiction. And the reason why we're talking a lot about this today is because there is a small number of people with a lot of money and probably too much time on their hands who are a little paranoid about this and are promoting this idea and funding a lot of organizations to kind of feed the fire, if you want.
Numerous safety research organizations.
Well, so there is an organization called Open Philanthropy. It changed name recently, but which is very active in funding think tanks and independent research groups that publish a lot about AI safety and, you know, come up with those elaborate scenarios of how AI could lead to human extinction. It's not scientific. Like the vast majority of the AI research community does not believe at all in those stories.
But I think that the reason why people are talking about it is because AI labs like OpenAI or Anthropic or people in those companies are very vocal about it. So why are they communicating in that way?
So Anthropic is an interesting case. Anthropic was initially funded by Open Philanthropy, and the chair of the board of Anthropic, who is the sister of Dario, is married to the co-founder of Open Philanthropy.
So it's the same people, and so Dario belongs to that community called effective altruism. He denies it, but he really does. That basically pays a lot of attention to this question, and so he's in a very difficult position because on the one hand he has to say that, you know, AI is potentially dangerous, but on the other hand he's actually building the most powerful AI systems today, right? And so his way of kind of reconciling those two views is to say AI is intrinsically dangerous. Therefore, it should not be given to everyone.
It should be heavily regulated and controlled. And to me that is a new form of obscurantism.
It's akin to the Ottoman Empire in the 15th century who banned the printing press so as to keep control of the dogma and also to preserve some corporations like the scribes. AI is a wonderful way of accessing knowledge and disseminating and accessing knowledge, and if you keep it under wrap, if you keep it under control of a few companies or a few governments in the world, you cannot take advantage of all of its benefits.
So you mean the idea of effective altruism like influences people to, you know, or that influences the company to produce monopoly, so that's why it influences industry.
This is Anthropic's argument.
OpenAI is a little more ambiguous in this question because they're not as focused on safety as Anthropic, and it's pretty clear to me that Sam Altman's thinking in those directions has evolved.
And you can see this by the fact that the group that was working on AI safety within OpenAI is basically more or less being disbanded or pushed out or deemphasized. So a lot of those people have left, and of course when they leave they make a lot of noise. They say, well, you know, those companies are reckless, but in fact it's just that those people were being toxic and not very useful.
So when you see the current AI from a scientific point of view, how smart is it? Like, is it so you obviously state that it's not obviously smart enough to like wipe out humanity. So how smart is it?
So current AI technology is not very smart at all in most domains. It's a good distillation of all the human knowledge, and so it looks smart to us because it has accumulated an enormous amount of declarative knowledge and can basically regurgitate it at the right time. And most people always ask the same question. There are at least two domains where AI is actually more creative in the sense that it can invent new solutions that humans hadn't figured out yet to problems. And that's mathematics and computer code.
And the reason for those two domains to be more advanced is because AI systems can refine themselves automatically. You can produce a proof of a theorem and you can verify that it's correct. If it's not correct, you can try again, right? And so the AI systems can kind of automatically improve themselves by doing this. Same with programs. The system can generate a program, run the program, and check that the program actually does what it's supposed to do. If not, generate another program and then, you know, eventually we'll find one that works, right? Using hill climbing or reinforcement learning.
So those are the two domains. There are other domains where you can completely simulate the situation where the system also can self-improve if you can simulate the situation. But that's pretty rare. So we're not going to get systems that improve themselves at the same rate when it comes to, I don't know, designing a new drug or something of that type that requires an actual experiment in the real world.
So yeah, everyone's talking about like the AI improving by itself, like AI development next to AI, like RSI, recursive self-improvement. Yeah. Is it like so from your point of view it's not happening yet?
Well, it's happening only in very narrow domains where evaluation of the system can be done completely automatically without human intervention.
Okay. And so that's basically computer code, mathematics, and maybe a couple of the domains where you can run simulation. So we don't have to be scared about it as, you know, as scared about it as Anthropic warns like publicly.
No, I think there is a lot of delusion in Silicon Valley, and this is not the first time this kind of delusion is happening in AI. The delusion that we have a new technique now and that new technique is going to bring us to human-level AI. It's false.
The current technology that we have is not going to bring us to human-level AI, and you can see this. Okay, we have systems that can pass the bar exam and do all kinds of really impressive things. Also, you know, we play chess and Go, they're not LLMs, but or compute the conformational proteins. But like, where is our domestic robot? Where is our level five self-driving car? You know, where is even the robot that can accomplish what a house cat can do?
Like we don't even have the basic methodology to build AI systems that understand the physical world as well as they can understand natural language or computer language, right? Or mathematical language. The real world is just very, very complicated and messy.
So you mean that what LLM is missing to be human-level AI is sort of an understanding of the real world?
Yeah, and it's very important in my opinion. It's a long debate in philosophy whether intelligence needs to be grounded in reality or whether it can be abstract. I think it needs to be grounded, and there is an interesting sort of set of numbers that you can cite. All of the publicly available text on the internet represents about 20 trillion words.
When you tokenize those words, that's about 30 trillion tokens. A token is about three bytes. So the total size is about 10 to the 14 bytes. A one with 14 zeros. That looks like a lot of information, and it is in text. If you and I were to read through this, it would take 400,000 years or something like that, right?
Now it turns out a four-year-old child has seen that amount of information through vision. Through vision, our optic nerves carry about one megabyte per second each, and we have two of them. So it's about two megabytes per second getting to the visual cortex.
And you do the arithmetic, a four-year-old has been awake a total of about 16,000 hours, and that's about 10 to the 14 bytes. And so it tells you that there is just way more information in natural signals like vision, touch, audition than there is in text. And that because of that, we're never going to get to human-level intelligence by just training AI systems on text.
Admittedly, modern AI systems are trained with sort of multimodal inputs. But they basically have special encoders for images and other things that are bolted on top of an LLM and basically turn all of the signals into sequences of discrete symbols, which is not a good way to handle reality.
So what about the OpenAI hacking incident back in July? And everyone's kind of scared of the AI becoming like a violent, like not under control of humans. So how did you see that? How dangerous did you see it?
Okay. So first of all, nothing really bad happened. Like, you know, Hugging Face is not suing OpenAI. Like, you know, the agents didn't destroy anything. They just penetrated the server they were not supposed to penetrate.
So are people overreacting?
So there is a lot of overreaction. People in cybersecurity certainly think that the landscape of cybersecurity is changing, but they're not overly kind of worried about like a complete change of R&D.
The big question is whether... Okay, so first of all, the reason why those agents kind of escaped is because the sandbox that they were supposed to be contained inside of was leaky. It was very badly designed, mostly because OpenAI is not at all experienced in cybersecurity.
So it was either deliberate or just by incompetence, if you want. But I mean, it's... It's a human error, and they should have, you know, designed the sandbox much better. But otherwise, the agents did exactly what they were asked to do. They were asked to basically penetrate another system.
They were supposed to do it within their sandbox, and they escaped that sandbox, and that was really bad design on the part of OpenAI.
So that's the first thing. The big question is whether the emergence of, you know, agents that can penetrate other systems gives an advantage to the defense or to the attack in cybersecurity. And I don't know the answer to that, but my hunch is that it actually gives an advantage to the defense.
Because now we can use those systems when we build software, we can use the power of those AI systems to build software that is, you know, more secure and more reliable from the start, and then we can, you know, use them to attack our own software and solidify it before we release it. So I think we're going to enter an era where software is going to be actually way more secure than it used to be, thanks to AI.
Okay, but we've seen the recent like series of cyber attacks like everywhere in the world. In France, we just seen like a cyber attack to the supermarket refrigeration system. I think you saw it, and we've been talking about it in Japan for the past week or two. We're kind of scared about it right now, but how serious? So all the cyber attacks seem to be supported by AI. So how serious is this issue? Are you seeing...
I mean, it's serious in the sense that, you know, cybersecurity, like, you know, every area of activity around security is a game of cat and mouse, right? So someone comes up with a new attack technique, and then there's a response by the defense, and then the defense, you know, wise up a little bit, so the next generation system is more secure, and etc. I mean, we saw that in the early days of the internet, right? I mean, initially websites were not encrypted. Like, you would go to a website, but like the communication was not encrypted, you know, HTTP instead of HTTPS, right?
Initially it was kind of, you know, difficult to authenticate a person to sign up on a website, and for a number of years it was insecure. For a number of years, personal computers, particularly under Windows, were easy prey for attacks to online services because they were leaky, right? They were just badly designed, and they got fixed eventually, right? So I mean, I think we're going to see a transition period where, you know, a lot of systems are going to be fixed and solidified to resist those attacks.
Yeah. Okay. I want to talk about Meta before we go into what you do now at AMI Labs. But why did you leave Meta after 12 years of research there?
