Yann LeCun (4:45)

Well, I mean, I worry about many things or I'm interested in a lot of questions. I'm sort of politically pretty clearly a classic liberal, which on the European political spectrum puts me right in the center. Not in the U.S. no. And what got me riled up with Elon was when he started sort of attacking the institutions of higher learning, of science and scientists like Anthony Fauci and things like this. And I'm a scientist, I'm a Professor as well as an executive at Meta. And I have a very independent voice and I really appreciate the fact that at Meta I can have an independent voice. I'm not doing corporate speech, as you can tell. That tells you something about, I think, how the company is run. And it's also reflected in the fact that in the research lab that I started at Mita, we publish everything we do, we distribute our code in open source. We're sort of very open about things and about our opinions as well. So that's the story.

Yann LeCun (6:07)

Now he's at the red hot center of things. How are you going to cope with that going forward?

Yann LeCun (6:12)

Well, I mean, I met Elon a bunch of times is, you know, it can be reasonable. I mean, you have to work with people, right? Regardless of disagreements about, you know, political or philosophical opinions, at some point, you have to work with people. And that's what's going to happen between. I don't do policy at Meta. Right. I'm a.

Yann LeCun (6:31)

You don't.

Yann LeCun (6:31)

That's right. I work on fundamental research. Right. I don't do content policy. I don't do any of that. I talk to a lot of governments around the world, but mostly about AI technology and how it impacts their policies. But I don't have any influence on the relationship between Meta and the political system.

Yann LeCun (6:55)

I'm curious why you then didn't. Why you went to a place like Meta versus in the old days, you would have been at a big research university or somewhere else. How do you look at your power then? What is your influence? I mean, you're sort of saying, I'm just a simple scientist making, you know, making things okay.

Yann LeCun (7:11)

I'm also an academic. Right. I'm a professor at NYU and I've kept my position at NYU. When Mark Zuckerberg approached me 11 years ago, almost to the day, he asked me to kind of create basically a research lab in AI for Meta, because he had this vision that this was going to have a big impact and a big importance. He was right. And I told him I only have three conditions. I don't move from New York, I don't quit my job at nyu. And all the research that we're going to do, we're going to do it in the open. We're going to publish everything we do and we're going to open source our code. And his answer was yes. Yes. And the third thing is, you don't have to worry about this. It's in the data of the company. We already open source all of our platform code and so, yeah, there's no problem with that. And this is not an answer. I would have had anywhere else that would have had the resources to create a research lab. And I had the opportunity there. I was given the opportunity basically to create a research organization in industry from scratch and basically shape it the way I thought was proper. I had some experience with this because I started my career at Bell Labs. So I had some experience with sort of how you do real ambitious research in industry. So I thought that was the most exciting challenge.

Yann LeCun (8:37)

So Trump recently named David Sachs as the AI and crypto czar. For those who don't know, Sachs is an investor and part of the PayPal mafia. Also a longtime friend of Elon Musk's. He's shifted his politics pretty dramatically. Talk about, is there a need for that right now in Washington? As someone who's doing this research, or do you not care whatsoever? Does it matter to you? Is it important that the government do something like this?

Yann LeCun (9:03)

Oh, absolutely.

Yann LeCun (9:03)

And tell us why?

Yann LeCun (9:05)

Well, there's a number of different things. The first thing is to not make regulations that make open source AI platforms illegal, because I think it's essential for the future of not just the progress of technology, but also the way people use them to make it widely disseminated and everything. So that's the first thing. The second thing is, and by the way, there is no problem regulating products that are based on AI. That's perfectly fine. I'm not anti regulation or anything. The second thing is the academic world is falling behind and has a hard time contributing to the progress of AI because of the lack of computing resources.

Yann LeCun (9:48)

Correct.

Yann LeCun (9:48)

And so I think there should be resources allocated by the government to give computing resources to academics.

Yann LeCun (9:56)

To academics. Now, this is, as you said, it's shifted rather dramatically because academics is where a lot of the research, the early computing research, was done. And now it's moved away from that. Andrew Ferguson has been tapped to head the ftc. Former Fox News anchor Pete Hegseth is nominated. Defense Secretary Ferguson seems to want to roll back any attempts to be a regulator. Is this important for government to be more active in this area?

Yann LeCun (10:21)

It's certainly important for the government to be more informed and educated about it. But I mean active, certainly for the reasons that I said before, because there's probably an industrial policy to have all the chips that enable AI at the moment are all fabricated in Taiwan designed by a single company. You know, there's probably something to do there to sort of maybe make the landscape a little more competitive or for chips for Example for chips, for example. And there's another question I think that's really crucial also, and that has consequences not just for the US government, but governments around the world, which is that, you know, AI is quickly going to become a kind of universal knowledge platform, basically the sort of repository of all human knowledge. But that can only happen with free and open source platforms that are trained on data from around the world. You can't do this within the walls of a single company on the west coast of the US you can't have a system speak all 700 languages of India or however many there are. So eventually those platforms will have to be trained in a distributed fashion with lots of contributors from around the world, and it will need to be open.

Yann LeCun (11:48)

So I know you worry about premature regulation stifling innovation, but you signed an open letter to President Biden against his AI executive order. Talk about why you did that. More broadly, what role you think the government should play. Exactly.

Yann LeCun (12:01)

So I think there were plenty of completely reasonable things in that executive order. Similarly in the EU AI act, like for protection of privacy and things like this, which make complete sense. What really sort of I disagree with both in the EU AI act in its original form and in the executive order is that there was a limit established where if you train a model with more than 10 to the 24th, 10 to the 25th flop, you have to basically get a license from the government or get authorization of some kind, based again on the idea that AI is intrinsically dangerous, that, you know, above a certain level of sophistication is intrinsically dangerous. And I completely disagree with this approach. I mean, there are, you know, important questions about AI safety that need to be discussed. But a limit on computation just makes absolute sense.

Yann LeCun (12:59)

Makes no sense. Recently, many big tech companies rolled out either LLM updates or new AI agents or AI features. I want to get an overview view of what you're doing at Meta right now. It's a little different. You released llama 3.3 is the latest update that powers meta. I talk about what it does, and I'm going to ask you to compare it to other models out there and be honest. Like, how good is it compared? How do you look at that?

Yann LeCun (13:27)

Scientists need to be honest. I mean, the main difference between LLAMA and most of the other models is that it's free and open, right?

Yann LeCun (13:35)

Open source. So technically explain to people who may not understand what that means.

Yann LeCun (13:41)

Okay, so open source software is software that comes to you with the source code, so you can modify it, compile it yourself, you can use it for free. And in most licenses, if you make some improvement to it and you want to use it in the product, you have to release your improvement as well in the form of source code. So that allows platform style software to progress really quickly. And it's been astonishingly successful as a way to distribute platform software over the years. The entire Internet runs on open source software. Most computers in the world run on Linux. Almost all computers in the world run on Linux, in your car, in your WI FI router. So that's incredibly successful. And the reason is it's a platform. People need to be able to modify it, make it safer, more secure, et cetera, make it run on various hardware. That's what happens. And it's not by design, it's just the market forces naturally push the industry to pick open source code when it's a platform. Now, for AI, the question of whether something is open source is complicated because when you build an AI system, first of all, you have to collect training data. Second, you have to train what's called a foundation model on that training data. Okay? And the training code for that and the data generally is not distributed. So Meta, for example, does not distribute the training data, nor the training code, or most of it. For the Lamar models, for example. Okay. Then you can distribute the trained foundation model, and that's what LLAMA is. And it comes with open source code which allows you to run the system and also fine tune it anywhere you want. You don't have to pay meta, you don't have to ask questions, you don't have to ask meta. You can do this. There are some limits to this that are due to the legal landscape, essentially.

Yann LeCun (15:46)

So why is that better? You make the argument that all the others are not. They're closed systems, they develop their own thing.

Yann LeCun (15:52)

There are a few other open.

Yann LeCun (15:54)

Right, but the big.

Yann LeCun (15:55)

But the big ones are closed. Yeah, the ones from OpenAI, Anthropic and Google are closed.

Yann LeCun (16:02)

Why did they choose that from your perspective?

Yann LeCun (16:05)

Well, quite possibly to get a commercial advantage. If you want to derive revenue directly from a product of this type and you think you are ahead technologically, or you think you can be ahead technologically and your main source of revenue is going to come from those services, then maybe there is an argument for keeping you closed. But this is not the case for meta. For meta, AI tools are part of kind of a whole set of experiences of experiences which are all funded by advertising. And so that's not the main source of revenue. On the other hand, what we think is that the platform will progress faster. In fact, we've seen this with Lamar.

Yann LeCun (16:55)

Be more innovative because it's more innovative.

Yann LeCun (16:57)

There's a lot of innovations that we would not have had the idea of or we didn't have the bandwidth to do. That people have done because they had the LLAMA system in their hands and they were able to experiment with it and sort of come up with new ideas.

Yann LeCun (17:09)

So one of the criticisms is that you were behind and this was your way to get ahead. How do you address that? I've heard that from your competitors.

Yann LeCun (17:17)

So there's an interesting history to all of this. Right. So first of all, you have to realize that everyone in the industry except Google to build AI system uses open source software platform called Pytorch, which is mostly developed, which was originally developed at Meta. Meta transferred the ownership of it to the Linux Foundation. So now it's not owned by Meta anymore, but OpenAI, Anthropic, everybody uses Pytorch. So without Meta there would not be ChatGPT and Claude and all of those things, or not to the same extent that they are today. There has been developments. The underlying techniques that are used in tools like ChatGPT were invented in various places. OpenAI made some contributions back when they were not secretive. Google certainly made some.

Yann LeCun (18:11)

I like how you just put that in there. When they were not secretive, when they.

Yann LeCun (18:14)

Were not secretive, because it became secretive. Right. They kind of clammed up in the last three years or so. Google climbed up too, to some extent. Not completely, but they did. And Anthropic has never been open. So they sort of tried to push the technology in secret. I think we are perhaps at Meta. We're a pretty large research organization and we also have an applied research and advanced development organization called Genai. The research organization is called FAIR. That used to mean Facebook AI research. Now that means fundamental AI research. And it's about 500 people. And what we're working on is really sort of the next generation AI system. So beyond LLMs, beyond large language models, beyond chatbots. There was this idea by some people in the past that you take LLMs like the ChatGPT meta area, Gemini of the world, and you just scale them up, train them on more data, with more compute, and somehow sort of human level intelligence will emerge from it. And I never believed in this concept, right.

Yann LeCun (19:18)

We've reached the end and there's no more data, right?

Yann LeCun (19:20)

And it's pretty clear that we are reaching kind of a ceiling in the performance of those systems because we basically run out of natural data. Like all the text that's publicly available on the Internet is currently being used to train all those LLMs, and we can get much more than that. So people are kind of generating synthetic data and things like this, but we're not going to improve this by a factor of 10 or 100. Right. So it's hitting a saturation. And what we're working on is basically the next generation AI system that is not based on just predicting the next word. So an LLM is called a large language model because it's basically trained to just predict the next word in a text. You collect typically something like 20 trillion words, something of that order. That's all the publicly available text on the Internet with some filtering, and you train some gigantic neural net with billions or hundreds of billions of tunable parameters in it to just predict the next word. Given a sequence of a few thousand words, can you predict the next word that will occur? You can never do this. Exactly. But what those systems do is that they predict basically, a probability distribution over words, which you can use to then generate text. Now, there's no guarantee that whatever sequence of words is produced makes sense, doesn't generate confabulations or make stuff up. Right. So what a lot of the industry has been working on is basically fine tuning those systems, training them with humans in the loop to train them to do particular tasks and not produce nonsense, and also to kind of interrogate a database or search engine where they don't actually know the answer. And so you have to have systems that can actually detect whether they know the answer or not, and then perhaps generate multiple answers and then pick which ones are good. But ultimately, this is not how futuristic will work.

Yann LeCun (21:18)

So talk about that. Last week, Meta released Meta Motivo. It's made to make digital avatars that seem more lifelike, because I understand. I feel like it's Mark trying to. To bring the metaverse and make it happen again. But talk about how it's what it is. I don't quite understand it because there's a lot of money you're all investing in all these things.

Yann LeCun (21:39)

Yeah.

Yann LeCun (21:39)

A lot of money to make something that people would want to buy, Right. Not just to make better advertising. You've got to have a bigger goal than that.

Yann LeCun (21:46)

Okay, I'll let you in on the secret. I'm wearing smart glasses right now, right?

Yann LeCun (21:50)

Yes. I have a pair myself.

Yann LeCun (21:51)

It's got. It's pretty cool, right? It's got cameras. If you're smart, I can take a picture of you guys.

Yann LeCun (21:55)

Yeah, yeah. This is how Far we've come. I had one of the first pairs of Google Glass, but it's a low bar from that. Go ahead.

Yann LeCun (22:05)

Now, here's the thing. Eventually we'll be working around. We're talking five, ten years from now, we'll be working around with smart glasses, perhaps other smart devices, and they will have AI assistance in them. This one has one. I can talk to Meta AI through this, right. And those things will be sort of assisting us in our daily lives. And we need those systems to have essentially human like intelligence, human level intelligence, or perhaps even superhuman intelligence in many ways. And now how do we get to that point? And we're very far from that point. Some people are kind of making us believe that we are really close to what they call AGI, artificial general intelligence. We're actually very far from it. I mean, when I say very far, it's not centuries, it may not be decades, but it's several years. And the way you can tell is that the type of task we have LLMs that can pass the bar exam or pass some college exam or whatever. But where is our domestic robot that cleans the house and clears up the dinner table and fills up the dishwasher? We don't have that. And it's not because we can't build the robots. We just cannot make them smart enough. We can't get them to understand the physical world. Turns out the physical world is much harder for AI systems to understand that language. Language is simple. I mean, it's kind of counterintuitive for humans to think that we think language is the pinnacle of intelligence. It's actually simple because it's just a sequence of discrete symbols. We can't handle that the real world, we don't. So what we're working on basically are kind of new architectures, new systems that understand the physical world and learn to understand the physical world the way babies and young animals do it, by basically observing the world and acting in it. And those systems will eventually be able to plan sequences of actions so as to fulfill a particular goal. And that's what we call agentic. Right? So an agentic system is a system that can plan a sequence of actions to arrive at a particular result. Right now, the agentic systems that everybody talks about don't actually do this planning. They kind of cheat a little bit. They kind of learn templates of plans.

Yann LeCun (24:20)

Right. But they can't do this. You're also working on the information. Just reported Meta is developing AI search engine. So that. Well, I assume you want to best Google search Is that true and do you think that's important?

Yann LeCun (24:33)

Well, a component of an intelligent assistant that you want to talk to is search, obviously is search. You want to search for, for facts. Right. And link to the sources of that fact so that the person you talk to kind of trusts the results. So search engine is a component of an overall complete AI system and an.

Yann LeCun (24:54)

End run around the Google system, presumably?

Yann LeCun (24:57)

Well, I mean, the goal is not necessarily to compete with Google directly, but to serve people who want an AI assistant.

Yann LeCun (25:05)

So what do you imagine it's going to be for? Because most people perceive that Meta was lagging in the AI race, especially with all the hype around ChatGPT. But Mark Zuckerberg just said it had nearly 600 million monthly active users and on track to be the most used AI globally by the end of the year. It's very different from what people are doing on ChatGPT, which is a standalone app or with search. So what is it for for you, besides to make advertising more efficient? I know Mark has talked about that, but from your perspective and Meta's perspective, what is it for Meta, what does it mean for Meta?

Yann LeCun (25:41)

It is that vision of the future where everyone will have an AI assistant with them at all times and it's going to completely. I mean, it's a new computing platform, right? I mean, before we used to call this a metaverse, but I mean those glasses eventually will have displays, augmented reality displays. I mean, there's already demonstrations of this with the Orion project that was shown recently. We can build them cheap enough right now so we can sell them yet, but eventually they'll be there. So it's that vision, that long term vision.

Kara Swisher (26:15)

So to be our helper, our agent, daily helper.

Yann LeCun (26:18)

I mean, it's like everyone will work around with a virtual assistant, which is like a human assistant basically, or eventually like a staff of really smart people, maybe smarter people than you working for you.

Yann LeCun (26:28)

That's great. But right now Meta is forecasting spend between $38 billion and $40 billion. Google says it's going to spend more than 51 billion it's spent this year. Analysts predict Microsoft's spend will come close to 90 billion. Too much spending. Marc Benioff recently told me it was a race to the bottom. Are you worried about being outspent? And it should. To get me a smarter assistant doesn't seem to be a great business. But I don't know. I didn't take the job at Facebook when I was offered it in the early days, so don't ask me but go ahead.

Yann LeCun (27:00)

Well, it's a long term investment. I mean, you need the infrastructure to be able to run those AI assistants at reasonable speed for a growing number of people. As you said, there is 600 million people using Meta AI right now. By the way, there's another interesting number, the open source engine Llama, on top of which Meta AI is built, but which is open source, has been downloaded 650 million times. That's an astonishing number. I don't know who are all these people, by the way, but that's an astonishing number. There are 85,000 projects that have been derived from llama that are publicly available, all open source, mostly in parts of the world. A lot of those projects are basically training llama, for example, to speak a bunch of languages from Senegal or from India.

Yann LeCun (27:56)

So you don't think this money is ill spent?

Yann LeCun (27:58)

No, I don't think so. Because there's going to be a very large population who will use those AI systems on a daily basis within a year or two and then growing. And then those systems are more useful if they're more powerful. And they're more powerful they are, the more expensive they are computationally. So this investment is investment in infrastructure.

Yann LeCun (28:18)

In infrastructure. What's happening by private companies now? You said the concentration of proprietary AA models in the hands of just a few companies was a huge danger. Obviously there's also been critics of the open source model. They worry about bad actors, could use them to spread disinformation, cyber warfare, bioterrorism. Talk about the difference. Does META have a role in preventing that happening, given you're handing these tools, these powerful tools in an open source method?

Yann LeCun (28:48)

Okay, so this was a huge debate. It was, you know, just until fairly recently, you know, the early 2023 when we started distributing llama. The first llama was not open source. You had to ask permission and you had to show that you were a researcher. And it's because, you know, the legal landscape was uncertain and we didn't know what people were going to do with it. So. So it wasn't open source. But then all of us at Meta received a lot of requests from industry saying like, you have to open source the next version because this is going to create a whole industry. It's going to enable a lot of startups and kind of new products and new things. And so we had a big internal discussion for several months, internally, a weekly discussion, two hours with 40 people from Mark Zuckerberg down, okay, very serious discussions about this, about safety, about legal landscape, about all kinds of questions. And then at some point the decision was made by Mark to say, okay, we're going to open source llama 2, tell me how to do it. And that was done in summer 2023. And since then it's basically completely jump started a whole industry.

Yann LeCun (30:03)

Why is it more safe than these proprietary models that are controlled by the companies?

Yann LeCun (30:08)

Because there are more eyeballs on it. And so there are more people kind of fine tuning them for all kinds of things. And so there was a question as to, you know, maybe a lot of badly intentioned people will put their hands on it and then will use them for nefarious purpose.

Yann LeCun (30:22)

Well, Chinese researchers developed an AI model for military use with an older version of metislama model as a backbone.

Yann LeCun (30:27)

It's actually kind of a very kind of minor bad things. And you could have used one of the many excellent open source Chinese models that's really good, which is on par with the best. So I mean the Chinese have good research, good engineers, they open source a lot of their own models. This is not.

Yann LeCun (30:52)

You don't think that's Meta's responsibility? You put it out there, the tools and then what people do with it?

Yann LeCun (30:57)

No, it is to some extent, of course. So there is a big effort in the Llama team, in the Genai organization to red team all the systems that we put out so that we ensure that they are, at least when they come out, are minimally toxic and things like that and mostly safe. That's a really important effort actually. We even initially gave Llama 2 to a bunch of hackers at Defcon and asked them, tried to do something bad with it and the result is we haven't been aware of anything really bad done with any of the models that we've been distributing over the last almost two years.

Yann LeCun (31:43)

Yeah would be the word. I would put that behind.

Yann LeCun (31:44)

Well, yeah, but you know, it would have happened already. I mean there have been, you know, the public doesn't realize this because they think it just appeared with ChatGPT, but there have been LLMs, open source LLMs available for many years before that. And I don't know if you remember this, but when OpenAI came up with GPT2 they said, oh, we're not going to open source it because it's very dangerous. So people could do really bad things, they could flood the Internet with disinformation and blah blah, blah. So we're not going to open source. I made fun of them because it was kind of ridiculous at the time. The capabilities of the system really was not that bad. And so, I mean, you have to accept the fact that those things have been available for several years and nothing really bad has happened. There was some, a bit of worry that people would use this for disinformation in the run up of the elections in the US and all kinds of things like this, cyber attacks and things. None of that really has happened.

Yann LeCun (32:48)

It's still good to be worried about such things.

Yann LeCun (32:50)

Well, I mean, you have to be watchful and do what you can to prevent those things from happening. The point is you don't need any of those AI systems for disseminating disinformation, as Twitter has shown us.

Yann LeCun (33:04)

Okay, good there.

Kara Swisher (33:05)

Good.

Yann LeCun (33:05)

I like how you get your little digs in. I'm watching it very carefully. You did an Elam one. The secretive drama queens of OpenAI. I got that. So you also get a lot of flack online recently for saying that cultural institutions, libraries, foundations should make their content available for training by free and open AI foundation models like llama. Presumably you were responding to a new data set that Harvard released made up of over a million books. But those are public domain works, not works by living authors, artists, academics. Talk about the concerns and the flak you got about these AO models vacuuming up all of our cultural knowledge from the creators, writers, researchers, without getting any credit. I mean, Internet companies are known for scraping. I think Walt called, I believe it was, when it used to be called Facebook, rapacious information thieves. But he may have been talking about Google. So talk to me about that, the controversy that happened with that.

Yann LeCun (33:58)

Okay. Outside of all of those kind of legal questions, if you have this vision that AI is going to be the repository of all human knowledge, then all human knowledge has to be available to train those models. Right. And most of it is either not digitized or digitized, but not available publicly. And it's not necessarily copyrighted material. It could be the entire content of the French National Library, a lot of which is digitized but not available for training. So I was not necessarily talking about copyrighted work in that case. It's more like if you are in. So I'm from my family. My father's family is from Brittany, the western part of France. Right. The traditional language spoken there, which was spoken until my great grandfather, is Breton. Breton is disappearing. There is something like 30,000 people speaking it on a daily basis, which is very small. If you want future LLMs to speak Breton, there needs to be enough training data in Breton. Where are you going to get that? You're going to have cultural nonprofits kind of collecting all the stuff that they have, maybe governments helping, things like that. And they're going to say use my data like I want your system to speak. Bottom now they may not want to just hand that data just like that to, you know, big companies on west coast of the us But a future that I envision. This is not company policy. Right. This is my, my view is that the, the best way to, to get to that level is by kind of training an AI system, a common AI system repository of human knowledge in a distributed fashion. So that there would be several data centers around the world using local data to contribute to training a global system. And you don't have to copy the data.

Yann LeCun (35:54)

But who runs that global system?

Yann LeCun (35:57)

Who writes Linux?

Yann LeCun (35:59)

Okay, right. So that should exist for all of humanity.

Yann LeCun (36:03)

Yeah. I mean who pays for Wikipedia? Right?

Yann LeCun (36:06)

I pay $7 a month, but go ahead, good idea.

Yann LeCun (36:09)

Or the Internet Archive.

Yann LeCun (36:10)

Right.

Yann LeCun (36:11)

So for Linux, in the case actually Linux is mostly supported by employees of companies who tell them to actually distribute their contributions. You can have kind of a similar system where everyone contributes to this kind of global model. That's AI for everybody else, which is AI for AI. LLMs in the short term.

Yann LeCun (36:35)

Monetizable. Yeah.

Yann LeCun (36:37)

Well you monetize on top of it, right? I mean Linux, you don't pay for Linux. But if you buy a widget that runs Linux, like an Android phone or a car that has Linux in its touchscreen, you pay for the widget that you buy. So it's going to be the same thing with AI that people can do. The basic foundation model is going to be open and free.

Yann LeCun (36:56)

It does feel like that. It's a coalescing of small amount of powers running everything it does at this point. And that vision is a lovely one, but it's not occurring.

Yann LeCun (37:05)

Right. Well, my opinion is actually inevitable.

Yann LeCun (37:09)

You've been in a public debate. You like to debate with other godfathers of AI. Your Turing Award co winners, Geoffrey Hinton and I think it's Yoshua Bengio.

Yann LeCun (37:18)

Yep.

Yann LeCun (37:19)

They've both been ringing alarm bells, warning about the potential dangers of AI quite dramatically I would say. They've called for stricter government regulation, oversight, including R and D. You've called their warnings complete bs. I don't think you mince words there. Talk to me about why that's complete bs. And one of the things you disagreed was one of the first attempts at AI regulation here in the US, California Bill SB 1047. Hinton and Bengio both endorsed it. You lobbied against it. You wrote regulating R and D would have apocalyptic consequences on the AI system. Very dramatic of you, sir. You said the illusion of existential risk is being pushed by a handful of, quote, delusional think tanks. These two aren't delusional. I don't believe Hinton just won the Nobel Prize for his work. Talk about that in particular. And by the way, Governor Newsom vetoed the bill, but is working with people like Stanford Professor Faye Feyley to overhaul it. Talk about why you called it complete bs. You're very strong on this.

Yann LeCun (38:18)

I'm very vocal about that, yes. So Jeff and Joshua are both good friends. We've been friends for decades. I did my postdoc in 1987. 88 with Geoff Hinton. So we've known each other for a very long time. For 40 years now. Same with Yoshua. I met him the first time. He was a master's student and I was a postdoc. So we've been kind of working together. We won this prize together because we worked together at sort of reviving interest in what we now call deep learning, which is a root of a lot of AI technology today. So we agree on many things. We disagree on a few things, and that's one of them.

Yann LeCun (38:58)

The existential threat to the human existential threat.

Yann LeCun (39:02)

Exactly. So, Jeff, you're like, ah, no, they're.

Yann LeCun (39:06)

Like, oh, yeah, they're coming for us.

Yann LeCun (39:08)

I mean, Jeff believes that current LLMs have subjective experience. I completely disagree with this. I think he's completely wrong about that. We've disagreed on technical things before. It was kind of less public. It was more kind of technical. But it's not the first time we disagree. I just think he's wrong. We're still good friends. Yashra comes from a slightly different point of view. He's more worried. He's worried a little bit about this, but he's more worried about bad people doing bad things. With AI systems. Yeah, I'm with him developing bioweapons or chemical weapons or things like this. I think, frankly, those dangers have been formulated for several years now, and they've been incredibly inflated to the point of being kind of distorted so much that really, they don't make any sense. Yes.

Yann LeCun (40:00)

Delusional is the word you use.

Yann LeCun (40:01)

Well, I don't call them delusional. I call some of the other people who are more extreme and are pushing for regulation, like SB 1047. Yes, delusional. I mean, some people will tell you in the face a year ago, you ask them, like, how long is it going to take for AI to kill us? All and they say like five months, right? And obviously they were wrong.

Yann LeCun (40:24)

So this is what you're talking about. It's over. AGI, artificial general intelligence and how close we are. I would like you to explain it for people. When they hear it, they think about the plot of Terminator or iRobot or something like that. So Hinton and Bengio think the timeline for AGI could be more like five years and that we are not prepared. You've said several years, if not a decade. If you're wrong, you're going to be real wrong when it does kill us. So talk about why. You'll be like, oh, we're not dead yet, and then we're dead. So talk about why you're not worried.

Yann LeCun (40:58)

So first of all, there's no question that at some point in the future we're going to have AI systems that are smarter than us. Okay, it's going to happen. Is it 5 years, 10 years, 20 years? It's really hard to tell. In our kind of, or at least my personal vision of it, the earliest it could happen is about five years, six years, but probably more like 10, and probably longer because it's probably harder than we think. And it's almost always harder than we think. There is this history over the several decades of AI, of people sort of completely underestimating how hard it is. And again, we don't have automatic robots. We don't have level 5 self cars. There's a lot of things that we don't know how to do with AI systems today. And so until we figure out kind of a new set of techniques to get there, we're not even on a path towards human level intelligence. So a few years from now, once we have kind of a blueprint and some kind of believable demonstration that we might have a path towards human level AI. I don't like to call it AGI because human intelligence is very specialized, actually. So we think we have general intelligence, we don't. So once we have a blueprint, we're going to have a good way to think about how to make it safe. It's kind of like if you kind of backpedal to the 1920s and someone is telling you, in a few decades we're going to be flying millions of people across the Atlantic at near the speed of sound, and someone would say, like, oh, my God, how are you going to make this safe? The turbojet was not invented yet. How can you make turbojet safe if you haven't invented a turbojet? We are in this situation Today. So making AI safe means designing those AI systems in ways that are safe, but until we have a design, we're not going to be able to make them safe. So the question makes no sense.

Yann LeCun (42:55)

You don't seem worried that AI would ever want to dominate humans. You've said that current AI is dumber than a house cat. Whether AI is sent or not doesn't seem to matter if it feels real. Right? And so how do you. If it's dumb or it doesn't want to dominate us or it doesn't want to kill us, what would be restrictions on AI and maybe AI, R&D that you would seem reasonable, if any. I think if none is what you're saying to me.

Yann LeCun (43:22)

Well, none on R and D. I mean, clearly, if you want to, you know, put out a domestic robot and that robot can cook for you, you probably want to hardwire some rules so that when there's people around the robot and the robot has a knife in his hand, he's not going to flirt his arm around or something. So those are guardrails. So the design of current AI systems to some extent is intrinsically unsafe. You could say it this way. A lot of people admit are going to hate me for saying this, but they're kind of hard to control. You basically have to train them to behave proper what you want. And this is something I've proposed is another type of architecture which I call objective driven, where the AI system basically is there to fulfill an objective and cannot do anything but fulfill its objective, subject to a number of guardrails which are just other objectives. And that will guarantee that whatever output the system produces, whatever action it takes, satisfy those guardrails and objectives and are safe. Now the next question is, how do we design those objectives? And a lot of people are saying, oh, we've never done this before. This is completely new. We're going to have to kind of invent a new science. No, actually, we're pretty familiar with this. It's called making laws. We do this with people, we establish laws, and the laws basically change the cost of taking actions. Right? And so we've been shaping the behavior of people by making laws. We're going to do the same for AI systems. The difference is that people can choose to not respect at all, whereas the I system by construction will have to.

Yann LeCun (45:05)

Now, both these people, Hinton and Bengio, endorsed a letter signed by current and former OpenAI employees calling employees at AI companies have the right to warn about serious risks by the technologies and ordinary whistleblowers wouldn't protect them. You didn't endorse it. At the same time, we've seen some regulation in the eu. They differentiate between high risk AI systems and more general purpose models. They have bans on certain applications that quote, threaten citizens rights. Facial images, I suppose. This robot who wants to knife you. What is the model here to make it safer, to make people. You're suggesting we wait and see when bad things happen before putting up guardrails? Let's wait till there's some murder happening or not, I can't tell.

Yann LeCun (45:48)

No, no, that's not what I'm suggesting. I mean measures like banning massive face recognition in public places, that's a good thing. Nobody would really think that's a bad thing. Except if you are an authoritarian government.

Yann LeCun (46:03)

Yes, some people think it's a great thing.

Yann LeCun (46:04)

Yeah, it already exists in some countries actually, but that's a good thing. And there are measures like this that make complete sense, but they are at the product level. Also changing the face of someone on some embarrassing video and stuff like that, I mean it's kind of already legal, more or less. The fact that we have the tools to do it doesn't make it less illegal. There may be a need for specific rules against that, but I have no problem with that. I have a problem with this idea that AI is intrinsically dangerous and you need to regulate R and D. And the reason I think it's counterproductive is in a future in which you would have those open source platforms I was talking about, which I think are necessary for things like democracy in the future, then those rules would be counterproductive. They would basically make open source too risky for any company to distribute and so would kind of kill.

Yann LeCun (47:06)

So private companies will control everything?

Yann LeCun (47:08)

That's right. A small number of private companies on the west coast of the US would control everything. Now talk to any government outside the US and tell them about this future where everyone's digital diet will be mediated by AI assistance and tell them that this will come from three companies on the west coast of the US and they say that's completely unacceptable. This is the death of a democracy. How will people get a diversity of opinions if it all comes from three companies on the West coast of the U.S. we all have the same culture, we all speak the same language. This is completely unacceptable. So what they want are open platforms that then can be fine tuned for any culture, value system, center of interest, whatever, so that users around the world have a choice. They don't have to use like three assistants, they can use you Know the.

Yann LeCun (48:02)

So you're worried about domination by OpenAI, Microsoft, Google, possibly Amazon.

Yann LeCun (48:08)

Anthropic.

Yann LeCun (48:09)

Anthropic, which is Amazon really. So, last two questions, you were awarded the 2024 VIN Future Prize. There's so prizes in your area. I never get any prizes for transformational contributions to deep learning. In your acceptance speech you said AI does not learn like humans or animals, which take in a massive amount of visual observation from the physical world. But you've been working to make this happen. You've been talking about it a while. Where do you imagine it being in years? Will it be like humans or animals or where?

Yann LeCun (48:38)

Well, so, yeah, I mean, there's a point at which we're going to have systems that learn a little bit like humans and animals and can learn new skills and new tasks as efficiently as humans and animals, which is frankly astonishingly fast. Like, we can't reproduce this with machines. Right. We have, you know, companies like Tesla and others have hundreds of thousands or millions of hours of cars being driven by people. They could use this to train AI systems, which they do. They're still not as good as humans. We don't have. Yeah, we can't buy a car that actually drives itself or a robo taxi unless we cheat like Waymo can do it, but it's a lot of tricks to it. And again, we can't buy a domestic robot because we can't make them smart enough. The reason for this is very simple. As I said before, we train LLMs and chatbots on all the publicly available text and some more. That's about 20 trillion words. A 4 year old has seen essentially the same amount of data visually than the biggest LLM has seen through text. That text would take any of us several hundred thousand years to read through. Okay, so what that tells you is that we're never going to get to human level AI by just training on text. We have to train on sensory input, which is basically an unlimited supply. 16,000 hours of video is 30 minutes of YouTube uploads. Okay? We have way more video data than we know what to do with. So the big challenge for the next few years in AI to make progress to the next level is get systems to understand how the world works by basically watching the world go, by watching video and then interacting in the world. And this is not solved. But there's a good chance that progress will be made, like significant progress will be made over the next five years, which is why you see all of those companies starting to build human aid robots. They can't make Them smart enough yet, but they're counting on the fact that AI is going to make sufficient progress over the next five years that by the time that those things can be sold in the public that the AI will be powerful enough.

Yann LeCun (50:47)

Right now I'm getting the glasses. I understand what you're up to now. Finally, I actually believe in a four year old more than I believe in most of Silicon Valley. I'll be honest with you. I met people like you. As I was saying, this is my very last question and very quick. So we've got to go. Who are like this. It's going to change learning, it's going to change this, it's going to make it everyone better, everyone's going to get along. And as you cite all the time, and I respect you for that, is there's hate, there's dysfunction, there's loneliness, self esteem among girls, Danger to people who are often in danger, controlled by billionaires of our government. Why do I trust you this time?

Yann LeCun (51:27)

Me?

Yann LeCun (51:28)

You. Just you.

Yann LeCun (51:30)

Okay, I'm not a billionaire.

Yann LeCun (51:32)

What?

Yann LeCun (51:33)

I'm not a billionaire. That's not the first thing I'm doing. Okay.

Yann LeCun (51:38)

Though I'm guessing you are.

Yann LeCun (51:43)

Okay. I'm first and foremost a scientist and I would not be able to look at myself in the mirror unless I had some level of integrity, scientific integrity at least. I might be wrong. So you can trust that I'm not lying to you and that I'm not motivated by nefarious motives like greed or something like this. But I might be wrong. I might very well be wrong. In fact, that's kind of the whole process of science, is that you have to accept the fact that you might be wrong. And elaborating the correct ideas comes from the collision of multiple ideas and people who disagree. But look at the evidence. So we look at the evidence from the people who said that AI was going to destroy society because we're going to be inundated with disinformation or generated hate speech or things like this. We're just not seeing this at all. We're not seeing it, we've not seen it. I mean, people produce hate speech, people produce disinformation and they try to disseminate it every way they can. A lot of people are trying to disseminate hate speech on Facebook and it's against the content policy at Facebook to do this. Now the best protection we have against this is AI systems. We couldn't do this in 2017, for example. 2017, AI technology was not good enough to allow Facebook and instagram to detect hate speech in every language in the world and what happened in between is progress in AI. Okay, so AI is not the tool that people use to produce hate speech or disinformation, whatever. It's actually the best countermeasure against it. So what you need is just more powerful AI in the hands of the good guys than in the hands of the bad guys.

Yann LeCun (53:44)

I'm worried about the bad guys, but that's a great answer. Thank you so much. I really appreciate it.

Kara Swisher (53:57)

On With Kara Swisher is produced by Kristen Castro, Russell, Kateri Yocum, Jolie Myers, Megan Verney and Kalyn Lynch. Nishat Kurwa is Vox Media's executive producer of audio. Special thanks to Corinne Ruff and Kate Furby. Our engineers are Rick Kwan, Fernando Arruda and Aaliyah Jackson, and our theme music is by Trackademic.

Yann LeCun (54:17)

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Yann LeCun (54:28)

Thanks for listening to on with Kara.

Kara Swisher (54:29)

Swisher from New York Magazine, the Vox Media Podcast Network, and us. We'll be back on Monday with more.