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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recordings per month · last 12 monthsRecordings per month over the last 12 months — 4 in all, peaking in Jan 2026 with 2.
Appearances
It's because I see the danger of this concentration of power through proprietary AI systems as a much bigger danger than everything else. That if we really want diversity of opinion, AI systems that in this future where we'll all be interacting through AI systems.
We need those to be diverse for the preservation of diversity of ideas and creeds and political opinions and whatever, and the preservation of democracy. And what works against this is people who think that for reasons of security, we should keep AI systems under lock and key because it's too dangerous to put it in the hands of everybody because it could be used by terrorists or something.
That would lead to potentially A very bad future in which all of our information diet is controlled by a small number of companies through proprietary systems.
Isn't that what democracy and free speech is all about? I think so. Do you trust institutions to do the right thing? Do you trust...
people to do the right thing and yeah there's bad people who are going to do bad things but they're not going to have superior technology to the good people so then it's going to be my good ai against your bad ai right i mean it's the examples that we were just talking about of you know maybe some rogue country will build you know some ai system that's going to try to convince everybody to
go into a civil war or something or elect a favorable ruler. But then they will have to go past our AI systems.
And doesn't put any articles in their sentences.
Not soon, but it's going to happen. The next decade, I think, is going to be really interesting in robots. The emergence of the robotics industry has been in the waiting for 10, 20 years without really emerging other than for pre-programmed behavior and stuff like that. And the main issue is Again, the Moravec paradox, how do we get the system to understand how the world works and plan actions?
And so we can do it for really specialized tasks. And the way Boston Dynamics goes about it is basically with a lot of handcrafted dynamical models and careful planning in advance, which is very classical robotics with a lot of innovation, a little bit of perception. But it's still not, they can't build a domestic robot.
And we're still some distance away from completely autonomous level five driving. And we're certainly very far away from having level five autonomous driving by a system that can train itself by driving 20 hours like any 17 year old. So until we have, again, world models, systems that can train themselves to understand how the world works,
uh, we're not going to, we're not going to have significant progress in robotics. So a lot of the people working on robotic hardware at the moment are, are betting or banking on the fact that AI is going to make sufficient progress towards that.
I mean, there's, you know, cleaning up, cleaning the house, clearing up the table after a meal, washing the dishes, you know, all those tasks, you know, cooking. I mean, all the tasks that, you know, in principle could be automated, but are actually incredibly sophisticated, really complicated.
That sort of works. Like you can sort of do this now. Navigation is fine.
Yeah, it's not going to be, you know, necessarily. I mean, we have demos actually because, you know, there is a so-called embodied AI group at FAIR. And, you know, they've been not building their own robots, but using commercial robots. Yeah.
And you can tell a robot dog, like, you know, go to the fridge and they can actually open the fridge and they can probably pick up a can in the fridge and stuff like that and bring it to you. So it can navigate, it can grab objects as long as it's been trained to recognize them, which, you know, vision systems work pretty well nowadays.
But it's not like a completely general robot that would be sophisticated enough to do things like clearing up the dinner table.
Well, I mean, I hope things can work as planned. I mean, again, we've been kind of working on this idea of self-supervised learning from video for 10 years and only made significant progress in the last two or three years.
So basically, I've listed them already. This idea of how do you train a world model by observation? Mm-hmm. And you don't have to train necessarily on gigantic datasets. I mean, it could turn out to be necessary to actually train on large datasets to have emergent properties like we have with LLMs. But I think there's a lot of good ideas that can be done without necessarily scaling up.
Then there is how you do planning with a learned world model. If the world the system evolves in is not the physical world, but it's the world of... Let's say the Internet or some world where an action consists in doing a search in a search engine or interrogating a database or running a simulation or calling a calculator or solving a differential equation.
How do you get a system to actually plan a sequence of actions to give the solution to a problem? So the question of planning is not just a question of planning physical actions. It could be planning actions to use tools for a dialogue system or for any kind of intelligent system. And there's some work on this, but not a huge amount.
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