Pivot
Meta's Chief AI Scientist Yann LeCun Makes the Case for Open Source | On With Kara Swisher
Sat, 21 Dec 2024
We're bringing you a special episode of On With Kara Swisher! Kara sits down for a live interview with Meta's Yann LeCun, an “early AI prophet” and the brains behind the largest open-source large language model in the world. The two discuss the potential dangers that come with open-source models, the massive amounts of money pouring into AI research, and the pros and cons of AI regulation. They also dive into LeCun’s surprisingly spicy social media feeds — unlike a lot of tech employees who toe the HR line, LeCun isn’t afraid to say what he thinks of Elon Musk or President-elect Donald Trump. This interview was recorded live at the Johns Hopkins University Bloomberg Center in Washington, DC as part of their Discovery Series. Learn more about your ad choices. Visit podcastchoices.com/adchoices
Chapter 1: Who is Yann LeCun and why is he significant in AI?
And so I think there should be resources allocated by the government to give computing resources to academics.
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. Yeah. 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?
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.
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 U.S. government, but governments around the world, which is that AI is quickly going to become a kind of universal knowledge platform, basically, you know, 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
train in a distributed fashion with lots of contributors from around the world. And it will need to be open.
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Chapter 2: What are the dangers of unregulated AI research?
Okay. Outside of all of those 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'm not, this is, I was not necessarily talking about copyrighted work in that case. It's more like, you know, if you are in, so I'm from, my family, my father's family is from Brittany, okay, 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 non-profits, you know, kind of collecting all the stuff that they have, maybe governments helping, things like that. And they're going to say like, you know, use my data. Like, I want your system to speak Breton. Now, they may not want to just hand that data just like that to, you know, big companies on the U.S.
But a future that I envision, this is not company policy, all right, this is my view, is that the best way to get to that level is by kind of training an AI system, a common AI system, repository of all 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. You don't have to copy the data.
But who runs that global system?
Who writes Linux?
Right. So that should exist for all of humanity. Right.
Yeah, I mean, who pays for Wikipedia, right?
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