Yann LeCun

A nice piece by @AndrewYNg arguing that irrational fears about AI should not cause governments to regulate open source AI models out of existence.

Excellent piece by @Noahpinion on techno-optimism. So many good points. Like Noah, I'm a humanist who subscribes to both Positive and Normative forms of Active Techno-Optimism. Quote: "Techno-optimism is thus much more than an argument about the institutions of today or the…

Good article at FastCompany on the LLM craze, the future of AI, the necessity of open source AI platform, and my anti-AI-doomer stance.

A balanced view of the issues of open vs closed LLMs.

Quote: <<Princeton computer science professor Arvind Narayanan told Vox that “the bottleneck for bad actors isn’t generating misinformation — it’s distributing it and persuading people.” He added, “AI, whether open source or not, hasn’t made those steps any easier.”>>

Very nice article by Craig Smith in IEEE Spectrum about the debate on the power and limitations of LLMs between (among others) @ilyasut and me. Do AI systems ultimately need to be grounded in reality, not merely learn from language? I say yes.

ConvNets are a decent model of how the ventral pathway of the human visual cortex works. But LLMs don't seem to be a good model of how humans process language. There longer-term prediction taking place in the brain. Awesome work by the Brain-AI group at FAIR-Paris.

Excellent paper in which the word "criti-hype" is coined. Criti-hype designates the kind of academic and non-academic work that magnifies the imagined dangers of a new technology, feeding on and mirroring the hype from the advocates of said technology.

Good article on LLMs at Forbes. The media are starting to agree with my much-criticized statements about LLMs. "LLMs as they exist today will never replace Google Search. Why not? In short, because today’s LLMs make stuff up."

Excellent WaPo article about large language models and chatbots that corroborates what I've been posting recently: they are useful but they make stuff up. They detail the reasons why large tech cos have been hesitant to release such things for public use.

Nice piece.

Nice article in @ScienceMagazine about FAIR's ESM-2 protein folding prediction system, and the publication of 615 million protein structures. The article discusses how this kind of breakthrough piggybacks on large investments in deep learning by industry.

An *amazing* piece that humorously explains why content moderation on social networks is difficult, painful, expensive, & ultimately unpopular but necessary. A lesson for new owners of social networks who think they can do away with content moderation.