信息流

Tout cela est correct. Merci. Sauf une chose : j'ai bien créé FAIR en 2013, y compris la branche parisienne en 2015, et montréalaise en 2017. Mais j'en ai quitté la direction en 2018 pour devenir Chief AI Scientist: je me suis refocalisé sur la recherche (Self-Supervised Learning, JEPA, World Models, etc). @AntoineBordes et @joellepineau étaient les co-directeurs de 2018 à 2023, jusqu a ce qu'Antoine parte pour Helsing et que Joëlle prenne la direction de FAIR. Je n'ai jamais été impliqué dans les LLMs, ni pour la recherche (à FAIR), ni pour le développement (qui était dans le groupe de produit GenAI). J'applaudissais des gradins. Mes seules contributions aux LLMs ont été de défendre Galactica (en octobre 2022) contre les attaques aux vitriol de la twittersphere, de promouvoir le projet Llama en interne (développé par une douzaine de chercheurs à FAIR-Paris, dont @GuillaumeLample et Timothée Lacroix), d'aider à convaincre Mark Zuckerberg de créer un groupe de produit autour de Llama (GenAI, créé début 2023), et de convaincre la direction de distribuer Llama-2 en open source/open weight. Je n'étais pas du tout impliqué dans la direction de GenAI après sa création, préférant travailler sur la prochaine révolution: l'IA physique. Si vous cherchez un responsable pour le déception qu'a été Llama-4, c'est plutôt la pression de sortir un produit (trop) rapidement, qui a conduit à un excès de conservatisme et un manque d'innovation de la part de l'équipe de GenAI. Mon avis est que l'équipe aurait dû prendre un peu plus de temps et intégrer les dernières techniques venant de la recherche à FAIR. Mais ils n'en ont pas eu le loisir. En particulier, conserver plus de pouvoir de décision quant aux choix techniques dans les mains des créateurs originaux de Llama à Paris, au lieu de déplacer les décisions à Menlo Park, aurait peut-être permis de conserver les meilleurs talents un peu plus longtemps et de produire de meilleurs résultats.

@shostekofsky There are a few notable counter-examples (e.g. HAL) but this is basically right for a lot of pre-2000 sci-fi. I think getting AI right from e.g. somewhere in the ~1970s would require a fairly complicated set of inferences, probably the biggest things would be: Image the world in 50 years. 1. "Digital knowledge work / computing". The vast majority of knowledge work (which makes up a very large fraction of the economy) goes digital. As computers get cheaper, much of society’s work happens on screens, over billions of machines in homes and offices. 2. "Internet". Similar to ARPANET (and see Licklider’s “Intergalactic Computer Network” (1963)), this creates demand for a global network of computers where people work, argue, teach, joke, and socialize. 3. "Big data". This creates a very large, compounding repository of digital information, with traces of how people reason, how they create, what they know etc. 4. "Statistical learning". Trying to program intelligence by hand is extremely difficult. But Shannon's n-gram approximations of English (1948) show examples of how language has statistical structure that can be "learned". You could imagine doing so over extensions of cybernetic computing (e.g. in the style of Rosenblatt's Perceptron 1958). So instead of programming thought, we could fit a statistical model to the record. 5. "LLMs". This might lead to something along the lines of ELIZA (1966), but with enough data (per 1-3), and enough compute (see Moore's law 1965), the imitation might become of extreme hight fidelity to the point that it's economically extremely useful. 6. "Scaling laws". These networks may demand a huge amount of data / computing resources and expertise to train. It's plausible that the more of each you have, the better your ELIZA gets. The economics concentrate, where a few organizations build a few minds and then deploy copies of them digitally. Something like that. Basically, it's a lot of leaps. You have to start with computers, to internet, to big data, to big compute (Moore's law), which then allow large-scale statistical learning approach based on neural networks, and then posit scaling laws, and then imagine the form factor of language models as a lot more sophisticated ELIZA. And you'd have to short everything else, most predominantly I think the idea that we'd figure out how to program robots by hand.

Weekend hack: HackerTube. I was annoyed with YouTube's recommendations so I had Claude filter down 43M comments over 20 years of HN to 6,454 videos that keep being recommended. *Significantly* better viewing experience, ~2 hours with Opus 5.5: hackertube.org - Excellent recs without any logins (state is kept in local browser session) - Thumbnail is a representative frame from middle of the video instead of the super duper A/B test hyper-optimized user uploads - Underlying data from the comment analysis as one zip file, point your Claude at it and ask for your own version - HackerTube Longs ✊ A bit more on the process: I use Claude Code (desktop app for Mac), Opus 5.5 on Medium effort as my default. I only talk to Claude and have it do everything, the only "manual work" I helped with in tandem was in the browser (reminiscent of MenuGen): 1) I had to create some kind of API key from Google Cloud UI to query YouTube for video metadata, 2) I booted up a VPS box for serving and 3) I bought the domain name. Once the box was up I gave Claude the IP and told it update, secure, harden, deploy and optimize the code, and it did the whole thing. Many awesome projects that would have normally taken weeks/months even can now be few-shotted for breakfast. What else are we not thinking of.

People should always be in control of the future. OpenAI is against the notion that it would be acceptable for people to be relegated to a role of doing things that “AI systems think makes sense to reward in humans”. People should not cede control of the future to superintelligence, and that requirement should ultimately constrain AI progress.

@pondorasti Yeah this is really good. Btw you can also just tell your Claude Code "Create and deploy an extension for my browser that shows YouTube thumbnails from halfway of the video instead of the user-uploaded version. Include a toggle between the two", works great

Join me and 2000 other AI engineers and builders at AI Dev in New York City, Nov 30-Dec 1! A LinkedIn study found that over July-September, the job title AI Engineer has been one of the 10 fastest-growing occupations in seven major economies. AI engineering has become a core skill for all software work, and AI Dev is designed to help you develop it. Our sessions give you hands-on practice with: - Coding agents and harness design: Harness tuning, repository context, autonomous coding - Agent memory and knowledge graphs: GraphRAG, vector search, persistent memory, hybrid retrieval - Agents in production: Evals, observability, simulated testing, browser and voice agents @ylecun and I will also talk about what’s next in world models and physical AI. I hope to see you there! Tickets: ai-dev.deeplearning.ai

@davegoldblatt "Hi Claude good morning how are you I'm great thx build me a YouTube-style site but restricted to videos that have been repeatedly recommended on HN over the years pls. Don't make any mistakes."

@jestermolecule @HarshithLucky3 but only because...i actually do want the world to have tons of memory and people to put as much as they want in their computers, and it is fucking annoying that this was taken out of context. i have been arguing for the world to ramp up memory production for a long time.

Our latest work in closing the last-mile gap on long horizon robotics tasks. Wensi Ai@wensi_ai Long-horizon, whole-body robot policies still fail during deployment. How do we best close this last-mile gap? 🤖 🚨 Introducing I3L: Iterative Interactive Imitation Learning for Whole-Body Mobile Manipulation Website i-3l.github.io Oq Paper arxiv.org/abs/2610.17748 bI Your browser does not support the video tag. 🔗 View on Twitter 🔗 View Quoted Tweet

@RagavanThurair Same... I had to bring it down (RIP) because it was just too much work to maintain, it would have been a lot easier today. But today there's also alphaxiv.org , which I like & use and is in the same spirit.

@birdabo i want you to have so much ram lets build more

@BrianRoemmele It's as if my electronic wind instrument could stop me from playing along Miles Davis or John Coltrane solos while I'm (painfully) trying to learn from them. Anthropic is insane and insufferably moralizing. But we already knew that.

Plot twist: I was Vapnik's Department Head between 1996 and 2002, and made sure he had the space, resources, and collaborators to do his best work. To people who think I'm dogmatic: I famously disagreed with Vapnik about the best way forward in ML, but did what I could to promote his work, because research is about exploration and science is about results.

LLMs accumulate a lot of declarative knowledge. That's how they can answer a large number of questions. That's also why they require so many parameters and hence so much memory. But one can replace the accumulation of knowledge by *understanding* That what JEPA world models do. That's one reason they can be useful with considerably less memory and training data than LLMs. This paper merely shows that the underlying similarity structure of the data, whether text or image (e.g. co-occurence of words in text or objects in images) uniquely determines the shape of the representation point cloud, when the encoder is trained trained with a common anti-collapse criterion like SIGReg or VCReg (not by reconstruction).

@ZimingLiu11 Yes, the evil nature of non-linearities that cannot produce zero-mean output distributions has been known for a long time. They create badly conditioned cost functions. proceedings.neurips.cc/paper/1990/has… scholar.google.com/citations?view…