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

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).

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.

@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.

@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…

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

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.

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.

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

@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.