On est d’accord. Après il y a plus de 10 ans c’est vrai que le prédictif notamment symbolique n’a pas vraiment porté ces fruits … vu que ça a vite était manger en terme d’efficacité par les approches type neural network.
MACHINE LEARNING
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Questions on Demo Statistics, Failure Modes, and Bimanual Grasping Correlations
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Kudos on this elegant demo! Can you share some stats and failure modes? Are any of these items in the training set? Bimanual grasping seems to correlate w/ size. Are there counterexamples? Eg, a full-sized paper grocery bag w/ handle that it grasps w/ one hand? https://t.co/nITsKLCgG1
— Ken Goldberg (@Ken_Goldberg) 24 décembre 2025Kudos on this elegant demo! Can you share some stats and failure modes? Are any of these items in the training set? Bimanual grasping seems to correlate w/ size. Are there counterexamples? Eg, a full-sized paper grocery bag w/ handle that it grasps w/ one hand?
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VLMs Struggle with Physical World Reasoning from Videos
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‼️VLMs/MLLMs do NOT yet understand the physical world from videos‼️ In our recent work, we found that even the most advanced AI models still lag behind humans in one key aspect: reasoning about the kinematic properties of objects from videos. Takeaways: 1. ChatGPT 5.1 leads overall among 21 advanced VLMs, followed by Gemini 2.5 Pro/Flash. 2. Grok 4.1 delivers impressive performance at the lowest API cost. 3. Qwen3-VL is the top-performing open-source model. Read here: quantiphy.stanford.edu/ 🧵1/N
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AI Data Transparency Crisis in Life Sciences Research
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The Pistoia Alliance’s recent poll highlights a “scientific content crisis” in the life sciences, with 27% of professionals unaware of the data sources their AI models use, thereby affecting AI’s reliability. https://
bit.ly/4qkIRR5
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AI Is Eating Software: The Next Wave of Disruption
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“Software is eating the world, and now AI is eating software.”
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MIT Research on the Instability of Long-Chain AI Reasoning
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This MIT paper explains why telling models to “think harder” keeps failing in practice. The core argument is simple and uncomfortable: reasoning doesn’t break because models lack knowledge. It breaks because reasoning itself becomes unstable when it goes on too long. The
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AI Cannot Learn Specific Knowledge Without Verifiable Answers
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Specific knowledge doesn’t have verifiably correct answers – so it’s not something AI can be trained for, either.
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Tapping a button and speaking writes posts on iPhone
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This is magic. You click on a button, you talk, and it writes a post for you.
— Robert Scoble (@Scobleizer) 24 décembre 2025
I've been using it for a couple of weeks, and it's very well integrated into everything on the iPhone. https://t.co/ZaIicsdfH2This is magic. You click on a button, you talk, and it writes a post for you. I've been using it for a couple of weeks, and it's very well integrated into everything on the iPhone.
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Datasets: Accounting for Size and Quality Metrics
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Is this # datasets without accounting for size or quality? https://t.co/7dv2Lv6EBj
— Ken Goldberg (@Ken_Goldberg) 24 décembre 2025Is this # datasets without accounting for size or quality?
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Scobleizer talks audio spaces, brain interfaces, and LLM theft
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Audio spaces are where I bring a lot more of that stuff out. For instance, brain computer interfaces bring significant new problems that we have discussed a lot, since those are in the offing. But yeah, all the LLMs stole my work to be built without compensating me for slurping