What if your AI could reason smarter AND faster, especially on complex tasks? A collaboration from Fudan University, Peking University, and Meituan LongCat Team just made it happen! They've developed a new framework for Block Diffusion Language Models (BDLMs) that lets the AI
RESEARCH
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First AI War: Expert Analysis on Geopolitical Impact
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This is the first AI War : https://
newstatesman.com/international-
politics/2026/03/nina-schick-this-is-the-first-ai-war
… My interview with the @NewStatesman -

Industrial AI Execution Gap: Why 95% of Pilots Fail
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Why do 95% of industrial AI pilots fail? The math is fine. The problem is the Execution Gap.
Plants are drowning in sensors, yet decision-making remains stuck in reactive chaos. A new research initiative by MIT SMR India and Infinite Uptime is tackling this head-on. Visibility is -

Agentic AI Intelligence Explosion Through Socially Aggregated Cognition
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Our new essay is out in Science: "Agentic AI and the Next Intelligence Explosion" For decades, the AI "singularity" has been imagined as a single, godlike mind bootstrapping itself to omniscience. In this piece with the inimitable Benjamin Bratton (@bratton) and Blaise Agüera y Arcas (@blaiseaguera), we argue this vision is wrong in its most fundamental assumption. Every prior intelligence explosion—primate sociality, human language, writing, institutions—wasn't an upgrade to individual cognitive hardware. It was the emergence of a new socially aggregated unit of cognition. AI is extending this sequence, not breaking from it. The evidence is already inside the models themselves. In recent work, we showed that frontier reasoning models like DeepSeek-R1 don't improve by "thinking longer"—they spontaneously simulate internal multi-agent debates, what we call a "society of thought" (lnkd.in/guNfRtXh). Reinforcement learning for accuracy alone causes models to rediscover what epistemology and cognitive science have long suggested: robust reasoning is a social process, even within a single mind. This opens a vast design space. A century of research on team composition, hierarchy, role differentiation, and structured disagreement has barely been brought to bear on AI reasoning. The toolkits of organizational science become blueprints for next-generation AI. Outside the model, we've entered the era of human-AI centaurs—composite actors that are neither purely human nor purely machine. Agents that fork, differentiate, recombine. Recursive societies of thought that expand when complexity demands and collapse when problems resolve. The scaling frontier isn't just bigger models. It's richer social systems—and the institutions to govern them. Just as human societies rely on persistent institutional templates (courtrooms, markets, bureaucracies), scalable AI ecosystems will need digital equivalents. The Founders would have recognized the logic: no single concentration of intelligence should regulate itself. The intelligence explosion is already here. Not as a singular ascending mind, but as a combinatorial society complexifying—intelligence growing like a city. The question is whether we'll build the social infrastructure worthy of what it's becoming. No mind is an island. Read it here in Science (science.org/doi/10.1126/scie…) or free on the arXiv (arxiv.org/abs/2603.20639)
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War and AI, the Death of Sora: Gary Marcus’s Latest Insights
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War and AI, the death of Sora, and 3 ways you can catch me live today, https://
open.substack.com/pub/garymarcus
/p/war-and-ai-the-death-of-sora-and?r=8tdk6&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true
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Adam Smith Economic Principles Beyond Conventional Understanding
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If by "conventional" you mean stuff that Adam Smith came up with, then there are LOTS of things that he never accounted for, and yet the principles were still as applicable as ever.
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DeepSeek Releases Larger Base Model Amid Training Silence
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"A new, much larger (DeepSeek) base model will be released soon", from DeepSeek staff. I'm currently wondering why there's been so much silence surrounding DeepSeek. The last report stated that they attempted to train on Huawei chips but failed ("DeepSeek AI model failed to
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Best Model for Actual Machine Learning Work
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For actual ML work it's still the best overall model around.
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Beyond Random Search: Guiding Autoresearch Agents Toward Meaningful Exploration
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What are the best current techniques to have autoresearch behave better than (slightly improved) random search? By which I mean (in Sijun below example), having the agent understand that (given some constraints) exploring int5 quantization is more exciting and have more downstream fruits than playing with the random seed? I’m talking about the beginning of having an agent pushed a real research program. The ones where you know the current technique will not give crazy results out of the box but it still push it because it believe and can demonstrate that the general direction has potential. Like neural networks used to be a worse way to do AI performance-wise. But we still pushed them… Sijun Tan (@sijun_tan) We took @karpathy's autoresearch agent, scaled it into a collaborative swarm, and topped @OpenAI's Parameter Golf Challenge—twice. Here’s how we did it: — https://nitter.net/sijun_tan/status/2036584756729749802#m
→ View original post on X — @thom_wolf, 2026-03-25 12:29 UTC
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Robot Hand With Bidirectional Bending Achieves Superhuman Dexterity
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This #Robot Hand Bends Both Ways — Unlocking Superhuman Dexterity
— Ronald van Loon (@Ronald_vanLoon) 25 mars 2026
via @ZappyZappy7
#AI #Robotics #MachineLearning #ArtificialIntelligence #ML #MI pic.twitter.com/hjiYhdanj6This #Robot Hand Bends Both Ways — Unlocking Superhuman Dexterity
via @ZappyZappy7 #AI #Robotics #MachineLearning #ArtificialIntelligence #ML #MI
