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lar platforms like Health Data Nexus as examples of PhysioNet’s legacy. Although there are now more resources out there hosting similar electronic health data, according to Google DeepMind researcher Vivek Natarajan, both PhysioNet and MIMIC “set the standard,” he says, “and it’s still the standard right now.” That standard, according to those who use the platform, changed how research is conducted. Access to data should not be the determinant for which ideas are possible, according to Ziad Ober

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as follows : Anthropic – Claude Fable 5 OpenAI – GPT 5.6 Sol Moonshot AI – Kimi K3 (open weights ) SpaceXAI – Grok 4.5 Zhipu (Z.ai) – GLM 5.2 (open weights) Meta – Muse Spark 1.1 DeepMind – Gemini Flash 3.5 Alibaba – Qwen 3.7 Max (3.8 announced, also to be open-weights, when writing) It is astonishing to see DeepMind, and some of the other American giants this low. In many ways, the X AI team deserves more credit. A visual summary from Artificial Analysis is below: This release and other recent

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t and safety really go hand in hand with capabilities,” he said. Indeed, reinforcement learning from human feedback draws its roots back to a 2017 collaboration between OpenAI and DeepMind: One encouraging trend I put to him is that bigger models are turning out easier to align, not harder. Ted’s own framing was more guarded - it has “gone different from how many safety researchers expected,” he said - but his read is that “people feel pretty good about the alignment situation right now,” temper

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school software that can tell you yes or no, and you use that as your VR. What would corroborate your point is the initial attempts. Again, I’ll circle back to the IMO. Initially, DeepMind basically does that. Everything is in Lean, and then the next year it’s all in natural language. So to your point, it’s not needed. I do think there’s a yet-to-be-explored benefit of that formalization domain, which is that at the moment you still need a human reviewing that counterexample to the unit distance

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e window to be a builder rather than a buyer is measured in the time it takes to stand up a cluster, not a career. This should sting, because Europeans invented much of modern AI. DeepMind was founded in London and sold to Google in 2014, and a great deal of the talent that followed now lives in California. Today Europe faces a company worth almost $1tn and American tech giants spending an estimated $450bn a year on AI infrastructure. Its answer has been the EU AI Act and a capital commitment th

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plus a simple heuristic (update what’s specific, leave what’s general) that sidesteps the need to learn how to learn. There was a lot of hype around the (very lengthy) paper from DeepMind on nested learning, which starts with the observation that architecture and optimization aren’t separate things. Rather, they’re the same concept operating at different timescales. A transformer’s attention mechanism is associative memory updating every forward pass. The feedforward layers are associative memo

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e on the Trainium roadmap only grows from here. Put plainly: Trainium2 is converging toward an Anthropic custom-silicon program. This will enable Anthropic to be, alongside Google DeepMind, the only AI labs benefiting from tight hardware–software co-design in the near horizon. This report will dig into all aspects of Amazon’s AI resurgence: the Anthropic partnership, datacenters, and Trainium. At the end of the report, we provide a longer-term outlook on Anthropic, AWS Bedrock and internal model

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ai-models-dangerous-before-public-deployment/}} , year = {2025} , month = {01} , } Many frontier AI safety policies from scaling labs (e.g. OpenAI’s Preparedness Framework, Google DeepMind’s Frontier Safety Framework, etc.), as well as past work by third party evaluators including UK AISI , Apollo Research , and METR , focus on pre-deployment testing – ensuring that the AI model is safe and that the lab has sufficient security before the lab deploys the model to the public. Such pre-deployment s

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