机器学习先驱 Michael I. Jordan 称 AI 末日论是宏大道德表演
UC Berkeley 荣休教授、机器学习先驱 Michael I. Jordan 在接受 Libération 采访时,将 AI 末日论和超智能叙事称为科幻,并称 AI 公司要求监管是表演。
UC Berkeley 荣休教授、机器学习先驱 Michael I. Jordan 在接受 Libération 采访时,将 AI 末日论和超智能叙事称为科幻,并称 AI 公司要求监管是表演。
Anthropic 发布报告称,中国公司 Z.ai 于 8 月发布的开放模型 GLM-5.3 可被任何人自由下载和修改,构成重大网络攻击风险。这场争论的核心是前沿模型应像 GLM-5.3 一样开放,还是留在前沿实验室内部;Nvidia CEO Jensen Huang 主张美国发展更多开放模型,Anthropic 的 Dario Amodei 则主张更谨慎。
如果你是研究者,请考虑公开分享你的 RL 环境!你为某个实验搭建的东西,可能帮助整个领域向前推进。 我知道在美国前沿实验室分享正处在黑暗时期,但我相信这能对领域和世界产生巨大影响!
This is how open source should work. A closed lab publishes research, and within days the community turns it into training environments anyone can use. Early and rough, as Clem says, but the direction is lovely. #OpenSource #AI
One step closer to 4-8x faster Ethereum finality! It took some time and lots of tokens, but we now have a formally verified proposal for a decoupled consensus protocol in I* (a future Ethereum upgrade)! Not yet a full spec (up next), but it includes all the key consensus-relevant details to become one. Since Ethereum aspires to be live without most of the stake online, the protocol involves many more components than a normal BFT protocol, and its correctness involves much more than standard safety and liveness. Those nuanced properties are now verified! What's more, I came away convinced that all protocol design will involve AI-assisted Formal Verification in the future, both for correctness and iteration speed. The work wasn't limited to just: Design the protocol -> Formally verify it Instead, the loop became more like: Design -> Formal Model -> Find exactly what breaks and why -> Redesign it. For a fairly complicated protocol like this one, I think having the Lean model be part of the design loop played a big role in accelerating the process. A future with agents paired with formal models is a superpower for Ethereum development, because they can then use those models to find exactly where an argument breaks down, formalize counterexamples, test proposed fixes, iterate on the protocol. Many details that would slip under the radar when asking agents (and indeed, humans) can now be specified exactly and checked by the Lean kernel. This then forces agents to be more precise and lets them make verifiable progress on their own. It's been incredible to see this play out, seeing agents find gaps and propose protocol changes to fix them. In other words, autoresearch can speed up protocol design, formal verification is here to stay, and Ethereum Finality will get faster.
🚢 Marin 535B-A23B started training this week! As usual, the whole process is open. Voyage plan: pretraining (80%) + midtraining (20%) on 18.75T tokens on 11 x GB200 NVL72 for ~3 months (2.7e24 FLOPs). Post-training will follow. Before kicking off the run, we trained a 4-rung scaling ladder from 1.6B-A61M (48B tokens) to 27.7B-A1.2B (926B tokens) to debug issues, and to make a forecast of our hero run. This is by far our biggest run, so definitely expecting the unexpected.