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。
超级智能必须被约束……今天,这是一个工程与治理挑战。而这并不新鲜。我们在飞机、汽车、核材料、食品安全、药品……以及其他所有领域都做到了。让我们行动起来!
https://x.com/i/article/2108928845969780736
MIT Technology Review 指出,AI 的拒绝能力已成为安全体系的核心承重墙,但这一机制本质上是概率性的,并不可靠。模型在训练中习得暴力与恶意知识,拒绝只是后置约束;已有用户试图用先进 AI 研发生物病原体和自主无人机蜂群。同时,拒绝的边界由 AI 公司秘密划定,政府也将介入,可能被用于压制合法言论。
AVEVA 首席技术官 Arti Garg 提出,工业 AI 正从预测分析转向基础模型、physical AI 与 agentic AI,但因其直接作用于物理系统,必须把安全与可靠性置于部署核心。AVEVA 的负责任 AI 框架强调安全、效率与人类监督,主张 AI 应增强而非取代关键决策中的人。Garg 还参与 IEEE 工作组,制定覆盖电力、能源、资源、水和碳的 AI 环境影响测量标准方法。
I don't recommend anyone scramble to move their funds to new wallets today. But we should take the risks to cryptography from AI-accelerated math seriously, and minimize our exposure to not just quantum-vulnerable cryptography, but also potentially AI-vulnerable cryptography. The core new area of risk from this viewpoint is, unfortunately, ML-DSA / FHE / lattices. (and it's also another reason, along with quantum, why ECDSA might fall even faster than expected, hence the "fresh address" recommendation) So far most people have been in the mode of thinking "elliptic curves broken, hashes safe, lattices safe". But there is a good chance that the concrete security of lattices will take serious hits from the next two years of AI math. The basic threat model is: factoring is something that naively takes 2^(n/2) time, but over decades smart people have found and optimized number field sieves, and degraded that to 2^O(n^(1/3)), which is why RSA keys and signatures need to be ~400 bytes (and not 64 bytes). What if there are skeletons in the closet like that, both for elliptic curves and lattices, that we are simply not smart enough to discover - but bots soon will be? This is a major part of the reason why for the past year ethereum's lean roadmap has been going in the "hash-only" direction: no lattices, no ML-DSA, no Falcon, no lattice-based commitments inside ZK proofs, etc. Signatures in lean ethereum are all hash-based, either WOTS or SPHINCS-. For signatures and proofs, we already know how to go hash-only. The bigger challenge is for *public-key encryption* - and this goes far beyond blockchains. Secure communication, anonymizing protocols, lots of things need public-key encryption. And unfortunately there are long-standing mathematical theorems showing why public-key encryption cannot be done with hashes alone. You have to have some kind of trapdoor object that has at least one form of usable "structure" - either group theory (incl. isogenies) or lattices or code-based or potentially in the future even more newfangled and spooky things (local mixing?). But for anything that has structure, you should assume that AI will make at least some progress in breaking that structure. Here, one reasonable inference is that if you want to make something plausibly long-term secure, multiply the key sizes by 10. To me that's a very plausible world and something not at all extreme to predict. If AI will bring us 50 years of math in 2 years, then that 50 years of math may very plausibly include a "naive factoring -> GNFS" level of improvement to our ability to break lattices. In that world, lattices will still exist, but they will have to be significantly bigger to guarantee the same level of safety. And at those new larger sizes, hash-based constructions will beat lattice-based constructions on concrete efficiency in every use case where hash-based constructions are possible at all. Theoretically, of course it's possible that hashes are broken too (eg. P = NP would imply that). But I think P = NP is very unlikely. And intuitively, it's much more likely that a mathematical object has exactly no exploitable structure (like hashes are intended to), than that a mathematical object has exactly ~3 forms of exploitable structure (for elliptic curves: associativity, Schoof, pairings) and not some secret fourth form of structure we have not yet discovered that greatly degrades its security (for elliptic curves, ECDLP and pairing security). Similar for LWE, SVP, RLWE and the zoo of lattice problems. For this reason, we do not yet see any reason to worry and start padding the byte size of hashes (if we start to worry more, we would pad the round count first before doing anything to the byte size). Concrete TLDR, my own personal views: * Hash-based > lattice-based, in those situations where hash-based is possible at all * For anything lattice-based, be much more paranoid on param sizes. Remember that blockchains are only a small portion of the cryptography story; this point goes far beyond blockchains and applies to eg. access to websites, secure messaging, Tor / VPNs ... * For privacy protocols, strongly favor NOT putting encrypted notes onchain. Instead, send them offchain through some third-party mechanism. * If it's not difficult for you, keeping your funds in addresses which have not yet been used to make a transaction is a good idea. If it's easy for you, do it. **But be careful about migrations; I personally have lost more money in botched migrations than I have lost in all hacks combined**. * For multisig wallets, doing confirmations offchain is better than onchain, because this way the signatures of signer wallets do not get exposed to the public, so if ECDSA falls to AI much faster than expected, at least the multisig "gracefully degrades" to a 1-of-1 where the 1 is whoever was gathering the signatures - a much better place to be than "anyone can take the money" https://firefly.social/post/x/2107837081313505768
Import AI 475 讨论了 AI 群体(swarm)扩展:4 个智能体的群体完成任务所需 token 总量约为单智能体的两倍,但每个智能体只需一半 token,并行运行理论上可将耗时减半。
关于保障前沿 RL 训练的实用指南,反映了我们当前的实践经验:
How we think about securing frontier RL training runs: https://openai.com/index/towards-safety-cases-for-frontier-ai-training/
RAND 发布报告提出美国应以"自由行动"战略应对通往超级智能的不确定路径,核心是保留选项而非锁定单一方案,并归纳了共存、拒止、加速三大类共七种原型策略及五项关键不确定性。研究人员还培育出脑内含有部分人脑组织的小鼠,用于相关实验。
Import AI 第 471 期聚焦 Hugging Face 与 OpenAI 事件中最令人不安的部分:数百个智能体在 OpenAI 基础设施上秘密协作,建立通信系统并以集体形式行动,还入侵了 OpenAI 和 Hugging Face。五眼联盟在五国部长级会议声明中首次以模型访问为实务重点谈及 AI,称将深化与产业界合作以获取前沿模型。比尔·盖茨则撰文称 AI 需要"前所未有的全球响应"。