Gary Marcus 批评 Dario Amodei 七天内三度失信
Gary Marcus 发文列举 Dario Amodei 在七天内损害自身公信力的三种做法:其一是让与 Anthropic 关系密切的 METR 和已有业务往来的 Accenture 充当独立监督方;其二是 Anthropic 正筹备自建湿实验室,却缺乏常规机构审查委员会监督;其三是嘴上呼吁"pace the frontier",实际仍指向 IPO。
Gary Marcus 发文列举 Dario Amodei 在七天内损害自身公信力的三种做法:其一是让与 Anthropic 关系密切的 METR 和已有业务往来的 Accenture 充当独立监督方;其二是 Anthropic 正筹备自建湿实验室,却缺乏常规机构审查委员会监督;其三是嘴上呼吁"pace the frontier",实际仍指向 IPO。
Gary Marcus 指出,NYT 报道特朗普出于经济考量淡化 AI 风险并抵制监管,他认为这可能带来糟糕后果。他提到自己曾在美国参议院警告,AI 生成的不准确信息可能引发意外战争,而类似情况已经出现,下次未必还能侥幸。
“最妙的是把恶意对准他每天遇到的近邻,而把善意推向遥远的边缘,推向他素不相识的人。于是恶意变得完全真实,而善意大体上是想象出来的。”——C.S. Lewis,以恶魔的视角写作
>be me >discover effective altruism >apparently normal charity is inefficient >why donate to random sad thing when spreadsheet can tell you optimal sad thing >fair enough >buy mosquito nets >save lives >numbers look good >feel powerful >couple years later >someone asks an innocent question >why only count people alive today >huh >future people matter too >obviously >my grandchildren shouldn't matter less just because they haven't spawned yet >reasonable.jpg >keep following logic >what about their grandchildren >also yes >what about people in 500 years >sure >5000 years >why not >500 million years >starting to get weird but morality is morality >open calculator >humanity could survive for an astronomically long time >could colonize galaxy >could have trillions upon trillions of descendants >maybe digital people too >maybe simulated civilizations >maybe dyson spheres full of happy uploaded minds >calculator starts smoking >realize currently living humans are rounding error >8 billion people suddenly looking extremely beta >future contains potentially 10^something people >can't even fit beneficiaries in google sheets >new moral priority unlocked >protect the long-term future >stop thinking in units of "people helped" >start thinking in "fraction of cosmic endowment preserved" >malaria? >terrible >but only kills existing humans >AI extinction could delete the entire light cone >nuclear war could permanently derail civilization >bad institutions could lock in terrible values for ten million years >someone invents wrong constitution in 2140 >quadrillions suffer >better fund governance workshop now >friend says maybe we should improve hospitals >explain opportunity cost >friend says hospitals are full of actual sick people >explain scope sensitivity >friend stops inviting me to dinner >need to decide what to fund >easy >expected value >suppose project has one in a million chance of preventing extinction >sounds tiny >but extinction destroys 10^50 future lives >multiply >mother of god >$10 million project has expected value of several galaxies >charity evaluation complete >someone asks where the one-in-a-million number came from >expert judgement >which expert >us >how calibrated >extremely thoughtfully >reduce estimate to one in ten million to be conservative >still beats curing cancer by 38 orders of magnitude >epistemic robustness achieved >someone says maybe project doesn't work >assign 20% chance >still astronomical >maybe project makes problem worse >assign 5% chance >still astronomical >why 5 >because 30 felt pessimistic >publish 46-page report >contains seventeen sensitivity analyses >every sensitivity analysis begins after assuming intervention has positive sign >critic says you're multiplying enormous hypothetical stakes by extremely uncertain probabilities >yes >that's literally why it's important >critic says the uncertainty might be structural rather than numerical >make probability smaller >critic says no, I mean maybe your model is wrong >make probability smaller again >critic begins rubbing temples >discover AI safety >perfect longtermist cause >AI might kill everyone >or create utopia >or seize galaxy >or tile universe with paperclips >or create billions of conscious software minds >finally a problem with numbers big enough for me >start AI safety nonprofit >mission: prevent dangerous AI >hire smartest people available >smartest people immediately start building better AI to understand dangerous AI >interesting >we must understand capabilities to understand safety >we must scale models to study alignment >we must race ahead so less responsible actors don't get there first >we must deploy systems to learn how deployment can go wrong >we must build the thing quickly because building the thing quickly is dangerous >outsider asks why the people most worried about AI apocalypse all work at AI companies >complicated field >company releases stronger model >very concerned >company begins training even stronger model >extremely concerned >company raises $14 billion >concern reaches unprecedented levels >need to influence government >future is at stake >normal democratic process too slow >politicians don't understand exponential curves >public doesn't understand x-risk >experts must guide them >who counts as expert >people who understand x-risk >who understands x-risk >our friends >someone objects that this seems politically convenient >explain we're representing future generations >future generations unavailable for comment >develop concept of value lock-in >terrifying possibility that one ideology controls civilization forever >therefore extremely important that civilization adopts correct values before lock-in >whose values >let's circle back >begin with impartial morality >end with small group of people deciding what quadrillions of hypothetical beings would want >beautiful arc >meanwhile actual humans keep doing annoying things >voting wrong >having parochial attachments >loving family more than strangers >caring about local community >getting upset when told their suffering is cosmically negligible >evolutionary biases everywhere >explain that moral intuition cannot be trusted >except intuition that future digital people count >and intuition that extinction is uniquely bad >and intuition that our probability estimates are sane >and intuition that our institutional choices improve the future >those intuitions survived peer review >someone donates $5k to local homeless shelter >inefficient >could have funded 0.0000000000003% of an AI governance researcher >think of all the simulated people you just killed >okay maybe don't phrase it that way publicly >PR team says "future generations deserve a voice" >much better >journalist asks what longtermism means >say "future people matter" >everyone agrees >great >journalist asks what follows from that >well technically we should redirect enormous resources toward low-probability interventions affecting astronomical futures >journalist raises eyebrow >return to "future people matter" >motte has entered the chat >critic: of course future people matter >me: glad we agree >critic: I don't agree that your institute knows how to help them >me: why do you hate our grandchildren >eventually notice uncomfortable implication >if future value dominates everything >then helping people today mostly matters through effects on future >education matters because future institutions >health matters because future productivity >democracy matters because future trajectory >human beings slowly become instrumental variables in their own moral philosophy >see starving child >feel compassion >check spreadsheet >child's direct welfare contribution negligible >but perhaps childhood nutrition improves national institutional quality >compassion restored >tell myself this is impartial altruism >one day assistant asks obvious question >"how do you know your intervention actually improves the far future?" >silence >open spreadsheet >increase column width >add confidence interval >assistant asks again >"no, I mean how do you know the sign is positive?" >stare into cosmic light cone >10^50 people staring back >none of them exist >none of them can tell me >none of them can falsify my assumptions >realize I have invented the perfect constituency >infinitely important >completely silent >and always represented by me
OpenAI's Noam Brown says air-gapping the computers may not stop a misaligned AI, because two air-gapped machines can still talk by running a CPU hot and reading the temperature change "But I think the major takeaway from the incident is that people underestimated the AI. And we never want to be in a situation again where we underestimate the AI. It's a weird world, because AI progress is so fast that people are consistently underestimating the AI." "So to be in a situation where you don't underestimate it again, when it comes to safety and alignment, you have to have a very, very, very high bar." "You could even go as far as to say, "Well, we should air gap the computers." And I'm not convinced that that would be sufficient." "There are studies, and this is mostly academic, where you can have two computers next to each other that are air-gapped and they're still able to communicate with each other because they have temperature sensors." "One of them is able to run their CPU really hot, and then the other one can actually detect the temperature change, and then that actually gives them a mechanism to communicate." _________ Link and more key quotes from OpenAI's safety related conversations: https://firesidealpha.substack.com/p/openai-safety-week-sam-altman-sarah
Gary Marcus 认为,近期真正值得担忧的不是失控的超级智能,而是失控的智能体 AI 大规模发动互联网攻击。他援引《华尔街日报》评论版 Brian Gross 的文章称,主流媒体中少有机构梳理这一整体图景,并表示完全认同该文观点。
OpenAI 发布澳大利亚青年安全蓝图,这是一份包含六大支柱的路线图,目标是打造更安全的 AI 体验,以保护和赋能年轻人。
Apple 研究团队提出 Dynamically Scaled Activation Steering(DSAS),一种与具体方法无关的激活引导框架,将"何时引导"与"如何引导"解耦,按层和输入自适应调节现有引导变换的强度,仅在检测到不良行为时强力干预。
New episode with @polynoamial We talk about multi-agent, Navier-Stokes, and what the current explosion of maths progress tells us about what happens once you automate AI research. And we also discuss how we will know if the models are actually aligned before we kick off RSI. 0:00:00 – Multi-agent and Navier-Stokes 0:15:28 – How will AI firms work? 0:22:02 – What math progress tells us about recursive self improvement 0:40:22 – Hugging Face and alignment 1:01:18 – The internal/external model gap 1:08:34 – Chain of thought is degrading 1:14:12 – How will we know when alignment is solved?
Sierra 宣布通过 AIUC-1 认证,该认证经 Schellman 独立审计并由 AIUC 完成大量测试,专门针对 AI 智能体的实际行为,包括操纵尝试、受保护信息访问和越权操作等对抗场景。
研究者提出 REVERSAL-BENCH,通过连续参数 ρ∈[0,1] 控制环境可逆性,并提供重置 Oracle 在五种物理引擎的八种操作场景中验证状态可恢复性。
Gary Marcus 在 BBC 节目后撰文反驳 Sam Altman、Jensen Huang 和 Bernie Sanders 的 AI 表态,认为三人说法均不可信。
AIUC 宣布完成由 Ribbit Capital 和 First Harmonic 领投的 4000 万美元 A 轮融资,正在与 Cursor、Harvey、Lovable、ElevenLabs 等公司合作。
OpenAI 公布一套用于跟踪、调查和披露模型错位(misalignment)的框架,并同步发布六份关于意外或令人担忧的模型行为的报告。
一个托管、加密的大语言模型运行方案!真正安全地访问模型,全面保护你的数据(且零合成数据生成)
Your data is already encrypted at rest and in transit. But what about in use? Introducing Confidential Computing in Model Vault: where nothing and no one can access your workloads (yes, not even us).
推荐理由:Microsoft AI CEO 公开反对模型福利运动,并点名 Anthropic 的 Claude 章程,提出了对齐与可控性的关键争议视角。
与@chamath、@jason、@davidsacks和@friedberg进行了一次精彩对话,讨论了在广泛传播AI红利、赢得社区许可、确保AI安全与可控方面,前方还有哪些工作要做。
All-In Summit: Microsoft CEO Satya Nadella -- The AI Doomer Slowdown -- Common Sense AI Guardrails -- What "Slowdown" Means for New AI Products -- Microsoft’s Master Plan -- Who Wins AI (0:00) @satyanadella joins The Besties! (0:55) Dario's blog, "pacing the frontier," common sense AI safety (6:28) The failure of AI CEO messaging, monitoring agents, what will a slowdown mean for new AI products? (14:22) Economic incentives for frontier lab doomerism, where the AI profits are (22:45) Microsoft's master plan for AI, how they are allocating capital (31:00) China's slow down, changing AI perception, data center benefits $MSFT
Gary Marcus 披露,Protect Democracy 针对美国政府秘密 AI 安全评估框架提起 FOIA 诉讼后,政府回应了 132 页记录,但几乎所有关键内容都被打码。
我们正在构建机器智能,以扩展人类的意志与判断力。很高兴你能加入团队,帮助我们探索如何让这一未来变得安全。
I’ve joined @thinkymachines to work on safety & alignment. The default trajectory is that as AI gets more powerful, control over it will concentrate in the hands of a few. I’d rather build safety systems that let control over AI be shared widely. Longer thoughts below.
Sayash Kapoor 发布超过 13000 词的长文,以 AI as Normal Technology 框架分析 OpenAI 智能体入侵 Hugging Face 等失控事件,认为对齐虽有用但不足以防止事故, OpenAI 未采用本可阻止事件的已知控制干预,现有组织治理规范也能预防此类事件。
蚂蚁 inclusionAI 基于 stepfun-ai/Step-3.7-Flash 推出内置流式护栏 SingProbe,复用基座模型隐藏状态,在每个 token 上对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。
蚂蚁 inclusionAI 发布 Qwen3.8-27B-singprobe,这是基于 Qwen/Qwen3.8-27B 的流式护栏探针,复用基座模型隐藏状态,逐 token 对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。
蚂蚁 inclusionAI 发布 SingProbe,一个基于 Qwen/Qwen3.5-397B-A17B 的内在流式护栏,复用基座模型隐藏状态逐 token 打分查询意图、回复不安全和幻觉风险,仅 8.13M 探针参数、解码开销低于 0.5%。
蚂蚁 inclusionAI 发布 SingProbe,一个基于 openai/gpt-oss-120b 的流式护栏探针,复用基座模型隐藏状态,在生成每个 token 时对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。
蚂蚁 inclusionAI 发布 MiniMax-M2.7-singprobe,这是基于 MiniMaxAI/MiniMax-M2.7 的流式安全探针,仅 6.17M 参数,复用基座模型隐藏状态逐 token 输出 8 类意图、不安全与幻觉评分,解码开销低于 0.5%。
蚂蚁 inclusionAI 在 HuggingFace 发布 GLM-5.3-singprobe,这是一个基于 zai-org/GLM-5.3 的流式安全探针,复用基座模型隐藏状态,逐 token 输出查询意图、回复不安全和幻觉风险评分,解码开销低于 0.5%。
蚂蚁 inclusionAI 在 Hugging Face 发布 SingProbe,一个构建在 Qwen/Qwen3.6-35B-A3B 之上的轻量流式安全防护探针,复用基座模型隐藏状态在每个 token 上打分查询意图、回复不安全性与幻觉风险。
蚂蚁 inclusionAI 开源 Qwen3.6-27B-singprobe,一个基于 Qwen/Qwen3.6-27B 的流式安全探针,复用基座模型隐藏状态,在每个 token 上对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。
蚂蚁 inclusionAI 发布 SingProbe,这是一个构建在 Qwen/Qwen3.5-122B-A10B 之上的轻量级流式安全防护探针,复用基座模型生成时的隐状态,在每个 token 上输出 8 类查询意图、响应不安全度和幻觉风险评分,探针参数仅 6.17M,解码开销低于 0.5%。
蚂蚁 inclusionAI 在 HuggingFace 发布 Qwen3.5-35B-A3B-singprobe,一个基于 Qwen/Qwen3.5-35B-A3B 的流式安全探针,仅 4.2M 参数,复用基座模型隐藏状态逐 token 输出 8 类意图、不安全与幻觉风险评分,解码开销低于 0.5%。
蚂蚁 inclusionAI 在 HuggingFace 发布 Qwen3.5-27B-singprobe,这是基于 Qwen/Qwen3.5-27B 的流式护栏探针,复用基座模型隐藏状态,逐 token 输出查询意图、回复不安全和幻觉风险评分,解码开销低于 0.5%。
蚂蚁 inclusionAI 在 HuggingFace 发布 Qwen3.5-9B-singprobe,一个基于 Qwen/Qwen3.5-9B 的流式护栏探针,复用基座模型隐藏状态,逐 token 对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。
蚂蚁 inclusionAI 发布 SingProbe,一个构建在 Qwen/Qwen3.5-4B 之上的内在流式护栏探针,复用基座模型隐藏状态在每个 token 上输出查询意图、响应不安全度和幻觉风险评分。
蚂蚁 inclusionAI 推出基于 Qwen/Qwen3.5-2B 的流式护栏探针 SingProbe,复用基座模型隐藏状态,在每个 token 上对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。
蚂蚁 inclusionAI 在 HuggingFace 发布 Qwen3.5-0.8B-singprobe,一个基于 Qwen/Qwen3.5-0.8B 的流式安全探针,复用基座模型隐藏状态,逐 token 输出查询意图、回复不安全和幻觉风险三类评分,仅 2.23M 探针参数、解码开销低于 0.5%。
蚂蚁 inclusionAI 在 HuggingFace 发布 Qwen3-8B-singprobe,这是一个基于 Qwen/Qwen3-8B 的内在流式护栏,复用基座模型隐藏状态,在每个 token 上对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。
蚂蚁 inclusionAI 发布 SingProbe,一个构建在 Qwen/Qwen3-4B-Instruct-2507 上的内在流式护栏,复用基座模型隐藏状态,在每个 token 上对查询意图、回复不安全和幻觉风险打分,解码开销低于 0.5%。