AI大佬集体卷入政治口水战
Yann LeCun 密集转发批评马斯克政府效率部（DOGE）裁撤国际开发署（USAID）的推文，称其将导致数百万儿童死亡；Anthropic、OpenAI 官方账号同期发布无关日常内容。AI 顶级机构核心人物与官方账号呈现鲜明割裂。
LeCun 作为 Meta 首席科学家，过去两年刻意与马斯克保持距离，此次罕见高频介入美国国内政治议题，时间点选在 Meta 试图修复与特朗普政府关系的敏感窗口。这种'个人账号冲锋、机构账号沉默'的分裂策略，正在成为硅谷高管的标准政治操作范式。
对 AI 从业者而言，这意味着你的技术背书人（公司首席科学家）和你的商业利益（政府合同、监管关系）可能走向公开脱钩。如果你在头部 AI 公司负责公共事务或政府关系，需要提前设计'高管个人言论'的防火墙话术，而不是事后灭火。
更值得跟踪的是：OpenAI 和 Anthropic 的沉默是默契还是接到内部指令？这预示着 AI 监管游说联盟（如 AI Alliance）内部对特朗普政府的态度已经出现实质分歧。
RT @NickKristof: .@elonmusk says that no one can name a person who died from his aid cuts. In fact, I've met the kids who are dying, and I'…
查看原文 →RT @ML3democrats: As a European, I don't understand how the sitting U.S. president—who has to pay $5 million to a woman he r@ped and defame…
查看原文 →RT @dieworkwear: this is what you look like with low rise pants https://t.co/6c97K2M5wu
查看原文 →RT @joni_askola: Don't fall for Elon Musk's gaslighting. There is a massive, life-or-death difference between well-planned budget cuts an…
查看原文 →RT @Forbes: One study found Musk’s closing of the U.S. Agency for International Development could cause the deaths of millions of children.…
查看原文 →We’re introducing GeneBench-Pro, a research-level benchmark for a harder kind of AI progress: how well agents can navigate messy biological data, choose the right analysis path, and make judgment calls that real computational research depends on. https://t.co/AsilnnSxnE
查看原文 →RT @SteveRattner: Musk is right that many problems can't be solved just by "throwing money" at them. But US aid to fight AIDS, child hunge…
查看原文 →RT @arakharazian: We can finally say AI isn't killing jobs. A new paper from me, @tryramp, and @RevelioLabs uses firm-level spend and work…
查看原文 →RT @SteveRattner: It's not just the Lutnick sons cashing in on the Trump family's White House self-dealing. World Liberty Financial — the…
查看原文 →RT @aljhlester: A very damaged man who had the political power and the private wealth to save millions of lives but chose instead to allow…
查看原文 →RT @NickKristof: The discussion about @elonmusk's comments on humanitarian aid has mostly focused on our values, which seems right. But not…
查看原文 →RT @TyBealPhD: Marco Rubio and Elon Musk claim that no one died from shutting down USAID. Yet the best estimates are between 8–20 million b…
查看原文 →RT @BulwarkOnline: "Every one of these children had a name. They had dreams, loves, and futures. Their lives were as valuable and special a…
查看原文 →Today, we give robots a /skills library that self-evolves and compounds indefinitely! Introducing ASPIRE: a robot solving its 100th task is no longer as clueless as solving its first. Coding agents observe multimodal sensory traces from simulation and real robots, launch an evolutionary search over control programs, and distill the best know-how into an ever-expanding library. ASPIRE is a new type of continual learning: "training" is skill refinement instead of gradient descent. "Trained model" is a repo of sensorimotor skills instead of floating weights. “Distributed training” is a panel of agents each practicing a different skill instead of sharded minibatches. Here's the beauty: ASPIRE gives the tired terms "sim2real transfer" and "cross-embodiment transfer" a whole new meaning. Bridging the sim-to-real gap is notoriously brutal. An end-to-end policy has to swallow both the visual shift (sim looks toyish next to a real camera) and the subtle contact physics it never quite gets right. ASPIRE sidesteps the mess, because it doesn't ship pixels or weights across the gap, but ships the know-how. The robot still has to practice in the real world, not zero-shot, but it gets there way faster because it isn't rediscovering the strategy from scratch. Same for going single-arm to bimanual hardware, which usually requires new data and retraining from zero. ASPIRE achieves up to ~10x cut in "transfer learning” tokens (yes, tokens are the new unit of *training* compute ;) Check out our gallery of 150+ tasks and 90+ skills the robots taught themselves, all on the website! Kind of wild that we can ship the "learned weights" as an HTML page rather than a GGUF. We'll open-source the full stack so your own robot library starts compounding from ours! Deep dive in thread:
查看原文 →RT @GoogleDeepMind: We’re shipping 2 major releases: 🔘 Nano Banana 2 Lite: our fastest and cheapest Gemini Image model 🔘 Gemini Omni Flash…
查看原文 →RT @nytimes: President Trump reaped at least $2.2 billion from his family’s holdings last year, a new filing shows. https://t.co/Rdp8KHJLxl
查看原文 →RT @ClementDelangue: Open-source AI is booming, massively impactful for progress, competition, transparency & orders of magnitude less dang…
查看原文 →RT @randall_balestr: Can regularization based JEPA (e.g. SIGReg) scale and compete with SOTA foundation models (DINO)? Here is the answer:…
查看原文 →RT @gklambauer: LeWorld model becomes ADAPTIVE and meets MODEL-PREDICTIVE CONTROL AdaJEPA by Yann LeCun and colleagues performs actions, t…
查看原文 →RT @randall_balestr: The Sensorimotor World Model (https://t.co/K5iWbk7Izs): a deep dive into the role of inverse dynamics modeling as an a…
查看原文 →End-to-end example of a long-running AI agent that pauses, resumes, and never loses context. It simulates the onboarding of a new employee. Here are 3 patterns you'll learn from this example: 1. How to implement a durable state machine that persists over time. 2. How to build event-driven agents that stay dormant until they receive a webhook event. No active polling or blocked threads. 3. How to build multi-agent delegation instead of relying on a single agent to do everything. You can deploy this example in the Gemini Enterprise Platform: https://t.co/vPH9Xd6UsO You'll find a link to the GitHub repository with the complete source code and a complete explanation of this example in this article: https://t.co/tm2WDLKI8v Thanks to the Google Cloud AI team for partnering with me on this post.
查看原文 →ENPIRE -> ASPIRE, our 2nd work in the series for Physical AutoResearch. We are building the components for robot self-improvement, one /skill at a time.
查看原文 →RT @HaiyuWu1: Working on world model or SSL? You definitely need to try our new work: VISReg! What does it achieve? 💪 Strong collapse prev…
查看原文 →People have really soured on AI over the last few weeks. Overall sentiment: very negative. This mostly comes from the developers I talk to. The non-tech people I know have always been either meh or thumbs down toward AI. I think all the talk about companies replacing people with AI is finally catching up with folks. Twitter might tell you that AI is meant to help you and yada yada, but who are we kidding? Every company owner is looking at a spreadsheet right now, trying to figure out how to halve their headcount. I’m not sure how this will play out, but I don't see how folks will come around. Especially the younger generation.
查看原文 →AG-UI makes building agentic applications dramatically easier. Here's how it works. This is a model for a simple chatbot: User → LLM → Response But interactive agents that render UI, pause for approvals, and ask users for input need a much more complex model. When building these agents, a response from the LLM will include a series of state changes as the agent runs: • Agent started a task • Agent called a tool • Agent updated its state • Agent streams these tokens • Agent is waiting on a human • Agent is resuming the task The Agent-User Interaction Protocol (AG-UI) treats the LLM response as a stream of events rather than a text endpoint. In practice, here is what you get as an agent runs: 1. Lifecycle events so your UI knows where the agent is. 2. Text messages that stream tokens. 3. Tool calls so your UI can prefill a form with any required arguments. 4. State updates that keep your UI in sync with the agent. 5. Special events for human approvals, rich media, and custom needs. All of these events travel over standard transports (SSE, WebSockets, or plain HTTP) as JSON. As a result, you can build a frontend that stays in sync with the agent's progress without having to invent a custom process to make this happen. For example, building a human-in-the-loop workflow becomes an off-the-shelf component you can integrate rather than build from scratch. @CopilotKit is the creator of AG-UI, and you can use it when building frontend applications pretty much anywhere: • React • Angular • Vue • React Native • Slack • Teams • Discord • WhatsApp • Telegram Here is the link for you to check it out: https://t.co/CGEDOtd8V1 Thanks to the CopilotKit team for partnering with me on this post.
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