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All news · Today15 items

All AI news

All collected and published items, ordered by time. Picks are a selected subset.
Opinion

Viewpoints, commentary, analysis, interviews, and discussion of trends.

10/16Fri
  1. 量子位57

    Former Huawei Xiaoyi model lead Tang Duyu founds startup pursuing proactive physical AI

    Tang Duyu, former head of multimodal model development for Huawei’s Xiaoyi assistant, founded Guanxing Shuzhi to develop physical AI that identifies needs and decides when to intervene without explicit commands. Its ProAgent architecture pairs StreamingJev, a lightweight event monitor, with a Response Model that plans interventions and selects devices. The team reports testing guidance, warnings, and interaction on Reachy Mini across 60 cases, and introduced ProactiveXBench to evaluate intervention timing, progress, and outcomes.

  2. 量子位28

    How Antai MBA Is Training Future Business Leaders for the AI Era

    Shanghai Jiao Tong University Antai College of Economics and Management is reshaping its MBA education to prepare business leaders who can combine technical understanding with business judgment and responsibility. Its “Five Links” talent framework connects AI, science and technology innovation, industry, global perspectives, and Shanghai Jiao Tong University resources. The college also plans to co-create 1,000 AI projects with companies over the next decade and has announced curriculum and training changes for the 2027 cohort.

  4. Nathan Lambert · X28

    Guys this is like my superbowl I waited so long for US open weight models to be relevant again. Before today the US models were still behind Chinese models from as early as Feb. 2026. Still more work to do! This cut the gap by at least half, in time (say 3 months)

    Guys this is like my superbowl I waited so long for US open weight models to be relevant again. Before today the US models were still behind Chinese models from as early as Feb. 2026. Still more work to do! This cut the gap by at least half, in time (say 3 months)

  7. François Chollet · X81

    Chollet compares Gemini 4 Argon’s ARC-AGI-3 performance with Astra

    Argon has a similar performance profile on ARC-AGI-3 as Astra, François Chollet says, adding that Google is back at the frontier. https://t.co/a6GGEsMN4A The quoted ARC Prize post reports Gemini 4 Argon’s verified ARC-AGI-3 results of 97.5%, $4.8K with a provider adapter harness and 49.8%, $5.8K with the standard harness, plus 91.3%, $0.32/task on ARC-AGI-2 and 98.5%, $0.16/task on ARC-AGI-1. https://t.co/8siwgfBJcr

    Why it matters: The reported ARC-AGI-3 scores distinguish provider adapter and standard harness results, providing context for Chollet’s comparison of Argon with Astra and his assessment of Google’s frontier position.

  9. Nathan Lambert · X37

    Nathan Lambert Says He Needs to Blog After @AIatMeta Announces Muse Spark Open

    @AIatMeta says it is releasing Muse Spark Open, which it calls “the most capable US open-weight model to date.” The quoted post says the model is built for coding and agentic work and lists 816B total and 42B active parameters, plus a 1M token [context window](https://t.co/yAtoxNeYJm). Nathan Lambert reacts: “Okay now I really gotta blog 🎉” https://t.co/uKl5X18Xpw

  10. Nathan Lambert · X58

    Nathan Lambert predicts strong muse spark open adoption if reported scores hold up

    Nathan Lambert predicts adoption of muse spark open will surge if its performance roughly matches its reported scores, calling it the first US open-weight model with near-frontier agentic capabilities. He compares the shift with GLM 5.2 and Kimi K3 in China. In the quoted announcement, @alexandr_wang describes muse spark open as having 816B total params, 42B active, 1M-token context, and native multimodality, built for coding and agentic work. https://t.co/9elY7xigU5

  13. The Decoder75

    Google pitches Gemini 4 Argon for robot control, simulation, and training data evaluation

    Google is positioning Gemini 4 Argon as a single model for robotics simulation, control, and training data evaluation. Google reports that simulation-generated training episodes raised one robot’s success rate from 56 to 75 percent, but Argon ranked third in Artificial Analysis’s real robot control benchmark. The published demo prompts include extensive guidance, and Google does not report individual demo failure rates or the number of attempts behind the 75 percent result.

  14. The Verge · AI70

    Friends don’t let friends feed their data to AI agents

    Janus Rose examines how Meta’s Muse can collect sensitive information about users and their contacts, raising consent concerns for people who never installed the assistant. Privacy experts argue that permission interfaces obscure the scope of data sharing, while Meta says access is opt-in. The article also explains how uploading messages after decryption can undermine the privacy protections encrypted communications are intended to provide.

10/15Thu
  4. The Verge · AI71

    Benn Jordan on Flock, privacy, and the new surveillance backlash

    Nilay Patel interviews Benn Jordan about his investigations into Flock Safety and the bipartisan backlash against license plate surveillance. Jordan argues that the central problem extends beyond cameras to private networks that let police search people’s movements, and describes security vulnerabilities he uncovered. He advocates public, immutable audit logs, stronger oversight, and community input while explaining why he now reports vulnerabilities directly to Flock and engages with its leadership.

  7. Nathan Lambert · X12

    Nathan Lambert notes expected AI releases and rumors

    Lots of ecosystem players (Nvidia, Huggingface) vague posting about a lot of releases in the near future. Rumors include thinky, muse spark, and Chinese players who haven’t dropped in a while. Fable 5.5 and whatever OpenAI is cooking expected too at any moment.

  11. Alexandr Wang · X49

    Alexandr Wang Reviews Muse Wonder Full

    @davis7’s review says Muse Wonder Full has good writing and “voice,” capabilities on par with 6.1 sol for non-specialized tasks, and orchestration. It lists the naming scheme and price and usage limits as cons, and says the full model is available in Muse Code through a subscription, with no API. Alexandr Wang called it a thoughtful review, said there is lots to improve, and was glad people enjoy using the model. https://twitter.com/davis7/status/2110533786895356118

  13. Ethan Mollick · X30

    I think people believe that OpenAI and Anthropic, since they are so good at AI, are also somehow not normal companies that suffer from the normal problems of hypergrowth, but they are not immune from the inevitable politics, disfunction and confusion that haunt fast-growing firms

    I think people believe that OpenAI and Anthropic, since they are so good at AI, are also somehow not normal companies that suffer from the normal problems of hypergrowth, but they are not immune from the inevitable politics, disfunction and confusion that haunt fast-growing firms

  18. TechCrunch · AI41

    Ricursive Intelligence Is Developing AI to Design More Efficient Chips

    Ricursive Intelligence is developing AI that designs chips, learns from each iteration, and improves later designs to automate and accelerate chip design. At TechCrunch Disrupt, founders Anna Goldie and Azalia Mirhoseini said AI can find unconventional layouts and optimize entire chips; they cited a “donut-shaped” arrangement produced by their earlier system, AlphaChip. Goldie said demand for compute could grow at least 10,000x in the next five years.

  23. Latent Space60

    Crossing the Threshold: How OpenAI and Anthropic Approach Autonomy and Safety in Financial Agents

    Speakers from OpenAI, Anthropic, Two Sigma, and Bloomberg at AI Engineer New York described how financial agents can gain autonomy through targeted controls and human oversight. OpenAI’s Joseph Kim predicted agents could handle investment idea generation and trading in 2027, while Anthropic illustrated an agent that proposes order amendments for human approval. Two Sigma described classifiers that route important requests to people, and Bloomberg advocated deterministic controls outside the model, including tool-call checks, audit logs, and kill switches.

  25. Latent Space67

    Ian Quigley Explains Leash Bio’s Data Methods and Lab-in-the-Loop Agent Research

    Brandon Anderson interviews Leash Bio’s Ian Quigley about generating reproducible protein–small molecule interaction data using DNA Encoded Libraries and detecting misleading signals in experiments and models. They discuss a Kaggle competition with about 4B measurements where nobody beat chance on an unseen chemical library, and an agent sandbox searching 6.5M molecules on a budget of 100. Anderson reports that the sandbox has matured into Minos, with a preprint, blog, and open dataset being released today.