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All news · Today8 items

#Expert views

All collected and published items, ordered by time. Picks are a selected subset.
Today10/17Sat
  1. Alexandr Wang · X35

    Alexandr Wang Thanks Meta Superintelligence Labs for Reaching a Model-Building Milestone

    Alexandr Wang thanked Meta Superintelligence Labs’ research teams after a week of launches, including Muse Wonder [full] and Muse Spark open. He said MSL and TBD began a little more than a year ago with the goal of producing a frontier model within a year or so, despite the ambition of that target given where Llama 4 landed. Wang said the teams have also built a scalable system for continuing to advance models at the frontier.

  4. MIT Technology Review · AI44

    MIT Technology Review’s James O’Donnell Interviews AI-Designed Virus Creator Samuel King

    Senior AI reporter James O’Donnell interviews Stanford University bioengineering PhD candidate Samuel King about using a generative AI model to propose genetic blueprints for microscopic viruses. The session covers King’s work, his recognition as one of MIT Technology Review’s Innovators Under 35, and new ways of seeing biology. The source says this is not yet an example of AI-generated life.

  5. 量子位58

    Amazon CTO Werner Vogels urges companies to keep fear out of AI decisions

    Amazon CTO Werner Vogels argues that companies should understand their business problems and AI’s capabilities before rushing to adopt it. Speaking in Shenzhen, he emphasized task suitability, operating costs, explicit limits on Agent actions, and developers’ responsibility for outcomes. He also warned that AI-generated code can accumulate verification debt and stressed that junior engineers still need experience reading and reviewing code.

  6. Sam Altman · X55

    Sam Altman welcomes Nick Cammarata’s optimistic outlook on mechanistic interpretability

    Sam Altman says he is very happy to see Nick Cammarata’s post on interpretability. Cammarata says early work at OpenAI left him pessimistic, with primitive tools making about 2% of neurons legible. He now claims modern agentic tools make the vast majority legible in models up to trillions of parameters, and believes ambitious interpretability could be solved quite soon as automated scientists improve. https://already-clean.goodfire.com/

  7. TechCrunch · AI39

    Nvidia Veteran Says Physical AI Is More Than Robots

    Amulya Vishwanath, a former NVIDIA robotics product marketing leader and founder of Techable VC, says robotics is in its “Lego era,” with basic building blocks still being rebuilt. On TechCrunch’s Equity podcast, she discusses investing in physical AI across robotics, materials science, and life sciences, and defines it broadly to include “a molecule, a material, a medicine,” or a factory machine.

  8. TechCrunch · AI32

    Former Nvidia robotics executive says the field is still in its “Lego era”

    Amulya Vishwanath, who spent seven years leading product marketing for NVIDIA’s robotics platforms, says robotics is still rebuilding basic components such as actuators, robotic hands, software, and safety standards. On TechCrunch’s Equity podcast, she discussed where she is investing through Techable VC, the early-stage firm she founded to back physical AI across robotics, materials science, and life sciences.

10/16Fri
  4. The Decoder64

    Is AI interpretability close to a breakthrough? A new essay has researchers divided

    Cammarata argues that better tools and automated agents make neural networks more interpretable than earlier research suggested, while Google DeepMind's Neel Nanda disputes that a broad breakthrough is near. Cammarata reports preliminary findings that about 90 percent of neuron poles in Qwen3-32B have one main job and reinterprets classic superposition examples as related features. Nanda sees practical value in neuron analysis but expects significant gaps and maintains that superposition is real. The technical companion paper has not yet been released.

  6. Arthur Mensch · X55

    Should Europe leave frontier AI to the Americans and the Chinese?

    Arthur Mensch argues that Europe should build frontier AI models, warning that relying on American and Chinese providers would deepen economic dependence and limit control over access, pricing, security, and scientific research. He argues that open models only partly address this dependence and that frontier research supports industrial deployment. At Mistral, he says, compute capacity is set to increase sixfold over the coming year, and the company will raise capital to move faster.

  10. Ethan Mollick · X22

    This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.

    This paper shows why I am skeptical of the idea that frontier models can be easily replaced by cheaper ones (and why you should use the best models as orchestrators): Small improvements in accuracy & judgement compound over long-horizon tasks, creating huge gains in outcomes.

  11. Alexandr Wang · X30

    Alexandr Wang Reacts to @kirillk_web3’s Muse Spark Post

    @kirillk_web3 says Meta’s Llama 4 disappoints everyone, Meta goes closed with Muse Spark, Zuck writes a $100-200 billion check, Muse Wonder ships on Monday, and 3 days later Meta drops you with open weights: 816 billion parameters, only 42 billion active at a time. https://t.co/031gtR0nHm https://t.co/nEN1JMzvBa Alexandr Wang: “lmao muse spark open balling out” https://t.co/AcUT9o8NEQ

  13. Ethan Mollick · X23

    The first review I’ve seen of my upcoming book, Co-Existence, from The New Scientist. (When I became an author, I was surprised to learn you don’t know who is reviewing your book until the review comes out, so it is nice to start with a positive one!) https://www.newscientist.com/article/2593188-how-to-build-a-real-relationship-with-ai-and-keep-your-cognitive-edge/

    The first review I’ve seen of my upcoming book, Co-Existence, from The New Scientist. (When I became an author, I was surprised to learn you don’t know who is reviewing your book until the review comes out, so it is nice to start with a positive one!) https://www.newscientist.com/article/2593188-how-to-build-a-real-relationship-with-ai-and-keep-your-cognitive-edge/

  14. Mira Murati · X53

    Mira Murati reflects on Tinker users turning their ideas into model weights they own

    Mira Murati says one of the best parts of this year, inside Thinky and out, has been watching people follow their own ideas into weights they own. https://twitter.com/thinkymachines/status/2110851669915570685 In the quoted post, @thinkymachines marks one year since Tinker’s launch with a look back at what it has shipped and what users are building. https://thinkingmachines.ai/news/one-year-of-tinker/

  15. The Verge · AI22

    Hollywood’s New Tech Films Take a Sharper View of Silicon Valley

    A new wave of films about Silicon Valley figures presents them more critically than earlier portrayals, reflecting audiences’ dimming views of Big Tech. The films include The Social Reckoning, which connects Facebook’s past decisions to current concerns about Meta’s influence, and Alex Gibney’s Musk documentary, which is screening in select theaters and has faced advertising restrictions. Luca Guadagnino’s Artificial and Nathan Fielder’s You Can See Everything also examine Sam Altman and Elizabeth Holmes, respectively.

  16. Clément Delangue · X30

    We're sending a bunch of Microducks to assess @AnthropicAI! Kidding aside, it hasn't been approved yet but in my opinion, it's very important that open-source engineers and scientists are included in these important endeavors for transparency. That's a big part of why @Thom_Wolf is starting the open alignment initiative. We're all in this together!

    We're sending a bunch of Microducks to assess @AnthropicAI! Kidding aside, it hasn't been approved yet but in my opinion, it's very important that open-source engineers and scientists are included in these important endeavors for transparency. That's a big part of why @Thom_Wolf is starting the open alignment initiative. We're all in this together!

  17. TechCrunch · AI36

    Amazon’s Panos Panay Sees Ambient AI Devices Making Apps Less Important

    Amazon devices chief Panos Panay said ambient devices that use sensors and AI could handle more tasks in the background, making app-based interactions less important. He described Amazon’s new Bee devices, introduced Thursday, which passively record audio and take notes; they process audio in real time and delete it afterward. Panay said he expects an intelligence layer connecting different devices, rather than one form factor replacing another.

  18. Aravind Srinivas · X57

    Aravind Srinivas argues neoclouds need to own the full agent inference platform

    Aravind Srinivas argues that neoclouds can't just rent GPUs and need to own the full agent inference platform, including open-weight models (SGLang), fine-tuned models (Miles, River), and secure sandboxes (SPACE). He looks forward to working with Coreweave, Akamai, Crusoe, and more partners. In the quoted post, Reed Albergotti reports that Perplexity is open sourcing its sandbox, SPACE, with partners including Akamai, Coreweave, and Crusoe. https://twitter.com/ReedAlbergotti/status/2110782821879144922

  19. 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.

  21. 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)

  23. 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.

  25. 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

  28. 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.

10/15Thu
  3. 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.