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#大佬观点

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Oct 17Sat
  4. TechCrunch · AI32

    Physical AI is more than robots, says the Nvidia veteran now investing in it

    Amulya Vishwanath, who spent seven years in robot platform product marketing at Nvidia, founded early-stage firm Techable VC to invest in physical AI. She says robotics is in its "Lego era," with building blocks like actuators, grippers, software and safety standards still being rebuilt, rather than nearing a "ChatGPT moment." She also defines physical AI as broader than robots, covering molecules, materials, drugs and machines on factory lines.

Oct 16Fri
  1. TechCrunch · AI36

    Mark Wahlberg on using AI in film ‘the right way’ and being open to an AI clone carrying on his legacy

    Speaking at TechCrunch Disrupt, Mark Wahlberg said AI has a place in the film industry if used "the right way." He plans to use AI to enhance visuals like stadium scenes in the upcoming Pete Rose biopic, but opposes using ChatGPT to write scripts directly or replace real actors. He has not visited the CAA Vault but is willing to try it, and in the future doesn't rule out an AI version of his likeness carrying on his work, nor object to the animated series Ted continuing with AI after his death.

  3. TechCrunch · AI46

    Replit’s CEO says the future of work is the ‘self-driving company’

    Replit CEO Amjad Masad laid out his vision of the "self-driving company" at TechCrunch Disrupt in San Francisco: AI agents handle most of the grunt work while human value shifts to judgment and taste. He noted that Replit's support tickets are first triaged by agents searching GitHub issues, codebases and Linear tickets, and after human fixes a one-click deploy closes the loop with agents. He argues agents aren't taking jobs but freeing people to spend more time on architecture and discussion.

  4. TechCrunch · AI51

    Amazon devices chief Panos Panay sees a future where AI devices replace the need to use apps

    Panos Panay, Amazon's SVP of Devices & Alexa, said at TechCrunch Disrupt that personal computing is heading toward ambient computing devices where sensors and AI work proactively in the background — the next generation of users won't open an app for everything but will expect devices to understand their needs, though smartphones will remain important.

Oct 15Thu
  1. AWS Machine Learning Blog56

    Trust in Depth: Giving agents more autonomy without losing control

    AWS released its Trust in Depth methodology, which lets agents operate safely without per-step human approval through four layers: Scope (limiting capabilities and access), Know (Agent Registry and independent identities), Bound (AgentCore Policy enforced at the Gateway, Guardrails, and the open-source sandbox Strands Box), and Observe (CloudWatch Omni tracking and evaluation).

  2. The Verge · AI58

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

    On The Verge’s Decoder podcast, investigative journalist Benn Jordan discussed Flock’s automated license plate recognition (ALPR) system: he recently testified before the Senate Judiciary Committee’s crime and counterterrorism subcommittee, Flock is facing hundreds of local governments canceling contracts, an investigation by Republican Senator Josh Hawley, and Reuters reported plans to cut about 18% of its workforce.

  3. Semiconductor Engineering Chips and Packaging51

    How Close Are We To Fully Autonomous Chip Design?

    In a SEMICON West interview, Synopsys CEO Sassine Ghazi said the company has deployed more than 40 task agents, and some customers can hand a spec to the Synopsys Autonomous Agent to automatically generate and verify RTL — a process that used to take multiple engineers several months — though fully autonomous chip design remains out of reach today.

  4. Simon Willison36

    Tree Appreciator

    Simon Willison had Claude Opus 5.5 Max build a Three.js game called Tree Appreciator in the Claude iPhone app, where the gameplay is appreciating differences between two algorithmically generated trees. Claude ran for 3 hours 7 minutes and produced a fairly playable spot-the-difference game with music, animations and an achievement system.

  6. The Decoder64

    Google says self-improving AI isn't anywhere close

    Google DeepMind engineer Haydn Belfield said AI-driven acceleration of AI research has not yet appeared and self-improving AI remains far off. AI now writes over 75% of production code, but roughly 88% of code changes still require human oversight or review; the share of code changes fully handed to AI rose from 7.5% to 11.9%, and the more complex the task, the lower the rate of independent AI completion.

Oct 14Wed
  3. TechCrunch · AI30

    Cerebras CEO says we’re nearing a future where everyone has a personal AI assistant at home

    Cerebras CEO Andrew Feldman said at TechCrunch Disrupt 2026 that the era of at-home AI devices is not far off. He predicted personal AI assistants will run locally, splitting work between devices and the cloud like phones do, and could eventually shop on users' behalf. He cited Meta's Muse and Instinct, a startup valued at $10 billion, and said he already uses a set of agents to manage his schedule and look up who is calling him.

Oct 13Tue
  2. TechCrunch · AI41

    Microsoft president says UK is best placed to drive AI regulation

    Microsoft President Brad Smith, on a BBC podcast, called for urgent AI regulation, saying AI is both a "weapon and a tool" and the moment for global safety rulemaking "has arrived." He argued that the UK, rather than the US or EU, is best placed to lead global safety rules because the UK AI Security Institute, established in 2023, can access primary models from leading US frontier labs for safety testing before deployment.

Oct 12Mon
Oct 11Sun
  3. The Verge · AI54

    Satya Nadella says we should assume all AI models are ‘compromised’

    Microsoft CEO Satya Nadella posted on X arguing that AI should no longer be treated as a "nested black box" whose advice is simply accepted or rejected, and that transparent systems should be built that are containable, observable, and leave tamper-proof human-readable evidence. He suggested timely incident disclosure, independent audits, and verifiable data, and went further to propose assuming from the start that models are compromised and isolating them accordingly—like an emergency brake, authorized personnel should be able to pause or shut down a model mid-run at any time; he believes more advanced models require standardized, more advanced containment techniques.

Oct 10Sat
Oct 9Fri
  1. Simon Willison38

    Quoting Matthew Green

    Cryptographer Matthew Green argues there is a 1% chance we live in a 'Minicrypt' world where public-key cryptography is fundamentally impossible, and a 15% chance we will actually lose confidence in existing public-key algorithms. He notes AI creates surprises far faster than humans (even AI-assisted) can replace standards, and only advance preparation enables recovery from such surprises.

  2. Simon Willison30

    Quoting Carson Gross

    Carson Gross argues that programming is essentially two things: solving problems with computers, and controlling complexity in the process. He finds it hard to imagine these two skills losing value, so programming will remain a viable career even as AI tools keep emerging.

  4. Latent Space53

    Synthesis Superintelligence: from Semiconductors to Superconductors — Periodic Labs’ Liam Fedus and Ekin Dogus Cubuk

    The Latent Space podcast interviews Periodic Labs co-founders Liam Fedus and Ekin Dogus Cubuk about their "synthesis superintelligence" vision: building reinforcement learning environments around physical labs where AI agents learn through an end-to-end cycle of predicting, synthesizing and characterizing materials, rather than relying only on internet data.

Oct 8Thu
  1. MIT Technology Review · AI26

    Building a safer path to autonomous industrial AI

    AVEVA CTO Arti Garg discusses how foundation models, physical AI, and agentic AI are pushing industrial automation into a new phase, with research showing industrial AI adoption has grown about 78% over the past two years. She stresses that AI should augment rather than replace human judgment in critical decisions, and AVEVA's responsible AI framework emphasizes safety, efficiency, and human oversight. She also serves on an IEEE working group creating standard methods for measuring AI's environmental impact in terms of power, water, and carbon.

  2. Simon Willison26

    Quoting Ben Affleck

    Ben Affleck says he has been involved with machine learning since the digital cinema era and can write fairly bad Python scripts. He explains how film VFX pipelines have used machine learning for years: applying convolutional neural networks to analyze tensors (numeric representations of images) for edge detection and feature extraction to key green screens and swap backgrounds, calling Transformers an evolution with far more compute.

Oct 7Wed
  3. Microsoft Research48

    What AI gets wrong and what failure teaches us

    In a Microsoft Research podcast, partner research manager Jennifer Neville speaks with applied scientist Chad Atalla about how evaluation drives AI systems past performance limits to meet user needs, and about the "unexpected failures" models show when tested beyond standard benchmarks. She shares practical advice on using existing AI systems and stresses the importance of carefully checking data when results don't match expectations.