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All news · Today68 items

All AI news

All collected and published items, ordered by time. Picks are a selected subset.
9/16Wed
  6. NVIDIA Blog62

    Jensen Huang at Dreamforce: Salesforce's first CRM reasoning model, Koa, is built on NVIDIA Nemotron 3 Super

    At Salesforce Dreamforce, NVIDIA founder and CEO Jensen Huang appeared alongside Marc Benioff as Salesforce released Koa, its first CRM reasoning model, built on NVIDIA Nemotron 3 Super.

    Why it matters: Huang discussed AI safety and shifts in the share of open models at Dreamforce, while the article explains how Salesforce's first reasoning model was trained and when it will be deployed.

  8. Latent Space38

    How Good Start Labs uses games to train AI models and transfer skills to real work

    Good Start Labs turned the 1830 railway board game into a training environment for experiments with 30B models. Both single-turn question-and-answer and multi-turn terminal-agent training improved performance in the game, but only the terminal-agent setup improved results on the Finance-Agent benchmark. The company, spun out of Every and funded with $3.6 million, argues that how a game is presented determines which skills a model learns and whether they transfer to work outside games.

  9. Google Research36

    Google Research proposes Retrieve-for-Train: compile diffusion models with RL to bypass inference bottlenecks in complex AI search

    Google Research proposes Retrieve-for-Train, a framework that uses offline RL to discover reward-aligned query fan-outs and compile them into supervision, then distills them into a 53.9M-parameter diffusion retriever. At inference, it generates a complete result set in one non-autoregressive pass without chain-of-thought tokens. The method fine-tunes fan-out language models based on Gemma3-4B and Qwen3-4B, using set-level attribute rewards to address paraphrase collapse in zero-shot LLMs and autoregressive latency.

  10. Logan Kilpatrick · X72

    Google releases Gemini 3.8 Live and 3.8 Live Extended Thinking real-time audio models

    Google has released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, new state-of-the-art real-time audio models positioned for leading price and performance. Gemini 3.8 Live supports 97 languages with seamless switching, as well as asynchronous tool calls and other features.

    Why it matters: Google's new real-time audio models offer language coverage and asynchronous tool calls, helping readers assess the practical limits of live voice interaction.

  11. Google DeepMind82

    Google DeepMind releases Gemini 3.8 Live and 3.8 Live Extended Thinking

    Google DeepMind has released two real-time voice-conversation models: Gemini 3.8 Live for scale and cost efficiency, and Gemini 3.8 Live Extended Thinking for complex tasks and multi-step reasoning.

    Why it matters: The two live voice models support parallel reasoning and tool calls and report results on several speech benchmarks, offering a way to assess the limits of voice agents.

  12. Google DeepMind · X77

    Google DeepMind releases Gemini 3.8 Live and 3.8 Live Extended Thinking

    Google DeepMind has released Gemini 3.8 Live and Gemini 3.8 Live Extended Thinking, calling them its best conversational AI yet. The company says the models can converse and think while handling tasks in the background without interrupting the user's workflow.

    Why it matters: The new conversational models give readers an update on Google DeepMind's positioning for real-time dialogue and background task handling.

  13. NVIDIA Blog62

    NVIDIA details DSX platform: MaxLPS delivers 24% more token throughput on the same power budget

    At the AI Infra Summit, NVIDIA presented its DSX platform. DSX MaxLPS was validated on Lambda's five-rack, 19-node HGX B200 cluster, where it increased token throughput by 24% under the same power budget—from about 4 million to 5 million tokens per second—and improved performance per watt by 23%.

    Why it matters: The article reports DSX MaxLPS measurements on a Lambda cluster, allowing readers to judge possible throughput gains under a fixed power budget.

  14. NVIDIA Blog72

    NVIDIA shares efficiency progress for Vera Rubin and DSX at AI Infra Summit

    At the AI Infra Summit, NVIDIA announced several advances in Vera Rubin and its DSX platform, shifting AI infrastructure measurement from peak performance to useful agentic tokens per megawatt.

    Why it matters: The article reports several measured token-output figures per megawatt for Vera Rubin and DSX, showing how efficiency metrics for AI factories are changing.

  15. Hugging Face Blog66

    IBM research proposes consistency guidelines, narrowing ReAct agents' Pass^5 gap from 24.4pp to 12.0pp

    IBM researchers describe new consistency guidelines in altk-evolve, designed to address unstable results when an agent runs the same task repeatedly.

    Why it matters: The paper quantifies the gap between Mean@k and Pass^k and shows that consistency guidelines cut it in half, offering a technique for agent-evaluation design.

9/15Tue
9/14Mon
9/13Sun
  1. Demis Hassabis · X58

    Demis Hassabis responds to Dario Amodei's proposal to slow frontier AI

    Demis Hassabis responded to Dario Amodei's new essay calling for the AI industry to slow frontier-model development. Hassabis said the direction was right but the details needed work. He also pointed to his own recent proposal for a frontier AI industry standards body as another way to address this critical moment. Amodei proposed a three-part slowdown plan and said Anthropic would unilaterally begin with the first step, giving third-party evaluators permanent employee-level access to verify safety measures, report incidents, and assess alignment in models under training.

  2. John Schulman · X64

    John Schulman welcomes embedded evaluators and support for pacing the AI frontier

    John Schulman calls embedding evaluators a big positive development, credits Sam/OpenAI for agreeing to do it as well, and welcomes how quickly “pacing the frontier” has gained traction. In the quoted post, Dario Amodei announces an essay proposing a three-part plan to slow the AI industry and says Anthropic will give third-party evaluators permanent, employee-level system access to verify safety measures, report incidents, and assess alignment during training. https://darioamodei.com/post/we-must-pace-the-frontier

9/12Sat
  3. Thinking Machines · X34

    Thinking Machines' John Schulman discusses where human judgment still matters as models self-improve

    Our own @johnschulman2 talks with Dwarkesh about where human judgment still matters as models improve and self-improve: teaching them to handle messy real-world tasks, applying taste to what works in the long run, and, above all, specifying what we actually want. Quoted post by @dwarkesh_sp (context, not the main author's work): New episode with @johnschulman2, @oneill_c and @BerenMillidge. I got together with some of the most insightful AI researchers I know who are at the openish companies, because I wanted to hear the details of what's actually happening at the frontier and what comes next. 0:00:00 – Steelmanning the case against RSI 0:18:39 – What's driving the Chinese labs' progress 0:28:06 – How will automated AI researchers be trained 0:33:51 – Will long-horizon RL elicit AGI? 0:45:24 – The sim-to-real gap 1:00:33 – How much progress is explained by data? 1:18:03 – Why is RL working so well? 1:24:54 – Move 37 and entropy collapse 1:28:31 – Rapid-fire timelines

9/11Fri
  1. Google DeepMind · X18

    Google DeepMind uses AI to recreate a memory that was never filmed

    How do you recreate a memory that was never filmed? Our team combined restored archival photos with pose-controlled models to capture Burt and Ethelle's mannerisms and micro-expressions. That helped recreate the day they first met for *Love, Rendered*, a new documentary made with @PrimordialSoup_ and @StorySyndicate_. Watch the full film on YouTube → https://goo.gle/3V8m94m

  4. GitHub Blog · AI & ML38

    Getting started with the GitHub Copilot app: using the diff, terminal, and browser panels

    The GitHub Copilot app includes diff, terminal, and browser panels, letting users review, run, and preview AI-agent code changes without leaving the app. The diff panel marks additions in green and deletions in red, and supports accepting changes, commenting, or asking Copilot to revise them. The terminal can run project commands directly and supports switching between multiple windows.

  5. Jan Leike · X19

    Now is a good time to build institutional mechanisms to pace the frontier of AI development. The industry is locked into an all-out scaling race to build superintelligence as quickly as possible, and we may need to give everyone more time for safety and alignment mitigations.

    Now is a good time to build institutional mechanisms to pace the frontier of AI development. The industry is locked into an all-out scaling race to build superintelligence as quickly as possible, and we may need to give everyone more time for safety and alignment mitigations.

  6. John Schulman · X44

    John Schulman Says User Data Likely Plays a Limited Role in Frontier Model Gains

    John Schulman says training on user data is exceedingly unlikely to contribute much to frontier models’ math gains, which he attributes to scaling pretraining and RLVR. He says user data may instead help identify failure modes that hired annotators cannot easily recreate, and calls for stronger disclosure norms about how companies train on user data and what capabilities they aim to improve.

9/10Thu