AI High Signal

Ce qui vient de franchir un palier de reprise, le plus récent en tête.

  • 14
    de 9 à 14 il y a 23 min
    Richard Sutton @RichardSSutton
    Brute force computation is not enough for intelligence. Clever new methods are needed. The science of AI is far from finished. IMHO
    incompleteideas.net https://incompleteideas.net/IncIdeas/BitterLesson.html
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    elie @eliebakouch a cité · 08/11 09:58
    i disagree with the first point, there is nothing that says that scaling up current transformer recipe is not enough for "intelligence". you take kimi K3 or your favorite open arch + some stability fixes, you scale it to 50T parameters, you do classical training end to end, you
    François Fleuret @francoisfleuret a cité · 08/11 07:44
    We need a model that learns *in spite* of its inductive bias, not thanks to it. We have to stop being cross-entropy fetishists.
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    M Saiful Bari (MARUF) @sbmaruf a cité · 08/11 07:02
    True! This is the era of the RL objective. The biggest economy on this earth (US stock market) runs on subscriptions. Subscriptions require vendor lock-in. Currently, the frontier is pushing “Variance” learning from customer data, like how Google won the search market. But I
    Denny Zhou @denny_zhou a cité · 08/11 06:26
    The Bitter Lesson 2.0
  • 11
    de 10 à 11 il y a 4 h
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    Justin Lo @loresjpg
    Nice DeepSWE score! Does it survive a lowercase filename? Introducing MaxxBench: 89 DeepSWE tasks with small changes to the instructions or starting code. Same engineering problem, slightly different requirement. Models clustered around 74% on DeepSWE end up 23 pp apart. Good
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    Julian Schrittwieser @Mononofu a cité · 01:37
    Making novel variations of evals is very useful, public evals tend to get memorized!
    Hamel Husain @HamelHusain a cité · 01:16
    I see some are drawing the wrong inference from this study: they are focusing on this meta benchmark as yet another benchmark for model picking, i.e. open vs closed models, model families etc. That’s too narrow of an interpretation! You need to do your own private evals on
    Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) @teortaxesTex a cité · 08/11 22:00
    holy shit, an art form of scientific grift next METR/Epoch/AA employee right here you all need to work on your epistemic hygiene
    Ofir Press @OfirPress a cité · 08/11 21:26
    GPT-5.6 Sol and Luna were built by roughly the same people using roughly the same infra, I don't think we can blame the 20 point difference in Luna just on memorization. There's something else being tracked here in addition to memorization. Maybe MaxxBench questions are slightly
    Mat Velloso @matvelloso a cité · 07/11 21:20
    I'm both surprised and also not at all
  • 3
    il y a 8 h
    alphaXiv @askalphaxiv
    We mapped funding raised vs founder citations across 32 neo-labs Their founding teams have amassed 8.35M citations and have raised nearly $26B Discovery Loop leads with 1.59M founder citations, followed by SSI (915K) and AMI Labs (752K) The two are correlated, but a huge
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    rohan anil @_arohan_ a cité · 08/11 21:24
    X axis can be number of uncs. Never beating allegations.
    Julian Togelius @togelius a cité · 08/11 04:22
    How can I not retweet this …hey, investors, look here
  • 4
    de 3 à 4 il y a 8 h
    Pushmeet Kohli @pushmeet
    Across our projects @GoogleDeepMind, we are witnessing a genuine phase transition in the pace of scientific discovery. You will see a glimpse of this in our releases in the next few weeks.
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    Lucas Beyer (bl16) @giffmana a cité · 08/11 20:37
    Pre-announcing the pre-announcement
    elvis @omarsar0 a cité · 08/11 17:08
    Been working for this moment for over a decade. What I've witnessed behind the scenes, together with what we have been building, tells me we are about to enter a truly incredible era of AI-powered scientific discovery. It's an incredible time to be doing research. I will be
    Chubby♨️ @kimmonismus a cité · 08/11 15:36
    2026 is the turning point for humanity. Google DeepMind’s Pushmeet Kohli says AI is accelerating scientific discovery and that upcoming releases will offer a glimpse of the progress. He says the team also aims to help the wider scientific community use these tools responsibly.
  • 23
    il y a 9 h
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    Reflection @reflection_ai
    Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active. - Frontier reasoning efficiency - Advances the Western open frontier on coding & agentic tasks - Trained end-to-end from scratch Full weights release this month. Learn more about
    huggingface.co reflection (Reflection) Org profile for Reflection on Hugging Face, the AI community building the future.
    luma.com Open by Design: NYC Developer Meetup | Together AI × NVIDIA × Reflection · Luma Open by Design NYC: Open Models in Production What does it take to move open models from experimentation to production? Join Together AI, NVIDIA, and…
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    Aaron Defazio @aaron_defazio a cité · 08/11 20:11
    Reflection's Beam was trained with my AdamC algorithm! Cool... Thinking Machine's blog post also mentioned using it, so it's seeing a lot of use. It really does fix late-stage gradient norm issues.
    Andreas Kirsch 🇺🇦 @BlackHC a cité · 06/10 14:33
    Congrats to everyone there!
    Ben Burtenshaw @ben_burtenshaw a cité · 06/10 13:00
    No one (actually) working on open source is surprised by this release. For months, @reflection_ai engineers have been down in the trenches contributing to projects like transformers, vLLM, SGLang, TRL, and OpenEnv. Go check the commits. I'm super excited to see an open lab
    Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) @teortaxesTex a cité · 06/10 03:22
    interesting fact about Beam: 23B*23,8T/37B*14,8T≈0,9996! It's an iso-flop replication of DeepSeek V3, 2 years later. Not of V3's efficiency, though. in tender loving Chinese quant hands, one gimped H100 is > one GB300 in NVL72.
    Together AI @togethercompute a cité · 06/10 01:46
    Congrats to the Reflection team on Beam 🎉 Be sure to join us next week at our NYC meet-up with the Reflection team and NVIDIA
  • 4
    il y a 11 h
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    Mark Gurman @markgurman
    NEW: Apple is cutting employee AI spending budgets even as it pushes workers to use the technology more. Engineers have seen daily limits cut from $300 to $200, with further reductions to $100 planned. Other teams could see limits fall to $25.
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    sunil pai @threepointone a cité · 08/11 17:47
    (voice note) spent a couple of days thinking about this. I'm finding it hard to reconcile what Apple did with its engineers where they're restricting them to 200, 300 dollars a day for using LLMs. Like, at this point, the engineer is actually kind of decoupled from the work they
    Theo - t3.gg @theo a cité · 05/11 08:26
    Apple software is dead
    Artificial Analysis @ArtificialAnlys a cité · 04/11 23:02
    A $100 budget buys 40 Intelligence Index tasks (a mix of task archetypes) with Opus 5.5 at medium reasoning effort, or 10 tasks at max effort. The same $100 is 166 tasks with DeepSeek V4.1 Flash 1017 (max) or 2,500 with GPT-6 Luna (medium).
    Zach Mueller @TheZachMueller a cité · 04/11 22:38
    Apple engineers aren’t using their Macs to code with local models? 🤔🧐
  • 4
    il y a 23 h
    Dwarkesh Patel @dwarkesh_sp
    I spent 20,000 dollars in API credits having different AI models play each other at Blood on the Clocktower (a Mafia or Werewolf type social deception game). The results were fascinating. Models have gotten much better over the course of this year at creative on-the-spot
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    Boris Power @BorisMPower a cité · 06/11 12:19
    Amazing, this is so fun, yet useful!
    Teortaxes▶️ (DeepSeek 推特🐋铁粉 2023 – ∞) @teortaxesTex a cité · 06/11 03:27
    Impressive. Dwarkesh says that over the course of the year models "have gotten much better", but it's mostly GPTs. OpenAI gained 583 Elo between 5.4 and Astra. Over *a longer* time, Anthropic's best improved by 163. OpenAI is training on something similar to this scenario.
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    Noam Brown @polynoamial a cité · 06/11 02:24
    Honestly quite impressed with the methodology on this benchmark. @dwarkesh_sp want a job designing evals at @OpenAI?