リサーチ — 今週のランキング
過去7日間に「リサーチ」カテゴリで最もエンゲージメントを獲得した投稿。
Fifteen years ago, @Coursera and online courses changed education. It worked better than almost anyone expected, expanding access by opening up where you can learn. But how you learn remains largely the same as it has for centuries: it is still one-size-fits-all, taught the same way to each person who shows up. We now have an opportunity to change how learning happens. With advances in AI, we can now build a custom learning guide for each person. We will turn learning from one‑to‑many to one‑to‑one. I'm starting LearnVector to invent this next generation of learning. We are starting with a $100M investment from Coursera, and plan to collaborate closely with Coursera and Udemy. Good learning needs much more than just a chatbot. Research shows that chatbots without guardrails harm learning. They help complete tasks and enable students to do better on homework. But cognitive offloading to a chatbot results in them being less skilled. And, you cannot always trust what a chatbot tells you. In contrast, LearnVector will plan a path with you, adapt to how you learn, and patiently stay with you until you’ve mastered new skills. One thing has not changed in all this time. People want learning they can trust: material that is accurate, relevant, and worth the effort you put into it. Anything less wastes the most valuable thing a learner has: time. Coursera has a trusted library of materials from authoritative sources. LearnVector plans to work with Coursera to bring this trustworthy learning to everyone. I'm grateful to Greg Hart and the entire Coursera team for supporting LearnVector. I look forward to working with our talented team to change how we learn, and accelerate human development. https://t.co/TqFUDFd1hb
Gemini Robotics 2 is here, with our new suite of models, robots can now reason through every movement to manage tasks that weren’t possible before, like tying delicate knots - and even team up to solve complex workflows. Huge congrats to the robotics team on this great milestone!
One brain. For any robot. 🤖 We’re launching Gemini Robotics 2: our next-generation physical AI bringing full body intelligence to humanoids, advanced dexterity, multi-robot teamwork and more.
ということで、やはりKimi-K3のモデル公開と同時に、論文も公開されました。史上最大のオープンモデルの論文です。 全体をざっと読んだ感じのコメントは以下。 ・残念ながら、コアだと思われる具体的なデータの量（トークン数）やレシピ、データ量などの情報はない ・それに対してアーキテクチャ、学習メソッドの細かい情報は極めて豊富。データはともかく、このような構成でFableレベルにたどり着けるということを示しただけでも偉大。ただ、工夫がありすぎて、結局どれが効いているのかよくわからない ・エージェンティックな能力、RLへの力の入れ方が大きい。この辺はDeepSeek V4などと非常に似た手法 ・ロングコンテキスト処理のこだわりが強く、データの整備、学習中の品質管理が細かい
Kimi-K3の技術報告(論文)は近日公開とされていて、今日27日に予定されているモデル公開と同時公開がまず有力候補です。 Fable/Mythosレベルのモデルの作り方の知見が公になるのはもちろん初めてなので、世界中のAI開発にかなり影響を与えて、性能上昇が加速するターニングポイントになるのではないか。
Fable/Mythos5では推論中に人の言語とも思えぬ意味不明な思考過程を出力することが報告されてましたが、Claude Opus5では推論の過程で「うおおおおおおおお」「NOO0!!」みたいな発狂ワードが出る模様 DeepSeek R1の論文でも、突然LLMの出力挙動が不自然になる（でも性能は上がる）「Ahaモーメント」が報告されてましたが、ある意味で、極限まで考えると発狂する人間の性質を受け継いでいるのか、言語的にも発狂ワードを入れた方が飛躍的な発想につながるのか、はたまた整理されてロジックが通った自然言語は邪魔になるのか
Good move by @JensenHuang. The Nvidia letter is well written and worth reading. As we saw with the OpenAI-Hugging Face hack, we need open models and harnesses for defense. Lets stop believing the PR that closed models are safer. - that's just regulatory capture.
Attackers have frontier AI. Defenders need a frontier AI ecosystem—the best open and closed models, force-multiplied by a global community. During the Hugging Face incident, closed AI blocked essential forensics. An open-weight frontier model helped contain the intrusion. That’s why we created the Open Secure AI Alliance.
Really enjoyed talking to @bzcohen about AlphaGo's famous move 37 and the significance of it 10 years on for math and science breakthroughs in verifiable domains
A decade ago, a computer did something that no human would have done. It was considered a breakthrough for AI. Now the world is full of them. https://on.wsj.com/4waySAp
A significant portion of current AI "discourse" is less about technological capabilities and more about frontier lab employees navigating their own self-esteem and sense of identity.
In science, you have to report the experiments that didn't work, not just the ones that did. Same with AI. (I wish.)
Dreaming in Voxels: How AI is Generating Playable Minecraft Worlds Generative AI has conquered images, video, text. But what about interactive 3D environments? We trained models on billions of cubes to generate fully playable, structured worlds. Blog: https://pub.sakana.ai/dream-cubed 🧱
We are excited to share our latest work, together with @nyuniversity: "Dream-Cubed: Controllable Generative Modeling in Minecraft by Training on Billions of Cubes." Blog: https://t.co/dASGUTAPIY Paper: https://t.co/EQbWkWgztP Code: https://t.co/QQBh2HmF6i Generative AI has made incredible progress in language modeling, far beyond other modalities, where words and tokens offer a natural compositional unit for scalable training. This is similar to Minecraft and many other popular video games, where developers rely on cubes, tiles, and other discrete primitives to build rich, interactive worlds. In this work, we show that using cubes as tokens allows large transformers to do the same. Our contribution is two-fold: 1/ We release Dream-Cubed to the research community, a large-scale dataset of Minecraft worlds designed for generative modeling. Our data comprises tens of billions of carefully-balanced cubes from procedurally generated Minecraft terrain and high-quality human-authored maps (obtained with the authors' consent). 2/ We use our data to train a family of powerful transformers for efficient generation of interactive 3D environments at cube resolution. We show how our models allow players to mold the world around them by generating structures, terrain, and maps that are immediately editable and playable. Using high-quality data, we demonstrate that these models can be successfully trained with different training objectives, including both continuous and discrete diffusion, unlocking targeted inpainting, large-scale outpainting, and user-conditioned generation of infinitely sized worlds with fine-grained block-level control.
Running K3 locally on m5 max at 0.3 tok/s This is probably as slow as it will ever be😅
That's a bit slow. Streaming directly the K3 official hugging face 1.6TB of weights in mxfp4 in an m5 max 128gb.
The kernel folks are living on the front-lines. Where the the "cognitive abstractions" should live in a stack is such an interesting question.
(6/6) Read the full blog post, written with @HazyResearch: https://hazyresearch.stanford.edu/blog/2026-08-05-retire-the-abstractions
Inkling-small. 2 weeks after inkling Nearly as good as Inkling but 4x smaller. We're just getting started...🔥
Today, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available. https://t.co/BtYNcpkDRA Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
NLPコロキウムのスライド https://speakerdeck.com/odashi/opunsosuainokai-fa-shi-qing-2026-07-22-at-nlpkorokiumu #speakerdeck
Our plan keeps open weights and safety compatible with each other. We hope that this will help resolve debates and policies that pit open weights and safety as opposite and incompatible; ensuring a future full of open weights!
Releasing weights indiscriminately isn't safe. Neither is keeping capable models inside a few labs. We think there's a path between them. We haven't mapped all of it. Our new post covers the part we can see: how we assessed Inkling, and why access should widen in stages. https://t.co/zGFbuXNr0Y
出力に自然言語制約をかけなければ実質的に中間層の一種になるので、当然と言えば当然である。 NMT時代にも中間層に"NLP的”制約を入れる話が流行ったが、実用的に邪魔なのでほぼ使われていない。問題を解くことだけをobjectiveにするなら自然言語の出力制約はモデルにとっては邪魔なだけでしょうね。
ちなみにMythos/Fableの強化学習中こちらで、System Card内で、カードパズルを解く過程で最初は人間が読める思考過程だったのが、どんどん解読不能の言語になっていくのが報告されています。 こちらもOpus5と同じくしっかり「AAAAARGH」と発狂している辺り、賢すぎるAI共通の心の叫び説
Edgy update of Waiting for Godot: Vladimir and Estragon are #NeurIPS authors waiting for any reviewer to respond to the pair's rebuttals.
学生さんから「IT系のおすすめの資格ってありますか」と聞かれれば迷わず「まずはITパスポートと基本情報」と答えますが、自分ではどちらも（その先も）受験したことがない。これはあまりよろしくない。次のターゲットはこのあたりの予定です。
配点が確定したので自己採点の点数が確定しました。 ①経済 56🔺 ②財務 52🔺 ③経営 69✅科目合格️ ④運営 66✅科目合格 ⑤法務 68✅科目合格 ⑥情報 80✅科目合格 ⑦中小 37 ❌️ 合計点 428 ✅️ ということで多分不合格ですが、エアプ状態は解消しました。この感じだと不合格科目だけ次回本気で行けば取れそうです。