Fable 5に続いて、GPT-5.6、Grok 4.5がすぐ来ることは公式が報告している。Gemini 3.5 Proに関してはグーグルが6月中に公開予定だと言っていたが、延期して今に至る（当然、Googleも諦めたわけではなく、競争が激化している）

We are opening up our platform and you can now build full native apps with plugin extensions, and ship them right in ChatGPT. We have over 1.2B weekly users and will surface relevant plugins right in the conversations.

1984年：ディズニーアニメは子どもたちには「暴力的すぎる」？ テレビ暴力反対全国連合（NCTV）は月曜日、ウォルト・ディズニーのケーブルテレビチャンネルで放映されているアニメの大半に、子どもにとって過度に暴力的な行為が含まれていると発表 3匹の子ぶたがオオカミに熱湯を浴びせる行為など

Today we announced the Claude-led discovery of a molecular machine that we suspect could represent a new gene editing mechanism. Its precise function, biotechnological utility (if any), or level of significance is not yet clear, but at minimum it is work I would have been proud to do as a PhD student. The work was done mostly, though not entirely, by Claude: our life sciences team suggested a broad area of research, Claude read through the literature and a bunch of genome data and discovered something interesting, then Claude proposed experiments to verify the discovery and our team carried them out. It’s easy to dismiss this as a one-off or curiosity, but we’ve repeatedly seen a pattern where AI performance in new intellectual domains goes from weak to superhuman in a matter of a few years. In 2023 models struggled to do math at the level of an average high-school student. In 2024 they started to do well on math competitions for the best high-schoolers in the country, in 2025 they started to solve minor open problems, in early 2026 more significant open problems, and in late 2026 they are beginning to solve the top few open problems in all of mathematics. We believe AI for biology is on a similar exponential trend. The main difference between biology and mathematics, of course, is that math can be done purely theoretically, while biology requires experimentation. Some have used this to draw the conclusion that AI’s utility in biology will be limited. We think this is wrong. As we’ve demonstrated today, humans can collaborate with AI to perform the experiments, validate key results in a few weeks and, if necessary, work with the AI to iterate on what they find. Eventually it may even be possible for Claude itself to safely perform the experiments by autonomously controlling lab equipment, with appropriate safeguards in place, but we aren’t doing that today (our lab is also a BSL1/BSL2 facility that doesn't handle materials dangerous to humans). More broadly, biomedical advancement has many stages — from fundamental biology discoveries, to translational research, to drug discovery, clinical trials, and finally the actual delivery of medicines and health care to patients. We are also interested in these later stages, but even simply accelerating the first stage of fundamental biological discoveries has the potential to speed up and broaden the entire pipeline. Improving our understanding of biology and sharpening biologists’ tools can drive forward all of the later stages, for example by identifying new drug targets, finding new therapeutic modalities, allowing for more precise measurement, and speeding up the experimental loop which itself further accelerates our understanding of biology. This will not in itself speed up clinical trial times, but if it succeeds it could greatly increase the number of promising candidates that go into the pipeline — an increase in throughput even though latency remains. In Machines of Loving Grace, I wrote about AI’s potential to “cure most diseases in 5-10 years” — a goal that sounds impossible, but one I believe is just barely possible if AI is applied to every stage of the pipeline. The first step is showing that AI can first help with, and then drive, biological discoveries. Claude’s discovery is the latest in a line of related prior work that goes back decades, beginning with systems like CRISPR, and continuing with discoveries like the bridge recombinase and VIPR in the past few years. Recently, there has been heightened interest in systems based on reverse transcriptase (RT) enzymes, the enzyme underlying the system Claude identified. And most recently, a Stanford team working independently described a novel RT system with an associated non-coding array that is in some ways similar to the one Claude found, though they are distinct systems that evolved independently from each other. I believe that we’re at the very beginning of finding such systems and developing them into powerful tools for biotechnology. I’m proud of the resources Anthropic has invested in accelerating the public benefits of AI through the life sciences, and we’re aiming both to grow our life sciences team and to work with other scientists to extend this approach to a broad range of problems. If you have a proposal for a research collaboration or are interested in joining our life sciences team, please reach out.

現在 ダリオ・アモデイ 「AI業界は減速するべきだ」 イーロン・マスク「ダリオは正しい」 サム・アルトマン「同意します」 デミス・ハサビス「正しい」 近未来 イーロン・マスク「Grok 4.7を発表します！性能が上がりました！」

数学の未解決問題が大量に証明されていく時代が近づいていて，ワクワクしてる

We’re working with an independent advisory group of mathematicians to help OpenAI responsibly share advances in AI and mathematics. The group will advise on how we assess and communicate new mathematical results, uphold academic and professional standards, and build tools that support mathematical research and learning. Through this work, we want mathematicians to be at the center of shaping how AI supports mathematical understanding and how its benefits reach the wider community. openai.com/index/advisory-gr…

現在 ダリオ・アモデイ 「AI業界は減速するべきだ」 イーロン・マスク「ダリオは正しい」 サム・アルトマン「同意します」 デミス・ハサビス「正しい」 近未来 イーロン・マスク「Grok 4.7を発表します！性能が上がりました！」

AI生成画像と食品サンプルについて、今の時代ならではの興味深い問いかけだと思いました。 「本物ではない」という共通点から、食品サンプルが好意的に受け止められる理由を考える。食品サンプルをつくる側としては、むしろうれしい問いかけでもあります✨ 食品サンプルは、最初から「本物ではない」ことを前提に、料理の魅力を伝えるもの。 本物そっくりなのに、本物ではない。その驚きや面白さを楽しむ感覚も、約100年にわたる日本の食品サンプル文化の中で育まれてきたのかもしれません。 食品サンプルは大正末期から昭和初期に飲食店の「販促物」として生まれ、料理のおいしさやお店の魅力を伝えるために、その表現技術を磨いてきました。 その価値は、料理をリアルに再現することだけではありません。 飲食店街に食品サンプルがずらりと並ぶ光景。眺めているだけでワクワクした経験のある方も、いらっしゃるのではないでしょうか。 遠くからでも人の目を引き、足を止めるきっかけになる。目の前に並ぶ実物大の料理たちは、食べるシーンや味わいを想像させ、「これが食べたい！」という気持ちを引き出してくれます。 画像や動画とは異なり、実際に空間に存在し、さまざまな角度から眺められることも大きな特徴です。 AIの表現技術は、これからもますます進化していくでしょう。 一方、食品サンプルが、職人の手と感性で一つひとつの料理と向き合い、「よりおいしそう」を追求しながらつくり上げられることは、これからも変わらないと思います。 その手仕事によって生み出される、一つひとつの表情や存在感も、食品サンプルならではの魅力です。 AI生成も食品サンプルも、料理の魅力を伝える表現として、これからも共存していくと考えています。 そして、AIが進化する今だからこそ、日本で育まれてきた食品サンプルという文化・技術が、改めて注目され、新たな価値を持ち始めていると感じています🍝 #食品サンプル

1858年：海底電信の功罪（ニューヨーク・タイムズ） 「金属の糸で海を結び合わせたという科学の輝かしい勝利には深く感銘を受けているものの、私は、たとえ海底電信が成功したとしても、そこに実際の利益を見出すことができる者たちの一人ではない」 「陸上電信はこれまで何か有益なことをしたか？」