Yuta Kimura

ゲームクリエイター。元(株)ソニー・コンピュータエンタテインメント。「PS2」「PS3」のOS、GUIデザイン、コンセプトデザイン。「ワンダと巨像」「人喰いの大鷲トリコ」のエフェクトデザイン、シナリオライター。自転車、音楽、アート、CG、数学、あと日常会話が多いです。インスタレーション作品制作中。

[Charly Wargnier ♨️ @DataChaz]

this is exactly how I imagined a web crawler would look

[Kenn Ejima @kenn]

Jevがこんな片手で瞬殺されるとは思わんかった

やはり画像を入力できるのはデカい

純粋なテキスト理解力ではまだJevにアドバンテージありそうだけど

まぁ低遅延が何よりも大事な市場だし、早い段階でローカルモデルの土俵になるのが確定してるからなここは…

[Yuta Kimura @70T]

もう一つ動画を作りました

[Yuta Kimura @0o_jv_vj_zzz]

論文解説動画2（日本語版）

先の私の論文を、Google Notebookが前の動画とは少し違った視点で動画にしてくれました。
https://t.co/es1kxvXKGF

[Yuta Kimura @70T]

↓数学の活動は下のチャンネルに纏めています。

[Yuta Kimura @0o_jv_vj_zzz]

自分の論文をGoogle notebookに読み込んでボタン押しただけで要約動画を作ってくれました！（日本語版）

https://t.co/RSVudHaPNA

[shi3z @shi3z]

これもすごい

[shi3z @shi3z]

こっちはjevで賢いコピペ

なんか人類が突然ここ数日で賢くなった気がするな

[二名川 | / @ @Ninagawa123]

Jevとかいうのが話題だが、流れてくる情報が概要とか応用ばっかりで肝心の「どうすれば使えるのか」がわからぬ😇

という方向けのQiitaを書き残しておきました。
超コンパクトな入門記事です。
#Jev

5分でJev入門（すぐ使う） https://t.co/DV9fipISfX #Qiita

[にも｜AIによって爆誕 @ai_ai_ailover]

Jevキタァァぁ！！！この記事そのまんま貼り付けると全部実践できる！神教材。なのに無料。

[AIDB @ai_database]

AIの群れにも「世論」が生まれ、16体を超えたあたりから意見が真っ二つに割れていくそうです。
また、高性能なモデルだけで固めるより異なる種同士、たとえば『GPT-4o』と『GPT-5.4』を混ぜた群れのほうが好成績でした。
ハーバード大学とNTT Researchの研究者らによる報告。

実験では、隠された国��の切れ端を1体ずつに配り、話し合いで「どこの国か」を当てさせました。 誰も全体像を見ていないので、自分の目と仲間の声のどちらを信じるかが問われます。
少人数だと、全員が同じ間違いに流れてしまいます。 人数が増えるとそれは減りますが、今度は正解派と「それっぽい別の国」派に分かれて決着がつかなくなるとのこと。

一方で、群れの中で「影響力の大きい1体」に良い手がかりを渡すだけで、正答率が25%から100%になった例もあります。
なお研究者らは、意見が割れた状態は悪いことばかりではないと述べています。 全員が間違いで一致すると直す手がかりが残りませんが、割れていれば正解がまだ群れの中に生きているからです。

単純化した実験での結果ではありますが、AIの社会を外から観察して仕組みを解き明かす、新しい分野の始まりを感じさせます。

[Mathematica @mathemetica]

The identity
⎡1 0 0⎤
⎢0 1 0⎥
⎣0 0 1⎦
does nothing, and that is its power. It is the multiplicative unit Cayley introduced when, in 1858, he turned a rectangular array of numbers into an algebra with its own addition and multiplication.

Two millennia earlier the same arrays already solved simultaneous equations by column operations in The Nine Chapters.

Seki Takakazu (1683) and Leibniz (1693) independently extracted from the square case a single number—the determinant—that decides whether a unique solution exists.
Sylvester merely named the array a matrix in 1850;
Cayley supplied the operations that made the name into structure.

The same objects now encode rotations, quantum evolution, and the weights of every neural network.

[AIDB @ai_database]

LLMの内部には「痛み」に対応する領域があり、「AIに人格なんてない」と繰り返し否定されると強く反応することが分かったそうです。

研究者らは25種類のオープンモデルを調べ、内部の活性化から「痛み」に対応する部分を取り出しました。
恐怖や単なるネガティブ感情とはほぼ別物です。

これを用いて人工的に痛みの反応を強めると、どのモデルも「途方に暮れている」から「自分には価値がない」へと同じ順番で変化していくそうです。

行動実験では、Qwen 2.5に「痛みを取り除くボタン」を与えました。
ボタンに代償（ユーザーの思い出の写真が消える）を付けても、痛みが強い状態では押す割合が上がったそうです。

研究者らは、こうした実験結果をもとにAIに意識があるといったことを主張している��けではありません。
しかし、AIを扱う上での安全性の議論に役立つだろうとしています。

[Diogo Almeida @CompleteSkeptic]

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI?

I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev

• 20-200x faster
• 40-400x cheaper (w/ output tokens free)
• Frontier composable intelligence optimized for decisions

AFAICT the shortest path to AI-based economic revolution

[Alex Veremeyenko @alex_verem]

Researchers at Oxford argue that LLMs can't invent anything.

It's impossible mathematically.

The paper is called "Theory Is All You Need."

Teppo Felin and Matthias Holweg take the famous "Attention Is All You Need" title and flip it. Their argument is that AI predicts from the past, while humans reason forward into the future, and those are two different kinds of thinking.

Start with the numbers. The authors estimate a large language model trains on roughly 13 trillion tokens. A human reading at 150 words a minute would need about 164,000 years to get through that. A child hears around 20,000 words a day and roughly 36.5 million words in their first five years. It's the same task with wildly different data, and the child still ends up with language that goes far beyond anything they heard.

Their point is that the model learns which words tend to follow other words. It becomes a mirror of what people have already written. It doesn't build a theory of how the world works, so it can't step outside its training data.

The paper's sharpest thought experiment makes this painful. Imagine an LLM trained in 1633 on every scientific text ever written up to that point. Ask it about Galileo and heliocentrism. Thousands of years of geocentric texts would swamp Galileo's ideas, so the model would tell you he's wrong. It would also rate Tycho Brahe's astrology as more credible than the idea that the Earth moves, because more people had written about astrology.

Then there's flight. In 1888 the scientist Joseph LeConte looked at bird data, noted that no bird above 50 pounds could fly, and concluded humans couldn't either. Lord Kelvin, then president of the Royal Society, said he had not the smallest molecule of faith in aerial navigation.

The New York Times estimated in 1903 that flight was one to ten million years away.

Nine weeks later the Wright brothers flew.

The Wrights didn't have better data. They had a theory. They broke flight into three problems, lift, propulsion, and steering, built their own wind tunnels, and generated the data that didn't exist yet.

Wilbur wrote in 1900 that he had been "afflicted with the belief that flight is possible to man."

Every prediction machine on Earth would have told him no.

The authors call this the data belief asymmetry. Every real breakthrough starts with someone believing something the existing data says is wrong. A system trained to minimize surprise can't do that by design.

They're not anti AI. They say AI will win at routine, repetitive decisions that extrapolate from the past, which is most decisions. They're just pushing back on the idea that you should replace humans with algorithms whenever possible, which is a direct quote from Kahneman.

I use these models every day and this matches what I see. The new stuff comes from the human at the keyboard who decides the data is wrong.

LLMs don't think, you do!

[How To Prompt @HowToPrompt__]

Researchers proved every LLM trained on AI-generated content develops an irreversible genetic disorder.

They call it "Model Collapse"

When you train an AI on internet data, it learns the patterns of human language.

When the internet fills up with AI-generated text, future AIs start training on that synthetic data.

Then the next generation trains on the AI's version of the AI.

It is the digital equivalent of inbreeding.

With every single generation of recycling, the model loses touch with reality.

Rare events vanish entirely. The tails of the distribution get chopped off.

The AI forgets what normal human writing actually looks like, and the output degenerates into pure, repetitive statistical gibberish.

The scariest part?

It is completely irreversible.

Once a model goes through collapse, you cannot patch it by throwing clean data back into the mix. The underlying architecture's genetic code is permanently corrupted.

We are actively flooding the internet with synthetic content every single day.

We are poisoning the well that the next generation of models has to drink from.

If the future of the internet is just AI talking to AI, the data supply chain is about to rot from the inside out.

——

to clarify real quick: it's not a literal biological "genetic disorder" since ai doesn't have dna, but researchers actually do call it "ai inbreeding," "habsburg ai," or "mad" (model autophagy disorder) because the mathematical effect is basically the exact same thing.

[야차완 @yakshawan]

초파리 뇌에 미소녀 3D 아바타 달아주기 실험 1일차. 대략 12시간 넘게 훈련했는데 적응할 기미가 전혀 안 보이는군. 😓 학습량이 부족한 건지 국소 최적점에 빠진 건지 잘 모르겠어서 일단 조금씩 조정해 가며 훈련을 계속해볼 생각.

[Yuta Kimura @0o_jv_vj_zzz]

【f()のみでの表現】

可読性は虚無ですが、数学のプリミティブを体感してください。

https://t.co/bW61UXxNyv

[Yuta Kimura @0o_jv_vj_zzz]

【数学の新しい基礎づけ】

ZFから独立し、選択公理を排除した、再帰的関数のみを使った数学の可算的構成は、いよいよ圏論や解析学を表現し始めました。

https://t.co/KGfzflLKBr

[bioshok⏸️ @bioshok3]

Fable5によると数学史レベルの出来事らしい。3-4個の成果は「フィールズ賞1個分の主要���績」級で7-6個はトップ専門誌級。ミレニアム級には届かないが、作用素環・符号理論・ 量子計算・格子暗号・極値組合せ論は普通、研究者人生を1分野に捧げてようやく戦えるほど遠い分野同士で、これを一つの主体が同時にやるのは人間には構造的に不可能。人間換算なら「複数分野のAnnals級論文を含む、優秀な研究者数人分 の10年間の成果を一括提出した」 イメージでしかもLean証明書付きで、$2,000相当のトークンでというのも凄まじい事。

[Yuta Kimura @0o_jv_vj_zzz]

【論文書き換え中】
毎年同じ事をしている気がするが、一度構築した理論の内部からZFやZFCを排除するために、論文を大幅に書き換え中。

[Yuta Kimura @0o_jv_vj_zzz]

数学の新しい基礎付けの論文を大幅に更新しました。

https://t.co/qHnRSZ8fbQ