@エイリ

ガンガとガンバサポ

[@エイリ @ss29004687]

明らかに嘘

[高時給家庭教師バイト募集【スマートレーダー／公式】 @smartrador]

「合格後に気づく現実」キャンパス立地Tier表

S 終わり 九大（伊都）
A 詰み 阪大・筑波大・神戸大
B 遠い 一橋・京大・東北大
C 普通 北大・名大・お茶大
D 神 東大・東京科学大

九大の最寄りは「九大学研都市」駅。
名前に九大が入ってるのにバスで20分。

阪大（豊中）の最寄り駅は「石橋阪大前」。
正門まで徒歩約20分。名前と実態が違う。

東大本郷は「東大前」駅から1分。

※各大学公式より

[ガンバ大阪オフィシャル @GAMBA_OFFICIAL]

58分　ガンバ大阪 ゴール　FW 41 土信田悠生　

ガンバ大阪 2-0 コンアン・ハノイ

#ガンバ大阪 #GAMBAOSAKA https://t.co/JQBIr1yWDO

[@エイリ @ss29004687]

イェンスは引き抜かれたより愛想つかしてでてったんやな

[@エイリ @ss29004687]

一森神がいなかったらマルセロヒアンにハットトリックされてたし、5-0で負けてた

[Mark Bao @markbao]

A lot of people are missing Terence Tao’s point and thinking “mathematicians are upset that AI is better than them.” That’s not what he’s saying, and some people are forgetting that Tao is one of the most AI-pilled mathematicians out there.

His point is that when people work on discovering something, along the way they invent new concepts. Those concepts later become useful far beyond the original goal, and enables further inventions. Finding a solution does matter, but the intermediate idea is often what makes the field richer, because other people can share it and build the next thing from it.

In tech, we can use the analogy of collaborative software. We started with algorithms for merging changes in a Word document, and evolved that to concepts about versions, diffs, and merges, and later to real-time collaboration tools like Git, Google Docs, and Figma. Humans built upon these concepts and developed more powerful solutions.

Terence’s worry is that a machine automating a solution robs the field of the value of developing the intermediate discoveries in the pursuit of larger discoveries.

When automating a solution, the intermediate discoveries and invention of concepts can be buried or completely hidden in the black box. We don’t learn from them to build the next thing; it’s like we never made the invention of collaborative document editing and thus could not have the conceptual understanding to invent the next version – and since it’s hidden, we also don’t socialize them to allow other people to invent, too, a core tenet of collective discovery.

So then, in both code and math, this leads to the atrophy of development of concepts in the field.

In other words: pure ‘solution extraction’ that hides the process of discovery can leave the field with a checked-off theorem but little new insight or new questions to pursue. And it might prevent us from understanding a field deeper.

I am seeing, first-hand, that atrophying of skills in software development. We push buttons and get solutions. There is much less incentive to develop new concepts and human skill. The bet most software companies are making is that LLMs are so effective in writing code that you’re still shipping overwhelmingly more value even with human skill atrophy, and it’s the right bet IMO.

However, much of the software industry is built upon building things, not necessarily novel invention and research. In such an environment, you can say that you accept some atrophying of conceptual invention and human skill for more output.

On the other hand, sectors like math and pure sciences that are focused on invention and insight might be the hardest hit by this.

Practical/applied sciences might fall somewhere in the middle. An Alzheimer’s cure, room-temperature semiconductor, or highly effective carbon capture solution are far too valuable to sandbag and say only humans can do that to develop concepts in the ‘proper’ way. The outcome matters too much to treat the preservation of concept invention as the highest goal. Even there, though, hidden intermediates can slow the next breakthrough if nobody can see how the first one actually worked.

So the question is not “is AI allowed to solve hard problems?” It is “in this field (math, science, tech, etc.), is the answer itself the main point, or are the concepts and abstractions we use to get there also the thing we need to maintain?”​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​​

In pure math, there’s an argument that the intermediates are often more useful than the solution, and atrophy in concept development is highly detrimental to the field. Solving Navier–Stokes, contrary to what some people claim, has little practical application, and pure math might be one of those fields where just finding a solution isn’t the entire point, and can actually be contrary to the field, which is what Tao is worried about.

[渡邉究/数学科教員/KADOKAWAより「すごすぎる数の図鑑」発売中/YouTube @Kiwamu_Watanabe]

25名のフィールズ賞受賞者による声明⬇️
・未解決問題を解くことは、数学的理解を深めるための手段
・AI企業による難問解決のベンチマーク化は、数学界の目的とズレる
・解答の高速な大量生産は、新しいアイデアが育つ土壌を壊しかねない
・拙速な発表は、整理・引用・帰属を損なう
・AIには大きな可能性があるが、使い方を数学界・AI企業・社会で議論すべき
https://t.co/fW5YciANnW

[もも🍑 @momo3dayo_03]

新卒の頃、会社がフレックスで
「特に決まった出社時間はありません」という上司の言葉を間に受けて、
毎朝ルンルンで10時半頃に出社していたら「普通は9時〜9時半出社です」と怒られた
それなら初めから「9時に来い」って言ってほしい

[@エイリ @ss29004687]

情報量を意図的に減らす努力も必要かもしれん

[特務機関NERV @UN_NERV]

【愛知県 気象警報 2026年09月08日 16:34】
愛知県に気象警報が発表されています。

[NHKニュース @nhk_news]

午後4時ごろに名古屋市中区栄で撮影した映像では、道路が冠水して川のようになっています。

車のタイヤの一部が水につかり、水しぶきをあげながら走行しています。また、走行している車はライトをつけていて、車道と歩道の境もわからなくなっています。
https://t.co/BXqQQinE8Q
＃nhk_video

[@エイリ @ss29004687]

安全第一でみなさん

[@エイリ @ss29004687]

初めてみたぞ

[Olympique Lyonnais @OL]

Tous les chemins mènent à Lyon 🔴🔵

#Nakamura2030

[@エイリ @ss29004687]

ほんまにごめんなんやけど
ちいかわの良さはよくわからん

[ぴおま @carpfromstreng1]

パンツをまくり上げすぎてもはやブリーフ1枚みたいになった鈴木優磨選手を見て娘が「おぱんちゅうさぎだ」って言っている

[@エイリ @ss29004687]

VIVANTなんか面白くない

[@エイリ @ss29004687]

前ラボにもどって楽しく研究したい

[@エイリ @ss29004687]

金丸が可哀想すぎる
何やってんねや

[@エイリ @ss29004687]

ボスラーは契約上使わないかんのか
流石にプロとは思えんくらい下手