ぺ～いち

ゴルフ、テニス、f1 など

[フラームジェクト @FLaMme_ject]

**┈┈┈┈┈┈┈┈┈**
𝐇𝐀𝐏𝐏𝐘 𝐁𝐈𝐑𝐓𝐇𝐃𝐀𝐘
𝐄 𝐑 𝐈 𝐊 𝐀
** ┈┈┈┈┈┈┈┈┈**

8月17日は
#戸田恵梨香 の誕生日💐

【FLaMme PRESS】では、バースデーメッセージを募集しています💠

▼メッセージはこちらから
https://t.co/jTmtzZTCfE

[ぺ～いち @orippe]

@T12626 めでてえ

[ぺ～いち @orippe]

戸田恵梨香誕生日おめでとう

[LIZLISA @LisLisa114514]

GPTとアルファロメオについて語り合ってたらめちゃ納得してしまった

[Nav Toor @heynavtoor]

🚨SHOCKING: Apple just proved that AI models cannot do math. Not advanced math. Grade school math. The kind a 10-year-old solves.

And the way they proved it is devastating.

Apple researchers took the most popular math benchmark in AI — GSM8K, a set of grade-school math problems — and made one change. They swapped the numbers. Same problem. Same logic. Same steps. Different numbers.

Every model's performance dropped. Every single one. 25 state-of-the-art models tested.

But that wasn't the real experiment.

The real experiment broke everything.

They added one sentence to a math problem. One sentence that is completely irrelevant to the answer. It has nothing to do with the math. A human would read it and ignore it instantly.

Here's the actual example from the paper:

"Oliver picks 44 kiwis on Friday. Then he picks 58 kiwis on Saturday. On Sunday, he picks double the number of kiwis he did on Friday, but five of them were a bit smaller than average. How many kiwis does Oliver have?"

The correct answer is 190. The size of the kiwis has nothing to do with the count.

A 10-year-old would ignore "five of them were a bit smaller" because it's obviously irrelevant. It doesn't change how many kiwis there are.

But o1-mini, OpenAI's reasoning model, subtracted 5. It got 185.

Llama did the same thing. Subtracted 5. Got 185.

They didn't reason through the problem. They saw the number 5, saw a sentence that sounded like it mattered, and blindly turned it into a subtraction.

The models do not understand what subtraction means. They see a pattern that looks like subtraction and apply it. That is all.

Apple tested this across all models. They call the dataset "GSM-NoOp" — as in, the added clause is a no-operation. It does nothing. It changes nothing.

The results are catastrophic.

Phi-3-mini dropped over 65%. More than half of its "math ability" vanished from one irrelevant sentence.

GPT-4o dropped from 94.9% to 63.1%.

o1-mini dropped from 94.5% to 66.0%.

o1-preview, OpenAI's most advanced reasoning model at the time, dropped from 92.7% to 77.4%.

Even giving the models 8 examples of the exact same question beforehand, with the correct solution shown each time, barely helped. The models still fell for the irrelevant clause.

This means it's not a prompting problem. It's not a context problem. It's structural.

The Apple researchers also found that models convert words into math operations without understanding what those words mean. They see the word "discount" and multiply. They see a number near the word "smaller" and subtract. Regardless of whether it makes any sense.

The paper's exact words: "current LLMs are not capable of genuine logical reasoning; instead, they attempt to replicate the reasoning steps observed in their training data."

And: "LLMs likely perform a form of probabilistic pattern-matching and searching to find closest seen data during training without proper understanding of concepts."

They also tested what happens when you increase the number of steps in a problem. Performance didn't just decrease. The rate of decrease accelerated. Adding two extra clauses to a problem dropped Gemma2-9b from 84.4% to 41.8%. Phi-3.5-mini from 87.6% to 44.8%. The more thinking required, the more the models collapse.

A real reasoner would slow down and work through it. These models don't slow down. They pattern-match. And when the pattern becomes complex enough, they crash.

This paper was published at ICLR 2025, one of the most prestigious AI conferences in the world.

You are using AI to help you make financial decisions. To check legal documents. To solve problems at work. To help your children with homework. And Apple just proved that the AI is not thinking about any of it. It is pattern matching. And the moment something unexpected shows up in your question, it breaks. It does not tell you it broke. It just quietly gives you the wrong answer with full confidence.

[ぺ～いち @orippe]

@miedndai ぱい

[ぺ～いち @orippe]

ファイアレッドおもれー

[ぺ～いち @orippe]

ストレス管理下手な人ってどうしたらいいのかわからんな

[ぺ～いち @orippe]

@413hooo 声も顔も変わらんよ！
すげーわ！笑

[ぺ～いち @orippe]

回答した人が可愛いかどうかが気になる

[ライブドアニュース @livedoornews]

【調査結果】「かわいいだけじゃだめですか？」→現役高校生の7割が「だめ」と回答
https://t.co/uxbiyAqPee

ワカモノリサーチは全国の高校生男女479人に「かわいいだけじゃだめだと思うか」を調査。「だめ」な理由には性格や中身を挙げる声が多く、一方「かわいいは正義」という声も多数挙がった。

[ライブドアニュース @livedoornews]

【調査結果】「かわいいだけじゃだめですか？」→現役高校生の7割が「だめ」と回答
https://t.co/uxbiyAqPee

ワカモノリサーチは全国の高校生男女479人に「かわいいだけじゃだめだと思うか」を調査。「だめ」な理由には性格や中身を挙げる声が多く、一方「かわいいは正義」という声も多数挙がった。

[ぺ～いち @orippe]

@elonmuskTN 17

[ぺ～いち @orippe]

僕のヒーローアカデミアで一番あちーのはエンデバーだよなー！

[ぺ～いち @orippe]

@__zz42 俺は？

[ぺ～いち @orippe]

@__zz42 生理ですねわかります

[ぺ～いち @orippe]

ふぁ！！！！ひゃ！！！！
ふふふふす

[F1-Gate.com @F1Gate]

カルロス・サインツJr. 2027年にフェラーリ復帰報道 ルクレール移籍の可能性
#F1 #f1jp #サインツ #フェラーリ #ルクレール https://t.co/OYULpTBtnH

[ぺ～いち @orippe]

@__zz42 ひぇ！

[ぺ～いち @orippe]

@__zz42 いや、爪