Dr. Yann LeCun 談大型語言模式 (LLM) 的限制： 缺乏真正的推理能力。為何語言能力 ≠ 智能。...... 7位得主齊獲殊榮 The winners of the 2025 Queen Elizabeth Prize for Engineering were awarded to seven individuals for their seminal contributions to the development of modern machine learning,

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The winners of the 

2025 Queen Elizabeth Prize for Engineering were awarded to seven individuals for their seminal contributions to the development of modern machine learning, a core component of artificial intelligence (AI) advancements. 

The 2025 laureates, who share the £500,000 prize, are:

• Dr. Bill Dally
• Dr. Fei-Fei Li
• Professor Geoffrey Hinton
• Professor John Hopfield
• Jensen Huang
• Dr. Yann LeCun
• Professor Yoshua Bengio 

Their combined work laid the conceptual and hardware foundations for modern machine learning and AI, including the development of artificial neural networks, essential high-performance computing hardware (GPUs), and high-quality datasets like ImageNet which are critical for training AI systems. 

The winners were announced in February 2025, and His Majesty King Charles III presented the award during a ceremony in November 2025. 

Yann LeCun 談大型語言模式 (LLM) 1.“LLM 會一個接一個地生成詞元。它生成一個詞元需要進行固定量的計算，這顯然屬於系統 1——它是被動的，沒有推理能力。” —— 解釋 LLM 為何缺乏真正的推理能力。 2.“LLM 很棒，它們很有用，我們應該投資它們——很多人會使用它們……但它們並非通往人類智能的途徑。它們只是不是。現在，它們佔據了所有資源——基本上沒有資源用於其他任何事情。” —— 解釋為何業界對 LLM 的執著是錯誤的。 3.“語言具有強大的統計特性……這就是為什麼我們擁有能夠通過律師資格考試或計算積分的系統，但我們的家用機器人在哪裡？在現實世界中，一隻貓的表現仍然遠遠超過它們。” —— 解釋為何語言能力 ≠ 智能。 4. “在通往人類水平人工智慧的道路上，大型語言模型基本上是一個出口——一個幹擾因素，一條死路。” ——論大型語言模式作為人工智慧研究中的一條演化死胡同。 ——圖片來源：印度時報，Gadgets Now Mark Bishop 已註明出處😜 原文連結： https://timesofindia.indiatimes.com/....../125428070.cms Yann LeCun tán dàxíng yǔyán móshì (LLM) 1.“LLM huì yīgè jiē yīgè dì shēngchéng cí yuán. Tā shēngchéng yīgè cí yuán xūyào jìnxíng gùdìng liàng de jìsuàn, zhè xiǎnrán shǔyú xìtǒng 1——tā shì bèidòng de, méiyǒu tuīlǐ nénglì.” —— Jiěshì LLM wèihé quēfá zhēnzhèng de tuīlǐ nénglì. 2.“LLM hěn bàng, tāmen hěn yǒuyòng, wǒmen yīnggāi tóuzī tāmen——hěnduō rén huì shǐyòng tāmen……dàn tāmen bìngfēi tōng wǎng rénlèi zhìnéng de tújìng. Tāmen zhǐshì bùshì. Xiànzài, tāmen zhànjùle suǒyǒu zīyuán——jīběn shàng méiyǒu zīyuán yòng yú qítā rènhé shìqíng.” —— Jiěshì wèihé yèjiè duì LLM de zhízhuó shì cuòwù de. 3.“Yǔyán jùyǒu qiángdà de tǒngjì tèxìng……zhè jiùshì wèishéme wǒmen yǒngyǒu nénggòu tōngguò lǜshī zīgé kǎoshì huò jìsuàn jīfēn de xìtǒng, dàn wǒmen de jiāyòng jīqìrén zài nǎlǐ? Zài xiànshí shìjiè zhōng, yī zhī māo de biǎoxiàn réngrán yuǎn yuǎn chāoguò tāmen.” —— Jiěshì wèihé yǔyán nénglì ≠ zhìnéng. 4. “Zài tōng wǎng rénlèi shuǐpíng réngōng zhìhuì de dàolù shàng, dàxíng yǔyán móxíng jīběn shàng shì yīgè chūkǒu——yīgè gànrǎo yīnsù, yītiáo sǐlù.” ——Lùn dàxíng yǔyán móshì zuòwéi réngōng zhìhuì yánjiū zhōng de yītiáo yǎnhuà sǐhútòng. ——Túpiàn láiyuán: Yìndù shíbào,Gadgets Now Mark Bishop yǐ zhùmíng chūchù 😜 yuánwén liánjié: Https://Timesofindia.Indiatimes.Com/....../125428070.Cms Show more

Yann LeCun on Large Language Models (LLMs)

1. “An LLM produces one token after another. It goes through a fixed amount of computation to produce a token, and that’s clearly System 1 — it’s reactive. There’s no reasoning.”

— On why LLMs lack genuine reasoning capacity.

2. “LLMs are great, they’re useful, we should invest in them — a lot of people are going to use them … But they are not a path to human-level intelligence. They’re just not. Right now, they’re sucking the air out of the room — there’s basically no resources for anything else.”

— On why industry obsession with LLMs is misplaced.

3. “Language has strong statistical properties… That’s why we have systems that can pass the bar exam or compute integrals, but where is our domestic robot? A cat still vastly outperforms them in the real world.”

— On why language competence ≠ intelligence.

4. “On the highway toward human-level AI, a large language model is basically an off-ramp — a distraction, a dead end.”

— On LLMs as an evolutionary cul-de-sac in AI research.

———

Image credit: Times of India, Gadgets Now

Mark Bishop i credited it

Here’s the source article:

https://timesofindia.indiatimes.com/....../125428070.cms

黃仁勳與AI先驅同列　7位得主齊獲殊榮

今年伊莉莎白女王工程獎的7位得主除有黃仁勳，還有華裔美籍科學家李飛飛，她是獲獎者中唯一的女性。另有NVIDIA首席科學家比爾達利（Bill Dally）、92歲的AI先驅約翰霍普菲爾德（John Hopfield）、喬書亞本吉奧（Yoshua Bengio）、傑佛瑞辛頓（Geoffrey Hinton）與Meta首席科學家楊立昆。他們被表彰為「讓電腦模仿人腦運作、進而發展出現代機器學習模型」的奠基者。