Yann LeCun
Professor of Computer Science at New York University and 2018 Turing Award laureate; a leading researcher in deep learning, known for convolutional neural networks.
Yann LeCun did not write this page.
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Their wordsSo no, Auto-Regressive LLMs in their current form will not take us to human-level AI.
- 2 months earlier
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Their wordsSB1047 attempts to regulate AI research and development, creating obstacles to the dissemination open research in AI and open source AI platforms. Regulating the deployment of AI applications is fine.
- 5 days earlier
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Their wordsThe problem isn't that it is a transformer. The problem is that it is an auto-regressive LLM. Auto-regressive LLMs that compute each token with a fixed number of computational steps can't reason, regardless of the details of the architecture.
- 2 weeks earlier
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Their wordsRegulators should regulate applications, not technology. - Regulating basic technology will put an end to innovation.
- 5 months earlier
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Their wordsA big bunch of nails in the coffin of the idea that open source AI models are more dangerous than closed ones.
- 3 months earlier
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Our reading
Scaling up auto-regressive LLMs will not fix their very limited reasoning and planning abilities
Their wordsAR-LLMs have very limited reasoning and planning abilities. This will not be fixed by making them bigger and training them on more data.
- 7 months earlier
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Their words1- Auto-Regressive LLMs are useful, particularly as writing aids, particularly for code. 2- they hallucinate too often
- 6 weeks earlier
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Their wordsBecause, unlike the real world, the universe that a program manipulates (the state of the variables) is limited, discrete, deterministic, and fully observable. The real world is none of that.