ClaimsEliezer Yudkowsky

“I don’t think it’s as smart as a human yet.”

Eliezer Yudkowsky, interviewee 30 March 2023 Lex Fridman Podcast #368 Eliezer Yudkowsky transcript (Podscript) archived copy

Factual AI capabilities

Yudkowsky said he did not think the AI system under discussion was as smart as a human yet.

Unresolvable The public record could not settle it.

Factual: Statements about the present or the past, checked against the record: did they hold up?

Reasoning

'As smart as a human' is vague and undefined for a general comparison.

Cite this claim

Eliezer Yudkowsky, “Yudkowsky said he did not think the AI system under discussion was as smart as a human yet.” (said 30 March 2023). Verdict: unresolvable. Reliability API, claim c_1183b812d2b9, method 0.1.0+c876030c, dataset release v1.1.2. https://epistacklabs.com/claims/c_1183b812d2b9

This claim is in dataset release v1.1.2, with the same verdict.

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More factual claims by Eliezer Yudkowsky

  1. Resolved true Factual September 2025
    “We learned that that's false, but we did not learn that you can have a thing that can redesign a tree and build an AI much smarter than itself, that builds an AI much smarter than itself, and that…”

    No AI has yet been shown to redesign a tree and build a much smarter AI that builds a still smarter AI; that scenario has not occurred.

  2. Mostly true Factual 25 March 2024
    “I was a passionate noticer of the human brain not being homogenous, and was therefore unpleasantly surprised by transformers scaling as well as they did.”

    Yudkowsky was unpleasantly surprised by how well transformers scaled, having believed the human brain is not homogeneous.

  3. Mostly true Factual 6 April 2023
    “First of all, you realize in principle that the task of minimizing losses on predicting human text does not stop when you’re as smart as a human, right?”

    In principle, the task of minimizing loss on predicting human text does not stop being improvable once a system is as smart as a human.

  4. Mostly true Factual 30 March 2023
    “Transformers are the main thing like that.”

    Transformers were the main architectural change that gave a non-linear jump in performance, compared with earlier approaches such as RNNs.

  5. Mostly true Factual 30 March 2023
    “Because the basic paradigm of machine learning that we are presently operating under is that you can have the loss function, but only for things you can evaluate.”

    The basic paradigm of machine learning currently in use is that a loss function can be defined only for things that can be evaluated.

All of them

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