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What Dario Amodei thinks about LLMs

@dario-amodei · 43 positions · 0 changes of mind

Co-founder and CEO of Anthropic; previously VP of research at OpenAI.

Everything they publish, on ppll ↗

Dario Amodei did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

5 dated positions, 2020 to 2026, in their own words. Our reading of what Dario Amodei has said — not written or endorsed by them.

  1. Our readingModels already generalise substantially from tasks that can be verified to tasks that cannot.

    Their wordsWe already see substantial generalization from things that that verify to things that don't verify. We're already seeing that.

    ↗Dario Amodei — “We are near the end of the exponential”youtube.com 6th of 29 in this recording

  3. Our readingA lab under real commercial pressure cannot fool itself about whether AI makes it productive, because the output is a model launch every few months.

    Their wordsLike we see the end productivity every few months in the form of model launches. Like there's no kidding yourself about this. Like the models make you more productive.

    ↗Dario Amodei — “We are near the end of the exponential”youtube.com 9th of 29 in this recording

  5. Our readingModels are far more differentiated from one another than clouds are, so AI will not commoditise the way cloud did.

    Their wordsLike models are good at different types of coding. Models have different styles. Like I think I think these things are actually, you know, quite different from each other. And so expect more differentiation than you see in in um cloud.

    ↗Dario Amodei — “We are near the end of the exponential”youtube.com 18th of 29 in this recording

  7. Our readingTraining a model on principles rather than a list of rules is not an ideology but an empirical finding: its behaviour is more consistent and covers edge cases.

    Their wordsit's it's kind of purely a practical and empirical thing that we've observed that by teaching the model principles, getting it to learn from principles, its behavior is more consistent, it's easier to cover edge cases, and the model is more likely to do what people want it to do.

    ↗Dario Amodei — “We are near the end of the exponential”youtube.com 27th of 29 in this recording

  8. 6 years earlier

  9. Our readingScaling up language models greatly improves task-agnostic few-shot performance, sometimes matching prior fine-tuning approaches.

    Their wordsHere we show that scaling up language models greatly improves task-agnostic, few-shot performance, sometimes even reaching competitiveness with prior state-of-the-art fine-tuning approaches.

    ↗Language models are few-shot learners (with 30 co-authors)arxiv.org 2nd of 3 in this piece