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Buck Shlegeris

Buck Shlegeris

@buck-shlegeris · 8 positions · On ppll ↗

CEO of Redwood Research, where he works on AI control: making sure a powerful model cannot cause a catastrophe even if it is misaligned and trying to.

Buck Shlegeris did not write this page.

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2 of Buck Shlegeris's claims fit 1 mental models. Most often: Margin of safety.

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Margin of safety

Leave room for being wrong, because sometimes you will be.

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A control evaluation that reports under one per cent risk should be read as several per cent, because the evaluation can itself fail.

  1. Buck Shlegeris CEO of Redwood Research In particular, suppose that our control evaluations directly estimate a less than 1% chance of catastrophe if our untrusted AIs are scheming, then the actual risk conditional on scheming is probably more like 5% to 20% due to the potential for failures in the evaluation. The case for ensuring that powerful AIs are controlledredwoodresearch.substack.com · 7 May 2024All korrents from this piece
    In particular, suppose that our control evaluations directly estimate a less than 1% chance of catastrophe if our untrusted AIs are scheming, then the actual risk conditional on scheming is probably more like 5% to 20% due to the potential for failures in the evaluation.

Risk from a scheming model should be managed the way insider security risk is: assume some of them are compromised and stay safe anyway.

  1. Buck Shlegeris CEO of Redwood Research We're advocating that companies handle risk from scheming models in a similar way–striving to ensure that they'll be safe even if their alignment efforts fail to prevent models from scheming. The case for ensuring that powerful AIs are controlledredwoodresearch.substack.com · 7 May 2024All korrents from this piece
    We're advocating that companies handle risk from scheming models in a similar way–striving to ensure that they'll be safe even if their alignment efforts fail to prevent models from scheming.