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Chess as a Testing Grounds for the Oracle Approach to AI Safety
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Chess as a Testing Grounds for the Oracle Approach to AI Safety
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To reduce the danger of powerful super-intelligent AIs, we might make the first such AIs oracles that can only send and receive messages. This paper proposes a possibly practical means of using machine learning to create two classes of narrow AI oracles that would provide chess advice: those aligned with the player's interest, and those that want the player to lose and give deceptively bad advice. The player would be uncertain which type of oracle it was interacting with. As the oracles would be vastly more intelligent than the player in the domain of chess, experience with these oracles might help us prepare for future artificial general intelligence oracles.
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Cited by 1 Pith paper
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Three-Body Alignment: Aligning Chess Agent with Human Reasoning through Reranked Rationale
Reranking retrieved grandmaster rationales by FEN similarity raises a chess LLM's semantic alignment with grandmaster explanations from 0.61 to 0.73 cosine similarity, while reducing tactical quality.
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