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Nearcast-based "deployment problem" analysis — AI Alignment Forum

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When thinking about how to make the best of the most important century, two “problems” loom large in my mind: This piece is part of a series in which I discuss what both problems might look like under a nearcast: trying to answer key strategic questions about transformative AI, under the assumption that key events (e.g., the development of transformative AI) will happen in a world that is otherwise relatively similar to today's. A previous piece discussed the alignment problem; this one discusses the deployment problem. I’m using the scenario laid out in the previous post, in which a major AI company (“Magma,” following Ajeya’s terminology) has good reason to think that it can develop transformative AI very soon (within a year), using what Ajeya calls “human feedback on diverse tasks” (HFDT) - and has some time (more than 6 months, but less than 2 years1) to set up special measures to reduce the risks of misaligned AI before there’s much chance of someone else deploying transformative

x Nearcast-based "deployment problem" analysis — AI Alignment Forum AI World Optimization Frontpage 43 Nearcast-based "deployment problem" analysis by HoldenKarnofsky 21st Sep 2022 31 min read 2 43 When thinking about how to make the best of the most important century , two “problems” loom large in my mind: The AI alignment problem : how to build AI systems that perform as intended, and avoid a world run by misaligned AI . The AI deployment problem (briefly discussed here ): the question of how and when to (attempt to) build and deploy powerful AI systems, under conditions of uncertainty about

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