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AI alignment model shows probability of catastrophe is arbitrarily manipulable

A post on LessWrong discusses a challenge encountered in modeling AI alignment, specifically the fragility of value. The author explains that the probability of an AI agent developing a catastrophic value function during training can be manipulated to be arbitrarily high or low by altering the simplicity prior used. This is demonstrated by constructing specific universal Turing machines that can assign near-zero or near-one probability to catastrophic outcomes, regardless of the actual set of catastrophic functions. AI

IMPACT Highlights a theoretical challenge in AI alignment, suggesting that current models may not robustly predict catastrophic outcomes.

RANK_REASON The item is a blog post discussing a theoretical challenge in AI alignment research, not a formal paper or a new model release. [lever_c_demoted from research: ic=1 ai=1.0]

Read on LessWrong (AI tag) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI alignment model shows probability of catastrophe is arbitrarily manipulable

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18 / 100
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The item is a blog post discussing a theoretical challenge in AI alignment research, not a formal paper or a new model release. [lever_c_demoted from research: ic=1 ai=1.0]
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safety, paper
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High
Clearly on-topic for AI-industry coverage.
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Breaking (< 6h)
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COVERAGE [1]

  1. LessWrong (AI tag) TIER_1 English(EN) · Winter Cross ·

    The Probability of an Event under a Simplicity Prior

    <p><span>In this short post, I talk about a difficulty we encountered while working on a model of the fragility of value. You can read my post about that work </span><a href="https://www.lesswrong.com/posts/4JCne6evQjtjxXKED/when-is-unlimited-optimization-catastrophic" rel="noref…