The wonderful world of prisoner dilemma names: This paper serves as an introduction to the wonderful and mostly inscrutable names for different prisoner dilemma games, including: Tit for Tat, Grim Trigger, Win-Stay, Lose-Shift, Generous Tit for Tat, Suspicious Tit for Tat, Prober, Random, Gradual, Alternator, and Bayesian.
Want to regulate AI but don't know what to target? Regulate the big scary companies, not the use-cases or models.
…FLOPs? Use cases? Maybe there's a better way - target companies at the frontier…
When you're trying to regulate AI, do you target the company or the AI system? That's the question a couple of researchers try to answer in Entity-Based Regulation in Frontier AI Governance, a new paper from the Carnegie Endowment for International Peace. In the paper, they try to reason through the difficulties in regulating companies according to a narrow property of an AI system like aggregate compute dumped in (leads to all kinds of collateral damage, and basically unprincipled with regard to safety) or use cases (which can lead to chilling effects on adoption) and instead propose a different approach - "an alternative paradigm of frontier AI regulation: one that focuses on the large business entities developing the most powerful AI models and systems"
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