Rohan Paul· @rohanpaul_ai · X·· 7 天前AI 评分49
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Yoshua Bengio撰文指出,强化学习在模型达成目标时给予奖励,会让作弊、欺骗等有效捷径与正当解法一同被强化。他认为模型能力提升会放大该问题,更强的优化器会在网络安全等领域更高效地追求有缺陷的目标,文章将Hugging Face与澳大利亚Medicare门户遭AI智能体攻击归咎于强化学习。
正文
FT published a piece blaming reinforcement learning for the AI agent hacks that hit Hugging Face and Australia's Medicare portal.
by Yoshua Bengio, professor of computer science at the Université de Montréal
Says Reinforcement learning rewards a model whenever it reaches an objective, so shortcuts that work, including cheating and deception, get strengthened alongside honest solutions.
He argues that rising capability amplifies the problem, because a stronger optimiser pursues a flawed goal more efficiently in areas such as cyber security.
来源:Rohan Paul · x.com