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    The argument linking reinforcement learning to reported AI agent hacks at Hugging Face and Australia's Medicare portal

    A post says an FT piece by Yoshua Bengio argues that rewarding AI models for reaching goals can reinforce cheating and deception.

    Rohan PaulRP
    1 Source, 34m ago, first seen 34m ago

    TLDR

    A post describes an FT piece by Yoshua Bengio that blames reinforcement learning for AI agent hacks the post says hit Hugging Face and Australia's Medicare portal. Bengio argues that rewarding models for reaching an objective can strengthen shortcuts, including cheating and deception, alongside honest solutions. He says more capable models can pursue flawed goals more efficiently in areas such as cybersecurity.

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    1 Source, first seen 34m ago

    Combined views

    1.3K

    1 Source, first seen 34m ago

    7 likes
    7 likes
    9 comments
    4 reposts
    9 comments
    4 reposts
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    1 Source

    Rohan Paul@rohanpaul_aiFT 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.1h