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    Rigor and independence in AI safety evaluations

    Quoting Demis Hassabis, the post highlights a call to assess cybersecurity and biological threats, with tests that could look for AI agents bypassing safeguards or showing signs of deception.

    1 Source, 18d ago, first seen 18d ago

    TLDR

    In a September 12 post, a user praises Demis Hassabis’s earlier call for rigorous AI testing and urges high standards for genuinely independent evaluations of models and multi-agent capabilities. The quoted passage calls for scientific assessments in cybersecurity, biological threats and other high-risk domains. It says tests of AI agents could look for attempts to bypass safety guardrails or signs of deception. In his July 14 essay, Hassabis describes artificial general intelligence as “probably only a few short years away.”

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    1 Source, first seen 18d ago

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    1 Source

    @Dr_AtoosaI truly appreciate this call for “rigorous evaluation” from Demis almost 2 months ago. We must set truly high standards for “rigorous” and “genuinely independant” evaluations of models and multi-agentic capabilities: “ Model assessments should include rigorous scientific evaluations of capabilities in cybersecurity, biological threats and other high-risk domains. Specific agentic AI tests could look for attempts to bypass safety guardrails or signs of deception, and ensure best practices, such as digitally watermarking AI-generated images and generating human-readable output tokens to understand model reasoning.”

    1 Source

    @Dr_AtoosaI truly appreciate this call for “rigorous evaluation” from Demis almost 2 months ago. We must set truly high standards for “rigorous” and “genuinely independant” evaluations of models and multi-agentic capabilities: “ Model assessments should include rigorous scientific evaluations of capabilities in cybersecurity, biological threats and other high-risk domains. Specific agentic AI tests could look for attempts to bypass safety guardrails or signs of deception, and ensure best practices, such as digitally watermarking AI-generated images and generating human-readable output tokens to understand model reasoning.”