Proposes a feasibility taxonomy of 20 hardware-level AI compute governance mechanisms organized by monitoring, verification, and enforcement, with mappings to regulatory scenarios that highlight immaturity of treaty-verification tools.
What does it take to catch a chinchilla? verifying rules on large-scale neural network training via compute monitoring
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The paper categorizes sources of catastrophic AI risks into malicious use, AI race, organizational risks, and rogue AIs, providing illustrative stories and mitigation suggestions for each.
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Hardware-Level Governance of AI Compute: A Feasibility Taxonomy for Regulatory Compliance and Treaty Verification
Proposes a feasibility taxonomy of 20 hardware-level AI compute governance mechanisms organized by monitoring, verification, and enforcement, with mappings to regulatory scenarios that highlight immaturity of treaty-verification tools.
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An Overview of Catastrophic AI Risks
The paper categorizes sources of catastrophic AI risks into malicious use, AI race, organizational risks, and rogue AIs, providing illustrative stories and mitigation suggestions for each.