Regulators in the US and EU are using thresholds based on training compute--the number of computational operations used in training--to identify general-purpose artificial intelligence (GPAI) models that may pose risks of large-scale societal harm. We argue that training compute currently is the most suitable metric to identify GPAI models that deserve regulatory oversight and further scrutiny. Training compute correlates with model capabilities and risks, is quantifiable, can be measured early in the AI lifecycle, and can be verified by external actors, among other advantageous features. These features make compute thresholds considerably more suitable than other proposed metrics to serve as an initial filter to trigger additional regulatory requirements and scrutiny. However, training compute is an imperfect proxy for risk. As such, compute thresholds should not be used in isolation to determine appropriate mitigation measures. Instead, they should be used to detect potentially risky GPAI models that warrant regulatory oversight, such as through notification requirements, and further scrutiny, such as via model evaluations and risk assessments, the results of which may inform which mitigation measures are appropriate. In fact, this appears largely consistent with how compute thresholds are used today. As GPAI technology and market structures evolve, regulators should update compute thresholds and complement them with other metrics into regulatory review processes.

翻译：美国和欧盟的监管机构正采用基于训练算力——即训练过程中使用的计算操作数量——的阈值来识别可能引发大规模社会危害风险的通用人工智能模型。我们认为，训练算力当前是识别值得监管关注和进一步审查的通用人工智能模型最适宜的度量指标。训练算力与模型能力和风险相关，具有可量化性，可在人工智能生命周期早期测量，并能由外部机构验证等优势特征。这些特征使得算力阈值相比其他提议的度量指标，更适合作为触发额外监管要求和审查的初步筛选工具。然而，训练算力仅是风险的不完美代理指标。因此，算力阈值不应单独用于确定适当的缓解措施，而应用于检测需要监管关注（如通过报备要求）和进一步审查（如通过模型评估和风险评估）的潜在高风险通用人工智能模型，审查结果可为制定适宜缓解措施提供依据。事实上，这与当前算力阈值的实际应用基本一致。随着通用人工智能技术和市场结构的发展，监管机构应更新算力阈值，并将其与其他度量指标共同纳入监管审查流程。