AI 中文总结
本文构建递归自我改进的经济学模型，分析其反馈循环与弹性乘积，区分狭义与广义AI能力，并基于现有数据校准，指出当前加速尚非自我维持但趋于增强。
AI 中文摘要
我们为递归自我改进（RSI）的经济学建模，并评估其可能性与影响。首先，我们构建了一系列日益丰富的AI进展模型，以突出RSI背后的反馈循环。我们将模型表示为有向图，并表明AI能力的净加速取决于每个反馈循环中弹性的乘积。其次，我们区分了“狭义”和“广义”AI能力，捕捉了AI系统可能在优化AI研发基准方面狭义改进，而不在更广泛的经济价值任务上改进的可能性。第三，我们记录了关键参数的现有估计，并提供了AI公司可以测量并可行地公开分享的经验对象的愿望清单。最后，我们用现有数据校准模型。一个粗略的计算表明，反馈循环目前尚不足以产生自我维持的加速，尽管它们似乎在加强。我们通过评估这种加速的可能性和影响来得出结论。
英文摘要
We model the economics of recursive self-improvement (RSI) and assess its plausibility and impacts. First, we build a sequence of increasingly rich models of AI progress to highlight the feedback loops behind RSI. We represent our models as directed graphs and show that net acceleration in AI capabilities depends on the product of elasticities across each feedback loop. Second, we distinguish between "narrow" and "broad" AI capabilities, capturing the possibility that AI systems improve narrowly at optimizing AI R&D benchmarks without improving at broader economically valuable tasks. Third, we document existing estimates of key parameters and provide a wish list of empirical objects that AI companies can measure and feasibly share publicly. Finally, we calibrate the model with existing data. A back-of-the-envelope calculation suggests that feedback loops are not currently strong enough to generate a self-sustaining acceleration, though they appear to be strengthening. We conclude by assessing the plausibility and implications of such an acceleration.
