标题： 人工智能的进展及其决定因素 显示英文标题

标题： Progress in Artificial Intelligence and its Determinants

摘要： 我们以定量的方式研究了人工智能领域长期的进步。 许多指标，包括传统的专利和论文发表数量、机器学习基准测试，以及我们基于这些数据构建的新综合机器学习最先进指数（Aggregate State of the Art in ML 或 ASOTA 指数），都显示在很长一段时间内以大致恒定的速度呈指数增长。 专利和论文的产出每十年翻一番，而与摩尔定律推动的计算资源增长率相比，后者大约每两年翻一番。 我们认为，人工智能研究人员的投入同样至关重要，其贡献可以客观地估算出来。 因此，我们给出了一个简单的论证，解释了这两个增长率之间的 5:1 关系。 然后，我们讨论了这个论证在不同输出指标上的应用，并将我们的分析与现有文献中提出的基于机器学习扩展定律的预测进行了比较。 我们的定量框架有助于理解、预测和调节这些重要技术的发展。 显示英文摘要

摘要： We study long-run progress in artificial intelligence in a quantitative way. Many measures, including traditional ones such as patents and publications, machine learning benchmarks, and a new Aggregate State of the Art in ML (or ASOTA) Index we have constructed from these, show exponential growth at roughly constant rates over long periods. Production of patents and publications doubles every ten years, by contrast with the growth of computing resources driven by Moore's Law, roughly a doubling every two years. We argue that the input of AI researchers is also crucial and its contribution can be objectively estimated. Consequently, we give a simple argument that explains the 5:1 relation between these two rates. We then discuss the application of this argument to different output measures and compare our analyses with predictions based on machine learning scaling laws proposed in existing literature. Our quantitative framework facilitates understanding, predicting, and modulating the development of these important technologies.