AI进步呈指数增长，研究者投入是关键驱动力。
Progress in Artificial Intelligence and its Determinants
- 用专利、论文和新构建的ASOTA指数量化AI长期进展
- 研究者数量每十年翻倍，比算力增速慢五倍但贡献相当
- 框架可预测并调控AI发展，适合政策制定者参考
我们以定量方式研究人工智能的长期进展。多种指标，包括传统专利与论文数量、机器学习基准，以及我们新构建的机器学习综合前沿指数（ASOTA），均显示在长期内呈现大致恒定速率的指数增长。专利与论文产出每十年翻倍，而算力增长受摩尔定律驱动，约每两年翻倍。我们认为，人工智能研究人员的投入同样至关重要，其贡献可被客观估计。据此，我们提出一个简单解释，说明为何这两者增速存在5:1的关系。随后，我们将该分析应用于不同输出度量，并与现有文献中基于机器学习缩放定律的预测进行对比。我们的定量框架有助于理解、预测和调控这些重要技术的发展。
原文摘要 · Abstract (English)
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.
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