图灵奖得主Richard S. Sutton 苦涩的教训(the bitter lesson)|AI经典重温

the bitter lesson导读

苦涩的教训(the bitter lesson)是richard sutton在2019年3月写的一篇博文，反思了过去AI研究中的一个教训，在设计人工智能算法时，人类试图用更多的专家知识来提升智能水平，而未充分考虑机器本身计算思维的特性，即利用计算能力可带来的智能的提升。
sutton在文中回顾70年来AI研究的经验，介绍了在计算象棋、围棋、语音处理、计算机视觉等领域，利用算力的方法在性能上最终超越了基于专家知识构造的特征方法，得出结论：AI研究者试图将专业知识嵌入智能体中来在各个领域中取得SOTA性能，而最终奏效的方法却是基于算力的搜索和学习方法。
反思这一苦涩的教训，sutton得出在AI研究中的两点启示，一是重视能随算力扩展的通用方法的威力，其中学习和搜索是两大能随算力扩展的通用方法；二是人类的心智是极其复杂的，应该避免人类中心论的思维，停止将专家知识嵌入智能体的方法，寻找能捕捉复杂性的元方法，让智能体能像人类一样去发现，而非将人类发现的知识嵌入智能体。
以下为the bitter lesson原文及翻译：

原文总结

70年AI研究中的教训

The biggest lesson that can be read from 70 years of AI research is that general methods that leverage computation are ultimately the most effective, and by a large margin. The ultimate reason for this is Moore’s law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation. There were many examples of AI researchers’ belated learning of this bitter lesson, and it is instructive to review some of the most prominent.

从70年的人工智能研究中得到的最重要的启示是，利用计算能力的通用方法最终是最有效的，而且优势巨大。其根本原因在于摩尔定律，或者更准确地说，是计算能力单位成本持续呈指数级下降的普遍趋势。大多数人工智能研究都假设智能体可利用的计算能力是固定的（在这种情况下，利用人类知识将是提升性能的少数途径之一），然而在比典型研究项目稍长的时