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 comput
核心贡献 · Key contributions
利用算力的通用方法在 AI 中最终最为有效，且优势巨大。 General methods leveraging computation are ultimately the most effective in AI, by a large margin.
搜索和学习是随算力增长而扩展的两项关键技术。 Search and learning are the two key techniques that scale with increased computation.
基于人类知识的方法常陷入平台期，阻碍长期进步。 Human-knowledge-based approaches often plateau and inhibit long-term progress.
AI 的突破性进展来自通过搜索和学习扩展算力，而非编码人类知识。 Breakthrough progress in AI comes from scaling computation via search and learning, not from encoding human knowledge.
心智的实际内容极其复杂；应构建能发现复杂性的元方法。 The actual contents of minds are irredeemably complex; we should build meta-methods that discover complexity.
国际象棋、围棋、语音识别和计算机视觉的例子证实了这一苦涩教训。 Examples from chess, Go, speech recognition, and computer vision confirm the bitter lesson.
局限 · Limitations
该教训可能不适用于算力极其有限或无明确扩展路径的领域。 The lesson may not apply to domains with extremely limited computation or no clear scaling path.
人类知识在短期或特定狭窄任务中仍有价值。 Human knowledge can still be valuable in the short term or for specific narrow tasks.
该方法可能忽视数据质量和超越 Scaling 的算法创新的重要性。 The approach may overlook the importance of data quality and algorithmic innovation beyond scaling.
它未涉及纯粹算力驱动 AI 带来的伦理或安全问题。 It does not address ethical or safety concerns that arise from purely computation-driven AI.
历史例子可能无法完全代表 AGI 或超级智能等未来 AI 范式。 The historical examples may not fully represent future AI paradigms like AGI or superintelligence.