2 posts tagged with "Machine Learning"

In his 2019 essay The Bitter Lesson, Richard Sutton outlined a key observation in AI research: general-purpose methods that leverage massive computation consistently outperform human-crafted, specialized approaches. While this claim has strong empirical support, its theoretical justification is often overlooked. This post explores the fundamental learning theories, computational complexity principles, and optimization frameworks that underpin The Bitter Lesson.

In 2019, Richard Sutton, one of the pioneers of reinforcement learning, wrote a short but impactful essay titled "The Bitter Lesson". The core argument is simple: history has repeatedly shown that leveraging more computation trumps human-designed domain knowledge in AI. Despite decades of attempts to encode expert rules and handcrafted heuristics into AI systems, raw computational power combined with scalable methods has consistently outperformed these approaches over time.