The Bitter Lesson: Scaling and Adaptation in AI for Success

The HackerNews post is about a paper titled "The Bitter Lesson" which discusses the idea that the most successful AI methods are those that can scale and adapt, rather than relying on specialized solutions. The post includes comments discussing the distinction between scaling methods and simply making algorithms bigger, as well as the role of search and learning in AI. There is also mention of the success of Deep Blue in chess and the use of reinforcement learning in AlphaGo. The comments also touch on the importance of parallelization, the debate between weak and strong search methods, and the potential for general artificial intelligence.

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