http://www.incompleteideas.net/IncIdeas/BitterLesson.html

• In AI research, general methods that leverage computation are the most effective

  • Moore’s law, leveraging compute is more effective than human knowledge
  • Example: chess and go
    • Researchers tried to improve algorithms with human knowledge of the games (ie architecture), but ultimately search and learning using massive
  • Another prime example is classic nlp vs deep learning…
  • Ongoing paradigm transition from building knowledge to building mechanisms into models, and ‘let compute figure out the knowledge’

  “The second general point to be learned from the bitter lesson is that the actual contents of minds are tremendously, irredeemably complex; we should stop trying to find simple ways to think about the contents of minds, such as simple ways to think about space, objects, multiple agents, or symmetries. All these are part of the arbitrary, intrinsically-complex, outside world. They are not what should be built in, as their complexity is endless; instead we should build in only the meta-methods that can find and capture this arbitrary complexity. Essential to these methods is that they can find good approximations, but the search for them should be by our methods, not by us. We want AI agents that can discover like we can, not which contain what we have discovered. Building in our discoveries only makes it harder to see how the discovering process can be done.”