Does the Bitter Lesson Have Limits?
Recently, “the bitter lesson” is having a moment. Coined in an essay by Rich Sutton, the bitter lesson is that, “general methods that leverage computation are ultimately the most effective, and by a large margin.” Why is the lesson bitter? Sutton writes:
The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning. The eventual success is tinged with bitterness, and often incompletely digested, because it is success over a favored, human-centric approach.
Sutton walks through how the fields of computer chess, computer go, speech recognition, and computer vision have all experienced the bitter lesson.
