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.
- AI researchers have often tried to build knowledge into their agents
- this always helps in the short term, and is personally satisfying to the researcher
- in the long run it plateaus and even inhibits further progress
- 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.