"The bitter lesson of machine learning is that building knowledge into agents does not work in the long run, and breakthrough progress comes from scaling comput

"The bitter lesson of machine learning is that building knowledge into agents does not work in the long run, and breakthrough progress comes from scaling computation by search and learning.02 This applies to domains where domain knowledge is weak or hard to express mathematically. The rapid progress of ML applied to LQCD, mol.2 dyn., protein folding, and computer graphics is the result of combining domain knowledge with ML." This passage says that the advantages of scaling a certain kind of learning are especially good when .. two conditions.. but a side-effect of that statement is, when knowledge is well known, and maybe straightforward to express, these kinds of learning systems, not as great.. this is true. without taking on big other topics, I think this "bitter lesson" is unspecific enough to include some self-serving utility.. just tell the other camps to give up, you lost. that sort of thing.