My original point was in response to OP's saying that not every paper needs to be brilliant. The thing about "normal science" is that begins with more or less verified theories and extends their theory and practice - until they fail, reach the edge of the theories, and the science community must search for alternative hypotheses. Machine learning is more working with a bunch of practices, rules of thumb and suggestions for doing practices. These can't really fail since they're roughly repeating themselves in different domains. The problem is that can be done forever and you never need new theories or approaches.

So I'd claim we're really at the situation right now, at the exploring new ideas, the "crisis of science" phase where essentially people have to start brainstorming (and not all ideas are good here either but they need to be somewhat original).

All this is using Thomas Kuhn's Structure Of Scientific Revolutions model very roughly.