> 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

> 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. Be careful when anyone, even a giant in the field such as Sutton, posits a sweeping claim like this. My take? Sutton's "bitter lesson" is rather vague and unspecified (i.e. hard to pin down and test) for at least two reasons: 1. The word "ultimately" is squishy, when you think about it. When has enough time passed to make the assessment? At what point can we say e.g. "Problem X has a most effective solution"? 2. What do we mean by "most effective"? There is a lot of variation, including but not limited to (a) some performance metric; (b) data efficiency; (c) flexibility / adaptability across different domains; and (d) energy efficiency. I'm a big fan of Sutton's work. I've read his RL book cover-to-cover and got to meet him briefly. But, to me, the bitter lesson (as articulated in Sutton's original post) is not even wrong. It is sufficiently open-ended that many of us will disagree about what the lesson is, even before we can get to the empirical questions of "First, has it happened in domain D at time T? Second, is it 'settled' now, or might things change?"