Sure, many people are aware of the NFL theorem, but they don't take it seriously. If you don't believe me, read almost any computer vision paper. Vision researchers study algorithms, not images.
Sure, many people are aware of the NFL theorem, but they don't take it seriously.
Legg's thesis says:
...Some, such as Edmonds (2006), argue that universal definitions of intelligence are impossible due to Wolpert’s so called “No Free Lunch” theorem (Wolpert and Macready, 1997). However this theorem, or any of the standard variants on it, cannot be applied to universal intelligence for the simple reason that we have not taken a uniform distribution over the space of environments. Instead we have used a highly non-uniform distribution based on Occam’s razor
I searched the posts but didn't find a great deal of relevant information. Has anyone taken a serious crack at it, preferably someone who would like to share their thoughts? Is the material worthwhile? Are there any dubious portions or any sections one might want to avoid reading (either due to bad ideas or for time saving reasons)? I'm considering investing a chunk of time into investigating Legg's work so any feedback would be much appreciated, and it seems likely that there might be others who would like some perspective on it as well.