I really disagree with this. There are lots of topics in AI research that are not just "make the best possible model on this task assuming you have unlimited compute".
I won't say I totally disagree, but I think the article overstates the position.
Two of the most important developments for ANN's were backprop and then the deep network training method. Without those we could only evolve ANN's which is much less efficient than the training methods.
If the scale argument held 100% then teams that purely focused on evolving ANN's (this is the "search" method of creating ANN's) to their target would be in the lead, but I'm not aware of anyone doing that other than smaller/hobby size projects.
Two of the most important developments for ANN's were backprop and then the deep network training method. Without those we could only evolve ANN's which is much less efficient than the training methods.
If the scale argument held 100% then teams that purely focused on evolving ANN's (this is the "search" method of creating ANN's) to their target would be in the lead, but I'm not aware of anyone doing that other than smaller/hobby size projects.