> I don't understand this kind of comment. To my mind what it amounts to is "look at how big it is". Alright. So it's big. So what?
It's a good question. A while ago, Rich Sutton wrote a good answer for it.: http://incompleteideas.net/IncIdeas/BitterLesson.html -- I recommend reading the whole essay. Quoting him (emphasis mine):
> 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. The ultimate reason for this is Moore's law, or rather its generalization of continued exponentially falling cost per unit of computation. Most AI research has been conducted as if the computation available to the agent were constant (in which case leveraging human knowledge would be one of the only ways to improve performance) but, over a slightly longer time than a typical research project, massively more computation inevitably becomes available. Seeking an improvement that makes a difference in the shorter term, researchers seek to leverage their human knowledge of the domain, but the only thing that matters in the long run is the leveraging of computation. These two need not run counter to each other, but in practice they tend to. Time spent on one is time not spent on the other. There are psychological commitments to investment in one approach or the other. And the human-knowledge approach tends to complicate methods in ways that make them less suited to taking advantage of general methods leveraging computation.
> We have to learn the bitter lesson that building in how we think we think does not work in the long run. The bitter lesson is based on the historical observations that 1) AI researchers have often tried to build knowledge into their agents, 2) this always helps in the short term, and is personally satisfying to the researcher, but 3) in the long run it plateaus and even inhibits further progress, and 4) breakthrough progress eventually arrives by an opposing approach based on scaling computation by search and learning.
> One thing that should be learned from the bitter lesson is the great power of general purpose methods, of methods that continue to scale with increased computation even as the available computation becomes very great. The two methods that seem to scale arbitrarily in this way are search and learning.
A key related question -- to which no one has the answer today -- is whether we must scale computation to match or exceed that of the human brain to be able to replicate or surpass its cognitive abilities. (Note that this question is independent of whether doing so would require future theoretical breakthroughs -- another question to which no one knows the answer today.)
PS. See also sanxiyn's response: https://news.ycombinator.com/item?id=28838745