(Before beginning, I want to note that these are solely my opinions, and therefore are probably wrong.) In the space of possible problems solvable by computers

(Before beginning, I want to note that these are solely my opinions, and therefore are probably wrong.) In the space of possible problems solvable by computers, there are those of which are "easy" and those of which are "hard". Arbitrarily defined, an "easy" problem is any problem that be solved by throwing more resources at it -- whether it'd be more data, or more compute. A "hard" problem on the other hand is the opposite: solvable only by a major, intellectual breakthrough; the benefit of solving a hard problem is that it allows us to do "more" with "less". Now, the question is: which type of problems are being looked at by today's AI practitioners. I'd argue it is the former. Chess, Go, Dota 2 -- these are all "easy" problems. Why? Because it is easy to find or generate more data, to use more CPUs and GPUs, and to get better results. Hell, I might even add self-driving cars to that list since they, along with neural networks, existed since the 1980s [1]. The only difference, it seems, is more compute. All and all, I think these recent achievements only qualify themselves as engineering achievements -- not as theoretical or scientific breakthroughs. One way to put it: have we, not the computers and machines, learned something fundamentally different? Maybe another approach to current ML / AI is needed? I remember a couple weeks ago there was a post on HN, about Judas Pearl advocating causality as an alternative [2]. Intuitively it makes sense: baby humans don't only perform glorified pattern matching, but they are able to discern cause-and-effect. Perhaps that is what today's AI practitioners are missing. [1] https://en.wikipedia.org/wiki/History_of_autonomous_cars#1980s https://en.wikipedia.org/wiki/History_of_autonomous_cars#198... [2] https://news.ycombinator.com/item?id=17108179 https://news.ycombinator.com/item?id=17108179