We now have protein folders and superhuman Go players -- that's new.
I agree that ML ("AI") is currently at the alchemy stage. And, guess what? A neural network isn't even Turing complete! [citation needed - correct me if I am wrong.] So ML can only compute SOME functions.
AGI, when it comes, and believe me, it will, will have ML as part of its structure, but only a small part.
It seems to me that that deep nets have effectively maximized the potential of using gradient pursuit to model patterns. But if you remove gradients from your data, or shrink your data down to tens of samples, or shift the problem to logic, or need to use functions that aren't convex or differentiable, deep nets run smack into a wall.
Luckily human perception makes extensive use of gradients, as does most search, so problems in those arenas have been unsurprisingly amenable to solution using deep nets (vision, speech, game play, etc). But many of the problems that remain untouched by DL, like human cognition, are NOT driven by gradients. Will deep nets eventually fill that void? I doubt it. You can convert only so many problems with big data into gradients to pursue them efficiently with DL before that transformation trick runs out of steam.
Personally I think deep net language modeling is one of those areas, and soon we'll encounter the limit of their generalizable contextual phrase association. Then because deep nets are so difficult to selectively revise or extend the specifics that they have learned, the vanguard of ever more complex deep nets (transformers) will eventually sink beneath their own weight, taking the last best hope for DL-based general AI with them.
We're making some progress on the convexity bit (heat functions, RL, etc.), but yes, there are other areas of statistical research all involved in trying to solve those sorts of problems.
ML is not necessarily a panacea, but just because you can point to problems that it doesn't solve doesn't mean it has no "real accomplishments."
> DL, like human cognition, are NOT driven by gradients.
???
This seems to me quite a deep insight. But how would you formally define a process that is gradient-based?
But it’s hard to deny that each boom doesn’t give us something useful. Neural Nets might not exactly make an AGI, but they do have uses.