While I sort of intuitively agree with you, I think there's a certain amount of circularity in the argument - any problem will seem to have well defined metrics once you've built and studied a machine which can solve it.

In the sixties, chess was thought to be a problem that required intelligence since it couldn't be brute-forced. We now have machines which can play it well, without brute-forcing, and yet it's seen as entirely procedural.

AI people thought chess was a problem that required intelligence. Critics back in the 60s such as Dreyfus probably didn't view chess as the hallmark of intelligence.

AGI is the hypothesis that someday the number viable human cons with well-defined metrics will dwarf the ones without.

The mechanics of speech synthesis are clear? The measure of success of style transfer is well defined?

I would say speech synthesis is governed by very clear mechanics. https://en.wikipedia.org/wiki/International_Phonetic_Alphabe...

As for style transfer, that is a very specific skill of making the patterns of one style map to the patterns of another. I am not particularly well versed with art, but that process seems well defined to me.

Perhaps your issue is with my more generalized definition of "clear" and "well-defined". I meant to use these terms to distinguish between autonomous driving and being a successful human. I really don't think there is anywhere close to a consensus on the latter. To the extent that there is, then yes, AI should be able to do it.

The IPA is nowhere close to sufficient for realistic speech synthesis, and style transfer is not just copy and paste. By the same token writing poetry is just "putting words into grammatical constructions that have certain patterns" or mathematics research is just "a form of proof search".

Of course we don't have human-level AI right now, but if that's the only thing you're claiming it's pretty vacuous.

I would say speech synthesis is governed by clear mechanics - and it's not the IPA, it's that the output comes out as a waveform, which has a structure that informs the algorithm.

Note that we have great raster-based deep visual effects, but vector is... not there yet (not saying it won't be) - vector is less structured than raster, so the choice of algorithm is less obvious.

As for well-defined criteria, I don't think that's really quite the right standard, I think the correct standard is that there is a way of metrizing success on a well-ordered set (like the [0,1] interval), even if it's noisy.

I fiddled with an idea where I wrote unit tests and used them for a scoring function to train a model. Writing the number of tests and encoding the logic for a simple Linked List took orders of magnitude more code than coding the list itself.

I suspect (based on stuff like https://arxiv.org/abs/1510.08419) that you'd have had more luck with property tests instead of unit tests.

Thanks for the paper I will check it out.