That's a false choice. There's also:
- Technology not suitable or adequate for this use case.
I mean, we've been to the "AI over-promises and under-delivers" rodeo before.
- Technology not suitable or adequate for this use case.
I mean, we've been to the "AI over-promises and under-delivers" rodeo before.
Yeah. Attempts at powered flight had a 100% failure rate in the 1800s.
It's kind of absurd to think that "AI" (especially in its current incarnation) must be able to solve any "hot" problem that it's thrown at.
And those older systems has more intelligence in them. Todays’s “AI” is a small number of tricks aimed at large amounts of data, with an unprecedented and enormous amount of marketing added.
If you're talking about deep neural networks, I can understand this viewpoint. But generally the last decade has proven widely successful for high quality vision recognition models that just weren't available before then. And with something like transfer learning, you can take a powerful off-the-shelf model and specialize it without a huge dataset.
The real barriers IME are around legal and privacy implications. There's also a strong argument about if these models creating enough value in the first place, but they can work on a technical level.
More or less. It's almost certain if "AI" makes any headway at all, it will fall far short of the hype.
> That seems like a fairly fringe view. Especially on a problem that has well defined data like medical imaging.
I doubt it.
Also on the wet side, there is very little progress on something like general antiviral drugs.
Some stuff is just really hard.