If it's as bad as you say, it seems like a critical evaluation would be pretty interesting and advance the field.
Source: Used to work for an 80s-era "AI Company"
We're way past memorization. We're into interpolation and extrapolation in high D spaces with Bayesian parameters. Sentiment analysis and contextual searches - search by idea, not keyword. Heuristic decision making. Massively accelerated 3D modeling with 99% accuracy. Generative nets for text, music, scientific models...
Sorry, but you're behind the times, and that's ok - one of us will be proven right in the next 1-5 years. Based solely on the work we're doing at the startup I'm working for, we're on the cusp of an ML revolution. Time will tell, but personally I'm pretty excited. And don't worry, I'm not working in adtech or anything useless.
That said, the driving problem seems to be quite far from being solved, I agree though it is outside my expertise; but I think the primary issue is that this is an application where error must be unrealistically low, a constraint which does not apply to many other domains. You can get away with a couple percent of uncertainty when people's lives aren't on the line!
There is no doubt in my mind that an AI with billions of parameters will be excellent at memorizing stuff.
I also have no doubt that research activity has exploded, which might be related to the generous hardware grants being handed out...
But all that research has produced surprisingly little progress over algorithms from 2004 in the field of optical flow.
The papers I looked at were the top-ranked optical flow algorithms on Sintel and KITTY. So those were the AIs that work best in practice and better than 99% of the other AI solutions.