The entire premise up front is false and probably a primary culprit. Expecting ML to do things it can't yet by extrapolating from what it can do today (after reading current capabilities through a filter of marketing hype):
>Today's work in artificial intelligence is amazing. We've taught computers to beat the most advanced players in the most complex games. We've taught them to drive cars and create photo-realistic videos and images of people. They can re-create works of fine-art and emulate the best writers.
Today's work in ML is amazing.
> We've taught computers to beat the most advanced players in the most complex games.
Not true. You can spend a zillion dollars on self play to get an AI superhuman at games simple enough that you can simulate at many many times real life speed, but we're just now learning to do games like poker, which intuitively seems less intellectual than Go or Chess, but so does starcraft and that came after those other games. In ML, placing tasks in order of achievable to currently impossible can be really unintuitive for lay people.
> We've taught them to drive cars and create photo-realistic videos and images of people.
No again. We're getting there with cars, but it turns out that it's really really hard. Harder than playing superhuman chess! But people who play chess better than computers can drive cars better than computers. Weird, right? Again, in ML, placing tasks in order of achievable to currently impossible can be really unintuitive for lay people.
We can make photorealistic pictures of people, but we're sorta limited (it's complicated) to faces at high resolutions and just really really really recently getting them without weird artifacts. But the face is the most complex part of the body, right? So the rest should be easy!
> They can re-create works of fine-art and emulate the best writers.
This is soooo much of a nope, and you know what I'm going to say anways.
This xkcd is always relevant, even if the bar has moved. Maybe it's even harder because the bar is moving quickly. https://xkcd.com/1425/
> In CS, it can be hard to explain the difference between the easy and the virtually impossible.
In ML, we're really good at some tasks, so it seems like we should be good at adjacent tasks, but that's not how it works.
>Today's work in artificial intelligence is amazing. We've taught computers to beat the most advanced players in the most complex games. We've taught them to drive cars and create photo-realistic videos and images of people. They can re-create works of fine-art and emulate the best writers.
Today's work in ML is amazing.
> We've taught computers to beat the most advanced players in the most complex games.
Not true. You can spend a zillion dollars on self play to get an AI superhuman at games simple enough that you can simulate at many many times real life speed, but we're just now learning to do games like poker, which intuitively seems less intellectual than Go or Chess, but so does starcraft and that came after those other games. In ML, placing tasks in order of achievable to currently impossible can be really unintuitive for lay people.
> We've taught them to drive cars and create photo-realistic videos and images of people.
No again. We're getting there with cars, but it turns out that it's really really hard. Harder than playing superhuman chess! But people who play chess better than computers can drive cars better than computers. Weird, right? Again, in ML, placing tasks in order of achievable to currently impossible can be really unintuitive for lay people.
We can make photorealistic pictures of people, but we're sorta limited (it's complicated) to faces at high resolutions and just really really really recently getting them without weird artifacts. But the face is the most complex part of the body, right? So the rest should be easy!
> They can re-create works of fine-art and emulate the best writers.
This is soooo much of a nope, and you know what I'm going to say anways.
This xkcd is always relevant, even if the bar has moved. Maybe it's even harder because the bar is moving quickly. https://xkcd.com/1425/
> In CS, it can be hard to explain the difference between the easy and the virtually impossible.
In ML, we're really good at some tasks, so it seems like we should be good at adjacent tasks, but that's not how it works.