basically, what a network does is that takes an entire animation clip as a single sample in a search space. and them from those samples can gives you the set of all sample that are close to the space.
That's pretty much how i imagined it to work. I won't go there - feel too old to learn it. I would be happy if could learn basics of math as you explained some posts above in my lifetime ;D
However, despite being always critical with hyped stuff, i became more open minded towards ML.
It first occurred to me here on this forum. Pretty early when i came here, and i think this was before the hype. A ML researcher contacted me by PM because he was interested in using it for character animation. We talked a bit, and he told me things hard to believe. He said his stuff could even learn to talk. Though, might take ten years for the training.
Now, all he said became true. A year ago i had a intellectual conversation with a ML chatbot. It did not pass the turing test on me, but the talk was meaningful. The bots reactions to my cynical remarks about the meaning of life from the perspective of an AI bot were eerie relaxed and almost outsmarting, reflecting my trolling with simple truth.
But what really convinced me was this:
https://soundcloud.com/yichao-zhou-555747812/sets/bandnet-sound-samples-1To me, as i'm a hobby musician, i always said 'ML will never be able to compose earwigs'. That's the highest art in music. Nobody has a recipe. We simply don't know why some melodies stick in our heads. And if you compose one yourself, it feels like a gift coming from the outside. You just pick it up and eventually refine it.
Those examples from the link are generated by ML from Beatles samples. And the songs are, while somewhat unconventional, pretty good. I know many human composer who never get there in their whole life. The examples surely are cherry picked, but that's no argument. Finding one good song from a million examples would be not much different than composing one yourself. Thus, many songs the ML has generated must have some higher quality i guess.
So, ML achieved what i personally thought would be impossible for a machine.
The magic surely comes from the Beatles samples, which are human, but the resulting songs are new ones. And the first one is really catchy. Could become a radio hit or at least a good commercial.
ML really seems useful. Especially for tasks which are fuzzy and lack a precise definition of the problem. Which i think is not true for our robotic topic here.
Still, my crystal ball remains showing me the picture of games industry jumping on this. You explained the reasons yourself.