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this post was submitted on 03 Dec 2023
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One of the overlooked aspects of generative AI is that effectively by definition generative models can also be classifiers.
So let's say you were Spotify and you fed into an AI all the songs as well as the individual user engagement metadata for all those songs.
You'd end up with a model that would be pretty good at effectively predicting the success of a given song on Spotify.
So now you can pair a purely generative model with the classifier, so you spit out song after song but only move on to promoting it if the classifier thinks there's a high likelihood of it being a hit.
Within five years systems like what I described above will be in place for a number of major creative platforms, and will be a major profit center for the services sitting on audience metadata for engagement with creative works.
Right, the trick will be quantifying what is 'likely to be a hit', which if we're honest, has already been done.
Also, neural networks and other evolutionary algorithms can inject random perturbations/mutations to the system which, operate a bit like uninformed creativity (something like banging on a piano and hearing something interesting that's worth pursuing). So, while not 'inspired' or 'soulful' as we would generally think of it, these algorithms are capable of being creative In some sense. But it would need to be recognized as 'good' by someone or something...and back to your point.
What you described in your second paragraph is basically how image generation AI works.
Starting from random noise and gradually moving towards the version a classifier identifies as best matching the prompt.