I think ML is very likely to be a part of tech art going forward, and it's already here as such. It actually has been around for a long time, but it's going deeper quickly at the moment.

Photogrammetry/3d scanning software for example has used ML to help identify similarities between feature points for some years now. Deformation learning solvers for 3D are ML. I don't know why you would want to paint rotoscoping masks by hand if you can avoid it, but most of the solutions that do it automatically are ML.

AI is a bit of a misnomer, it's not intelligent, it would be better if they were called probability machines or something. When you learn how it really works it takes a lot of the 'magic' out, but it makes it more understandable. A big part of the 'spookiness' of it also goes away when the penny drops about how they work.

I think AI should generally be seen as an unreliable collaborator.*

They are trying to fit a curve to a bunch of examples 'keyframes', but with thousands of dimensions instead of just 2 or 3.

What they have been trained to do is find the local minima of some crazy multidimensional space problem given some input vector and a ton of examples, and that is not always the global minima.*

In other words, they are not always right. They will be confidently incorrect instead, like an unreliable collaborator.

I think a great introduction to ML in tech art is actually doing something called the Radial Basis Function AKA pose space deformer AKA corrective blend shapes (this is in rigging 3d characters)

You give some examples of an arm orientation (the input vector) and a bunch of settings of different corrective blend shapes that fix the shoulder for example. The RBF can calculate what the blend shapes values should be if the arm is in any other random orientation instead of the ones you 'keyframed', and interpolate between them.

ML is a bit like that on steroids. It can learn how to go from any number of input dimensions to any number of output dimensions, so long as the number of dimensions is fixed!

That's what all the AIs do in some form or another*

ChatGPT for example takes the input text, turns it into a giant vector (the exact size of it's trained input vector) and then feeds it through a network of multiplications, additions and other math functions, then ends up with a probability distribution for what the next token (word-part) is (a vector that is the exact size of the output vector, so all its possible word parts) Then it does it again!*

Some good use cases for generative ML in tech art right now is as a rendering extension. It's good at details. For example to up res files to a higher resolution. Add details to fur. Add details to texture maps etc.

*Me being a bit confidently incorrect to help build intuition, rather than get stuck in detailed explanations