[allovernow]

Well, there are pretty convincing examples in other domains: try hardcoding rules to classify animals or objects in photos, especially an algorithm which can handle thousands of different categories. Totally impractical - but if we appropriately design the net and structure the training data, you can train a pretty accurate net on a mid-range GPU in a matter of hours to do what would take far, far longer to hardcode!

Perhaps not quite appropriate to call them heuristics in this context, but the principle is the same - you are leveraging joint probabilities of pixels to generate some conditional output. Similar principle in ML accelerated modeling.

[YeGoblynQueenne]

I think I understand what you meant by heuristics. I agree that it's impractical to try and hand-code image recognition rules and all attempts to do that in the past have failed as they have in similarly complex domains (like machine translation, say). My concern is particularly about the use of neural networks (or in general machine learning models that learn to approximate a function) in domains where precision is normally required, like engineering. I mean, I know there's plenty of approximation in engineering already but of course we're not talking about computing integrals here (er, I think?).

Anyway I was especially trying to understand the OP's comment about speedup using a neural network. I'm still a bit confused about that. But thanks for the conversation.