>If you train a NN to generate a representative knowledge model that solves a "simple" problem that could have been solved with an explicit solution, you're still doing ML.

I guess my question would be when do you know that what you have is a representative knowledge model rather than just a simple function? Another question that might help clear it up for me is - what would have to change in order for the strandbeest program to be considered machine learning?

That makes sense. After all it doesn't make sense to call something Artificial Intelligence if it doesn't act intelligently. I feel like it boils down to when the machine/neural net/function has learned to do a task which has reached a certain level of general applicability/usefulness then it can be considered AI / Machine Learning. And it makes sense that people draw this line at different points and disagree about where it should be drawn.

For example if you train a car to drive around a track but the track is a fixed width it's possible that you trained only a single parameter - the amount which the car should turn based on the difference between the distances to the left and right wall. Once that number is dialed in the car will be able to handle any track of that fixed width and will look pretty smart, but it could have been achieved with a simple function instead of a neural network.

I've heard similar concerns raised with AI related to Radiology for cancer screening since there is no way to actually know what factors the neural network is considering and how then it's possible that it was making the judgement based on something completely unrelated to the cancer. I tried to find a source for that but hopefully you get what I mean, basically just the black box problem.

Yeah, it seems that this is an old question without an agreed upon answer. I've seen a lot of YouTube videos which claim to be AI which use this method but maybe there's a more agreed upon definition in academia or industry. It's not a big deal either way really I was just curious but I think I'll just go with "it depends who you ask".

Yeah that makes sense, thank you

Cool thanks, I hadn't thought of the connection to board games like chess that learn with self-play sometimes.

Interesting, thanks. It seems a lot of people do lump it in with machine learning such as this video using a neural network and evolutionary algorithm to teach a car to drive around a track. Does the use of a feed forward neural network make it qualify as machine learning or still no? Or is it just a gray area?

I'm not trying to be defensive just wanted to have the discussion and see what other people's takes on this was. What do you think of the car example?

The data is the curve generated by each leg orientation. Each curve in the batch is then scored based on some criteria. If that isn't machine learning then neither is using a neural network and evolutionary algorithm and I think most people would say that it is.

What about the use of neural network and evolutionary algorithms then?

Edit: I often see this referred to as AI