I was talking about attempts such as expert systems. We attempted to symbolically encode things as sums of their parts in plain terms, e.g. a bicycle is two wheels, an axle, a chain, some peddles, etc. but in more detail than that. The problem is the level of detail required for general knowledge: firstly it is hard to codify, secondly as it grows computation becomes exponentially more expensive.

We as humans have the intelligence to simply see for fractions of a second and then just know — we can know the answers to math problems and how a person is feeling. The symbolic AI programming was an attempt to codify what symbols we are seeing but they are so diverse and nuanced it became apparent this was an ineffective scientific route.

Yeah, that's because you decided it should be a hand-coded expert system. If you review the set of hand-coded neural networks, you will discover they even more bloated, and have an extreme low ceiling on usefulness.

Besides, keep in mind that the currently useful NNs are stuff that requires dozens of GB of memory to run.

Anyway, training a NN is also an attempt to codify what symbols we see on the world.

I’m not sure I understand your point at all. Of course I decided what I wrote — I wrote it. Of course training some AI is an attempt to codify the world, that’s the point.

My entire premise was about an _if_ between hand-coded logic and probabilistic training. Hand coding the logical internals of a neural network is one marriage, but there could be others.