Hard to explain well. Let me try another way. Thinking through this more right now, it seems like the default way of computers to solve this penalizes them. Ev

Hard to explain well. Let me try another way. Thinking through this more right now, it seems like the default way of computers to solve this penalizes them. Every time they get more depth it hurts them unless you account for more breadth. So lets say the programers know this. This STILL probably doesn't help much because...well idk. What's interesting is Tensor Flow is supposed to be massively parallel (breadth) and abstract the software/algorithms to do this. But if you follow this logic the answer must be gradient decent algorithms also get stuck somewhere... This must have to do with hyper-sensitivity to really having the full decision tree before optimizing. The answer seems to be if computers are modeling this exactly as humans do, (using neural networks which then simplify this problem with heuristics), it just takes this much computer power to simulate 1 human brain optimizing for this. Alternatively if they model it in a more brute force way it must take even more computer power because this is proving to be less efficient.