I have a background in Computer Engineering and have been working on developing better algorithms to mimic human thought. (One of my favorites is Analogical Modeling as applied to language processing and decision making.) However, the more I research, the more I realize just how complicated AI is.
I have tried to tackle many problems in this field, but sometimes I find that I am reinventing the wheel or am trying to solve a problem that has already been proven to be unsolvable (ie. the halting problem). So, to help in furthering AI, I want to better understand the current obstacles that are hindering our progress in this field.
For example, time and space complexity of some machine learning algorithms is super-polynomial which means that even with fast computers, it can take a while for the program to complete. Even still, some algorithms may be fast on a desktop or other computer while dealing with a small data set, but when increasing the size of the data, the algorithm becomes intractable.
What are other issues currently facing AI development?