Problems of Symbolic AI

Two key factors in AI systems are important but distracting.
Turing complete: just because a system is Turing complete, it does not mean that it is a good basis for an AI.
Logical completeness: just because a system can represent things logically, it does not mean that it is a good basis for an AI.
A system needs to do both of these things (people do), but it needs to do a lot of other things.
Cyc was a really huge knowledge based system. One lesson to take from its unintelligence is that you can't just fill up the KB, the system needs to learn it.
Learning is critical. People are learning all the time.
Cognitive models like ACT and Soar can learn, but they can not readily learn new symbols. (Symbol Grounding Problem.)
People can ground symbols.
Neural systems probably can too.
Most AI systems work on either trivial systems, a very simple domain, or use highly preprocessed data.
People do not.
People learn unexpected associations and have a vast range of mechanisms for solving problems.
These are frequently based on the environment that they are in. (The behaviour of an ant seems complex, but that is because of its environment.)