Lack of inference.
The AI is told "you're right, you're wrong" but it does not, can not and will not ever work out WHY it's wrong. It just shifts it's detection to finer and finer and finer criteria, outside of the control of the programmer or operator, until it's "success" improves by 0.00001%. This, as all "AI" in use today demonstrates, plateaus REALLY quickly. You get "convincing" results that then can't be untrained or retrained or trained out of the system and it gets stuck and can make progress only at glacial rates. And all you're really doing is statistical analysis, and modifying that data slightly. It's really no different to a Bayesian spam filter on your email.
What we lack is any way to provide the machine with the capability to infer the causes of those results, why that result is wrong, how it can modify, what questions it can ask to distinguish between a Cavendish banana and any other.
It's an inherently one-way system. This is data. Eat it. Now eat more data and tell me if it's the same. At no point do we assist the machine - even human tampering in the data selection process isn't helping it at all, any more than doing a child's homework for them. The AI is still stuck in a blind maze of problems that it has no way to escape out of, but punishment if it doesn't manage to. And that works fine for maze-like problems (like Chess or Go or anything else that logical and graph-theory) that are small enough to get out by brute-force-and-a-bit-of-help in a reasonable time.
We do not have AI. And we won't until we work out the inference problem.
Biting the hand that feeds IT
