I think the problem is a disagreement over what "AI" means. We don't know how to do strong, general-purpose AI with artificial neural nets. We do know how to do a lot of smaller tasks with neural nets. Just because they're not currently a panacea doesn't mean they're not useful.
I once read something along the lines of "AI is whatever computers can't currently do". There is a ring of truth to this. We once thought that it would take AI for a computer to be the best chess player on the planet, but now most phones can beat 99.99% of people of the planet without trying. One day the Turing Test will be easily passed and I am sure we (humans) still won't be satisfied.
No, neural nets have topped out long before that saying came into effect. They aren't useless, but don't be fooled by facile analogies to the human brain; neural nets as we actually know how to make them do useful things are rarely the best solution. We don't know how to train complicated ones very well, and the simple ones we know how to train are still very slow to learn vs. many other techniques, yet simultaneously bring very little to the table that aren't done better by numerous other techniques.
Reminds me of genetic algorithms and programming; no, they aren't necessarily useless, but remove the facile analogies to real-world processes and they carry a lot of expense for not a lot of gain. Only rarely are they the right choice.
I thought that there was plenty of progress in the field...
http://en.wikipedia.org/wiki/Artificial_neural_network