Yes, we can do a lot that we couldn't do 30 years ago. However, this only falls under the banner of "AI" because AI has been redefined from the promises of 50 years ago [1], when the goal of AI was to build "human-level" intelligent agents. The "Dartmouth Summer Research Project on Artificial Intelligence" proposal [1] in 1955 when revered AI researchers Marvin Minsky, (inventor of Lisp) John McCarthy, and (inventor of information theory) Claude Shannon claimed "a significant advance can be made in one or more of these problems if a carefully selected group of scientists work on it together for a summer". They failed at this lofty goal of solving the AI problem over a summer, and over time the field of AI was redefined to be narrow fancy mathematical tricks to solve domain-specific problems. Most "AI" is not intelligent at all, instead they are approaches for searching some search space with a non-general-purpose tool, such as genetic algorithms, neural networks, or first-order logic theorem provers. A "true AI" would discover regularities [2], or patterns, in any search space, and exploit these patterns to incrementally improve its ability to navigate the search space. Examples of regularities in the world are that navigation is often best performed by sticking to the regularly appearing feature that we call a "road" instead of attempting to climb over buildings, and that navigation can be effectively guided by attempting to minimize the Euclidean distance between the current location and the goal location. Instead, most of the currently available tools are essentially performing a stochastic search through the state space, possibly guided by some heuristics.

While a lot of progress is being made in the redefined and inaccurately named field of AI, object recognition and speech recognition are still narrow domains. They are not using general purpose approaches, but instead tweaking and tuning specific algorithms to these areas. In reality, I am not championing for "human-level AI", as I think it would be very difficult to duplicate a human's strengths and deficiencies [3] accurately. Instead, I am saying that we should be encouraging research that produces a general intelligence; software that can recognize and exploit regularities (patterns) in the state spaces of many problem domains, and apply the abstracted knowledge gained from earlier experiences to new domains and problems. Such software would have a tremendous impact on society, assisting us in solving and analyzing many problems and likely leading to an "intelligence explosion" after crossing some threshold at which the software is able to recursively improve itself. [4]

You asked "just about every specific useful task humans do better than computers, there are a few dozen AI researchers working on it. Why is that not a reasonable approach to the problem?" How do we take all these domain specific solutions and get a cohesive general intelligence out of them? I see these domain specific algorithms as being possibly useful to a general intelligence as an optimization to shift some of the load off of the more general purpose algorithms, but I don't see how throwing a bunch of face recognition, path finding, neural network and genetic algorithms into a box would automatically cause a general intelligence to pop out. These research areas seem mostly tangential or supportive to genuine Artificial General Intelligence research.

1. http://www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html

2. http://en.wikipedia.org/wiki/Kolmogorov_complexity

3. http://www.singinst.org/Biases.pdf

4. http://en.wikipedia.org/wiki/Technological_singularity