I read and enjoyed the article. Was not boring to me. I am extremely interested in machine learing and get excited about things like graphical models, MCMC, hybrid monte carlo, online learning, reinforcement learning, deep learners, sparse learners, co-evolution, ensembles etc.

I found this article interesting and actually very well informed as such things go. It does not focus on Human like AI but practical approaches currently being used that are able to do the most basic type of learning at a complex level - ability to infer albeit on restricted spaces. For example, its 9 AM someone is at the door, given my past experience on situations like this (data) who is it most likely to be? I have a best guess based on what I have learned about who tends to come knocking when.

They can also pick out basic patterns whose details are very intricate, sometimes nuanced. What is interesting is that in these small spaces they can account for and infer on and recommend basic actions on vast number of parameters so accurately that they blow away any human attempt to match them to smitherings. This is why they are used as they are. Creativity, abstraction, self aware introspection,emotions, exploration for exploration's sake etc. are nice to think of as AI but that would be a waste of the type of niche intelligence computers are particularly well suited to based on their architecture. They do need to handle nuanced ambiguity better though. As well as transfering learning and dealing with situations far outside their training. The logic overflow of the 1960s movies is quite apt sometimes.

I do believe though that when (if) AI arrives it will be sudden as a unifying framework, a Standard Model of ML if you will is created. But in the mean time there is a lot of very interesting things going on and the combined explotion of introspection in the field (of Statistical Inference/Learning), data and processing power means very interesting times are ahead.