Most Interesting quote I found in first 5 minutes of browsing your G+ feed:

Unfortunately, the bubble was to burst once again, following a series of attacks on connectionism’s representational capabilities and lack of grounding. Connectionist models were criticized for being incapable of capturing the compositionality and productivity characteristic of language processing and other cognitive representations (Fodor & Pylyshyn 1988); for being too opaque (e.g., in the distribution and dynamics of their weights) to offer insight into their own operation, much less that of the brain (Smolensky 1988); and for using learning rules that are biologically implausible and amount to little more than a generalized regression (Crick 1989). The theoretical position underlying connectionism was thus reduced to the vague claim that that the brain can learn through feedback to predict its environment, without a psychological explanation being offered of how it does so. As before, once the excitement over computational power was tempered, the shortage of theoretical substance was exposed.

"One reason that research in connectionism suffered such setbacks is that, although there were undeniably important theoretical contributions made during this time, overall there was insufficient critical evaluation of the nature and validity of the psychological claims underlying the approach. During the initial explosions of connectionist research, not enough effort was spent asking what it would mean for the brain to be fundamentally governed by distributed representations and tuning of association strengths, or which possible specific assumptions within this framework were most consistent with the data. Consequently, when the limitations of the metaphor were brought to light, the field was not prepared with an adequate answer. On the other hand, pointing out the shortcomings of the approach (e.g., Marcus 1998; Pinker & Prince 1988) was productive in the long run, because it focused research on the hard problems. Over the last two decades, attempts to answer these criticisms have led to numerous innovative approaches to computational problems such as object binding (Hummel & Biederman 1992), structured representation (Pollack 1990), recurrent dynamics (Elman 1990), and executive control (e.g., Miller & Cohen 2001; Rougier et al. 2005). At the same time, integration with knowledge of anatomy and physiology has led to much more biologically realistic networks capable of predicting neurological, pharmacological, and lesion data (e.g., Boucher et al. 2007; Frank et al. 2004). As a result, connectionist modeling of cognition has a much firmer grounding than before."

-- Matt Jones & Bradley C. Love, Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition.

I would love more detailed/referenced high-level analyses of different approaches to AI (e.g. connectionism v. computationalism v. WBE).

I suppose this would be a good place to start at the very least: