I have been compiling evidence and argumentation that artificial intelligence (AI) will not (any time soon or probably ever) match or exceed our most important human abilities. Many current AI projects have much to offer -- in medical research, autonomous vehicles, and across science and the economy. Deep learning and other AI techniques can process and parse previously unimaginable volumes of data, make sense of complex systems, and even mimic some human senses, such as vision and hearing. As for the fashionable economic worry that AI is a widespread threat to employment, however, I'm skeptical. Among many new entries in the growing literature of AI reality, let's highlight two. In the first of a new four-part broadside against AI alarmism, Massachusetts Institute of Technology robotics professor Rodney Brooks categorizes what he views as the four historical approaches to AI and grades their strengths and weaknesses.

Distributed knowledge based applications in open domain rely on common sense information which is bound to be uncertain and incomplete. To draw the useful conclusions from ambiguous data, one must address uncertainties and conflicts incurred in a holistic view. No integrated frameworks are viable without an in-depth analysis of conflicts incurred by uncertainties. In this paper, we give such an analysis and based on the result, propose an integrated framework. Our framework extends definite argumentation theory to model uncertainty. It supports three views over conflicting and uncertain knowledge. Thus, knowledge engineers can draw different conclusions depending on the application context (i.e. view). We also give an illustrative example on strategical decision support to show the practical usefulness of our framework.