Historically, AI research has understandably focused on those aspects of cognition that distinguish humans from other animals - in particular, our capacity for complex problem solving. However, with a few notable exceptions, narratives in popular media generally focus on those aspects of human experience that we share with other social animals: attachment, mating and child rearing, violence, group affiliation, and inter-group and inter-individual conflict. Moreover, the stories we tell often focus on the ways in which these processes break down. In this paper, I will argue that current agent architectures don't offer particularly good models of these phenomena, and discuss specific phenomena that I think it would be illuminating to understand at a computational level.
This paper is aimed at furthering discussions about the properties which computational cognitive models of attentional control should have if they are meant to be applied in interactive human-computer systems, namely for predicting human attention shifts across control levels. The paper discusses a number of issues of attentional control and implications that follow for computational modeling. It concludes by proposing the term anticipatory cognitive computing for those computational approaches that incorporate cognitive processing mechanisms found to exist in humans and that employ these mechanisms to generate hypothesis about imminent human cognitive and external actions.
IBM Watson Health has formed a medical imaging collaborative with more than 15 leading healthcare organizations. The goal: To take on some of the most deadly diseases. The collaborative, which includes health systems, academic medical centers, ambulatory radiology providers and imaging technology companies, aims to help doctors address breast, lung, and other cancers; diabetes; eye health; brain disease; and heart disease and related conditions, such as stroke. Watson will mine insights from what IBM calls previously invisible unstructured imaging data and combine it with a broad variety of data from other sources, such as data from electronic health records, radiology and pathology reports, lab results, doctors' progress notes, medical journals, clinical care guidelines and published outcomes studies. As the work of the collaborative evolves, Watson's rationale and insights will evolve, informed by the latest combined thinking of the participating organizations.
We discover the patterns of autistic reasoning in the conditions requiring change in representation of domain knowledge. The formalism of nonmonotonic logic of defaults is used to simulate the autistic decision-making while learning how to adjust an action to the environment which forces new representation structure. Our main finding is that while autistic reasoning may be able to process single default rules, they have a characteristic difficulty in cases with nontrivial representation changes, where multiple default rules conflict. We evaluate our hypothesis that the skill of representation adjustment can be advanced by learning default reasoning patterns via a set of exercises.