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.
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.
We have, up to this point, had decades of benefits accrued through computing -- but what innovations will push the envelope for our seniors? Artificial intelligence (AI) is the information of science. AI is misunderstood and glamorized by some, making it out to be something other than what it really is. It gives us the ability to use computers to perform tasks that usually require human intelligence. What was dubbed "artificial" is coming to be more appropriately thought to be cognitive intelligence, giving us tremendous benefits in all fields including aging, health, and safety.
An important area of KDD research involves development of techniques which transform raw data into forms more useful for prediction or explanation. We present an approach to automating the search for "indicator functions" which mediate such transformations. The fitness of a function is measured as its contribution to discerning different classes of data. Genetic programming techniques are applied to the search for and improvement of the programs which make up these functions. Rough set theory is used to evaluate the fitness of functions. Rough set theory provides a unique evaluator in that it allows the fitness of each function to depend on the combined performance of a population of functions. This is desirable in applications which need a population of programs that perform well in concert and contrasts with traditional genetic programming applications which have as there goal to find a single program which performs well. This approach has been applied to a small database of iris flowers with the goal of learning to predict the species of the flower given the values of four iris attributes and to a larger breast cancer database with the goal of predicting whether remission will occur within a five year period.