On the May 8th edition of Closing Bell on CNBC, venture capitalist Chamath Palihapitiya, founder and CEO of Social Capital, created quite a stir in enterprise artificial intelligence (AI) circles, when he took on Watson, Big Blue's AI platform. "Human intelligence outperforms machine-learning applications in complex decision making routinely required during the course of care, because machines do not yet possess mature capabilities for perceiving, reasoning, or explaining," explained Ernest Sohn, a chief data scientist in Booz Allen's Data Solutions and Machine Intelligence group; Joachim Roski, a principal at Booz Allen Hamilton; Steven Escaravage, vice president in Booz Allen's Strategic Innovation Group; and Kevin Maloy, MD, assistant professor of emergency medicine at Georgetown University School of Medicine. "A health care organization that relies on a single EHR [Electronic Health Record] vendor's analytic solutions, as well as its own legacy analytics infrastructure created before the era of big data, may see limited progress," they continued. "While many machine-learning solutions are not yet mature and sophisticated enough to support complex clinical decisions, machine learning can be effectively deployed today to reduce more routine, time-consuming, and resource-intensive tasks, allowing freed-up personnel to be redeployed to support higher-end work."

There are countless internet quizzes that helps sort you into Hogwarts houses depending on your personality, but nothing comes quite close to this: an actual sorting hat powered by IBM Watson. The hat, created by IBM engineer Ryan Anderson, started off as a fun project for him and his two daughters to help expose the girls to STEM while bridging it with their interests in the Harry Potter series. His daughter helped by adding lines of established "ground truths" for each of the four houses, with Watson using deep learning to figure out more attributes and expanding known qualities of each house every time the hat is worn. With above 90 percent certainty, the hat sorted Hawking and Clinton into Ravenclaw for their wits.

The newly emerging field of machine ethics (Anderson and Anderson 2006) is concerned with adding an ethical dimension to machines. Unlike computer ethics -- which has traditionally focused on ethical issues surrounding humans' use of machines -- machine ethics is concerned with ensuring that the behavior of machines toward human users, and perhaps other machines as well, is ethically acceptable. In this article we discuss the importance of machine ethics, the need for machines that represent ethical principles explicitly, and the challenges facing those working on machine ethics. We also give an example of current research in the field that shows that it is possible, at least in a limited domain, for a machine to abstract an ethical principle from examples of correct ethical judgments and use that principle to guide its own behavior.