 While the links between such sentences and the correct answer are indirect (e.g., dependent on resolving he and who to Jimmy Carter, and associating restored diplomatic relations with recognized, and Communist China with China), correct analysis of those links is not a requirement for success—it is sufficient for the cluster of sentences favoring the answer Jimmy Carter (in virtue of their word and phrasal content and numerous other features) to provide a larger net weight to that answer than any competing clusters. This type of statistical evidence combination based on stored texts seems unlikely to provide a path to the kind of understanding that even first-graders betray in answering simple commonsense questions, such as “How do people keep from getting wet when it rains?”, or “If you eat a cookie, what happens to the cookie?” At the same time, vast data banks utilized in the manner of Watson can make up for inferential weakness in various applications, and IBM is actively redeveloping Watson as a resource for physicians, one that should be able to provide diagnostic and treatment possibilities that even specialists may not have at their fingertips. In sum, however, the goal of open-domain QA based on genuine understanding and knowledge-based reasoning remains largely unrealized. 