Building a Mind for Life
published: Nov. 14, 2013, recorded: July 2013, views: 3189
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Sometimes two hard problems are easier than one. Making sense of the explosion to data about life and human health that arises from exponentially improving DNA sequencing technology is one of the primary scientific challenges of our time. Artificial intelligence is based in the equally profound question of what constitutes a mind and how one could be constructed artificially. These two challenges mutually inform and constrain each other. Computational biology needs tools that relate enormous experimental results to vast amounts of relevant prior knowledge in ways that illuminate the mechanisms underlying the phenomena under study. This is a fundamentally semantic process that requires programs represent complex, incomplete and partially incorrect knowledge, reason about it and its relation to experimental results, and engage in effective, ongoing scientific communication about hypotheses. This is an "AI-complete" problem. However, the domain is special in several ways: (1) All the relevant knowledge is explicit, and can be found in the roughly 10,000 textbooks and 20 million journal articles that make up the biomedical literature. (2) The biomedical research community has a long-standing and effective project in ontology development, provide an increasingly comprehensive conceptual foundation of entities, processes, functions, locations, relations and more that are explicitly defined, rigorously organized and maintained by domain experts. These biomedical ontologies underpin several large-scale annotation projects, the results of which are available in standardized semantic formats like RDF and OWL. (3) There is a large community of biomedical research scientists desperate for effective means of explaining and contextualizing their data. Although they are demanding and less interested in the technological means than the scientific ends, a thoughtful human community eager to interact in an extended intellectual partnership with programs is a rare and valuable resource for AI research. This combination of factors suggests that the first wildly acknowledged genuine AI may think about biology. I will talk about progress to date and prospects in building a mind for life.
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