Building reliable, robust software is hard. It is even harder when we move from deterministic domains (such as balancing a checkbook) to uncertain domains (such as recognizing speech or objects in an image). The field of machine learning allows us to use data to build systems in these uncertain domains, but the field mostly concentrates on accuracy of results. Peter Norvig looks at techniques for achieving reliability (and some of the other -ilities).
Peter Norvig is a director of research at Google. Previously, he directed Google’s core search algorithms group. Peter is coauthor of Artificial Intelligence: A Modern Approach, the leading textbook in the field, and coteacher of an artificial intelligence course that signed up 160,000 students, helping to kick off the current round of massive open online classes. He is a fellow of the AAAI, ACM, California Academy of Science, and American Academy of Arts & Sciences.
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