Distinguished Lecture - Tom M. Mitchell

We describe our effort to build a Never-Ending Language Learner (NELL) that runs 24 hours per day, forever, learning to read the Web. Each day NELL extracts (reads) more facts from the Web, and integrates these into its growing knowledge base of beliefs. Each day NELL also learns to read better than yesterday, enabling it to go back to the text it read yesterday, and extract more facts, more accurately. NELL has now been running 24 hours/day for over two years. The result so far is a collection of 15 million interconnected beliefs [e.g., servedWtih(coffee, applePie), isA(applePie, bakedGood)], that NELL is considering at different levels of confidence, along with hundreds of thousands of learned phrasings, morphological features, and Web page structures that NELL uses to extract beliefs from the Web. The approach implemented in NELL is based on three key ideas: (1) coupling the semi-supervised training of thousands of different functions that extract different types of information from different Web sources, (2) automatically discovering new constraints that more tightly couple the training of these functions over time, and (3) a curriculum or sequence of increasing difficult learning tasks. Track NELL's progress at http://rtw.ml.cmu.edu

 

Bio: Tom M. Mitchell is the E. Fredkin University Professor and founding head of the Machine Learning Department at Carnegie Mellon University. His research interests lie in machine learning, artificial intelligence, and cognitive neuroscience.  Mitchell is a member of the U.S. National Academy of Engineering, a Fellow of the American Association for the Advancement of Science (AAAS), and a Fellow and Past President of the Association for the Advancement of Artificial Intelligence (AAAI) Mitchell believes the field of machine learning will be the fastest growing branch of computer science during the 21st century.