August 27 ACM Webcast with Tom Mitchell of CMU: "Never-Ending Learning to Read the Web"

  • Articolo

One of the great technical challenges in big data is to construct computer systems that learn continuously over years, from a continuing stream of diverse data, improving their competence at a variety of tasks, and becoming better learners over time. This webinar describes Carnegie Mellon University's research 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, today. NELL has been running 24 hours/day for over three years now. The result so far is a collection of 50 million interconnected beliefs (e.g., servedWith(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 has learned to use to extract beliefs from the web. Track NELL's progress at https://rtw.ml.cmu.edu.

Register TODAY to attend the next free ACM Webcast, "Never-Ending Learning to Read the Web," presented on Tuesday, August 27, 2013 at 1 pm ET (12 noon CT/11 am MT/10 am PT/5 pm GMT), part of the ACM Learning Webinar series, presented by Tom M. Mitchell, Founder and Chair of the Machine Learning Department at Carnegie Mellon University. The talk will be followed by a live question and answer session. If you'd like to attend but can't make it to the virtual event, you still need to register to receive a recording of the webinar when it becomes available. Note: You can stream this and all ACM Learning Webinars on your mobile device, including smartphones and tablets.

Duration: 60 minutes

Presenter:
Tom M. Mitchell, Carnegie Mellon University
Tom M. Mitchell founded and chairs the Machine Learning Department at Carnegie Mellon University, where he is the E. Fredkin University Professor. His research uses machine learning to develop computers that are learning to read the web, and uses brain imaging to study how the human brain understands what it reads. 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 of the Association for the Advancement of Artificial Intelligence (AAAI). He believes the field of machine learning will be the fastest growing branch of computer science during the 21st century. Mitchell's web page is https://www.cs.cmu.edu/~tom.

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