Tom M. Mitchell
E. Fredkin University Professor
Machine Learning Department
Carnegie Mellon University

Never-Ending Language Learning


We will never really understand learning until we can build machines 
that learn many different things, over years, and become better 
learners over time.

We describe our 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, into its 
growing knowledge base of beliefs.  Each day NELL also learns to read 
better than the day before.  NELL has been running 24 hours/day for 
over four years now. The result so far is a collection of 70 million 
interconnected beliefs (e.g., servedWtih(coffee, applePie)), NELL is 
considering at different levels of confidence, along with millions of 
learned phrasings, morphological features, and web page structures 
that NELL uses to extract beliefs from the web. NELL is also learning 
to reason over its extracted knowledge, and to automatically extend its 
ontology. Track NELL's progress at http://rtw.ml.cmu.edu, or follow it 
on Twitter at @CMUNELL.



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 and Past 
President 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.