Never-Ending Learning

Whereas people learn many different types of knowledge from diverse experiences over many years, most current machine learning systems acquire just a single function or data model from just a single data set. We propose a never-ending learning paradigm for machine learning, to better reflect the more ambitious and encompassing type of learning performed by humans. As a case study, we describe the Never- Ending Language Learner (NELL), which achieves some of the desired properties of a never-ending learner, and we discuss lessons learned. NELL has been learning to read the web 24 hours/day since January 2010, and so far has acquired a knowledge base with over 80 million confidence-weighted beliefs (e. g. , servedWith(tea, biscuits)), while learning continually to improve its reading competence over time. NELL has also learned to reason over its knowledge base to infer new beliefs from old ones, and is now beginning to extend its ontology by synthesizing new relational predicates. NELL can be tracked online at http: //rtw. ml. cmu. edu, and followed on Twitter at @CMUNELL.

Authors

• Tom Mitchell
• William Cohen
• Estevam Hruschka
• Partha Talukdar
• Justin Betteridge
• Andrew Carlson
• Bhavana Dalvi Mishra
• Matthew Gardner
• Bryan Kisiel
• Jayant Krishnamurthy
• Ni Lao
• Kathryn Mazaitis
• Thahir Mohamed
• Ndapa Nakashole
• Emmanouil Platanios
• Alan Ritter Georgia Institute of Technology
• Mehdi Samadi
• Burr Settles
• Richard Wang
• Derry Wijaya
• Abhinav Gupta
• Xinlei Chen
• Abulhair Saparov
• Malcolm Greaves
• Joel Welling

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