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Technical Perspective: Breaking the Mold of Machine Learning


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The field of artificial intelligence (AI) is rife with misnomers and machine learning (ML) is a big one. ML is a vibrant and successful subfield, but the bulk of it is simply "function approximation based on a sample." For example, the learning portion of AlphaGo—which defeated the human world champion in the game of GO—is in essence a method for approximating a non-linear function from board position to move choice, based on tens of millions of board positions labeled by the appropriate move in that position.a As pointed out in my Wired article,4 function approximation is only a small component of a capability that would rival human learning, and might be rightfully called machine learning.

Tom Mitchell and his collaborators have been investigating how to broaden the ML field for over 20 years under headings such as multitask learning,2 life-long learning,7 and more. The following paper, "Never-ending Learning," is the latest and one of the most compelling incarnations of this research agenda. The paper describes the NELL system, which aims to learn to identify instances of concepts (for example, city or sports team) in Web text. It takes as input more than 500M sentences drawn from Web pages, an initial hierarchy of interrelated concepts, and small number of examples of each concept. Based on this information, and the relationships between the concepts, it is able to learn to identify millions of concept instances with high accuracy. Over time, NELL has also begun to identify relationships between concept classes, and extend its input concept set.


 

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