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Smart MachinesIBM's Watson and the Era of Cognitive Computing$
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Steve Hamm and John Kelly

Print publication date: 2013

Print ISBN-13: 9780231168564

Published to Columbia Scholarship Online: November 2015

DOI: 10.7312/columbia/9780231168564.001.0001

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Building Learning Systems

Building Learning Systems

Chapter:
(p.23) 2 Building Learning Systems
Source:
Smart Machines
Author(s):

John E. Kelly

Steve Hamm

Publisher:
Columbia University Press
DOI:10.7312/columbia/9780231168564.003.0002

This chapter examines machine learning systems. The development of IBM's Watson represented a major advance in the science of machine learning, a branch of artificial intelligence that focuses on building systems that learn from data. These systems will be able to learn from both structured and unstructured data, discover important correlations, create hypotheses for those correlations, and suggest actions that produce better outcomes. In addition, they will have feedback loops where they measure the results of their work, in order to modify the way they do things. There are two stages in the evolution of learning systems. The first is static learning, in which teams of technology and domain experts are needed to provide a learning system with the rules and information it needs to perform a certain well-defined task—then train it to use the information. The second is dynamic learning, wherein machines will continually obtain information from a variety of sources, expanding their hypothesis generation across multiple domains that frequently intersect with one another.

Keywords:   machine learning systems, IBM, machine learning, artificial intelligence, static learning, dynamic learning

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