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arXiv:1705.00594cs.AI

A System for Accessible Artificial Intelligence

Randal S. Olson, Moshe Sipper, William La Cava, Sharon Tartarone, Steven Vitale, Weixuan Fu, Patryk Orzechowski, Ryan J. Urbanowicz, John H. Holmes, Jason H. Moore

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Abstract

While artificial intelligence (AI) has become widespread, many commercial AI systems are not yet accessible to individual researchers nor the general public due to the deep knowledge of the systems required to use them. We believe that AI has matured to the point where it should be an accessible technology for everyone. We present an ongoing project whose ultimate goal is to deliver an open source, user-friendly AI system that is specialized for machine learning analysis of complex data in the biomedical and health care domains. We discuss how genetic programming can aid in this endeavor, and highlight specific examples where genetic programming has automated machine learning analyses in previous projects.

arXiv comments
14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 workshop
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence cs.HCHuman-Computer Interaction cs.NENeural and Evolutionary Computing

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No comments linking this paper were found in the collected sources. Found in 2 HN submissions from May 2017 to May 2017. Open submission discussions.

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