A System for Accessible Artificial Intelligence
May 01, 2017 Β· Declared Dead Β· π Genetic Programming Theory and Practice
"No code URL or promise found in abstract"
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Authors
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
arXiv ID
1705.00594
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.HC,
cs.NE
Citations
32
Venue
Genetic Programming Theory and Practice
Last Checked
4 months ago
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.
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