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.
@article{arxiv.1705.00594,
title = {A System for Accessible Artificial Intelligence},
author = {Randal S. Olson and Moshe Sipper and William La Cava and Sharon Tartarone and Steven Vitale and Weixuan Fu and Patryk Orzechowski and Ryan J. Urbanowicz and John H. Holmes and Jason H. Moore},
journal= {arXiv preprint arXiv:1705.00594},
year = {2017}
}
Comments
14 pages, 5 figures, submitted to Genetic Programming Theory and Practice 2017 workshop