{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-system-for-accessible-artificial","title":"A System for Accessible Artificial Intelligence","arxiv_id":"1705.00594","date":"2017-05-01","proceeding":null,"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"],"abstract":"While artificial intelligence (AI) has become widespread, many commercial AI\nsystems are not yet accessible to individual researchers nor the general public\ndue to the deep knowledge of the systems required to use them. We believe that\nAI has matured to the point where it should be an accessible technology for\neveryone. We present an ongoing project whose ultimate goal is to deliver an\nopen source, user-friendly AI system that is specialized for machine learning\nanalysis of complex data in the biomedical and health care domains. We discuss\nhow genetic programming can aid in this endeavor, and highlight specific\nexamples where genetic programming has automated machine learning analyses in\nprevious projects.","url_abs":"http://arxiv.org/abs/1705.00594v2","url_pdf":"http://arxiv.org/pdf/1705.00594v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-system-for-accessible-artificial","repo_url":"https://github.com/EpistasisLab/pennai","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-system-for-accessible-artificial","repo_url":"https://github.com/epistasislab/aliro","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}