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A System for Accessible Artificial Intelligence

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Genetic Programming Theory and Practice XV

Part of the book series: Genetic and Evolutionary Computation ((GEVO))

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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Notes

  1. 1.

    FGLab: https://github.com/Kaixhin/FGLab.

  2. 2.

    http://lacava.github.io/few.

  3. 3.

    http://epistasislab.github.io/ellyn.

  4. 4.

    https://github.com/rhiever/tpot.

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Acknowledgements

This work was generously funded by the Perelman School of Medicine and the University of Pennsylvania Health System. Additional funding was provided by National Institutes of Health grants AI116794, DK112217, ES013508, and TR001878.

Author information

Authors and Affiliations

  1. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

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

  2. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Moshe Sipper

  3. Department of Computer Science, Ben-Gurion University, Beer-Sheva, Israel

    Moshe Sipper

  4. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA

    Patryk Orzechowski

  5. Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Krakow, Poland

    Patryk Orzechowski

Authors
  1. Randal S. Olson
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  2. Moshe Sipper
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  3. William La Cava
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  4. Sharon Tartarone
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  5. Steven Vitale
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  6. Weixuan Fu
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  7. Patryk Orzechowski
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  8. Ryan J. Urbanowicz
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  9. John H. Holmes
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  10. Jason H. Moore
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Corresponding author

Correspondence to Jason H. Moore .

Editor information

Editors and Affiliations

  1. BEACON Center for the Study of Evolution in Action and Department of Computer Science, Michigan State University, East Lansing, Michigan, USA

    Wolfgang Banzhaf

  2. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Randal S. Olson

  3. Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    William Tozier

  4. Center for the Study of Complex Systems, University of Michigan, Ann Arbor, Michigan, USA

    Rick Riolo

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Olson, R.S. et al. (2018). A System for Accessible Artificial Intelligence. In: Banzhaf, W., Olson, R., Tozier, W., Riolo, R. (eds) Genetic Programming Theory and Practice XV. Genetic and Evolutionary Computation. Springer, Cham. https://doi.org/10.1007/978-3-319-90512-9_8

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  • DOI: https://doi.org/10.1007/978-3-319-90512-9_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-90511-2

  • Online ISBN: 978-3-319-90512-9

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