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A collective AI via lifelong learning and sharing at the edge

Nature Machine Intelligence volume 6, pages 251–264 (2024)Cite this article

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Abstract

One vision of a future artificial intelligence (AI) is where many separate units can learn independently over a lifetime and share their knowledge with each other. The synergy between lifelong learning and sharing has the potential to create a society of AI systems, as each individual unit can contribute to and benefit from the collective knowledge. Essential to this vision are the abilities to learn multiple skills incrementally during a lifetime, to exchange knowledge among units via a common language, to use both local data and communication to learn, and to rely on edge devices to host the necessary decentralized computation and data. The result is a network of agents that can quickly respond to and learn new tasks, that collectively hold more knowledge than a single agent and that can extend current knowledge in more diverse ways than a single agent. Open research questions include when and what knowledge should be shared to maximize both the rate of learning and the long-term learning performance. Here we review recent machine learning advances converging towards creating a collective machine-learned intelligence. We propose that the convergence of such scientific and technological advances will lead to the emergence of new types of scalable, resilient and sustainable AI systems.

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Fig. 1: Research fields contributing to ShELL.
Fig. 2: Desired learning behaviour for an on-knowledge-demand ShELL system.
Fig. 3: Types of information sharing and timing.
Fig. 4: List of ShELL operations and their likely hardware allocation on a typical computer architecture on commercially available edge devices.
Fig. 5: List of application areas, task categories and learning tasks that are suitable to ShELL systems.

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Acknowledgements

This material is based on work supported by DARPA under contracts HR00112190132, HR00112190133, HR00112190134, HR00112190135, HR00112190130 and HR00112190136. Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. The authors would like to thank B. Bertoldson, A. Carta, B. Clipp, N. Jennings, K. Stanley, C. Ekanadham, N. Ketz, M. Paravia, M. Petrescu, T. Senator and J. Steil for constructive discussions and comments on early versions of the manuscript.

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Authors and Affiliations

  1. Computer Science Department, Loughborough University, Loughborough, UK

    Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Saptarshi Nath & Christos Peridis

  2. Computer Science Department, Rice University, Houston, TX, USA

    Vladimir Braverman, Michael A. Jacobs & Guangyao Zheng

  3. University of Pennsylvania, Philadelphia, PA, USA

    Eric Eaton, Long Le & Kyle Vedder

  4. ECS Federal, Arlington, VA, USA

    Benjamin Epstein

  5. Thomas Lord Department of Computer Science, Viterbi School of Engineering, University of Southern California, Los Angeles, CA, USA

    Yunhao Ge & Laurent Itti

  6. Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Lucy Halperin & Jonathan How

  7. Department of Diagnostic and Interventional Imaging, The University of Texas McGovern Medical School at Houston, Houston, TX, USA

    Michael A. Jacobs

  8. The Department of Radiology and Oncology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Michael A. Jacobs

  9. Smart Information Flow Technologies, Minneapolis, MN, USA

    Pavan Kantharaju & David Musliner

  10. Aurora Flight Sciences, Cambridge, MA, USA

    Steven Lee & Sildomar T. Monteiro

  11. Department of Computer Science, Vanderbilt University, Nashville, TN, USA

    Xinran Liu & Soheil Kolouri

  12. Massachusetts Institute of Technology, Cambridge, MA, USA

    Sildomar T. Monteiro

  13. Department of Electrical Engineering, Yale University, New Haven, CT, USA

    Priyadarshini Panda

  14. Department of Computer Science, University of California, Davis, Davis, CA, USA

    Hamed Pirsiavash

  15. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Vishwa Parekh

  16. Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA

    Kaushik Roy

  17. Department of Software and Information System Engineering, Ben-Gurion University, Beer Sheva, Israel

    Shahaf Shperberg

  18. University of Massachusetts, Amherst, Amherst, MA, USA

    Hava T. Siegelmann

  19. Department of Computer Science, The University of Texas at Austin, Austin, TX, USA

    Peter Stone

  20. Sony AI America, Sony AI, Austin, TX, USA

    Peter Stone

  21. Simons Institute, University of California, Berkeley, Berkeley, CA, USA

    Jingfeng Wu

  22. Department of Electrical and Computer Engineering, University of California, Los Angeles, Los Angeles, CA, USA

    Lin Yang

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  1. Andrea Soltoggio
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Contributions

All authors contributed with insights during brainstorming, ideas and writing the paper. A.S. conceived the main idea and led the integration of all contributions.

Corresponding author

Correspondence to Andrea Soltoggio.

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Competing interests

P.S. serves as the executive director of Sony AI America and receives financial compensation for this work. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research. All other authors declare no competing interests.

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Nature Machine Intelligence thanks Senen Barro, Vincenzo Lomonaco, Xiaoying Tang, Gido van de Ven and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Information

Supplementary Section 1: ShELL algorithms and their implementations. Supplementary Section 2: additional technical details are provided on application scenarios and performance metrics.

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Soltoggio, A., Ben-Iwhiwhu, E., Braverman, V. et al. A collective AI via lifelong learning and sharing at the edge. Nat Mach Intell 6, 251–264 (2024). https://doi.org/10.1038/s42256-024-00800-2

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