Abstract

A. Soltoggio, E. Ben-Iwhiwhu, V. Braverman, E. Eaton, B. Epstein, Y. Ge, L. Halperin, J. How, L. Itti, M. A. Jacobs, others, A collective AI via lifelong learning and sharing at the edge, Nature Machine Intelligence, Vol. 6, No. 3, pp. 251--264, Nature Publishing Group UK London, Mar 2024. [2023 impact factor: 18.8] (Cited by 14)

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.

Themes: Machine Learning