A collective AI via lifelong learning and sharing at the edge

Andrea Soltoggio, Eseoghene Ben-Iwhiwhu, Vladimir Braverman, ERIC EATON, Benjamin Epstein, Yunhao Ge, Lucy Halperin, JONATHAN P HOW, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Xinran Liu, Sildomar Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash, Vishwa Sanjay Parekh et al. (8 additional authors not shown)

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. In this paper, we review recent machine learning advances converging towards creating a collective machine-learned intelligence. We put forward that the convergence of such scientific and technological advances will lead to the emergence of new types of scalable, resilient, and sustainable AI systems.