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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
How, Laurent Itti, Michael A. Jacobs, Pavan Kantharaju, Long Le, Steven
Lee, Xinran Liu, Sildomar T. Monteiro, David Musliner, Saptarshi Nath, Priyadarshini Panda, Christos Peridis, Hamed Pirsiavash,
Vishwa Parekh, Kaushik Roy, Shahaf Shperberg, Hava T. Siegelmann, Peter Stone,
Kyle Vedder, Jingfeng Wu, Lin Yang, Guangyao Zheng, and Soheil Kolouri.
nature
machine intelligence, 2024.
One vision of a future artificial intelligence (AI) is where many separate unitscan learn independently over a lifetime and share their knowledge with eachother. The synergy between lifelong learning and sharing has the potential tocreate a society of AI systems, as each individual unit can contribute to andbenefit from the collective knowledge. Essential to this vision are the abilitiesto learn multiple skills incrementally during a lifetime, to exchange knowledgeamong units via a common language, to use both local data and communication tolearn, and to rely on edge devices to host the necessary decentralizedcomputation and data. The result is a network of agents that can quickly respondto and learn new tasks, that collectively hold more knowledge than a single agentand that can extend current knowledge in more diverse ways than a single agent.Open research questions include when and what knowledge should be shared tomaximize both the rate of learning and the long-term learning performance. Herewe review recent machine learning advances converging towards creating acollective machine-learned intelligence. We propose that the convergence of suchscientific and technological advances will lead to the emergence of new types ofscalable, resilient and sustainable AI systems.
@Article{peter_nature2024,
author = {Andrea Soltoggio and Eseoghene Ben-Iwhiwhu and Vladimir Braverman and Eric Eaton and Benjamin Epstein and Yunhao Ge and Lucy Halperin and Jonathan How and Laurent Itti and Michael A. Jacobs and Pavan Kantharaju and Long Le and Steven Lee and Xinran Liu and Sildomar T. Monteiro and David Musliner and Saptarshi Nath and Priyadarshini Panda and Christos Peridis and Hamed Pirsiavash and Vishwa Parekh and Kaushik Roy and Shahaf Shperberg and Hava T. Siegelmann and Peter Stone and Kyle Vedder and Jingfeng Wu and Lin Yang and Guangyao Zheng and Soheil Kolouri},
title = {A collective AI via lifelong learning and sharing at the edge},
journal = {nature machine intelligence},
year = {2024},
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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