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Biological underpinnings for lifelong learning machines

journal contribution
posted on 31.03.2022, 13:08 by Dhireesha Kudithipudi, Mario Aguilar-Simon, Jonathan Babb, Maxim Bazhenov, Douglas Blackiston, Josh Bongard, Andrew P Brna, Suraj Chakravarthi Raja, Nick Cheney, Jeff Clune, Anurag Daram, Stefano Fusi, Peter Helfer, Leslie Kay, Nicholas Ketz, Zsolt Kira, Soheil Kolouri, Jeffrey L Krichmar, Sam Kriegman, Michael Levin, Sandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K Pilly, Sebastian Risi, Terrence J Sejnowski, Andrea SoltoggioAndrea Soltoggio, Nicholas Soures, Andreas S Tolias, Darío Urbina-Meléndez, Francisco J Valero-Cuevas, Gido M van de Ven, Joshua T Vogelstein, Felix Wang, Ron Weiss, Angel Yanguas-Gil, Xinyun Zou, Hava Siegelmann
Biological organisms learn from interactions with their environment throughout their lifetime. For artificial systems to successfully act and adapt in the real world, it is desirable to similarly be able to learn on a continual basis. This challenge is known as lifelong learning, and remains to a large extent unsolved. In this Perspective article, we identify a set of key capabilities that artificial systems will need to achieve lifelong learning. We describe a number of biological mechanisms, both neuronal and non-neuronal, that help explain how organisms solve these challenges, and present examples of biologically inspired models and biologically plausible mechanisms that have been applied to artificial systems in the quest towards development of lifelong learning machines. We discuss opportunities to further our understanding and advance the state of the art in lifelong learning, aiming to bridge the gap between natural and artificial intelligence.

Funding

DARPA Lifelong Learning Machines programme

History

School

  • Science

Department

  • Computer Science

Published in

Nature Machine Intelligence

Volume

4

Issue

3

Pages

196 - 210

Publisher

Springer Nature

Version

AM (Accepted Manuscript)

Rights holder

© Springer Nature

Publisher statement

This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1038/s42256-022-00452-0

Acceptance date

20/01/2022

Publication date

2022-03-23

Copyright date

2022

eISSN

2522-5839

Language

en

Depositor

Dr Andrea Soltoggio. Deposit date: 30 March 2022

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