Biological underpinnings for lifelong learning machines

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 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

doi:10.1038/s42256-022-00452-0

Biological underpinnings for lifelong learning machines

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 LevinSandeep Madireddy, Santosh Manicka, Ali Marjaninejad, Bruce McNaughton, Risto Miikkulainen, Zaneta Navratilova, Tej Pandit, Alice Parker, Praveen K. Pilly, Sebastian Risi, Terrence J. Sejnowski, Andrea 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

Whiting School of Engineering

Research output: Contribution to journal › Review article › peer-review

Abstract

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.

Original language	English (US)
Pages (from-to)	196-210
Number of pages	15
Journal	Nature Machine Intelligence
Volume	4
Issue number	3
DOIs	https://doi.org/10.1038/s42256-022-00452-0
State	Published - Mar 2022

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications
Artificial Intelligence

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10.1038/s42256-022-00452-0

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Kudithipudi, D., Aguilar-Simon, M., Babb, J., Bazhenov, M., Blackiston, D., Bongard, J., Brna, A. P., Chakravarthi Raja, S., Cheney, N., Clune, J., Daram, A., Fusi, S., Helfer, P., Kay, L., Ketz, N., Kira, Z., Kolouri, S., Krichmar, J. L., Kriegman, S., ... Siegelmann, H. (2022). Biological underpinnings for lifelong learning machines. Nature Machine Intelligence, 4(3), 196-210. https://doi.org/10.1038/s42256-022-00452-0

Kudithipudi, D, Aguilar-Simon, M, Babb, J, Bazhenov, M, Blackiston, D, Bongard, J, Brna, AP, Chakravarthi Raja, S, Cheney, N, Clune, J, Daram, A, Fusi, S, Helfer, P, Kay, L, Ketz, N, Kira, Z, Kolouri, S, Krichmar, JL, Kriegman, S, Levin, M, Madireddy, S, Manicka, S, Marjaninejad, A, McNaughton, B, Miikkulainen, R, Navratilova, Z, Pandit, T, Parker, A, Pilly, PK, Risi, S, Sejnowski, TJ, Soltoggio, A, Soures, N, Tolias, AS, Urbina-Meléndez, D, Valero-Cuevas, FJ, van de Ven, GM, Vogelstein, JT, Wang, F, Weiss, R, Yanguas-Gil, A, Zou, X & Siegelmann, H 2022, 'Biological underpinnings for lifelong learning machines', Nature Machine Intelligence, vol. 4, no. 3, pp. 196-210. https://doi.org/10.1038/s42256-022-00452-0

@article{26d328082c64434ab58317d6ea290716,

title = "Biological underpinnings for lifelong learning machines",

abstract = "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.",

author = "Dhireesha Kudithipudi and Mario Aguilar-Simon and Jonathan Babb and Maxim Bazhenov and Douglas Blackiston and Josh Bongard and Brna, {Andrew P.} and {Chakravarthi Raja}, Suraj and Nick Cheney and Jeff Clune and Anurag Daram and Stefano Fusi and Peter Helfer and Leslie Kay and Nicholas Ketz and Zsolt Kira and Soheil Kolouri and Krichmar, {Jeffrey L.} and Sam Kriegman and Michael Levin and Sandeep Madireddy and Santosh Manicka and Ali Marjaninejad and Bruce McNaughton and Risto Miikkulainen and Zaneta Navratilova and Tej Pandit and Alice Parker and Pilly, {Praveen K.} and Sebastian Risi and Sejnowski, {Terrence J.} and Andrea Soltoggio and Nicholas Soures and Tolias, {Andreas S.} and Dar{\'i}o Urbina-Mel{\'e}ndez and Valero-Cuevas, {Francisco J.} and {van de Ven}, {Gido M.} and Vogelstein, {Joshua T.} and Felix Wang and Ron Weiss and Angel Yanguas-Gil and Xinyun Zou and Hava Siegelmann",

note = "Funding Information: This work was partly supported by the DARPA Lifelong Learning Machines programme. We wish to express our thanks to the technical leadership team of DARPA L2M, specifically R. McFarland, B. Epstein, R. McFarland and T. Senator. R. McFarland and B. Epstein offered several insights on organization of the paper, contributed in brainstorming sessions, and provided graphics suggestions. T. Senator seeded the idea to develop a review article. R. McFarland and other members of the L2M team spurred insightful discussions and provided feedback on the Perspective. We thank G. Vallabha, E. Johnson, M. Peot, F. Sha for reviewing the manuscript. Publisher Copyright: {\textcopyright} 2022, Springer Nature Limited.",

year = "2022",

month = mar,

doi = "10.1038/s42256-022-00452-0",

language = "English (US)",

volume = "4",

pages = "196--210",

journal = "Nature Machine Intelligence",

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T1 - Biological underpinnings for lifelong learning machines

AU - Kudithipudi, Dhireesha

AU - Aguilar-Simon, Mario

AU - Babb, Jonathan

AU - Bazhenov, Maxim

AU - Blackiston, Douglas

AU - Bongard, Josh

AU - Brna, Andrew P.

AU - Chakravarthi Raja, Suraj

AU - Cheney, Nick

AU - Clune, Jeff

AU - Daram, Anurag

AU - Fusi, Stefano

AU - Helfer, Peter

AU - Kay, Leslie

AU - Ketz, Nicholas

AU - Kira, Zsolt

AU - Kolouri, Soheil

AU - Krichmar, Jeffrey L.

AU - Kriegman, Sam

AU - Levin, Michael

AU - Madireddy, Sandeep

AU - Manicka, Santosh

AU - Marjaninejad, Ali

AU - McNaughton, Bruce

AU - Miikkulainen, Risto

AU - Navratilova, Zaneta

AU - Pandit, Tej

AU - Parker, Alice

AU - Pilly, Praveen K.

AU - Risi, Sebastian

AU - Sejnowski, Terrence J.

AU - Soltoggio, Andrea

AU - Soures, Nicholas

AU - Tolias, Andreas S.

AU - Urbina-Meléndez, Darío

AU - Valero-Cuevas, Francisco J.

AU - van de Ven, Gido M.

AU - Vogelstein, Joshua T.

AU - Wang, Felix

AU - Weiss, Ron

AU - Yanguas-Gil, Angel

AU - Zou, Xinyun

AU - Siegelmann, Hava

N1 - Funding Information: This work was partly supported by the DARPA Lifelong Learning Machines programme. We wish to express our thanks to the technical leadership team of DARPA L2M, specifically R. McFarland, B. Epstein, R. McFarland and T. Senator. R. McFarland and B. Epstein offered several insights on organization of the paper, contributed in brainstorming sessions, and provided graphics suggestions. T. Senator seeded the idea to develop a review article. R. McFarland and other members of the L2M team spurred insightful discussions and provided feedback on the Perspective. We thank G. Vallabha, E. Johnson, M. Peot, F. Sha for reviewing the manuscript. Publisher Copyright: © 2022, Springer Nature Limited.

PY - 2022/3

Y1 - 2022/3

N2 - 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.

AB - 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.

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U2 - 10.1038/s42256-022-00452-0

DO - 10.1038/s42256-022-00452-0

M3 - Review article

AN - SCOPUS:85127349740

SN - 2522-5839

VL - 4

SP - 196

EP - 210

JO - Nature Machine Intelligence

JF - Nature Machine Intelligence

IS - 3

ER -

Biological underpinnings for lifelong learning machines

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