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Catalyzing next-generation Artificial Intelligence through NeuroAI

Author

Listed:
  • Anthony Zador

    (Cold Spring Harbor Laboratory)

  • Sean Escola

    (Columbia University)

  • Blake Richards

    (Mila
    McGill University
    McGill University
    McGill University)

  • Bence Ölveczky

    (Harvard University)

  • Yoshua Bengio

    (Mila)

  • Kwabena Boahen

    (Stanford University)

  • Matthew Botvinick

    (Google Deepmind)

  • Dmitri Chklovskii

    (Simons Foundation)

  • Anne Churchland

    (University of California Los Angeles)

  • Claudia Clopath

    (Imperial College London)

  • James DiCarlo

    (MIT)

  • Surya Ganguli

    (Stanford University)

  • Jeff Hawkins

    (Numenta)

  • Konrad Körding

    (University of Pennsylvania)

  • Alexei Koulakov

    (Cold Spring Harbor Laboratory)

  • Yann LeCun

    (Meta
    NYU)

  • Timothy Lillicrap

    (Google Deepmind)

  • Adam Marblestone

    (MIT)

  • Bruno Olshausen

    (University of California Berkeley)

  • Alexandre Pouget

    (University of Geneva)

  • Cristina Savin

    (NYU)

  • Terrence Sejnowski

    (Salk Institute for Biological Studies)

  • Eero Simoncelli

    (NYU)

  • Sara Solla

    (Northwestern University)

  • David Sussillo

    (Meta
    Stanford University)

  • Andreas S. Tolias

    (Baylor College of Medicine)

  • Doris Tsao

    (University of California Berkeley)

Abstract

Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which challenges AI animal models to interact with the sensorimotor world at skill levels akin to their living counterparts. The embodied Turing test shifts the focus from those capabilities like game playing and language that are especially well-developed or uniquely human to those capabilities – inherited from over 500 million years of evolution – that are shared with all animals. Building models that can pass the embodied Turing test will provide a roadmap for the next generation of AI.

Suggested Citation

  • Anthony Zador & Sean Escola & Blake Richards & Bence Ölveczky & Yoshua Bengio & Kwabena Boahen & Matthew Botvinick & Dmitri Chklovskii & Anne Churchland & Claudia Clopath & James DiCarlo & Surya Gangu, 2023. "Catalyzing next-generation Artificial Intelligence through NeuroAI," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-37180-x
    DOI: 10.1038/s41467-023-37180-x
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    References listed on IDEAS

    as
    1. Agrim Gupta & Silvio Savarese & Surya Ganguli & Li Fei-Fei, 2021. "Embodied intelligence via learning and evolution," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    2. Kwabena Boahen, 2022. "Dendrocentric learning for synthetic intelligence," Nature, Nature, vol. 612(7938), pages 43-50, December.
    3. Josh Merel & Matthew Botvinick & Greg Wayne, 2019. "Hierarchical motor control in mammals and machines," Nature Communications, Nature, vol. 10(1), pages 1-12, December.
    4. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    5. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
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