Review

. 2023 Mar 22;14(1):1597.

doi: 10.1038/s41467-023-37180-x.

Catalyzing next-generation Artificial Intelligence through NeuroAI

Anthony Zador^#¹, Sean Escola^#², Blake Richards^{3

4

5

6

7}, Bence Ölveczky⁸, Yoshua Bengio³, Kwabena Boahen⁹, Matthew Botvinick¹⁰, Dmitri Chklovskii¹¹, Anne Churchland¹², Claudia Clopath¹³, James DiCarlo¹⁴, Surya Ganguli¹⁵, Jeff Hawkins¹⁶, Konrad Körding¹⁷, Alexei Koulakov¹⁸, Yann LeCun^{19

20}, Timothy Lillicrap¹⁰, Adam Marblestone²¹, Bruno Olshausen²², Alexandre Pouget²³, Cristina Savin²⁴, Terrence Sejnowski²⁵, Eero Simoncelli²⁶, Sara Solla²⁷, David Sussillo^{19

28}, Andreas S Tolias²⁹, Doris Tsao²²

Affiliations

¹ Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA. zador@cshl.edu.
² Department of Psychiatry, Columbia University, New York, NY, 10027, USA.
³ Mila, Montréal, QC, H2S 3H1, Canada.
⁴ School of Computer Science, McGill University, Montreal, Canada.
⁵ Montreal Neurological Institute, McGill University, Montreal, Canada.
⁶ Department of Neurology & Neurosurgery, McGill University, Montreal, Canada.
⁷ Learning in Machines and Brains Program, CIFAR, Toronto, Canada.
⁸ Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA.
⁹ Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
¹⁰ Google Deepmind, London, N1C 4AG, UK.
¹¹ Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
¹² Department of Neurobiology, University of California Los Angeles, Los Angeles, CA, 90095, USA.
¹³ Department of Bioengineering, Imperial College London, London, SW7 2BW, UK.
¹⁴ Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA.
¹⁵ Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA.
¹⁶ Numenta, Redwood City, CA, 94063, USA.
¹⁷ Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA.
¹⁸ Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
¹⁹ Meta, Menlo Park, CA, 94025, USA.
²⁰ Department of Electrical and Computer Engineering, NYU, Brooklyn, NY, 11201, USA.
²¹ Media Lab, MIT, Cambridge, MA, 02140, USA.
²² Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA.
²³ Department of Basic Neurosciences, University of Geneva, Genève, 1211, Switzerland.
²⁴ Center for Neural Science, NYU, New York, NY, 10003, USA.
²⁵ Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
²⁶ Departments of Neural Science, Mathematics, and Psychology, NYU, New York, NY, 10003, USA.
²⁷ Department of Physiology, Northwestern University, Chicago, IL, 60611, USA.
²⁸ Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
²⁹ Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA.

^# Contributed equally.

PMID: 36949048
PMCID: PMC10033876
DOI: 10.1038/s41467-023-37180-x

Review

Catalyzing next-generation Artificial Intelligence through NeuroAI

Anthony Zador et al. Nat Commun. 2023.

. 2023 Mar 22;14(1):1597.

doi: 10.1038/s41467-023-37180-x.

Authors

Affiliations

¹ Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA. zador@cshl.edu.
² Department of Psychiatry, Columbia University, New York, NY, 10027, USA.
³ Mila, Montréal, QC, H2S 3H1, Canada.
⁴ School of Computer Science, McGill University, Montreal, Canada.
⁵ Montreal Neurological Institute, McGill University, Montreal, Canada.
⁶ Department of Neurology & Neurosurgery, McGill University, Montreal, Canada.
⁷ Learning in Machines and Brains Program, CIFAR, Toronto, Canada.
⁸ Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, 02138, USA.
⁹ Department of Bioengineering, Stanford University, Stanford, CA, 94305, USA.
¹⁰ Google Deepmind, London, N1C 4AG, UK.
¹¹ Flatiron Institute, Simons Foundation, New York, NY, 10010, USA.
¹² Department of Neurobiology, University of California Los Angeles, Los Angeles, CA, 90095, USA.
¹³ Department of Bioengineering, Imperial College London, London, SW7 2BW, UK.
¹⁴ Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, 02139, USA.
¹⁵ Department of Applied Physics, Stanford University, Stanford, CA, 94305, USA.
¹⁶ Numenta, Redwood City, CA, 94063, USA.
¹⁷ Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, 19104, USA.
¹⁸ Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, 11724, USA.
¹⁹ Meta, Menlo Park, CA, 94025, USA.
²⁰ Department of Electrical and Computer Engineering, NYU, Brooklyn, NY, 11201, USA.
²¹ Media Lab, MIT, Cambridge, MA, 02140, USA.
²² Helen Wills Neuroscience Institute, University of California Berkeley, Berkeley, CA, 94720, USA.
²³ Department of Basic Neurosciences, University of Geneva, Genève, 1211, Switzerland.
²⁴ Center for Neural Science, NYU, New York, NY, 10003, USA.
²⁵ Salk Institute for Biological Studies, La Jolla, CA, 92037, USA.
²⁶ Departments of Neural Science, Mathematics, and Psychology, NYU, New York, NY, 10003, USA.
²⁷ Department of Physiology, Northwestern University, Chicago, IL, 60611, USA.
²⁸ Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA.
²⁹ Department of Neuroscience, Baylor College of Medicine, Houston, TX, 77030, USA.

^# Contributed equally.

PMID: 36949048
PMCID: PMC10033876
DOI: 10.1038/s41467-023-37180-x

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.

PubMed Disclaimer

Conflict of interest statement

The authors declare their engagements with the following relevant for-profit entities—A.Z.: Cajal Neuroscience; S.E.: Herophilus; R.B.: Google DeepMind; B.Ö.: Blackbird Neuroscience; K.B.: Femtosense Inc., Radical Semiconductor, Neurovigil; C.C.: Google DeepMind; S.G.: Meta; J.H.: Numenta; K.K.: Paradromics; T.L.: Google DeepMind; A.M.: Google DeepMind, Kernel; D.S.: Meta; A.T.: Vathes Inc., Upload AI LLC, BioAvatar LLC. The remaining authors declare no competing interests.

Figures

**Fig. 1. Turing tests: comparisons between the performance of AI systems and their living counterparts.**
*Left*: The original Turing test as proposed by Alan Turing. If a human tester cannot determine whether their interlocutor is an AI system or another human, the AI passes the test. Modern large language models have made substantial progress towards passing this test. *Right*: The embodied Turing test. An AI animal model—whether robotic or in simulation—passes the test if its behavior is indistinguishable from that of its living counterpart. No AI systems are close to passing this test. Here, an artificial beaver is tested on the species-specific behavior of dam construction.

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Catalyzing next-generation Artificial Intelligence through NeuroAI

Affiliations

Catalyzing next-generation Artificial Intelligence through NeuroAI

Authors

Affiliations

Abstract

Conflict of interest statement

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