Catalyzing next-generation Artificial Intelligence through NeuroAI

作者：Anthony M. Zador, G. Sean Escola, Blake A. Richards, Bence P. Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri B. Chklovskii, Anne K. Churchland, Claudia Clopath, James J. DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad P. Körding, Alexei A. Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno A. Olshausen, Alexandre Pouget, Cristina Savin, Terrence J. Sejnowski, Eero P. Simoncelli, Sara A. Solla, David Sussillo, Andreas S. Tolias, Doris Y. Tsao · 发表于：Nature Communications · 年份：2023 · DOI：10.1038/s41467-023-37180-x · 被引用次数：302 · 研究领域：Reinforcement Learning in Robotics、Neural dynamics and brain function、Advanced Memory and Neural Computing

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.