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NeuroAI - publication version - for arXiv

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.

Computer Science > Artificial Intelligence

By Anthony Zador*, Sean Escola*, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Körding, Alexei Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, Doris Tsao

  • Equal contributions

Over the coming decades, Artificial Intelligence (AI) will transform society and the world economy in ways that are as profound as the computer revolution of the last half century and likely at an even faster pace. This AI revolution presents tremendous opportunities to unleash human creativity and catalyze economic growth, relieving workers from performing the most dangerous and menial jobs. However, to reach this potential, we still require advances that will make AI more human-like in its capabilities. Historically, neuroscience has been a critical driver and source of inspiration for improvements in AI, particularly those that made AI more proficient in areas that humans and other animals excel at, such as vision, reward-based learning, interacting with the physical world, and language 1,2 . It can still play this role. To accelerate progress in AI and realize its vast potential, we must invest in fundamental research in 'NeuroAI.'

The seeds of the current AI revolution were planted decades ago, mainly by researchers attempting to understand how brains compute 3 . Indeed, the earliest efforts to build an 'artificial brain' led to the invention of the modern 'von Neumann computer architecture,' for which John von Neumann explicitly drew upon the very limited knowledge of the brain available to him in the 1940s 4,5 . Later, the Nobel-prize winning work of David Hubel and Torsten Wiesel on visual processing circuits in the cat neocortex inspired the deep convolutional networks that have catalyzed the recent revolution in modern AI 6-8 . Similarly, the development of reinforcement learning was directly inspired by insights into animal behavior and neural activity during learning 9-15 . Now, decades later, applications of ANNs and RL are coming so quickly that many observers assume that the long-elusive goal of human-level intelligence sometimes referred to as 'artificial general intelligence' - is within our grasp. However, in contrast to the optimism of those outside the field, many front-line AI researchers believe that major breakthroughs are needed before we can build artificial systems capable of doing all that a human, or even a much simpler animal like a mouse, can do.

Although AI systems can easily defeat any human opponent in games such as chess 16 and Go 17 , they are not robust and often struggle when faced with novel situations. Moreover, we have yet to build effective systems that can walk to the shelf, take down the chess set, set up the pieces, and move them around during a game, although recent progress is encouraging 18 . Similarly, no machine can build a nest, forage for berries, or care for young. Today's AI systems cannot compete with the sensorimotor capabilities of a four-year old child or even simple animals. Many basic capacities required to navigate new situations capacities that animals have or acquire effortlessly - turn out to be deceptively challenging for AI, partly because AI systems lack even the basic abilities to interact with an unpredictable world. A growing number of AI researchers doubt that merely scaling up current approaches will overcome these limitations. Given the need to achieve more natural intelligence in AI, it is quite likely that new inspiration from naturally intelligent systems is needed 19 .

Historically, many key AI advances, such as convolutional ANNs and reinforcement learning, were inspired by neuroscience. Neuroscience continues to provide guidance - e.g., attention-based neural networks were loosely inspired by attention mechanisms in the brain 20-23 - but this is often based on findings that are decades old. The fact that such cross-pollination between AI and neuroscience is far less common than in the past represents a missed opportunity. Over the last decades, through efforts such as the NIH BRAIN initiative and others, we have amassed an enormous amount of knowledge about the brain. The emerging field of NeuroAI, at the intersection of neuroscience and AI, is based on the premise that a better understanding of neural computation will reveal fundamental ingredients of intelligence and catalyze the next revolution in AI. This will eventually lead to artificial agents with capabilities that match those of humans. The NeuroAI program we advocate is driven by the recognition that AI historically owes much to neuroscience and the promise that AI will continue to learn from it-but only if there is a large enough community of researchers fluent in both domains. We believe the time is right for a large-scale effort to identify and understand the principles of biological intelligence and abstract those for application in computer and robotic systems.

It is tempting to focus on the most characteristically human aspects of intelligent behavior, such as abstract thought and reasoning. However, the basic ingredients of intelligence - adaptability, flexibility, and the ability to make general inferences from sparse observations - are already present in some form in basic sensorimotor circuits which have been evolving for hundreds of millions of years. As AI pioneer Hans Moravec 24 put it, abstract thought 'is a new trick, perhaps less than 100 thousand years old….effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge.' This implies that the bulk of the work in developing general AI can be achieved by building systems that match the perceptual and motor abilities of animals and that the subsequent step to human-level intelligence would be considerably smaller. This is good news because progress on the first goal can rely on the favored subjects of neuroscience research - rats, mice, and non-human primates - for which extensive and rapidly expanding behavioral and neural datasets can guide the way. Thus, we believe that the NeuroAI path will lead to necessary advances if we figure out the core capabilities that all animals possess in embodied sensorimotor interaction with the world.

Title:Toward Next-Generation Artificial Intelligence: Catalyzing the NeuroAI Revolution

By Anthony Zador*, Sean Escola*, Blake Richards, Bence Ölveczky, Yoshua Bengio, Kwabena Boahen, Matthew Botvinick, Dmitri Chklovskii, Anne Churchland, Claudia Clopath, James DiCarlo, Surya Ganguli, Jeff Hawkins, Konrad Körding, Alexei Koulakov, Yann LeCun, Timothy Lillicrap, Adam Marblestone, Bruno Olshausen, Alexandre Pouget, Cristina Savin, Terrence Sejnowski, Eero Simoncelli, Sara Solla, David Sussillo, Andreas S. Tolias, Doris Tsao

  • Equal contributions

Over the coming decades, Artificial Intelligence (AI) will transform society and the world economy in ways that are as profound as the computer revolution of the last half century and likely at an even faster pace. This AI revolution presents tremendous opportunities to unleash human creativity and catalyze economic growth, relieving workers from performing the most dangerous and menial jobs. However, to reach this potential, we still require advances that will make AI more human-like in its capabilities. Historically, neuroscience has been a critical driver and source of inspiration for improvements in AI, particularly those that made AI more proficient in areas that humans and other animals excel at, such as vision, reward-based learning, interacting with the physical world, and language 1,2 . It can still play this role. To accelerate progress in AI and realize its vast potential, we must invest in fundamental research in 'NeuroAI.'

The seeds of the current AI revolution were planted decades ago, mainly by researchers attempting to understand how brains compute 3 . Indeed, the earliest efforts to build an 'artificial brain' led to the invention of the modern 'von Neumann computer architecture,' for which John von Neumann explicitly drew upon the very limited knowledge of the brain available to him in the 1940s 4,5 . Later, the Nobel-prize winning work of David Hubel and Torsten Wiesel on visual processing circuits in the cat neocortex inspired the deep convolutional networks that have catalyzed the recent revolution in modern AI 6-8 . Similarly, the development of reinforcement learning was directly inspired by insights into animal behavior and neural activity during learning 9-15 . Now, decades later, applications of ANNs and RL are coming so quickly that many observers assume that the long-elusive goal of human-level intelligence sometimes referred to as 'artificial general intelligence' - is within our grasp. However, in contrast to the optimism of those outside the field, many front-line AI researchers believe that major breakthroughs are needed before we can build artificial systems capable of doing all that a human, or even a much simpler animal like a mouse, can do.

Although AI systems can easily defeat any human opponent in games such as chess 16 and Go 17 , they are not robust and often struggle when faced with novel situations. Moreover, we have yet to build effective systems that can walk to the shelf, take down the chess set, set up the pieces, and move them around during a game, although recent progress is encouraging 18 . Similarly, no machine can build a nest, forage for berries, or care for young. Today's AI systems cannot compete with the sensorimotor capabilities of a four-year old child or even simple animals. Many basic capacities required to navigate new situations capacities that animals have or acquire effortlessly - turn out to be deceptively challenging for AI, partly because AI systems lack even the basic abilities to interact with an unpredictable world. A growing number of AI researchers doubt that merely scaling up current approaches will overcome these limitations. Given the need to achieve more natural intelligence in AI, it is quite likely that new inspiration from naturally intelligent systems is needed 19 .

Historically, many key AI advances, such as convolutional ANNs and reinforcement learning, were inspired by neuroscience. Neuroscience continues to provide guidance - e.g., attention-based neural networks were loosely inspired by attention mechanisms in the brain 20-23 - but this is often based on findings that are decades old. The fact that such cross-pollination between AI and neuroscience is far less common than in the past represents a missed opportunity. Over the last decades, through efforts such as the NIH BRAIN initiative and others, we have amassed an enormous amount of knowledge about the brain. The emerging field of NeuroAI, at the intersection of neuroscience and AI, is based on the premise that a better understanding of neural computation will reveal fundamental ingredients of intelligence and catalyze the next revolution in AI. This will eventually lead to artificial agents with capabilities that match those of humans. The NeuroAI program we advocate is driven by the recognition that AI historically owes much to neuroscience and the promise that AI will continue to learn from it-but only if there is a large enough community of researchers fluent in both domains. We believe the time is right for a large-scale effort to identify and understand the principles of biological intelligence and abstract those for application in computer and robotic systems.

It is tempting to focus on the most characteristically human aspects of intelligent behavior, such as abstract thought and reasoning. However, the basic ingredients of intelligence - adaptability, flexibility, and the ability to make general inferences from sparse observations - are already present in some form in basic sensorimotor circuits which have been evolving for hundreds of millions of years. As AI pioneer Hans Moravec 24 put it, abstract thought 'is a new trick, perhaps less than 100 thousand years old….effective only because it is supported by this much older and much more powerful, though usually unconscious, sensorimotor knowledge.' This implies that the bulk of the work in developing general AI can be achieved by building systems that match the perceptual and motor abilities of animals and that the subsequent step to human-level intelligence would be considerably smaller. This is good news because progress on the first goal can rely on the favored subjects of neuroscience research - rats, mice, and non-human primates - for which extensive and rapidly expanding behavioral and neural datasets can guide the way. Thus, we believe that the NeuroAI path will lead to necessary advances if we figure out the core capabilities that all animals possess in embodied sensorimotor interaction with the world.

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unmanned aerial vehicles. Bioinspir. Biomim. 5 , 045003 (2010).

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Figure 1. Turing tests: comparisons between the performance of AI systems and their living counterparts. Left : The original Turing test as proposed by Alan Turing 25 . 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 26 . 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.