Similarly, the development of reinforcement learning was directly inspired by insights into animal behavior and neural activity during learning 9, 10, 11, 12, 13, 14, 15. 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, 7, 8. 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. The seeds of the current AI revolution were planted decades ago, mainly by researchers attempting to understand how brains compute 3. To accelerate progress in AI and realize its vast potential, we must invest in fundamental research in “NeuroAI.” 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. However, to reach this potential, we still require advances that will make AI more human-like in its capabilities. This AI revolution presents tremendous opportunities to unleash human creativity and catalyze economic growth, relieving workers from performing the most dangerous and menial jobs. 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. Nature Communications volume 14, Article number: 1597 ( 2023) Catalyzing next-generation Artificial Intelligence through NeuroAI
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