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Deep Intelligence: What AI Should Learn from Nature’s Imagination

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Abstract

Artificial intelligence (AI) has recently seen explosive growth and remarkable successes in several application areas. However, it is becoming clear that the methods that have made this possible are subject to several limitations that might inhibit progress towards replicating the more general intelligence seen in humans and other animals. In contrast to current AI methods that focus on specific tasks and rely on large amounts of offline data and extensive, slow, and mostly supervised learning, this natural intelligence is quick, versatile, agile, and open-ended. This position paper brings together ideas from neuroscience, evolutionary and developmental biology, and complex systems to analyze why such natural intelligence is possible in animals and suggests that AI should exploit the same strategies to move in a different direction. In particular, it argues that integrated embodiment, modularity, synergy, developmental learning, and evolution are key enablers of natural intelligence and should be at the core of AI systems as well. The analysis in the paper leads to the description of a biologically grounded deep intelligence (DI) framework for understanding natural intelligence and developing a new approach to building more versatile, autonomous, and integrated AI. The paper concludes that the dominant paradigm of AI today is unlikely to lead to truly natural general intelligence and that something like the biologically inspired DI framework is needed for that.

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    Ali A. Minai

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Minai, A.A. Deep Intelligence: What AI Should Learn from Nature’s Imagination. Cogn Comput 16, 2389–2404 (2024). https://doi.org/10.1007/s12559-023-10124-9

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