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Cognitive Computation
Erschienen in:

06.03.2023

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

verfasst von: Ali A. Minai

Erschienen in: Cognitive Computation | Ausgabe 5/2024

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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.
Vorheriger Artikel Does Deep Learning Have Epileptic Seizures? On the Modeling of the Brain
Nächster Artikel Re-Thinking the Organization of Cortico-Basal Ganglia-Thalamo-Cortical Loops

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Metadaten
Titel
Deep Intelligence: What AI Should Learn from Nature’s Imagination
verfasst von
Ali A. Minai
Publikationsdatum
06.03.2023
Verlag
Springer US
Erschienen in
Cognitive Computation / Ausgabe 5/2024
Print ISSN: 1866-9956
Elektronische ISSN: 1866-9964
DOI
https://doi.org/10.1007/s12559-023-10124-9

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