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

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 intel...

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Bibliographic Details
Published in:Cognitive computation 2024-09, Vol.16 (5), p.2389-2404
Main Author: Minai, Ali A.
Format: Article
Language:English
Subjects:
Algorithms
Animal cognition
Animals
Artificial Intelligence
Behavior
Cognitive ability
Complex systems
Computation by Abstract Devices
Computational Biology/Bioinformatics
Computer Science
Developmental biology
Evolution & development
Intelligence
Intelligent systems
Machine learning
Memory
Modularity
Neural networks
Neurosciences
Robotics
Supervised learning
Systems analysis
Task complexity
Citations: Items that this one cites
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Summary: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.
ISSN:1866-9956
1866-9964
DOI:10.1007/s12559-023-10124-9