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Quantitative Biology > Neurons and Cognition

arXiv:2411.15243 (q-bio)
[Submitted on 22 Nov 2024]

Title:Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence

Authors:Nima Dehghani, Michael Levin
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Abstract:The pursuit of creating artificial intelligence (AI) mirrors our longstanding fascination with understanding our own intelligence. From the myths of Talos to Aristotelian logic and Heron's inventions, we have sought to replicate the marvels of the mind. While recent advances in AI hold promise, singular approaches often fall short in capturing the essence of intelligence. This paper explores how fundamental principles from biological computation--particularly context-dependent, hierarchical information processing, trial-and-error heuristics, and multi-scale organization--can guide the design of truly intelligent systems. By examining the nuanced mechanisms of biological intelligence, such as top-down causality and adaptive interaction with the environment, we aim to illuminate potential limitations in artificial constructs. Our goal is to provide a framework inspired by biological systems for designing more adaptable and robust artificial intelligent systems.
Subjects: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV); Neural and Evolutionary Computing (cs.NE); Symbolic Computation (cs.SC)
Cite as: arXiv:2411.15243 [q-bio.NC]
  (or arXiv:2411.15243v1 [q-bio.NC] for this version)
  https://doi.org/10.48550/arXiv.2411.15243

Submission history

From: Nima Dehghani [view email]
[v1] Fri, 22 Nov 2024 02:55:39 UTC (29 KB)
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