Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916493051035648 |
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| author | Dehghani, Nima Levin, Michael |
| author_facet | Dehghani, Nima Levin, Michael |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_15243 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence Dehghani, Nima Levin, Michael Neurons and Cognition Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Neural and Evolutionary Computing Symbolic Computation 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. |
| title | Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence |
| topic | Neurons and Cognition Artificial Intelligence Computation and Language Computer Vision and Pattern Recognition Neural and Evolutionary Computing Symbolic Computation |
| url | https://arxiv.org/abs/2411.15243 |