Bio-inspired AI: Integrating Biological Complexity into Artificial Intelligence

TL;DR

This paper explores how principles from biological computation, like hierarchical processing and adaptive interaction, can inspire the development of more adaptable and robust artificial intelligence systems.

Contribution

It introduces a biologically inspired framework that incorporates hierarchical, context-dependent processing and adaptive mechanisms into AI design.

Findings

Biological principles can enhance AI adaptability.

Hierarchical and context-aware processing improves robustness.

Biological mechanisms reveal limitations in current AI models.

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.