This paper revisits the origins of artificial intelligence (AI) in a biomimicry/bio-inspired design framework as an approach to accelerating the development of beneficial AI. Currently, the issue of designing long-term security in and ensuring benefits from AI systems is one of the most important. Just as AI emerged from learning from and mimicking biological systems, its further development can benefit from models in biology. The ideas here are based on the philosophical grounds of natural computing (e.g. Dodig-Crnkovic 2020, 2022). A first sketch of the model has already been proposed (Polak & Krzanowski, 2023).
Biomimicry—drawing on nature’s evolutionary solutions—has long influenced disciplines such as material science, robotics, and computing. In the realm of AI, this project proposes a broader biological lens—embracing intelligence as it emerges across a wide variety of organisms, from microbial colonies to complex mammalian nervous systems.
The proposed investigation centers on several interrelated themes. The first concerns expanding AI’s sensory capabilities by emulating mechanisms that exceed human perception—such as echolocation, chemical sensing, or distributed tactile sensitivity. A second theme focuses on swarm intelligence and multi-agent systems, drawing lessons from collective behavior to improve the coordination and emergent problem-solving among AI agents.
A third track explores alternative and hybrid computational architectures, inspired by biochemical and physiological processes.
We propose including the study of biosemiotics, or how living systems use signs and symbols to interpret their environments, opening the door to more context-aware and symbolically grounded embedded AI systems, as well as different mechanisms for generating intelligence.
Ultimately, it is necessary to study and model the relationships between biological organisms in order to be able to harmoniously integrate AI into existing networks of biological relationships and shape them in the desired way. This paper seeks to build a multidisciplinary framework for biomimicry-inspired beneficial AI, enabling new forms of cognition, perception, and resilience. By modeling the diversity and adaptability of biological intelligence, this project aspires to develop AI systems that are not only more powerful and efficient but also more robust, self-regulating, and ecologically integrated.
Bibliography
Dodig-Crnkovic, G., 2020. Natural Morphological Computation as Foundation of Learning to Learn in Humans, Other Living Organisms, and Intelligent Machines. Philosophies, 5(3), pp.17–32.
Dodig-Crnkovic, G., 2022. In search of common, information-processing, agency-based framework for anthropogenic, biogenic, and abiotic cognition and intelligence. Philosophical Problems in Science, (73), pp.17–46.
Polak, P. and Krzanowski, R., 2023. How to Tame Artificial Intelligence? A Symbiotic AI Model for Beneficial AI. Ethos 36(3(143)), pp.92–106.
