Bio-inspired Artificial Intelligence attempts to synthetically produce systems that exhibit intelligence by taking inspiration from the processes of natural life systems. Examples of bio-inspired artificial intelligence include behaviour-based robotics, artificial neural networks, evolutionary algorithms, particle swarm optimisation and ant colony optimisation. Biology is a branch of science that seeks to determine the laws of nature that lie behind the structure and behaviour of living organisms. A biologist will postulate a theory or model in order to explain certain natural phenomena, and will then experimentally verify how well the model predicts what is observed in nature. If the experimental results do not match with what occurs in nature, then in the words of the acclaimed physicist Richard Feynman, âitâs wrongâ. Although there is no design constraint in the field of Artificial Intelligence to restrict oneself to applying exactly the same mechanisms to those used by natural life systems, the âwrongnessâ of the particular model in reproducing natural intelligence can be a useful guide in measuring the success of a bio-inspired AI model. Furthermore, in attempting to reproduce human level intelligence (since few AI systems to date has come close to human capabilities), insight can be gained that can potentially lead to further breakthroughs. The purpose of this article is to emphasize the importance of this process of validation against real-world systems in evaluating bio-inspired AI models. We can gain insights from the comparison with observations of naturally occurring processes in order to inform the design of our intelligent artificial life systems. (Directions for Bio-Inspired Artificial Intelligence, William J. Teahan)
Last date updated on March, 2024