Constrained disorder principle-based second-generation algorithms implement quantified variability signatures to improve the function of complex systems
Tal Sigawi, Hillel S. Lehmann, Noa Hurvitz, Yaron Ilan
Abstract
Improving the efficacy and overcoming the malfunctions of systems are significant challenges. Variability characterizes all levels of complex biological systems. We reviewed the relevant publications and described a method for improving the systems' function. The constrained disorder principle (CDP) defines the function of living systems based on their degree of variability. Per the CDP, the boundaries of a system define its function and efficiency. The present paper aims to describe the role of variability in biological systems and the generation of CDP-based second-generation artificial intelligence (AI) algorithms designed to improve effectiveness and correct malfunctions of biological organisms by focusing on implementing personalized variability signatures. The paper describes some of the challenges of first-generation AI systems, focusing on the three steps process of establishing the second-generation platforms comprising: the use of a pseudorandom number generator in an openloop system, implementing variability based on feedback in a closed-loop system, and quantifying variability signatures in a personalized way for improving algorithm' output. Examples of its use in humans are provided. The CDP provides a platform for improving disturbed systems' functions using second-generation AI systems.