Nature inspired computing, or NIC, is a very new discipline that strives to develop new computing techniques through observing how naturally occurring phenomena behave to solve complex problems in various environmental situations. This has produced groundbreaking research that has created new branches, like neural networks, swarm intelligence, evolutionary computation and artificial immune systems. NIC techniques are used in the fields of physics, biology, engineering, economics and even management.
Computer Science vs. Natural Phenomena
Advances in big data, computer science and computational analysts all contributed to the creation of nature-based computing. Researchers and scientists study how biological groups, like ant colonies, bee hives and flocks of birds, react to stimuli, process information and make decisions. For example, certain flocks of birds collectively display altruistic behavior by leaving a healthy bird to remain with an injured bird on the ground.
Regardless of the result, modeling software can imitate reactions and behaviors to understand very complex and dynamic organic systems. A typical NIC software system is based on the concepts of self-organization and complex biological systems. The software used can recreate how populations of autonomous entities in natural environments make decisions.
Autonomous entities within NIC software systems consist of two concepts: effectors and detectors. There may be multiple detectors that receive information regarding neighboring agents and the environment. There may be multiple effectors that exhibit certain behaviors, cause changes to their internal state and drive changes to the environment. Effectors facilitate the sharing of information among autonomous entities.
NIC software systems are comprised of specific behavior rules that are crucial to autonomous entity. They are typically used to decide how an autonomous entity must act on information or react to local stimuli that are collected and shared by the detectors. Autonomous entities are capable of learning because they respond to local changing conditions by modifying their collective rules of behavior over time.
How to Enter this Unique Career?
There are only a handful of colleges that offer graduate level degrees in this fascinating field. For example, nature inspired engineering and computing students study how nature presents the best tools and models to efficiently and effectively solve complex problems. The main objective of these research programs is to develop computational models and advanced algorithms that are based on natural intelligence found in chemical, physical and biological systems. These students study how to create innovative solutions in engineering fields related to health, energy, security and the environment.
Some programs focus on top-down or objective-driven approaches build computational models for understanding social and biological intelligence found in nature. These programs are mostly interested in the neural information processing and the organizing principles of neural development in animals. The bottom-up approach concentrates on developing efficient statistical, mathematical and machine learning algorithms that can solve complex issues. These include data mining, pattern recognition, knowledge extraction, signal processing, group decision-making and self-organization of collective systems.
Nature inspired computing is an exciting new field that promises real-world applications with things like robotics, source localization, threat detection, motion tracking, medical image analysis, intelligent heat solutions and aerodynamic design optimization. The National Center for Biotechnology Information (NCBI) offers an overview of nature-based computing here.