👉 Inspired computing is a paradigm that draws inspiration from natural processes and biological systems to develop novel computational models and algorithms. It borrows concepts from areas such as neuroscience, genetics, and ecology to create more efficient, adaptive, and robust computing systems. For instance, artificial neural networks emulate the structure and function of biological neurons, enabling parallel processing and learning from data. Genetic algorithms mimic natural selection to optimize solutions to complex problems, while swarm intelligence models the collective behavior of decentralized, self-organized systems, such as flocks of birds or schools of fish. These inspired approaches often lead to innovative solutions that surpass traditional computing methods, particularly in handling large datasets and dynamic environments.
