👉 Concentration computing, also known as concentration-based computing or attention mechanisms in the context of artificial intelligence and machine learning, refers to a computational paradigm that focuses on the selective processing of information. It mimics how human attention works, where our cognitive resources are not evenly distributed but rather concentrated on specific aspects of a task or input. In this approach, a system identifies and prioritizes relevant data (or "concentration") while filtering out less pertinent information. This is often achieved through mechanisms like attention vectors or weights, which determine the importance of different inputs in a given context. By concentrating computational resources on the most critical elements, concentration computing enhances efficiency and performance, particularly in complex tasks like natural language processing, image recognition, and decision-making processes.
