👉 Abstract computing, also known as abstract machine learning or abstract data types, is a theoretical framework that focuses on defining computational processes and data structures in high-level, abstract terms rather than through specific programming languages or hardware implementations. It aims to separate the essential properties and operations of algorithms from their concrete realizations, enabling a more generalized understanding of computation. In this context, abstract computing involves creating models or representations that capture the core logic and behavior of algorithms, allowing researchers to reason about their correctness, efficiency, and scalability independently of particular programming paradigms or hardware architectures. This abstraction facilitates the development of more reusable and portable algorithms, as well as aids in the design of more efficient computational systems by focusing on the fundamental principles of computation rather than the specifics of implementation.
