From Formalism to Functionality: Leveraging AI and Ml to Advance Foundational Computer Science Paradigms

Abstract

In the traditional foundations of computer science, theoretical abstraction has traditionally been valued over practical implementation in formal logic, automata theory, algorithm analysis, and structured programming. With the increasing complexity of AI and ML, there has been a massive trend towards more functional, adaptive, and context-aware computing frameworks. This article discusses the combination of AI and ML and basic computer science methodologies and their impact on turning theoretical models into practical, application-oriented systems. Applying ML to algorithm improvement, automata pattern detection, and logical inference for formal verification, AI closes the old divide between computer science abstractions and actual implementation. Using both literature review and a structured conceptual framework, we attempt to find the significant advances in such fields as compiler design improved with the help of deep learning, Turing Machine simulation directed by reinforcement learning, and AID-based methods for code synthesis. Tables, graphical charts, model performance indicators, and detailed figures present concrete evidence to explain the actual usage of this interdisciplinary approach. To the argument, these developments greatly enhance computational efficiencies and bring new possibilities for educational and architectural opportunities in computer science education and system architecture. The article concludes by discussing moving the research further to build a stronger, more powerful, and more flexible foundation for the present-day computing practices from the traditional computational theories using schematics of AI paradigms.