From algorithms to intelligence: exploring the fusion of AI and computer science for emerging technological solutions

Abstract

Artificial intelligence and computer science will converge to produce the next set of emerging technologies. Artificial intelligence enables machines to learn, make intelligent decisions, and adapt, but it is built on the foundations of computer science, including algorithms, data structures, optimization, complexity theory, and computing platforms. This theoretical review examines the role of these foundations in modern artificial intelligence systems and their contributions to the development of scalable, explainable, efficient, and deployable technologies. The paper discusses the technical foundations of machine learning, deep learning, graph-based intelligence, neuro-symbolic systems, explainable artificial intelligence, and edge intelligence in terms of algorithmic reasoning, data structures, learning optimization, computational efficiency, and computational infrastructure. It also discusses the role of integrating artificial intelligence and computer science across major application areas, including smart health, cybersecurity, robotics, natural language processing, Internet of Things systems, edge computing, and sustainable digital infrastructure. To improve the paper's conceptual framework, it proposes an Algorithm-to-Intelligence Integration Framework that connects computer science foundations, artificial intelligence paradigms, system requirements, application domains, and future technologies. The survey finds that intelligent systems should, in the future, combine adaptive learning with robust computational design to achieve responsible, secure, sustainable, and deployable technological advancement.