Artificial Intelligence in Computer Science: Evolution, Techniques, Challenges, and Multidisciplinary Applications

Artificial Intelligence (AI) has become an influential paradigm in computer science that has sparked revolutionary changes in various industries with its learning capabilities, reasonability, and adaptability. This paper gives an exhaustive overview of AI's presence in general-purpose computational fields and important intersections with algorithmic work, data work, software work, human-computer interaction (HCI), security and privacy concerns, and foundations. The paper exhaustively addresses state-of-the-art AI methods like machine learning, deep learning, natural language processing, computer vision, knowledge representation, recommender systems, and optimization techniques. Additionally, it rigorously examines current AI problems, including ethical concerns, algorithmic biases, interpretability issues, data constraints, insecurity issues, and resource issues. It highlights the need for interdisciplinary collaboration in finding solutions to these problems. In extensive critiques, this work highlights the strategic relevance of AI and proposes directions toward responsible, equitable and sustainable AI development and application. Directions on future AI work focus on transparency, privacy preservation, computational performance improvement, increased human-AI interaction, and strong foundational theories to maximize AI benefits to society and reduce its harmful risks to the maximum extent.