EMPOWERING USER INTERFACES WITH AI: PRACTICAL INSIGHTS AND LIMITATIONS

Abstract

Artificial Intelligence (AI), conceptualized by John McCarthy in 1950 as the science and engineering of intelligent machines, has evolved into a multifaceted discipline. Its myriad definitions by researchers encompass the synthesis and analysis of computational agents exhibiting intelligent behavior. More recently, AI is characterized as a system's capacity to proficiently interpret external data, learn from it, and employ those insights to accomplish specific objectives through adaptable adaptation.

The trajectory of artificial learning has yielded diverse branches, including machine learning, deep learning, and narrow learning. Machine learning employs computational algorithms to enhance experiences, with supervised learning utilizing labeled data to predict outcomes, while unsupervised learning identifies patterns autonomously by discerning underlying structures within data.

Deep learning leverages extensive multilayer artificial neural networks to calculate precise numerical representations. Moreover, AI progressions have significantly impacted Human-Computer Interaction (HCI), a domain focused on conceiving, assessing, and deploying interactive computing systems for human utilization, while also investigating consequential phenomena related to them