Author: Alessandro Melis
Affiliation: New York Institute of Technology
Artificial Intelligence (AI) endeavors to construct machines capable of undertaking tasks that traditionally necessitate human intelligence. The foundational pillars of AI were
established by seminal figures such as Alan Turing, known for conceptualizing computational machines and intelligence testing, and John McCarthy, who notably coined the term
"artificial intelligence" at the 1956 Dartmouth Conference. This era marked the genesis of AI research, with a primary focus on rule-based systems designed to emulate human problem-solving and decision-making capabilities. These systems operated on a set of predefined instructions but often faltered in complex or unpredictable scenarios. The field experienced a transformative shift with the introduction of machine learning algorithms in the late 20th century. Transitioning from static, rule-based systems to dynamic, learning models signified a critical evolution in AI technology. Machine learning, a cornerstone of AI, entails developing algorithms that learn and improve from experience, enabling systems to analyze vast datasets, discern patterns, and make decisions autonomously. Data acquisition and preprocessing emerged as crucial steps, ensuring the information fed into machine learning algorithms was clean, structured, and representative, thereby enhancing the algorithms' learning efficacy (Russell & Norvig, 2016). Upon completion of their training, AI models are deployed across various sectors—healthcare, finance, automotive, and entertainment—where they continue to adapt and refine their capabilities through continuous feedback mechanisms. This adaptability underscores the potential of AI to revolutionize industries by introducing efficiencies and innovations previously unattainable.
