Artificial Intelligence vs. Machine Learning: Definitions, History, and Key Differences

In recent years, the terms artificial intelligence (AI) and machine learning (ML) have become common in tech news, often used interchangeably, yet experts note subtle but real differences: AI predates ML, and most consider ML a subset of AI.

The relationship is illustrated by Nvidia’s blog graphic, which serves as a useful starting point for understanding the distinction.

AI is broadly defined as creating machines that think like humans, a concept dating back to ancient myths but gaining traction after Alan Turing’s 1950 paper and the famous Turing test. The term "Artificial Intelligence" was coined by John McCarthy in 1956 at the Dartmouth conference, which set the agenda for research areas such as natural language processing, image recognition, and machine learning.

Machine learning traces its roots to Arthur Samuel’s 1959 definition of a program that learns without explicit programming. It gained popularity with the rise of data mining in the 1990s, where algorithms discover patterns in data, and later evolved to modify program behavior based on learned information.

Modern ML applications include image recognition—trained by humans labeling thousands of pictures—and recommendation engines used by Facebook, Amazon, and Netflix, which predict user preferences based on data patterns. Enterprises now employ ML for predictive analytics, integrating it with big‑data analysis and statistical methods.

While some view ML as a distinct field linked to statistics and data mining, others see it as an essential component of AI, especially as deep learning and neural networks—systems modeled after the brain—drive advances in both domains.

The article also mentions related terms such as cognitive computing (often used by IBM) and clarifies that neural networks form the foundation of deep learning, a specialized form of ML that leverages multi‑layered algorithms and GPU acceleration.