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

In recent years, the terms artificial intelligence and machine learning have begun to appear frequently in tech news and websites. They are often used as synonyms, but many experts believe there are subtle yet real differences.

Of course, experts sometimes disagree on what those differences are.

However, overall, two things seem clear: first, the term AI predates ML, and second, most people consider machine learning a subset of AI.

One of the best graphic representations of this relationship comes from Nvidia's blog, which provides a good starting point for understanding the differences between AI and ML.

Artificial Intelligence vs. Machine Learning – First, what is artificial intelligence?

Computer scientists have defined AI in many ways, but essentially AI involves machines that think like humans. Since it's hard to determine if a machine truly "thinks," in practice AI creation involves building computer systems that excel at tasks humans are good at.

The idea of creating machines as intelligent as humans dates back to ancient Greek myths about automata created by gods, but it only truly took off in 1950.

That year, Alan Turing published the seminal paper "Computing Machinery and Intelligence," posing the question of whether machines can think. He introduced the famous Turing test, which essentially says that if a human judge cannot distinguish between a human and a machine, the computer can be considered intelligent.

The term artificial intelligence was coined by John McCarthy in 1956, who organized a Dartmouth conference dedicated to the topic. At the end of the conference, participants suggested further research on "any aspect of learning or any other intelligent feature that could, in principle, be described precisely enough for a machine to simulate it. They would try to find ways for machines to use language, form abstractions and concepts, solve problems currently reserved for humans, and improve themselves."

That proposal foreshadowed many of the topics that dominate AI today, including natural language processing, image recognition and classification, and machine learning.

In the years following the first conference, AI research flourished. Yet over decades it was evident that the technology to build truly self‑thinking machines was many years away.

In the past decade, AI has moved from science‑fiction to scientific fact. Stories such as IBM Watson winning a game show, and Google's AI defeating human champions in Go, have brought AI back to the forefront of public awareness.

Today, all the biggest tech companies invest in AI projects, and we interact with AI software daily when using smartphones, social media, web search engines, or e‑commerce sites. One of the AI types we interact with most is machine learning.

Artificial Intelligence vs. Machine Learning – So, what is machine learning?

The phrase "machine learning" also dates back to the mid‑20th century. In 1959, Arthur Samuel defined machine learning as "the ability to learn without being explicitly programmed." He went on to create a checkers program, one of the first to learn from its mistakes and improve performance over time.

Like AI research, machine learning was not popular for a long time, but it regained attention when data mining emerged in the 1990s. Data mining uses algorithms to find patterns in a given data set. Machine learning does the same, but takes a step further—it changes program behavior based on what it learns.

A very popular recent application of machine learning is image recognition. First, these applications must be trained—humans must look at many pictures and label their contents. After thousands of repetitions, the software learns which pixel patterns correspond to horses, dogs, cats, flowers, trees, houses, etc., and can reliably guess image content.

Many web‑based companies also use machine learning to power their recommendation engines. For example, Facebook decides what appears in your news feed, Amazon highlights products you might want to buy, and Netflix suggests movies you might watch; all these recommendations are based on predictions derived from patterns in existing data.

Currently, many enterprises use machine learning for predictive analytics. As big‑data analytics becomes more popular, machine learning techniques become increasingly common and are standard features in many analytical tools.

In fact, machine learning has become intertwined with statistics, data mining, and predictive analytics, and some people argue it should be classified as a field separate from AI. After all, a system can exhibit AI capabilities such as natural language processing or automated reasoning without any machine‑learning component, and a machine‑learning system does not necessarily need any other AI functions.

Others prefer the term "machine learning" because it sounds more technical and intimidating. One internet commentator even said the distinction is that "machine learning actually works."

However, machine learning has been part of the AI discussion from the start, and the two remain closely linked in many modern applications. For example, personal assistants and robots often have many AI features, including ML.

AI and ML Frontiers: Deep Learning, Neural Networks, and Cognitive Computing

Of course, "machine learning" and "artificial intelligence" are not the only terms related to the field of computer science. IBM often uses the term "cognitive computing," which is more or less synonymous with AI.

But some other terms have very distinct meanings. For example, an artificial neural network or neural network is a system designed to process information in a way similar to the biological brain. Things get confusing because neural networks are especially good at machine learning, so the two terms are sometimes conflated.

Furthermore, neural networks provide the foundation for deep learning, a special kind of machine learning. Deep learning uses a set of machine‑learning algorithms that run across multiple layers and can be partially implemented by systems that use GPUs to process large amounts of data at once.

If you feel confused by all these different terms, you are not alone. Computer scientists continue to debate their exact definitions, and they may do so for some time. As companies continue to pour money into AI and ML research, more terminology may emerge, adding further complexity to the problem.