Artificial Intelligence and Machine Learning are two terms thought to be synonyms. In the 21st century, these terms have become inseparable from the applications of Information Technology. However, not understanding these terms and confusing them with each other could potentially be a setback to becoming proficient in these concepts. This post will explain the difference between AI and ML and how they are related to each other.

What Does it Mean to be “Intelligent”?

The Oxford Advanced American Dictionary defines intelligence as “the ability to learn, understand and think in a logical way about things; the ability to do this well.” Therefore, we can count anything that shows these abilities to be intelligent. Hence, Artificial Intelligence simply refers to any program or algorithm that shows the capability to “understand”, “think” about, and find a solution to a problem.

So, How is Machine Learning any Different?

Machine learning is nothing but an application of artificial intelligence – think of it as a type of artificial intelligence. Artificial intelligence is a broader concept that deals with the creation of human-like thinking algorithms. The “learning” part of these algorithms might be encoded by the person, who also encodes the logic the algorithm must follow, for instance, to find the shortest route to a place, or to play a game such as tic-tac-toe. Some such algorithms are Minimax, Monte Carlo Tree Search etc.

Machine learning is an application of artificial intelligence in which the person does not explicitly encode the learning part of the AI. Instead, all the person does is tell the AI how it should learn from data, and make inferences or “predictions” on new data. Hence, the machine “learns” on its own through past data. The following Venn diagram illustrates the relationship between artificial intelligence and machine learning:

Venn diagram showing the relationship between AI and ML.

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