Artificial Intelligence vs. Machine Learning: What's the Difference?

A.I. and machine learning are not the same thing

Artificial intelligence is a catch-all term used to describe many different types of virtual 'intelligence' of the style and kind found in humans. Machine learning is a type of artificial intelligence, but it's not the style and kind of A.I. found in humans we see represented on TV or in movies; Machine learning is the process used to create virtual intelligence.

What Is Artificial Intelligence?

Artificial intelligence is the measure of a computer's intellectual ability. But there isn't a scientific body that decides what is or is not, technically, artificial intelligence, so it's important to understand how the term is being defined by whoever is using the term.

The Encyclopedia Britannica defines artificial intelligence as "the ability of a digital computer or computer-controlled robot to perform tasks commonly associated with intelligent beings." In this sense, a computer that can make predictions is artificially intelligent.

Britannica, however, goes on to note that the "term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from experience."

This is what we tend to see represented in popular culture: completely human-looking Androids that talk, think, and feel just like we humans do. Androids, or robots, of this kind are forms of artificial intelligence too, but they're much higher-level A.I. that would require lower-level A.I., like machine learning, to work.

What Is Machine Learning?

While artificial intelligence is a measure of a computer's intellectual ability, machine learning is a type of artificial intelligence used to build intellectual ability in computers.

Investopedia defines machine learning as "the concept that a computer program can learn and adapt to new data without human intervention." An example you've likely used is when you search for specific photos in your phone's photo library. You can search for 'tree' and pictures of trees will show up without you having specifically said to the phone, "This is a tree."

Much of machine learning is done by hubs of interconnected computers or supercomputers that process massive quantities of data in order to train a program to be able to give a certain output with a given input.

Examples of Artificial Intelligence vs. Machine Learning

In 2011, two champions of the long-running Jeopardy game show were defeated by a new challenger: the supercomputer made by IBM known as Watson. This room-sized machine was capable of understanding and answering the complicated, specific questions characteristic of the show better than the best players on the show at the time. Watson is an example of artificial intelligence.

Today, IBM offers a service called IBM Watson Machine Learning that allows third parties to use their technology to build, train, and test predictive software like the kind used by the Watson supercomputer, which needed the ability to independently 'understand' and 'respond' to human writing and speech. This ability is an example of machine learning.

Watson the supercomputer is artificial intelligence, while Watson's ability to 'understand' language and respond using it is machine learning, much the same kind a digital assistant like Alexa uses to be able to talk to you.

Artificial intelligence as we most often see it in the movies is much more advanced than IBM's Watson, but machine learning will be an essential component of higher-level A.I., like proper robots and androids, just as it's an important component of Watson.

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