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

They're not the same thing although they can work together

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 sort of A.I. we see on TV or in movies; instead, it's 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; the term is defined by whoever is using it.

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."

In popular culture, we tend to see 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 said to the phone, "This is a tree."

Machine learning is powered by hubs of interconnected computers or supercomputers processing massive quantities of data to train a program to give a particular output with a given input.

Examples of Artificial Intelligence vs. Machine Learning

In 2011, a new challenger. IBM's Watson supercomputer defeated two champions of the long-running Jeopardy game show.

This room-sized machine could understand and answer 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.

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 Watson supercomputer uses.

Watson needs to independently 'understand' and 'respond' to human writing and speech, which is an example of machine learning.

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

Artificial intelligence, as portrayed in the movies, is much more advanced than IBM's Watson. However, machine learning will be an essential component of higher-level A.I., like robots and androids, just as it's an integral component of Watson.

  • What is cross-validation in machine learning?

    Cross-validation is a statistical method used to evaluate machine learning models. Subsets of the available input data are used to train the model, and a complementary subset of the data is used for evaluation.

  • What is a feature in machine learning?

    In machine learning, a feature is a measurable property of a phenomenon. For example, in speech recognition algorithms, features include noise ratios and the length of sounds.

  • What is a neural network?

    An artificial neural network is a series of interconnected artificial neurons modeled after those in the human brain. Neural networks are capable of processing new information to learn and making predictions.

Was this page helpful?