Machine-learning algorithms use statistics to find patterns in massive* amounts of data. And data, here, encompasses a lot of things—numbers, words, images, clicks, what have you.

History of machine learning. ML’s rise began with a humble checkers game and has since rewritten the rulebook of what computers can do. Let’s dive into this data-driven tale.

Game-playing machine learning is strongly successful for checkers, chess, shogi, and Go, having beaten human world champions. Automatic language translation has been largely successful, although ...

Machine learning is a subfield of artificial intelligence, which explores how to computationally simulate (or surpass) humanlike intelligence. While some AI techniques (such as expert systems) use ...

“Successful machine learning is only as good as the data available, which is why it needs new, updated data to provide the most accurate outputs or predictions for any given need,” said ...

To a large extent, supervised ML is for domains where automated machine learning does not perform well enough. Scientists add supervision to bring the performance up to an acceptable level.

Machine Learning continues to transform the ways we live our lives and run our businesses. However, the meaning and implications of what machine learning is in 2017 are not fully understood by ...

Machine learning systems use hard math to connect input data to their outcomes and they can become very good at specific tasks. In some cases, they can even outperform humans.

Our latest video explainer – part of our Methods 101 series – explains the basics of machine learning and how it allows researchers at the Center to analyze data on a large scale. To learn more about ...

Machine learning is the process by which computer programs grow from experience. This isn’t science fiction, where robots advance until they take over the world. When we talk about machine ...