Facebook Inc. has open-sourced several of its artificial intelligence tools by making them available on the Torch open-source library. The tools will allow developers to take advantage of some of Facebook’s deep learning algorithms and use them to create faster, smarter AI.

You may not realize how much Facebook uses artificial intelligence in its day-to-day operation. Every time you see a recommended friend or are given suggestions for who to tag in a photo, you are seeing Facebook’s deep learning AI in action.

“Deep learning AI” is a phrase stuffed with buzzwords, but it is one of the current hot topics in computing. Traditional AI requires programmers to account for hundreds or thousands of different scenarios, but deep learning allows AIs to do most of the work. As the name suggests, deep learning AI learns from past experience and the data it analyzes to streamline the process in the future. The more data the AI  takes it, the better it becomes at recognizing patterns.

“For everyone out there, these kernels are faster than anything else in the open-source community,” Facebook AI expert Soumith Chintala told VentureBeat.

An AI “lego set”

Facebook’s newly open-sourced tools have been uploaded as a suite of modules that allow developers to take advantage of a range of features, including tools designed to scan images and recognize objects or faces. They also significantly speed up the learning process for neural networks, improving efficiency so that information can be parsed quickly.

Chintala believes the modular nature of the Torch open-source project will help developers find the tools and programs that meet their specific needs.

“It’s like building some kind of electronic contraption or, like, a Lego set,” Chintala said. “You just can plug in and plug out all these blocks that have different dynamics and that have complex algorithms within them.”

Chintala also notes that Torch is easy to pick up for engineers, which allows them to “start churning out research really fast.”

Facebook’s open-source tools can be found on the company’s Github page, which includes links to several different modules for download.