Here’s what Google’s AI software can do, and how you can help improve it

The future of Google lies in its artificial intelligence technologies, CEO Sundar Pichai has said multiple times.

That means Google’s products increasingly rely on machine learning — programming that allows computers to “learn” on their own.

You’ll notice AI at work in Google Assistant to understand questions you ask, as well as in Google Photos, which uses AI to identify things like objects, animals and people.

But Google also wants developers to use its open source AI software including translation and visual recognition, to build new tools. Google has said making software open source allows outsiders to improve the company’s technology.

Google publishes A.I. Experiments to show in simple ways the different things Google’s AI software can do. It offers interactive demonstrations of Google’s open source technology. The site is aimed at promoting the software and encouraging developers to use it, with some of the code for the experiments available on the site.

The tools are easy to play with if you’re not a programmer and can offer a window into what Google is teaching computers to do. Sometimes users’ interactions with the tools support experiments and development by Google into the future.

At least one of the 10 experiments posted on the site, Quick, Draw!, has played a role in Google’s AI research. The widget prompts users to draw a specific thing, like a seesaw, in under 20 seconds. While the user draws, the program tries to guess what the user is drawing.

Google used doodles from Quick, Draw! to teach artificial intelligence software how to draw on its own.

Last week, Google added another drawing tool, this one called AutoDraw, which turns doodles into clip art by comparing them against a database of professional drawings.

Other experiments include:

• A demonstration in which the AI tries to imitate your handwriting strokes.
• An app that identifies what’s in a photo, in English an an alternate language.
• An experiment that visualizes how machine learning organizes sounds by how similar they are.

This article originally appeared on Recode.net.

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