“Machine Learning” is a technique that allows computers to acquire skills by looking at several examples,

instead of through sets of rules.

Machine Learning makes it easier for you to do things everyday: Searching for photos of what you love,

Speaking to anyone in any language and getting you exactly where you want to go anywhere in the world.

So how do machines learn?

In machine learning, computers find, identify, and learn common patterns through sets of data.

for example,

Showing a computer many images of cars teaches it how to recognize a car in any picture.

The more variety of car images we show it, the better it gets at recognition.

That’s why your contributions to the Crowdsource app are important.

They help create and verify accurate examples for computers to learn,

which in turn enables features that can benefit everyone.

When you verify image labels, you help apps, like Google Photos and Google Lens, get better at classifying photos and identifying objects.

When you label the sentiment of sentences, you help Google Maps and Google Play organize reviews in your language.

When you verify translations, you help Google Translate make more accurate translations in your language.

So your favorite apps get better for everyone, thanks to you.

As part of the global Crowdsource community, you’re joining contributors in your country and throughout the world to contribute millions of examples.

Your responses are combined with thousands of other users’ answers to determine a “best” response, which is called “ground truth.”

The ground truth is fed to machine learning models that find patterns to learn specific skills -

such as how to identify cars in a photo, or how to translate from one language to another.

What a machine learns is limited by the data it is given.

If we develop an image recognition algorithm with images from only a small part of the world,

it will only recognize objects from that part of the world.

In order for apps like Photos to work well for everyone, we must train machines using images from every part of the world.

By using Crowdsource, you’re representing your region, language and opinions in training data.

Thank you for being a part of the community!