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

「機器學習」這種技術可讓電腦藉由觀察一些範例來獲取技能

instead of through sets of rules.

並不是採用一些預設的規則來進行判斷

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

機器學習能夠讓你輕鬆處理日常大小事 例如搜尋你喜愛的人事物相片

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

使用任何一種語言與他人交談 還有順利抵達你想去的任何地方

So how do machines learn?

那麼機器要如何學習呢？

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

在機器學習領域中，電腦會透過一組組資料 尋找、識別及學習常見模式

for example,

舉個例子

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

如果為電腦提供許多汽車圖片 就能訓練電腦從任何一張相片中辨識出汽車

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

我們提供的汽車圖片越多樣化 電腦辨識汽車的能力就越強

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

因此，你的貢獻對群眾外包應用程式非常重要

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

這些資料有助於建立及驗證正確的範例，供電腦學習

which in turn enables features that can benefit everyone.

進而協助開發種種功能，造福所有使用者

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

當你驗證圖片標籤時，將協助 Google 相簿和 Google 智慧鏡頭等應用程式 更加準確地分類相片和識別物品

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

當你為語句加上語氣標籤時，將協助 Google 地圖和 Google Play 以你慣用的語言整理評論

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

當你驗證翻譯內容時，則可協助 Google 翻譯 為你慣用的語言提供更準確的譯文

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

因為有你的貢獻，你喜愛的應用程式將更臻完善，使人人受惠

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

在群眾外包全球社群中，你將攜手所在地和世界各地的其他貢獻者 一同提供數百萬個樣本

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

你的回覆會與另外數千名使用者的答案彙整 供系統決定「最佳」答案，也就是「實際資料」

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

這類實際資料接著會饋送給機器學習模型 讓模型找出模式來學習特定技能

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

例如識別相片中的汽車 或是將一種語言翻譯成另一種語言

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

機器學習的成果受限於其獲得的資料

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

如果我們開發圖片辨識演算法時 只能參考世界上一小部分地區的圖片

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

這套演算法就只能辨識來自該地區的物件

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

為了確保 Google 相簿等應用程式能夠為每個人提供優質服務 我們訓練機器時必須使用世界各地的圖片

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

使用群眾外包應用程式時 你是代表著你的地區、語言和立場訓練資料

Thank you for being a part of the community!

感謝你參與群眾外包社群！