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智能設備走進(jìn)你生活的八大方式
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2017/4/28 18:03:50

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智能設備走進(jìn)你生活的八大方式

Eight ways intelligent machines are already in your life

Many people are unsure about exactly what machine learning is. But the reality is that it is already part of everyday life.

許多人都不確定機器學(xué)習的概念是什么，但現實(shí)是它已經(jīng)是我們每天生活的一部分。

A form of artificial intelligence, it allows computers to learn from examples rather than having to follow step-by-step instructions.

這是人工智能的一種形式，它可以使計算機從范例中進(jìn)行學(xué)習而不是一步一步按照指示去做。

The Royal Society believes it will have an increasing impact on people’s lives and is calling for more research, to ensure the UK makes the most of opportunities.

英國皇家學(xué)會(huì )認為它對我們的生活的影響會(huì )逐漸加深，并呼吁對其進(jìn)行更多的研究，從而保證英國能夠充分利用機會(huì )。

Machine learning is already powering systems from the seemingly mundane to the life-changing. Here are just a few examples.

機器學(xué)習能力已經(jīng)使其看似平凡的系統變得能夠改變生活。這里僅僅是一些例子：

1.On your phone

在你的手機上

Using spoken commands to ask your phone to carry out a search, or make a call, relies on technology supported by machine learning.

用口頭指令讓你的手機進(jìn)行搜索或者撥打電話(huà)。這些命令是基于機器學(xué)習提供的科技。

Virtual personal assistants - the likes of Siri, Alexa, Cortana and Google Assistant - are able to follow instructions because of voice recognition.

因為語(yǔ)音識別，虛擬私人助手可以服從指令，例如Siri，Alexa，小娜和谷歌助手。

They process natural human speech, match it to the desired command and respond in an increasingly natural way.

他們處理自然的人類(lèi)語(yǔ)言并且將語(yǔ)言和命令進(jìn)行匹配，最終以越來(lái)越自然的方式進(jìn)行回應。

The assistants learn over a number of conversations and in many different ways.

這些虛擬助手通過(guò)各種各樣的談話(huà)和不同的方式進(jìn)行學(xué)習。

They might ask for specific information - for example how to pronounce your name, or whose voice is whose in a household.

他們也許會(huì )要求詳細信息—比如說(shuō)，你的名字怎么讀或者家里誰(shuí)的聲音是誰(shuí)的聲音。

Data from large numbers of conversations by all users is also sampled, to help them recognise words with different pronunciations or how to create natural discussion.

大量的用戶(hù)談話(huà)數據也被采樣，以幫助他們識別不同的發(fā)音或者如何自然地交談

2.In your shopping basket

在你的購物車(chē)中

Many of us are familiar with shopping recommendations - think of the supermarket that reminds you to add cheese to your online shop, or the way Amazon suggests books it thinks you might like.

我們很多人都熟悉購物建議—想想超市提醒你在網(wǎng)上購買(mǎi)奶酪，或者亞馬遜推薦你可能喜歡的書(shū)。

Machine learning is the technology that helps deliver these suggestions, via so-called recommender systems.

機器學(xué)習是一項提供建議的技術(shù)，因此也被稱(chēng)作推薦系統。

By analysing data about what customers have bought before, and any preferences they have expressed, recommender systems can pick up on patterns in purchasing history. They use this to make predictions about the products you might like.

通過(guò)分析顧客之前的購買(mǎi)數據，他們之前表現出的喜好，推薦系統可以從選擇購買(mǎi)歷史中選擇范例。他們用這些來(lái)預測你可能喜歡的產(chǎn)品。

3.On your TV

在你的電視上

Similar systems are used to recommend films or TV shows on streaming services like Netflix.Recommender systems use machine learning to analyse viewing habits and pick out patterns in who watches - and enjoys - which shows.

類(lèi)似的系統也用于在Netflix這類(lèi)流媒體服務(wù)上推薦電影或電視節目。推薦系統運用機器學(xué)習來(lái)分析觀(guān)看習慣并選擇人們喜歡看的節目。

By understanding which users like which films - and what shows you have watched or awarded high ratings - recommender systems can identify your tastes.

通過(guò)了解哪些用戶(hù)喜歡哪些電影，以及你觀(guān)看的節目或者得高分的內容，推薦系統可以識別你的口味。

They are also used to suggest music on streaming services, like Spotify, and articles to read on Facebook

他們也在流媒體服務(wù)上推薦音樂(lè )，比如Spotify。還可以在臉書(shū)上推薦文章。

4.In your email

在你的電子郵件中

Machine learning can also be used to distinguish between different categories of objects or items.

機器學(xué)習也可以用來(lái)區分不同類(lèi)別的對象或項目。

This makes it useful when sorting out the emails you want to see from those you don’t.

當你整理想看的郵件和不想看的郵件時(shí)，這項功能十分有用。

Spam detection systems use a sample of emails to work out what is junk - learning to detect the presence of specific words, the names of certain senders, or other characteristics.

垃圾郵件檢測系統利用郵件樣本來(lái)區分哪些是垃圾郵件，并學(xué)習通過(guò)檢測特定詞句、特定寄件人的名稱(chēng)和其他的特點(diǎn)進(jìn)行辨別。

Once deployed, the system uses this learning to direct emails to the right folder. It continues to learn as users’ flag emails, or move them between folders.

一旦部署，系統運用機器學(xué)習將電子郵件定位到正確的文件夾。它可以繼續學(xué)習用戶(hù)的標志電子郵件，或者在文件夾之間移動(dòng)它們。

5.On your social media

在你的社交媒體上

Ever wondered how Facebook knows who is in your photos and can automatically label your pictures?

有沒(méi)有想過(guò)臉書(shū)怎么知道你照片中的人是誰(shuí)，并且自動(dòng)給你的照片貼上標簽？

The image recognition systems that Facebook - and other social media - uses to automatically tag photos is based on machine learning.

臉譜和其他社交媒體運用圖片識別系統自動(dòng)給照片加標簽，這項系統也是基于機器學(xué)習的

When users upload images and tag their friends and family, these image recognition systems can spot pictures that are repeated and assigns these to categories - or people.

當用戶(hù)上傳圖像并標記他們的朋友和家人時(shí)，這些圖像識別系統可以發(fā)現重復的圖像并將其分配特定類(lèi)別或人群。

6.At your bank

在你的銀行中

By analysing large amounts of data and looking for patterns, activity which might not otherwise be visible to human analysts can be identified.

通過(guò)分析大量數據并尋求典例，那些之前不能被人類(lèi)發(fā)現的行為現在可以被識別。

One common application of this ability is in the fight against debit and credit card fraud. Machine learning systems can be trained to recognise typical spending patterns and which characteristics of a transaction - location, amount, or timing - make it more or less likely to be fraudulent.

這種能力的一種常見(jiàn)應用是打擊借記卡和信用卡欺詐。通過(guò)訓練，機器學(xué)習系統可以識別典型的消費模式并識別交易的位置，數量或時(shí)間的特征，從而判別是否為欺詐行為。

When a transaction seems out of the ordinary, an alarm can be raised - and a message sent to the user.

當一筆交易看起來(lái)不正常時(shí)，該系統可以發(fā)出警報并給用戶(hù)發(fā)出信息。

7. In hospitals

在醫院里

Doctors are just starting to consider machine learning to make better diagnoses, for example to spot cancer and eye disease.

醫生開(kāi)始考慮利用機器學(xué)習來(lái)更好地進(jìn)行診斷，比如說(shuō)發(fā)現癌癥或者眼部疾病。

Learning from images that have been labelled by doctors, computers can analyse new pictures of a patient’s retina, a skin spot, or an image of cells taken under a microscope. In doing so, they look for visual clues that indicate the presence of medical conditions.This type of image recognition system is increasingly important in healthcare diagnostics.

通過(guò)學(xué)習醫生標記好的圖片，計算機可以分析病人視網(wǎng)膜圖片、皮膚半點(diǎn)圖片或者顯微鏡下的細胞圖片。在這個(gè)過(guò)程中，他們會(huì )尋找能顯示醫療條件的視覺(jué)線(xiàn)索。這種類(lèi)型的圖片識別系統在健康診斷方面愈發(fā)重要。

8. In science

在科學(xué)方面

Machine learning is also powering scientists’ ability to make new discoveries.

機器學(xué)習也在幫助科學(xué)家尋求新的發(fā)現。

In particle physics, it has allowed them to find patterns in immense data sets generated from the Large Hadron Collider at Cern.

在量子物理學(xué)方面，科學(xué)家可以利用這種技術(shù)，從歐洲粒子物理研究所的大型例子對撞機產(chǎn)生的大量數據中發(fā)現典型模式。

It was instrumental in the discovery of the Higgs Boson, for example, and is now being used to search for "new physics" that no-one has yet imagined.

例如，它在發(fā)現希格斯玻色子時(shí)起了重要作用，現在正在用來(lái)探索沒(méi)有人想象過(guò)的“新物理學(xué)”。

Similar ideas are being used to search for new medicines, for example by looking for new small molecules and antibodies to fight diseases.

類(lèi)似的想法被用于探索新型藥物，比如說(shuō)尋求新的小分子和抗體來(lái)對抗疾病。

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