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双语:智能设备走进你生活的八大方式

2017-05-16 08:16[双语新闻] 来源: 浏览: 次 评论:0条

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

  许多人都不确定机器学习的概念是什么,但现实是它已经是我们每天生活的一部分。

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

  这是人工智能的一种形式,它可以使计算机从范例中进行学习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.

  英国皇家学会认为它对我们的生活的影响会逐渐加深,并呼吁对其进行更多的研究,从而保证英国能够充分利用机会。

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

  机器学习能力已经使其看似平凡的系统变得能够改变生活。这里仅仅是一些例子:

  1.On your phone

  在你的手机上

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

  用口头指令让你的手机进行搜索或者拨打电话。这些命令是基于机器学习提供的科技。

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

  因为语音识别,虚拟私人助手可以服从指令,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.

  他们处理自然的人类语言并且将语言和命令进行匹配,最终以越来越自然的方式进行回应。

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

  这些虚拟助手通过各种各样的谈话和不同的方式进行学习。

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

  他们也许会要求详细信息—比如说,你的名字怎么读或者家里谁的声音是谁的声音。

  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.

  大量的用户谈话数据也被采样,以帮助他们识别不同的发音或者如何自然地交谈

  2.In your shopping basket

  在你的购物车中

  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.

  我们很多人都熟悉购物建议—想想超市提醒你在网上购买奶酪,或者亚马逊推荐你可能喜欢的书。

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

  机器学习是一项提供建议的技术,因此也被称作推荐系统。

  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.

  通过分析顾客之前的购买数据,他们之前表现出的喜好,推荐系统可以从选择购买历史中选择范例。他们用这些来预测你可能喜欢的产品。

  3.On your TV

  在你的电视上

  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.

  类似的系统也用于在Netflix这类流媒体服务上推荐电影或电视节目。推荐系统运用机器学习来分析观看习惯并选择人们喜欢看的节目。

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

  通过了解哪些用户喜欢哪些电影,以及你观看的节目或者得高分的内容,推荐系统可以识别你的口味。

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

  他们也在流媒体服务上推荐音乐,比如Spotify。还可以在脸书上推荐文章。

  4.In your email

  在你的电子邮件中

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

  机器学习也可以用来区分不同类别的对象或项目。

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

  当你整理想看的邮件和不想看的邮件时,这项功能十分有用。

  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.

  垃圾邮件检测系统利用邮件样本来区分哪些是垃圾邮件,并学习通过检测特定词句、特定寄件人的名称和其他的特点进行辨别。

  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,系统运用机器学习将电子邮件定位到正确的文件夹。它可以继续学习用户的标志电子邮件,或者在文件夹之间移动它们。

  5.On your social media

  在你的社交媒体上

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

  有没有想过脸书怎么知道你照片中的人是谁,并且自动给你的照片贴上标签?

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

  脸谱和其他社交媒体运用图片识别系统自动给照片加标签,这项系统也是基于机器学习的

  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.

  当用户上传图像并标记他们的朋友和家人时,这些图像识别系统可以发现重复的图像并将其分配特定类别或人群。

  6.At your bank

  在你的银行中

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

  通过分析大量数据并寻求典例,那些之前不能被人类发现的行为现在可以被识别。

  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.

  这种能力的一种常见应用是打击借记卡和信用卡欺诈。通过训练,机器学习系统可以识别典型的消费模式并识别交易的位置,数量或时间的特征,从而判别是否为欺诈行为。

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

  当一笔交易看起来不正常时,该系统可以发出警报并给用户发出信息。

  7. In hospitals

  在医院里

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

  医生开始考虑利用机器学习来更好地进行诊断,比如说发现癌症或者眼部疾病。

  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.

  通过学习医生标记好的图片,计算机可以分析病人视网膜图片、皮肤半点图片或者显微镜下的细胞图片。在这个过程中,他们会寻找能显示医疗条件的视觉线索。这种类型的图片识别系统在健康诊断方面愈发重要。

  8. In science

  在科学方面

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

  机器学习也在帮助科学家寻求新的发现 。

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

  在量子物理学方面,科学家可以利用这种技术,从欧洲粒子物理研究所的大型例子对撞机产生的大量数据中发现典型模式。

  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,它在发现希格斯玻色子时起了重要作用,现在正在用来探索没有人想象过的“新物理学”。

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

  类似的想法被用于探索新型药物,比如说寻求新的小分子和抗体来对抗疾病。


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