Google’s big bet on computers that can teach themselves is about to face its most significant examination.

谷歌(Google)押注计算机可以自主学习的赌局，即将面临最重大的考验。

Machine learning has brought artificial intelligence (AI) back into the technology mainstream¹ which, for Google, means using its computing² resources to analyse mountains of data to identify patterns and make predictions, from calculating the adverts⁴ users are likely to find relevant to whether a digital image shows a cat or a dog.

机器学习把人工智能(AI)带回到科技主流中，对谷歌而言，这意味着利用它的计算能力来分析海量数据以识别模式并作出预测，从计算用户可能觉得相关的广告，到一幅数字图像显示的是猫还是狗。

It’s now solving problems we don’t know how to solve in any other way, said Jeff Dean, the engineer who has spearheaded Google’s efforts since it began to focus on the area nearly five years ago.

它现在正在解决我们完全不知道如何解决的问题，自谷歌在近5年前开始聚焦该领域以来一直引领研究的工程师杰夫•迪恩(Jeff Dean)表示。

About 100 product teams at Google now apply the technology, he added.

他补充称，谷歌如今约有100个产品团队正在应用这项技术。

The latest — and most visible — product of the push is an intelligent digital assistant, intended to usher⁵ in a more natural and intelligent form of human-computer interaction, based on the use of everyday language.

最新（也最显眼）的产品是一个智能数字助理，旨在开启一个更自然、更智能的人机交互模式，基于日常语言的使用。

The feature — called Assistant — is due to appear, in different guises⁶, in a range of Google products and services in the coming weeks.

被称为助手(Assistant)的这项功能将于未来几周以不同形式出现在谷歌一系列产品和服务中。

That will give it a central place in the company’s efforts to steal users away from some of its rivals’ most successful recent ventures.

它将有助于谷歌从某些竞争对手最成功的新项目夺取用户。

These include Amazon’s voice-activated home device, Echo; Apple’s smart assistant, Siri; and Facebook’s messaging services, Messenger and WhatsApp.

这些包括亚马逊(Amazon)的家庭声控设备Echo；苹果(Apple)的智能助手Siri；以及Facebook的通讯服务——Messenger和WhatsApp。

But even for a company with Google’s massive computing power and engineering brains, teaching computers to act more naturally and intelligently has required it to confront some of the most intractable computer science problems.

但是，即使是对于像谷歌那样拥有庞大计算能力和工程设计人才的公司来说，教会计算机更自然更智能地行动，也需要面对一些最棘手的计算机科学问题。

Google certainly has the bench strength to make a dent³ in this problem but no one has cracked the code yet, said Tim Tuttle, chief executive of MindMeld, an AI start-up that is building its own platform for conversational⁷ computing.

谷歌当然拥有足够强大的人才实力来挑战这个问题，但是迄今还没人能完全破解，AI初创企业MindMeld的首席执行官蒂姆•塔特尔(Tim Tuttle)表示。该公司正在打造自己的对话式计算平台。

Many experts in the AI field credit Google with having edged ahead of its main rivals in machine learning.

AI领域的很多专家承认，谷歌在机器学习方面领先于其主要竞争对手。

It has been showing leading edge results in the field, said Oren Etzioni, head of artificial intelligence at the research institute of Microsoft co-founder Paul Allen.

在微软(Microsoft)共同创始人保罗•艾伦(Paul Allen)的研究所负责AI研究的奥伦•埃齐奥尼(Oren Etzioni)称，谷歌在该领域展现了前沿成果。

He credits it with taking a more open approach than rivals, publishing its research and making its technologies freely available.

他认为，这是由于谷歌采取了比对手更开放的姿态，发表研究结果，并使其技术可以免费获得。

This open-sourcing has helped it build a wider ecosystem⁸ around its approach.

这种开源模式帮助它围绕自己的方法建立了一个更大的生态系统。

Amazon has adopted a much more closed model and is playing catch-up in machine learning, said Mr Etzioni. The people that they have attracted are not at the same level.

亚马逊采用了更封闭的模式，在机器学习领域正追赶谷歌，埃齐奥尼称，他们吸引到的人才不是同一水平的。

All of this has served to raise expectations that Google’s Assistant will reach new standards in understanding language and supplying more intelligent guidance, from answering direct questions to steering⁹ users through tasks such as finding a restaurant for dinner or arranging a flight.

所有这一切都起到了提高期望值的作用，即谷歌Assistant在理解语音和提供更智能的指引上将达到新水平，从回答直接的问题，到指导用户完成寻找餐厅或安排航班等任务。

But the heightened expectations have also greatly elevated the risks.

但是，期望值提高也大大提升了风险。

Users are often quick to impute¹⁰ high levels of intelligence to computers that appear to understand language, leaving plenty of room for disappointment when the results fall short.

用户往往很快认为似乎理解语言的计算机具有高智能，当结果不尽人意时会非常失望。

Google first disclosed its plans for Assistant at its annual developer conference in May.

谷歌于今年5月在年度开发者大会上首次透露了Assistant计划。

The technology will take different forms, depending on the device or service where it is used.

该技术将根据使用的设备或服务而采取不同形式。

It is set to be used in a product called Home, a voice-activated gadget¹¹ modelled on Amazon’s breakthrough Echo.

预计将用于一款被称为Home的语音工具产品（效仿亚马逊的Echo）。

Google also said in May that it would power a text-based intelligent service to appear inside Allo, an app launched yesterday (see below) that is intended to propel Google, belatedly, into messaging.

谷歌5月时还表示，该技术将用于在应用软件Allo中驱动基于文本的智能服务。近日已发布的Allo旨在推动谷歌进入即时信息领域。

With these new approaches, the search company is betting that many people are ready to try new ways of interacting with digital devices.

凭借这些新方法，这家搜索公司押注很多人都已准备好尝试与数字化设备交互的新方式。

Around 20 per cent of searches on Android devices in the US are already conducted by voice, according to Google.

据谷歌表示，在美国，Android设备上进行的搜索约20%通过语音完成。

Advances in the quality of techniques like speech recognition have brought the technology to a stage where it is ready for a mass market, said Mr Dean.

迪恩称，语音识别等技术的进步，使得AI达到了可以面向大众市场的阶段。

For instance, Google says its error rate in understanding spoken words, even in a noisy room, has fallen to 8 per cent.

例如，谷歌称其理解口语单词的错误率（即使是在嘈杂的房间内）已降至8%。

The company has done a remarkable¹² job in areas such as speech recognition and the text-to-speech feature that turns search results into spoken answers, said Mr Tuttle.

塔特尔称，该公司还在语音识别和文本转换语音（将搜索结果转换为语音回答）等领域取得了出色的表现。

Each of these draws on Google’s roots in internet search, which supplies it with mountains of data about general language usage to fuel its core language engines.

这一切成功都利用了谷歌在互联网搜索方面的根基，后者使其可以利用有关一般语言用法的海量数据来推动其核心语言引擎。

In these contexts, Google has an advantage, says Mr Tuttle.

在这些方面，谷歌具有优势，塔特尔表示。

However, understanding language at the deeper level involves grasping the context of a statement, which is often not obvious, or being able to follow a sequence of comments that follow human but not computer logic¹³.

然而，若要在更深层面上理解语言，就必然涉及掌握一句话的背景（往往不明显）或是能够理解一系列遵循人类（而非计算机）逻辑的评论。

These are things that trip up general-purpose tools such as Assistant, said Mr Tuttle.

塔特尔称，这些任务会使Assistant等通用工具出错。

In taking on the more intractable challenges, Google is looking to draw on deep learning, the most advanced form of machine learning.

为了应对更棘手的挑战，谷歌正在寻求利用深度学习——机器学习的最高级形式。

Patterned on the workings of the human brain, deep learning systems use multiple processing layers, like artificial neural¹⁴ networks, to filter data to reach their results.

深度学习系统借鉴人类大脑的工作方式，利用多个处理层（就像人工神经网络那样）来过滤数据以得到结果。

The technology is particularly well suited to things that computers have traditionally found impossible, such as image recognition, and has been applied¹⁵ most strikingly in Google’s Photos app to automatically identify people or objects in users’ albums.

这项技术特别适合于处理传统电脑不可能完成的任务，比如图像识别。该技术迄今最引人瞩目的应用是在谷歌相册(Photos)的用户相簿中自动识别人或物体。

According to Mr Dean, the sort of breakthroughs made in image recognition are now beginning to be seen in language, divining context and meaning where other programs have foundered¹⁶.

据迪恩表示，图像识别上的这种突破，如今已经开始出现在语音、语境和语意推测方面；在这些方面，其他程序已失败。

What’s happened recently is the deep learning approaches have started showing an ability to understand language for many different tasks, he said.

最近出现的情况是，深度学习方法开始在很多不同的任务中表现出了理解语言的能力，他称。

He concedes, though, that Google’s computers are still far from matching human levels of language comprehension, or replicating¹⁷ the broad understanding of the world that people draw on when holding a conversation.

尽管如此，他承认谷歌的计算机距离人类语言理解能力、或者人类在对话时利用深厚背景知识的程度仍然很远。

We have a pretty good ability to understand shorter sentences or utterances¹⁸, said Mr Dean. But we don’t have the ability in long-range context, or the deep background models a human has from other areas when you are talking.

我们在理解较短的句子或表达时拥有相当出色的能力，迪恩称，但是我们无法理解长程语境和人类在说话时来自其他方面的深层背景模式。

A further challenge will be to restrict the situations in which Assistant can handle tasks automatically, limiting it to areas where there is little chance of it making a mistake.

还有一个挑战将限制Assistant自动处理任务的情形，把它限制在犯错几率很小的领域。

It is one thing to unleash¹⁹ a deep learning program to identify pictures of cats, said Mr Dean, but it is another to set the same program free to make changes to your travel itinerary²⁰, where a slight misunderstanding would cause deep inconvenience.

迪恩称，释放一款深度学习程序来识别猫咪照片是一回事，而放手让同样的程序来更改你的行程则是另一回事。在后面一种情形中，细微的误解都会造成极大的不便。

As a result, the packaging of the new Assistant technology — finding a useful set of tasks that it can do well, without over-promising or disappointing — is likely to be as important to its success as the underlying²¹ technical achievements themselves.

其结果是，新Assistant技术的包装——在不过度承诺或让人失望的情况下，找到一套它可以顺利完成的任务——可能会和它本身作为根本性技术成就的成功同样重要。

The best technologies don’t always translate to the best product or the winner in the market place, said Mr Etzioni. Google has already seen Amazon steal a march with the groundbreaking Echo, and Apple catch the popular imagination with Siri. With Assistant, it is time to get back into the conversation.

最好的技术并不总是转化为最棒的产品或市场上的赢家，埃齐奥尼称。在眼看着亚马逊以开创性的Echo先声夺人、苹果以Siri抓住大众想象力之后，谷歌是时候在Assistant的帮助下重新成为关注焦点。