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托福双语阅读素材:谷歌的海马体

2018年04月25日16:05 来源:小站整理
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摘要:想要提高托福阅读能力,我们一定要在日常生活中有意识地增加英语阅读量,提升语感和熟练度,这其中比较常用也比较方便地一个方式就是利用各类英文报刊杂志文章进行精读与泛读练习。下面我们来看一篇经济学人文章:谷歌的海马体。

想要提高托福阅读能力,我们一定要在日常生活中有意识地增加英语阅读量,提升语感和熟练度,这其中比较常用也比较方便地一个方式就是利用各类英文报刊杂志文章进行精读与泛读练习。下面我们来看一篇经济学人文章:谷歌的海马体。

托福双语阅读素材:谷歌的海马体图1

Artificial intelligence

Google’s hippocampus

Alphabet has plenty of AI expertise, so why does it need DeepMind?

人工智能

谷歌的海马体

Alphabet已拥有大量人工智能专门技术,为何还需要DeepMind?

DEEPMIND’S office is tucked away in a nondescript building next to London’s Kings Cross train station. From the outside, it doesn’t look like something that two of the world’s most powerful technology companies, Facebook and Google, would have fought to acquire. Google won, buying DeepMind for £400m ($660m) in January 2014. But why did it want to own a British artificial-intelligence (AI) company in the first place? Google was already on the cutting edge of machine learning and AI, its newly trendy cousin. What value could DeepMind provide?

人工智能(以下简称AI)科技公司DeepMind的办公室藏身于伦敦国王十字火车站旁边一座不起眼的建筑物内,从外看去,完全不像是Facebook和谷歌这两大科技巨头争相收购的对象。最终,谷歌胜出,在2014年1月以4亿英镑(6.6亿美元)成功收购了DeepMind。但谷歌当初为何要收购这样一家英国AI公司呢?在机器学习及与之相近的AI技术方面,谷歌早已走在前列。DeepMind能给谷歌带来什么价值?

That question has become a little more pressing. Before October 2015 Google’s gigantic advertising revenues had cast a comfortable shade in which ambitious, zero-revenue projects like DeepMind could shelter. Then Google conjured up a corporate superstructure called Alphabet, slotting itself in as the only profitable firm. For the first time, other businesses had their combined revenues broken out from Google’s on the balance-sheet, placing them under more scrutiny (see next article). But understanding DeepMind’s worth is not a simple financial question. Its value is deeper than that.

这个问题现在变得更迫切了些。2015年10月之前,谷歌的巨额广告收入为DeepMind这类雄心勃勃的零收入项目提供了充足的庇荫。而后谷歌构建了名为Alphabet的母公司架构,并成为公司旗下唯一盈利的公司。其他业务的综合营收首次从谷歌的资产负债表中拆分出来,因而会受到更多审视。但要了解DeepMind的价值所在并非一个简单的财务问题。其意义更为深远。

DeepMind’s most immediate benefit to Google and Alphabet is the advantage it gives in the strategic battle that technology companies are waging over AI (see chart). It hoovers up talent, keeping researchers away from competitors like Facebook, Microsoft and Amazon. The Kings Cross office already houses about 400 computer scientists and neuroscientists, and there is talk of expanding that to 1,000.

DeepMind对谷歌和Alphabet最直接的好处是使其在科技公司围绕AI展开的战略竞争中处于有利位置(见图表)。它吸纳了众多人才,令Facebook、微软、亚马逊等竞争对手对其研究人员求之而不得。公司在国王十字火车站旁的办公楼内现有约400名计算机科学家及神经科学家,据说规模将扩至1000人。

Another boost to the mother ship comes in the form of prestige. DeepMind has reached the cover of Nature, a highly regarded academic journal, twice since it was acquired. Gigantic copies of the relevant covers adorn the walls of the office lobby. The first was for a video-game-playing AI programme the second for one that learned to play the ancient Asian board game of Go. Named AlphaGo for its parent, that software went on to make headlines around the world when it beat Lee Sedol, a South Korean champion, in March 2016 (the match is pictured here).

DeepMind为母公司带来的另外一个好处是声望的提升。被收购后,DeepMind已两次登上权威学术期刊《自然》的封面,相关封面的巨幅复制品就张贴在公司大堂的墙上。首次登上封面是因为一款能玩电子游戏的AI程序,第二次则是由于一款学会了下古老的亚洲棋盘游戏围棋的程序。这一以母公司名字命名的软件AlphaGo在2016年3月击败了韩国围棋冠军李世石(如图),一举登上世界各地的新闻头条。

DeepMind’s horizons stretch far beyond talent capture and public attention, however. Demis Hassabis, its CEO and one of its co-founders, describes the company as a new kind of research organisation, combining the long-term outlook of academia with “the energy and focus of a technology startup”—to say nothing of Alphabet’s cash. He founded it in 2010, along with Mustafa Suleyman and Shane Legg. Mr Legg and Mr Hassabis met as neuroscience researchers at University College, London; Mr Suleyman is a childhood friend of Mr Hassabis’s.

然而,DeepMind的眼光远不止于吸引人才和公众关注。其CEO及联合创始人德米斯·哈萨比斯(Demis Hassabis)将公司描述为一种新型的研究机构:既拥有学术领域的长远眼界,也具备“科技创业公司的活力和专注”,而Alphabet的资金就更不用说了。哈萨比斯在2010年与穆斯塔法·苏莱曼(Mustafa Suleyman)和谢恩·列格(Shane Legg)一起创立了DeepMind。列格与哈萨比斯在伦敦大学学院(University College, London)从事神经科学研究时相识,苏莱曼则是哈萨比斯儿时的玩伴。

The firm’s overall mission, as Mr Hassabis puts it, is to “solve intelligence”. This would allow the firm to create multifunctional, “general” artificial intelligence that can think as broadly and effectively as a human. Being bought by Google had several attractions. One was access to the technology firm’s computing power. Another was Google’s profitability; a weaker buyer would have been more likely to require DeepMind to make money. This way Mr Hassabis can focus on research rather than the detail of running a firm. And by keeping DeepMind in London, at a safe distance from Google’s Silicon Valley base in Mountain View, he can retain more control over the operation.

正如哈萨比斯所说,公司的整体使命是“解密智能”。这将使公司创造能像人类那样广泛高效思考的多功能“通用型”人工智能。公司接受谷歌收购有几个诱因。一是可藉此获得谷歌的计算能力。另一个则是谷歌的盈利能力:如果是由财力较弱的买家来收购,则更可能对DeepMind设下盈利要求。而谷歌没有这样的要求,哈萨比斯便可专注于研究,而非公司的运营细节。通过把DeepMind留在伦敦,与谷歌位于山景城的硅谷总部保持一段安全距离,他还可以对运营保留更大的控制权。

Were he to succeed in creating a general-purpose AI, that would obviously be enormously valuable to Alphabet. It would in effect give the firm a digital employee that could be copied over and over again in service of multiple problems. Yet DeepMind’s research agenda is not—or not yet—the same thing as a business model. And its time frames are extremely long. Mr Hassabis says the company is following a 20-year road map. DeepMind aims to invent new kinds of AI algorithms, he adds, that are inspired by the way the human brain works. This explains the firm’s large number of neuroscientists. Mr Hassabis claims that seeking inspiration from the brain sets his firm far apart from other machine-learning research units and in particular from “deep learning”, the powerful branch of machine-learning that is being used by the Google Brain unit.

假如他成功实现了通用AI技术,显然将会为Alphabet带来巨大的价值,等于为之提供了一名可以被无穷复制的数字化员工,用于解决各种问题。但DeepMind的研究计划并不是——或者说尚未成为——一种商业模式,而且其未来规划极为长远。哈萨比斯表示公司正在执行一个20年期的规划。他补充道,DeepMind的目标是发明类似人脑运作方式的AI新算法。正因如此,公司聘用了大批神经科学家。哈萨比斯声称,从人脑寻求灵感使DeepMind大大有别于其他机器学习研究团队,尤其是“深度学习”这一正为“谷歌大脑”团队使用的机器学习的强大分支。

Even if DeepMind never achieves human-level (or indeed, superhuman) artificial intelligence, however, the learning software that it creates along the way can still benefit other Alphabet businesses. This has already happened. In July the company announced that its learning software had found a way to reduce the quantity of electricity that is needed to cool Google data centres, by two-fifths. The software learned about the task by crunching data-centre operation logs, and then optimised the process by running it over and over again in a simulation.

即便DeepMind从来都没研发出达到人类水平(或甚至超人类)的人工智能,但在研究过程中创建的学习软件仍可为Alphabet的其他业务带来好处,而且效果已经显现。今年七月,公司宣布其学习软件已找到方法将谷歌数据中心的制冷用电量减少五分之二。该软件先是分析数据中心的操作日志来理解任务,然后通过反复模拟运行来优化过程。

DeepMind is also applying its AI research to solve problems in its own right. Mr Suleyman, who leads these efforts, has expressed an ambition for DeepMind to help manage energy infrastructure, hone health-care systems and improve access to clean water, in return for revenue streams. The company has already started on health care. Its first paid work came in November in the form of a five-year deal with the Royal Free London, an NHS Foundation Trust, to process 1.7m patient records. Earlier this year it gained access to two data sets from other London hospitals: one million retina scans that it can mine and thereby identify early signs of degenerative eye conditions, and head and neck cancer imagery which, fed into its models, will allow DeepMind’s AI to distinguish between healthy and cancerous tissues.

DeepMind也在应用AI研究来自主解决问题。主管这些工作的苏莱曼曾表达过此种抱负:希望DeepMind能帮助管理能源基础设施,完善医疗保健系统,改善洁净水的供给,以此开拓公司的收入来源。DeepMind已经启动了医疗保健方面的工作。今年11月,公司获得了首个付费工作,与NHS公立医院皇家自由伦敦医院(Royal Free London)签下五年的合同,为其处理170万份病历。今年早前,DeepMind从伦敦其他医院获得了两组数据集:100万份视网膜扫描图,可从中挖掘并辨别出退行性眼病的早期征兆;头颈部癌症病例的医学影像,可输入到DeepMind的模型中,让其AI系统学习区分健康和癌变组织。

Da Neu Ron Ron

Skilful programmers and powerful computers are crucial to this applied AI business. But access to data about the real-world environment is also vital. When systems like hospitals, electricity grids and factories are targeted for improvement using AI and machine learning, data about their specific operations are needed.

神经网络在延展

熟练的程序员及强大的计算机是这类应用型AI业务的关键,不过获取现实世界的数据也至关重要。运用AI及机器学习技术改进医院、电网及工厂等系统时,获取其具体操作数据是必需的。

Alphabet, of course, holds huge volumes of data that can be mined for these purposes. But DeepMind will have to acquire lots more in each of the fields it aims to examine. In the case of a recent project it was involved in on lip-reading, for example, it was the acquisition of an unprecedentedly large data set that made it a success. A group of researchers at the University of Oxford, headed by Andrew Zisserman, a computer-vision researcher, led the work. The BBC gave the researchers hundreds of thousands of hours of newscaster footage, in the absence of which they would not have been able to train their AI systems.

当然,在这些方面,Alphabet公司拥有大量数据可供挖掘,但DeepMind必须还要从其有意探究的各个领域获取更多数据。例如,最近它参与一个关于唇读的项目之所以取得成功,就是因为获得了前所未有的大数据集。由计算机视觉专家安德鲁·基泽曼(Andrew Zisserman)带领的一组牛津大学的科研人员负责了该项目。BBC向这些研究者提供了数十万小时的新闻播音员录像。没有这些数据,他们就无法训练其AI系统。

Alphabet, of course, holds huge volumes of data that can be mined for these purposes. But DeepMind will have to acquire lots more in each of the fields it aims to examine. In the case of a recent project it was involved in on lip-reading, for example, it was the acquisition of an unprecedentedly large data set that made it a success. A group of researchers at the University of Oxford, headed by Andrew Zisserman, a computer-vision researcher, led the work. The BBC gave the researchers hundreds of thousands of hours of newscaster footage, in the absence of which they would not have been able to train their AI systems.

关于数据采集之于DeepMind未来的重要性,哈萨比斯轻描淡写地表示,人类工程师只要能就有待解决的问题构建模拟情境就足够了,然后DeepMind便可将学习主体置于这些模拟情境中。但目前运行的大多数机器学习系统并非如此操作。AlphaGo本身就是先在收录了16万盘人类棋局、包含数百万着棋的数据库中学习之后,才反复自我对弈训练,加以改进。不过,DeepMind如果真的需要掌握大量个人信息,就必须解决消费者对于企业获取数据的顾虑。

If it can solve these problems, however, DeepMind will hold immense value as something entirely new for Alphabet: an algorithm factory. That would go far beyond simply being the technology giant’s long-term AI research outfit and talent-holding pool. The data that DeepMind processes can remain the property of the organisations they come from (which should help to allay concerns about privacy), but the software that learns from that data will belong to Alphabet.

但如果这些问题得到解决,DeepMind将为Alphabet带来巨大的价值,成为其一个全新的部分:一家算法工厂。这样一来,DeepMind将远不止是该科技巨头的AI技术长远研究机构及人才储备库。DeepMind处理的数据的所有权可归其来源机构(这应有助于减轻人们对隐私外泄的担忧),但通过学习这些数据而打造出的软件将属于Alphabet。

DeepMind may not ever make significant revenue of its own by applying AI programmes to complex problems. But the knowledge it sends into learning software from those same sets of data may justify the bidding war that brought it into Alphabet’s compass.

DeepMind自己运用AI程序解决复杂问题也许永远赚不了大钱,但学习软件从那些数据集中获取的知识却意义重大。科技巨头们掀起收购战,Alphabet把DeepMind纳入麾下,原因或许就在于此。

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