大数据造就老大哥？
令人炫目的“大数据”技术有着无限的潜力改变世界，它能让普通人也过上《唐顿庄园》里的贵族生活——轻松得到量身定做的个性化服务。但是，斯诺登泄密事件也深刻反映了大数据可能带来的对个人隐私的侵犯。FT专栏作家John Gapper认为，必须警惕政府有关部门和网络公司通过大数据正在积聚着的巨大权力。难怪，斯诺登事件爆出后，《1984》的销量猛增。
测试中可能遇到的词汇和知识：
Big Brother “老大哥”，乔治·奥威尔的名著《1984》中的经典形象，一个令人感到窒息和恐怖的、控制一切的“大洋国领袖”。"Big Brother is watching"是书中一句名言，指个人言行随时被官方监视着。
artificial intelligence 人工智能，即AI
Taj Mahal 泰姬陵
Uttar Pradesh 印度的北方邦
Brick Lane 砖块街/布里克巷，在东伦敦，现在是孟加拉移民的社区。
Big data has to show that it’s not like Big Brother(977 words)
Sales of George Orwell’s Nineteen Eighty-Four have risen since Edward Snowden revealed how the National Security Agency of the US gains access to telephone records and data from technology companies. So far, if people do not exactly love Big Brother, they are prepared to accept some invasion of their privacy in return for security.
What about “big data”? Companies that hold rapidly expanding amounts of personal information are using new kinds of data analysis and artificial intelligence to shape products and services, and to predict what customers will want. Larry Page, Google’s chief executive, describes his ideal form of technology as “a really smart assistant doing things for you so you don’t have to think about it”.
The vision of living in a virtual Downton Abbey, with a computer to plan your day, suggest the best route to travel, the films you might want to watch and the best flight to catch – even to book it for you – has an allure. We are all pressed for time and want an easy life. Instead of being bombarded with information and forced to choose, it’s nice to get personal service.
But just as the NSA disclosures have taken people by surprise, although it has existed for 60 years, I doubt whether many grasp either the size of the data trail they create daily, or the advances in technology that are permitting a select group of big data enterprises to exploit it. The technology is evolving so quickly that what was unthinkable two years ago is routine.
“It is both a wonderful and scary future. Companies with huge amounts of data will know more about you than yourself. They will be able to predict what you might do next,” says Kai-Fu Lee, a Beijing-based investor and the former head of Google in China.
In a column last week I compared Google to General Electric in the late 19th century – an innovative industrial enterprise riding a wave of new technology. The flip side of that is that Google, Amazon, Microsoft and other technology giants are amassing powers that need to be controlled carefully.
The NSA and big data companies put their databases and computing power to different uses – one to identify spies and terrorists, and the others to match services to users. They have in common the use of very large databases and techniques such as pattern recognition and network analysis.
At the advanced end, this shades into artificial intelligence of the kind that, for example, intuits what you meant to search for even when you misspell the key words; can translate speech into another language in real time (as Microsoft demonstrated in China last year); or learns to recognise a photograph of a cat by viewing thousands of images.
The ability of computers to learn in a similar manner to humans is known as “deep learning” and it is notable that Google has hired several pioneers in the field, including the scientist and author Ray Kurzweil. Among the technology transfer offered by the NSA to private US companies are “cutting-edge machine learning technologies”.
Such software can infer a lot from scraps of information, provided that it has enough of them, as shown by the NSA’s effort to analyse phone call metadata from Verizon (and perhaps other operators). President Barack Obama assured Americans that “no one is listening to your phone calls”, but this alone is a trove.
A study by Latanya Sweeney, a professor at Harvard University, found that 87 per cent of people can be identified simply by knowing their age, gender and postcode, if these are cross-checked against public databases. That is typical of the data collected by social networks and internet companies.
The extraordinary power of big data companies comes from being able to combine the personal data of customers with observations about them, from which products they buy to where (as measured by global positioning satellite data from mobile phones) they are. That produces a set of “inferred data” about what they probably want.
If I search on an Android phone for “Taj Mahal” while standing in India, for example, Google will prioritise results for the shrine in Uttar Pradesh. If I do the same in Brick Lane, east London, it will suggest local Bangladeshi restaurants. How long before it offers to book a restaurant based on how I rated others as I walk around a foreign city at dusk?
At one level, I would be pleased if it did (as long as it was a good one) since it would save me doing the work myself. At another, as a World Economic Forum report on personal data put it: “Inferred data can feel like an all-knowing Big Brother watching the security camera.”
One of the concerns that springs from this is that big data companies with such software are very difficult to compete with. The more data that I and other users provide them with, the better they are at predicting what we want. The machine brain becomes cleverer with use.
Another is trust. Social networks have been poor at protecting users’ data, and they hold only a fraction of the information on people’s behaviour, habits and intentions on the new generation of services. It is no wonder that the NSA turns to them – it has computing power and they have swaths of material.
A third is ownership. We each have rights over our own information, but what happens when it gets mixed up with that of others and combined into a vast database of intentions? If I change my mind, how can it be unscrambled?
Above all, we don’t know what this technology means because we are only at the beginning of the era of big data. There are plenty of aspects to admire but it will take some time to love.
请根据你所读到的文章内容，完成以下自测题目：
1.About artificial intelligence, which of the following is not correct?
A. It is based on data analysis.
B. Computers will be able to predict what customers want.
C. It was an idea first brought up by Google’s cofounder Larry Page.
D. "A really smart assistant doing things for you so you don’t have to think about it."
答案(1)
2.There are technology giants that "are amassing powers that need to be controlled carefully", which company is not among them?
A. Google.
B. Amazon.
C. Facebook.
D. Verizon.
E. Cisco.
答案(2)
3.What best explains the concern that big data companies are "very difficult to compete with"?
A. They usually have ponopoly power on the market.
B. The more data they have, the better they can do.
C. They are very rich and have armies of genius engineers.
D. They have already forged powerful lobby groups.
答案(3)
4.Which of the following is significantly different from the other three?
A. "network analysis"
B. "deep learning"
C. "cross-checking"
D. "inferred data"
答案(4)
* * *
(1) 答案：C.It was an idea first brought up by Google’s cofounder Larry Page.
解释：人工智能的概念很早就有了，AB都正确，D是拉里·佩奇说的。
(2) 答案：E.Cisco.
解释：从文中不难得知，那些可以得到大量用户数据的、可以进行大数据业务的公司，正在积聚着巨大的权力，这样的公司有搜索引擎、社交网络、电信公司、网上商城等。ABCD都是典型。而思科主营电信硬件设备，不大可能拥有这样的数据。
(3) 答案：B.The more data they have, the better they can do.
解释：ACD在某种程度上是有道理的，但是最根本的原因还是大数据的“自然垄断”性——类似水电燃气等基础设施，用的人多了，再多的人就只能用它们的。
(4) 答案：C."cross-checking"
解释：ABD都是大数据技术专有的术语，而C相互检验和反复核实则是早就有的技术，至少信息化社会之前就有了。
