造福社会影响文明 你不必与科技为敌

Every one of today’s smartphones has thousands of times more processing power than the computers that guided astronauts to the moon. And if Moore’s law – the theory that computing capacity doubles roughly every two years – continues to be accurate, tomorrow’s computers will be even stronger.
与当年指导宇航员登月的计算机相比，今天每一部智能手机的数据处理能力都要胜出几千倍。如果摩尔定律依旧准确，未来计算机将更加强大。根据摩尔定律，计算能力大约每两年增加一倍

But Americans today dream less often of feats that computers will help us to accomplish; more and more we have nightmares about computers taking away our jobs. The optimism that many felt in the 1960s over labour-saving technology is giving way to a fearful question: will your labour be good for anything in the future? Or will you be replaced by a machine?
但今日的美国人较少再幻想计算机将帮人类实现丰功伟绩，而是越来越恐惧计算机将夺走我们的工作。20世纪60年代时，许多人对节省人力的技术抱持乐观态度，而今这种乐观已让位于一个可怕的问题：你的劳动在未来有利用价值吗？或者是，你会被机器取代吗？

Fear of replacement is not new. Fifteen years ago American workers were worried about competition from cheaper Mexican substitutes. In 1992 US presidential candidate Ross Perot predicted that a “giant sucking sound” would be heard along the country’s southern border as soon as the North American Free Trade Agreement was signed.
这种害怕被取代的情绪并不新鲜。15年前，美国工人曾担心来自墨西哥廉价劳力的竞争。1992年美国总统候选人罗斯•佩罗(Ross Perot)曾预言，一旦《北美自由贸易协定》(North American Free Trade Agreement)签订，那么沿着美国南部边境，人们将听到一个“巨大的吮吸声”。

Today people think they can hear that sound once more – but they trace it to server farms in Texas instead of cut-rate factories in Tijuana. Americans fear the technology of the near future because they see it as a replay of the globalisation of the near past.
今天人们认为自己又再度听到这个声音，但当他们循声追踪，找到的却不是蒂华纳（Tijuana，墨西哥西北部城市）的廉价工厂，而是得克萨斯州的服务器群。美国人害怕“不久的将来”的科技，因为他们将此视作“不久的以前”出现的全球化的重演。

But the situations are very different: unlike fellow humans of different nationalities, computers are not substitutes for American labour. Men and machines are good at different things. People form plans and make decisions in complicated situations. We are less good at making sense of enormous amounts of data. Computers are exactly the opposite: they excel at efficient data processing but struggle to make basic judgments that would be simple for any human.
但两种情况有很大不同：电脑不像他国人类同胞，它们无法取代美国劳工。人和机器擅长的事情是不同的。人类可以在复杂情况下制定计划和作出决定。我们不擅长分析海量数据。计算机则正相反，它们擅长高效处理数据，但很难作出任何人都能轻易作出的基本判断。

I came to understand this from my experience as chief executive of PayPal. In mid-2000 we had survived the dotcom crash and we were growing fast but we faced one huge problem: we were losing upwards of $10m a month to credit card fraud. Since we were processing hundreds or even thousands of trans­actions each minute, we could not possibly review each one. No human quality control team could work that fast.
我是从担任Paypal首席执行官的经验中得出这一结论的。在21世纪头十年中期，我们挺过了互联网泡沫破灭，得到了快速增长，但我们遇到了一个大问题：我们每月因信用卡欺诈起码要损失1000万美元。由于我们每分钟要处理成百上千笔交易，所以不可能对每笔交易进行审查。由人力构成的质量管理团队绝不可能审查得这么快。