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哪些职业最有可能被机器人取代
[导读]信贷业务员是最容易被机器人和智能设备取代的职业，记者被取代的概率为11%。
机器人和智能设备技术越来越成熟，逐渐取代人工劳动。美国彭博社网站在日前的一篇报道中指出，有700多种职业，面临被机器人取代。
根据英国牛津大学的一项研究，最有可能被机器人取代的职业包括：
——信贷员（被取代的概率是98%）
——前台接待员和信息类人员（96%）
——法律助理和初级律师（94%）
——零售行业导购员（92%）
——出租车司机、专职司机（89%）
——保安（84%）
——厨师、快餐业者（81%）
——酒吧服务生（77%）
——个人理财顾问（58%）
——记者（11%）
——音乐艺人和歌手（7.4%）
——律师（小于5%）
彭博社报道指出，信贷业务员是最容易被机器人和智能设备取代的职业。比如美国的Daric公司，在富国银行董事长Richard-Kovacevich的支持下，提供了P2P信贷业务，他们通过软件和算法，可以遴选出贷款风险最小的借款人。从去年11月开始，该公司不再雇佣信贷员，估计未来也不会再雇佣这一岗位。
零售和快餐行业，也是另外一个高风险行业，许多餐馆已经部署了自动化系统。比如许多餐馆，可以让顾客通过智能手机点餐，这削减了对收款员、服务生、销售人员的人力需求。
原文链接
http://www.businessinsider.com.au/jobs-at-risk-of-being-replaced-by-robots-2014-3
http://www.bloomberg.com/news/2014-03-12/your-job-taught-to-machines-puts-half-u-s-work-at-risk.html
牛津大学的论文，第57页有七百多种职业和他们可能被计算机取代的概率
http://www.oxfordmartin.ox.ac.uk ... e_of_Employment.pdf
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比尔·盖茨：很多工作将会被机器人取代
[导读]在未来20年内，很多技术对于劳动力的需求将会大大减少。
比尔·盖茨：很多工作将会被机器人取代
BI中文站 3月14日报道
微软创始人比尔-盖茨（Bill Gates）认为，劳动力市场将迎来政府和人民都未曾准备好面对的巨大变化。
周四在华盛顿哥伦比亚特区经济智库美国企业研究所演讲时，盖茨声称在20年内，很多工作将会消失，将会被自动化软件（用技术行话就是“机器人”）所取代。
他的原话如下：
“无论是司机、服务员，还是护士，它们都可能会被软件所取代，这种趋势已变得越来越明显了。随着时间的推移，技术的发展将会减少工作的需求，尤其是技术含量较低的工种。在未来20年内，很多技术对于劳动力的需求将会大大减少。我想，人们并没有意识到这一点。”
他并不是唯一预测就业市场灰暗前景的人。在1月，《经济学家》（Economist）杂志就详细列出了在未来20年内将会被机器人取代的十多种工作，包括电话营销员、会计和零售人员。
盖茨认为，税法应作出相应的调整，例如取消所得税和工资税，以鼓励企业招聘员工。此外，他也不主张提高最低工资标准，担心这样会挫伤企业招聘员工的积极性，尤其是在受机器人影响最大的领域。
他解释说，“每当人们主张提高最低工资标准时，我就担心这样做可能会导致工作岗位减少，尤其是那些我最担心被机器人取代的工作岗位。”
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Your Job Taught to Machines Puts Half U.S. Work at Risk By Aki ItoMar 12, 2014 5:01 PM
Aethon Inc.’s self-navigating TUG robot transports soiled linens, drugs and meals in...
Who needs an army of lawyers when you have a computer?
When Minneapolis attorney William Greene faced the task of combing through 1.3 million electronic documents in a recent case, he turned to a so-called smart computer program. Three associates selected relevant documents from a smaller sample, “teaching” their reasoning to the computer. The software’s algorithms then sorted the remaining material by importance.
“We were able to get the information we needed after reviewing only 2.3 percent of the documents,” said Greene, a Minneapolis-based partner at law firm Stinson Leonard Street LLP.
Full Coverage: Technology and the Economy
Artificial intelligence has arrived in the American workplace, spawning tools that replicate human judgments that were too complicated and subtle to distill into instructions for a computer. Algorithms that “learn” from past examples relieve engineers of the need to write out every command.
The advances, coupled with mobile robots wired with this intelligence, make it likely that occupations employing almost half of today’s U.S. workers, ranging from loan officers to cab drivers and real estate agents, become possible to automate in the next decade or two, according to a study done at the University of Oxford in the U.K.
“These transitions have happened before,” said Carl Benedikt Frey, co-author of the study and a research fellow at the Oxford Martin Programme on the Impacts of Future Technology. “What’s different this time is that technological change is happening even faster, and it may affect a greater variety of jobs.”
Profound Imprint
It’s a transition on the heels of an information-technology revolution that’s already left a profound imprint on employment across the globe. For both physical and mental labor, computers and robots replaced tasks that could be specified in step-by-step instructions -- jobs that involved routine responsibilities that were fully understood.
That eliminated work for typists, travel agents and a whole array of middle-class earners over a single generation.
Yet even increasingly powerful computers faced a mammoth obstacle: they could execute only what they’re explicitly told. It was a nightmare for engineers trying to anticipate every command necessary to get software to operate vehicles or accurately recognize speech. That kept many jobs in the exclusive province of human labor -- until recently.
Oxford’s Frey is convinced of the broader reach of technology now because of advances in machine learning, a branch of artificial intelligence that has software “learn” how to make decisions by detecting patterns in those humans have made.
702 Occupations
The approach has powered leapfrog improvements in making self-driving cars and voice search a reality in the past few years. To estimate the impact that will have on 702 U.S. occupations, Frey and colleague Michael Osborne applied some of their own machine learning.
They first looked at detailed descriptions for 70 of those jobs and classified them as either possible or impossible to computerize. Frey and Osborne then fed that data to an algorithm that analyzed what kind of jobs make themselves to automation and predicted probabilities for the remaining 632 professions.
The higher that percentage, the sooner computers and robots will be capable of stepping in for human workers. Occupations that employed about 47 percent of Americans in 2010 scored high enough to rank in the risky category, meaning they could be possible to automate “perhaps over the next decade or two,” their analysis, released in September, showed.
Safe Havens
“My initial reaction was, wow, can this really be accurate?” said Frey, who’s a Ph.D. economist. “Some of these occupations that used to be safe havens for human labor are disappearing one by one.”
Loan officers are among the most susceptible professions, at a 98 percent probability, according to Frey’s estimates. Inroads are already being made by Daric Inc., an online peer-to-peer lender partially funded by former Wells Fargo & Co. Chairman Richard Kovacevich. Begun in November, it doesn’t employ a single loan officer. It probably never will.
The startup’s weapon: an algorithm that not only learned what kind of person made for a safe borrower in the past, but is also constantly updating its understanding of who is creditworthy as more customers repay or default on their debt.
It’s this computerized “experience,” not a loan officer or a committee, that calls the shots, dictating which small businesses and individuals get financing and at what interest rate. It doesn’t need teams of analysts devising hypotheses and running calculations because the software does that on massive streams of data on its own.
Lower Rates
The result: An interest rate that’s typically 8.8 percentage points lower than from a credit card, according to Daric. “The algorithm is the loan officer,” said Greg Ryan, the 29-year-old chief executive officer of the Redwood City, California, company that consists of him and five programmers. “We don’t have overhead, and that means we can pass the savings on to our customers.”
Similar technology is transforming what is often the most expensive part of litigation, during which attorneys pore over e-mails, spreadsheets, social media posts and other records to build their arguments.
Each lawsuit was too nuanced for a standard set of sorting rules, and the string of keywords lawyers suggested before every case still missed too many smoking guns. The reading got so costly that many law firms farmed out the initial sorting to lower-paid contractors.
Training Software
The key to automate some of this was the old adage to show not tell -- to have trained attorneys illustrate to the software the kind of documents that make for gold. Programs developed by companies such as San Francisco-based Recommind Inc. then run massive statistics to predict which files expensive lawyers shouldn’t waste their time reading. It took Greene’s team of lawyers 600 hours to get through the 1.3 million documents with the help of Recommind’s software. That task, assuming a speed of 100 documents per hour, could take 13,000 hours if humans had to read all of them.
“It doesn’t mean you need zero people, but it’s fewer people than you used to need,” said Daniel Martin Katz, a professor at Michigan State University’s College of Law in East Lansing who teaches legal analytics. “It’s definitely a transformation for getting people that first job while they’re trying to gain additional skills as lawyers.”
Robot Transporters
Smart software is transforming the world of manual labor as well, propelling improvements in autonomous cars that make it likely machines can replace taxi drivers and heavy truck drivers in the next two decades, according to Frey’s study.
One application already here: Aethon Inc.’s self-navigating TUG robots that transport soiled linens, drugs and meals in now more than 140 hospitals predominantly in the U.S. When Pittsburgh-based Aethon first installs its robots in new facilities, humans walk the machines around. It would have been impossible to have engineers pre-program all the necessary steps, according to Chief Executive Officer Aldo Zini.
“Every building we encounter is different,” said Zini. “It’s an infinite number” of potential contingencies and “you could never ahead of time try to program everything in. That would be a massive effort. We had to be able to adapt and learn as we go.”
Human-level Cognition
To be sure, employers won’t necessarily replace their staff with computers just because it becomes technically feasible to do so, Frey said. It could remain cheaper for some time to employ low-wage workers than invest in expensive robots. Consumers may prefer interacting with people than with self-service kiosks, while government regulators could choose to require human supervision of high-stakes decisions.
Even more, recent advances still don’t mean computers are nearing human-level cognition that would enable them to replicate most jobs. That’s at least “many decades” away, according to Andrew Ng, director of the Stanford Artificial Intelligence Laboratory near Palo Alto, California.
Machine-learning programs are best at specific routines with lots of data to train on and whose answers can be gleaned from the past. Try getting a computer to do something that’s unlike anything it’s seen before, and it just can’t improvise. Neither can machines come up with novel and creative solutions or learn from a couple examples the way people can, said Ng.
机械式的工作，有大量的数据来训练并且这些答案能从过去收集，最适合机器学习程序。
Employment Impact
“This stuff works best on fairly structured problems,” said Frank Levy, a professor emeritus at the Massachusetts Institute of Technology in Cambridge who has extensively researched technology’s impact on employment. “Where there’s more flexibility needed and you don’t have all the information in advance, it’s a problem.”
人工智能在非常结构化的问题上工作的非常好。
That means the positions of Greene and other senior attorneys, whose responsibilities range from synthesizing persuasive narratives to earning the trust of their clients, won’t disappear for some time. Less certain are prospects for those specializing in lower-paid legal work like document reading, or in jobs that involve other relatively repetitive tasks.
As more of the world gets digitized and the cost to store and process that information continues to decline, artificial intelligence will become even more pervasive in everyday life, says Stanford’s Ng.
“There will always be work for people who can synthesize information, think critically, and be flexible in how they act in different situations,” said Ng, also co-founder of online education provider Coursera Inc. Still, he said, “the jobs of yesterday won’t the same as the jobs of tomorrow.”
能综合处理信息，谨慎地思考，在不同的环境下灵活地采取行动的人永远能找到工作。未来的职业不会和过去的相同。
Workers will likely need to find vocations involving more cognitively complex tasks that machines can’t touch. Those positions also typically require more schooling, said Frey. “It’s a race between technology and education.”
工作者将会需要从事涉及复杂认知的工作。而这些职位需要更多的教育，这是技术和教育的竞赛。
这篇文章希望大家都能仔细看看，这对将来自己和下一代的职业规划有很重要的参考意义。
本来想翻译的，意思很好懂，但是翻译起来真的很费劲，所以只好原文发出来了
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谢谢分享！慢慢领会~~~
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这种科幻的小说的东西贴在这里不合适吧
这种洋人学者的推测无非是哗众取宠的小丑表演，捏造事实就是为了吸引注意力
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现在人类的肢体机能确实比不上原始人，原始人能徒手抓猎物，现在的人类只能依靠工具
但是人类回到原始社会的事永远不会发生
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並非科幻，如果你覺得在你帖子出現不合適，我刪掉！打攪了！
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我的意思是说，我们要相信科学，这些洋人危言耸听的推测完全是反科学的
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我贴这个帖子的目的是告诉大家，人工智能的优点和局限，其实有很多工作是人工智能完全不可能取代的。但是在有些方面，人工智能比人类高效很多，文章中已经指出，非常结构化的惯例操作，并且有大量的过去数据可以用来训练的工作（比如例子当中的贷款软件，他需要大量的过去贷款案例的数据来训练，用这些过去的数据来判断贷款人是否是有信用），非常适合人工智能。
但是人工智能无法应付突发情况，如果一个任务是从来没有出现过的，没有过去的数据可以参考，这种情况下人工智能是不能工作的。
人工智能并不像科幻作家描绘的那样无所不能，其实如果你仔细了解过人工智能，你会发现人工智能更像是人工弱智
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牛津大学的论文，第57页有七百多种职业和他们可能被计算机取代的概率
http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf