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ai, radiology and the future of work
人工智能、放射学以及工作的未来
images aren’t everything
图像不是一切
clever machines will make workers more productive more often than they will replace them
聪明的机器将使工人更有效率，而不是取代他们
radiologists, say the pessimists, will be first against the wall when the machines take over.
悲观主义者说，当人类的工作由机器代替时，放射科医生将首先走向绝路。
analysing medical images is a natural fit for “deep learning”, an artificial-intelligence (ai) technique which first attracted attention for its ability to teach computers to recognise objects in pictures.
分析医学图像非常适合“深度学习”（一种人工智能技术），它最先引起了人们对教授计算机识别图像中物体能力的关注。
a variety of companies hope that bringing ai into the clinic will make diagnosis faster and cheaper.
许多公司希望将人工智能带入诊所使诊断变得更快和更便宜。
the machines may even be able to see nuances that humans cannot, assessing how risky a patient’s cancer is simply by looking at a scan.
这些机器甚至可以看到人类无法看到的细微差别，只需通过扫描即可评估患者患癌症的危险程度。
some ai researchers think that human beings can be dispensed with entirely.
一些人工智能研究人员认为人类可以完全被替代。
“it’s quite obvious that we should stop training radiologists,” said geoffrey hinton, an ai luminary, in 2016.
人工智能杰出人才杰弗里·辛顿在2016年表示：“很明显，我们应该停止培训放射科医生。”
in november andrew ng, another superstar researcher, when discussing ai’s ability to diagnose pneumonia from chest x-rays, wondered whether “radiologists should be worried about their jobs”.
同年11月份，另一位巨星级研究员吴恩达在讨论人工智能从胸部x光片诊断肺炎的能力时，想弄明白“放射科医生是否应该担心他们的工作”。
given how widely applicable machine learning seems to be, such pronouncements are bound to alarm white-collar workers, from engineers to lawyers.
鉴于机器学习的适用范围之广泛，这种声明必然会引起白领们（从工程师到律师）的警觉。
in fact the application of ai to medicine suggests that the story is more complicated.
事实上，人工智能在医学中的应用表明情节是更复杂的。
machine learning will indeed change many fields, allowing the rapid analysis of enormous piles of data to uncover insights that people might overlook.
机器学习确实会改变许多领域，可以快速分析大量数据，从而发现人们可能忽略的地方。
but it is not about to make humans redundant.
但它不是要使人类变得多余。
and radiology, the very field that is used as a cautionary tale about the robopocalypse, shows why.
放射学（被用于创作关于机器人启示录的警示故事）这一领域说明了原因。
one is the nature of ai itself.
其一是人工智能自身的本质。
the field issuffusedwith hype.
这个领域充满了炒作。
some papers show artificial radiologists outperforming the ones in white coats.
一些论文显示人工智能在放射科的表现优于身着白大褂的放射科医生。
others, though,still put the humans ahead.
不过，其他人仍然认为人类优于人工智能。
the machines may eventually take an unambiguous lead.
这些机器可能最终会取得明显的 优势。
but it is important to remember that ai, for the foreseeable future, will remain “narrow”, not general.
但重要的是要记住，在可预见的未来，人工智能将保持“局限”，而不能全面。
no human is as good at mental arithmetic as a $10 pocket calculator, but that is all the calculator can do.
没有人像10美元大小的袖珍计算器一样擅长心算，但这就是计算器所能做到的全部了。
deep learning is broader.
深度学习会更广泛些。
it is a pattern-recognition technique, and patterns are everywhere in nature.
它是一种模式识别技术，而模式无处不在。
but in the end it, too, is limited―a sort of electronic idiot-savant which excels at one particular mental task but is baffled by others.
但最终它也是有限的—— 一种电子低能特才，擅长于一项特殊的智力任务，却对其他的任务迷惑。
instead of wondering whether ai can replace a job, it is better to ponder whether it could replace humans at a specific task.
不是考虑人工智能是否可以取代工作，而是考虑是否可以在特定任务中取代人类。
(红色标注词为考研重点词汇)
考研重点词汇：
dispense [d?’spens] vi. 免除，豁免 vt. 分配，分发；免除；执行
l
uminary [‘lu?m?n(?)r?] n. 发光体；杰出人物；知识渊博的人
suffuse [s?’fju?z] vt. 充满；弥漫
baffle [‘b?f(?)l] n. 挡板；困惑 vt. 使…困惑；使…受挫折；用挡板控制 vi. 做徒劳挣扎
小编寄语：许多人认为人工智能会给人类带来很大的威胁，不过相反，我并不认为ai会给人来带来巨大的威胁，机器人不但能 人类完成简单的工作，给人类带来更多的机会，和休闲的时间。根据目前形式来看，ai会对人类造成威胁担忧，但只要人们管理好，监督好以及限制好就会阻止这些事情的发生。对于人工智能，你怎么看呢？
来源：每日双语经济学人
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