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In a rapidly changing work world it's important to be a constant learner to be able to change and evolve your skills.
在一个瞬息万变的工作环境中，不断地学习和发展自己的技能是很重要的。
Especially when you're facing automation of certain types of work. So I want you to think about a spectrum of work that gets automated.
尤其是当你面对某些工作类型的自动化时。所以我想让你们思考一个自动化的工作范围。
And one part of the spectrum is chess. Chess is based on rules is very clear. Patterns repeat. That is a great situation for computer.
其中一部分是国际象棋。国际象棋的规则是非常明确的。模式重复。这对计算机来说是一个很好的情况。
Computers are really good at patterns which is why they made an exponential progress in chess and now the chess app on your iphone can beat the best human chess player in the world.
电脑真的很擅长模式，因此它们在国际象棋上取得了指数级的进步，现在你iPhone上的国际象棋应用可以打败世界上最好的人类棋手。
In the middle of spectrum maybe you think about self driving cars. Self driving cars we have made great progress. There are rules of the road. There are regular repeating patterns. But there's significant challenges that remain.
在光谱的中间，你可能会想到自动驾驶汽车。我们已经取得了很大的进步。我们制定了交通规则，还有规律的重复模式。但仍存在重大挑战。
And on the far end of the spectrum we have something like, say, cancer research where IBM's Watson had a lot of hype but actually was underperform that hype in a such away that I talk to AI researcher, some of them worried that they would damage the reputation of AI in health research going forward.
我们的远端频谱上有，癌症研究。IBM 的沃森为癌症研究做了很多宣传，但实际上这种宣传效果不佳了。在我与人工智能研究人员交谈时，他们中的一些人担心他们会损害人工智能在未来健康研究中的声誉。
As one chologist I talk to put it, the reason Watson destroyed at jeopardy but failed in cancer research because was we know the answer to jeopardy.
正如我交谈过的一位心理学家所说，沃森在《危险边缘》(jeopardy)节目中失败却在癌症研究中失败的原因是，我们知道《危险边缘》的答案。
So if you want to have skills that continue to be valuable, you have to keep learning things and you have to be some of these more amorphous field almost.
因此，如果你想拥有持续有价值的技能，你就必须不断学习，你必须在这些更不固定的领域里学习。
So I wanna share on one example how this has played out in the past. When ATMs were created the thought was that this would do away with bank tellers for good, right?
我想分享一个过去的例子。当自动柜员机被创造出来的时候，人们认为这将永远消除银行柜员，对吗？
Bank tellers did repetitive transaction and see you would not need them anymore. But in fact as more ATM came online there were more jobs for bank tellers.
银行柜员反复做交易，发现你不再需要他们了。但事实上，随着越来越多的自动取款机上线，银行柜员的工作机会也越来越多。
What happend was that each branch needed few tellers so each of the bank became cheaper and banks open more branches so there were more tellers. But the job of tellers changed completely.
结果是每家分行只需要很少的出纳员，所以每家银行的成本都降低了，而银行又开设了更多的分行，这样就有了更多的柜员。但是出纳员的工作完全变了。
It was no longer someone who could do repetitive transaction rather they had to learn marketing skills and customers service and have this much wider array of broad skills.
他们不再能够做重复的交易，而是必须学习营销技巧和客户服务，并拥有更广泛的技能。
Because those broader skills and integrating different types of information are what's difficult for computer.
因为那些更广泛的技能和整合不同类型的信息对计算机来说非常困难。
The psychologist Robbin Hogart categorize domain of learning as going from the kind to the wicked.
心理学家罗宾斯·霍格特（Robbin Hogart）将学习领域归类为善和恶。
Kind learning environment where areas or patterns repeated, there is wealth of previous data, there were clear rules, and feed back was apparent.
在良好的学习环境中，区域或模式反复出现，有丰富的前期数据，有明确的规则，反馈明显。
And in those kind of areas like chess, computers really thrive.
在像国际象棋这样的领域，电脑真的很发达。
On the other end of the spectrum are wicked environments where not all the information is clear, rules don't necessarily repeat, people aren't waiting for each other to take turns, feed back maybe delayed.
另一极端是恶劣的环境，在这种环境中，所有的信息都不明确，规则不一定重复，人们不等待彼此轮流。 反馈可能会延迟。
If you get it all, it maybe inaccurate. And human behaviour is involved. Those are areas wheres computer dont do as well there are quiet a lot of the so called soft skills.
如果你全得到了，可能就不准确。这涉及到人类行为。这些都是电脑做不到的领域，有很多所谓的软技能。
How to deal with human behaviour and how to adjust things that are changing in real time and interpret signals that are very difficult to quantify.
如何处理人类行为，如何调整实时变化的事物，如何解释很难量化的信号。
That's an area thats very very difficult for computers but humans have a huge advantages. So those kinds of soft skills are really important and will be for long time to come.
这是一个对计算机来说非常困难的领域，但人类有巨大的优势。所以这些软技能真的很重要，而且会持续很长一段时间。
