英语流利说levelu文本.docx

1、英语流利说levelu文本 Document serial number【UU89WT-UU98YT-UU8CB-UUUT-UUT108】英语流利说levelu文本Level 7Unit 2Part3Machine intelligence makes human morals more important机器智能使人类道德更重要by Zeynep TufekciSo, I started my first job as a computer programmer in my very first year of college - basically, as a teenager.所以，我在

2、大学一年级时就开始了我的第一份电脑程序员的工作，基本上是一个十几岁的孩子。Soon after I started working, writing software in a company, a manager who worked at the company came down to where I was, and he whispered to me, Can he tell if Im lying There was nobody else in the room.我开始工作后不久，在一家公司写软件，一位在公司工作的经理来到我所在的地方，他低声对我说：“他能告诉我我在撒谎吗”房间

3、里没有其他人。Can who tell if youre lying And why are we whispering“谁能告诉我你在撒谎我们为什么要窃窃私语”The manager pointed at the computer in the room. Can he tell if Im lying Well, that manager was having an affair with the receptionist.经理指着房间里的电脑。“他能告诉我我在撒谎吗”嗯，那个经理和接待员有暧昧关系。(Laughter)（笑声）And I was still a teenager. So

4、I whisper-shouted back to him, Yes, the computer can tell if youre lying.我还是个十几岁的孩子。于是我小声地对他喊道：“是的，电脑能分辨出你在撒谎。”(Laughter)（笑声）Well, I laughed, but actually, the laughs on me. Nowadays, there are computational systems that can suss out emotional states and even lying from processing human faces. Adver

5、tisers and even governments are very interested.嗯，我笑了，但事实上，我笑了。现在，有一些计算系统可以解决情绪状态，甚至可以从处理人脸上撒谎。广告商甚至政府都很感兴趣。I had become a computer programmer because I was one of those kids crazy about math and science. But somewhere along the line Id learned about nuclear weapons, and Id gotten really concerned w

6、ith the ethics of science. I was troubled. However, because of family circumstances, I also needed to start working as soon as possible. So I thought to myself, hey, let me pick a technical field where I can get a job easily and where I dont have to deal with any troublesome questions of ethics. So

7、I picked computers.我已经成为一名电脑程序员，因为我是一个对数学和科学着迷的孩子。但我在某个地方学到了核武器，我真的很关心科学的伦理学。我很烦恼。然而，由于家庭情况，我也需要尽快开始工作。因此，我想，嘿，让我选择一个技术领域，我可以轻松地找到一份工作，在那里我不需要处理任何棘手的道德问题。所以我选择了电脑。(Laughter)（笑声）Well, ha, ha, ha! All the laughs are on me. Nowadays, computer scientists are building platforms that control what a billio

8、n people see every day. Theyre developing cars that could decide who to run over. Theyre even building machines, weapons, that might kill human beings in war. Its ethics all the way down.哈，哈，哈！所有的笑声都在我身上。如今，计算机科学家正在构建一个平台，控制着每天有十亿人看到的东西。他们正在开发可以决定谁来跑的汽车。他们甚至制造机器，武器，可能会在战争中杀死人类。这是道德的一路下滑。Machine inte

9、lligence is here. Were now using computation to make all sort of decisions, but also new kinds of decisions. Were asking questions to computation that have no single right answers, that are subjective and open-ended and value-laden.机器智能在这里。我们现在使用计算来做所有的决定，但也有新的决定。我们问的问题是没有一个正确答案的计算，这是主观的，开放的和价值的。Wer

10、e asking questions like, Who should the company hire Which update from which friend should you be shown Which convict is more likely to reoffend Which news item or movie should be recommended to people我们在问这样的问题：“公司应该雇佣谁”“你应该从哪个朋友那里得到更新”“哪一个犯人更有可能重新犯罪”“应该向人们推荐哪种新闻或电影”Look, yes, weve been using comput

11、ers for a while, but this is different. This is a historical twist, because we cannot anchor computation for such subjective decisions the way we can anchor computation for flying airplanes, building bridges, going to the moon. Are airplanes safer Did the bridge sway and fall There, we have agreed-u

12、pon, fairly clear benchmarks, and we have laws of nature to guide us. We have no such anchors and benchmarks for decisions in messy human affairs.看，是的，我们已经使用了一段时间的电脑，但这是不同的。这是一个历史的转折，因为我们不能锚定计算这样的主观决定的方式，我们可以锚定计算的飞行飞机，建造桥梁，去月球。飞机安全吗这座桥摇晃了吗在那里，我们已经达成一致，相当明确的基准，我们有自然法则来指导我们。在混乱的人类事务中，我们没有这样的锚定和基准。To m

13、ake things more complicated, our software is getting more powerful, but its also getting less transparent and more complex. Recently, in the past decade, complex algorithms have made great strides. They can recognize human faces. They can decipher handwriting. They can detect credit card fraud and b

14、lock spam and they can translate between languages. They can detect tumors in medical imaging. They can beat humans in chess and Go.为了使事情变得更复杂，我们的软件变得越来越强大，但它也变得越来越不透明，越来越复杂。最近，在过去的十年中，复杂的算法取得了很大的进步。他们可以识别人脸。他们能辨认笔迹。他们可以检测信用卡诈骗和阻止垃圾邮件，他们可以翻译之间的语言。他们可以在医学影像中发现肿瘤。他们可以在国际象棋中击败人类。Much of this progress c

15、omes from a method called machine learning. Machine learning is different than traditional programming, where you give the computer detailed, exact, painstaking instructions. Its more like you take the system and you feed it lots of data, including unstructured data, like the kind we generate in our

16、 digital lives. And the system learns by churning through this data. And also, crucially, these systems dont operate under a single-answer logic. They dont produce a simple answer; its more probabilistic: This one is probably more like what youre looking for.这种进步很大程度上来自一种叫做“机器学习”的方法。机器学习不同于传统编程，在那里你

17、给计算机详细、精确、细致的指令。它更像是你采取的系统，你喂它大量的数据，包括非结构化数据，像我们在我们的数字生活中产生的那种。系统通过这些数据来学习。而且，关键的是，这些系统不在一个单一的答案逻辑下运作。他们并没有给出一个简单的答案，而是更多的概率：“这一个可能更像你正在寻找的。”Now, the upside is: this method is really powerful. The head of Googles AI systems called it, the unreasonable effectiveness of data. The downside is, we dont

18、really understand what the system learned. In fact, thats its power. This is less like giving instructions to a computer; its more like training a puppy-machine-creature we dont really understand or control. So this is our problem. Its a problem when this artificial intelligence system gets things w

19、rong. Its also a problem when it gets things right, because we dont even know which is which when its a subjective problem. We dont know what this thing is thinking.现在，好处是：这种方法真的很强大。谷歌的人工智能系统的负责人称之为“数据的不合理有效性”。缺点是，我们并不真正理解系统所学到的东西。事实上，这就是它的力量。这不像是给电脑指令，更像是训练 puppy-machine-creature 我们并不真正理解或控制。这就是我们的

20、问题。当这种人工智能系统出错时，这是个问题。这也是一个问题，当它得到正确的东西，因为我们甚至不知道这是什么时候，这是一个主观的问题。我们不知道这是什么想法。So, consider a hiring algorithm - a system used to hire people, using machine-learning systems. Such a system would have been trained on previous employees data and instructed to find and hire people like the existing high

21、 performers in the company. Sounds good. I once attended a conference that brought together human resources managers and executives, high-level people, using such systems in hiring. They were super excited. They thought that this would make hiring more objective, less biased, and give women and mino

22、rities a better shot against biased human managers.因此，考虑一种雇佣算法一种用来雇佣人的系统，使用机器学习系统。这样的系统将被培训在以前的员工的数据，并指示找到和雇用的人，如现有的高绩效的公司。听起来不错。我曾经参加过一个会议，汇集了人力资源经理和高级管理人员，高层人员，在招聘中使用这种系统。他们非常兴奋。他们认为这会使招聘更加客观，减少偏见，给女性和少数群体一个更好的机会来对付有偏见的人类管理者。And look - human hiring is biased. I know. I mean, in one of my early job

23、s as a programmer, my immediate manager would sometimes come down to where I was really early in the morning or really late in the afternoon, and shed say, Zeynep, lets go to lunch! Id be puzzled by the weird timing. Its 4pm. Lunch I was broke, so free lunch. I always went. I later realized what was

24、 happening. My immediate managers had not confessed to their higher-ups that the programmer they hired for a serious job was a teen girl who wore jeans and sneakers to work. I was doing a good job, I just looked wrong and was the wrong age and gender.看，人的雇佣是有偏见的。我知道。我的意思是，在我作为一名程序员的早期工作中，我的直属经理有时会到我

25、真正早到的地方，或者是在下午很晚的时候，她会说：“Zeynep，我们去吃午饭吧！”我会被奇怪的时间所迷惑。下午四点。午餐我破产了，所以免费的午餐。我总是去。后来我意识到发生了什么。我的直属经理们还没有承认他们的上司，他们雇用的一个严肃的工作是一个十几岁的女孩穿着牛仔裤和运动鞋上班。我做得很好，我只是看起来错了，是错误的年龄和性别。So hiring in a gender- and race-blind way certainly sounds good to me. But with these systems, it is more complicated, and heres why:

26、Currently, computational systems can infer all sorts of things about you from your digital crumbs, even if you have not disclosed those things. They can infer your sexual orientation, your personality traits, your political leanings. They have predictive power with high levels of accuracy. Remember

27、- for things you havent even disclosed. This is inference.因此，在一个性别和种族的盲目的方式雇用听起来对我很好。但是，随着这些系统，它是更复杂的，这就是为什么：目前，计算系统可以推断出各种各样的事情，你从你的数字面包屑，即使你没有透露这些东西。他们可以推断出你的性取向，你的个性特征，你的政治倾向。他们具有高精度的预测能力。记住-对于你还没有透露的事情。这是推理。I have a friend who developed such computational systems to predict the likelihood of cli

28、nical or postpartum depression from social media data. The results are impressive. Her system can predict the likelihood of depression months before the onset of any symptoms - months before. No symptoms, theres prediction. She hopes it will be used for early intervention. Great! But now put this in

29、 the context of hiring.我有一个朋友开发了这样的计算系统，从社会媒体数据预测临床或产后抑郁症的可能性。结果令人印象深刻。她的系统可以预测几个月前出现任何症状之前的抑郁的可能性。没有症状，有预测她希望这将被用于早期干预。太棒了！但现在把这放在招聘的背景下。So at this human resources managers conference, I approached a high-level manager in a very large company, and I said to her, Look, what if, unbeknownst to you, y

30、our system is weeding out people with high future likelihood of depression Theyre not depressed now, just maybe in the future, more likely. What if its weeding out women more likely to be pregnant in the next year or two but arent pregnant now What if its hiring aggressive people because thats your

31、workplace culture You cant tell this by looking at gender breakdowns. Those may be balanced. And since this is machine learning, not traditional coding, there is no variable there labeled higher risk of depression, higher risk of pregnancy, aggressive guy scale. Not only do you not know what your sy

32、stem is selecting on, you dont even know where to begin to look. Its a black box. It has predictive power, but you dont understand it.所以在这次人力资源经理会议上，我找了一家非常大的公司的高级经理，我对她说：“看，如果你不知道，你的系统正在淘汰那些未来可能有抑郁症的人呢他们现在不沮丧，只是可能在未来，更有可能。如果在接下来的一年或两年内淘汰妇女更可能怀孕，但现在又没有怀孕怎么办如果它雇佣有侵略性的人，因为那是你的工作场所文化”你不能通过看性别问题来判断。这些可能是平衡的。而且由于这是机器学习，而不是传统的编码，没有可变的标签“高风险的抑郁症，”“更高的风险怀孕，”“侵略性的家伙规模。”不仅你不知道你的系统在选择什么，你甚至不知道从哪里开始看。它是一个黑匣子。它具有预测力，但你不理解它。What safeguards,