TED Talk Machine intelligence makes human morals more important 机器智能使人类道德更重要 Speaker: Zeynep Tufekci 第二课
Machine intelligence is here. 机器智能在这里。
We're now using computation to make all sort of decisions, but also new kinds of decisions. 我们现在使用计算机运算来做所有的决定，但也有新的决定。
We're asking questions to computation that have no single right answers, that are subjective and open-ended and value-laden. 我们向运算提问的问题没有单个正确答案，这是主观的，开放的，有价值的。
We're asking questions like, "Who should the company hire?" 我们在问这样的问题：“公司应该雇佣谁？”
"Which update from which friend should you be shown?"“你应该从哪个朋友那里得到更新？”
"Which convict is more likely to reoffend?" “哪一个犯人更有可能重新犯罪？”
"Which news item or movie should be recommended to people?" “应该向人们推荐哪种新闻或电影？”
Look, yes, we've been using computers for a while, but this is different.看，是的，我们已经使用了一段时间的电脑，但这是不同的。
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.这是一个历史的转折，因为我们不能讲计算用于主观决定的方式，我们可以将计算用于飞行飞机，建造桥梁，登录月球。
Are airplanes safer? Did the bridge sway and fall?飞机安全吗？这座桥摇晃了吗？
There, we have agreed-upon, fairly clear benchmarks, and we have laws of nature to guide us. 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。
We have no such anchors and benchmarks for decisions in messy human affairs.在无规律的人类事务中，我们没有这样的锚定和基准。
【选择】What does Tufekci mean by historical twist? -Computers are being used to solve subjective problems for the first time in history.
【选择】According to Tufekci, machine intelligence should not be trusted to... hire new employees.
【选择】If sth. reflects your personal values, it is... value-laden. value-laden adj. 受主观价值影响的，主观的
To make things more complicated, our software is getting more powerful, but it's also getting less transparent and more complex. 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。
Recently, in the past decade, complex algorithms have made great strides. 【跟读】最近，在过去的十年中，复杂的算法取得了很大的进步。
They can recognize human faces. They can decipher handwriting.他们可以识别人脸。他们能辨认笔迹。
They can detect credit card fraud and block spam and they can translate between languages. 【跟读】他们可以检测信用卡诈骗和阻止垃圾邮件，他们可以翻译不同的语言。
They can detect tumors in medical imaging. 他们可以在医学影像中发现肿瘤。
They can beat humans in chess and Go. 他们可以在国际象棋中击败人类。
Much of this progress comes from a method called "machine learning." 这种进步很大程度上来自一种叫做“机器学习”的方法。
Machine learning is different than traditional programming, where you give the computer detailed, exact, painstaking instructions. 机器学习不同于传统编程，会你给计算机详细、精确、细致的指令。
It's more like you take the system and you feed it lots of data, including unstructured data, like the kind we generate in our digital lives. 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。
And the system learns by churning through this data. 系统通过这些数据来学习。
And also, crucially, these systems don't operate under a single-answer logic. 而且，关键的是，这些系统不在一个单一的答案逻辑下运作。
They don't produce a simple answer; it's more probabilistic: "This one is probably more like what you're looking for." 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. 现在，好处是：这种方法真的很强大。
The head of Google's AI systems called it, "the unreasonable effectiveness of data." 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”。
The downside is, we don't really understand what the system learned. 【跟读】 缺点是，我们并不真正理解系统所学到的东西。
In fact, that's its power. This is less like giving instructions to a computer; it's more like training a puppy-machine-creature we don't really understand or control. 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。
So this is our problem. It's a problem when this artificial intelligence system gets things wrong. 这就是我们的问题。当这种人工智能系统出错时，这是个问题。
It's also a problem when it gets things right, because we don't even know which is which when it's a subjective problem. 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。
We don't know what this thing is thinking. 我们不知道这是什么想法。
【选择】Why is it a problem while machine intelligence gets things right？ -People can't examine how the system reaches its conclusion.
【选择】How is machine learning different from traditional programming? -It leads to probabilistic answers. 机器学习与传统编程有何不同?它导致了概率性的答案。
【选择】If a method or argument is probabilistic, it is based on what is most likely to be true.
So, consider a hiring algorithm -- a system used to hire people, right, using machine-learning systems. 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。
Such a system would have been trained on previous employees' data and instructed to find and hire people like the existing high performers in the company. 这样的系统将被培训以前的员工的数据，并指示找到和雇用的人，如现有的高绩效的公司。
Sounds good. 听起来不错。
I once attended a conference that brought together human resources managers and executives, high-level people, using such systems in hiring. 我曾经参加过一个汇集了人力资源经理和高级管理人员，高层人员的会议，他们在招聘中使用这种系统。
They were super excited.他们非常兴奋。
They thought that this would make hiring more objective, less biased, and give women and minorities a better shot against biased human managers.
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. 看，人的雇佣是有偏见的。
I know. I mean, in one of my early jobs 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 she'd say, "Zeynep, let's go to lunch!" 我知道。我的意思是，在早期我作为一名程序员时，我的直属经理有时会到我早到或者是在下午很晚的时候，她会说：“Zeynep，我们去吃午饭吧！”
I'd be puzzled by the weird timing. 我会被奇怪的时间所迷惑。
It's 4 pm. Lunch? I was broke, so free lunch. I always went. I later realized what was happening. 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。
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. 我的直属经理们还没有向上司承认，他们雇用来做一项严肃的工作的员工，是一个穿着牛仔裤和运动鞋上班的十几岁女孩。
I was doing a good job, I just looked wrong and was the wrong age and gender. 我做得很好，我只是看起来错了，是错误的年龄和性别。
【跟读】 I was doing a good job, but I was the wrong age and gender.
So hiring in a gender- and race-blind way certainly sounds good to me. 【跟读】 因此，通过一个没有性别和种族偏见的方式雇人听起来很好。
But with these systems, it is more complicated, and here's why: 但是，这些系统，它是更复杂的，以下是原因：
Currently, computational systems can infer all sorts of things about you from your digital crumbs, even if you have not disclosed those things. 目前，计算系统可以从你的数字碎屑推断出各种各样的事情，即使你没有透露这些东西。
They can infer your sexual orientation, your personality traits, your political leanings. 他们可以推断出你的性取向，你的个性特征，你的政治倾向。
They have predictive power with high levels of accuracy. 他们具有高精度的预测能力。
Remember -- for things you haven't even disclosed. This is inference. 记住--甚至是你还没有透露的事情。这是推理。
【选择】-How a hiring algorithm more complicated than it appeared to be? - The infer undisclosed information and make predictions.
【选择】A hiring algorithm would find and hire strong candidates by... basing its criteria on existing employees.
【跟读】
