Can machines read your emotions

机器能读懂你的情绪吗？

来源视频

With every year, machines surpass humans in more and more activities

we once thought only we were capable of.

Today's computers can beat us in complex board games,

transcribe speech in dozens of languages,

and instantly identify almost any object.

But the robots of tomorrow may go futher

by learning to figure out  what we're feeling.

每年，机器逐渐在一些我们以前认为

只有人类可以做的事情中超越人类

如今，电脑可以在复杂的桌面游戏中打败我们

能够转录各种语言

并能迅速识别几乎所有物体

而未来的机器人

或许能在感知我们的情绪方面取得突破

And why does that matter?

Because if machines  and the people who run them

can accurately read our emotional states,

they may be able to assist us or manipulate us

at unprecedented scales.

But before we get there,

how can something so complex as emotion be converted into mere numbers,

the only language machines understand?

为什么这很重要？

因为如果机器和操作他们的人

可以准确地感知到我们的情绪

他们可以前所未有地帮助我们

甚至是操纵我们

但是在这之前

我们先来探讨一下 为什么像情绪这么复杂的东西

可以被转化为数字， 这种计算机唯一能够理解的语言呢？

Essentially the same way our own brains interpret emotions,

by learning how to spot them.

American psychologist Paul Ekman identified certain universal emotions

whose visual cues are understood the same way across cultures.

For example, an image of a smile signals joy to modern urban dwellers

and aboriginal tribesmen alike.

And according to Ekman,

anger,

disgust,

fear,

joy,

sadness,

and surprise are equally recognizable.

本质上，机器理解感情的方式与我们大脑一样，

通过情绪识别。

美国心理学家保罗·艾克曼 定义了几种全球通用的情绪

这些情绪的视觉信号在不同文化中是相同的。

例如，微笑的画面对于现代城市人而言意味着愉悦

对于土著原始人而言也是如此。

根据艾克曼的理论，

愤怒，

厌恶，

恐惧，

愉悦

悲伤

和惊喜都一样容易被识别。

As it turns out, computers are rapidly getting better at image recognition

thanks to machine learning algorithms, such as neural networks.

These consist of artificial nodes that mimic our biological neurons

by forming connections  and exchanging information.

To train the network, sample inputs pre-classified into different categories,

such as photos marked happy or sad,

are fed into the system.

The network then learns to classify those samples

by adjusting the relative weights assigned to particular features.

The more training data it's given,

the better the algorithm becomes at correctly identifying new images.

事实证明，电脑的图像识别能力正在迅速提高

这归功于神经网络这样的机器学习算法。

这些人工节点通过建成关联和交换信息，

模仿人们的生物神经元。

为了训练这样的网络， 输入的样例被预分类到不同类别，

譬如被标记成快乐或伤心的图片，

被输入到这个系统里。

然后，这个系统网络通过改变不同特征的比重

来辨别不同的样例。

这样的训练越多，

算法就能更准确地识别新的图像。

This is similar to our own brains,

which learn from previous experiences to shape how new stimuli are processed.

Recognition algorithms aren't just limited to facial expressions.

Our emotions manifest in many ways.

There's body language and vocal tone,

changes in heart rate, complexion, and skin temperature,

or even word frequency and sentence structure in our writing.

这一原理正与我们的大脑相像，

我们的大脑依据过往的经历来处理新的刺激。

识别算法并不只限于面部表情。

我们的情感通过许多不同的方式被表露。

比如肢体语言，语音语调

心跳的改变，面色和皮肤温度，

甚至写作的用词频率和句型结构。

You might think that training neural networks to recognize these

would be a long and complicated task

until you realize just how much  data is out there,

and how quickly modern computers can process it.

From social media posts,

uploaded photos and videos,

and phone recordings,

to heat-sensitive security cameras

and wearables that monitor physiological signs,

the big question is not how to collect enough data,

but what we're going to do with it.

你也许会认为通过训练神经网络来识别这些特征

会是一个漫长而复杂的过程

考虑到当下巨大的数据量，

以及现代电脑的数据处理速度。

从社交网络的更新，

上传的图片和视频，

电话录音，

到热敏感安全摄像机

和可穿戴的生理信号监视器，

关键问题并不是如何获得足够的数据，

而是我们应该如何运用这些数据。

There are plenty of beneficial uses for computerized emotion recognition.

Robots using algorithms to identify facial expressions

can help children learn

or provide lonely people with a sense of companionship.

Social media companies are considering using algorithms

to help prevent suicides by flagging posts that contain specific words or phrases.

And emotion recognition software can help treat mental disorders

or even provide people with low-cost automated psychotherapy.

电子情感识别的用途是多方面的。

比如，用算法识别面部表情的机器人

可以用于帮助儿童学习

或者为孤独的人作伴。

许多社交网络公司正在考虑使用算法

来标记帖子里的特殊字词以防范自杀行为。

情感识别软件可以帮助治疗精神疾病

或者提供低价的自动化心理治疗。

Despite the potential benefits,

the prospect of a massive network automatically scanning our photos,

communications,

and physiological signs is also quite disturbing.

What are the implications for our privacy when such impersonal systems

are used by corporations to exploit our emotions through advertising?

And what becomes of our rights

if authorities think they can identify the people likely to commit crimes

before they even make  a conscious decision to act?

尽管情感识别有这些好处，

通过一个巨大的网络自动扫描我们的照片，

通信，

和生理信号也让人感到不安。

当我们的隐私信息被这个没有人情味的系统收集， 进而被公司利用到广告中来欺骗我们的感情

这意味着什么？

我们的权利又是什么

如果任何的权力机构认为 他们可以在人们决定做任何事情之前，

就能辨别有可能作案的人？

Robots currently have a long way to go

in distinguishing emotional nuances, like irony,

and scales of emotions, just how happy or sad someone is.

Nonetheless, they may eventually be able to accurately read our emotions

and respond to them.

Whether they can empathize with our fear of unwanted intrusion, however,

that's another story.

当前的机器人在辨别情感的微妙变化上

还需要提升，比如辨识讽刺

以及识别情绪的程度， 分辨一个人有多么的开心或者难过。

无论如何， 它们或许终究能够正确识别我们的情绪

并且做出回应。

至于他们能否体会到我们不想被过度入侵的恐惧，

这就是另外一回事了