Can machines read your emotions

機(jī)器能讀懂你的情緒嗎？

來(lái)源視頻

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，機(jī)器逐漸在一些我們以前認(rèn)為

只有人類可以做的事情中超越人類

如今，電腦可以在復(fù)雜的桌面游戲中打敗我們

能夠轉(zhuǎn)錄各種語(yǔ)言

并能迅速識(shí)別幾乎所有物體

而未來(lái)的機(jī)器人

或許能在感知我們的情緒方面取得突破

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?

為什么這很重要？

因?yàn)槿绻麢C(jī)器和操作他們的人

可以準(zhǔn)確地感知到我們的情緒

他們可以前所未有地幫助我們

甚至是操縱我們

但是在這之前

我們先來(lái)探討一下 為什么像情緒這么復(fù)雜的東西

可以被轉(zhuǎn)化為數(shù)字， 這種計(jì)算機(jī)唯一能夠理解的語(yǔ)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.

本質(zhì)上，機(jī)器理解感情的方式與我們大腦一樣，

通過(guò)情緒識(shí)別。

美國(guó)心理學(xué)家保羅·艾克曼 定義了幾種全球通用的情緒

這些情緒的視覺(jué)信號(hào)在不同文化中是相同的。

例如，微笑的畫面對(duì)于現(xiàn)代城市人而言意味著愉悅

對(duì)于土著原始人而言也是如此。

根據(jù)艾克曼的理論，

憤怒，

厭惡，

恐懼，

愉悅

悲傷

和驚喜都一樣容易被識(shí)別。

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.

事實(shí)證明，電腦的圖像識(shí)別能力正在迅速提高

這歸功于神經(jīng)網(wǎng)絡(luò)這樣的機(jī)器學(xué)習(xí)算法。

這些人工節(jié)點(diǎn)通過(guò)建成關(guān)聯(lián)和交換信息，

模仿人們的生物神經(jīng)元。

為了訓(xùn)練這樣的網(wǎng)絡(luò)， 輸入的樣例被預(yù)分類到不同類別，

譬如被標(biāo)記成快樂(lè)或傷心的圖片，

被輸入到這個(gè)系統(tǒng)里。

然后，這個(gè)系統(tǒng)網(wǎng)絡(luò)通過(guò)改變不同特征的比重

來(lái)辨別不同的樣例。

這樣的訓(xùn)練越多，

算法就能更準(zhǔn)確地識(shí)別新的圖像。

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.

這一原理正與我們的大腦相像，

我們的大腦依據(jù)過(guò)往的經(jīng)歷來(lái)處理新的刺激。

識(shí)別算法并不只限于面部表情。

我們的情感通過(guò)許多不同的方式被表露。

比如肢體語(yǔ)言，語(yǔ)音語(yǔ)調(diào)

心跳的改變，面色和皮膚溫度，

甚至寫作的用詞頻率和句型結(jié)構(gòu)。

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.

你也許會(huì)認(rèn)為通過(guò)訓(xùn)練神經(jīng)網(wǎng)絡(luò)來(lái)識(shí)別這些特征

會(huì)是一個(gè)漫長(zhǎng)而復(fù)雜的過(guò)程

考慮到當(dāng)下巨大的數(shù)據(jù)量，

以及現(xiàn)代電腦的數(shù)據(jù)處理速度。

從社交網(wǎng)絡(luò)的更新，

上傳的圖片和視頻，

電話錄音，

到熱敏感安全攝像機(jī)

和可穿戴的生理信號(hào)監(jiān)視器，

關(guān)鍵問(wèn)題并不是如何獲得足夠的數(shù)據(jù)，

而是我們應(yīng)該如何運(yùn)用這些數(shù)據(jù)。

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.

電子情感識(shí)別的用途是多方面的。

比如，用算法識(shí)別面部表情的機(jī)器人

可以用于幫助兒童學(xué)習(xí)

或者為孤獨(dú)的人作伴。

許多社交網(wǎng)絡(luò)公司正在考慮使用算法

來(lái)標(biāo)記帖子里的特殊字詞以防范自殺行為。

情感識(shí)別軟件可以幫助治療精神疾病

或者提供低價(jià)的自動(dòng)化心理治療。

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?

盡管情感識(shí)別有這些好處，

通過(guò)一個(gè)巨大的網(wǎng)絡(luò)自動(dòng)掃描我們的照片，

通信，

和生理信號(hào)也讓人感到不安。

當(dāng)我們的隱私信息被這個(gè)沒(méi)有人情味的系統(tǒng)收集， 進(jìn)而被公司利用到廣告中來(lái)欺騙我們的感情

這意味著什么？

我們的權(quán)利又是什么

如果任何的權(quán)力機(jī)構(gòu)認(rèn)為 他們可以在人們決定做任何事情之前，

就能辨別有可能作案的人？

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.

當(dāng)前的機(jī)器人在辨別情感的微妙變化上

還需要提升，比如辨識(shí)諷刺

以及識(shí)別情緒的程度， 分辨一個(gè)人有多么的開(kāi)心或者難過(guò)。

無(wú)論如何， 它們或許終究能夠正確識(shí)別我們的情緒

并且做出回應(yīng)。

至于他們能否體會(huì)到我們不想被過(guò)度入侵的恐懼，

這就是另外一回事了