TedTest

From the Ted Talk by Kostas Karpouzis: Can machines read your emotions?

Unscramble the Blue Letters

With every year, machines susarps hmunas in more and more activities we once thought only we were capable of. Today's computers can beat us in complex board gmeas, transcribe speech in dozens of languages, and instantly identify almost any object. But the robots of tooormrw may go futher by learning to figure out what we're felnieg. 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 eitmoon be converted into mere numbers, the only language mehcnias understand? Essentially the same way our own brains interpret emotions, by learning how to spot them. American pghlsocsoyit Paul Ekman identified certain universal emotions whose vsaiul cues are understood the same way across cultures. For example, an image of a smile signals joy to modern uabrn dwellers and agroiianbl tribesmen aklie. And according to Ekman, agner, disgust, fear, joy, sadness, and surprise are equally recognizable. As it trnus out, copmrutes are rapidly getting better at image rtnoiiogecn thanks to machine learning algorithms, such as neural networks. These consist of artificial ndeos that mimic our biological neurons by fiomrng 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 rltiavee weights assigned to particular features. The more training data it's given, the better the algorithm becomes at crotclery identifying new images. This is similar to our own brains, which learn from previous experiences to shape how new smlitui are processed. Recognition algorithms aren't just limtied 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. 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 mredon computers can process it. From sacoil media posts, uploaded photos and vodeis, and phone recordings, to heat-sensitive scuetiry 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. There are ptlney of beneficial uses for computerized emotion recognition. Robots using algorithms to identify facial expressions can help children learn or podrvie lonely people with a ssene 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 stwraofe can help treat mental disorders or even provide poplee with low-cost automated psychotherapy. Despite the potential benefits, the prospect of a massive network automatically scanning our photos, cnunmitcooiams, and physiological snigs is also quite drsbtiuing. What are the implications for our privacy when such iprmoeasnl systems are used by corporations to exploit our emotions through advertising? And what becomes of our rights if authorities think they can itniedfy the people likely to commit crimes before they even make a ciconusos decision to act? 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 istronuin, however, that's another sorty.

Open Cloze

With every year, machines _______ ______ in more and more activities we once thought only we were capable of. Today's computers can beat us in complex board _____, transcribe speech in dozens of languages, and instantly identify almost any object. But the robots of ________ may go futher by learning to figure out what we're _______. 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 _______ be converted into mere numbers, the only language ________ understand? Essentially the same way our own brains interpret emotions, by learning how to spot them. American ____________ Paul Ekman identified certain universal emotions whose ______ cues are understood the same way across cultures. For example, an image of a smile signals joy to modern _____ dwellers and __________ tribesmen _____. And according to Ekman, _____, disgust, fear, joy, sadness, and surprise are equally recognizable. As it _____ out, _________ are rapidly getting better at image ___________ thanks to machine learning algorithms, such as neural networks. These consist of artificial _____ that mimic our biological neurons by _______ 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 ________ weights assigned to particular features. The more training data it's given, the better the algorithm becomes at _________ identifying new images. This is similar to our own brains, which learn from previous experiences to shape how new _______ are processed. Recognition algorithms aren't just _______ 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. 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 ______ computers can process it. From ______ media posts, uploaded photos and ______, and phone recordings, to heat-sensitive ________ 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. There are ______ of beneficial uses for computerized emotion recognition. Robots using algorithms to identify facial expressions can help children learn or _______ lonely people with a _____ 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 ________ can help treat mental disorders or even provide ______ with low-cost automated psychotherapy. Despite the potential benefits, the prospect of a massive network automatically scanning our photos, ______________, and physiological _____ is also quite __________. What are the implications for our privacy when such __________ systems are used by corporations to exploit our emotions through advertising? And what becomes of our rights if authorities think they can ________ the people likely to commit crimes before they even make a _________ decision to act? 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 _________, however, that's another _____.

Solution

machines
disturbing
psychologist
people
conscious
forming
security
modern
aboriginal
stimuli
intrusion
identify
visual
humans
sense
social
signs
plenty
impersonal
alike
communications
videos
emotion
limited
provide
nodes
urban
software
anger
correctly
tomorrow
feeling
story
surpass
recognition
games
turns
computers
relative

Original Text

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. 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? 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. 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. 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. 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. 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. 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? 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.

Frequently Occurring Word Combinations

ngrams of length 2

collocation	frequency
accurately read	2
neural networks	2
facial expressions	2
social media	2
emotion recognition	2

Important Words

aboriginal
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advertising
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