Postagens
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Will computers ever truly understand what we're saying?
Date: January 11, 2016
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human communication, think again. Computers like Siri often get confused because they judge meaning by looking at a word’s statistical regularity. This is unlike humans, for whom context is more important than the word or signal, according to a researcher who invented a communication game allowing only nonverbal cues, and used it to pinpoint regions of the brain where mutual understanding takes place.
From Apple’s Siri to Honda’s robot Asimo, machines seem to be getting better and better at communicating with humans. But some neuroscientists caution that today’s computers will never truly understand what we’re saying because they do not take into account the context of a conversation the way people do.
Specifically, say University of California, Berkeley, postdoctoral fellow Arjen Stolk and his Dutch colleagues, machines don’t develop a shared understanding of the people, place and situation - often including a long social history - that is key to human communication. Without such common ground, a computer cannot help but be confused.
"People tend to think of communication as an exchange of linguistic signs or gestures, forgetting that much of communication is about the social context, about who you are communicating with," Stolk said.
The word "bank," for example, would be interpreted one way if you're holding a credit card but a different way if you're holding a fishing pole. Without context, making a “V” with two fingers could mean victory, the number two, or “these are the two fingers I broke.”
"All these subtleties are quite crucial to understanding one another," Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication. “In fact, we can understand one another without language, without words and signs that already have a shared meaning.”
(Adapted from http://www.sciencedaily.com/releases/2016/01/1 60111135231.htm)
_____________________________________________________________________________
👉 Questão 16 :
The title of Text I reveals that the author of this text is:
[a] unsure;
[b] trustful;
[c] careless;
[d] annoyed;
[e] confident.
_____________________________________________________________________________
👉 Questão 17 :
Based on the summary provided for Text I, mark the statements below as TRUE (T) or FALSE (F).
[ ] Contextual clues are still not accounted for by computers.
[ ] Computers are unreliable because they focus on language patterns.
[ ] A game has been invented based on the words people use.
The statements are, respectively:
[a] F – T – T;
[b] T – F – T;
[c] F – F – T;
[d] F – T – F;
[e] T – T – F.
_____________________________________________________________________________
👉 Questão 18 :
According to the researchers from the University of California, Berkeley:
[a] words tend to have a single meaning;
[b] computers can understand people’s social history;
[c] it is easy to understand words even out of context;
[d] people can communicate without using actual words;
[e] social context tends to create problems in communication.
_____________________________________________________________________________
👉 Questão 19 :
If you are holding a fishing pole, the word “bank” means a:
[a] safe;
[b] seat;
[c] boat;
[d] building;
[e] coastline.
_____________________________________________________________________________
👉 Questão 20 :
The word “so” in “perhaps more so than the words and signals” is used to refer to something already stated in Text I. In this context, it refers to:
[a] key;
[b] crucial;
[c] subtleties;
[d] understanding;
[e] communication
READ TEXT II AND ANSWER QUESTIONS 21 TO 25:
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voicerecognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers. (From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)
👉QUESTION 21:
The use of the phrase “the backlash” in the title of Text II means the:
[a] backing of;
[b] support for;
[c] decision for;
[d] resistance to;
[e] overpowering of.
👉QUESTION 22:
The three main arguments against big data raised by Text II in the second paragraph are:
[a] large numbers; old theories; consistent relations;
[b] intrinsic partiality; outdated concepts; casual links;
[c] clear views; updated assumptions; weak associations;
[d] objective approaches; dated models; genuine connections;
[e] scientific impartiality; unfounded theories; strong relations.
👉QUESTION 23:
The base form, past tense and past participle of the verb “fall” in “The criticisms fall into three areas” are, respectively:
[a] fall-fell-fell;
[b] fall-fall-fallen;
[c] fall-fell-fallen;
[d] fall-falled-fell;
[e] fall-felled-falling.
👉QUESTION 24:
When Text II mentions “grumblers” in “to face the grumblers”, it refers to:
[a] scientists who use many tests;
[b] people who murmur complaints;
[c] those who support large data sets;
[d] statisticians who promise solid results;
[e] researchers who work with the internet.
👉QUESTION 25:
The phrase “lots of data to chew on” in Text II makes use of figurative language and shares some common characteristics with:
[a] eating;
[b] drawing;
[c] chatting;
[d] thinking;
[e] counting.
The title of Text I reveals that the author of this text is...O título de Texto I revela que o autor deste texto é:
[a] unsure;
[b] trustful;
[c] careless;
[d] annoyed;
[e] confident.
👉QUESTION 17:
Based on the summary provided for Text I, mark the statements below as TRUE (T) or FALSE (F).Com base no resumo fornecido para o Texto I, marque as sentenças abaixo como TRUE (T) ou FALSE (F)
[ ] Contextual clues are still not accounted for by computers.
[ ] Computers are unreliable because they focus on language patterns.
[ ] A game has been invented based on the words people use.
The statements are, respectively:
[a] F – T – T;
[b] T – F – T;
[c] F – F – T;
[d] F – T – F;
[e] T – T – F.
👉QUESTION 18:
According to the researchers from the University of California, Berkeley:Segundo os pesquisadores da Universidade da Califórnia, Berkeley:
[a] words tend to have a single meaning;
[b] computers can understand people’s social history;
[c] it is easy to understand words even out of context;
[d] people can communicate without using actual words;
[e] social context tends to create problems in communication.
👉QUESTION 19:
If you are holding a fishing pole, the word "bank" means a.Se você estiver segurando uma VARA DE PESCAR, a palavra "BANK" significa uma
[a] safe;cofre,caixa
[b] seat;assento,banco
[c] boat;barco de pesca,bote
[d] building;edificio,construção
[e] coastline.litoral
MUITA ATENÇAO...a palavra "bank" pode ser:
"bank"=banco(financeiro),banco(assento) e banco(de areia).
O texto sugere que o "bank" é de areia,veja:
"The word "bank," for example, would be interpreted one way if you're holding a credit card but a different way if you're holding a fishing pole.A palavra "banco", por exemplo, seria interpretada de uma maneira se você estiver segurando um cartão de crédito, mas de uma maneira diferente se você estiver segurando uma vara de pesca"
Areia tem a ver com LITORAL ou seja COASTLINE.
👉QUESTION 20: The word "so" in "perhaps more so than the words and signals" is used to refer to something already stated in Text I. In this context, it refers to:A palavra "SO" em "talvez mais do que as palavras e sinais" é usada para se referir a algo já indicado no Texto I. Neste contexto, ele se refere a:
[a] key;
[b] crucial;
[c] subtleties;
[d] understanding;
[e] communication
Vamos ao texto:
""All these subtleties are quite crucial to understanding one another," Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication.Todas essas sutilezas são bastante cruciais para se entenderem ", disse Stolk, talvez mais CRUCIAL do que as palavras e sinais de que os computadores e muitos neurocientistas se concentram como a chave da comunicação."
TEXTO II
Hey, what's up dear friends!!!...How have you been?!
Neste post, veremos a Prova de INGLÊS - Concurso Público do IBGE(Instituto Brasileiro de Geografia e Estatística ) - Cargo:Tecnologista Estatística - Prova aplicada em 10/04/2016.
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[a]Banca Organizadora do VESTIBULAR
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[b]Padrão/Composição da prova
[b]Padrão/Composição da prova
👉20 Questões.
👉Reading Comprehension(Compreensão textual).
👉Reading Comprehension(Compreensão textual).
👉Use of english(uso do inglês).
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[c]Dictionary:
Caso necessário,sugiro que consulte os excelentes dicionários a seguir:
http://www.thefreedictionary.com/
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➽[ → ]
http://www.thefreedictionary.com/
_____________________________________________________________________________
🔄 VERBS :
➽[ → ]
🔄 Phrasal Verbs :
➽[ → ]
➽[ → ]
🔄 Phrasal Verbs :
➽[ → ]
🔄Expressões verbais com o TO BE(simple present/simple past/simple future/ be going to/present continuous/past continuous/future continuous):
➽[ → ]
🔄Expressões verbais no PERFECT TENSE(present perfect/past perfect/present perfect continuous/past perfect continuous):
➽[ → ]
🔄Expressões com os 10 modais(can/could/may/might/must/should/would/ought to/will/shall):
➽[ → ]
🔄Expressões com verbos com ING:
➽[ → ]
🔄Expressões VERBAIS EM GERAL:
➽["]
🔄Substantivos(NOUNS):
➽[ → ]
🔄Adjetivos/Locuções adjetivas :
➽[ → ]
🔄Advérbios/Locução adverbial:
➽[ → ]
🔄 Pronomes Relativos(who, which, whom, that) :
➽[ → ]
🔄 Coordination Conjunctions (for, and, nor, but, or, yet, so):
➽["]
🔄 Subordination Conjunctions (however/nonetheless/nevertheless/notwithstanding)-(although/though/even though)-(as if/as though)-(as/so long as/provided that)-(despite/in spite of)-(as)-(once)-(otherwise)-(unless)-(untill)-(when/by the time)-(whenever)-(whereas)-(while)-(so that/so as to/in order that/in order to)-(since):
➽[)
🔄 Correlative Conjunctions(not only...but also, both...and, as...as, either...or, wheter...or, neither...nor) :
➽["]
🔄 Preposition (in ➜ mês), (on ➜ dia/data), (at➜ hora/momento específico: at night, at midnight, at lunchtime) :
➽["]
🔄 Passive Voice: Verbo TO BE(no tempo verbal contextual)+VP no particípio passado. :
➽[ → ]
🔄Expressões verbais no PERFECT TENSE(present perfect/past perfect/present perfect continuous/past perfect continuous):
➽[ → ]
🔄Expressões com os 10 modais(can/could/may/might/must/should/would/ought to/will/shall):
➽[ → ]
🔄Expressões com verbos com ING:
➽[ → ]
🔄Expressões VERBAIS EM GERAL:
➽["]
🔄Substantivos(NOUNS):
➽[ → ]
🔄Adjetivos/Locuções adjetivas :
➽[ → ]
🔄Advérbios/Locução adverbial:
➽[ → ]
🔄 Pronomes Relativos(who, which, whom, that) :
➽[ → ]
🔄 Coordination Conjunctions (for, and, nor, but, or, yet, so):
➽["]
🔄 Subordination Conjunctions (however/nonetheless/nevertheless/notwithstanding)-(although/though/even though)-(as if/as though)-(as/so long as/provided that)-(despite/in spite of)-(as)-(once)-(otherwise)-(unless)-(untill)-(when/by the time)-(whenever)-(whereas)-(while)-(so that/so as to/in order that/in order to)-(since):
➽[)
🔄 Correlative Conjunctions(not only...but also, both...and, as...as, either...or, wheter...or, neither...nor) :
➽["]
🔄 Preposition (in ➜ mês), (on ➜ dia/data), (at➜ hora/momento específico: at night, at midnight, at lunchtime) :
➽["]
🔄 Passive Voice: Verbo TO BE(no tempo verbal contextual)+VP no particípio passado. :
➽["]
🔄Expressões idiomáticas :
➽[ → ]
🔄Expressões ADJETIVO+SUBSTANTIVO:
➽[ → ]
🔄IF-CLAUSE:
➽["]
🔄Expressões Técnicas :
🔄 Comparativos & Superlativos :
➽[ → ]
🔄 Afixos :
➽[ → ]
🔄Expressões com frações/números:
➽[."]
🔄 Questions :
➽[ → ]
🔄Expressões idiomáticas :
➽[ → ]
🔄Expressões ADJETIVO+SUBSTANTIVO:
➽[ → ]
🔄IF-CLAUSE:
➽["]
🔄Expressões Técnicas :
➽["]
➽[ → ]
🔄Expressões com 'S (Genitive case=proprietário 'S propriedade) :
➽[ → ]🔄 Afixos :
➽[ → ]
🔄Expressões com frações/números:
➽[."]
🔄 Questions :
➽[ → ]
🔄 Falso cognato :
_____________________________________________________________________________
Agora vamos à prova.
_____________________________________________________________________________
READ TEXT I AND ANSWER QUESTIONS 16 TO 20
_____________________________________________________________________________
TEXT I
_____________________________________________________________________________
_____________________________________________________________________________
Source University of California - Berkeley
Summary:
If you think computers are quickly approaching true human communication, think again. Computers like Siri often get confused because they judge meaning by looking at a word’s statistical regularity. This is unlike humans, for whom context is more important than the word or signal, according to a researcher who invented a communication game allowing only nonverbal cues, and used it to pinpoint regions of the brain where mutual understanding takes place.
_____________________________________________________________________________
Specifically, say University of California, Berkeley, postdoctoral fellow Arjen Stolk and his Dutch colleagues, machines don’t develop a shared understanding of the people, place and situation - often including a long social history - that is key to human communication. Without such common ground, a computer cannot help but be confused.
"People tend to think of communication as an exchange of linguistic signs or gestures, forgetting that much of communication is about the social context, about who you are communicating with," Stolk said.
The word "bank," for example, would be interpreted one way if you're holding a credit card but a different way if you're holding a fishing pole. Without context, making a “V” with two fingers could mean victory, the number two, or “these are the two fingers I broke.”
"All these subtleties are quite crucial to understanding one another," Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication. “In fact, we can understand one another without language, without words and signs that already have a shared meaning.”
(Adapted from http://www.sciencedaily.com/releases/2016/01/1 60111135231.htm)
_____________________________________________________________________________
👉 Questão 16 :
The title of Text I reveals that the author of this text is:
[a] unsure;
[b] trustful;
[c] careless;
[d] annoyed;
[e] confident.
_____________________________________________________________________________
👉 Questão 17 :
Based on the summary provided for Text I, mark the statements below as TRUE (T) or FALSE (F).
[ ] Contextual clues are still not accounted for by computers.
[ ] Computers are unreliable because they focus on language patterns.
[ ] A game has been invented based on the words people use.
The statements are, respectively:
[a] F – T – T;
[b] T – F – T;
[c] F – F – T;
[d] F – T – F;
[e] T – T – F.
_____________________________________________________________________________
👉 Questão 18 :
According to the researchers from the University of California, Berkeley:
[a] words tend to have a single meaning;
[b] computers can understand people’s social history;
[c] it is easy to understand words even out of context;
[d] people can communicate without using actual words;
[e] social context tends to create problems in communication.
_____________________________________________________________________________
👉 Questão 19 :
If you are holding a fishing pole, the word “bank” means a:
[a] safe;
[b] seat;
[c] boat;
[d] building;
[e] coastline.
_____________________________________________________________________________
👉 Questão 20 :
The word “so” in “perhaps more so than the words and signals” is used to refer to something already stated in Text I. In this context, it refers to:
[a] key;
[b] crucial;
[c] subtleties;
[d] understanding;
[e] communication
READ TEXT II AND ANSWER QUESTIONS 21 TO 25:
TEXT II
The backlash against big data
[…]
Big data refers to the idea that society can do things with a large body of data that weren’t possible when working with smaller amounts. The term was originally applied a decade ago to massive datasets from astrophysics, genomics and internet search engines, and to machine-learning systems (for voicerecognition and translation, for example) that work well only when given lots of data to chew on. Now it refers to the application of data-analysis and statistics in new areas, from retailing to human resources. The backlash began in mid-March, prompted by an article in Science by David Lazer and others at Harvard and Northeastern University. It showed that a big-data poster-child—Google Flu Trends, a 2009 project which identified flu outbreaks from search queries alone—had overestimated the number of cases for four years running, compared with reported data from the Centres for Disease Control (CDC). This led to a wider attack on the idea of big data.
The criticisms fall into three areas that are not intrinsic to big data per se, but endemic to data analysis, and have some merit. First, there are biases inherent to data that must not be ignored. That is undeniably the case. Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete. In fact, subject-area knowledge remains necessary even when dealing with large data sets. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance—increases with more data. Although there are new statistical techniques to identify and banish spurious correlations, such as running many tests against subsets of the data, this will always be a problem.
There is some merit to the naysayers' case, in other words. But these criticisms do not mean that big-data analysis has no merit whatsoever. Even the Harvard researchers who decried big data "hubris" admitted in Science that melding Google Flu Trends analysis with CDC’s data improved the overall forecast—showing that big data can in fact be a useful tool. And research published in PLOS Computational Biology on April 17th shows it is possible to estimate the prevalence of the flu based on visits to Wikipedia articles related to the illness. Behind the big data backlash is the classic hype cycle, in which a technology’s early proponents make overly grandiose claims, people sling arrows when those promises fall flat, but the technology eventually transforms the world, though not necessarily in ways the pundits expected. It happened with the web, and television, radio, motion pictures and the telegraph before it. Now it is simply big data’s turn to face the grumblers. (From http://www.economist.com/blogs/economist explains/201 4/04/economist-explains-10)
👉QUESTION 21:
The use of the phrase “the backlash” in the title of Text II means the:
[a] backing of;
[b] support for;
[c] decision for;
[d] resistance to;
[e] overpowering of.
👉QUESTION 22:
The three main arguments against big data raised by Text II in the second paragraph are:
[a] large numbers; old theories; consistent relations;
[b] intrinsic partiality; outdated concepts; casual links;
[c] clear views; updated assumptions; weak associations;
[d] objective approaches; dated models; genuine connections;
[e] scientific impartiality; unfounded theories; strong relations.
👉QUESTION 23:
The base form, past tense and past participle of the verb “fall” in “The criticisms fall into three areas” are, respectively:
[a] fall-fell-fell;
[b] fall-fall-fallen;
[c] fall-fell-fallen;
[d] fall-falled-fell;
[e] fall-felled-falling.
👉QUESTION 24:
When Text II mentions “grumblers” in “to face the grumblers”, it refers to:
[a] scientists who use many tests;
[b] people who murmur complaints;
[c] those who support large data sets;
[d] statisticians who promise solid results;
[e] researchers who work with the internet.
👉QUESTION 25:
The phrase “lots of data to chew on” in Text II makes use of figurative language and shares some common characteristics with:
[a] eating;
[b] drawing;
[c] chatting;
[d] thinking;
[e] counting.
RESOLUÇÃO DA PROVA
TEXTO I
Will computers ever truly understand what we're saying?
👉QUESTION 16: The title of Text I reveals that the author of this text is...O título de Texto I revela que o autor deste texto é:
[a] unsure;
[b] trustful;
[c] careless;
[d] annoyed;
[e] confident.
👉QUESTION 17:
Based on the summary provided for Text I, mark the statements below as TRUE (T) or FALSE (F).Com base no resumo fornecido para o Texto I, marque as sentenças abaixo como TRUE (T) ou FALSE (F)
[ ] Contextual clues are still not accounted for by computers.
[ ] Computers are unreliable because they focus on language patterns.
[ ] A game has been invented based on the words people use.
The statements are, respectively:
[a] F – T – T;
[b] T – F – T;
[c] F – F – T;
[d] F – T – F;
[e] T – T – F.
👉QUESTION 18:
According to the researchers from the University of California, Berkeley:Segundo os pesquisadores da Universidade da Califórnia, Berkeley:
[a] words tend to have a single meaning;
[b] computers can understand people’s social history;
[c] it is easy to understand words even out of context;
[d] people can communicate without using actual words;
[e] social context tends to create problems in communication.
👉QUESTION 19:
If you are holding a fishing pole, the word "bank" means a.Se você estiver segurando uma VARA DE PESCAR, a palavra "BANK" significa uma
[a] safe;cofre,caixa
[b] seat;assento,banco
[c] boat;barco de pesca,bote
[d] building;edificio,construção
[e] coastline.litoral
MUITA ATENÇAO...a palavra "bank" pode ser:
"bank"=banco(financeiro),banco(assento) e banco(de areia).
O texto sugere que o "bank" é de areia,veja:
"The word "bank," for example, would be interpreted one way if you're holding a credit card but a different way if you're holding a fishing pole.A palavra "banco", por exemplo, seria interpretada de uma maneira se você estiver segurando um cartão de crédito, mas de uma maneira diferente se você estiver segurando uma vara de pesca"
Areia tem a ver com LITORAL ou seja COASTLINE.
👉QUESTION 20: The word "so" in "perhaps more so than the words and signals" is used to refer to something already stated in Text I. In this context, it refers to:A palavra "SO" em "talvez mais do que as palavras e sinais" é usada para se referir a algo já indicado no Texto I. Neste contexto, ele se refere a:
[a] key;
[b] crucial;
[c] subtleties;
[d] understanding;
[e] communication
Vamos ao texto:
""All these subtleties are quite crucial to understanding one another," Stolk said, perhaps more so than the words and signals that computers and many neuroscientists focus on as the key to communication.Todas essas sutilezas são bastante cruciais para se entenderem ", disse Stolk, talvez mais CRUCIAL do que as palavras e sinais de que os computadores e muitos neurocientistas se concentram como a chave da comunicação."
TEXTO II