Prof. Gabriela Knoblauch

12/04/2016 | 20:33

IBGE: Prova de Inglês comentada

Oi, pessoal! 

O que acharam da prova do IBGE? Eu considerei a prova bem tranquila e quem fez meu curso certamente não tomou susto! :)

Passo para comentar a prova e já adianto que não encontrei possibilidade de recurso.



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 60111135231.htm)


16 The title of Text I reveals that the author of this text is:


(A) unsure; inseguro, em dúvida;

(B) trustful; confiante;

(C) careless; descuidado;

(D) annoyed; irritado;

(E) confident. confiante.


Vejamos o título:

Will computers ever truly understand what we’re saying?

Os computadores vão algum dia realmente entender o que estamos dizendo?

A pergunta nos dá a entender que o autor está em dúvida, sem certeza, ou seja, UNSURE.


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.

Correta. Se o autor diz que os computadores nunca entenderão de verdade (will never truly understand) o que estamos dizendo (saying), pois não levam em conta (do not take into account) o contexto (contexto), fica claro que pistas contextuais (contextual clues) ainda não são contabilizadas (not accounted) por computadores.

 ( ) Computers are unreliable because they focus on language patterns.

Correta. Se os computadores se confundem (get confused), pois julgam (judge) o significado (meaning) com base na regularidade estatística das palavras (word’s statistical regularity), devemos entender que computadores não são confiáveis (are unreliable), porque eles se concentram (focus) em padrões de linguagem (language patterns).

( ) A game has been invented based on the words people use.

Errada. O jogo (game) permite apenas o uso de dicas (cues) não-verbais (nonverbal), ou seja, é contradição afirmar que o jogo é baseado em palavras (words).


The statements are, respectively:

(A) F – T – T;

(B) T – F – T;

(C) F – F – T;

(D) F – T – F;

(E) T – T – F.


18 According to the researchers from the University of California, Berkeley:


(A) words tend to have a single meaning; palavras tendem a ter um único significado;

Errada. Vimos acima que o contexto e a comunicação não–verbal contam muito na interpretação do que é dito. Logo, as palavras podem ter vários sentidos.

(B) computers can understand people’s social history; os computadores podem entender a história social das pessoas;

Errada. Não possuem essa capacidade. 

(C) it is easy to understand words even out of context; é fácil entender as palavras, mesmo fora de contexto;

Errada. O contexto é importante. Sem ele, é difícil entender as palavras.

(D) people can communicate without using actual words; as pessoas podem se comunicar sem o uso de palavras reais;

Correta. O texto fala da possibilidade da comunicação não-verbal.

(E) social context tends to create problems in communication. o contexto social tende a criar problemas na comunicação.

 Errada. O texto não afirma isso.



19 If you are holding a fishing pole, the word “bank” means a:


(A) safe; cofre

(B) seat; assento

(C) boat;  barco

(D) building; edifício

(E) coastline. litoral, costa


O texto nos ensina que as palavras podem ter diferentes sentidos de acordo com o contexto o oferece como exemplo a palavra BANK.


  • Aterro, dique, barragem, barreira, ladeira, margem, ribanceira (de rio ou lago), banco, baixio, escolho, recife, terra ao longo de rio ou lago, banco de terra, banco de areia
  • Banco, estabelecimento de crédito, casa bancária.


Se o contexto é uma pessoa segurando uma vara de pescar (fishing pole), ficamos com o primeiro sentido apresentado.


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; chave

(B) crucial; cruciais

(C) subtleties; sutilezas

(D) understanding; compreendermos

(E) communication. comunicação


“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 compreendermos uns aos outros", disse Stolk, talvez mais do que as palavras e sinais que os computadores e muitos neurocientistas concentram-se como a chave para comunicação.


A que a palavra SO (MAIS) se refere? A “cruciais”. Vejam:

"Todas essas sutilezas são bastante cruciais para compreendermos uns aos outros", disse Stolk, talvez mais CRUCIAIS do que as palavras e sinais que os computadores e muitos neurocientistas concentram-se como a chave para comunicação.




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 explains/201 4/04/economist-explains-10)


21 The use of the phrase “the backlash” in the title of Text II means the:

(A) backing of; apoio a

(B) support for;  apoio para

(C) decision for;  decisão a favor

(D) resistance to; resistência a

(E) overpowering of. subjugação de


BACKLASH = reação forte e negativa de um grande número de pessoas, repercussão ruim.

Se houve backlash ao Big Data, houve resistência a ele.


22 The three main arguments against big data raised by Text II in the second paragraph are:

(A) large numbers; old theories; consistent relations;

números grandes; velhas teorias; relações consistentes;

(B) intrinsic partiality; outdated concepts; casual links;

parcialidade intrínseca; conceitos ultrapassados; ligações casuais;

(C) clear views; updated assumptions; weak associations;

visão clara; pressupostos atualizados; associações fracas;

(D) objective approaches; dated models; genuine connections;

abordagens objetivas; modelos datados; conexões genuínas;

(E) scientific impartiality; unfounded theories; strong relations.

imparcialidade científica; teorias infundadas; relações fortes.


As palavras-chave para encontrarmos a resposta são:

biases = preconceito, tendência, inclinação, parcialidade = intrinsic partiality

theory is obsolete = teoria está obsoleta = outdated concepts

risk of spurious correlations = risco de correlações falsas = casual links


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.


Base form é a o verbo não flexionado de forma nenhuma. É ele no infinitivo sem o “to”: FALL.

Past tense é o verbo no passado simples: FELL

Notem que estamos tratando de verbo irregular. O passado dos verbos é – regra geral – feito quando acrescentamos ED ao final.

Past Participle é o particípio passado do verbo. Em Português isso ocorre quando colocamos ADO ou IDO ao final do verbo. Ex: falado, ouvido. Em inglês, o particípio passado do verbo TO FALL  é FALLEN (caído).


24 When Text II mentions “grumblers” in “to face the grumblers”, it refers to:

(A) scientists who use many tests; os cientistas que usam muitos testes;

(B) people who murmur complaints; pessoas que murmuram reclamações;

(C) those who support large data sets; os que apoiam grandes conjuntos de dados;

(D) statisticians who promise solid results; estatísticos que prometem resultados sólidos;

(E) researchers who work with the internet. pesquisadores que trabalham com a internet.


GRUMBLER = resmungão, reclamão.


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; comendo

(B) drawing; desenhando

(C) chatting; batendo papo

(D) thinking; pensando

(E) counting. contando








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  • 26/05/2016 - LETÍZIA XAVIER BUENO
    Gostei de tudo! Suas explicações são perfeitas.
  • 26/05/2016 - Prof Gabriela Knoblauch
    Muito obrigada, Letícia! Que bom que gostou. Não deixe de me seguir nas redes sociais e de ver meu canal no Youtube. Dou muitas dicas legais. Em caso de dúvida, escreva para Abs!
  • 18/04/2016 - Caroline
    Professora, na questão "If you are holding a fishing pole, the word 'bank' means a...", não se poderia considerar a resposta "seat" também?
    Pq eu procurei em diversos sites de tradução e vi que essa também poderia ser uma opção de resposta..
  • 18/04/2016 - Prof Gabriela Knoblauch
    Oi, Caroline.

    Banco com sentido de assento é bench e não bank.

    BANK tem os dois sentidos que apresentei no comentário. Confira a definição de BANK no dicionário Macmillan:

    Agora no Cambridge:


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