Questões de Inglês

An 18-Minute Plan for Managing Your Day

According to STEP 3,

An 18-Minute Plan for Managing Your Day

Check the only alternative in which the expression in bold type has the same meaning as the item given.

An 18-Minute Plan for Managing Your Day

The main purpose of the text is to

An 18-Minute Plan for Managing Your Day

Otherwise in the sentence "Otherwise, take it off your list." (lines 41-42) can be substituted, without changing the meaning of the sentence, by

An 18-Minute Plan for Managing Your Day

According to paragraph 1, the author had problems at work because he

An 18-Minute Plan for Managing Your Day

In "But it may just help you leave the office feeling productive and successful." (lines 59-60) may just help could be correctly replaced, by

An 18-Minute Plan for Managing Your Day

The only adequate title to refer to STEP 1 is

An 18-Minute Plan for Managing Your Day

Which option correctly indicates the referent of that in "...isn't that a higher priority?" (line 61)?

An 18-Minute Plan for Managing Your Day

The only advice that is in line with STEP 2 is

An 18-Minute Plan for Managing Your Day

Check the option that contains a correct correspondence of meaning.

An 18-Minute Plan for Managing Your Day

Check the only alternative in which the expression in bold type has the same meaning as the item given.

READ TEXT I AND ANSWER QUESTIONS 16 TO 20 TEXT I 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) 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:

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:

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

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

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

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

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

READ TEXT I AND ANSWER QUESTIONS 11 TO 15 TEXT I 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) 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:

READ TEXT II AND ANSWER QUESTIONS 16 TO 20: 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) When Text II mentions “grumblers” in “to face the grumblers”, it refers to:

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:

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

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

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:

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

READ TEXT I AND ANSWER QUESTIONS 16 TO 20 TEXT I 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) If you are holding a fishing pole, the word “bank” means a:

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

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

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

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: