www.inglesparaconcursos.blog.br
PROVA
❑ PROVA:
01-A, 02-E, 03-D, 04-E, 05-B
06-D, 07-B, 08-C, 09-B, 10-A
➧ VOCABULĂRIO:
1) A WORD'S STATISTICAL REGULARITY - a regularidade estatĂstica de uma palavra.
2) BACKLASH - reação negativa de um grande nĂșmero de pessoas, repercussĂŁo ruim.
3) COMPUTERS ARE QUICKLY APPROACHING - os computadores estĂŁo rapidamente abordando.
4) SUBTLETIES (SĂĄrĂłthis) - sutilezas, requintes.
5) TO TAKE INTO ACCOUNT - levar em conta, levar em consideração.
➧ 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/r
eleases/2016/01/160111135231.htm)
The title of Text I reveals that the author of this text is:
(A) unsure;
(B) trustful;
(C) careless;
(D) annoyed;
(E) confident.
đ Gabarito A
O tĂtulo do Texto I revela que o autor deste texto Ă©/estĂĄ...
*Alternativa (A): inseguro;(O tĂtulo Ă© um questionamento que transmite incerteza, dĂșvida quanto evolução da tecnologia e da ciĂȘncia e os computadores ➜ Will computers ever truly understand what we're saying?(Os computadores algum dia vĂŁo entender verdadeiramente o que estamos dizendo?);
*Alternativa (B): trustful=confident ➜ "confiante", "seguro";
*Alternativa (C): careless ➜ "descuidado", "desatento", "negligente";
*Alternativa (D): annoyed ➜ "aborrecido, "irritado";
*Alternativa (E): confident=trustful ➜ "confiante" ,"seguro";
(B) trustful;
(C) careless;
(D) annoyed;
(E) confident.
đ Gabarito A
O tĂtulo do Texto I revela que o autor deste texto Ă©/estĂĄ...
*Alternativa (A): inseguro;(O tĂtulo Ă© um questionamento que transmite incerteza, dĂșvida quanto evolução da tecnologia e da ciĂȘncia e os computadores ➜ Will computers ever truly understand what we're saying?(Os computadores algum dia vĂŁo entender verdadeiramente o que estamos dizendo?);
*
*
*
*
02 – (FGV-2016-IBGE-ANALISTA)
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.
(B) T – F – T;
(C) F – F – T;
(D) F – T – F;
(E) T – T – F.
đ ComentĂĄrios e Gabarito E
Com base no resumo fornecido pelo Texto I, marque as sentenças abaixo como TRUE (T) ou FALSE (F).
*Alternativa (T): As pistas(dicas) contextuais ainda nĂŁo sĂŁo levadas em conta pelos computadores.(informação no 2Âș parĂĄgrafo).
*Alternativa (T): Os computadores sĂŁo duvidosos porque eles focam nos padrĂ”es de linguagem.(informação no 1Âș parĂĄgrafo)
*
Veja as ideias contextuais dos 1Âș e 2Âș parĂĄgrafos:
➦IDEIA CONTEXTUAL do 2Âș parĂĄgrafo ➜ Os computadores NĂO LEVAM EM CONTA o contexto de uma conversa do jeito como os humanos fazem.
"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."
(Da Siri, da Apple, ao robĂŽ Asimo, da Honda, as mĂĄquinas parecem estar cada vez melhores na comunicação com os humanos. Mas alguns neurocientistas advertem que os computadores de hoje nunca entenderĂŁo verdadeiramente o que estamos dizendo porque ELES NĂO LEVAM EM CONTA o contexto de uma conversa como as pessoas fazem.)
➦IDEIA CONTEXTUAL do 1Âș parĂĄgrafo ➜ Os computadores ficam duvidosos porque eles focam nos padrĂ”es de linguagem, ou seja, porque eles julgam o significado observando a regularidade estatĂstica de uma palavra.
"[...] "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.
(Se vocĂȘ acha que os computadores estĂŁo se aproximando rapidamente da verdadeira comunicação humana, pense novamente. Computadores como o Siri muitas vezes se confundem porque julgam o significado observando a regularidade estatĂstica de uma palavra. Isso Ă© diferente dos humanos, para quem o contexto Ă© mais importante do que a palavra ou o sinal, segundo um pesquisador que inventou um jogo de comunicação que permite apenas sinais nĂŁo-verbais e o usou para identificar regiĂ”es do cĂ©rebro onde a compreensĂŁo mĂștua acontece.)
➦IDEIA CONTEXTUAL do 1Âș parĂĄgrafo ➜ foi inventado um jogo de comunicação que permite apenas SINAIS NĂO-VERBAIS e o usou para identificar regiĂ”es do cĂ©rebro onde a compreensĂŁo mĂștua acontece, ou seja, entre os humanos, hĂĄ comunicação nĂŁo–verbal que conta muito na interpretação do que Ă© dito.
"[...] 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.
(Isso Ă© diferente dos humanos, para quem o contexto Ă© mais importante do que a palavra ou o sinal, segundo um pesquisador que INVENTOU UM JOGO DE COMUNICAĂĂO que permite apenas sinais nĂŁo-verbais e o usou para identificar regiĂ”es do cĂ©rebro onde a compreensĂŁo mĂștua acontece.)
03 – (FGV-2016-IBGE-ANALISTA)
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.
đ ComentĂĄrios e Gabarito D
Segundo os pesquisadores da Universidade da CalifĂłrnia, Berkeley:
*
*
*
*Alternativa (D): as pessoas podem se comunicar sem usar palavras reais;(O texto fala da possibilidade da comunicação não-verbal entre os humanos.)
*
04 – (FGV-2016-IBGE-ANALISTA)
If you are holding a fishing pole, the word "bank" means a:(A) safe;
(B) seat;
(C) boat;
(D) building;
(E) coastline.
đ ComentĂĄrios e Gabarito E
*Se vocĂȘ estiver segurando uma vara de pesca, a palavra BANK significa um/uma ...
*
*
*
*
*Alternativa (E): litoral/costa.
đŽ "bank" em um contexto de litoral onde uma pessoa segura uma vara de pescar, refere-se a um "BANCO de areia".
➦IDEIA CONTEXTUAL do 5Âș parĂĄgrafo ➜ as palavras podem ter diferentes sentidos de acordo com o contexto e oferece como exemplo a palavra BANK: Se o contexto Ă© uma pessoa segurando uma vara de pescar (fishing pole), o sentido de BANK Ă© banco de areia. Em outra situação, uma pessoa segurando um cartĂŁo de crĂ©dito, o significado de BANK Ă© banco(BB, CEF, etc):
"[...] 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."
(A palavra "banco", por exemplo, seria interpretada de uma forma se vocĂȘ estivesse segurando um cartĂŁo de crĂ©dito, mas de uma maneira diferente, se estivesse segurando uma vara de pescar. Sem contexto, fazer um "V" com dois dedos pode significar vitĂłria, o nĂșmero dois, ou "estes sĂŁo os dois dedos que eu quebrei.)
05 – (FGV-2016-IBGE-ANALISTA)
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.
đ ComentĂĄrios e Gabarito B
TĂPICO - Uso do "SO" para se referir a algo dito anteriormente. :
A palavra SO em "perhaps more SO than the words and signals"('talvez mais do que as palavras e sinais) Ă© usada para se referir a algo jĂĄ indicado no Texto I. Neste contexto, ele se refere a:
*Alternativa (A): chave;
*Alternativa (B): crucial/decisivo;
*Alternativa (C): sutilezas;
Alternativa (E): comunicação.
➦Ăltimo parĂĄgrafo: SO ➜ crucial.
"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."
(Todas essas sutilezas sĂŁo CRUCIAIS para a compreensĂŁo mĂștua, disse Stolk, talvez mais CRUCIAIS que as palavras e sinais que os computadores e muitos neurocientistas focalizam como a chave para a comunicação. Na verdade, podemos entender um ao outro sem linguagem, sem palavras. e sinais que jĂĄ tĂȘm um significado compartilhado.)
(B) crucial;
(C) subtleties;
(D) understanding;
(E) communication.
đ ComentĂĄrios e Gabarito B
A palavra SO em "perhaps more SO than the words and signals"('talvez mais do que as palavras e sinais) Ă© usada para se referir a algo jĂĄ indicado no Texto I. Neste contexto, ele se refere a:
*
*Alternativa (B): crucial/decisivo;
*
*Alternativa (D): compreensĂŁo;
*➦Ăltimo parĂĄgrafo: SO ➜ crucial.
"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."
(Todas essas sutilezas sĂŁo CRUCIAIS para a compreensĂŁo mĂștua, disse Stolk, talvez mais CRUCIAIS que as palavras e sinais que os computadores e muitos neurocientistas focalizam como a chave para a comunicação. Na verdade, podemos entender um ao outro sem linguagem, sem palavras. e sinais que jĂĄ tĂȘm um significado compartilhado.)
➧ TEXT II:
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.
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)
06 – (FGV-2016-IBGE-ANALISTA)
The use of the phrase 'the backlash' in the title of Text IImeans the:
(A) backing of;
(B) support for;
(C) decision for;
(D) resistance to;
(E) overpowering of.
đ ComentĂĄrios e Gabarito D
TĂPICO - VOCABULĂRIO CONTEXTUAL :
O uso da frase "a reação" no tĂtulo do Texto II significa que:
*Alternativa (A): backing of ➜ APOIO A;
*Alternativa (B): support for➜ APOIO PARA;
*Alternativa (C): decision for ➜ DECISĂO A FAVOR;
*Alternativa (D): resistance to➜ RESISTĂNCIA A;
*Alternativa (E): overpowering of ➜ SUBJUGAĂĂO DE.
(B) support for;
(C) decision for;
(D) resistance to;
(E) overpowering of.
đ ComentĂĄrios e Gabarito D
O uso da frase "a reação" no tĂtulo do Texto II significa que:
*
*
*
*
*
07 – (FGV-2016-IBGE-ANALISTA)
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.
(C) clear views; updated assumptions; weak associations;
(D) objective approaches; dated models; genuine connections;
(E) scientific impartiality; unfounded theories; strong relations.
đ ComentĂĄrios e Gabarito B
Os trĂȘs principais argumentos contra grandes dados levantados pelo Texto II no segundo parĂĄgrafo sĂŁo:
*Alternativa (A): grandes nĂșmeros; antigas teorias; relaçÔes consistentes;
*Alternativa (B): parcialidade intrĂnseca; conceitos desatualizados; ligaçÔes casuais;*
*
*
➦IDEIAS CONTEXTUAIS do 2Âș parĂĄgrafo: BIASES(Preconceitos/InclinaçÔes ou parcialidade intrĂnseca), THEORY IS OBSOLETE(teoria estĂĄ obsoleta ou conceitos desatualizados), THE RISK OF SPURIOUS CORRELATIONS(risco de correlaçÔes espĂșrias ou ligaçÔes casuais)
"[...] 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."
(As crĂticas caem em trĂȘs ĂĄreas que nĂŁo sĂŁo intrĂnsecas aos grandes dados em si, mas sĂŁo endĂȘmicas Ă anĂĄlise de dados e tĂȘm algum mĂ©rito. Primeiro, hĂĄ PRECONCEITOS/INCLINAĂĂES/PARCIALIDADES inerentes aos dados que nĂŁo devem ser ignorados. Isso Ă© inegavelmente o caso. Em segundo lugar, alguns proponentes do big data afirmaram que a TEORIA (isto Ă©, modelos generalizĂĄveis sobre como o mundo funciona) ESTĂ OBSOLETA. De fato, o conhecimento da ĂĄrea de assunto permanece necessĂĄrio mesmo quando se lida com grandes conjuntos de dados. Terceiro, o RISCO DE CORRELAĂĂES ESPĂRIAS - associaçÔes que sĂŁo estatisticamente robustas, mas acontecem apenas por acaso - aumenta com mais dados. Embora existam novas tĂ©cnicas estatĂsticas para identificar e banir correlaçÔes espĂșrias, como a execução de muitos testes contra subconjuntos de dados, isso sempre serĂĄ um problema.)
08 – (FGV-2016-IBGE-ANALISTA)
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.
đ ComentĂĄrios e Gabarito C
A forma base, o passado e o particĂpio passado do verbo TO FALL ➜ fall-fell-fallen.
09 – (FGV-2016-IBGE-ANALISTA)
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.
đ ComentĂĄrios e Gabarito B
TĂPICO - VOCABULĂRIO CONTEXTUAL :
Quando o Texto II menciona "grumblers" em 'to face the grumblers'(enfrentar os reclamÔes), refere-se:
*Alternativa (A): cientistas que usam muitos testes;
*Alternativa (B): pessoas que murmuram queixas;
*Alternativa (C): aqueles que apoiam grandes conjuntos de dados;
*Alternativa (D): estatĂsticos que prometem resultados sĂłlidos;
*Alternativa (E): pesquisadores que trabalham com a internet.
"[...] 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."
(Por trås da grande repercussão dos dados estå o ciclo clåssico do hype, no qual os primeiros defensores de uma tecnologia fazem afirmaçÔes excessivamente grandiosas, as pessoas lançam flechas quando essas promessas caem, mas a tecnologia acaba transformando o mundo, embora não necessariamente da maneira esperada pelos especialistas. Aconteceu com a web, a televisão, o rådio, o cinema e o telégrafo antes dele. Agora é simplesmente a vez do big data para enfrentar os resmungÔes.)
(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.
đ ComentĂĄrios e Gabarito B
Quando o Texto II menciona "grumblers" em 'to face the grumblers'(enfrentar os reclamÔes), refere-se:
*
*Alternativa (B): pessoas que murmuram queixas;
*
*
*
"[...] 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."
(Por trås da grande repercussão dos dados estå o ciclo clåssico do hype, no qual os primeiros defensores de uma tecnologia fazem afirmaçÔes excessivamente grandiosas, as pessoas lançam flechas quando essas promessas caem, mas a tecnologia acaba transformando o mundo, embora não necessariamente da maneira esperada pelos especialistas. Aconteceu com a web, a televisão, o rådio, o cinema e o telégrafo antes dele. Agora é simplesmente a vez do big data para enfrentar os resmungÔes.)
10 – (FGV-2016-IBGE-ANALISTA)
The phrase 'lots of data to chew on' in Text IImakes use of figurative language and shares some common characteristics with:
(A) eating;
(B) drawing;
(C) chatting;
(D) thinking;
(E) counting.
đ ComentĂĄrios e Gabarito A
A frase "muitos dados para mascarar" no Texto II faz uso da linguagem figurativa e compartilha algumas caracterĂsticas comuns com:
*
*
*
*
*
"[...] 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."
(Big data refere-se Ă ideia de que a sociedade pode fazer coisas com um grande volume de dados que nĂŁo eram possĂveis quando se trabalha com quantidades menores. O termo foi originalmente aplicado hĂĄ uma dĂ©cada a grandes conjuntos de dados de astrofĂsica, genĂŽmica e mecanismos de busca na Internet, e a sistemas de aprendizado de mĂĄquina (para reconhecimento de voz e tradução, por exemplo) que funcionam bem apenas quando recebem muitos dados para serem usados.)
(Big data refere-se Ă ideia de que a sociedade pode fazer coisas com um grande volume de dados que nĂŁo eram possĂveis quando se trabalha com quantidades menores. O termo foi originalmente aplicado hĂĄ uma dĂ©cada a grandes conjuntos de dados de astrofĂsica, genĂŽmica e mecanismos de busca na Internet, e a sistemas de aprendizado de mĂĄquina (para reconhecimento de voz e tradução, por exemplo) que funcionam bem apenas quando recebem muitos dados para serem usados.)





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