SóProvas

ID
1884790
Banca
FGV
Órgão
IBGE
Ano
2016
Provas
Disciplina
Inglês
Assuntos

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 voice-recognition 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 Second, some proponents of big data have claimed that theory (ie, generalisable models about how the world works) is obsolete.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)

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

Alternativas
Comentários

  • (A) large numbers; old theories; consistent relations; = grandes números; velhas teorias; relações consistentes;

    (B) intrinsic partiality; outdated concepts; casual links; = parcialidade intrinseca; conceitos desatualizados; links casuais.

    biases inherent to data that must not be ignored.= intrinsic partiality = parcialidades inerentes aos dados que não devem ser ignoradas

    theory is obsolete = teoria é obsoleta = outdated concepts = conceitos desatualizados

    causual links = ligações casuais = correlations that happen only by chance = correlações que acontecem apenas por acaso

    A assertiva usa os sinônimos dos argumentos no texto. Essa é a correta.

    (C) clear views; updated assumptions; weak associations; = visões claras; pressupostos atualizados; associações fracas

    (D) objective approaches; dated models; genuine connections; = abordagens objetivas; modelos datados; conecções genuínas;

    (E) scientific impartiality; unfounded theories; strong relations. = imparcialidade científica; teorias infundadas; relações fortes

    GABARITO: B

    FONTE: https://www.estrategiaconcursos.com.br/

  • FONTE: https://www.estrategiaconcursos.com.br/

  • b)intrinsic partiality; outdated concepts; casual links;

    The text snippet that gives away the answer starts at the second sentence, by making the claim that big data is overrated in that the data fed to it is subject to biased intent, followed by the assertion that its underlying framework is passé and loosely-tied associations that are too haphazard to be taken as a serious device for data analysis.

  • Rapaz se você não for fluente em inglês não responde isso nem ferrando....

  • A dica para resolução dessa questão, é aplicar a estratégia de leitura selectivity, a qual  selecionamos apenas o trecho ( segundo parágrafo) necessário do conteúdo para encontrar a informação, por meio do uso de palavras-chave, palavras cognatas e um vocabulário específico.
    Os três principais argumentos contra o banco de dados (big data)  levantados pelo Texto II no segundo parágrafo são:
    A) grandes números; velhas teorias; relações consistentes;
    B) parcialidade intrínseca; conceitos desatualizados; links casuais;
    C) vistas claras; pressupostos atualizados; associações fracas;
    D) abordagens objetivas; modelos datados; conexões genuínas;
    E) imparcialidade científica; teorias infundadas; relações fortes.
    O segundo parágrafo aponta os argumentos contra o banco de dados, usando palavras sinônimas às apresentadas na alternativa B.
    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. [...] Second, some proponents of big data have claimed that theory [...] is obsolete. [...]. Third, the risk of spurious correlations—associations that are statistically robust but happen only by chance —increases with more data[...]

    As críticas recaem em três áreas que não são intrínsecas ao banco de dados em si, mas endêmicas à análise de dados e têm algum mérito. Em primeiro lugar, existem vieses inerentes aos dados que não devem ser ignorados. [...] Em segundo lugar, alguns defensores do banco de dados afirmam que a teoria [...] está obsoleta. [...]Terceiro, o risco de correlações espúrias - associações que são estatisticamente robustas, mas acontecem apenas por acaso - aumenta com mais dados

    Gabarito do Professor: Letra B.