Questões de Linguagens do ENEM

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25th 2014 – from the print edition

      In 2012, Erik Brynjolfsson and Andrew McAfee took a ride in one of Google’s driverless cars. The car’s performance, they report, was flawless, boring and, above all, “weird”. Only a few years earlier, “We were sure that computers would not be able to drive cars.” Only humans, they thought, could make sense of the countless, shifting patterns of driving a car – with oncoming1 traffic, changing lights and wayward2 jaywalkers3 .

      Machines have mastered driving. And not just driving. In ways that are only now becoming apparent, the authors argue, machines can forecast home prices, design beer bottles, teach at universities, grade exams and do countless other things better and more cheaply than humans. (…)

      This will have one principal good consequence, and one bad. The good is bounty4 . Households will spend less on groceries, utilities and clothing; the deaf will be able to hear, the blind to see. The bad is spread5 . The gap is growing between the lucky few whose abilities and skills are enhanced6 by technology, and the far more numerous middle-skilled people competing for the remaining7 jobs that machines cannot do, such as folding towels and waiting at tables. (…) People should develop skills that complement, rather than compete with computers, such as idea generation and complex communication. (…)

<http://tinyurl.com/m2zmazg>Acesso em: 27.07.2015. Adaptado.

Glossário

1oncoming: iminente; próximo.

2wayward: desobediente; instável.

3jaywalker: pedestre imprudente.

4bounty: recompensa.

5spread: propagação; extensão.

6enhanced: aprimorado(a).

7remaining: remanescente.

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25th 2014 – from the print edition

      In 2012, Erik Brynjolfsson and Andrew McAfee took a ride in one of Google’s driverless cars. The car’s performance, they report, was flawless, boring and, above all, “weird”. Only a few years earlier, “We were sure that computers would not be able to drive cars.” Only humans, they thought, could make sense of the countless, shifting patterns of driving a car – with oncoming1 traffic, changing lights and wayward2 jaywalkers3 .

      Machines have mastered driving. And not just driving. In ways that are only now becoming apparent, the authors argue, machines can forecast home prices, design beer bottles, teach at universities, grade exams and do countless other things better and more cheaply than humans. (…)

      This will have one principal good consequence, and one bad. The good is bounty4 . Households will spend less on groceries, utilities and clothing; the deaf will be able to hear, the blind to see. The bad is spread5 . The gap is growing between the lucky few whose abilities and skills are enhanced6 by technology, and the far more numerous middle-skilled people competing for the remaining7 jobs that machines cannot do, such as folding towels and waiting at tables. (…) People should develop skills that complement, rather than compete with computers, such as idea generation and complex communication. (…)

<http://tinyurl.com/m2zmazg>Acesso em: 27.07.2015. Adaptado.

Glossário

1oncoming: iminente; próximo.

2wayward: desobediente; instável.

3jaywalker: pedestre imprudente.

4bounty: recompensa.

5spread: propagação; extensão.

6enhanced: aprimorado(a).

7remaining: remanescente.

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25th 2014 – from the print edition

      In 2012, Erik Brynjolfsson and Andrew McAfee took a ride in one of Google’s driverless cars. The car’s performance, they report, was flawless, boring and, above all, “weird”. Only a few years earlier, “We were sure that computers would not be able to drive cars.” Only humans, they thought, could make sense of the countless, shifting patterns of driving a car – with oncoming1 traffic, changing lights and wayward2 jaywalkers3 .

      Machines have mastered driving. And not just driving. In ways that are only now becoming apparent, the authors argue, machines can forecast home prices, design beer bottles, teach at universities, grade exams and do countless other things better and more cheaply than humans. (…)

      This will have one principal good consequence, and one bad. The good is bounty4 . Households will spend less on groceries, utilities and clothing; the deaf will be able to hear, the blind to see. The bad is spread5 . The gap is growing between the lucky few whose abilities and skills are enhanced6 by technology, and the far more numerous middle-skilled people competing for the remaining7 jobs that machines cannot do, such as folding towels and waiting at tables. (…) People should develop skills that complement, rather than compete with computers, such as idea generation and complex communication. (…)

<http://tinyurl.com/m2zmazg>Acesso em: 27.07.2015. Adaptado.

Glossário

1oncoming: iminente; próximo.

2wayward: desobediente; instável.

3jaywalker: pedestre imprudente.

4bounty: recompensa.

5spread: propagação; extensão.

6enhanced: aprimorado(a).

7remaining: remanescente.

Pai? Mito do passado.

Esta pergunta e sua resposta, de acordo com o conteúdo da crônica, sintetizam um dos argumentos que, aparentemente, fundamentaram o projeto:

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25th 2014 – from the print edition

      In 2012, Erik Brynjolfsson and Andrew McAfee took a ride in one of Google’s driverless cars. The car’s performance, they report, was flawless, boring and, above all, “weird”. Only a few years earlier, “We were sure that computers would not be able to drive cars.” Only humans, they thought, could make sense of the countless, shifting patterns of driving a car – with oncoming1 traffic, changing lights and wayward2 jaywalkers3 .

      Machines have mastered driving. And not just driving. In ways that are only now becoming apparent, the authors argue, machines can forecast home prices, design beer bottles, teach at universities, grade exams and do countless other things better and more cheaply than humans. (…)

      This will have one principal good consequence, and one bad. The good is bounty4 . Households will spend less on groceries, utilities and clothing; the deaf will be able to hear, the blind to see. The bad is spread5 . The gap is growing between the lucky few whose abilities and skills are enhanced6 by technology, and the far more numerous middle-skilled people competing for the remaining7 jobs that machines cannot do, such as folding towels and waiting at tables. (…) People should develop skills that complement, rather than compete with computers, such as idea generation and complex communication. (…)

<http://tinyurl.com/m2zmazg>Acesso em: 27.07.2015. Adaptado.

Glossário

1oncoming: iminente; próximo.

2wayward: desobediente; instável.

3jaywalker: pedestre imprudente.

4bounty: recompensa.

5spread: propagação; extensão.

6enhanced: aprimorado(a).

7remaining: remanescente.

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25th 2014 – from the print edition

      In 2012, Erik Brynjolfsson and Andrew McAfee took a ride in one of Google’s driverless cars. The car’s performance, they report, was flawless, boring and, above all, “weird”. Only a few years earlier, “We were sure that computers would not be able to drive cars.” Only humans, they thought, could make sense of the countless, shifting patterns of driving a car – with oncoming1 traffic, changing lights and wayward2 jaywalkers3 .

      Machines have mastered driving. And not just driving. In ways that are only now becoming apparent, the authors argue, machines can forecast home prices, design beer bottles, teach at universities, grade exams and do countless other things better and more cheaply than humans. (…)

      This will have one principal good consequence, and one bad. The good is bounty4 . Households will spend less on groceries, utilities and clothing; the deaf will be able to hear, the blind to see. The bad is spread5 . The gap is growing between the lucky few whose abilities and skills are enhanced6 by technology, and the far more numerous middle-skilled people competing for the remaining7 jobs that machines cannot do, such as folding towels and waiting at tables. (…) People should develop skills that complement, rather than compete with computers, such as idea generation and complex communication. (…)

<http://tinyurl.com/m2zmazg>Acesso em: 27.07.2015. Adaptado.

Glossário

1oncoming: iminente; próximo.

2wayward: desobediente; instável.

3jaywalker: pedestre imprudente.

4bounty: recompensa.

5spread: propagação; extensão.

6enhanced: aprimorado(a).

7remaining: remanescente.

Instrução: A questão toma por base um fragmento de Glória moribunda, do poeta romântico brasileiro Álvares de Azevedo (1831-1852).

É uma visão medonha uma caveira?

Não tremas de pavor, ergue-a do lodo.

Foi a cabeça ardente de um poeta,

Outrora à sombra dos cabelos loiros.

Quando o reflexo do viver fogoso

Ali dentro animava o pensamento,

Esta fronte era bela. Aqui nas faces

Formosa palidez cobria o rosto;

Nessas órbitas — ocas, denegridas! —

Como era puro seu olhar sombrio!

Agora tudo é cinza. Resta apenas

A caveira que a alma em si guardava,

Como a concha no mar encerra a pérola,

Como a caçoula a mirra incandescente.

Tu outrora talvez desses-lhe um beijo;

Por que repugnas levantá-la agora?

Olha-a comigo! Que espaçosa fronte!

Quanta vida ali dentro fermentava,

Como a seiva nos ramos do arvoredo!

E a sede em fogo das ideias vivas

Onde está? onde foi? Essa alma errante

Que um dia no viver passou cantando,

Como canta na treva um vagabundo,

Perdeu-se acaso no sombrio vento,

Como noturna lâmpada apagou-se?

E a centelha da vida, o eletrismo

Que as fibras tremulantes agitava

Morreu para animar futuras vidas?

Sorris? eu sou um louco. As utopias,

Os sonhos da ciência nada valem.

A vida é um escárnio sem sentido,

Comédia infame que ensanguenta o lodo.

Há talvez um segredo que ela esconde;

Mas esse a morte o sabe e o não revela.

Os túmulos são mudos como o vácuo.

Desde a primeira dor sobre um cadáver,

Quando a primeira mãe entre soluços

Do filho morto os membros apertava

Ao ofegante seio, o peito humano

Caiu tremendo interrogando o túmulo...

E a terra sepulcral não respondia.

                    (Poesias completas, 1962.)