Questões de Inglês

No último quadrinho, conclui-se que, ao dizer I’m not, o personagem

No segundo quadrinho da tirinha, o personagem diz que

No primeiro quadrinho da tirinha, um dos personagens comunica a sua decisão de

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25^th 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 yearsearlier, “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.

O modal verb should em “People should develop skills that complement” (terceiro parágrafo) expressa a ideia de

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25^th 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 yearsearlier, “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.

A respeito das tarefas que as máquinas podem desempenhar, e de acordo com o que é mencionado diretamente no segundo parágrafo, pode-se afirmar corretamente que

Learn ‘n’ go

How quickly can people learn new skills?

Jan 25^th 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 yearsearlier, “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.

De acordo com o primeiro parágrafo, em 2012, Erik Brynjolfsson e Andrew McAfee