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Ano: 2023
Banca:
COTEC
Órgão:
Prefeitura de Unaí - MG
Prova:
COTEC - 2023 - Prefeitura de Unaí - MG - Professor de Educação Básica II – Inglês |
Q2328565
Inglês
Texto associado
INSTRUÇÃO: Leia o texto a seguir para responder à questão.
What does the hurricane scale tell us?
Hurricanes are categorized by their wind speeds on the Saffir-Simpson Hurricane Scale. The scale was first
developed by Herb Saffir, a structural engineer, and Bob Simpson, a meteorologist.
Hurricanes are split into five categories based on the wind speeds they produce.
To be considered a “major” hurricane, according to the National Hurricane Center, a storm must reach Category 3 or
above.
A hurricane’s strength matters because it helps meteorologists give residents in its path an idea of what type of
damage is possible.
A Category 2 hurricane, for example, has the potential to cause major roof damage to homes, snap or uproot
shallowly rooted trees, and knock out power in an area for days to weeks.
When a hurricane reaches Category 5 strength, the center can predict that “catastrophic damage will occur,”
according to the Saffir-Simpson scale. Winds from a Category 5 hurricane can destroy homes, fell trees and power
lines and possibly leave an area without power for weeks or months.
Because the hurricane category scale is based only on wind speeds, a number of factors are not considered.
“Wind is only one of four hazards, four primary hazards, associated with a tropical cyclone,” said Dr. Michael Brennan,
the acting deputy director of the National Hurricane Center, using the broader term for a hurricane. “You can also
have rainfall and flooding, storm surge, tornadoes, rip currents.”
Other hurricane-related dangers can occur after the storms have moved through an area.
When an affected area loses power, for example, many people often turn to portable generators to produce electricity.
But when they are used improperly, they can lead to carbon monoxide poisoning.
And a weak Category 1 hurricane, or even a tropical storm, can still cause serious damage. A tropical storm can have
wind speeds between 39 m.p.h. and 73 m.p.h. If the storm strengthens and produces winds up to 74 m.p.h., it
becomes a Category 1 hurricane.
Disponível em: https://www.nytimes.com/2023/08/29/climate/hurricane-categories-scale-saffir-simpson.html. Acesso em: 27 set. 2023. Adaptado.
O furacão é um fenômeno atmosférico constituído por ventos giratórios que se deslocam em alta velocidade formado
em regiões oceânicas, especialmente em zonas tropicais, constituídas por elevados níveis de umidade. De acordo
com a reportagem, pode-se considerar:
Ano: 2023
Banca:
FGV
Órgão:
Câmara dos Deputados
Prova:
FGV - 2023 - Câmara dos Deputados - Analista Legislativo - Informática Legislativa - Manhã |
Q2326035
Inglês
Texto associado
Read Text II and answer the question that follow it
Text II
Boy cries Wolf
After astonishing breakthroughs in artificial intelligence,
many people worry that they will end up on the economic
scrapheap. Global Google searches for “is my job safe?” have
doubled in recent months, as people fear that they will be
replaced with large language models (LLMS). Some evidence
suggests that widespread disruption is coming. In a recent paper
Tyna Eloundou of OpenAI and colleagues say that “around 80% of
the US workforce could have at least 10% of their work tasks
affected by the introduction of LLMS”. Another paper suggests
that legal services, accountancy and travel agencies will face
unprecedented upheaval.
Economists, however, tend to enjoy making predictions about
automation more than they enjoy testing them. In the early
2010s many of them loudly predicted that robots would kill jobs
by the millions, only to fall silent when employment rates across
the rich world rose to all-time highs. Few of the doom-mongers
have a good explanation for why countries with the highest rates
of tech usage around the globe, such as Japan, Singapore and
South Korea, consistently have among the lowest rates of
unemployment.
Here we introduce our first attempt at tracking AI’s impact on
jobs. Using American data on employment by occupation, we
single out white-collar workers. These include people working in
everything from back-office support and financial operations to
copy-writers. White-collar roles are thought to be especially
vulnerable to generative AI, which is becoming ever better at
logical reasoning and creativity.
However, there is as yet little evidence of an AI hit to
employment. In the spring of 2020 white-collar jobs rose as a
share of the total, as many people in service occupations lost
their job at the start of the covid-19 pandemic. The white-collar
share is lower today, as leisure and hospitality have recovered.
Yet in the past year the share of employment in professions
supposedly at risk from generative AI has risen by half a
percentage point.
It is, of course, early days. Few firms yet use generative-AI
tools at scale, so the impact on jobs could merely be delayed.
Another possibility, however, is that these new technologies will
end up destroying only a small number of roles. While AI may be
efficient at some tasks, it may be less good at others, such as
management and working out what others need.
AI could even have a positive effect on jobs. If workers using
it become more efficient, profits at their company could rise
which would then allow bosses to ramp up hiring. A recent survey
by Experis, an IT-recruitment firm, points to this possibility. More
than half of Britain’s employers expect AI technologies to have a
positive impact on their headcount over the next two years, it
finds.
To see how it all shakes out, we will publish updates to this
analysis every few months. But for now, a jobs apocalypse seems
a way off.
From The Economist June 17th 2023, p. 71
In the last sentence of the first paragraph, when the paper
mentions an “upheaval”, it refers to the possibility of a future
Ano: 2023
Banca:
FGV
Órgão:
Câmara dos Deputados
Prova:
FGV - 2023 - Câmara dos Deputados - Analista Legislativo - Informática Legislativa - Manhã |
Q2326034
Inglês
Texto associado
Read Text II and answer the question that follow it
Text II
Boy cries Wolf
After astonishing breakthroughs in artificial intelligence,
many people worry that they will end up on the economic
scrapheap. Global Google searches for “is my job safe?” have
doubled in recent months, as people fear that they will be
replaced with large language models (LLMS). Some evidence
suggests that widespread disruption is coming. In a recent paper
Tyna Eloundou of OpenAI and colleagues say that “around 80% of
the US workforce could have at least 10% of their work tasks
affected by the introduction of LLMS”. Another paper suggests
that legal services, accountancy and travel agencies will face
unprecedented upheaval.
Economists, however, tend to enjoy making predictions about
automation more than they enjoy testing them. In the early
2010s many of them loudly predicted that robots would kill jobs
by the millions, only to fall silent when employment rates across
the rich world rose to all-time highs. Few of the doom-mongers
have a good explanation for why countries with the highest rates
of tech usage around the globe, such as Japan, Singapore and
South Korea, consistently have among the lowest rates of
unemployment.
Here we introduce our first attempt at tracking AI’s impact on
jobs. Using American data on employment by occupation, we
single out white-collar workers. These include people working in
everything from back-office support and financial operations to
copy-writers. White-collar roles are thought to be especially
vulnerable to generative AI, which is becoming ever better at
logical reasoning and creativity.
However, there is as yet little evidence of an AI hit to
employment. In the spring of 2020 white-collar jobs rose as a
share of the total, as many people in service occupations lost
their job at the start of the covid-19 pandemic. The white-collar
share is lower today, as leisure and hospitality have recovered.
Yet in the past year the share of employment in professions
supposedly at risk from generative AI has risen by half a
percentage point.
It is, of course, early days. Few firms yet use generative-AI
tools at scale, so the impact on jobs could merely be delayed.
Another possibility, however, is that these new technologies will
end up destroying only a small number of roles. While AI may be
efficient at some tasks, it may be less good at others, such as
management and working out what others need.
AI could even have a positive effect on jobs. If workers using
it become more efficient, profits at their company could rise
which would then allow bosses to ramp up hiring. A recent survey
by Experis, an IT-recruitment firm, points to this possibility. More
than half of Britain’s employers expect AI technologies to have a
positive impact on their headcount over the next two years, it
finds.
To see how it all shakes out, we will publish updates to this
analysis every few months. But for now, a jobs apocalypse seems
a way off.
From The Economist June 17th 2023, p. 71
By calling some economists “doom-mongers” in “Few of the
doom-mongers have a good explanation” (2nd paragraph), the
authors
Ano: 2023
Banca:
FGV
Órgão:
Câmara dos Deputados
Prova:
FGV - 2023 - Câmara dos Deputados - Analista Legislativo - Informática Legislativa - Manhã |
Q2326033
Inglês
Texto associado
Read Text II and answer the question that follow it
Text II
Boy cries Wolf
After astonishing breakthroughs in artificial intelligence,
many people worry that they will end up on the economic
scrapheap. Global Google searches for “is my job safe?” have
doubled in recent months, as people fear that they will be
replaced with large language models (LLMS). Some evidence
suggests that widespread disruption is coming. In a recent paper
Tyna Eloundou of OpenAI and colleagues say that “around 80% of
the US workforce could have at least 10% of their work tasks
affected by the introduction of LLMS”. Another paper suggests
that legal services, accountancy and travel agencies will face
unprecedented upheaval.
Economists, however, tend to enjoy making predictions about
automation more than they enjoy testing them. In the early
2010s many of them loudly predicted that robots would kill jobs
by the millions, only to fall silent when employment rates across
the rich world rose to all-time highs. Few of the doom-mongers
have a good explanation for why countries with the highest rates
of tech usage around the globe, such as Japan, Singapore and
South Korea, consistently have among the lowest rates of
unemployment.
Here we introduce our first attempt at tracking AI’s impact on
jobs. Using American data on employment by occupation, we
single out white-collar workers. These include people working in
everything from back-office support and financial operations to
copy-writers. White-collar roles are thought to be especially
vulnerable to generative AI, which is becoming ever better at
logical reasoning and creativity.
However, there is as yet little evidence of an AI hit to
employment. In the spring of 2020 white-collar jobs rose as a
share of the total, as many people in service occupations lost
their job at the start of the covid-19 pandemic. The white-collar
share is lower today, as leisure and hospitality have recovered.
Yet in the past year the share of employment in professions
supposedly at risk from generative AI has risen by half a
percentage point.
It is, of course, early days. Few firms yet use generative-AI
tools at scale, so the impact on jobs could merely be delayed.
Another possibility, however, is that these new technologies will
end up destroying only a small number of roles. While AI may be
efficient at some tasks, it may be less good at others, such as
management and working out what others need.
AI could even have a positive effect on jobs. If workers using
it become more efficient, profits at their company could rise
which would then allow bosses to ramp up hiring. A recent survey
by Experis, an IT-recruitment firm, points to this possibility. More
than half of Britain’s employers expect AI technologies to have a
positive impact on their headcount over the next two years, it
finds.
To see how it all shakes out, we will publish updates to this
analysis every few months. But for now, a jobs apocalypse seems
a way off.
From The Economist June 17th 2023, p. 71
If someone ends up “on the economic scrapheap” (1st paragraph),
this person will feel
Ano: 2023
Banca:
CESPE / CEBRASPE
Órgão:
MRE
Prova:
CESPE / CEBRASPE - 2023 - MRE - Oficial de Chancelaria |
Q2324514
Inglês
Texto associado
Text 1A2-III
In January 1948, before three pistol shots put an end to his
life, Gandhi had been on the political stage for more than fifty
years. He had inspired two generations of Indian patriots, shaken
an empire and sparked off a revolution which was to change the
face of Africa and Asia. To millions of his own people, he was
the Mahatma — the great soul — whose sacred glimpse was a
reward in itself.
By the end of 1947 he had lived down much of the
suspicion, ridicule and opposition which he had to face, when he
first raised the banner of revolt against racial exclusiveness and
imperial domination. His ideas, once dismissed as quaint and
utopian, had begun to strike answering chords in some of the
finest minds in the world. “Generations to come, it may be,”
Einstein had said of Gandhi in July 1944, “will scarcely believe
that such a one as this ever in flesh and blood walked upon
earth.”
Though his life had been a continual unfolding of an
endless drama, Gandhi himself seemed the least dramatic of men.
It would be difficult to imagine a man with fewer trappings of
political eminence or with less of the popular image of a heroic
figure. With his loin cloth, steel-rimmed glasses, rough sandals, a
toothless smile and a voice which rarely rose above a whisper, he
had a disarming humility. He was, if one were to use the famous
words of the Buddha, a man who had “by rousing himself, by
earnestness, by restraint and control, made for himself an island
which no flood could overwhelm.”
Gandhi’s deepest strivings were spiritual, but he did
not — as had been the custom in his country — retire to a cave in
the Himalayas to seek his salvation. He carried his cave within
him. He did not know, he said, any religion apart from human
activity; the spiritual law did not work in a vacuum, but
expressed itself through the ordinary activities of life.
This aspiration to relate the spirit of religion to the
problems of everyday life runs like a thread through Gandhi’s
career: his uneventful childhood, the slow unfolding and the
near-failure of his youth, the reluctant plunge into the politics of
Natal, the long unequal struggle in South Africa, and the
vicissitudes of the Indian struggle for freedom, which under his
leadership was to culminate in a triumph not untinged with
tragedy.
B. R. Nanda. Gandhi: a pictorial biography, 1972 (adapted).
The word “quaint” (second sentence of the second paragraph), in
its use in text 1A2-III, means
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