CAT Mock Test- 7 - CAT
66 Questions MCQ Test CAT Mock Test Series - CAT Mock Test- 7
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Read the passage carefully and answer the following questions:
It is almost universally agreed that the persistence of extreme poverty in many parts of the world is a bad thing. It is less well-agreed, even among philosophers, what should be done about it and by who. An influential movement founded by the philosopher Peter Singer argues that we should each try to do the best we can by donating our surplus income to charities that help those in greatest need. This ‘effective altruism’ movement has two components: i) encouraging individuals in the rich world to donate more; and ii) encouraging us to donate more rationally, to the organisations most efficient at translating those donations into gains in human well-being.
The problem with the first component of effective altruism was that it focuses on the internal moral economy of the giver rather than on the real-world problems our giving is supposed to address. The second component of effective altruism might not seem to have that problem because it is explicitly concerned with maximising the amount of good that each unit of resources achieves. However, this concern is better understood as efficiency than as effectiveness. This might seem an innocuous distinction since efficiency is about how we ought to get things done, i.e. a way of being effective. However, there are significant consequences for practical reasoning in the kind of cases effective altruism is concerned with.
If one takes the efficiency view promoted by the effective altruism movement then one assumes a fixed set of resources and the choice of which goal to aim for follows from a calculation of how to maximise the expected value those resources can generate; i.e. the means justifies the end. This should ensure that your donation will achieve the most good, which is to say that you have done the best possible job of giving. However, despite doing so well at the task effective altruism has set you, if you step back you will notice that very little has actually been achieved. The total amount of good we can achieve with our donations is limited to the partial alleviation of some of the symptoms of extreme poverty, symptoms that will recur so long as poverty persists. But effective altruism supplies no plan for the elimination of poverty itself.
The underlying problem is that effective altruism's distinctive combination of political pessimism and consumer-hero hubris forecloses the consideration of promising possibilities for achieving far more good. Singer and other effective altruist philosophers believe that their most likely customers find institutional reform too complicated and political action too impersonal and hit and miss to be attractive. So instead they flatter us by promising that we can literally be life-saving heroes from the comfort of our chairs and using only the super-power of our rich-world wallets.
But it just doesn't work. Singer and others have been making this argument for nearly 50 years, yet the level of private donations remain orders of magnitude below what would be required to eliminate global poverty, however efficiently allocated. Also, it needlessly squanders the most obvious and powerful tool we have: the political sphere and institutions of government that we invented to solve complicated and large collective action problems.
Q. The central idea of the passage is that
It is almost universally agreed that the persistence of extreme poverty in many parts of the world is a bad thing. It is less well-agreed, even among philosophers, what should be done about it and by who. An influential movement founded by the philosopher Peter Singer argues that we should each try to do the best we can by donating our surplus income to charities that help those in greatest need. This ‘effective altruism’ movement has two components: i) encouraging individuals in the rich world to donate more; and ii) encouraging us to donate more rationally, to the organisations most efficient at translating those donations into gains in human well-being.
The problem with the first component of effective altruism was that it focuses on the internal moral economy of the giver rather than on the real-world problems our giving is supposed to address. The second component of effective altruism might not seem to have that problem because it is explicitly concerned with maximising the amount of good that each unit of resources achieves. However, this concern is better understood as efficiency than as effectiveness. This might seem an innocuous distinction since efficiency is about how we ought to get things done, i.e. a way of being effective. However, there are significant consequences for practical reasoning in the kind of cases effective altruism is concerned with.
If one takes the efficiency view promoted by the effective altruism movement then one assumes a fixed set of resources and the choice of which goal to aim for follows from a calculation of how to maximise the expected value those resources can generate; i.e. the means justifies the end. This should ensure that your donation will achieve the most good, which is to say that you have done the best possible job of giving. However, despite doing so well at the task effective altruism has set you, if you step back you will notice that very little has actually been achieved. The total amount of good we can achieve with our donations is limited to the partial alleviation of some of the symptoms of extreme poverty, symptoms that will recur so long as poverty persists. But effective altruism supplies no plan for the elimination of poverty itself.
The underlying problem is that effective altruism's distinctive combination of political pessimism and consumer-hero hubris forecloses the consideration of promising possibilities for achieving far more good. Singer and other effective altruist philosophers believe that their most likely customers find institutional reform too complicated and political action too impersonal and hit and miss to be attractive. So instead they flatter us by promising that we can literally be life-saving heroes from the comfort of our chairs and using only the super-power of our rich-world wallets.
But it just doesn't work. Singer and others have been making this argument for nearly 50 years, yet the level of private donations remain orders of magnitude below what would be required to eliminate global poverty, however efficiently allocated. Also, it needlessly squanders the most obvious and powerful tool we have: the political sphere and institutions of government that we invented to solve complicated and large collective action problems.
Read the passage carefully and answer the following questions:
It is almost universally agreed that the persistence of extreme poverty in many parts of the world is a bad thing. It is less well-agreed, even among philosophers, what should be done about it and by who. An influential movement founded by the philosopher Peter Singer argues that we should each try to do the best we can by donating our surplus income to charities that help those in greatest need. This ‘effective altruism’ movement has two components: i) encouraging individuals in the rich world to donate more; and ii) encouraging us to donate more rationally, to the organisations most efficient at translating those donations into gains in human well-being.
The problem with the first component of effective altruism was that it focuses on the internal moral economy of the giver rather than on the real-world problems our giving is supposed to address. The second component of effective altruism might not seem to have that problem because it is explicitly concerned with maximising the amount of good that each unit of resources achieves. However, this concern is better understood as efficiency than as effectiveness. This might seem an innocuous distinction since efficiency is about how we ought to get things done, i.e. a way of being effective. However, there are significant consequences for practical reasoning in the kind of cases effective altruism is concerned with.
If one takes the efficiency view promoted by the effective altruism movement then one assumes a fixed set of resources and the choice of which goal to aim for follows from a calculation of how to maximise the expected value those resources can generate; i.e. the means justifies the end. This should ensure that your donation will achieve the most good, which is to say that you have done the best possible job of giving. However, despite doing so well at the task effective altruism has set you, if you step back you will notice that very little has actually been achieved. The total amount of good we can achieve with our donations is limited to the partial alleviation of some of the symptoms of extreme poverty, symptoms that will recur so long as poverty persists. But effective altruism supplies no plan for the elimination of poverty itself.
The underlying problem is that effective altruism's distinctive combination of political pessimism and consumer-hero hubris forecloses the consideration of promising possibilities for achieving far more good. Singer and other effective altruist philosophers believe that their most likely customers find institutional reform too complicated and political action too impersonal and hit and miss to be attractive. So instead they flatter us by promising that we can literally be life-saving heroes from the comfort of our chairs and using only the super-power of our rich-world wallets.
But it just doesn't work. Singer and others have been making this argument for nearly 50 years, yet the level of private donations remain orders of magnitude below what would be required to eliminate global poverty, however efficiently allocated. Also, it needlessly squanders the most obvious and powerful tool we have: the political sphere and institutions of government that we invented to solve complicated and large collective action problems.
Q. All of the following options represent causes reflective of effective altruism EXCEPT:
It is almost universally agreed that the persistence of extreme poverty in many parts of the world is a bad thing. It is less well-agreed, even among philosophers, what should be done about it and by who. An influential movement founded by the philosopher Peter Singer argues that we should each try to do the best we can by donating our surplus income to charities that help those in greatest need. This ‘effective altruism’ movement has two components: i) encouraging individuals in the rich world to donate more; and ii) encouraging us to donate more rationally, to the organisations most efficient at translating those donations into gains in human well-being.
The problem with the first component of effective altruism was that it focuses on the internal moral economy of the giver rather than on the real-world problems our giving is supposed to address. The second component of effective altruism might not seem to have that problem because it is explicitly concerned with maximising the amount of good that each unit of resources achieves. However, this concern is better understood as efficiency than as effectiveness. This might seem an innocuous distinction since efficiency is about how we ought to get things done, i.e. a way of being effective. However, there are significant consequences for practical reasoning in the kind of cases effective altruism is concerned with.
If one takes the efficiency view promoted by the effective altruism movement then one assumes a fixed set of resources and the choice of which goal to aim for follows from a calculation of how to maximise the expected value those resources can generate; i.e. the means justifies the end. This should ensure that your donation will achieve the most good, which is to say that you have done the best possible job of giving. However, despite doing so well at the task effective altruism has set you, if you step back you will notice that very little has actually been achieved. The total amount of good we can achieve with our donations is limited to the partial alleviation of some of the symptoms of extreme poverty, symptoms that will recur so long as poverty persists. But effective altruism supplies no plan for the elimination of poverty itself.
The underlying problem is that effective altruism's distinctive combination of political pessimism and consumer-hero hubris forecloses the consideration of promising possibilities for achieving far more good. Singer and other effective altruist philosophers believe that their most likely customers find institutional reform too complicated and political action too impersonal and hit and miss to be attractive. So instead they flatter us by promising that we can literally be life-saving heroes from the comfort of our chairs and using only the super-power of our rich-world wallets.
But it just doesn't work. Singer and others have been making this argument for nearly 50 years, yet the level of private donations remain orders of magnitude below what would be required to eliminate global poverty, however efficiently allocated. Also, it needlessly squanders the most obvious and powerful tool we have: the political sphere and institutions of government that we invented to solve complicated and large collective action problems.
Read the passage carefully and answer the following questions:
It is almost universally agreed that the persistence of extreme poverty in many parts of the world is a bad thing. It is less well-agreed, even among philosophers, what should be done about it and by who. An influential movement founded by the philosopher Peter Singer argues that we should each try to do the best we can by donating our surplus income to charities that help those in greatest need. This ‘effective altruism’ movement has two components: i) encouraging individuals in the rich world to donate more; and ii) encouraging us to donate more rationally, to the organisations most efficient at translating those donations into gains in human well-being.
The problem with the first component of effective altruism was that it focuses on the internal moral economy of the giver rather than on the real-world problems our giving is supposed to address. The second component of effective altruism might not seem to have that problem because it is explicitly concerned with maximising the amount of good that each unit of resources achieves. However, this concern is better understood as efficiency than as effectiveness. This might seem an innocuous distinction since efficiency is about how we ought to get things done, i.e. a way of being effective. However, there are significant consequences for practical reasoning in the kind of cases effective altruism is concerned with.
If one takes the efficiency view promoted by the effective altruism movement then one assumes a fixed set of resources and the choice of which goal to aim for follows from a calculation of how to maximise the expected value those resources can generate; i.e. the means justifies the end. This should ensure that your donation will achieve the most good, which is to say that you have done the best possible job of giving. However, despite doing so well at the task effective altruism has set you, if you step back you will notice that very little has actually been achieved. The total amount of good we can achieve with our donations is limited to the partial alleviation of some of the symptoms of extreme poverty, symptoms that will recur so long as poverty persists. But effective altruism supplies no plan for the elimination of poverty itself.
The underlying problem is that effective altruism's distinctive combination of political pessimism and consumer-hero hubris forecloses the consideration of promising possibilities for achieving far more good. Singer and other effective altruist philosophers believe that their most likely customers find institutional reform too complicated and political action too impersonal and hit and miss to be attractive. So instead they flatter us by promising that we can literally be life-saving heroes from the comfort of our chairs and using only the super-power of our rich-world wallets.
But it just doesn't work. Singer and others have been making this argument for nearly 50 years, yet the level of private donations remain orders of magnitude below what would be required to eliminate global poverty, however efficiently allocated. Also, it needlessly squanders the most obvious and powerful tool we have: the political sphere and institutions of government that we invented to solve complicated and large collective action problems.
Q. The passage makes all of the following claims about effective altruism, EXCEPT
It is almost universally agreed that the persistence of extreme poverty in many parts of the world is a bad thing. It is less well-agreed, even among philosophers, what should be done about it and by who. An influential movement founded by the philosopher Peter Singer argues that we should each try to do the best we can by donating our surplus income to charities that help those in greatest need. This ‘effective altruism’ movement has two components: i) encouraging individuals in the rich world to donate more; and ii) encouraging us to donate more rationally, to the organisations most efficient at translating those donations into gains in human well-being.
The problem with the first component of effective altruism was that it focuses on the internal moral economy of the giver rather than on the real-world problems our giving is supposed to address. The second component of effective altruism might not seem to have that problem because it is explicitly concerned with maximising the amount of good that each unit of resources achieves. However, this concern is better understood as efficiency than as effectiveness. This might seem an innocuous distinction since efficiency is about how we ought to get things done, i.e. a way of being effective. However, there are significant consequences for practical reasoning in the kind of cases effective altruism is concerned with.
If one takes the efficiency view promoted by the effective altruism movement then one assumes a fixed set of resources and the choice of which goal to aim for follows from a calculation of how to maximise the expected value those resources can generate; i.e. the means justifies the end. This should ensure that your donation will achieve the most good, which is to say that you have done the best possible job of giving. However, despite doing so well at the task effective altruism has set you, if you step back you will notice that very little has actually been achieved. The total amount of good we can achieve with our donations is limited to the partial alleviation of some of the symptoms of extreme poverty, symptoms that will recur so long as poverty persists. But effective altruism supplies no plan for the elimination of poverty itself.
The underlying problem is that effective altruism's distinctive combination of political pessimism and consumer-hero hubris forecloses the consideration of promising possibilities for achieving far more good. Singer and other effective altruist philosophers believe that their most likely customers find institutional reform too complicated and political action too impersonal and hit and miss to be attractive. So instead they flatter us by promising that we can literally be life-saving heroes from the comfort of our chairs and using only the super-power of our rich-world wallets.
But it just doesn't work. Singer and others have been making this argument for nearly 50 years, yet the level of private donations remain orders of magnitude below what would be required to eliminate global poverty, however efficiently allocated. Also, it needlessly squanders the most obvious and powerful tool we have: the political sphere and institutions of government that we invented to solve complicated and large collective action problems.
Read the passage carefully and answer the following questions:
It is almost universally agreed that the persistence of extreme poverty in many parts of the world is a bad thing. It is less well-agreed, even among philosophers, what should be done about it and by who. An influential movement founded by the philosopher Peter Singer argues that we should each try to do the best we can by donating our surplus income to charities that help those in greatest need. This ‘effective altruism’ movement has two components: i) encouraging individuals in the rich world to donate more; and ii) encouraging us to donate more rationally, to the organisations most efficient at translating those donations into gains in human well-being.
The problem with the first component of effective altruism was that it focuses on the internal moral economy of the giver rather than on the real-world problems our giving is supposed to address. The second component of effective altruism might not seem to have that problem because it is explicitly concerned with maximising the amount of good that each unit of resources achieves. However, this concern is better understood as efficiency than as effectiveness. This might seem an innocuous distinction since efficiency is about how we ought to get things done, i.e. a way of being effective. However, there are significant consequences for practical reasoning in the kind of cases effective altruism is concerned with.
If one takes the efficiency view promoted by the effective altruism movement then one assumes a fixed set of resources and the choice of which goal to aim for follows from a calculation of how to maximise the expected value those resources can generate; i.e. the means justifies the end. This should ensure that your donation will achieve the most good, which is to say that you have done the best possible job of giving. However, despite doing so well at the task effective altruism has set you, if you step back you will notice that very little has actually been achieved. The total amount of good we can achieve with our donations is limited to the partial alleviation of some of the symptoms of extreme poverty, symptoms that will recur so long as poverty persists. But effective altruism supplies no plan for the elimination of poverty itself.
The underlying problem is that effective altruism's distinctive combination of political pessimism and consumer-hero hubris forecloses the consideration of promising possibilities for achieving far more good. Singer and other effective altruist philosophers believe that their most likely customers find institutional reform too complicated and political action too impersonal and hit and miss to be attractive. So instead they flatter us by promising that we can literally be life-saving heroes from the comfort of our chairs and using only the super-power of our rich-world wallets.
But it just doesn't work. Singer and others have been making this argument for nearly 50 years, yet the level of private donations remain orders of magnitude below what would be required to eliminate global poverty, however efficiently allocated. Also, it needlessly squanders the most obvious and powerful tool we have: the political sphere and institutions of government that we invented to solve complicated and large collective action problems.
Q. According to the author, the efficiency view promoted by effective altruism
Read the passage carefully and answer the following questions:
Once upon a time — just a few years ago, actually — it was not uncommon to see headlines about prominent scientists, tech executives, and engineers warning portentously that the revolt of the robots was nigh. The mechanism varied, but the result was always the same: Uncontrollable machine self-improvement would one day overcome humanity. A dismal fate awaited us.
Today we fear a different technological threat, one that centers not on machines but other humans. We see ourselves as imperilled by the terrifying social influence unleashed by the Internet in general and social media in particular. We hear warnings that nothing less than our collective ability to perceive reality is at stake, and that if we do not take corrective action we will lose our freedoms and way of life.
Primal terror of mechanical menace has given way to fear of angry primates posting. Ironically, the roles have reversed. The robots are now humanity’s saviors, suppressing bad human mass behavior online with increasingly sophisticated filtering algorithms. We once obsessed about how to restrain machines we could not predict or control — now we worry about how to use machines to restrain humans we cannot predict or control. But the old problem hasn’t gone away: How do we know whether the machines will do as we wish?
The shift away from the fear of unpredictable robots and toward the fear of chaotic human behavior may have been inevitable. For the problem of controlling the machines was always at heart a problem of human desire — the worry that realizing our desires using automated systems might prove catastrophic. The promised solution was to rectify human desire. But once we lost optimism about whether this was possible, the stage was set for the problem to be flipped on its head.
The twentieth-century cyberneticist Norbert Wiener made what was for his time a rather startling argument: "The machine may be the final instrument of doom, but humanity may be the ultimate cause." In his 1960 essay “Some Moral and Technical Consequences of Automation,” Wiener recounts tales in which a person makes a wish and gets what was requested but not necessarily what he or she really desired. Hence, it's imperative that we be absolutely sure of what desire we put into the machine. Wiener was of course not talking about social media, but we can easily see the analogy: It too achieves purposes, like mob frenzy or erroneous post deletions, that its human designers did not actually desire, even though they built the machines in a way that achieves those purposes. Nor does he envision, as in Terminator, a general intelligence that becomes self-aware and nukes everyone. Rather, he imagined a system that humans cannot easily stop and that acts on a misleading substitute for the military objectives humans actually value.
However, there is a risk in Wiener’s distinction between what we desire and what actually happens in the end. It may create a false image of ourselves — an image in which our desires and our behaviors are wholly separable from each other. Instead of examining carefully whether our desires are in fact good, we may simply assume they are, and so blame bad behavior on the messy cooperation between ourselves and the “system.”
Q. In the third paragraph, why does the author remark that "ironically, the roles have reversed?"?
Read the passage carefully and answer the following questions:
Once upon a time — just a few years ago, actually — it was not uncommon to see headlines about prominent scientists, tech executives, and engineers warning portentously that the revolt of the robots was nigh. The mechanism varied, but the result was always the same: Uncontrollable machine self-improvement would one day overcome humanity. A dismal fate awaited us.
Today we fear a different technological threat, one that centers not on machines but other humans. We see ourselves as imperilled by the terrifying social influence unleashed by the Internet in general and social media in particular. We hear warnings that nothing less than our collective ability to perceive reality is at stake, and that if we do not take corrective action we will lose our freedoms and way of life.
Primal terror of mechanical menace has given way to fear of angry primates posting. Ironically, the roles have reversed. The robots are now humanity’s saviors, suppressing bad human mass behavior online with increasingly sophisticated filtering algorithms. We once obsessed about how to restrain machines we could not predict or control — now we worry about how to use machines to restrain humans we cannot predict or control. But the old problem hasn’t gone away: How do we know whether the machines will do as we wish?
The shift away from the fear of unpredictable robots and toward the fear of chaotic human behavior may have been inevitable. For the problem of controlling the machines was always at heart a problem of human desire — the worry that realizing our desires using automated systems might prove catastrophic. The promised solution was to rectify human desire. But once we lost optimism about whether this was possible, the stage was set for the problem to be flipped on its head.
The twentieth-century cyberneticist Norbert Wiener made what was for his time a rather startling argument: "The machine may be the final instrument of doom, but humanity may be the ultimate cause." In his 1960 essay “Some Moral and Technical Consequences of Automation,” Wiener recounts tales in which a person makes a wish and gets what was requested but not necessarily what he or she really desired. Hence, it's imperative that we be absolutely sure of what desire we put into the machine. Wiener was of course not talking about social media, but we can easily see the analogy: It too achieves purposes, like mob frenzy or erroneous post deletions, that its human designers did not actually desire, even though they built the machines in a way that achieves those purposes. Nor does he envision, as in Terminator, a general intelligence that becomes self-aware and nukes everyone. Rather, he imagined a system that humans cannot easily stop and that acts on a misleading substitute for the military objectives humans actually value.
However, there is a risk in Wiener’s distinction between what we desire and what actually happens in the end. It may create a false image of ourselves — an image in which our desires and our behaviors are wholly separable from each other. Instead of examining carefully whether our desires are in fact good, we may simply assume they are, and so blame bad behavior on the messy cooperation between ourselves and the “system.”
Q. According to the author, what was the reason for the shift toward the fear of chaotic human behavior?
Read the passage carefully and answer the following questions:
Once upon a time — just a few years ago, actually — it was not uncommon to see headlines about prominent scientists, tech executives, and engineers warning portentously that the revolt of the robots was nigh. The mechanism varied, but the result was always the same: Uncontrollable machine self-improvement would one day overcome humanity. A dismal fate awaited us.
Today we fear a different technological threat, one that centers not on machines but other humans. We see ourselves as imperilled by the terrifying social influence unleashed by the Internet in general and social media in particular. We hear warnings that nothing less than our collective ability to perceive reality is at stake, and that if we do not take corrective action we will lose our freedoms and way of life.
Primal terror of mechanical menace has given way to fear of angry primates posting. Ironically, the roles have reversed. The robots are now humanity’s saviors, suppressing bad human mass behavior online with increasingly sophisticated filtering algorithms. We once obsessed about how to restrain machines we could not predict or control — now we worry about how to use machines to restrain humans we cannot predict or control. But the old problem hasn’t gone away: How do we know whether the machines will do as we wish?
The shift away from the fear of unpredictable robots and toward the fear of chaotic human behavior may have been inevitable. For the problem of controlling the machines was always at heart a problem of human desire — the worry that realizing our desires using automated systems might prove catastrophic. The promised solution was to rectify human desire. But once we lost optimism about whether this was possible, the stage was set for the problem to be flipped on its head.
The twentieth-century cyberneticist Norbert Wiener made what was for his time a rather startling argument: "The machine may be the final instrument of doom, but humanity may be the ultimate cause." In his 1960 essay “Some Moral and Technical Consequences of Automation,” Wiener recounts tales in which a person makes a wish and gets what was requested but not necessarily what he or she really desired. Hence, it's imperative that we be absolutely sure of what desire we put into the machine. Wiener was of course not talking about social media, but we can easily see the analogy: It too achieves purposes, like mob frenzy or erroneous post deletions, that its human designers did not actually desire, even though they built the machines in a way that achieves those purposes. Nor does he envision, as in Terminator, a general intelligence that becomes self-aware and nukes everyone. Rather, he imagined a system that humans cannot easily stop and that acts on a misleading substitute for the military objectives humans actually value.
However, there is a risk in Wiener’s distinction between what we desire and what actually happens in the end. It may create a false image of ourselves — an image in which our desires and our behaviors are wholly separable from each other. Instead of examining carefully whether our desires are in fact good, we may simply assume they are, and so blame bad behavior on the messy cooperation between ourselves and the “system.”
Q. The risk in Wiener's distinction between what we desire and what actually happens, in the end, is that:
Read the passage carefully and answer the following questions:
Once upon a time — just a few years ago, actually — it was not uncommon to see headlines about prominent scientists, tech executives, and engineers warning portentously that the revolt of the robots was nigh. The mechanism varied, but the result was always the same: Uncontrollable machine self-improvement would one day overcome humanity. A dismal fate awaited us.
Today we fear a different technological threat, one that centers not on machines but other humans. We see ourselves as imperilled by the terrifying social influence unleashed by the Internet in general and social media in particular. We hear warnings that nothing less than our collective ability to perceive reality is at stake, and that if we do not take corrective action we will lose our freedoms and way of life.
Primal terror of mechanical menace has given way to fear of angry primates posting. Ironically, the roles have reversed. The robots are now humanity’s saviors, suppressing bad human mass behavior online with increasingly sophisticated filtering algorithms. We once obsessed about how to restrain machines we could not predict or control — now we worry about how to use machines to restrain humans we cannot predict or control. But the old problem hasn’t gone away: How do we know whether the machines will do as we wish?
The shift away from the fear of unpredictable robots and toward the fear of chaotic human behavior may have been inevitable. For the problem of controlling the machines was always at heart a problem of human desire — the worry that realizing our desires using automated systems might prove catastrophic. The promised solution was to rectify human desire. But once we lost optimism about whether this was possible, the stage was set for the problem to be flipped on its head.
The twentieth-century cyberneticist Norbert Wiener made what was for his time a rather startling argument: "The machine may be the final instrument of doom, but humanity may be the ultimate cause." In his 1960 essay “Some Moral and Technical Consequences of Automation,” Wiener recounts tales in which a person makes a wish and gets what was requested but not necessarily what he or she really desired. Hence, it's imperative that we be absolutely sure of what desire we put into the machine. Wiener was of course not talking about social media, but we can easily see the analogy: It too achieves purposes, like mob frenzy or erroneous post deletions, that its human designers did not actually desire, even though they built the machines in a way that achieves those purposes. Nor does he envision, as in Terminator, a general intelligence that becomes self-aware and nukes everyone. Rather, he imagined a system that humans cannot easily stop and that acts on a misleading substitute for the military objectives humans actually value.
However, there is a risk in Wiener’s distinction between what we desire and what actually happens in the end. It may create a false image of ourselves — an image in which our desires and our behaviors are wholly separable from each other. Instead of examining carefully whether our desires are in fact good, we may simply assume they are, and so blame bad behavior on the messy cooperation between ourselves and the “system.”
Q. Which of the following could be an example of Weiner's desire-outcome disparity argument?
I. A weapons system, which cannot be stopped easily, starts bombing after receiving an erroneous command.
II. An AI program developed to mitigate global warming starts eliminating a fraction of the human population to complete its objective.
III. A Social media platform allows groups of militants to communicate their plans and coordinate their attacks.
Read the passage carefully and answer the following questions:
Iceland, Norway, Finland and Sweden are, according to the World Economic Forum, the most gender-equal countries in the world, while Denmark is in 14th place. Iceland has been named the most gender-equal in the world for 11 years running. Strong economic and work participation, together with political empowerment, has led many to see the Nordic countries as a “gender equality utopia”. However, behind women participation statistics and progressive policies, gender stereotypes prevail, particularly in the workplace, and women in the region say that there is still a lot of work to be done.
A recent report by intergovernmental forum the Nordic Council of Ministers found that, whereas Nordic governments’ policies have contributed to reducing the income disparities between men and women, financial gender equality is far from a reality yet. Occupational segregation still exists across the region’s industries and sectors and “social norms continue to restrict occupational choices”, the study points out. This gender segregation is more pronounced in Stem industries, which in turn is linked to a segregation in education on these subjects
Gabriele Griffin, professor of gender research at the University of Uppsala, says that closer examination of the statistics about gender equality in Nordic countries shows that most of the people who believe it has already been achieved are men, whereas women are more sceptical. Griffin says that there is still a rooted stereotype of technology being a male field and humanities and medicine being female. Progressive legislation and policy have not prevented the continuation of gender stereotypes.
The modern concept of gender equality has its foundations in the postwar welfare state. In Sweden, it was motivated by the need for more women in the workforce after the Second World War, explains Jenny Björklund, associate professor of gender studies at the University of Uppsala. During the 1960s and 1970s, the feminist movement demanded that the social democratic government introduce childcare to allow women to have full-time jobs. Men were also encouraged to take care of the family. “There’s this dual-earner/dual-carer ideal that Swedish gender equality is based on,” says Björklund.
Policies in Sweden have since then focused on facilitating that work-family balance. However, the expectations on women to be full-time workers, self-sacrificing mothers and still have leisure time have put unrealistic pressure on this ideal. Expectations on men are not as high, and Björklund says that fathers can get away with being less caring than mothers - an idea underpinned by traditional stereotypes and middle-class values.
Furthermore, the ideal of gender equality has been made a key element of a white and middle-class “Swedishness” - a national trait hijacked by far-right political parties promoting anti-immigration policies, says Björklund. These parties stereotype the immigrant woman as “less gender-equal” and repressed, and present immigrant men as patriarchal and aggressive, diverting attention away from the issues still at stake. Professor Griffin adds that this rising conservatism in Sweden has led to a liberalisation of discourses that are in many ways discriminatory, where it becomes acceptable to say that gender equality has gone too far.
Q. Which of the following statements CANNOT be inferred from the passage concerning the Nordic regions?
I. Progressive policies have not addressed the presence of gender stereotypes in the workplace.
II. Competition among women has exacerbated the income gap between men and women.
III. Occupational gender segregation has led to segregation in education on major subjects.
IV. Social norms discourage women from taking up certain occupations.
Read the passage carefully and answer the following questions:
Iceland, Norway, Finland and Sweden are, according to the World Economic Forum, the most gender-equal countries in the world, while Denmark is in 14th place. Iceland has been named the most gender-equal in the world for 11 years running. Strong economic and work participation, together with political empowerment, has led many to see the Nordic countries as a “gender equality utopia”. However, behind women participation statistics and progressive policies, gender stereotypes prevail, particularly in the workplace, and women in the region say that there is still a lot of work to be done.
A recent report by intergovernmental forum the Nordic Council of Ministers found that, whereas Nordic governments’ policies have contributed to reducing the income disparities between men and women, financial gender equality is far from a reality yet. Occupational segregation still exists across the region’s industries and sectors and “social norms continue to restrict occupational choices”, the study points out. This gender segregation is more pronounced in Stem industries, which in turn is linked to a segregation in education on these subjects
Gabriele Griffin, professor of gender research at the University of Uppsala, says that closer examination of the statistics about gender equality in Nordic countries shows that most of the people who believe it has already been achieved are men, whereas women are more sceptical. Griffin says that there is still a rooted stereotype of technology being a male field and humanities and medicine being female. Progressive legislation and policy have not prevented the continuation of gender stereotypes.
The modern concept of gender equality has its foundations in the postwar welfare state. In Sweden, it was motivated by the need for more women in the workforce after the Second World War, explains Jenny Björklund, associate professor of gender studies at the University of Uppsala. During the 1960s and 1970s, the feminist movement demanded that the social democratic government introduce childcare to allow women to have full-time jobs. Men were also encouraged to take care of the family. “There’s this dual-earner/dual-carer ideal that Swedish gender equality is based on,” says Björklund.
Policies in Sweden have since then focused on facilitating that work-family balance. However, the expectations on women to be full-time workers, self-sacrificing mothers and still have leisure time have put unrealistic pressure on this ideal. Expectations on men are not as high, and Björklund says that fathers can get away with being less caring than mothers - an idea underpinned by traditional stereotypes and middle-class values.
Furthermore, the ideal of gender equality has been made a key element of a white and middle-class “Swedishness” - a national trait hijacked by far-right political parties promoting anti-immigration policies, says Björklund. These parties stereotype the immigrant woman as “less gender-equal” and repressed, and present immigrant men as patriarchal and aggressive, diverting attention away from the issues still at stake. Professor Griffin adds that this rising conservatism in Sweden has led to a liberalisation of discourses that are in many ways discriminatory, where it becomes acceptable to say that gender equality has gone too far.
Q. All of the following have been discussed about gender equality in Sweden, EXCEPT:
Read the passage carefully and answer the following questions:
Iceland, Norway, Finland and Sweden are, according to the World Economic Forum, the most gender-equal countries in the world, while Denmark is in 14th place. Iceland has been named the most gender-equal in the world for 11 years running. Strong economic and work participation, together with political empowerment, has led many to see the Nordic countries as a “gender equality utopia”. However, behind women participation statistics and progressive policies, gender stereotypes prevail, particularly in the workplace, and women in the region say that there is still a lot of work to be done.
A recent report by intergovernmental forum the Nordic Council of Ministers found that, whereas Nordic governments’ policies have contributed to reducing the income disparities between men and women, financial gender equality is far from a reality yet. Occupational segregation still exists across the region’s industries and sectors and “social norms continue to restrict occupational choices”, the study points out. This gender segregation is more pronounced in Stem industries, which in turn is linked to a segregation in education on these subjects
Gabriele Griffin, professor of gender research at the University of Uppsala, says that closer examination of the statistics about gender equality in Nordic countries shows that most of the people who believe it has already been achieved are men, whereas women are more sceptical. Griffin says that there is still a rooted stereotype of technology being a male field and humanities and medicine being female. Progressive legislation and policy have not prevented the continuation of gender stereotypes.
The modern concept of gender equality has its foundations in the postwar welfare state. In Sweden, it was motivated by the need for more women in the workforce after the Second World War, explains Jenny Björklund, associate professor of gender studies at the University of Uppsala. During the 1960s and 1970s, the feminist movement demanded that the social democratic government introduce childcare to allow women to have full-time jobs. Men were also encouraged to take care of the family. “There’s this dual-earner/dual-carer ideal that Swedish gender equality is based on,” says Björklund.
Policies in Sweden have since then focused on facilitating that work-family balance. However, the expectations on women to be full-time workers, self-sacrificing mothers and still have leisure time have put unrealistic pressure on this ideal. Expectations on men are not as high, and Björklund says that fathers can get away with being less caring than mothers - an idea underpinned by traditional stereotypes and middle-class values.
Furthermore, the ideal of gender equality has been made a key element of a white and middle-class “Swedishness” - a national trait hijacked by far-right political parties promoting anti-immigration policies, says Björklund. These parties stereotype the immigrant woman as “less gender-equal” and repressed, and present immigrant men as patriarchal and aggressive, diverting attention away from the issues still at stake. Professor Griffin adds that this rising conservatism in Sweden has led to a liberalisation of discourses that are in many ways discriminatory, where it becomes acceptable to say that gender equality has gone too far.
Q. The central idea in the fifth paragraph is that
Read the passage carefully and answer the following questions:
Iceland, Norway, Finland and Sweden are, according to the World Economic Forum, the most gender-equal countries in the world, while Denmark is in 14th place. Iceland has been named the most gender-equal in the world for 11 years running. Strong economic and work participation, together with political empowerment, has led many to see the Nordic countries as a “gender equality utopia”. However, behind women participation statistics and progressive policies, gender stereotypes prevail, particularly in the workplace, and women in the region say that there is still a lot of work to be done.
A recent report by intergovernmental forum the Nordic Council of Ministers found that, whereas Nordic governments’ policies have contributed to reducing the income disparities between men and women, financial gender equality is far from a reality yet. Occupational segregation still exists across the region’s industries and sectors and “social norms continue to restrict occupational choices”, the study points out. This gender segregation is more pronounced in Stem industries, which in turn is linked to a segregation in education on these subjects
Gabriele Griffin, professor of gender research at the University of Uppsala, says that closer examination of the statistics about gender equality in Nordic countries shows that most of the people who believe it has already been achieved are men, whereas women are more sceptical. Griffin says that there is still a rooted stereotype of technology being a male field and humanities and medicine being female. Progressive legislation and policy have not prevented the continuation of gender stereotypes.
The modern concept of gender equality has its foundations in the postwar welfare state. In Sweden, it was motivated by the need for more women in the workforce after the Second World War, explains Jenny Björklund, associate professor of gender studies at the University of Uppsala. During the 1960s and 1970s, the feminist movement demanded that the social democratic government introduce childcare to allow women to have full-time jobs. Men were also encouraged to take care of the family. “There’s this dual-earner/dual-carer ideal that Swedish gender equality is based on,” says Björklund.
Policies in Sweden have since then focused on facilitating that work-family balance. However, the expectations on women to be full-time workers, self-sacrificing mothers and still have leisure time have put unrealistic pressure on this ideal. Expectations on men are not as high, and Björklund says that fathers can get away with being less caring than mothers - an idea underpinned by traditional stereotypes and middle-class values.
Furthermore, the ideal of gender equality has been made a key element of a white and middle-class “Swedishness” - a national trait hijacked by far-right political parties promoting anti-immigration policies, says Björklund. These parties stereotype the immigrant woman as “less gender-equal” and repressed, and present immigrant men as patriarchal and aggressive, diverting attention away from the issues still at stake. Professor Griffin adds that this rising conservatism in Sweden has led to a liberalisation of discourses that are in many ways discriminatory, where it becomes acceptable to say that gender equality has gone too far.
Q. Which of the following is likely to be the next course of discussion?
Read the passage carefully and answer the following questions:
Five years ago we launched the Simons Foundation Powering Autism Research for Knowledge (SPARK) to harness the power of big data by engaging hundreds of thousands of individuals with autism and their family members to participate in research. The more people who participate, the deeper and richer these data sets become, catalyzing research that is expanding our knowledge of both biology and behavior to develop more precise approaches to medical and behavioural issues.
Genetic research has taught us that what we commonly call autism is actually a spectrum of hundreds of conditions that vary widely among adults and children. Across this spectrum, individuals share core symptoms and challenges with social interaction, restricted interests and/or repetitive behaviours.
We now know that genes play a central role in the causes of these “autisms,” which are the result of genetic changes in combination with other causes including prenatal factors. Essentially, we will take a page from the playbook that oncologists use to treat certain types of cancer-based upon their genetic signatures and apply targeted therapeutic strategies to help people with autism.
But in order to get answers faster and be certain of these results, SPARK and our research partners need a huge sample size: “bigger data.” To ensure an accurate inventory of all the major genetic contributors, and learn if and how different genetic variants contribute to autistic behaviours, we need not only the largest but also the most diverse group of participants.
The genetic, medical and behavioural data SPARK collects from people with autism and their families is rich in detail and can be leveraged by many different investigators. Access to rich data sets draws talented scientists to the field of autism science to develop new methods of finding patterns in the data, better predicting associated behavioural and medical issues, and, perhaps, identifying more effective supports and treatments.
Genetic research is already providing answers and insights about prognosis. For example, one SPARK family’s genetic result is strongly associated with a lack of spoken language but an ability to understand language. Armed with this information, the medical team provided the child with an assistive communication device that decreased tantrums that arose from the child’s frustration at being unable to express himself.
SPARK has identified genetic causes of autism that can be treated. Through whole exome sequencing, SPARK identified a case of phenylketonuria (PKU) that was missed during newborn screening. This inherited disorder causes a buildup of amino acid in the blood, which can cause behaviour and movement problems, seizures and developmental disabilities. With this knowledge, the family started their child on treatment with a specialized diet including low levels of phenylalanine.
We know that big data, with each person representing their unique profile of someone impacted by autism, will lead to many of the answers we seek. Better genetic insights, gleaned through a complex analysis of rich data, will help provide the means to support individuals—children and adults across the spectrum—through early intervention, assistive communication, tailored education and, someday, genetically-based treatments. We strive to enable every person with autism to be the best possible version of themselves.
Q. The purpose of the last three paragraphs is to:
Read the passage carefully and answer the following questions:
Five years ago we launched the Simons Foundation Powering Autism Research for Knowledge (SPARK) to harness the power of big data by engaging hundreds of thousands of individuals with autism and their family members to participate in research. The more people who participate, the deeper and richer these data sets become, catalyzing research that is expanding our knowledge of both biology and behavior to develop more precise approaches to medical and behavioural issues.
Genetic research has taught us that what we commonly call autism is actually a spectrum of hundreds of conditions that vary widely among adults and children. Across this spectrum, individuals share core symptoms and challenges with social interaction, restricted interests and/or repetitive behaviours.
We now know that genes play a central role in the causes of these “autisms,” which are the result of genetic changes in combination with other causes including prenatal factors. Essentially, we will take a page from the playbook that oncologists use to treat certain types of cancer-based upon their genetic signatures and apply targeted therapeutic strategies to help people with autism.
But in order to get answers faster and be certain of these results, SPARK and our research partners need a huge sample size: “bigger data.” To ensure an accurate inventory of all the major genetic contributors, and learn if and how different genetic variants contribute to autistic behaviours, we need not only the largest but also the most diverse group of participants.
The genetic, medical and behavioural data SPARK collects from people with autism and their families is rich in detail and can be leveraged by many different investigators. Access to rich data sets draws talented scientists to the field of autism science to develop new methods of finding patterns in the data, better predicting associated behavioural and medical issues, and, perhaps, identifying more effective supports and treatments.
Genetic research is already providing answers and insights about prognosis. For example, one SPARK family’s genetic result is strongly associated with a lack of spoken language but an ability to understand language. Armed with this information, the medical team provided the child with an assistive communication device that decreased tantrums that arose from the child’s frustration at being unable to express himself.
SPARK has identified genetic causes of autism that can be treated. Through whole exome sequencing, SPARK identified a case of phenylketonuria (PKU) that was missed during newborn screening. This inherited disorder causes a buildup of amino acid in the blood, which can cause behaviour and movement problems, seizures and developmental disabilities. With this knowledge, the family started their child on treatment with a specialized diet including low levels of phenylalanine.
We know that big data, with each person representing their unique profile of someone impacted by autism, will lead to many of the answers we seek. Better genetic insights, gleaned through a complex analysis of rich data, will help provide the means to support individuals—children and adults across the spectrum—through early intervention, assistive communication, tailored education and, someday, genetically-based treatments. We strive to enable every person with autism to be the best possible version of themselves.
Q. Which of the following is the author most likely to agree with?
Read the passage carefully and answer the following questions:
Five years ago we launched the Simons Foundation Powering Autism Research for Knowledge (SPARK) to harness the power of big data by engaging hundreds of thousands of individuals with autism and their family members to participate in research. The more people who participate, the deeper and richer these data sets become, catalyzing research that is expanding our knowledge of both biology and behavior to develop more precise approaches to medical and behavioural issues.
Genetic research has taught us that what we commonly call autism is actually a spectrum of hundreds of conditions that vary widely among adults and children. Across this spectrum, individuals share core symptoms and challenges with social interaction, restricted interests and/or repetitive behaviours.
We now know that genes play a central role in the causes of these “autisms,” which are the result of genetic changes in combination with other causes including prenatal factors. Essentially, we will take a page from the playbook that oncologists use to treat certain types of cancer-based upon their genetic signatures and apply targeted therapeutic strategies to help people with autism.
But in order to get answers faster and be certain of these results, SPARK and our research partners need a huge sample size: “bigger data.” To ensure an accurate inventory of all the major genetic contributors, and learn if and how different genetic variants contribute to autistic behaviours, we need not only the largest but also the most diverse group of participants.
The genetic, medical and behavioural data SPARK collects from people with autism and their families is rich in detail and can be leveraged by many different investigators. Access to rich data sets draws talented scientists to the field of autism science to develop new methods of finding patterns in the data, better predicting associated behavioural and medical issues, and, perhaps, identifying more effective supports and treatments.
Genetic research is already providing answers and insights about prognosis. For example, one SPARK family’s genetic result is strongly associated with a lack of spoken language but an ability to understand language. Armed with this information, the medical team provided the child with an assistive communication device that decreased tantrums that arose from the child’s frustration at being unable to express himself.
SPARK has identified genetic causes of autism that can be treated. Through whole exome sequencing, SPARK identified a case of phenylketonuria (PKU) that was missed during newborn screening. This inherited disorder causes a buildup of amino acid in the blood, which can cause behaviour and movement problems, seizures and developmental disabilities. With this knowledge, the family started their child on treatment with a specialized diet including low levels of phenylalanine.
We know that big data, with each person representing their unique profile of someone impacted by autism, will lead to many of the answers we seek. Better genetic insights, gleaned through a complex analysis of rich data, will help provide the means to support individuals—children and adults across the spectrum—through early intervention, assistive communication, tailored education and, someday, genetically-based treatments. We strive to enable every person with autism to be the best possible version of themselves.
Q. Which of the following cannot be inferred?
I. The effectiveness of 'big data' is determined not by its size but by its diversity.
II. Genes are a major factor influencing autism.
III. Consumption of a diet containing low levels of phenylalanine helps decrease the level of amino acid in the blood.
Read the passage carefully and answer the following questions:
Five years ago we launched the Simons Foundation Powering Autism Research for Knowledge (SPARK) to harness the power of big data by engaging hundreds of thousands of individuals with autism and their family members to participate in research. The more people who participate, the deeper and richer these data sets become, catalyzing research that is expanding our knowledge of both biology and behavior to develop more precise approaches to medical and behavioural issues.
Genetic research has taught us that what we commonly call autism is actually a spectrum of hundreds of conditions that vary widely among adults and children. Across this spectrum, individuals share core symptoms and challenges with social interaction, restricted interests and/or repetitive behaviours.
We now know that genes play a central role in the causes of these “autisms,” which are the result of genetic changes in combination with other causes including prenatal factors. Essentially, we will take a page from the playbook that oncologists use to treat certain types of cancer-based upon their genetic signatures and apply targeted therapeutic strategies to help people with autism.
But in order to get answers faster and be certain of these results, SPARK and our research partners need a huge sample size: “bigger data.” To ensure an accurate inventory of all the major genetic contributors, and learn if and how different genetic variants contribute to autistic behaviours, we need not only the largest but also the most diverse group of participants.
The genetic, medical and behavioural data SPARK collects from people with autism and their families is rich in detail and can be leveraged by many different investigators. Access to rich data sets draws talented scientists to the field of autism science to develop new methods of finding patterns in the data, better predicting associated behavioural and medical issues, and, perhaps, identifying more effective supports and treatments.
Genetic research is already providing answers and insights about prognosis. For example, one SPARK family’s genetic result is strongly associated with a lack of spoken language but an ability to understand language. Armed with this information, the medical team provided the child with an assistive communication device that decreased tantrums that arose from the child’s frustration at being unable to express himself.
SPARK has identified genetic causes of autism that can be treated. Through whole exome sequencing, SPARK identified a case of phenylketonuria (PKU) that was missed during newborn screening. This inherited disorder causes a buildup of amino acid in the blood, which can cause behaviour and movement problems, seizures and developmental disabilities. With this knowledge, the family started their child on treatment with a specialized diet including low levels of phenylalanine.
We know that big data, with each person representing their unique profile of someone impacted by autism, will lead to many of the answers we seek. Better genetic insights, gleaned through a complex analysis of rich data, will help provide the means to support individuals—children and adults across the spectrum—through early intervention, assistive communication, tailored education and, someday, genetically-based treatments. We strive to enable every person with autism to be the best possible version of themselves.
Q. All of the following have been discussed in the passage as benefits of having richer and bigger datasets EXCEPT:
The four sentences (labelled 1, 2, 3, 4) below, when properly sequenced, would yield a coherent paragraph. Decide on the proper sequencing of the order of the sentences and key in the sequence of the four numbers as your answer:
1. On the contrary, the industry is happy reducing the wage bills, doing mechanisation and raising its profits.
2. During the pandemic, nearly 31 million families have moved down from the middle class and nearly 100 million people have lost jobs.
3. The industries that are most likely to create employment, i.e. the medium and small industries, are going down under and the large ones which do not create employment are the poster boys.
4. They are the ones that will get the 6 per cent productivity-linked incentive from the tax paid by the average taxpayers, with unknown consequences.
The four sentences (labelled 1, 2, 3, 4) below, when properly sequenced, would yield a coherent paragraph. Decide on the proper sequencing of the order of the sentences and key in the sequence of the four numbers as your answer:
1. For others, Trump’s loss makes him into a loser—especially damaging given how much Trump hates losers.
2. In the more immediate future, however, no one will remain as personally angry about it as Trump.
3. Polls show that many Republicans believe the 2020 election was tainted, and the damage that will do to faith in democracy in the long term is dangerous.
4. Some followers who saw him as a man who could challenge the establishment will view his defeat as proof that politics is irredeemable, and will slide into apathy and disengagement.
Five sentences related to a topic are given below. Four of them can be put together to form a meaningful and coherent short paragraph. Identify the odd one out.
1. Without robust national privacy safeguards, entire databases of citizen information are ready for purchase, whether to predatory loan companies, law enforcement agencies, or even malicious foreign actors.
2. Federal privacy bills that don’t give sufficient attention to data brokerage will therefore fail to tackle an enormous portion of the data surveillance economy.
3. Data brokerage is a threat to democracy.
4. This is why the largest data brokers are lobbying more aggressively in Washington.
5. This will leave civil rights, national security, and public-private boundaries vulnerable in the process.
Five sentences related to a topic are given below. Four of them can be put together to form a meaningful and coherent short paragraph. Identify the odd one out.
1. Researchers see signs of this in sperm whales in the Galápagos and the Caribbean, in humpbacks across the South Pacific, in Arctic belugas, and in the Pacific Northwest’s killer whales.
2. Today many scientists believe some whales and dolphins, like humans, have distinct cultures.
3. Whale culture, it seems, is rattling timeworn conceptions of ourselves.
4. The possibility is prompting new thinking about how some marine species evolve.
5. Cultural traditions may help drive genetic shifts, altering what it means to be a whale.
The four sentences (labelled 1, 2, 3, and 4) below, when properly sequenced, would yield a coherent paragraph. Decide on the proper sequencing of the order of the sentences and key in the sequence of the four numbers as your answer:
1. However, the very ubiquity of such technology poses critical questions about data privacy and individual autonomy.
2. The integration of advanced algorithms and everyday technology has streamlined countless processes, improving efficiency exponentially.
3. As we entrust more of our lives to digital gatekeepers, the parameters of the debates shift from technical concerns to ethical dilemmas.
4. Such an environment, teeming with both subtle surveillance and convenience, highlights the double-edged nature of progress.