Reading Comprehension || RC PRACTICE SET - 7 || CAT 2021 || 20 April

Direction: Read the given passage carefully and answer the questions that follow. Certain words are printed in bold to help you locate them while answering some of these.

In recent months, a number of high-profile figures have spoken frankly about their experiences of mental health. Prince Harry has disclosed the anxiety and panic attacks he experienced following the sudden death of his mother, while former England cricketer Freddie Flintoff and rapper Professor Green have also both opened up about their mental health.

Experts welcome such open discussions on mental health, which has been a taboo subject for far too long. However, there is still a long way to go – especially in many workplaces – but things are definitely moving in the right direction.

This move to increase open discussions on the subject comes as two-thirds of British adults have admitted to having experienced mental ill-health at some point in their lives, according to research published by the Mental Health Foundation. In the Queen’s speech last month, the government set out details on how it plans to improve mental health services but failed to provide any further information on the additional 10,000 extra staff for the sector, as outlined in its manifesto.

For those experiencing mental health issues, one must try to approach the employer about one’s concerns. If one does not speak out, one is potentially denying support. One of the biggest issues facing employees in relation to their mental wellbeing is the inability to switch off after work, with smartphones continually pinging with email notifications. Knowing when it’s getting too much and stopping accordingly is crucial.

Workplaces need to become mental-health friendly as this will help more people feel comfortable about disclosing when they have a health problem. It is also important that employers themselves understand their responsibilities under the law, as well as other steps they can take to get the best out of a diverse workforce.

However, despite the challenges of working in an office, a professor of occupational health psychology and head of psychology at Birmingham City University, is keen to add that there are benefits to working life: “We should remember that work also provides many benefits for individuals – psychological, emotional, cognitive, social.”

Source: https://www.theguardian.com/careers/2017/jul/11/how-to-manage-mental-health-at-work

Direction: Read the given passage carefully and answer the questions that follow. Certain words are printed in bold to help you locate them while answering some of these.

In recent months, a number of high-profile figures have spoken frankly about their experiences of mental health. Prince Harry has disclosed the anxiety and panic attacks he experienced following the sudden death of his mother, while former England cricketer Freddie Flintoff and rapper Professor Green have also both opened up about their mental health.

Experts welcome such open discussions on mental health, which has been a taboo subject for far too long. However, there is still a long way to go – especially in many workplaces – but things are definitely moving in the right direction.

This move to increase open discussions on the subject comes as two-thirds of British adults have admitted to having experienced mental ill-health at some point in their lives, according to research published by the Mental Health Foundation. In the Queen’s speech last month, the government set out details on how it plans to improve mental health services but failed to provide any further information on the additional 10,000 extra staff for the sector, as outlined in its manifesto.

For those experiencing mental health issues, one must try to approach the employer about one’s concerns. If one does not speak out, one is potentially denying support. One of the biggest issues facing employees in relation to their mental wellbeing is the inability to switch off after work, with smartphones continually pinging with email notifications. Knowing when it’s getting too much and stopping accordingly is crucial.

Workplaces need to become mental-health friendly as this will help more people feel comfortable about disclosing when they have a health problem. It is also important that employers themselves understand their responsibilities under the law, as well as other steps they can take to get the best out of a diverse workforce.

However, despite the challenges of working in an office, a professor of occupational health psychology and head of psychology at Birmingham City University, is keen to add that there are benefits to working life: “We should remember that work also provides many benefits for individuals – psychological, emotional, cognitive, social.”

Source: https://www.theguardian.com/careers/2017/jul/11/how-to-manage-mental-health-at-work

Direction: Read the given passage carefully and answer the questions that follow. Certain words are printed in bold to help you locate them while answering some of these.

In recent months, a number of high-profile figures have spoken frankly about their experiences of mental health. Prince Harry has disclosed the anxiety and panic attacks he experienced following the sudden death of his mother, while former England cricketer Freddie Flintoff and rapper Professor Green have also both opened up about their mental health.

Experts welcome such open discussions on mental health, which has been a taboo subject for far too long. However, there is still a long way to go – especially in many workplaces – but things are definitely moving in the right direction.

This move to increase open discussions on the subject comes as two-thirds of British adults have admitted to having experienced mental ill-health at some point in their lives, according to research published by the Mental Health Foundation. In the Queen’s speech last month, the government set out details on how it plans to improve mental health services but failed to provide any further information on the additional 10,000 extra staff for the sector, as outlined in its manifesto.

For those experiencing mental health issues, one must try to approach the employer about one’s concerns. If one does not speak out, one is potentially denying support. One of the biggest issues facing employees in relation to their mental wellbeing is the inability to switch off after work, with smartphones continually pinging with email notifications. Knowing when it’s getting too much and stopping accordingly is crucial.

Workplaces need to become mental-health friendly as this will help more people feel comfortable about disclosing when they have a health problem. It is also important that employers themselves understand their responsibilities under the law, as well as other steps they can take to get the best out of a diverse workforce.

However, despite the challenges of working in an office, a professor of occupational health psychology and head of psychology at Birmingham City University, is keen to add that there are benefits to working life: “We should remember that work also provides many benefits for individuals – psychological, emotional, cognitive, social.”

Source: https://www.theguardian.com/careers/2017/jul/11/how-to-manage-mental-health-at-work

Direction: Read the given passage carefully and answer the questions that follow. Certain words are printed in bold to help you locate them while answering some of these.

In recent months, a number of high-profile figures have spoken frankly about their experiences of mental health. Prince Harry has disclosed the anxiety and panic attacks he experienced following the sudden death of his mother, while former England cricketer Freddie Flintoff and rapper Professor Green have also both opened up about their mental health.

Experts welcome such open discussions on mental health, which has been a taboo subject for far too long. However, there is still a long way to go – especially in many workplaces – but things are definitely moving in the right direction.

This move to increase open discussions on the subject comes as two-thirds of British adults have admitted to having experienced mental ill-health at some point in their lives, according to research published by the Mental Health Foundation. In the Queen’s speech last month, the government set out details on how it plans to improve mental health services but failed to provide any further information on the additional 10,000 extra staff for the sector, as outlined in its manifesto.

For those experiencing mental health issues, one must try to approach the employer about one’s concerns. If one does not speak out, one is potentially denying support. One of the biggest issues facing employees in relation to their mental wellbeing is the inability to switch off after work, with smartphones continually pinging with email notifications. Knowing when it’s getting too much and stopping accordingly is crucial.

Workplaces need to become mental-health friendly as this will help more people feel comfortable about disclosing when they have a health problem. It is also important that employers themselves understand their responsibilities under the law, as well as other steps they can take to get the best out of a diverse workforce.

However, despite the challenges of working in an office, a professor of occupational health psychology and head of psychology at Birmingham City University, is keen to add that there are benefits to working life: “We should remember that work also provides many benefits for individuals – psychological, emotional, cognitive, social.”

Source: https://www.theguardian.com/careers/2017/jul/11/how-to-manage-mental-health-at-work

Direction: Read the given passage carefully and answer the questions that follow. Certain words are printed in bold to help you locate them while answering some of these.

In recent months, a number of high-profile figures have spoken frankly about their experiences of mental health. Prince Harry has disclosed the anxiety and panic attacks he experienced following the sudden death of his mother, while former England cricketer Freddie Flintoff and rapper Professor Green have also both opened up about their mental health.

Experts welcome such open discussions on mental health, which has been a taboo subject for far too long. However, there is still a long way to go – especially in many workplaces – but things are definitely moving in the right direction.

This move to increase open discussions on the subject comes as two-thirds of British adults have admitted to having experienced mental ill-health at some point in their lives, according to research published by the Mental Health Foundation. In the Queen’s speech last month, the government set out details on how it plans to improve mental health services but failed to provide any further information on the additional 10,000 extra staff for the sector, as outlined in its manifesto.

For those experiencing mental health issues, one must try to approach the employer about one’s concerns. If one does not speak out, one is potentially denying support. One of the biggest issues facing employees in relation to their mental wellbeing is the inability to switch off after work, with smartphones continually pinging with email notifications. Knowing when it’s getting too much and stopping accordingly is crucial.

Workplaces need to become mental-health friendly as this will help more people feel comfortable about disclosing when they have a health problem. It is also important that employers themselves understand their responsibilities under the law, as well as other steps they can take to get the best out of a diverse workforce.

However, despite the challenges of working in an office, a professor of occupational health psychology and head of psychology at Birmingham City University, is keen to add that there are benefits to working life: “We should remember that work also provides many benefits for individuals – psychological, emotional, cognitive, social.”

Source: https://www.theguardian.com/careers/2017/jul/11/how-to-manage-mental-health-at-work

At the Centre for Vision, Cognition, Learning, and Autonomy at University of California Los Angeles, we are interested in what factors make machines more trustworthy, and how well different learning algorithms enable trust. In our latest research, we experimented with different ways a robot could explain its actions to a human observer. Interestingly, the forms of explanation that fostered the most human trust did not correspond to the learning algorithms that produced the best task performance. This suggests performance and explanation are not inherently dependent upon each other. This divergence calls for robot designs that takes into account both good task performance and trustworthy explanations.

We taught a robot to learn from human demonstrations how to open a medicine bottle with a safety lock. A person wore a tactile glove that recorded the poses and forces of the human hand as it opened the bottle. That information helped the robot learn what the human did in two ways: symbolic and haptic. Symbolic refers to meaningful representations of your actions: for example, the word “grasp.” Haptic refers to the feelings associated with your body’s postures and motions: for example, the sensation of your fingers closing together.

It turns out the robot was able to achieve its best performance when combining the symbolic and haptic components. The robot did better, using knowledge of the steps for performing the task and real-time sensing from its gripper than using either alone.

To explain its actions, the robot can draw on its internal decision process as well as its behaviour. The symbolic model provides step-by-step descriptions of the robot’s actions, and the haptic model provides a sense of what the robot gripper is “feeling.”

In our experiment, we added an additional explanation for humans: a text write-up that provided a summary after the robot has finished attempting to open the medicine bottle. We asked 150 human participants, divided into four groups, to observe the robot attempting to open the medicine bottle. The robot then gave each group a different explanation of the task: symbolic, step-by-step, haptic – arm positions and motions, text summary, or symbolic and haptic together. A baseline group observed only a video of the robot attempting to open the bottle, without providing any additional explanations.

We found that providing both the symbolic and haptic explanations fostered the most trust, with the symbolic component contributing the most. Interestingly, the explanation in the form of a text summary didn’t foster more trust than simply watching the robot perform the task, indicating that humans prefer robots to give step-by-step explanations of what they’re doing.

The most interesting outcome of this research is that what makes robots perform well is not the same as what makes people see them as trustworthy. The robot needed both the symbolic and haptic components to do the best job. But it was the symbolic explanation that made people trust the robot most.

This divergence highlights important goals for future artificial intelligence and robotics research: to focus on pursuing both task performance and explainability. Only focussing on task performance may not lead to a robot that explains itself well. Performance and explanation do not naturally complement each other, so both goals need to be a priority from the start when building artificial intelligence systems.

source: The next frontier of human-robot relationships is building trust

At the Centre for Vision, Cognition, Learning, and Autonomy at University of California Los Angeles, we are interested in what factors make machines more trustworthy, and how well different learning algorithms enable trust. In our latest research, we experimented with different ways a robot could explain its actions to a human observer. Interestingly, the forms of explanation that fostered the most human trust did not correspond to the learning algorithms that produced the best task performance. This suggests performance and explanation are not inherently dependent upon each other. This divergence calls for robot designs that takes into account both good task performance and trustworthy explanations.

We taught a robot to learn from human demonstrations how to open a medicine bottle with a safety lock. A person wore a tactile glove that recorded the poses and forces of the human hand as it opened the bottle. That information helped the robot learn what the human did in two ways: symbolic and haptic. Symbolic refers to meaningful representations of your actions: for example, the word “grasp.” Haptic refers to the feelings associated with your body’s postures and motions: for example, the sensation of your fingers closing together.

It turns out the robot was able to achieve its best performance when combining the symbolic and haptic components. The robot did better, using knowledge of the steps for performing the task and real-time sensing from its gripper than using either alone.

To explain its actions, the robot can draw on its internal decision process as well as its behaviour. The symbolic model provides step-by-step descriptions of the robot’s actions, and the haptic model provides a sense of what the robot gripper is “feeling.”

In our experiment, we added an additional explanation for humans: a text write-up that provided a summary after the robot has finished attempting to open the medicine bottle. We asked 150 human participants, divided into four groups, to observe the robot attempting to open the medicine bottle. The robot then gave each group a different explanation of the task: symbolic, step-by-step, haptic – arm positions and motions, text summary, or symbolic and haptic together. A baseline group observed only a video of the robot attempting to open the bottle, without providing any additional explanations.

We found that providing both the symbolic and haptic explanations fostered the most trust, with the symbolic component contributing the most. Interestingly, the explanation in the form of a text summary didn’t foster more trust than simply watching the robot perform the task, indicating that humans prefer robots to give step-by-step explanations of what they’re doing.

The most interesting outcome of this research is that what makes robots perform well is not the same as what makes people see them as trustworthy. The robot needed both the symbolic and haptic components to do the best job. But it was the symbolic explanation that made people trust the robot most.

This divergence highlights important goals for future artificial intelligence and robotics research: to focus on pursuing both task performance and explainability. Only focussing on task performance may not lead to a robot that explains itself well. Performance and explanation do not naturally complement each other, so both goals need to be a priority from the start when building artificial intelligence systems.

source: The next frontier of human-robot relationships is building trust

At the Centre for Vision, Cognition, Learning, and Autonomy at University of California Los Angeles, we are interested in what factors make machines more trustworthy, and how well different learning algorithms enable trust. In our latest research, we experimented with different ways a robot could explain its actions to a human observer. Interestingly, the forms of explanation that fostered the most human trust did not correspond to the learning algorithms that produced the best task performance. This suggests performance and explanation are not inherently dependent upon each other. This divergence calls for robot designs that takes into account both good task performance and trustworthy explanations.

We taught a robot to learn from human demonstrations how to open a medicine bottle with a safety lock. A person wore a tactile glove that recorded the poses and forces of the human hand as it opened the bottle. That information helped the robot learn what the human did in two ways: symbolic and haptic. Symbolic refers to meaningful representations of your actions: for example, the word “grasp.” Haptic refers to the feelings associated with your body’s postures and motions: for example, the sensation of your fingers closing together.

It turns out the robot was able to achieve its best performance when combining the symbolic and haptic components. The robot did better, using knowledge of the steps for performing the task and real-time sensing from its gripper than using either alone.

To explain its actions, the robot can draw on its internal decision process as well as its behaviour. The symbolic model provides step-by-step descriptions of the robot’s actions, and the haptic model provides a sense of what the robot gripper is “feeling.”

In our experiment, we added an additional explanation for humans: a text write-up that provided a summary after the robot has finished attempting to open the medicine bottle. We asked 150 human participants, divided into four groups, to observe the robot attempting to open the medicine bottle. The robot then gave each group a different explanation of the task: symbolic, step-by-step, haptic – arm positions and motions, text summary, or symbolic and haptic together. A baseline group observed only a video of the robot attempting to open the bottle, without providing any additional explanations.

We found that providing both the symbolic and haptic explanations fostered the most trust, with the symbolic component contributing the most. Interestingly, the explanation in the form of a text summary didn’t foster more trust than simply watching the robot perform the task, indicating that humans prefer robots to give step-by-step explanations of what they’re doing.

The most interesting outcome of this research is that what makes robots perform well is not the same as what makes people see them as trustworthy. The robot needed both the symbolic and haptic components to do the best job. But it was the symbolic explanation that made people trust the robot most.

This divergence highlights important goals for future artificial intelligence and robotics research: to focus on pursuing both task performance and explainability. Only focussing on task performance may not lead to a robot that explains itself well. Performance and explanation do not naturally complement each other, so both goals need to be a priority from the start when building artificial intelligence systems.

source: The next frontier of human-robot relationships is building trust

At the Centre for Vision, Cognition, Learning, and Autonomy at University of California Los Angeles, we are interested in what factors make machines more trustworthy, and how well different learning algorithms enable trust. In our latest research, we experimented with different ways a robot could explain its actions to a human observer. Interestingly, the forms of explanation that fostered the most human trust did not correspond to the learning algorithms that produced the best task performance. This suggests performance and explanation are not inherently dependent upon each other. This divergence calls for robot designs that takes into account both good task performance and trustworthy explanations.

We taught a robot to learn from human demonstrations how to open a medicine bottle with a safety lock. A person wore a tactile glove that recorded the poses and forces of the human hand as it opened the bottle. That information helped the robot learn what the human did in two ways: symbolic and haptic. Symbolic refers to meaningful representations of your actions: for example, the word “grasp.” Haptic refers to the feelings associated with your body’s postures and motions: for example, the sensation of your fingers closing together.

It turns out the robot was able to achieve its best performance when combining the symbolic and haptic components. The robot did better, using knowledge of the steps for performing the task and real-time sensing from its gripper than using either alone.

To explain its actions, the robot can draw on its internal decision process as well as its behaviour. The symbolic model provides step-by-step descriptions of the robot’s actions, and the haptic model provides a sense of what the robot gripper is “feeling.”

In our experiment, we added an additional explanation for humans: a text write-up that provided a summary after the robot has finished attempting to open the medicine bottle. We asked 150 human participants, divided into four groups, to observe the robot attempting to open the medicine bottle. The robot then gave each group a different explanation of the task: symbolic, step-by-step, haptic – arm positions and motions, text summary, or symbolic and haptic together. A baseline group observed only a video of the robot attempting to open the bottle, without providing any additional explanations.

We found that providing both the symbolic and haptic explanations fostered the most trust, with the symbolic component contributing the most. Interestingly, the explanation in the form of a text summary didn’t foster more trust than simply watching the robot perform the task, indicating that humans prefer robots to give step-by-step explanations of what they’re doing.

The most interesting outcome of this research is that what makes robots perform well is not the same as what makes people see them as trustworthy. The robot needed both the symbolic and haptic components to do the best job. But it was the symbolic explanation that made people trust the robot most.

This divergence highlights important goals for future artificial intelligence and robotics research: to focus on pursuing both task performance and explainability. Only focussing on task performance may not lead to a robot that explains itself well. Performance and explanation do not naturally complement each other, so both goals need to be a priority from the start when building artificial intelligence systems.

source: The next frontier of human-robot relationships is building trust

At the Centre for Vision, Cognition, Learning, and Autonomy at University of California Los Angeles, we are interested in what factors make machines more trustworthy, and how well different learning algorithms enable trust. In our latest research, we experimented with different ways a robot could explain its actions to a human observer. Interestingly, the forms of explanation that fostered the most human trust did not correspond to the learning algorithms that produced the best task performance. This suggests performance and explanation are not inherently dependent upon each other. This divergence calls for robot designs that takes into account both good task performance and trustworthy explanations.

We taught a robot to learn from human demonstrations how to open a medicine bottle with a safety lock. A person wore a tactile glove that recorded the poses and forces of the human hand as it opened the bottle. That information helped the robot learn what the human did in two ways: symbolic and haptic. Symbolic refers to meaningful representations of your actions: for example, the word “grasp.” Haptic refers to the feelings associated with your body’s postures and motions: for example, the sensation of your fingers closing together.

It turns out the robot was able to achieve its best performance when combining the symbolic and haptic components. The robot did better, using knowledge of the steps for performing the task and real-time sensing from its gripper than using either alone.

To explain its actions, the robot can draw on its internal decision process as well as its behaviour. The symbolic model provides step-by-step descriptions of the robot’s actions, and the haptic model provides a sense of what the robot gripper is “feeling.”

In our experiment, we added an additional explanation for humans: a text write-up that provided a summary after the robot has finished attempting to open the medicine bottle. We asked 150 human participants, divided into four groups, to observe the robot attempting to open the medicine bottle. The robot then gave each group a different explanation of the task: symbolic, step-by-step, haptic – arm positions and motions, text summary, or symbolic and haptic together. A baseline group observed only a video of the robot attempting to open the bottle, without providing any additional explanations.

We found that providing both the symbolic and haptic explanations fostered the most trust, with the symbolic component contributing the most. Interestingly, the explanation in the form of a text summary didn’t foster more trust than simply watching the robot perform the task, indicating that humans prefer robots to give step-by-step explanations of what they’re doing.

The most interesting outcome of this research is that what makes robots perform well is not the same as what makes people see them as trustworthy. The robot needed both the symbolic and haptic components to do the best job. But it was the symbolic explanation that made people trust the robot most.

This divergence highlights important goals for future artificial intelligence and robotics research: to focus on pursuing both task performance and explainability. Only focussing on task performance may not lead to a robot that explains itself well. Performance and explanation do not naturally complement each other, so both goals need to be a priority from the start when building artificial intelligence systems.

source: The next frontier of human-robot relationships is building trust