Đề MH T.Anh 08 (reading 1)

When it comes to discussing AI and the future of work we exhale in relief when someone says machines will have a hard time replacing us. This statement is backed by the argument that most AI systems are ‘narrow’. AI systems only do one thing but do it really well, for example, predicting what you want to watch on Netflix. If you ask the same system to make you a cup of coffee or drive your car, you’re likely to be disappointed.
But what happens when the AI system can perform the most significant tasks that a job entails? One interesting example we found came from the fashion industry, where a company had implemented two AI systems to produce novel designs. Together, they did all work and the human’s role was only to surveil the work.
Research published earlier this year coined the term ‘Shadow Learning’. As explained in the Harvard Business Review article titled ‘Learning to work with intelligent machines’, the researcher studied the challenges new surgeons faced when learning robotic surgery skills. Previously, they learned how to perform surgery by working alongside expert surgeons, but now they’re forced to watch over the surgeon’s shoulder as, thanks to robotics, individuals can handle entire surgeries with one pair of hands. The term, however, does not mean that you learn by shadowing someone. The phrase refers to students who gained experience with robotic tools by taking it upon themselves to acquire new skills outside of the curriculum.
If we look beyond the current discussion of AI and the future of work – which usually revolves around the number of jobs that will be impacted by AI – we can focus on how to create inspiring new ways to work with machines.

Question 35: According to paragraph 1, why should we not worry about AI snatching away our jobs?

• A. Because creating something new and unique is an ability exclusive to humans.

•    B. Because nothing can replace the caring and empathy that a live person can extend.

• C. Because AI can take over certain tasks, but possibly not entire multidisciplinary roles.

•   D. Because AI cannot proactively find and start new tasks without some sort of prompt.

Answer explanation

• * take over certain tasks: đảm nhận một số nhiệm vụ nhất định

• * entire multidisciplinary roles: vai trò đa ngành

When it comes to discussing AI and the future of work we exhale in relief when someone says machines will have a hard time replacing us. This statement is backed by the argument that most AI systems are ‘narrow’. AI systems only do one thing but do it really well, for example, predicting what you want to watch on Netflix. If you ask the same system to make you a cup of coffee or drive your car, you’re likely to be disappointed.
But what happens when the AI system can perform the most significant tasks that a job entails? One interesting example we found came from the fashion industry, where a company had implemented two AI systems to produce novel designs. Together, they did all work and the human’s role was only to surveil the work.
Research published earlier this year coined the term ‘Shadow Learning’. As explained in the Harvard Business Review article titled ‘Learning to work with intelligent machines’, the researcher studied the challenges new surgeons faced when learning robotic surgery skills. Previously, they learned how to perform surgery by working alongside expert surgeons, but now they’re forced to watch over the surgeon’s shoulder as, thanks to robotics, individuals can handle entire surgeries with one pair of hands. The term, however, does not mean that you learn by shadowing someone. The phrase refers to students who gained experience with robotic tools by taking it upon themselves to acquire new skills outside of the curriculum.
If we look beyond the current discussion of AI and the future of work – which usually revolves around the number of jobs that will be impacted by AI – we can focus on how to create inspiring new ways to work with machines.

Question 36: The word “they” in paragraph 2 refers to _______.                                                                             

• A. designs    

•  B. systems  

• C. tasks  

• D. students

When it comes to discussing AI and the future of work we exhale in relief when someone says machines will have a hard time replacing us. This statement is backed by the argument that most AI systems are ‘narrow’. AI systems only do one thing but do it really well, for example, predicting what you want to watch on Netflix. If you ask the same system to make you a cup of coffee or drive your car, you’re likely to be disappointed.
But what happens when the AI system can perform the most significant tasks that a job entails? One interesting example we found came from the fashion industry, where a company had implemented two AI systems to produce novel designs. Together, they did all work and the human’s role was only to surveil the work.
Research published earlier this year coined the term ‘Shadow Learning’. As explained in the Harvard Business Review article titled ‘Learning to work with intelligent machines’, the researcher studied the challenges new surgeons faced when learning robotic surgery skills. Previously, they learned how to perform surgery by working alongside expert surgeons, but now they’re forced to watch over the surgeon’s shoulder as, thanks to robotics, individuals can handle entire surgeries with one pair of hands. The term, however, does not mean that you learn by shadowing someone. The phrase refers to students who gained experience with robotic tools by taking it upon themselves to acquire new skills outside of the curriculum.
If we look beyond the current discussion of AI and the future of work – which usually revolves around the number of jobs that will be impacted by AI – we can focus on how to create inspiring new ways to work with machines.

Question 37: According to paragraph 3, what is the problem for surgical apprentices in the time of AI universality?

• A. Offer of exposure to new technologies would be used as recruiting strategy.

•      B. There would be fewer hands-on learning opportunities for the students.

•   C. Ability to work individually, however brilliantly, would not give a competitive edge.

• D. The students could advance only by upstaging other people around them.

Answer explanation

fewer hands-on learning opportunities: ít cơ hội học tập thực hành

When it comes to discussing AI and the future of work we exhale in relief when someone says machines will have a hard time replacing us. This statement is backed by the argument that most AI systems are ‘narrow’. AI systems only do one thing but do it really well, for example, predicting what you want to watch on Netflix. If you ask the same system to make you a cup of coffee or drive your car, you’re likely to be disappointed.
But what happens when the AI system can perform the most significant tasks that a job entails? One interesting example we found came from the fashion industry, where a company had implemented two AI systems to produce novel designs. Together, they did all work and the human’s role was only to surveil the work.
Research published earlier this year coined the term ‘Shadow Learning’. As explained in the Harvard Business Review article titled ‘Learning to work with intelligent machines’, the researcher studied the challenges new surgeons faced when learning robotic surgery skills. Previously, they learned how to perform surgery by working alongside expert surgeons, but now they’re forced to watch over the surgeon’s shoulder as, thanks to robotics, individuals can handle entire surgeries with one pair of hands. The term, however, does not mean that you learn by shadowing someone. The phrase refers to students who gained experience with robotic tools by taking it upon themselves to acquire new skills outside of the curriculum.
If we look beyond the current discussion of AI and the future of work – which usually revolves around the number of jobs that will be impacted by AI – we can focus on how to create inspiring new ways to work with machines.

Question 38: The word “revolves” in paragraph 4 is closest in meaning to ______.

                                                                    

• A. spins   

•  B. worries    

•  C. muses

• D. pivots

Answer explanation

revolves around (phr.v): xoay quanh cái gì, có cái gì như là mối quan tâm hay vấn đề chính ~ pivots around sth (phr.v)

When it comes to discussing AI and the future of work we exhale in relief when someone says machines will have a hard time replacing us. This statement is backed by the argument that most AI systems are ‘narrow’. AI systems only do one thing but do it really well, for example, predicting what you want to watch on Netflix. If you ask the same system to make you a cup of coffee or drive your car, you’re likely to be disappointed.
But what happens when the AI system can perform the most significant tasks that a job entails? One interesting example we found came from the fashion industry, where a company had implemented two AI systems to produce novel designs. Together, they did all work and the human’s role was only to surveil the work.
Research published earlier this year coined the term ‘Shadow Learning’. As explained in the Harvard Business Review article titled ‘Learning to work with intelligent machines’, the researcher studied the challenges new surgeons faced when learning robotic surgery skills. Previously, they learned how to perform surgery by working alongside expert surgeons, but now they’re forced to watch over the surgeon’s shoulder as, thanks to robotics, individuals can handle entire surgeries with one pair of hands. The term, however, does not mean that you learn by shadowing someone. The phrase refers to students who gained experience with robotic tools by taking it upon themselves to acquire new skills outside of the curriculum.
If we look beyond the current discussion of AI and the future of work – which usually revolves around the number of jobs that will be impacted by AI – we can focus on how to create inspiring new ways to work with machines.

Question 34: Which best serves as the title for the passage? 
                                            

•   A. AI and the future of work.   

•  B. Finding the sweet spot of trust.

• C. Overreliance on AI systems.           

• D. Ensure that work remains meaningful.