一、Part Ⅱ Listening Comprehension
1、Question 1 is based on the conversation you have just heard.
A、A driving test.
B、A video game.
C、Traffic routes.
D、Cargo logistics.
2、Question 2 is based on the conversation you have just heard.
A、He found it instructive and realistic.
B、He bought it when touring Europe.
C、He was really drawn to its other versions.
D、He introduced it to his brother last year.
3、Question 3 is based on the conversation you have just heard.
A、Travelling all over the country.
B、Driving from one city to another.
C、The details in the driving simulator.
D、The key role of the logistics industry.
4、Question 4 is based on the conversation you have just heard.
A、Clearer road signs.
B、More people driving safely.
C、Stricter traffic rules.
D、More self-driving trucks on the road.
5、Question 5 is based on the conversation you have just heard.
A、It isn’t so enjoyable as he expected.
B、It isn’t so motivating as he believed.
C、It doesn’t enable him to earn as much money as he used to.
D、It doesn’t seem to offer as much freedom as he anticipated.
6、Question 6 is based on the conversation you have just heard.
A、Not all of them care about their employees’ behaviors.
B、Few of them are aware of their employees’ feelings.
C、Few of them offer praise and reward to their employees.
D、Not all of them know how to motivate their employees.
7、Question 7 is based on the conversation you have just heard.
A、Job satisfaction.
B、Self-awareness.
C、Autonomy.
D、Money.
8、Question 8 is based on the conversation you have just heard.
A、The importance of cultivating close relationships with clients.
B、The need for getting recommendations from their managers.
C、The advantages of permanent full-time employment.
D、The way to explore employees’ interests and talents.
9、Question 9 is based on the passage you have just heard.
A、Consumers visualize their activities in different weather.
B、Good weather triggers consumers’ desire to go shopping.
C、Weather conditions influence consumers’ buying behavior.
D、Consumers’ mental states change with the prices of goods.
10、Question 10 is based on the passage you have just heard.
A、Active consumption.
B、Direct correlation.
C、Individual association.
D、Mental visualization.
11、Question 11 is based on the passage you have just heard.
A、Enabling them to simplify their mathematical formulas.
B、Helping them determine what to sell and at what price.
C、Enabling them to sell their products at a higher price.
D、Helping them advertise a greater variety of products.
12、Question 12 is based on the passage you have just heard.
A、A naturally ventilated office is more comfortable.
B、A cool office will boost employees’ productivity.
C、Office air-conditioning should follow guidebooks.
D、Air-conditioning improves ventilation in the office.
13、Question 13 is based on the passage you have just heard.
A、People in their comfort zone of temperature are more satisfied with their productivity.
B、People in different countries vary in their tolerance to uncomfortable temperatures.
C、Twenty-two degrees is the optimal temperature for office workers.
D、There is a range of temperatures for people to feel comfortable.
14、Question 14 is based on the passage you have just heard.
A、It will have no negative impact on work.
B、It will be immediately noticeable.
C、It will sharply decrease work efficiency.
D、It will cause a lot of discomfort.
15、Question 15 is based on the passage you have just heard.
A、They tend to favor lower temperatures.
B、They suffer from rapid temperature changes.
C、They are not bothered by temperature extremes.
D、They become less sensitive to high temperatures.
16、Question 16 is based on the recording you have just heard.
A、It overlooked the possibility that emotions may be controlled.
B、It ignored the fact that emotions are personal and subjective.
C、It classified emotions simply as either positive or negative.
D、It measured positive and negative emotions independently.
17、Question 17 is based on the recording you have just heard.
A、Sitting alone without doing anything seemed really distressing.
B、Solitude adversely affected the participants’ mental well-being.
C、Siting alone for 15 minutes made the participants restless.
D、Solitude had a reductive effect on high-arousal emotions.
18、Question 18 is based on the recording you have just heard.
A、It proved hard to depict objectively.
B、It went hand in hand with sadness.
C、It helped increase low-arousal emotions.
D、It tended to intensify negative emotions.
19、Question 19 is based on the recording you have just heard.
A、It uses up much less energy than it does in deep thinking.
B、It remains inactive without burning calories noticeably.
C、It continues to burn up calories to help us stay in shape.
D、It consumes almost a quarter of the body’s total energy.
20、Question 20 is based on the recording you have just heard.
A、Much of the consumption has nothing to do with conscious activities.
B、It has something to do with the difficulty of the activities in question.
C、Energy usage devoted to active learning accounts for a big part of it.
D、A significant amount of it is for performing difficult cognitive tasks.
21、Question 21 is based on the recording you have just heard.
A、It is believed to remain basically constant.
B、It is a prerequisite for any mental activity.
C、It is conducive to relieving mental exhaustion.
D、It is thought to be related to food consumption.
22、Question 22 is based on the recording you have just heard.
A、Job candidates rarely take it seriously.
B、Job seekers tend to have a ready answer.
C、Job seekers often feel at a loss where to start in answering it.
D、Job candidates can respond freely due to its open-ended nature.
23、Question 23 is based on the recording you have just heard.
A、Follow their career coaches’ guidelines.
B、Strive to take control of their narrative.
C、Do their best to impress the interviewer.
D、Repeat the information on their resume.
24、Question 24 is based on the recording you have just heard.
A、To reflect on their past achievements as well as failures.
B、To produce examples for different interview questions.
C、To discuss important details they are going to present.
D、To identify a broad general strength to elaborate on.
25、Question 25 is based on the recording you have just heard.
A、Getting acquainted with the human resources personnel.
B、Finding out why the company provides the job opening.
C、Figuring out what benefits the company is able to offer them.
D、Tailoring their expectations to the company’s long-term goal.
二、Part III Reading Comprehension
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
26、(1)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
27、(2)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
28、(3)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
29、(4)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
30、(5)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
31、(6)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
32、(7)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
33、(8)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
34、(9)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The idea of taxing things that are bad for society has a powerful allure. It offers the possibility of a double benefit —(26)_____ harmful activities, while also providing the government with revenue.
Take sin taxes. Taxes on alcohol make it more expensive to get drunk, which reduces excessive drinking and (27)_____ driving. At the same time, they provide state and local governments with billions of dollars of revenue. Tobacco taxes, which generate more than twice as much, have proven (28)_____ in the decline of smoking, which has saved millions of lives.
Taxes can also be an important tool for environmental protection, and many economists say taxing carbon would be the best way to reduce greenhouse gas emissions. Economic theory says that unlike income or sales taxes, carbon taxes can actually increase economic efficiency; because companies that (29)_____ carbon dioxide into the sky don’t pay the costs of the climate change they cause, carbon taxes would restore the proper (30)_____ to the market.
In reality, carbon taxes alone won’t be enough to halt global warming, but they would be a useful part of any climate plan. What’s more, the revenue from this tax, which would (31)_____ be hundreds of billions of dollars per year, could be handed out to citizens as a (32)_____ or used to fund green infrastructure projects.
Similarly, a wealth tax has been put forward as a way to reduce inequality while raising revenue. The revenue from this tax, which some experts (33)_____ will be over $4 trillion per decade, would be designated for housing, child care, health care and other government benefits. If you believe, as many do, that wealth inequality is (34)_____ bad, then these taxes improve society while also (35)_____ government coffers (金库).
35、(10)
A、merging
B、inherently
C、instrumental
D、emotional
E、predict
F、pump
G、probably
H、initially
I、swelling
J、incentives
K、dividend
L、fragments
M、discouraging
N、imprisoned
O、impaired
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
36、36. Farmers will not profit from replanting once they have applied most of the fertilizer and other chemicals to their fields.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
37、37. Agriculture differs from the medical science of the human body in that its environment is not a contained one.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
38、38. The agronomist is sure that he will obtain a near accurate count of plant population with his software.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
39、39. The application of artificial intelligence to agriculture is much more challenging than to most other industries.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
40、40. Even the farmers know the data provided by the UAV is not correct.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
41、41. The pressure for quick results leads to product failure, which, in turn, arouses doubts about the applicability of AI technology to agriculture.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
42、42. Remote sensors are aimed to help farmers improve decision-making to increase yields.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
43、43. The farmer expects the software to tell him whether he will have to replant any parts of his farm fields.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
44、44. Agriculture proves very difficult to quantify because of the constantly changing conditions involved.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
The Challenges for Artificial Intelligence in Agriculture
【A】A group of corn farmers stands huddled around an agronomist (农学家) and his computer on the side of an irrigation machine in central South Africa. The agronomist has just flown over the field with a hybrid unmanned aerial vehicle (UAV) that takes off and lands using propellers yet maintains distance and speed for scanning vast hectares of land through the use of its fixed wings.
【B】The UAV is fitted with a four spectral band precision sensor that conducts onboard processing immediately after the flight, allowing farmers and field staff to address, almost immediately, any crop abnormalities that the sensor may have recorded, making the data collection truly real-time.
【C】In this instance, the farmers and agronomist are looking to specialized software to give them an accurate plant population count. It’s been 10 days since the corn emerged and the farmer wants to determine if there are any parts of the field that require replanting due to a lack of emergence or wind damage, which can be severe in the early stages of the summer rainy season.
【D】At this growth stage of the plant’s development, the farmer has another 10 days to conduct any replanting before the majority of his fertilizer and chemical applications need to occur. Once these have been applied, it becomes economically unviable to take corrective action, making any further collected data historical and useful only to inform future practices for the season to come.
【E】The software completes its processing in under 15 minutes producing a plant population count map. It’s difficult to grasp just how impressive this is, without understanding that just over a year ago it would have taken three to five days to process the exact same data set, illustrating the advancements that have been achieved in precision agriculture and remote sensing in recent years. With the software having been developed in the United States on the same variety of crops in seemingly similar conditions, the agronomist feels confident that the software will produce a near accurate result.
【F】As the map appears on the screen, the agronomist’s face begins to drop. Having walked through the planted rows before the flight to gain a physical understanding of the situation on the ground, he knows the instant he sees the data on his screen that the plant count is not correct, and so do the farmers, even with their limited understanding of how to read remote sensing maps.
【G】Hypothetically, it is possible for machines to learn to solve any problem on earth relating to the physical interaction of all things within a defined or contained environment by using artificial intelligence and machine learning.
【H】Remote sensors enable algorithms (算法) to interpret a field’s environment as statistical data that can be understood and useful to farmers for decision-making. Algorithms process the data, adapting and learning based on the data received. The more inputs and statistical information collected, the better the algorithm will be at predicting a range of outcomes. And the aim is that farmers can use this artificial intelligence to achieve their goal of a better harvest through making better decisions in the field.
【I】In 2011, IBM, through its R&D Headquarters in Haifa, Israel, launched an agricultural cloud-computing project. The project, in collaboration with a number of specialized IT and agricultural partners, had one goal in mind—to take a variety of academic and physical data sources from an agricultural environment and turn these into automatic predictive solutions for farmers that would assist them in making real-time decisions in the field.
【J】 Interviews with some of the IBM project team members at the time revealed that the team believed it was entirely possible to “algorithm” agriculture, meaning that algorithms could solve any problem in the world. Earlier that year, IBM’s cognitive learning system, Watson, competed in the game Jeopardy against former winners Brad Rutter and Ken Jennings with astonishing results. Several years later, Watson went on to produce ground-breaking achievements in the field of medicine.
【K】So why did the project have such success in medicine but not agriculture? Because it is one of the most difficult fields to contain for the purpose of statistical quantification. Even within a single field, conditions are always changing from one section to the next. There’s unpredictable weather, changes in soil quality, and the ever-present possibility that pests and diseases may pay a visit. Growers may feel their prospects are good for an upcoming harvest, but until that day arrives, the outcome will always be uncertain.
【L】By comparison, our bodies are a contained environment. Agriculture takes place in nature, among ecosystems of interacting organisms and activity, and crop production takes place within that ecosystem environment. But these ecosystems are not contained. They are subject to climatic occurrences such as weather systems, which impact upon hemispheres as a whole, and from continent to continent. Therefore, understanding how to manage an agricultural environment means taking literally many hundreds if not thousands of factors into account.
【M】What may occur with the same seed and fertilizer program in the United States’ Midwest region is almost certainly unrelated to what may occur with the same seed and fertilizer program in Australia or South Africa. A few factors that could impact on variation would typically include the measurement of rain per unit of a crop planted, soil type, patterns of soil degradation, daylight hours, temperature and so forth.
【O】Practically, to say that AI and Machine Learning can be developed to solve all problems related to our physical environmentto basically say that we have a complete understanding of all aspects of the interaction of physical or material activity on the planet. After all, it is only through our understanding of ‘the nature of things’ that protocols and processes are designed for the rational capabilities of cognitive systems to take place. And, although AI and Machine Learning are teaching us many things about how to understand our environment, we are still far from being able to predict critical outcomes in fields like agriculture purely through the cognitive ability of machines.
【P】Backed by the venture capital community, which is now investing billions of dollars into the sector, most agricultural technology startups today are pushed to complete development as quickly as possible and then encouraged to flood the market as quickly as possible with their products.
【Q】This usually results in a failure of a product, which leads to skepticism from the market and delivers a blow to the integrity of Machine Learning technology. In most cases, the problem is not that the technology does not work, the problem is that industry has not taken the time to respect that agriculture is one of the most uncontained environments to manage. For technology to truly make an impact on agriculture, more effort, skills, and funding is needed to test these technologies in farmers’ fields.【R】There is huge potential for artificial intelligence and machine learning to revolutionize agriculture by integrating these technologies into critical markets on a global scale. Only then can it make a difference to the grower, where it really counts.
45、45. The same seed and fertilizer program may yield completely different outcomes in different places.
A、A
B、B
C、C
D、D
E、E
F、F
G、G
H、H
I、I
J、J
K、K
L、L
M、M
N、N
O、O
P、P
Q、Q
R、R
What is the place of art in a culture of inattention? Recent visitors to the Louvre report that tourists can now spend only a minute in front of the Mona Lisa before being asked to move on. Much of that time, for some of them, is spent taking photographs not even of the painting but of themselves with the painting in the background.
One view is that we have democratised tourism and gallery-going so much that we have made it effectively impossible to appreciate what we’ve travelled to see. In this oversubscribed society, experience becomes a commodity like any other. There are queues to climb Mt. Jolmo Lungma as well as to see famous paintings. Leisure, thus conceived, is hard labour, and returning to work becomes a well-earned break from the ordeal.
What gets lost in this industrialised haste is the quality of looking. Consider an extreme example, the late philosopher Richard Wollheim. When he visited the Louvre he could spend as much as four hours sitting before a painting. The first hour, he claimed, was necessary for misperceptions to be eliminated. It was only then that the picture would begin to disclose itself. This seems unthinkable today, but it is still possible to organise. Even in the busiest museums there are many rooms and many pictures worth hours of contemplation which the crowds largely ignore. Sometimes the largest crowds are partly the products of bad management; the Mona Lisa is such a hurried experience today partly because the museum is being reorganised. The Uffizi in Florence, another site of cultural pilgrimage, has cut its entry queues down to seven minutes by clever management. And there are some forms of art, those designed to be spectacles as well as objects of contemplation, which can work perfectly well in the face of huge crowds.
Olafur Eliasson’s current Tate Modern show, for instance, might seem nothing more than an entertainment, overrun as it is with kids romping (喧闹地玩耍) in fog rooms and spray mist installations. But it’s more than that: where Eliasson is at his most entertaining, he is at his most serious too, and his disorienting installations bring home the reality of the destructive effects we are having on the planet—not least what we are doing to the glaciers of Eliasson’s beloved Iceland.
Marcel Proust, another lover of the Louvre, wrote: “It is only through art that we can escape from ourselves and know how another person sees a universe, whose landscapes would otherwise have remained as unknown as any on the moon.” If any art remains worth seeing, it must lead us to such escapes. But a minute in front of a painting in a hurried crowd won’t do that.
46、46. What does the scene at the Louvre demonstrate according to the author?
A、The enormous appeal of a great piece of artistic work to tourists.
B、The near impossibility of appreciating art in an age of mass tourism.
C、The ever-growing commercial value of long-cherished artistic works.
D、The real difficulty in getting a glimpse at a masterpiece amid a crowd.
What is the place of art in a culture of inattention? Recent visitors to the Louvre report that tourists can now spend only a minute in front of the Mona Lisa before being asked to move on. Much of that time, for some of them, is spent taking photographs not even of the painting but of themselves with the painting in the background.
One view is that we have democratised tourism and gallery-going so much that we have made it effectively impossible to appreciate what we’ve travelled to see. In this oversubscribed society, experience becomes a commodity like any other. There are queues to climb Mt. Jolmo Lungma as well as to see famous paintings. Leisure, thus conceived, is hard labour, and returning to work becomes a well-earned break from the ordeal.
What gets lost in this industrialised haste is the quality of looking. Consider an extreme example, the late philosopher Richard Wollheim. When he visited the Louvre he could spend as much as four hours sitting before a painting. The first hour, he claimed, was necessary for misperceptions to be eliminated. It was only then that the picture would begin to disclose itself. This seems unthinkable today, but it is still possible to organise. Even in the busiest museums there are many rooms and many pictures worth hours of contemplation which the crowds largely ignore. Sometimes the largest crowds are partly the products of bad management; the Mona Lisa is such a hurried experience today partly because the museum is being reorganised. The Uffizi in Florence, another site of cultural pilgrimage, has cut its entry queues down to seven minutes by clever management. And there are some forms of art, those designed to be spectacles as well as objects of contemplation, which can work perfectly well in the face of huge crowds.
Olafur Eliasson’s current Tate Modern show, for instance, might seem nothing more than an entertainment, overrun as it is with kids romping (喧闹地玩耍) in fog rooms and spray mist installations. But it’s more than that: where Eliasson is at his most entertaining, he is at his most serious too, and his disorienting installations bring home the reality of the destructive effects we are having on the planet—not least what we are doing to the glaciers of Eliasson’s beloved Iceland.
Marcel Proust, another lover of the Louvre, wrote: “It is only through art that we can escape from ourselves and know how another person sees a universe, whose landscapes would otherwise have remained as unknown as any on the moon.” If any art remains worth seeing, it must lead us to such escapes. But a minute in front of a painting in a hurried crowd won’t do that.
47、47. Why did the late philosopher Richard Wollheim spend four hours before a picture?
A、It takes time to appreciate a piece of art fully.
B、 It is quite common to misinterpret artistic works.
C、The longer people contemplate a picture, the more likely they will enjoy it.
D、The more time one spends before a painting, the more valuable one finds it.
What is the place of art in a culture of inattention? Recent visitors to the Louvre report that tourists can now spend only a minute in front of the Mona Lisa before being asked to move on. Much of that time, for some of them, is spent taking photographs not even of the painting but of themselves with the painting in the background.
One view is that we have democratised tourism and gallery-going so much that we have made it effectively impossible to appreciate what we’ve travelled to see. In this oversubscribed society, experience becomes a commodity like any other. There are queues to climb Mt. Jolmo Lungma as well as to see famous paintings. Leisure, thus conceived, is hard labour, and returning to work becomes a well-earned break from the ordeal.
What gets lost in this industrialised haste is the quality of looking. Consider an extreme example, the late philosopher Richard Wollheim. When he visited the Louvre he could spend as much as four hours sitting before a painting. The first hour, he claimed, was necessary for misperceptions to be eliminated. It was only then that the picture would begin to disclose itself. This seems unthinkable today, but it is still possible to organise. Even in the busiest museums there are many rooms and many pictures worth hours of contemplation which the crowds largely ignore. Sometimes the largest crowds are partly the products of bad management; the Mona Lisa is such a hurried experience today partly because the museum is being reorganised. The Uffizi in Florence, another site of cultural pilgrimage, has cut its entry queues down to seven minutes by clever management. And there are some forms of art, those designed to be spectacles as well as objects of contemplation, which can work perfectly well in the face of huge crowds.
Olafur Eliasson’s current Tate Modern show, for instance, might seem nothing more than an entertainment, overrun as it is with kids romping (喧闹地玩耍) in fog rooms and spray mist installations. But it’s more than that: where Eliasson is at his most entertaining, he is at his most serious too, and his disorienting installations bring home the reality of the destructive effects we are having on the planet—not least what we are doing to the glaciers of Eliasson’s beloved Iceland.
Marcel Proust, another lover of the Louvre, wrote: “It is only through art that we can escape from ourselves and know how another person sees a universe, whose landscapes would otherwise have remained as unknown as any on the moon.” If any art remains worth seeing, it must lead us to such escapes. But a minute in front of a painting in a hurried crowd won’t do that.
48、48. What does the case of the Uffizi in Florence show?
A、Art works in museums should be better taken care of.
B、Sites of cultural pilgrimage are always flooded with visitors.
C、Good management is key to handling large crowds of visitors.
D、 Large crowds of visitors cause management problems for museums.
What is the place of art in a culture of inattention? Recent visitors to the Louvre report that tourists can now spend only a minute in front of the Mona Lisa before being asked to move on. Much of that time, for some of them, is spent taking photographs not even of the painting but of themselves with the painting in the background.
One view is that we have democratised tourism and gallery-going so much that we have made it effectively impossible to appreciate what we’ve travelled to see. In this oversubscribed society, experience becomes a commodity like any other. There are queues to climb Mt. Jolmo Lungma as well as to see famous paintings. Leisure, thus conceived, is hard labour, and returning to work becomes a well-earned break from the ordeal.
What gets lost in this industrialised haste is the quality of looking. Consider an extreme example, the late philosopher Richard Wollheim. When he visited the Louvre he could spend as much as four hours sitting before a painting. The first hour, he claimed, was necessary for misperceptions to be eliminated. It was only then that the picture would begin to disclose itself. This seems unthinkable today, but it is still possible to organise. Even in the busiest museums there are many rooms and many pictures worth hours of contemplation which the crowds largely ignore. Sometimes the largest crowds are partly the products of bad management; the Mona Lisa is such a hurried experience today partly because the museum is being reorganised. The Uffizi in Florence, another site of cultural pilgrimage, has cut its entry queues down to seven minutes by clever management. And there are some forms of art, those designed to be spectacles as well as objects of contemplation, which can work perfectly well in the face of huge crowds.
Olafur Eliasson’s current Tate Modern show, for instance, might seem nothing more than an entertainment, overrun as it is with kids romping (喧闹地玩耍) in fog rooms and spray mist installations. But it’s more than that: where Eliasson is at his most entertaining, he is at his most serious too, and his disorienting installations bring home the reality of the destructive effects we are having on the planet—not least what we are doing to the glaciers of Eliasson’s beloved Iceland.
Marcel Proust, another lover of the Louvre, wrote: “It is only through art that we can escape from ourselves and know how another person sees a universe, whose landscapes would otherwise have remained as unknown as any on the moon.” If any art remains worth seeing, it must lead us to such escapes. But a minute in front of a painting in a hurried crowd won’t do that.
49、49. What do we learn from Olafur Eliasson’s current Tate Modern show?
A、Children learn to appreciate art works most effectively while they are playing.
B、It is possible to combine entertainment with appreciation of serious art.
C、Art works about the environment appeal most to young children.
D、Some forms of art can accommodate huge crowds of visitors.
What is the place of art in a culture of inattention? Recent visitors to the Louvre report that tourists can now spend only a minute in front of the Mona Lisa before being asked to move on. Much of that time, for some of them, is spent taking photographs not even of the painting but of themselves with the painting in the background.
One view is that we have democratised tourism and gallery-going so much that we have made it effectively impossible to appreciate what we’ve travelled to see. In this oversubscribed society, experience becomes a commodity like any other. There are queues to climb Mt. Jolmo Lungma as well as to see famous paintings. Leisure, thus conceived, is hard labour, and returning to work becomes a well-earned break from the ordeal.
What gets lost in this industrialised haste is the quality of looking. Consider an extreme example, the late philosopher Richard Wollheim. When he visited the Louvre he could spend as much as four hours sitting before a painting. The first hour, he claimed, was necessary for misperceptions to be eliminated. It was only then that the picture would begin to disclose itself. This seems unthinkable today, but it is still possible to organise. Even in the busiest museums there are many rooms and many pictures worth hours of contemplation which the crowds largely ignore. Sometimes the largest crowds are partly the products of bad management; the Mona Lisa is such a hurried experience today partly because the museum is being reorganised. The Uffizi in Florence, another site of cultural pilgrimage, has cut its entry queues down to seven minutes by clever management. And there are some forms of art, those designed to be spectacles as well as objects of contemplation, which can work perfectly well in the face of huge crowds.
Olafur Eliasson’s current Tate Modern show, for instance, might seem nothing more than an entertainment, overrun as it is with kids romping (喧闹地玩耍) in fog rooms and spray mist installations. But it’s more than that: where Eliasson is at his most entertaining, he is at his most serious too, and his disorienting installations bring home the reality of the destructive effects we are having on the planet—not least what we are doing to the glaciers of Eliasson’s beloved Iceland.
Marcel Proust, another lover of the Louvre, wrote: “It is only through art that we can escape from ourselves and know how another person sees a universe, whose landscapes would otherwise have remained as unknown as any on the moon.” If any art remains worth seeing, it must lead us to such escapes. But a minute in front of a painting in a hurried crowd won’t do that.
50、50. What can art do according to Marcel Proust?
A、Enable us to live a much fuller life.
B、Allow us to escape the harsh reality.
C、Help us to see the world from a different perspective.
D、Urge us to explore the unknown domain of the universe.
Every five years, the government tries to tell Americans what to put in their bellies. Eat more vegetables. Dial back the fats. It’s all based on the best available science for leading a healthy life. But the best available science also has a lot to say about what those food choices do to the environment, and some researchers are annoyed that new dietary recommendations of the USDA (United States Department of Agriculture) released yesterday seem to utterly ignore that fact.
Broadly, the 2016-2020 dietary recommendations aim for balance: More vegetables, leaner meats and far less sugar.
But Americans consume more calories per capita than almost any other country in the world. So the things Americans eat have a huge impact on climate change. Soil tilling releases carbon dioxide, and delivery vehicles emit exhaust. The government’s dietary guidelines could have done a lot to lower that climate cost. Not just because of their position of authority: The guidelines drive billions of dollars of food production through federal programs like school lunches and nutrition assistance for the needy.
On its own, plant and animal agriculture contributes 9 percent of all the country’s greenhouse gas emissions. That’s not counting the fuel burned in transportation, processing, refrigeration, and other waypoints between farm and belly. Red meats are among the biggest and most notorious emitters, but trucking a salad from California to Minnesota in January also carries a significant burden. And greenhouse gas emissions aren’t the whole story. Food production is the largest user of fresh water, largest contributor to the loss of biodiversity, and a major contributor to using up natural resources.
All of these points and more showed up in the Dietary Guidelines Advisory Committee’s scientific report, released last February. Miriam Nelson chaired the subcommittee in charge of sustainability for the report, and is disappointed that eating less meat and buying local food aren’t in the final product. “Especially if you consider that eating less meat, especially red and processed, has health benefits,” she says.
So what happened? The official response is that sustainability falls too far outside the guidelines’ official scope, which is to provide “nutritional and dietary information.”
Possibly the agencies in charge of drafting the decisions are too close to the industries they are supposed to regulate. On one hand, the USDA is compiling dietary advice. On the other, their clients are US agriculture companies.
The line about keeping the guidelines’ scope to nutrition and diet doesn’t ring quite right with researchers. David Wallinga, for example, says “In previous guidelines, they’ve always been concerned with things like food security—which is presumably the mission of the USDA. You absolutely need to be worried about climate impacts and future sustainability if you want secure food in the future.”
51、51. Why are some researchers irritated at the USDA’s 2016-2020 Dietary Guidelines?
A、It ignores the harmful effect of red meat and processed food on health.
B、Too much emphasis is given to eating less meat and buying local food.
C、The dietary recommendations are not based on medical science.
D、It takes no notice of the potential impact on the environment.
Every five years, the government tries to tell Americans what to put in their bellies. Eat more vegetables. Dial back the fats. It’s all based on the best available science for leading a healthy life. But the best available science also has a lot to say about what those food choices do to the environment, and some researchers are annoyed that new dietary recommendations of the USDA (United States Department of Agriculture) released yesterday seem to utterly ignore that fact.
Broadly, the 2016-2020 dietary recommendations aim for balance: More vegetables, leaner meats and far less sugar.
But Americans consume more calories per capita than almost any other country in the world. So the things Americans eat have a huge impact on climate change. Soil tilling releases carbon dioxide, and delivery vehicles emit exhaust. The government’s dietary guidelines could have done a lot to lower that climate cost. Not just because of their position of authority: The guidelines drive billions of dollars of food production through federal programs like school lunches and nutrition assistance for the needy.
On its own, plant and animal agriculture contributes 9 percent of all the country’s greenhouse gas emissions. That’s not counting the fuel burned in transportation, processing, refrigeration, and other waypoints between farm and belly. Red meats are among the biggest and most notorious emitters, but trucking a salad from California to Minnesota in January also carries a significant burden. And greenhouse gas emissions aren’t the whole story. Food production is the largest user of fresh water, largest contributor to the loss of biodiversity, and a major contributor to using up natural resources.
All of these points and more showed up in the Dietary Guidelines Advisory Committee’s scientific report, released last February. Miriam Nelson chaired the subcommittee in charge of sustainability for the report, and is disappointed that eating less meat and buying local food aren’t in the final product. “Especially if you consider that eating less meat, especially red and processed, has health benefits,” she says.
So what happened? The official response is that sustainability falls too far outside the guidelines’ official scope, which is to provide “nutritional and dietary information.”
Possibly the agencies in charge of drafting the decisions are too close to the industries they are supposed to regulate. On one hand, the USDA is compiling dietary advice. On the other, their clients are US agriculture companies.
The line about keeping the guidelines’ scope to nutrition and diet doesn’t ring quite right with researchers. David Wallinga, for example, says “In previous guidelines, they’ve always been concerned with things like food security—which is presumably the mission of the USDA. You absolutely need to be worried about climate impacts and future sustainability if you want secure food in the future.”
52、52. Why does the author say the USDA could have contributed a lot to lowering the climate cost through its dietary guidelines?
A、It has the capacity and the financial resources to do so.
B、Its researchers have already submitted relevant proposals.
C、Its agencies in charge of drafting the guidelines have the expertise.
D、It can raise students’ environmental awareness through its programs.
Every five years, the government tries to tell Americans what to put in their bellies. Eat more vegetables. Dial back the fats. It’s all based on the best available science for leading a healthy life. But the best available science also has a lot to say about what those food choices do to the environment, and some researchers are annoyed that new dietary recommendations of the USDA (United States Department of Agriculture) released yesterday seem to utterly ignore that fact.
Broadly, the 2016-2020 dietary recommendations aim for balance: More vegetables, leaner meats and far less sugar.
But Americans consume more calories per capita than almost any other country in the world. So the things Americans eat have a huge impact on climate change. Soil tilling releases carbon dioxide, and delivery vehicles emit exhaust. The government’s dietary guidelines could have done a lot to lower that climate cost. Not just because of their position of authority: The guidelines drive billions of dollars of food production through federal programs like school lunches and nutrition assistance for the needy.
On its own, plant and animal agriculture contributes 9 percent of all the country’s greenhouse gas emissions. That’s not counting the fuel burned in transportation, processing, refrigeration, and other waypoints between farm and belly. Red meats are among the biggest and most notorious emitters, but trucking a salad from California to Minnesota in January also carries a significant burden. And greenhouse gas emissions aren’t the whole story. Food production is the largest user of fresh water, largest contributor to the loss of biodiversity, and a major contributor to using up natural resources.
All of these points and more showed up in the Dietary Guidelines Advisory Committee’s scientific report, released last February. Miriam Nelson chaired the subcommittee in charge of sustainability for the report, and is disappointed that eating less meat and buying local food aren’t in the final product. “Especially if you consider that eating less meat, especially red and processed, has health benefits,” she says.
So what happened? The official response is that sustainability falls too far outside the guidelines’ official scope, which is to provide “nutritional and dietary information.”
Possibly the agencies in charge of drafting the decisions are too close to the industries they are supposed to regulate. On one hand, the USDA is compiling dietary advice. On the other, their clients are US agriculture companies.
The line about keeping the guidelines’ scope to nutrition and diet doesn’t ring quite right with researchers. David Wallinga, for example, says “In previous guidelines, they’ve always been concerned with things like food security—which is presumably the mission of the USDA. You absolutely need to be worried about climate impacts and future sustainability if you want secure food in the future.”
53、53. What do we learn from the Dietary Guidelines Advisory Committee’s scientific report?
A、Food is easily contaminated from farm to belly.
B、Greenhouse effect is an issue still under debate.
C、Modern agriculture has increased food diversity.
D、Farming consumes most of our natural resources.
Every five years, the government tries to tell Americans what to put in their bellies. Eat more vegetables. Dial back the fats. It’s all based on the best available science for leading a healthy life. But the best available science also has a lot to say about what those food choices do to the environment, and some researchers are annoyed that new dietary recommendations of the USDA (United States Department of Agriculture) released yesterday seem to utterly ignore that fact.
Broadly, the 2016-2020 dietary recommendations aim for balance: More vegetables, leaner meats and far less sugar.
But Americans consume more calories per capita than almost any other country in the world. So the things Americans eat have a huge impact on climate change. Soil tilling releases carbon dioxide, and delivery vehicles emit exhaust. The government’s dietary guidelines could have done a lot to lower that climate cost. Not just because of their position of authority: The guidelines drive billions of dollars of food production through federal programs like school lunches and nutrition assistance for the needy.
On its own, plant and animal agriculture contributes 9 percent of all the country’s greenhouse gas emissions. That’s not counting the fuel burned in transportation, processing, refrigeration, and other waypoints between farm and belly. Red meats are among the biggest and most notorious emitters, but trucking a salad from California to Minnesota in January also carries a significant burden. And greenhouse gas emissions aren’t the whole story. Food production is the largest user of fresh water, largest contributor to the loss of biodiversity, and a major contributor to using up natural resources.
All of these points and more showed up in the Dietary Guidelines Advisory Committee’s scientific report, released last February. Miriam Nelson chaired the subcommittee in charge of sustainability for the report, and is disappointed that eating less meat and buying local food aren’t in the final product. “Especially if you consider that eating less meat, especially red and processed, has health benefits,” she says.
So what happened? The official response is that sustainability falls too far outside the guidelines’ official scope, which is to provide “nutritional and dietary information.”
Possibly the agencies in charge of drafting the decisions are too close to the industries they are supposed to regulate. On one hand, the USDA is compiling dietary advice. On the other, their clients are US agriculture companies.
The line about keeping the guidelines’ scope to nutrition and diet doesn’t ring quite right with researchers. David Wallinga, for example, says “In previous guidelines, they’ve always been concerned with things like food security—which is presumably the mission of the USDA. You absolutely need to be worried about climate impacts and future sustainability if you want secure food in the future.”
54、54. What may account for the neglect of sustainability in the USDA’s Dietary Guidelines according to the author?
A、Its exclusive concern with Americans’ food safety.
B、Its sole responsibility for providing dietary advice.
C、Its close ties with the agriculture companies.
D、Its alleged failure to regulate the industries.
Every five years, the government tries to tell Americans what to put in their bellies. Eat more vegetables. Dial back the fats. It’s all based on the best available science for leading a healthy life. But the best available science also has a lot to say about what those food choices do to the environment, and some researchers are annoyed that new dietary recommendations of the USDA (United States Department of Agriculture) released yesterday seem to utterly ignore that fact.
Broadly, the 2016-2020 dietary recommendations aim for balance: More vegetables, leaner meats and far less sugar.
But Americans consume more calories per capita than almost any other country in the world. So the things Americans eat have a huge impact on climate change. Soil tilling releases carbon dioxide, and delivery vehicles emit exhaust. The government’s dietary guidelines could have done a lot to lower that climate cost. Not just because of their position of authority: The guidelines drive billions of dollars of food production through federal programs like school lunches and nutrition assistance for the needy.
On its own, plant and animal agriculture contributes 9 percent of all the country’s greenhouse gas emissions. That’s not counting the fuel burned in transportation, processing, refrigeration, and other waypoints between farm and belly. Red meats are among the biggest and most notorious emitters, but trucking a salad from California to Minnesota in January also carries a significant burden. And greenhouse gas emissions aren’t the whole story. Food production is the largest user of fresh water, largest contributor to the loss of biodiversity, and a major contributor to using up natural resources.
All of these points and more showed up in the Dietary Guidelines Advisory Committee’s scientific report, released last February. Miriam Nelson chaired the subcommittee in charge of sustainability for the report, and is disappointed that eating less meat and buying local food aren’t in the final product. “Especially if you consider that eating less meat, especially red and processed, has health benefits,” she says.
So what happened? The official response is that sustainability falls too far outside the guidelines’ official scope, which is to provide “nutritional and dietary information.”
Possibly the agencies in charge of drafting the decisions are too close to the industries they are supposed to regulate. On one hand, the USDA is compiling dietary advice. On the other, their clients are US agriculture companies.
The line about keeping the guidelines’ scope to nutrition and diet doesn’t ring quite right with researchers. David Wallinga, for example, says “In previous guidelines, they’ve always been concerned with things like food security—which is presumably the mission of the USDA. You absolutely need to be worried about climate impacts and future sustainability if you want secure food in the future.”
55、55. What should the USDA do to achieve food security according to David Wallinga?
A、Give top priority to things like nutrition and food security.
B、 Endeavor to ensure the sustainable development of agriculture.
C、 Fulfill its mission by closely cooperating with the industries.
D、Study the long-term impact of climate change on food production.
三、Part IV Translation
56、 港珠澳大桥（Hong Kong-Zhuhai-Macau Bridge）全长55公里，是我国一项不同寻常的工程壮举。大桥将三个城市连接起来，是世界上最长的跨海桥梁和隧道系统。大桥将三个城市之间的旅行时间从3小时缩短到30分钟。这座跨度巨大的钢筋混凝土大桥充分证明中国有能力建造创纪录的巨型建筑。它将助推区域一体化，促进经济增长。大桥是中国发展自己的大湾区总体规划的关键。中国希望将大湾区建成在技术创新和经济繁荣上能与旧金山、纽约和东京的湾区相媲美的地区。
参考答案：
参考译文
With a total length of 55 kilometers, the Hong Kong-Zhuhai-Macau Bridge is an extraordinary feat of engineering in our country. As the longest cross-sea bridge and tunnel system in the world, the bridge connects Hong Kong, Zhuhai and Macau, shortening the traveling time between the three cities from 3 hours to 30 minutes. This steel reinforced concrete bridge with its huge span fully proves that China has the ability to build record-breaking giant constructions. It will enhance regional integration and promote economic growth. The bridge is the key to China’s general plan to develop its own Greater Bay Area. China hopes to build the Greater Bay Area into an area comparable to the bay areas of San Francisco, New York and Tokyo in terms of technological innovation and economic prosperity.
四、Part I Writing
57、Directions: For this part, you are allowed 30 minutes to write an essay on why students should be encouraged to develop effective communication skills. You should write at least 150 words but no more than 200 words.
参考答案：
参考范文
Nowadays, universities are embracing a more diverse culture, as a result of which, college students are supposed to socialize with people from various backgrounds. Therefore, it is beneficial for all of us to develop effective communication skills, and there are a few arguments that can justify my opinion.
In the first place, it is necessary for students to speak properly and avoid offending or hurting others when facing different groups of people, such as roommates, classmates or professors, because this is the primary social etiquette. Furthermore, those who can communicate effectively are often confident, popular, and they can make more friends. Thus, they tend to gain more respect and support from others when they are in trouble. Last but not least, effective communication with different people can broaden our horizons and cultivate our empathy, both of which are of utmost importance to our future life.
All in all, whether shy or outgoing, we should pay attention to developing our communication skills while interacting with others, especially those who have a different personality. Only in this way can we become more tolerant and get along well with others.
参考译文
如今，大学正提倡更加多元的文化，所以大学生们应与来自不同背景的群体交往。因此，培养有效的沟通技能对我们每个人都有益。以下几点可以证明我的观点。
首先，学生在面对室友、同学或教授等不同人群时，如室友、同学或教授，说话得体、避免冒犯或伤害他人是很有必要的，因为这是最基本的社交礼仪。除此之外，善于沟通的人通常都很自信、受欢迎，并且能交到更多的朋友。这使得他们更容易赢得尊重，在遇到困难时也会得到更多支持。最后也是最重要的，与不同的人进行有效的沟通可以拓宽我们的视野，培养我们的同理心，这两点对我们将来的生活都至关重要。
总的来说，不管是内向还是外向，在与他人交往时，尤其是在面对与自己性格不同的人时，我们应该注意提升沟通技巧。只有这样，我们才能变得更加包容，并与他人融洽相处。
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