| A．took to; get it across | B．took up; figure it out |
| C．took off; bring it up | D．took down; work it out |
As the world seeks to slow the pace of climate change and preserve wildlife, trees undoubtedly hold a major part of the answer. Yet the mass destruction of trees--deforestation-continues, sacrificing （牺牲）the long-term benefits of standing trees for short-term gain.
So, what are the factors leading to deforestation? Farming, animal raising, mining, and drilling combined account for more than half of all deforestation.
Luckily, a movement is under way to preserve existing forest ecosystems and restore lost tree cover. Organizations and activists are working to fight illegal mining and logging. As consumers, it makes sense to look for sustainably produced sources.
| A．Yet the effects of deforestation reach much farther. |
| B．Globally, forests are disappearing at an alarming rate. |
| C．Wildfires and urbanization also account for a small part, |
| D．Deforestation affects people and animals and even the wider world. |
| E．With these joint efforts, many conservationists see reasons for hope. |
| F．Deforestation is responsible for around 15% of all greenhouse gas emissions. |
| G．Some deforestation is caused by a combination of human and natural factors. |
My family’s house was close to a road. And on the far end of the road stood a big tree. My parents, together with me, often crossed the road to the big tree to have fun with our neighbors. But I wasn’t allowed to cross the road to go there by myself for my safety.
One day, for some reason, the big tree would be cut down. It was just before dinner time. A large crowd of neighbors gathered at the far end of the road to watch the falling of this tree. I watched from my house as the tree, tied with ropes, was lowered down. Soon, the huge tree fell safely to the ground with a big sound. But the kids and grown - ups didn’t leave. They moved closer to stay around this wonderful old tree.
Like them, I wanted to touch the tree with my hands and say good - bye. But I was only five years old and wasn’t allowed to cross the busy road alone. But as I became sadder when watching the tree nearly disappear, I thought to myself, “What’s the worst thing that could happen?” I didn’t see any cars coming, so I ran across the road to say good - bye to the fallen tree.
After standing around and talking with some of the neighbors, I realized that I had stayed there too long. “I am late for dinner, and I’m in trouble.”I told myself. Without even thinking, and without seeing if there were any cars coming, I dashed out between two parked cars and raced toward the other side of the road.
I didn’t see a car rushing forward from the other end of the road. And the driver didn’t expect I’d cross the road at that moment. When spotting me, he tried to stop his car, whose tires produced loud and unpleasant noises. But it was too late.
“Ouch!” I howled in pain as I dropped the hammer onto my foot. I felt foolish.
It was pouring with rain, and lame for a moment, I hopped around to the driver's side.
“No luck?” said George. We had a flat tyre (轮胎) and I was trying to change the wheel. But it was stuck firm.
We had driven ten hours along the road through the jungle without seeing another soul. In the distance was an ox working in the field.
“So what do we do now? How can we find a garage?” asked George, as I got back into the car.
About fifteen minutes later, the rain stopped. Out of the jungle came an old woman. She walked towards us carrying something long and thin.
“Uh, oh, here comes danger!” said George,” She's got a spear. ”
She was wearing traditional garments, with short sleeves, a colorful necklace and other jewelry. As she got closer, we saw the spear was just a wooden pole. I got out.
“Hello!” I said, in English,” Can you help us?” I pointed to the wheel.
She stared at it, and then made a gesture to follow her back to the jungle. George and I looked at each other, and then set off after her.
Soon we came to a tent under some trees. It had a firm framework of wooden poles. It looked like the one the woman was carrying, covered with heavy cloth, and fastened with rope made of some kind of loose plant fiber. All round were corn plants. It was harvest time for the grain, and a spade, a fork and other tools were lying on the ground. Nearby were some chicks and a rooster, and a pig tied to a post.
The woman folded back a cloth and invited us in. My eyes had to adjust to the darkness inside. The tent was furnished with a few mats on the floor, and a low table with a teapot. She picked up a photo and showed it to us. Was it her husband? The man in the photo was bare to the waist. He looked much too young. She was probably a widow (寡妇).Maybe her son or her nephew?
Our hostess poured some tea and watched smiling as we drank.
“Say something.” said George.
I was wondering what to say when I heard the sound of footsteps out of the tent.
Together we walked back to the car.
When I was 13, I climbed my first mountain — a fairly gentle 3, 900-foot peak near where I live in Surrey, British Columbia. I loved the challenge of conquering something bigger than myself. Soon I’d climbed nearly 100 peaks.
I often go climbing with my friend Mel Olsen. Two years ago, on December 30, when I was 16, she and I drove to Oregon to conquer 11, 240-foot Mount Hood.
It's safer to start winter climbs at night when there's less risk of the sun melting the snowpack. That day, we started at 3 a. m. The temperature was about 14 degrees, and we wore clothes we could easily remove. Along the way, we met two other climbers, and the four of us continued on together.
After about five hours, we reached Devil's Kitchen, a plateau at about 10, 000 feet, just before the final push to the top. By this point, the wind conditions were horrible. My exposed skin felt as though it were burning. The other climbers decided to turn back, but Mel and I went ahead. We had ice axes (斧), helmets, and crampons (钉鞋). We were prepared for the climb.
The trail we followed grew narrower and steeper. At around 9 a. m., we reached a patch of ice called an ice step. It was about three or four feet tall and sloped at a 75-degree angle. I volunteered to go first. I placed my left foot on the ice step.
I gained a sense of the ice when I stuck my ax and crampons into it, and it felt good. Confident that I was safe, I put my full weight on it. Suddenly, I heard a crack, and a whole block of ice broke off the step, right under my foot.
In an instant, I fell backward.
As Mel made her way down, I yelled for help.
By the age of seven months, most children have learned that objects still exist even when they are out of sight. Put a toy under a blanket and a child that old will know it is still there, and that he can reach underneath the blanket to get it back. This understanding, of “object permanence”, is a normal developmental milestone, as well as a basic tenet of reality. It is also something that self-driving cars do not have. And that is a problem. For a self-driving car, a bicycle that is momentarily hidden by a passing van is a bicycle that has ceased to exist.
This failing is basic to the now-widespread computing discipline that has arrogated to itself the slightly misleading moniker of artificial intelligence (AI). Current AI, based on the idea of machine learning, works by building up complex statistical models of the world, but it lacks a deeper understanding of reality. Similar techniques are used to train self-driving cars to operate in traffic. Cars thus learn how to obey lane markings, avoid other vehicles, hit the brakes at a red light and so on. But they do not understand many things a human driver takes for granted—that other cars on the road have engines and four wheels, or that they obey traffic regulations (usually) and the laws of physics (always). And they do not understand object permanence.
In a recent paper in Artificial Intelligence, Mehul Bhatt of Orebro University, in Sweden, describes a different approach. He and his colleagues took some existing AI programs which are used by self-driving cars and bolted onto them a piece of software called a symbolic-reasoning engine.
Instead of approaching the world probabilistically, as machine learning does, this software was programmed to apply basic physical concepts to the output of the programs that process signals from an autonomous vehicle's sensors. This modified output was then fed to the software which drives the vehicle. The concepts involved included the ideas that discrete objects continue to exist over time, that they have spatial relationships with one another-such as “in-front-of” and “behind”—and that they can be fully or partly visible, or completely hidden by another object. The improvement was not huge, but it proved the principle. And it also yielded something else. For, unlike a machine-learning algorithm, a reasoning engine can tell you the reason why it did what it did. A machine-learning program cannot do that. Besides helping improve program design, such information will, Dr Bhatt reckons, help regulators and insurance companies. It may thus speed up public acceptance of autonomous vehicles.
1．Why does the author mention a bicycle hidden by a van in the first paragraph?| A．To show the self-driving car isn't as able to know an object permanently exists as a 7-month-old child. |
| B．To make a comparison between a self-driving car and a bicycle that can for a moment cease to exist. |
| C．To consolidate the problem a self-driving car has as opposed to a 7-month-old child. |
| D．To verify the fact that a self-driving car isn't as intelligent as a 7-month-old child. |
| A．It fails as a misleading computing discipline used on self-driving cars. |
| B．It basically works on machine learning which is effective to train cars how to operate in traffic. |
| C．It is not that intelligent compared with the real human intelligence, hence the name AI. |
| D．It can teach cars many things except the reasons why they have engines and four wheels. |
| A．When an accident is around the corner, the car automatically alarms the driver. |
| B．If the car momentarily blocked the sight of another, it could predict and take steps to avoid bumping. |
| C．The car can make up reasons for hitting the brakes when a bicycle hidden by a van is about to appear. |
| D．When you are at a loss how you can make it to the destination, the car can always figure out the best route. |
| A．Is reasoning-engine better than machine learning? |
| B．Is it smarter than a seven-month-old? |
| C．Al---a misleading moniker |
| D．The self-reflection of a self-driving car |
| A. suspects B. maintains C. angles D. devotion E. lend F. favor G. determined H. analytical I. inventive J. credit K. stirring |
Tony Randall stars as the Belgian detective Hercule Poirot in the director Frank Tashlin’s extravagant 1965 adaptation of Agatha Christie’s “The ABC Murders,” infusing the sleuth’s punctilious style with
Russell Crowe teams up with the director Ron Howard for the story of the boxer James J. Braddock, who fell from
Braddock is presented as a man without sin; his wife, Mae (Renée Zellweger),
Yours,
Li Hua
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