英语 热门试卷

C

Turning the lights out or wearing a blindfold while eating could be a quick way to lose weight, according to scientists. The simple trick works because it stops diners eating for pleasure rather than for calories. It also triggers (引发) a part of the brain that is worried that unseen food may go bad.

An experiment by the University of Konstanz, in Germany, found that people who were blindfolded consumed nine percent fewer calories before they felt full, compared to those who could see. They also vastly overestimated how much they had eaten because they could not see how much was left on the plate. Blindfolded volunteers estimated they had eaten 88 percent more than they actually had.

Scientists believe that not seeing food on the table also allows the body to know when it is full in real time rather than remembering past experiences where it might have taken a full plate to feel full.

In the experiment, 50 people were blindfolded and 40 were allowed to see their food. All were told not to eat within two hours of the experiment. They were then given three 95g bowls of chocolate ice cream and invited to eat for 15 minutes. Their bowls were taken away and the remaining ice-cream weighed, while the participants were quizzed on how much they thought they had eaten.

On average the group who could see ate 116g while the blindfolded groups ate 105g. However, the blindfolded group believed they had eaten 197g while compared with 159g for the non-blind volunteers. They were also asked how pleasant the ice-cream tasted and the blindfolded group rated lower than those who could see.

“The experienced pleasure of eating was significantly lower in the blindfolded group. Not seeing the food might have decreased the appetite. Sight plays an important role in the eating experience and in the overall dining experience.”

Previous studies have shown that the visual influence of food plays a large part in the taste. While restaurants that allow diners to eat in the dark state that it triggers other senses, in fact eating in darkness is likely to taste far milder than usual.

B

Two of the saddest words in the English language are “if only”. I live my life with the goal of never having to say those words, because they convey regret, lost opportunities, mistakes, and disappointment.

My father is famous in our family for saying, “Take the extra minute to do it right.” I always try to live by the “extra minute” rule. When my children were young and likely to cause accidents, I always thought about what I could do to avoid an “if only” moment, whether it was something minor like moving a cup full of hot coffee away from the edge of a counter, or something that required a little more work such as taping padding (衬垫) onto the sharp corners of a glass coffee table.

I don’t only avoid those “if only” moments when it comes to safety. It’s equally important to avoid “if only” in our personal relationships. We all know people who lost a loved one and regretted that they had foregone an opportunity to say “I love you” or “I forgive you.” When my father announced he was going to the eye doctor across from my office on Good Friday, I told him that it was a holiday for my company and I wouldn’t be here. But then I thought about the fact that he’s 84 years old and I realized that I shouldn’t give up an opportunity to see him. I called him and told him I had decided to go to work on my day off after all.

I know there will still be occasions when I have to say “if only” about something, but my life is definitely better because of my policy of doing everything possible to avoid that eventuality. And even though it takes an extra minute to do something right, or it occasionally takes an hour or two in my busy schedule to make a personal connection, I know that I’m doing the right thing. I’m buying myself peace of mind and that’s the best kind of insurance for my emotional well-being.

D

       Let us all raise a glass to AlphaGo and the advance of artificial intelligence. AlphaGo, DeepMind’s Go-playing AI, just defeated the best Go-playing human, Lee Sedol. But as we drink to its success, we should also begin trying to understand what it means for the future.

The number of possible moves in a game of Go is so huge that, in order to win against a player like Lee, AlphaGo was designed to adopt a human-like style of gameplay by using a relatively recent development—deep learning. Deep learning uses large data sets, “machine learning” algorithms (计算程序) and deep neural (神经的) networks to teach the AI how to perform a particular set of tasks. Rather than programming complex Go rules and strategies into AlphaGo, DeepMind designers taught AlphaGo to play the game by feeding it data based on typical Go moves. Then, AlphaGo played against itself, tirelessly learning from its own mistakes and improving its gameplay over time. The results speak for themselves.

Deep learning represents a shift in the relationship humans have with their technological creations. It results in AI that displays surprising and unpredictable behaviour. Commenting after his first loss, Lee described being shocked by an unconventional move he claimed no human would ever have made. Demis Hassabis, one of DeepMind’s founders, echoed this comment: “We’re very pleased that AlphaGo played some quite surprising and beautiful moves.”

Unpredictability and surprises are—or can be—a good thing. They can indicate that a system is working well, perhaps better than the humans that came before it. Such is the case with AlphaGo. However, unpredictability also indicates a loss of human control. That Hassabis is surprised at his creation’s behaviour suggests a lack of control in the design. And though some loss of control might be fine in the context of a game such as Go, it raises urgent questions elsewhere.

How much and what kind of control should we give up to AI machines? How should we design appropriate human control into AI that requires us to give up some of that very control? Is there some AI that we should just not develop if it means any loss of human control? How much of a say should corporations, governments, experts or citizens have in these matters? These important questions, and many others like them, have emerged in response, but remain unanswered. They require human, not human-like, solutions.

So as we drink to the milestone in AI, let’s also drink to the understanding that the time to answer deeply human questions about deep learning and AI is now.