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When Elinor Lobel was 16, a “smart” insulin (胰岛素) pump was attached to her body. Powered by AI, it tracks her glucose levels and administers the right dose of insulin at the right time to keep her healthy. It is one of the new ways that data and AI can help improve lives.

Books that criticize the dark side of data are plentiful. They generally suggest there is much more to fear than fete in the algorithmic(算法的)age.

But the intellectual tide may be turning. One of the most persuasive supporters of a more balanced view is Elinor Lobel’s mother, Orly, a law professor. In The Equality Machine she acknowledges AI’s capacity to produce harmful results. But she shows how, in the right hands, it can also be used to fight inequality and discrimination.

A principle of privacy rules is “minimization”: collect and keep as little information as possible, especially in areas such as race and gender. Ms Lobel flips the script, showing how in hiring, pay and the legal system, knowing such characteristics leads to fairer outcomes.

Ms Lobel’s call to use more, not less, personal information challenges data-privacy orthodoxy(正统观念). But she insists that “tracking differences is key to detecting unfairness.” She advocates g loosening of privacy rules to provide more transparency(透明)over algorithmic decisions.

The problems with algorithmic formulae(公式) are tackled in depth in Escape from Model Land by Erica Thompson of the School of Economics. These statistical models are the backbone of big data and AL. Yet a perfect model will always be beyond reach. “All models are wrong,” runs a wise saying. “Some are useful.”

Ms Thompson focuses on a challenge she calls the Hawkmoth Effect. In the better known Butterfly Effect, a serviceable model, Vin the prediction of climate change, becomes less reliable over time because of the complexity of what it is simulating(模拟), or because of inaccuracies in the original data. In the Hawkmoth Effect, by contrast, the model itself is flawed; it might fail to take full account of the interplay between humidity, wind and temperature.

The author calls on data geeks to improve their solutions to real-world issues, not merely refine their formulae—in other words, to escape from model land. “We do not need to have the best possible answer,” she writes, “only a reasonable one.”

Both these books exhibit a healthy realism about data, algorithms and their limitations. Both recognize that making progress involves accepting limitations, whether in law or coding. As Ms Lobel puts it: “It’s always better to light a candle than to curse the darkness.”


Ms Lobel intends to convey that________
A.minimisation is a good privacy rule to go by
B.algorithms are currently challenged by data privacy
C.employing more personal data should be encouraged
D.identifying algorithms’ problems leads to better outcomes
2024高三·北京·专题练习
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What is life? Like most great questions, this one is easy to ask but difficult to answer. The reason is simple: we know of just one type of life and it’s challenging to do science with a sample size of one. The field of artificial life-called ALife for short — is the systematic attempt to spell out life’s fundamental principles. Many of these practitioners, so-called ALifers, think that somehow making life is the surest way to really understand what life is.

So far no one has convincingly made artificial life. This track record makes ALife a ripe target for criticism, such as declarations of the field’s doubtful scientific value. Alan Smith, a complexity scientist, is tired of such complaints. Asking about “the point” of ALife might be, well, missing the point entirely, he says. “The existence of a living system is not about the use of anything.” Alan says. “Some people ask me, ‘So what’s the worth of artificial life?’ Do you ever think, ‘What is the worth of your grandmother?’”

As much as many ALifers hate emphasizing their research’s applications, the attempts to create artificial life could have practical payoffs. Artificial intelligence may be considered ALife’s cousin in that researchers in both fields are enamored by a concept called open-ended evolution (演化). This is the capacity for a system to create essentially endless complexity, to be a sort of “novelty generator”. The only system known to exhibit this is Earth’s biosphere. If the field of ALife manages to reproduce life’s endless “creativity” in some virtual model, those same principles could give rise to truly inventive machines.

Compared with the developments of Al, advances in ALife are harder to recognize. One reason is that ALife is a field in which the central concept — life itself — is undefined. The lack of agreement among ALifers doesn’t help either. The result is a diverse line of projects that each advance along their unique paths. For better or worse, ALife mirrors the very subject it studies. Its muddled (混乱的) progression is a striking parallel (平行线) to the evolutionary struggles that have shaped Earth biosphere.

Undefined and uncontrolled, ALife drives its followers to repurpose old ideas and generated novelty. It may be, of course, that these characteristics aren’t in any way surprising or singular. They may apply universally to all acts of evolution. Ultimately ALife may be nothing special. But even this dismissal suggests somethingperhaps, just like life itself throughout the universe, the rise of ALife will prove unavoidable.


What can we learn from this passage?
A.ALife holds the key to human future.
B.ALife and AI share a common feature.
C.AI mirrors the developments of ALife.
D.AI speeds up the process of human evolution.

Lam Hon-ming, director of the State Key Laboratory at the Chinese University of Hong Kong, is a top expert in soybean (大豆) research. Since 1998, Lam’s team has been cooperating with scientists in Chinese mainland. In 2010, he came across Zhang Guohong, an agricultural expert from Gansu, China, at a national soybean conference. With the same major, they hit it off and decided to improve farmers’ lives and promote local agriculture.

Farmers in Gansu depend largely on the weather for their livelihood, mainly on rainfall, which is also a cause of severe poorness in the area. In 2016, they developed three new soybean varieties suited to salty soil and rare rainfall of Northwest China. All received official government approval.

As the land in Northwest China is not suitable for the growth of common varieties of soybeans, local farmers never planted soybeans, and it became a major problem for spreading new soybeans. Lam and Zhang increased communication with farmers through various ways. To ensure farmers’ income, Lam struck a partnership with a Hong Kong food company that will purchase all soybeans at market price when they are harvested.

By 2020, the planting area of the three approved soybeans in Gansu had gone beyond 2.4 million square kilometers, covering 46 of the province’s 80-plus counties, and the output had reached 7.71 million kilograms, adding about 30 million yuan to local farmers’ income.

Zhang said that Professor Lam’s contribution has greatly pushed the poorness relief and agricultural research in Northwest China. “It is hard to keep doing agricultural research with less funding. And it is more difficult to travel from Hong Kong to the poor areas of the Northwest to do agricultural research,” he added. In the future, Lan will continue to work with mainland scientists and lead more “Hong Kong power” into the development of the country’s Northwest.


What does the underlined word “it” in Paragraph 3 refer to?
A.Rainfall is not enough.B.The locals lived a poor life.
C.Little land is rich in nutrition.D.The farmers never planted soybeans.

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