考研英一阅读真题2021

考研英一阅读真题2021

考研英一阅读真题1

Any fair-minded assessment of the dangers of the deal between Britain's

National Health Service (NHS) and DeepMind must start by acknowledging that

both sides mean well. DeepMind is one of the leading artificial intelligence (AI)

companies in the world. The potential of this work applied to healthcare is very

great, but it could also lead to further concentration of power in the tech giants. It

Is against that background that the information commissioner, Elizabeth Denham,

has issued her damning verdict against the Royal Free hospital trust under the NHS,

which handed over to DeepMind the records of 1.6 million patients In 2022年 on

the basis of a vague agreement which took far too little account of the patients'

rights and their expectations of privacy.

DeepMind has almost apologized. The NHS trust has mended its ways. Further

arrangements- and there may be many-between the NHS and DeepMind will be

carefully scrutinised to ensure that all necessary permissions have been asked of

patients and all unnecessary data has been cleaned. There are lessons about

informed patient consent to learn. But privacy is not the only angle in this case and

not even the most important. Ms Denham chose to concentrate the blame on the

NHS trust, since under existing law it “controlled” the data and DeepMind merely

“processed" it. But this distinction misses the point that it is processing and

aggregation, not the mere possession of bits, that gives the data value.

The great question is who should benefit from the analysis of all the data that

our lives now generate. Privacy law builds on the concept of damage to an

individual from identifiable knowledge about them. That misses the way the

surveillance economy works. The data of an individual there gains its value only

when it is compared with the data of countless millions more.

The use of privacy law to curb the tech giants in this instance feels slightly

maladapted. This practice does not address the real worry. It is not enough to say

that the algorithms DeepMind develops will benefit patients and save lives. What

matters is that they will belong to a private monopoly which developed them using

public resources. If software promises to save lives on the scale that dugs now can,

big data may be expected to behave as a big pharm has done. We are still at the

beginning of this revolution and small choices now may turn out to have gigantic

consequences later. A long struggle will be needed to avoid a future of digital

feudalism. Ms Denham's report is a welcome start.

is true of the agreement between the NHS and DeepMind ?

[A] It caused conflicts among tech giants.

[B] It failed to pay due attention to patient’s rights.

[C] It fell short of the latter's expectations

[D] It put both sides into a dangerous situation.

32. The NHS trust responded to Denham's verdict with

[A] empty promises.

[B] tough resistance.

[C] necessary adjustments.

[D] sincere apologies.

author argues in Paragraph 2 that

[A] privacy protection must be secured at all costs.

[B] leaking patients' data is worse than selling it.

[C] making profits from patients' data is illegal.

[D] the value of data comes from the processing of it

ing to the last paragraph, the real worry arising from this deal is

[A] the vicious rivalry among big pharmas.

[B] the ineffective enforcement of privacy law.

[C] the uncontrolled use of new software.

[D] the monopoly of big data by tech giants.

author's attitude toward the application of AI to healthcare is

[A] ambiguous.

[B] cautious.

[C] appreciative.

[D] contemptuous.

考研英一阅读真题2

The journal Science is adding an extra source at Peer-review process, editor-in-

chief Marcia McNott announced today. The Follows similar efforts from other

journals, after widespread concern that Mistakes in data analysis are contributing

to the Published research findings.

"Readers must have confidence in the conclusions published in our

journal,"writes McNutt in an editorial. Working with the American Statistical

Association, the Journal has appointed seven experts to a statistics board of

reviewing Manuscript will be flagged up for additional scrutiny by the Journal's

editors, or by its existing Board of Reviewing Editors or by outside peer The SBoRE

panel will then find external statisticians to review these

Asked whether any particular papers had impelled the change, McNutt

said,"The creation of the'statistics board'was motivated by concerns broadly with

the application of statistics and data analysis in scientific research and is part of

Science's overall drive to increase reproducibility in the research we publish."

Giovanni Parmigiani，a biostatistician at the Harvard School of Public Health,

a member of the SBoRE group, says he expects the board to "play primarily on

advisory role." He agreed to join because he "found the foresight behind the

establishment of the SBoRE to be novel, unique and likely to have a lasting impact.

This impact will not only be through the publications in Science itself, but hopefully

through a larger group of publishing places that may want to model their approach

after Science."

John Ioannidis, a physician who studies research methodology, says that the

policy is "a most welcome step forward"and "long overdue,""Most journals are

weak in statistical review，and this damages the quality of what they publish. I think

that, for the majority of scientific papers nowadays, statistical review is more

essential than expert review,"he says. But he noted that biomedical journals such

as Annals of Internal Medicine, the Journal of the American Medical Association

and The Lancet pay strong attention to statistical review.

Professional scientists are expected to know how to analyze data, but statistical

errors are alarmingly common in published research，according to David Vaux，a

cell biologist. Researchers should improve their standards, he wrote in 2022年，

but journals should also take a tougher line，"engaging reviewers who are

statistically literate and editors who can verify the process."Vaux says that Science's

idea to pass some papers to statisticians "has some merit，but a weakness is that

it relies on the board of reviewing editors to identify'the papers that need

scrutiny'in the first place."

31. It can be learned from Paragraph I that

[A] Science intends to simplify its peer-review process.

[B]journals are strengthening their statistical checks.

[C]few journals are blamed for mistakes in data analysis.

[D]lack of data analysis is common in research projects.

32. The phrase "flagged up "(Para.2)is the closest in meaning to

[A]found.

[B]revised.

[C]marked

[D]stored