How A.I. may help solve science’s ‘reproducibility’ crisis

Researchers from Northwestern University's Kellogg School of Management developed an A.I. tool to weed through the deluge of academic and corporate research papers.

How A.I. may help solve science’s ‘reproducibility’ crisis

Researchers often have trouble reproducing, or verifying, supposedly groundbreaking work described in scientific papers, raising questions about whether the findings in studies are genuine.

Over the past few years, they’ve been increasingly sounding alarms about this so-called reproducibility (or replication) crisis, concerned that scholars are routinely overstating their findings. The problem is particularly acute in the field of artificial intelligence, in which researchers have published a number of non-peer reviewed papers about topics like speech recognition and diagnosing medical conditions that others have been unable to replicate. 

Now, a team from Northwestern University’s Kellogg School of Management and its Institute on Complex Systems has published a paper detailing a deep-learning system that it claims can figure out whether certain papers can be replicated or not. The paper, published Monday in the Proceedings of National Academy of Sciences of the United States of America journal, is noteworthy because it could help companies, universities, and other groups screen thousands of research papers while surfacing studies that are most likely to be reliable.

Brian Uzzi, a Kellogg School of Management professor and one of the paper’s authors, said his team wants to test the technology on the thousands of published coronavirus-related studies that scientists hope will lead to greater understanding about the virus. He’s concerned about a possible influx of low-quality coronavirus studies being rushed to publication. 

“We want to begin to apply this to the COVID issue—an issue right now where a lot of things are becoming lax, and we need to build on a very strong foundation of prior work,” Uzzi said. “It’s unclear what prior work is going to be replicated or not and we don’t have time for replications.”

What’s unusual is that the Northwestern A.I. system doesn’t analyze the empirical or mathematical evidence that researchers typically detail in their papers to support their theses. Instead, it uses advances in natural-language processing to analyze the text of the paper, finding hidden clues in the writing that tips the software to flag that certain papers are more replicable than others.

Some of the paper’s ideas take from psychology research in the 1960’s that “found that people often reveal in the words they choose the level of confidence they have in what they’re saying,” Uzzi said. The same may apply to authors of scientific papers, who may unintentionally reveal their confidence in their findings through the language that they use.

For the paper, the researchers trained the software twice using two different data sets of academic papers. The researchers first trained the system on two million scientific abstracts so that it could discover patterns and relationships between words used in the academic papers. They then trained the system on academic papers taken from the “Reproducibility Project: Psychology,” an initiative by other researchers to manually recreate psychology papers to determine whether they could be confirmed.

When the researchers then tested their newly-trained A.I. system on hundreds of other papers taken from fields like economics and the social sciences, they found that the machine-learning model performed better than more traditional statistical techniques used to determine whether a paper’s findings could be reproduced.

Despite his technology’s promising results, however, Uzzi said his team is still unsure just what their A.I. system learned about the language of certain academic papers that make them more easily replicated. It’s part of a larger problem about the difficulties A.I. researchers have describing just how exactly neural networks reach their conclusions.

As a result, other researchers may be apprehensive about using the technology as a screening tool. Still, Uzzi is hopeful that the technology can eventually be used to help other researchers. 

For future work, Uzzi said he would like to apply some of the natural-language processing techniques his team developed to analyzing corporate earnings calls. His team has already compiled the transcripts of 30,000 earnings calls as a dataset for the research project, which he hopes can help researchers understand “what it is about earnings calls that help us get more accurate predictions on what the company will do in the future.”

If the research is successful, it may be valuable for investors or analysts to use it as a financial forecasting tool.

“What we want to do us use the machine to help us predict whether the earnings call is going to lead to analysts giving a thumbs up or thumbs down to a company—like a buy or sell recommendation,” Uzzi said. 

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Would you book a cruise for $28 a night? Carnival bets on budget travelers for planned August 1 restart

As of Monday, the company was offering five-day cruises from Galveston, to Cozumel, Mexico, in early August starting at $139, plus taxes, fees and port expenses.

Would you book a cruise for $28 a night? Carnival bets on budget travelers for planned August 1 restart

Carnival Corp. plans to resume sailing on Aug. 1, becoming the first major cruise operator in the Americas to outline a return to operations after coronavirus outbreaks on several ships shuttered the industry.

The company’s flagship Carnival brand said Monday it will restart initially from Galveston, Texas, and Miami and Port Canaveral, Florida. Departures from other home ports in North America and Australia are canceled through Aug. 31, and other pauses will last even longer.

With several states starting to reopen their economies, the company is offering discounts of as little as $28 a day to get customers back. Still, many questions linger about the safety of cruising. When the industry shut down in mid-March, coronavirus outbreaks at sea wreaked havoc by trapping passengers, some of whom died, and placing demands on local health-care systems at a critical time in the pandemic.

“We will use this additional time to continue to engage experts, government officials and stakeholders on additional protocols and procedures to protect the health and safety of our guests, crew and the communities we serve,” the company said.

Read more: Carnival facing house panel probe

The cruise industry has historically used discounts to attract customers, but the extent of the sale shows the crisis of confidence Carnival is trying to overcome.

As of Monday, the company was offering five-day cruises from Galveston, to Cozumel, Mexico, in early August starting at $139, plus taxes, fees and port expenses. The base rate comes to about $28-a-day including food, cheaper than staying home for many people.

Turned Away

The industry stoppage in March came only after ports around the world started turning ships away in fear of the virus. Several vessels lingered at sea for weeks as they sought a place to dock.

Although most cruise companies — including Carnival — have portrayed the pause in operations as voluntary, President Donald Trump tweeted that he personally intervened.

The coronavirus swept the cruise industry broadly, but Carnival — the largest operator — had many of the earliest and most dramatic viral episodes at sea. The Diamond Princess, part of Carnival’s Princess Cruises brand, at one time had the biggest coronavirus outbreak outside of mainland China as it floated off Yokohama, Japan.

Compared with some other brands in the portfolio, Carnival Cruise Line has been relatively unscathed by the pandemic. Along with Princess, Holland America Line has been at the center of international crises tracked round-the-clock by the news media.

A “no-sail” order from the U.S. Centers for Disease Control and Prevention is due to expire on July 24 after a 100-day extension of the agency’s original edict.

Investors applauded the plan to restart. Carnival shares rose as much as 4.7% to $14.58 after the announcement. The stock was down 73% this year through last week.

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