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How to Break the Habit of Provincial Thinking
If I'm looking to find out what is the case with the war and whether the war is legitimate, I'm probably not going to be fully satisfied with the New York Times or theWashington Post or Fox News or CNN.
I'm a citizen and I want to know what my government is doing because my government is supposed to represent me, so I look to the news to find out what they’re doing and to form a judgment on whether I think that is right or whether I think that is wrong.
If I think it’s wrong I'm going to try to express my opinion. I'm going to try to communicate that, not just through my vote, but maybe through various forms of political organization or expression. So if I'm looking to find out what is the case with the war and whether the war is legitimate, I'm probably not going to be fully satisfied with the New York Times or the Washington Post or Fox News or CNN. I'm going to want to get on various sites that allow me to see how the same events are covered in other countries, how the political values of the war are being openly debated and I'm going to move from site to site to see what happens to my thinking as I move through those sites.
I'm a professor of comparative literature among other things, so I'm able to read in a couple of other languages and I understand that not everyone is, not everyone can, although it is quite stunning how many people do read Spanish in the United States, but moving between languages is also extremely helpful.
Events that get covered in the US one way are not very important elsewhere or are given a completely different slant and one needs to have a kind of comparative way of thinking in order to arrive at a judgment that is not completely provincial, that doesn’t end up ratifying one’s own national perspective and hence, one’s own national agendas.
In Their Own Words is recorded in Big Think's studio.
Image courtesy of Shutterstock
Northwell Health is using insights from website traffic to forecast COVID-19 hospitalizations two weeks in the future.
- The machine-learning algorithm works by analyzing the online behavior of visitors to the Northwell Health website and comparing that data to future COVID-19 hospitalizations.
- The tool, which uses anonymized data, has so far predicted hospitalizations with an accuracy rate of 80 percent.
- Machine-learning tools are helping health-care professionals worldwide better constrain and treat COVID-19.
The value of forecasting<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTA0Njk2OC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMzM2NDQzOH0.rid9regiDaKczCCKBsu7wrHkNQ64Vz_XcOEZIzAhzgM/img.jpg?width=980" id="2bb93" class="rm-shortcode" data-rm-shortcode-id="31345afbdf2bd408fd3e9f31520c445a" data-rm-shortcode-name="rebelmouse-image" data-width="1546" data-height="1056" />
Northwell emergency departments use the dashboard to monitor in real time.
Credit: Northwell Health<p>One unique benefit of forecasting COVID-19 hospitalizations is that it allows health systems to better prepare, manage and allocate resources. For example, if the tool forecasted a surge in COVID-19 hospitalizations in two weeks, Northwell Health could begin:</p><ul><li>Making space for an influx of patients</li><li>Moving personal protective equipment to where it's most needed</li><li>Strategically allocating staff during the predicted surge</li><li>Increasing the number of tests offered to asymptomatic patients</li></ul><p>The health-care field is increasingly using machine learning. It's already helping doctors develop <a href="https://care.diabetesjournals.org/content/early/2020/06/09/dc19-1870" target="_blank">personalized care plans for diabetes patients</a>, improving cancer screening techniques, and enabling mental health professionals to better predict which patients are at <a href="https://healthitanalytics.com/news/ehr-data-fuels-accurate-predictive-analytics-for-suicide-risk" target="_blank" rel="noopener noreferrer">elevated risk of suicide</a>, to name a few applications.</p><p>Health systems around the world have already begun exploring how <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7315944/" target="_blank" rel="noopener noreferrer">machine learning can help battle the pandemic</a>, including better COVID-19 screening, diagnosis, contact tracing, and drug and vaccine development.</p><p>Cruzen said these kinds of tools represent a shift in how health systems can tackle a wide variety of problems.</p><p>"Health care has always used the past to predict the future, but not in this mathematical way," Cruzen said. "I think [Northwell Health's new predictive tool] really is a great first example of how we should be attacking a lot of things as we go forward."</p>
Making machine-learning tools openly accessible<p>Northwell Health has made its predictive tool <a href="https://github.com/northwell-health/covid-web-data-predictor" target="_blank">available for free</a> to any health system that wishes to utilize it.</p><p>"COVID is everybody's problem, and I think developing tools that can be used to help others is sort of why people go into health care," Dr. Cruzen said. "It was really consistent with our mission."</p><p>Open collaboration is something the world's governments and health systems should be striving for during the pandemic, said Michael Dowling, Northwell Health's president and CEO.</p><p>"Whenever you develop anything and somebody else gets it, they improve it and they continue to make it better," Dowling said. "As a country, we lack data. I believe very, very strongly that we should have been and should be now working with other countries, including China, including the European Union, including England and others to figure out how to develop a health surveillance system so you can anticipate way in advance when these things are going to occur."</p><p>In all, Northwell Health has treated more than 112,000 COVID patients. During the pandemic, Dowling said he's seen an outpouring of goodwill, collaboration, and sacrifice from the community and the tens of thousands of staff who work across Northwell.</p><p>"COVID has changed our perspective on everything—and not just those of us in health care, because it has disrupted everybody's life," Dowling said. "It has demonstrated the value of community, how we help one another."</p>
A leading British space scientist thinks there is life under the ice sheets of Europa.
- A British scientist named Professor Monica Grady recently came out in support of extraterrestrial life on Europa.
- Europa, the sixth largest moon in the solar system, may have favorable conditions for life under its miles of ice.
- The moon is one of Jupiter's 79.
Neil deGrasse Tyson wants to go ice fishing on Europa<div class="rm-shortcode" data-media_id="GLGsRX7e" data-player_id="FvQKszTI" data-rm-shortcode-id="f4790eb8f0515e036b24c4195299df28"> <div id="botr_GLGsRX7e_FvQKszTI_div" class="jwplayer-media" data-jwplayer-video-src="https://content.jwplatform.com/players/GLGsRX7e-FvQKszTI.js"> <img src="https://cdn.jwplayer.com/thumbs/GLGsRX7e-1920.jpg" class="jwplayer-media-preview" /> </div> <script src="https://content.jwplatform.com/players/GLGsRX7e-FvQKszTI.js"></script> </div>
Water Vapor Above Europa’s Surface Deteced for First Time<span style="display:block;position:relative;padding-top:56.25%;" class="rm-shortcode" data-rm-shortcode-id="9c4abc8473e1b89170cc8941beeb1f2d"><iframe type="lazy-iframe" data-runner-src="https://www.youtube.com/embed/WQ-E1lnSOzc?rel=0" width="100%" height="auto" frameborder="0" scrolling="no" style="position:absolute;top:0;left:0;width:100%;height:100%;"></iframe></span>
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Study confirms the existence of a special kind of groupthink in large groups.
- Large groups of people everywhere tend to come to the same conclusions.
- In small groups, there's a much wider diversity of ideas.
- The mechanics of a large group make some ideas practically inevitable.
The grouping game<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDE2Ni9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYxMjI2MzA4OX0.RLrswIWbuEzHNqsw0F7EUrp9jPn7OulLPqCxcZT11ik/img.jpg?width=980" id="159b8" class="rm-shortcode" data-rm-shortcode-id="0feb15d2d7dde144c710c2f4f1e5350c" data-rm-shortcode-name="rebelmouse-image" data-width="2767" data-height="382" />
Some of the shapes used in the experiment
Credit: Guilbeault, et al./University of Pennsylvania<p>The researchers tested their theory with 1,480 people playing an online "Grouping Game" via Amazon's Mechanical Turk platform. The individuals were paired with another participant or made a member of a group of 6, 8, 24, or 50 people. Each pair and group were tasked with categorizing the symbols shown above, and they could see each other's answers.</p><p>The small groups came up with wildly divergent categories—the entire experiment produced nearly 5,000 category suggestions—while the larger groups came up with categorization systems that were virtually identical to each other.</p><p><a href="https://www.asc.upenn.edu/news-events/news/why-independent-cultures-think-alike-its-not-in-the-brain" target="_blank">Says Centol</a>a, "Even though we predicted it, I was nevertheless stunned to see it really happen. This result challenges many long-held ideas about culture and how it forms."</p><p>Nor was this unanimity a matter of having teamed-up like-minded individuals. "If I assign an individual to a small group," says lead author Douglas Guilbeault, "they are much more likely to arrive at a category system that is very idiosyncratic and specific to them. But if I assign that same individual to a large group, I can predict the category system that they will end up creating, regardless of whatever unique viewpoint that person happens to bring to the table."</p>
Why this happens<img type="lazy-image" data-runner-src="https://assets.rebelmouse.io/eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpbWFnZSI6Imh0dHBzOi8vYXNzZXRzLnJibC5tcy8yNTQ1NDE4NC9vcmlnaW4uanBnIiwiZXhwaXJlc19hdCI6MTYyMjkzMDg0Nn0.u2hdEIgNw4drFZ2frzx0AJ_MAxIizuM98rdovQrIblk/img.jpg?width=980" id="d3444" class="rm-shortcode" data-rm-shortcode-id="5da57d66e388fad0f1c17afb09af90a7" data-rm-shortcode-name="rebelmouse-image" data-width="1440" data-height="822" />
The many categories suggested by small groups on the left, the few from large groups on the right
Credit: Guilbeault, et al./Nature Communications<p>The striking results of the experiment correspond to a <a href="https://www.nature.com/articles/s41562-019-0607-5" target="_blank">previous study</a> done by NDG that investigated tipping points for people's behavior in networks.</p><p>That study concluded that after an idea enters a discussion among a large network of people, it can gain irresistible traction by popping up again and again in enough individuals' conversations. In networks of 50 people or more, such ideas eventually reach critical mass and become a prevailing opinion.</p><p>The same phenomenon does not happen often enough within a smaller network, where fewer interactions offer an idea less of an opportunity to take hold.</p>