The U.S. is updating the way elevation is measured -- old techniques date back to before GPS, supercomputers, and sophisticated gravity measurements -- and the impact will be changes, mostly decreases, in stated elevations across the U.S. Because inaccuracies in the current measurement system accumulate as one moves diagonally from Florida to Alaska, south Florida may actually gain in stated elevation whereas parts of Alaska and the Pacific Northwest are likely to lose five or six (or more) feet. The country's tallest mountains will be shorter, and some neighborhoods will almost certainly be designated to lie in floodplains, requiring special insurance. This map from The New York Times shows anticipated impacts of the new geodetic system to be rolled out in 2022 or 2023. (from www.nytimes.com/2020/05/22/science/maps-elevation-geodetic-survey.html)
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This article from MIT Technology Review features an interview with Jess Whittlestone at the University of Cambridge's Leverhulme Centre for the Future of [Artificial] Intelligence. Whittlestone is discussing the need for what she and colleagues are referring to as "ethics with urgency" for AI.
"With this pandemic we’re suddenly in a situation where people are really talking about whether AI could be useful, whether it could save lives. But the crisis has made it clear that we don’t have robust enough ethics procedures for AI to be deployed safely, and certainly not ones that can be implemented quickly. ... Compared to something like biomedical ethics, the ethics we have for AI isn’t very practical. It focuses too much on high-level principles. We can all agree that AI should be used for good. But what does that really mean? And what happens when high-level principles come into conflict? For example, AI has the potential to save lives but this could come at the cost of civil liberties like privacy. How do we address those trade-offs in ways that are acceptable to lots of different people? We haven’t figured out how to deal with the inevitable disagreements. "AI ethics also tends to respond to existing problems rather than anticipate new ones. Most of the issues that people are discussing today around algorithmic bias came up only when high-profile things went wrong, such as with policing and parole decisions. ... We need to think about ethics differently. It shouldn’t be something that happens on the side or afterwards—something that slows you down. It should simply be part of how we build these systems in the first place: ethics by design. ... What we’re saying is that machine-learning researchers and engineers need to be trained to think through the implications of what they’re building, whether they’re doing fundamental research like designing a new reinforcement-learning algorithm or something more practical like developing a health-care application. If their work finds its way into real-world products and services, what might that look like? What kinds of issues might it raise? ... NeurIPS [a leading AI conference] now asks researchers to include a statement at the end of their papers outlining potential societal impacts of their work. ... What you need at all levels of AI development are people who really understand the details of machine learning to work with people who really understand ethics. Interdisciplinary collaboration is hard, however. People with different areas of expertise often talk about things in different ways. What a machine-learning researcher means by privacy may be very different from what a lawyer means by privacy, and you can end up with people talking past each other. That’s why it’s important for these different groups to get used to working together." www.technologyreview.com/2020/06/24/1004432/ai-help-crisis-new-kind-ethics-machine-learning-pandemic Fireworks are not the only thing to light up the night sky. This map shows "fireball events" recorded by U.S. government sensors from April 1988 to March 2020. Recent research suggests that meteorites do not hit all parts of the earth equally, being more likely to land in the tropics than near the poles. This finding suggests not only where to find meteorites should one want to do so but also where to avoid meteor impacts should one need to, say, choose a new location for the global seed vault.
www.sciencenews.org/wp-content/uploads/2020/05/052220_sh_meteorites_inline2_desktop_rev.png (From www.sciencenews.org/article/meteorites-might-be-more-likely-strike-near-equator.) Practice your geography (and probably learn a few new things) with this 35-question British pub quiz. (Please note: unless it has been fixed, the year in Question #11 is incorrect; it should be 1872, not 1862.) www.radiotimes.com/news/2020-05-02/pub-quiz-geography/
The Sahel is the wide, lightly populated band of shrubland at the southern edge of the Sahara Desert. From northern Nigeria to Mali to, more recently, Burkina Faso, the Sahel has seen a surge in violence over the last decade. This map, from The Economist (UK), underscores the geographic concentration of violence in the Sahel over the last 18 months. www.economist.com/graphic-detail/2020/06/20/fighting-in-the-sahel-has-forced-17m-people-from-their-homes
One of the questions continually being batted about at present is, "Has China been fudging its COVID-19 numbers?" This provocative article from Foreign Policy, from well before the pandemic, argues that China fudges *all* of its numbers because China's government -- perhaps all authoritarian governments -- rewards not accuracy but politically desirable numbers.
"We don’t know China. Nor, however, do the Chinese — not even the government. We don’t know China because, in ways that have generally not been acknowledged, virtually every piece of information issued from or about the country is unreliable, partial, or distorted. The sheer scale of the country, mixed with a regime of ever-growing censorship and a pervasive paranoia about sharing information, has crippled our ability to know China. Official data is repeatedly smoothed for both propaganda purposes and individual career ambitions. ... GDP growth has long been one of the main criteria used to judge officials’ careers — as a result, the relevant data is warped at every level, since the folk reporting it are the same ones benefitting from it being high. If you add up the GDP figures issued by the provinces, the sum is 10 percent higher than the figure ultimately issued by the national government, which in itself is tweaked to hit politicized targets. Provincial governments have increasingly admitted to this in recent years, but the fakery has been going on for decades. We don’t know the extent of bad loans, routinely concealed by banks. We don’t know the makeup of most Chinese financial assets. ... But what we don’t know goes far beyond just economics. Look at any sector in China and you’ll find distorted or unreported public information; go to the relevant authorities and they’ll generally admit the most shocking practices in private. ... We don’t know the true size of the Chinese population because of the reluctance to register unapproved second children or for the family planning bureau to report that they’d failed to control births. We don’t know where those people are; rural counties are incentivized to overreport population to receive more benefits from higher levels of government, while city districts report lower figures to hit population control targets. Beijing’s official population is 21.7 million; it may really be as high as 30 or 35 million. Tens — perhaps hundreds — of millions of migrants are officially in the countryside but really in the cities. ... We don’t know how good Chinese schools really are because the much-quoted statistics provided by the Program for International Student Assessment (PISA) that placed China first in the world were taken from the study of a small group of elite Shanghai schools. As soon as that was expanded merely to Beijing — another metropolis — and two rich provinces, the results dropped sharply. ... We don’t know the extent of the collapse of rural education. We don’t know the real literacy figures, not least because rural and urban literacy is measured by different standards — a common trick for many figures. ...We don’t know the real crime figures, especially in the cities, which may represent as little as 2.5 percent of the actual total. We don’t know the death toll for the ethnic Uighur insurgency in Xinjiang, where local officials, in the words of one government terrorism expert, 'bend figures as much as during the Great Leap Forward,' nor do we know how many people are currently held in 're-education camps.' (Incidentally, we don’t know how many people died in the Great Leap Forward [1958-62], piled up in village ditches or abandoned on empty grasslands: the 16.5 million once given in official tolls or the 45 million estimated by some historians.) And we don’t know what we don’t know." foreignpolicy.com/2018/03/21/nobody-knows-anything-about-china |
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