Justin Worland in Time Magazine: “…Out of that realization came a plan to reshape disaster management using big data. Just a few months later, Wani worked with two fellow Stanford students to create a platform to predict the toll of natural disasters. The concept is simple but also revolutionary. The One Concern software pulls geological and structural data from a variety of public and private sources and uses machine learning to predict the impact of an earthquake down to individual city blocks and buildings. Real-time information input during an earthquake improves how the system responds. And earthquakes represent just the start for the company, which plans to launch a similar program for floods and eventually other natural disasters….
Previous software might identify a general area where responders could expect damage, but it would appear as a “big red blob” that wasn’t helpful when deciding exactly where to send resources, Dayton says. The technology also integrates information from many sources and makes it easy to parse in an emergency situation when every moment matters. The instant damage evaluations mean fast and actionable information, so first responders can prioritize search and rescue in areas most likely to be worst-hit, rather than responding to 911 calls in the order they are received.
One Concern is not the only company that sees an opportunity to use data to rethink disaster response. The mapping company Esri has built rapid-response software that shows expected damage from disasters like earthquakes, wildfires and hurricanes. And the U.S. government has invested in programs to use data to shape disaster response at agencies like the National Oceanic and Atmospheric Administration (NOAA)….(More)”.
Dave Gershgorn in Quartz: “Predictive policing, or the idea that software can foresee where crime will take place, is being adopted across the country—despite being riddled with issues. These algorithms have been shown to disproportionately target minorities, and private companies won’t reveal how their software reached those conclusions.
In an attempt to stand out from the pack, predictive-policing startup CivicScape has released its algorithm and data online for experts to scour, according to Government Technology magazine. The company’s Github page is already populated with its code, as well as a variety of documents detailing how its algorithm interprets police data and what variables are included when predicting crime.
“By making our code and data open-source, we are inviting feedback and conversation about CivicScape in the belief that many eyes make our tools better for all,” the company writes on Github. “We must understand and measure bias in crime data that can result in disparate public safety outcomes within a community.”…
CivicScape claims to not use race or ethnic data to make predictions, although it is aware of other indirect indicators of race that could bias its software. The software also filters out low-level drug crimes, which have been found to be heavily biased against African Americans.
While this startup might be the first to publicly reveal the inner machinations of its algorithm and data practices, it’s not an assurance that predictive policing can be made fair and transparent across the board.
“Lots of research is going on about how algorithms can be transparent, accountable, and fair,” the company writes. “We look forward to being involved in this important conversation.”…(More)”.
Emily Matchar in Smithsonian: “In many ways, the ocean is the Wild West. The distances are vast, the law enforcement agents few and far between, and the legal jurisdiction often unclear. In this environment, illegal activity flourishes. Illegal fishing is so common that experts estimate as much as a third of fish sold in the U.S. was fished illegally. This illegal fishing decimates the ocean’s already dwindling fish populations and gives rise to modern slavery, where fishermen are tricked onto vessels and forced to work, sometimes for years.
A new use of data technology aims to help curb these abuses by shining a light on the high seas. The technology uses ships’ satellite signals to detect instances of transshipment, when two vessels meet at sea to exchange cargo. As transshipment is a major way illegally caught fish makes it into the legal supply chain, tracking it could potentially help stop the practice.
“[Transshipment] really allows people to do something out of sight,” says David Kroodsma, the research program director at Global Fishing Watch, an online data platform launched by Google in partnership with the nonprofits Oceana and SkyTruth. “It’s something that obscures supply chains. It’s basically being able to do things without any oversight. And that’s a problem when you’re using a shared resource like the oceans.”
Global Fishing Watch analyzed some 21 billion satellite signals broadcast by ships, which are required to carry transceivers for collision avoidance, from between 2012 and 2016. It then used an artificial intelligence system it created to identify which ships were refrigerated cargo vessels (known in the industry as “reefers”). They then verified this information with fishery registries and other sources, eventually identifying 794 reefers—90 percent of the world’s total number of such vessels. They tracked instances where a reefer and a fishing vessel were moving at similar speeds in close proximity, labeling these instances as “likely transshipments,” and also traced instances where reefers were traveling in a way that indicated a rendezvous with a fishing vessel, even if no fishing vessel was present—fishing vessels often turn off their satellite systems when they don’t want to be seen. All in all there were more than 90,000 likely or potential transshipments recorded.
Even if these encounters were in fact transshipments, they would not all have been for nefarious purposes. They may have taken place to refuel or load up on supplies. But looking at the patterns of where the potential transshipments happen is revealing. Very few are seen close to the coasts of the U.S., Canada and much of Europe, all places with tight fishery regulations. There are hotspots off the coast of Peru and Argentina, all over Africa, and off the coast of Russia. Some 40 percent of encounters happen in international waters, far enough off the coast that no country has jurisdiction.
The tracked reefers were flying flags from some 40 different countries. But that doesn’t necessarily tell us much about where they really come from. Nearly half of the reefers tracked were flying “flags of convenience,” meaning they’re registered in countries other than where the ship’s owners are from to take advantage of those countries’ lax regulations….(More)”
Read more: http://www.smithsonianmag.com/innovation/fighting-illegal-fishing-big-data-180962321/#7eCwGrGS5v5gWjFz.99
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William Cook at BBC Arts: “Eighty years ago, on 30th January 1937, the New Statesman published a letter which launched the largest (and strangest) writers’ group in British literary history.
An anthropologist called Tom Harrisson, a journalist called Charles Madge and a filmmaker called Humphrey Jennings wrote to the magazine asking for volunteers to take part in a new project called Mass Observation. Over a thousand readers responded, offering their services. Remarkably, this ‘scientific study of human social behaviour’ is still going strong today.
Mass Observation was the product of a growing interest in the social sciences, and a growing belief that the mass media wasn’t accurately reflecting the lives of so-called ordinary people. Instead of entrusting news gathering to jobbing journalists, who were under pressure to provide the stories their editors and proprietors wanted, Mass Observation recruited a secret army of amateur reporters, to track the habits and opinions of ‘the man in the street.’
Ironically, the three founders of this egalitarian movement were all extremely well-to-do. They’d all been to public schools and Oxbridge, but this was the ‘Age of Anxiety’, when capitalism was in chaos and dangerous demagogues were on the rise (plus ça change…).
For these idealistic public schoolboys, socialism was the answer, and Mass Observation was the future. By finding out what ‘ordinary’ folk were really doing, and really thinking, they would forge a new society, more attuned to the needs of the common man.
Mass Observation selected 500 citizen journalists, and gave them regular ‘directives’ to report back on virtually every aspect of their daily lives. They were guaranteed anonymity, which gave them enormous freedom. People opened up about themselves (and their peers) to an unprecedented degree.
Even though they were all unpaid, correspondents devoted a great deal of time to this endeavour – writing at great length, in great detail, over many years. As well as its academic value, Mass Observation proved that autobiography is not the sole preserve of the professional writer. For all of us, the urge to record and reflect upon our lives is a basic human need.
The Second World War was the perfect forum for this vast collective enterprise. Mass Observation became a national diary of life on the home front. For historians, the value of such uncensored revelations is enormous. These intimate accounts of air raids and rationing are far more revealing and evocative than the jolly state-sanctioned reportage of the war years.
After the war, Mass Observation became more commercial, supplying data for market research, and during the 1960s this extraordinary experiment gradually wound down. It was rescued from extinction by the historian Asa Briggs….
The founders of Mass Observation were horrified by what they called “the revival of racial superstition.” Hitler, Franco and Mussolini were in the forefront of their minds. “We are all in danger of extinction from such outbursts of atavism,” they wrote, in 1937. “We look to science to help us, only to find that science is too busy forging new weapons of mass destruction.”
For its founders, Mass Observation was a new science which would build a better future. For its countless correspondents, however, it became something more than that – not merely a social science, but a communal work of art….(More)”.
Steven Aftergood in SecrecyNews: “Since 2010, the field of artificial intelligence (AI) has been “jolted” by the “broad and unforeseen successes” of one of its component technologies, known as multi-layer neural networks, leading to rapid developments and new applications, according to a new study from the JASON scientific advisory panel.
The JASON report distinguishes between artificial intelligence — referring to the ability of computers to perform particular tasks that humans do with their brains — and artificial general intelligence (AGI) — meaning a human-like ability to pursue long-term goals and exercise purposive behavior.
“Where AI is oriented around specific tasks, AGI seeks general cognitive abilities.” Recent progress in AI has not been matched by comparable advances in AGI. Sentient machines, let alone a revolt of robots against their creators, are still somewhere far over the horizon, and may be permanently in the realm of fiction.
While many existing DoD weapon systems “have some degree of ‘autonomy’ relying on the technologies of AI, they are in no sense a step–not even a small step–towards ‘autonomy’ in the sense of AGI, that is, the ability to set independent goals or intent,” the JASONs said.
“Indeed, the word ‘autonomy’ conflates two quite different meanings, one relating to ‘freedom of will or action’ (like humans, or as in AGI), and the other the much more prosaic ability to act in accordance with a possibly complex rule set based on possibly complex sensor input, as in the word ‘automatic’. In using a terminology like ‘autonomous weapons’, the DoD may, as an unintended consequence, enhance the public’s confusion on this point.”…
This week the Department of Defense announced the demonstration of swarms of “autonomous” micro-drones. “The micro-drones demonstrated advanced swarm behaviors such as collective decision-making, adaptive formation flying, and self-healing,” according to a January 9 news release.
A journalistic account of recent breakthroughs in the use of artificial intelligence for machine translation appeared in the New York Times Magazine last month. See “The Great A.I. Awakening” by Gideon Lewis-Kraus, December 14, 2016…(More)”
Max Read in NewYork Magazine: “My favorite story about the internet is the one about the anonymous Japanese guy who liberated Czechoslovakia. In 1989, as open dissent was spreading across the country, dissidents were attempting to coordinate efforts outside the watchful eye of Czechoslovak state security. The internet was a nascent technology, and the cops didn’t use it; modems were banned, and activists were able to use only those they could smuggle over the border, one at a time. Enter our Japanese guy. Bruce Sterling, who first told the story of the Japanese guy in a 1995 Wired article, says he talked to four different people who’d met the quiet stranger, but no one knew his name. What really mattered, anyway, is what he brought with him: “a valise full of brand-new and unmarked 2400-baud Taiwanese modems,” which he handed over to a group of engineering students in Prague before walking away. “The students,” Sterling would later write, “immediately used these red-hot 2400-baud scorcher modems to circulate manifestos, declarations of solidarity, rumors, and riot news.” Unrest expanded, the opposition grew, and within months, the Communist regime collapsed.
Is it true? Were free modems the catalyst for the Velvet Revolution? Probably not. But it’s a good story, the kind whose logic and lesson have become so widely understood — and so foundational to the worldview of Silicon Valley — as to make its truth irrelevant. Isn’t the best way to fortify the town square by giving more people access to it? And isn’t it nice to know, as one storied institution and industry after another falls to the internet’s disrupting sword, that everything will be okay in the end — that there might be some growing pains, but connecting billions of people to one another is both inevitable and good? Free speech will expand, democracy will flower, and we’ll all be rich enough to own MacBooks. The new princes of Silicon Valley will lead us into the rational, algorithmically enhanced, globally free future.
Or, they were going to, until earlier this month. The question we face now is: What happens when the industry destroyed is professional politics, the institutions leveled are the same few that prop up liberal democracy, and the values the internet disseminates are racism, nationalism, and demagoguery?
Powerful undemocratic states like China and Russia have for a while now put the internet to use to mislead the public, create the illusion of mass support, and either render opposition invisible or expose it to targeting…(More)”
James Bridle in the New Humanist: “In a 2008 article in Wired magazine entitled “The End of Theory”, Chris Anderson argued that the vast amounts of data now available to researchers made the traditional scientific process obsolete. No longer would they need to build models of the world and test them against sampled data. Instead, the complexities of huge and totalising datasets would be processed by immense computing clusters to produce truth itself: “With enough data, the numbers speak for themselves.” As an example, Anderson cited Google’s translation algorithms which, with no knowledge of the underlying structures of languages, were capable of inferring the relationship between them using extensive corpora of translated texts. He extended this approach to genomics, neurology and physics, where scientists are increasingly turning to massive computation to make sense of the volumes of information they have gathered about complex systems. In the age of big data, he argued, “Correlation is enough. We can stop looking for models.”
This belief in the power of data, of technology untrammelled by petty human worldviews, is the practical cousin of more metaphysical assertions. A belief in the unquestionability of data leads directly to a belief in the truth of data-derived assertions. And if data contains truth, then it will, without moral intervention, produce better outcomes. Speaking at Google’s private London Zeitgeist conference in 2013, Eric Schmidt, Google Chairman, asserted that “if they had had cellphones in Rwanda in 1994, the genocide would not have happened.” Schmidt’s claim was that technological visibility – the rendering of events and actions legible to everyone – would change the character of those actions. Not only is this statement historically inaccurate (there was plenty of evidence available of what was occurring during the genocide from UN officials, US satellite photographs and other sources), it’s also demonstrably untrue. Analysis of unrest in Kenya in 2007, when over 1,000 people were killed in ethnic conflicts, showed that mobile phones not only spread but accelerated the violence. But you don’t need to look to such extreme examples to see how a belief in technological determinism underlies much of our thinking and reasoning about the world.
“Big data” is not merely a business buzzword, but a way of seeing the world. Driven by technology, markets and politics, it has come to determine much of our thinking, but it is flawed and dangerous. It runs counter to our actual findings when we employ such technologies honestly and with the full understanding of their workings and capabilities. This over-reliance on data, which I call “quantified thinking”, has come to undermine our ability to reason meaningfully about the world, and its effects can be seen across multiple domains.
The assertion is hardly new. Writing in the Dialectic of Enlightenment in 1947, Theodor Adorno and Max Horkheimer decried “the present triumph of the factual mentality” – the predecessor to quantified thinking – and succinctly analysed the big data fallacy, set out by Anderson above. “It does not work by images or concepts, by the fortunate insights, but refers to method, the exploitation of others’ work, and capital … What men want to learn from nature is how to use it in order wholly to dominate it and other men. That is the only aim.” What is different in our own time is that we have built a world-spanning network of communication and computation to test this assertion. While it occasionally engenders entirely new forms of behaviour and interaction, the network most often shows to us with startling clarity the relationships and tendencies which have been latent or occluded until now. In the face of the increased standardisation of knowledge, it becomes harder and harder to argue against quantified thinking, because the advances of technology have been conjoined with the scientific method and social progress. But as I hope to show, technology ultimately reveals its limitations….
“Eroom’s law” – Moore’s law backwards – was recently formulated to describe a problem in pharmacology. Drug discovery has been getting more expensive. Since the 1950s the number of drugs approved for use in human patients per billion US dollars spent on research and development has halved every nine years. This problem has long perplexed researchers. According to the principles of technological growth, the trend should be in the opposite direction. In a 2012 paper in Nature entitled “Diagnosing the decline in pharmaceutical R&D efficiency” the authors propose and investigate several possible causes for this. They begin with social and physical influences, such as increased regulation, increased expectations and the exhaustion of easy targets (the “low hanging fruit” problem). Each of these are – with qualifications – disposed of, leaving open the question of the discovery process itself….(More)
Science Magazine: “For years, researchers at the MIT Media Lab have been developing a database of images captured at regular distances around several major cities. The images are scored according to different visual characteristics — how safe the depicted areas look, how affluent, how lively, and the like….Adjusted for factors such as population density and distance from city centers, the correlation between perceived safety and visitation rates was strong, but it was particularly strong for women and people over 50. The correlation was negative for people under 30, which means that males in their 20s were actually more likely to visit neighborhoods generally perceived to be unsafe than to visit neighborhoods perceived to be safe.
In the same paper, the researchers also identified several visual features that are highly correlated with judgments that a particular area is safe or unsafe. Consequently, the work could help guide city planners in decisions about how to revitalize declining neighborhoods.,,,
Jacobs’ theory, Hidalgo says, is that neighborhoods in which residents can continuously keep track of street activity tend to be safer; a corollary is that buildings with street-facing windows tend to create a sense of safety, since they imply the possibility of surveillance. Newman’s theory is an elaboration on Jacobs’, suggesting that architectural features that demarcate public and private spaces, such as flights of stairs leading up to apartment entryways or archways separating plazas from the surrounding streets, foster the sense that crossing a threshold will bring on closer scrutiny….(More)”
Robert M. Patton, Christopher G. Stahl and Jack C. Wells at DLib Magazine: “Measuring scientific progress remains elusive. There is an intuitive understanding that, in general, science is progressing forward. New ideas and theories are formed, older ideas and theories are confirmed, rejected, or modified. Progress is made. But, questions such as how is it made, by whom, how broadly, or how quickly present significant challenges. Historically, scientific publications reference other publications if the former publication in some way shaped the work that was performed. In other words, one publication “impacted” a latter one. The implication of this impact revolves around the intellectual content of the idea, theory, or conclusion that was formed. Several metrics such as h-index or journal impact factor (JIF) are often used as a means to assess whether an author, article, or journal creates an “impact” on science. The implied statement behind high values for such metrics is that the work must somehow be valuable to the community, which in turn implies that the author, article, or journal somehow has influenced the direction, development, or progress of what others in that field do. Unfortunately, the drive for increased publication revenue, research funding, or global recognition has lead to a variety of external factors completely unrelated to the quality of the work that can be used to manipulate key metric values. In addition, advancements in computing and data sciences field have further altered the meaning of impact on science.
The remainder of this paper will highlight recent advancements in both cultural and technological factors that now influence scientific impact as well as suggest new factors to be leveraged through full content analysis of publications….(More)”
Clive Thompson at the Smithsonian magazine: “As the 2016 election approaches, we’re hearing a lot about “red states” and “blue states.” That idiom has become so ingrained that we’ve almost forgotten where it originally came from: a data visualization.
In the 2000 presidential election, the race between Al Gore and George W. Bush was so razor close that broadcasters pored over electoral college maps—which they typically colored red and blue. What’s more, they talked about those shadings. NBC’s Tim Russert wondered aloud how George Bush would “get those remaining 61 electoral red states, if you will,” and that language became lodged in the popular imagination. America became divided into two colors—data spun into pure metaphor. Now Americans even talk routinely about “purple” states, a mental visualization of political information.
We live in an age of data visualization. Go to any news website and you’ll see graphics charting support for the presidential candidates; open your iPhone and the Health app will generate personalized graphs showing how active you’ve been this week, month or year. Sites publish charts showing how the climate is changing, how schools are segregating, how much housework mothers do versus fathers. And newspapers are increasingly finding that readers love “dataviz”: In 2013, the New York Times’ most-read story for the entire year was a visualization of regional accents across the United States. It makes sense. We live in an age of Big Data. If we’re going to understand our complex world, one powerful way is to graph it.
But this isn’t the first time we’ve discovered the pleasures of making information into pictures. Over a hundred years ago, scientists and thinkers found themselves drowning in their own flood of data—and to help understand it, they invented the very idea of infographics.
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The idea of visualizing data is old: After all, that’s what a map is—a representation of geographic information—and we’ve had maps for about 8,000 years. But it was rare to graph anything other than geography. Only a few examples exist: Around the 11th century, a now-anonymous scribe created a chart of how the planets moved through the sky. By the 18th century, scientists were warming to the idea of arranging knowledge visually. The British polymath Joseph Priestley produced a “Chart of Biography,” plotting the lives of about 2,000 historical figures on a timeline. A picture, he argued, conveyed the information “with more exactness, and in much less time, than it [would take] by reading.”
Still, data visualization was rare because data was rare. That began to change rapidly in the early 19th century, because countries began to collect—and publish—reams of information about their weather, economic activity and population. “For the first time, you could deal with important social issues with hard facts, if you could find a way to analyze it,” says Michael Friendly, a professor of psychology at York University who studies the history of data visualization. “The age of data really began.”
An early innovator was the Scottish inventor and economist William Playfair. As a teenager he apprenticed to James Watt, the Scottish inventor who perfected the steam engine. Playfair was tasked with drawing up patents, which required him to develop excellent drafting and picture-drawing skills. After he left Watt’s lab, Playfair became interested in economics and convinced that he could use his facility for illustration to make data come alive.
“An average political economist would have certainly been able to produce a table for publication, but not necessarily a graph,” notes Ian Spence, a psychologist at the University of Toronto who’s writing a biography of Playfair. Playfair, who understood both data and art, was perfectly positioned to create this new discipline.
In one famous chart, he plotted the price of wheat in the United Kingdom against the cost of labor. People often complained about the high cost of wheat and thought wages were driving the price up. Playfair’s chart showed this wasn’t true: Wages were rising much more slowly than the cost of the product.
Playfair’s trade-balance time-series chart, published in his Commercial and Political Atlas, 1786 (Wikipedia)
“He wanted to discover,” Spence notes. “He wanted to find regularities or points of change.” Playfair’s illustrations often look amazingly modern: In one, he drew pie charts—his invention, too—and lines that compared the size of various country’s populations against their tax revenues. Once again, the chart produced a new, crisp analysis: The British paid far higher taxes than citizens of other nations.
Neurology was not yet a robust science, but Playfair seemed to intuit some of its principles. He suspected the brain processed images more readily than words: A picture really was worth a thousand words. “He said things that sound almost like a 20th-century vision researcher,” Spence adds. Data, Playfair wrote, should “speak to the eyes”—because they were “the best judge of proportion, being able to estimate it with more quickness and accuracy than any other of our organs.” A really good data visualization, he argued, “produces form and shape to a number of separate ideas, which are otherwise abstract and unconnected.”
Soon, intellectuals across Europe were using data visualization to grapple with the travails of urbanization, such as crime and disease….(More)”
