News Article | April 21, 2017
The people who know the most about life on Earth tend to be the most impressed by its staying power. Harvard professor Andrew Knoll marvels that our planet has sustained life continuously for four billion years -- most of its 4.5 billion years in existence. This is not just a matter of location, said Knoll, who is an earth and planetary scientist. Mars and Venus are both in what astronomers would consider a “habitable” zone, getting sunlight in a range suitable for living organisms. Now both are barren (or close to it). Earth has special features that may or may not be present on many of the other planets detected around the galaxy. Earth’s geology helps regulate the climate through the cycling of carbon dioxide. When exposed rocks weather, carbon dioxide gets pulled out of the atmosphere, allowing the globe to cool. When those rocks get covered in ice, the weathering stops, and carbon can build up as it’s replenished by volcanoes. We can thank Earth’s system of plate tectonics for this, said Peter Ward, a paleontologist from the University of Washington and co-author of the book “Rare Earth: Why Complex Life Is Uncommon in the Universe.” As new crust continues to be exposed in some places and old crust is buried, carbon can cycle in and of the atmosphere. We’re also very lucky, said Ward, that the Earth got just the right amount of water. It’s thought that most came from impacts with comets early in the history of the solar system. If we’d gotten a bit more, and ended up like that third-rate Kevin Costner movie, he said, Earth would be a lot hotter -- maybe too hot for complex life. Complex life, including plants and animals, are particular. They didn’t get going until the most recent 600 million years. Bacteria are another story. It’s hard to put a date on the origin of simple life because it happened so early. What we know, said Harvard’s Knoll, is that the very oldest rocks on Earth were formed 3.8 billion years ago, and they hold preserved signatures of life. That’s fast given the widely held view that a few million years after its formation, the infant Earth collided with another early planet, creating debris that became the moon. After the crash, some scientists have calculated that the Earth’s surface temperature reached 3,600 degrees Fahrenheit and our planet shone like a star. After it cooled off, there were further radical changes: periods when tropical plants grew at the poles, and periods when ice flowed down to the equator. But the extremes always eventually gave way to more moderate periods, and life was never extinguished. All this recovery and cycling may sound reassuring, backing a longstanding popular belief in an inherent balance of nature. As historian Spencer Weart describes it in his book “The Discovery of Global Warming”: “Hardly anyone could imagine that human actions, so puny among the vast natural powers, could offset the balance that governed the planet as a whole. This view of Nature -- suprahuman, benevolent and inherently stable -- lay deep in most human cultures.” But in the last few decades, scientists have learned that there’s no real barrier between the physical processes of the planet and the biological ones. Earth was not born a blue planet rich with oxygen. Single-celled organisms called cyanobacteria started releasing oxygen into the atmosphere. The emergence of plants changed the climate. Animals changed the climate. Even the evolution of poop changed the physical world, said Ward, by creating a new mechanism by which carbon and other materials would get packaged up and sink to the bottom of the ocean. That still leaves the argument that human-generated greenhouse gases -- like early fish poop -- represent nothing the Earth can’t handle. Knoll said he recalled a newspaper column by George Will, still available online, arguing that current climate change is nothing to worry about because the past periods of climate change were not the end of the world. But the column focused on recent, small blips in the climate, not on the bigger, longer-term upheavals. Some periods of climate change were terrible. Take one 252 million years ago called the End Permian extinction. Large volcanic eruptions, possibly combined with ignition of coal beds, led to a rapid enough global warming to kill off about 90 percent of the planet’s species. This was good for some -- especially sulfur-excreting bacteria -- whose flourishing is preserved in the fossil record. But it was bad for plants and animals. In another of his popular books, “Under a Green Sky,” Ward describes the End Permian seashore this way: “No fish break its surface, no birds of any kind. We are under a pale green sky and it has the smell of death and poison.” So life went on, in an altered form, and plants and animals again flourished after a few million years. Knoll doesn’t find this particularly reassuring. “We are changing the climate at a geologically unusual rate,” he said -- changes comparable to an era of volcanism a million times more powerful than anything in human history. Earth’s climate will probably recover from this human-fueled round of global warming, but “on time scales that are unimaginable to humans.” And perhaps without humans. This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners. To contact the author of this story: Faye Flam at email@example.com To contact the editor responsible for this story: Philip Gray at firstname.lastname@example.org
News Article | April 27, 2017
It’s easy to make fun of last weekend's March for Science. People groan at the nerdy signs. The critics question the purpose of the march, beyond simply bringing scientists into the public eye. And they point out that although the march was aimed mainly at President Donald Trump and his plans to roll back environmental protection and spending on research, in reality there are elements on both sides of the political spectrum that deny or challenge the scientific consensus. But the demonstrations in dozens of cities across the U.S. -- which drew between 250,000 and 600,000 people -- were a crucial sign that our society is a healthy one. And it’s a reminder that respect for science, as an institution and as an idea, is necessary in order to keep our civilization on the right track. To understand why science is so important, it’s essential to take a broad view of history. Economic historians estimate that modern developed countries are more than 25 times as rich as they were in the Middle Ages. For example, here are estimates for the U.K.: The developing world is now repeating that transformation, following in the footsteps of places like Britain. In the space of a couple of centuries, the human race went from scratching in the dirt for a living to driving cars and ordering pizza. Industrial technology made that amazing change possible. Not all of the Industrial Revolution was due to science -- a lot of inventions came from tinkering. But there’s no question that science was essential to the process. From the germ theory of disease to the quantum physics that created semiconductors, the process of careful experimentation and practical, mathematical theory that we now call “science” has been the most powerful tool humanity has ever discovered. It has let us master the natural world and improve the human condition. Science was also the secret sauce of Western civilization. At first, Western nations harnessed science for war -- it was the systematic advancement of engineering, chemistry and materials science that turned Europe from a disease-ridden chaotic backwater in the 1300s into the wealthy masters of the globe in the 1800s. But in contrast to earlier world-conquerors like the Mongols or the Romans, the modern Europeans remained rich, healthy and successful even after they lost their colonial empires. Today, despite a shrinking population, Europe remains one of the centers of the global economy, even as science and its bounty have spread to the rest of Earth’s nations. That success stands in contrast to several other civilizations that, in earlier centuries, were on the verge of scientific revolutions but shied away, probably dooming humanity to additional centuries of poverty. During the Middle Ages, the Islamic empire under the Abbasids of Baghdad boasted many of the world’s leading thinkers, some of whom were experimenting with ideas eerily similar to those eventually embraced by European scientists. But for some reason, the Caliphate turned away from these ideas. No one knows exactly why, but many blame the rise of anti-scientific and anti-rationalist schools of thought. Another frustrating historical example is China’s Ming Dynasty. China bounced back from the Mongol conquests, and in the 15th and 16th centuries it was the world’s most technologically advanced civilization. Proto-science was common in early modern China, but the country cut itself off from foreign influences and de-emphasized science in the civil-service examinations. Eventually, China ended up importing Jesuit astronomers from Europe. No one will ever know how close the Abbasids and the Ming came to full-blown scientific revolutions, or exactly which factors led to the fizzling of their promise. History, of course, isn’t very scientific. But it seems very likely that each dynasty probably could have used a March for Science. If there’s something that makes the U.S. and other modern developed nations more successful than those old empires, it’s not the strength of their armies or the superiority of their religious beliefs -- it’s a healthy respect for the march of systematically acquired knowledge of the natural world. For now, that respect for science is intact. Americans of all political leanings and educational backgrounds evince a healthy respect for science as an institution: And most Americans support government investment in basic science: So the March for Science would seem to have the public on its side. Still, there is no guarantee that scientific values will continue to prevail in the U.S. Research funding has stagnated over the last few years. And scientific issues like climate change are way too politicized -- opposition to the scientific consensus on global warming is a badge of honor for too many partisans. Meanwhile, dangerous anti-scientific ideas like the thoroughly debunked anti-vaccination movement are spreading, and have even gained the ear of the president. That’s exactly why the U.S. -- and the world -- needed a March for Science, and why they need a durable pro-science movement now. Science is too valuable to risk. It’s all that stands between the human race and the poverty and darkness that once engulfed us. This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners. To contact the author of this story: Noah Smith at email@example.com To contact the editor responsible for this story: James Greiff at firstname.lastname@example.org
News Article | May 1, 2017
A new argument has started to crop up in debates over climate change. It goes like this: Science couldn’t predict the outcome of the last election, or the bumps in the economy, so why should we believe scientists when they try to predict the future of Earth’s climate? For example, a recent New York Times column -- the first from new op-ed writer Bret Stephens -- starts with a cautionary tale about the failure of data analytics to guide Team Clinton to victory in 2016, then segues into a discussion of climate-change skepticism. Given the “inherent uncertainties of data,” Stephens argues, doubters have a right to distrust “overweening scientism.” He writes: But to put this in context, science makes all kinds of predictions that do hold up. Consider last year’s finding of gravitational waves: Scientists reported that they’d detected ripples in space-time generated by a collision of two black holes some 1.3 billion light years away. The invisible waves were predicted by Einstein’s theory of general relativity a century ago. Even if someone later finds this individual claim was in error, it’s part of a body of knowledge. If physics weren’t on reasonably good footing, we wouldn’t be walking around with devices that talk to satellites to pinpoint our locations. If not for a general trust in physics, airlines would have to drag people kicking and screaming to get them on their planes. Why, then, can some areas of science predict invisible space-time ripples, but others can’t predict elections? I’ve been talking to scientists, philosophers and historians about this situation for months. There are, it turns out, some common characteristics of scientific pursuits that make good predictions. One is the tradition scientists in some fields have of submitting to peer review, and making their procedures transparent so other people can reproduce their results. This creates an interconnected body of knowledge. Great science combines great minds. Einstein himself wavered over whether his theory predicted the existence of the gravitational waves. Other scientists realized that it did, and they dreamed up a creative way to detect them. Fields of science with good track records for prediction often work by discerning patterns and insights that explain the world. The better the insights, the better the predictions -- on subjects ranging from eclipses to chemical reactions to the behavior of ants to the existence of black holes. In contrast, many data-driven algorithms developed by private companies and used to, say, predict election results, are opaque. They aren’t peer-reviewed. Their claims aren’t subject to replication. They don’t reveal insights or explanations that others can test. Established fields of science also gain predictive power by requiring scientists to quantify their uncertainties. For some, this isn’t just good practice but part of the very definition of science. When scientists graph their measurements, they draw vertical lines -- error bars -- which indicate how inherently imprecise their measurement systems are. There are good cautionary tales about failure to use error bars. One comes from forensic science -- the use of fingerprints, hair analysis and the like to solve crimes. A group of scientists looking into forensics for a recent government report concluded that it shouldn’t be considered a science at all, because people are doing such a poor job of calculating error bars. Expert witnesses mislead juries with statements about “matches” when all they have are probabilities. So it’s important to look closely at climate science and make sure they’re not making the same mistakes. And investigations by the National Academy of Sciences and others don’t reveal the kinds of problems that plague forensics. Climate science grew out of physics and chemistry -- disciplines with explicit rules for dealing with uncertainty. The first climate model came from the calculations of Swedish chemist Svante Arrhenius in 1896. The basic principles behind his model have been tested in laboratory experiments and used to predict temperatures on Venus and Mars. Earth is more complex than its neighbors because it’s covered in water. Atmospheric temperatures affect the state of the water -- ice, liquid or vapor -- which in turn affects the temperature. But that’s okay -- scientists are allowed to deal in complex phenomena as long as they do a good job of calculating their uncertainties. Individual scientists make mistakes, like everyone else, but if you really want a cautionary tale that’s relevant to climate change, it should involve a whole field misleading the public and being used to make harmful policy. It’s hard to find a better example than the now-discredited belief that dietary fat is killing people. As journalist Gary Taubes described it in Science in 2001, and later in the New York Times Magazine, the idea had political appeal with those on the left who were upset by consumption, cruelty to animals and the environmental toll of raising animals for meat. As Taubes tells it, scientists were in disagreement and lacked the kind of long-range health data they needed to understand the effects of dietary fat. The National Academy of Sciences investigated and was blasted for failing to approve the anti-fat belief. Back in the labs, scientists were coming across evidence that different fats had different physiological effects, some quite beneficial. But demand was growing for a simple recommendation. “Once politicians, the press, and the public had decided dietary fat policy,” Taubes wrote, “the science was left to catch up.” If there are lessons to be learned from the fat debacle, it’s that the press and policy makers shouldn’t get ahead of scientific consensus. Scientists do make mistakes, but scientific methods in many fields your guard against unwarranted certainty. (Science can make some predictions with near-certainty -- that solar eclipse will certainly happen on Aug. 21.) And of course, there is a consensus on climate change. Scientists shouldn’t be trusted blindly, but stubborn distrust in the face of evidence defeats the purpose. This column does not necessarily reflect the opinion of the editorial board or Bloomberg LP and its owners. To contact the author of this story: Faye Flam at email@example.com To contact the editor responsible for this story: Tracy Walsh at firstname.lastname@example.org
News Article | May 2, 2017
Orbital Insight Inc., the startup betting on using big-data analysis of satellite imagery to give investors a more accurate and timely view of the global economy, raised $50 million from U.S. and Japanese investors to bring its total funding to $78.7 million. The Series C funding was led by Sequoia Capital, one of Silicon Valley’s top venture capital firms, and additional investors included Geodesic Capital and Japan’s Itochu Corp., Orbital said in a statement Tuesday. The four-year-old company’s valuation wasn’t disclosed. The fresh capital will be used to expand its partnerships, increase its analytics products, and build bigger international sales operations in Europe and Asia, Orbital said. The Mountain View, California-based company founded by former NASA scientist and Google engineer James Crawford also will step up recruiting in engineering, data science and design. Falling satellite launch costs are helping make geospatial imagery a bigger and better source for economists and investors tracking everything from China’s manufacturing to the number of cars parked outside Wal-Mart stores. Venture capital investment in space companies jumped to a record nearly $1.4 billion last year, bringing the total since 2000 to $13.3 billion, Goldman Sachs Group Inc. said in a report last month. "I’d met with a lot of satellite and rocket launch companies prior to our investment in Orbital. One of the things I didn’t see was the big data, machine learning piece," said Sequoia partner Bill Coughran, who formerly worked with Crawford at Google. "There’s a big change taking place. That old spy-movie stuff where you have people hovering over an image will go away, with computers doing a lot of the work." Envision Ventures, Intellectus Partners, and Balyasny Asset Management also participated in the Series C investment, Orbital said. Orbital has received past funding from Bloomberg Beta, a venture-capital unit of Bloomberg LP. Orbital buys satellite and drone imagery from commercial providers including Airbus SE, DigitalGlobe Inc. and Planet Labs Inc., then uses advanced image processing and machine learning algorithms to track trends visible in cars, farms, and buildings. Customers include financial firms, U.S. government agencies and non-profit organizations. Geodesic invested in Orbital after working with it to assess the Japanese market, according to Nate Mitchell, a partner at the Foster City, California, firm, which has a Tokyo subsidiary. Japanese banks, insurers, retailers, manufacturers, and tech companies are looking to space imagery for better understanding of daily business and broader market forces, Mitchell said. Last year, Orbital calculated from photos showing the depth of shadows cast by floating lids of giant oil tanks in China that the world’s largest energy consumer may have stored more crude than official government estimates. The company has also teamed up with World Bank researchers to help better identify areas of extreme poverty. Analysts see the space business beginning a broader transformation, led by companies like Elon Musk’s Space Exploration Technologies Corp. and Amazon.com Inc. founder Jeff Bezos’s Blue Origin LLC. Commercial space will be a multi-trillion dollar industry within two decades, Goldman Sachs analysts led by Noah Poponak in New York said in an April 4 research report. "The second Space Age has begun, and the forces of innovation and disruption are overtaking formerly stagnant industries," they wrote. "We are witnessing an inflection point in the significance of the space economy, where it becomes central in providing Internet access and basic services to more than half the world’s population, compounding growth."
Agency: European Commission | Branch: FP7 | Program: CP | Phase: ICT-2011.4.2 | Award Amount: 4.75M | Year: 2012
The goal of the X-LIKE project is to develop technology to monitor and aggregate knowledge that is currently spread across global mainstream and social media, and to enable cross-lingual services for publishers, media monitoring and business intelligence.In terms of research contributions, the aim is to combine scientific insights from several scientific areas to contribute in the area of cross-lingual text understanding. By combining modern computational linguistics, machine learning, text mining and semantic technologies we plan to deal with the following two key open research problems:- to extract and integrate formal knowledge from multilingual texts with cross-lingual knowledge bases, and- to adapt linguistic techniques and crowdsourcing to deal with irregularities in informal language used primarily in social media.As an interlingua, knowledge resources from Linked Open Data cloud (http://linkeddata.org/) will be used with special focus on general common sense knowledge base CycKB (http://www.cyc.com/). For the languages where no required linguistic resources will be available, we will use a probabilistic interlingua representation trained from a comparable corpus drawn from the Wikipedia.The solution will be applied on two case studies, both from the area of news. For the Bloomberg case study the domain will be financial news, while for the Slovenian Press Agency we will deal with general news. The technology developed in the project will be used to introduce cross-lingual and information from social media in services for publishers and end-users in the area of summarization, contextualization, personalization, and plagiarism detection. Special attention will be paid to analysing news reporting bias from multilingual sources. The developed technology will be language-agnostic, while within the project we will specifically address English, German, Spanish, and Chinese as major world languages and Catalan and Slovenian as minority languages.
Bloomberg L.P. | Date: 2013-04-02
Methods and systems for allocating trades to multiple brokers are disclosed. An execution algorithm for an order is selected. A weighting allocation is for the order to brokers is specified. The order is allocated based on the weighting allocation and execution algorithm. The order is subsequently transmitted for execution.
Bloomberg L.P. | Date: 2013-06-25
A system conducts anonymous negotiations and supports indications of interest in trading stock. The system includes a database for storing public orders received from a public stock trading system; and a server for receiving hidden orders from a plurality of users and for conducting anonymous negotiations between first and second users with the hidden orders. The server repeatedly accesses the database to determine a match of any one of the hidden orders with any one of the public orders, and to execute a pair of orders selected from the hidden orders and the public orders. The system also transmits indications of interest (IOIs) into a trading environment using the server for processing a trading order from a first user and for maintaining a profile of a user. The profile includes a current IOI setting for controlling transmission of the IOI from the user. The server responds to a toggle command from the first user to control transmission of the RN opposite to the current RN setting. The server responds to the ICH setting being set to allow transmission by transmitting the IOI of the first user associated with the trading order.
Bloomberg L.P. | Date: 2013-06-26
A system for performing a computer-based method and a computer-based method include: receiving, at a computer-based interface device, data that is determined to be relevant to estimating cost information associated with a transfer of a low liquidity security; calculating, with a computer-based processor coupled to the computer-based interface device, an estimated fair value for the low liquidity security, based on the received data; receiving, at the computer-based interface device, an indication of at least one of an executable bid for the financial instrument and an executable offer for the financial instrument; and presenting, for display at a user interface terminal, a scaled, graphical representation of the estimated fair value for the low liquidity security and at least one of the executable bid for the financial instrument and the executable offer for the financial instrument.
Bloomberg L.P. | Date: 2014-07-15
In electronic trading venues, there may be orders for which the full information is not publicly displayed. For example, the full quantity of an order available for trading or the most aggressive price at which an order can be traded may not be made public. A system and method are disclosed that facilitates trading based on this non-public information. A first order associated with a financial instrument is placed at a venue to probe for non-public information related to the financial instrument. The results of the probe may then be used to place a second order at the venue that takes advantage of any discovered non-public information.
Bloomberg L.P. | Date: 2013-09-16
Computer systems, methods, and computer program products are provided for facilitating trading of financial interests over computer networks. A computer system may send through the network a first order for a financial interest, where the first order is not presently executable in an execution venue but has associated with it a quantity that can be committed to a possible future trade. On learning of a possible match between the first order and a second order, the computer system may attempt to firm up the first order into a tradable third order, possibly by asking a trader workstation to recall a quantity that was previously committed to a broker for trading. If firming up succeeds, the third order may be transmitted to a venue for execution. Firming up may include receiving from the trader workstation in formation identifying a broker to give up to the venue.