“The Copernicus Complex addresses some of the deepest questions humans have ever asked. How weird are we? Was our existence highly probable, or improbable? Even miraculous? You can break this question down into more specific questions: How probable was our universe? Our galaxy? Solar system? Planet? How probable was life? And how probable were creatures like us, who can ponder their probability?
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Are Scientists on "Cusp of Knowing" How Weird We Are? | Cross-Check, Scientific American Blog Network - 1 views
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Scientists still don’t have a clue why our universe has the form we observe, or how life began on the Earth some 3.6 billion years ago, or whether life exists elsewhere. During his talk at Stevens, Scharf acknowledged that we may never observe exoplanets in sufficient detail to know, with certainty, that they harbor life.
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“Unfortunately, you cannot determine the probability of the universe or of life on Earth when you have only one universe and one history of life to contemplate. Statistics require more than one data point.
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BBC World Service - The Science Hour, The Medical Scandal Engulfing Top Swedish University - 0 views
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Predicting the Next Financial CrisisWhy do financial crises occur – and when will the next one come? In the past, economic theory has failed to answer these questions. In this week’s Science Journal Perspectives, economists, physicists, epidemiologists, climate scientists and ecologists call to establish a new early warning system to avoid future global financial crises. They argue that the methods used by scientists to predict weather, traffic or disease epidemics should be used to simulate the financial systems, which could help to avoid the failures we have seen in the past. Professor Doyne tells Jack how the analysis of complex networks could and should be applied to the economy.
What Google Learned From Its Quest to Build the Perfect Team - The New York Times - 1 views
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Five years ago, Google — one of the most public proselytizers of how studying workers can transform productivity — became focused on building the perfect team. In the last decade, the tech giant has spent untold millions of dollars measuring nearly every aspect of its employees’ lives. Google’s People Operations department has scrutinized everything from how frequently particular people eat together (the most productive employees tend to build larger networks by rotating dining companions) to which traits the best managers share (unsurprisingly, good communication and avoiding micromanaging is critical; more shocking, this was news to many Google managers).The company’s top executives long believed that building the best teams meant combining the best people. They embraced other bits of conventional wisdom as well, like ‘‘It’s better to put introverts together,’’ said Abeer Dubey, a manager in Google’s People Analytics division, or ‘‘Teams are more effective when everyone is friends away from work.’’ But, Dubey went on, ‘‘it turned out no one had really studied which of those were true.’’In 2012, the company embarked on an initiative — code-named Project Aristotle — to study hundreds of Google’s teams and figure out why some stumbled while others soared.
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As they struggled to figure out what made a team successful, Rozovsky and her colleagues kept coming across research by psychologists and sociologists that focused on what are known as ‘‘group norms.’’
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As the researchers studied the groups, however, they noticed two behaviors that all the good teams generally shared. First, on the good teams, members spoke in roughly the same proportion, a phenomenon the researchers referred to as ‘‘equality in distribution of conversational turn-taking.’’ On some teams, everyone spoke during each task; on others, leadership shifted among teammates from assignment to assignment. But in each case, by the end of the day, everyone had spoken roughly the same amount. ‘‘As long as everyone got a chance to talk, the team did well,’’ Woolley said. ‘‘But if only one person or a small group spoke all the time, the collective intelligence declined.’’Second, the good teams all had high ‘‘average social sensitivity’’ — a fancy way of saying they were skilled at intuiting how others felt based on their tone of voice, their expressions and other nonverbal cues. One of the easiest ways to gauge social sensitivity is to show someone photos of people’s eyes and ask him or her to describe what the people are thinking or feeling — an exam known as the Reading the Mind in the Eyes test. People on the more successful teams in Woolley’s experiment scored above average on the Reading the Mind in the Eyes test. They seemed to know when someone was feeling upset or left out. People on the ineffective teams, in contrast, scored below average. They seemed, as a group, to have less sensitivity toward their colleagues.
The Great A.I. Awakening - The New York Times - 1 views
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Translation, however, is an example of a field where this approach fails horribly, because words cannot be reduced to their dictionary definitions, and because languages tend to have as many exceptions as they have rules. More often than not, a system like this is liable to translate “minister of agriculture” as “priest of farming.” Still, for math and chess it worked great, and the proponents of symbolic A.I. took it for granted that no activities signaled “general intelligence” better than math and chess.
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A rarefied department within the company, Google Brain, was founded five years ago on this very principle: that artificial “neural networks” that acquaint themselves with the world via trial and error, as toddlers do, might in turn develop something like human flexibility. This notion is not new — a version of it dates to the earliest stages of modern computing, in the 1940s — but for much of its history most computer scientists saw it as vaguely disreputable, even mystical. Since 2011, though, Google Brain has demonstrated that this approach to artificial intelligence could solve many problems that confounded decades of conventional efforts. Speech recognition didn’t work very well until Brain undertook an effort to revamp it; the application of machine learning made its performance on Google’s mobile platform, Android, almost as good as human transcription. The same was true of image recognition. Less than a year ago, Brain for the first time commenced with the gut renovation of an entire consumer product, and its momentous results were being celebrated tonight.
The Tilted Road Illusion - Scientific American Blog Network - 0 views
Is Economics More Like History Than Physics? | Guest Blog, Scientific American Blog Network - 3 views
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"Is economics like physics, or more like history? Steven Pinker says, "No sane thinker would try to explain World War I in the language of physics." Yet some economists aim close to such craziness. Pinker says the "mindset of science" eliminates errors by "open debate, peer review, and double-blind methods," and especially, experimentation. But experiments require repetition and control over all relevant variables. We can experiment on individual behavior, but not with history or macroeconomics."
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