The Filter Bubble The Grandmaster in the Corner Office: What the Study of Chess Experts Teaches Us about Building a Remarkable Life January 6th, 2010 · 213 comments Becoming a Grandmaster How do great chess players become great? Here’s how he summarized it in a recent interview: When we look at any kind of cognitively complex field — for example, playing chess, writing fiction or being a neurosurgeon — we find that you are unlikely to master it unless you have practiced for 10,000 hours. There seems to be no escape from this work. The full story, however, is more complex. Put another way, you need to put in a lot of hours to become exceptional, but raw hours alone doesn’t cut it. To understand what else is necessary, I’ll turn your attention to a fascinating 2005 study on chess players, published in the journal Applied Cognitive Psychology. In more detail: …chess players at the highest skill level (i.e. grandmasters) expended about 5000 hours on serious study alone during their first decade of serious chess play — nearly five times the average amount reported by intermediate-level players. Deliberate Practice Why?
Credibility Check Algorithmic Literacies - THE LATE AGE OF PRINT I’ve spent the last few weeks here auditioning ideas for my next book, on the topic of “algorithmic culture.” By this I mean the use of computers and complex mathematical routines to sort, classify, and create hierarchies for our many forms of human expression and association. I’ve been amazed by the reception of these posts, not to mention the extent of their circulation. Even more to the point, the feedback I’ve been receiving has already prompted me to address some of the gaps in the argument — among them, the nagging question of “what is to be done?” I should be clear that however much I may criticize Google, Facebook, Netflix, Amazon, and other leaders in the tech industry, I’m a regular user of their products and services. In other words, I don’t mean to suggest that life would be better off without algorithmic culture. It’s this question that’s brought me to the idea of algorithmic literacies, which is something Eli Pariser also talks about in the conclusion of The Filter Bubble.
NYHEN: New York Home Educators' Network Censorship Siva Vaidhyanathan Vaidhyanathan speaking at the 2011 Personal Democracy Forum Siva Vaidhyanathan (born June 16, 1966) is a cultural historian and media scholar and is a professor of Media Studies and Law at the University of Virginia. Vaidhyanathan is a frequent contributor on media and cultural issues in various periodicals including The Chronicle of Higher Education, New York Times Magazine, The Nation, MSNBC.com, and Salon.com. He is a fellow of the New York Institute for the Humanities and the Institute for the Future of the Book. Biography[edit] Vaidhyanathan was born in Buffalo, New York, and attended the University of Texas at Austin, earning a BA in History in 1994 and a Ph.D. in 1999 in American Studies.[3] From 1999 through the summer of 2007 he worked in the Department of Culture and Communication at New York University, the School of Library and Information Studies at the University of Wisconsin–Madison and Columbia University. Critical Information Studies[edit] Selected books[edit] See also[edit]
The Stanford Education Experiment Could Change Higher Learning Forever | Wired Science Sebastian Thrun and Peter Norvig in the basement of Thrun's guesthouse, where they record class videos.Photo: Sam Comen Stanford doesn’t want me. I can say that because it’s a documented fact: I was once denied admission in writing. I took my last math class back in high school. Which probably explains why this quiz on how to get a computer to calculate an ideal itinerary is making my brain hurt. I’m staring at a crude map of Romania on my MacBook. Last fall, the university in the heart of Silicon Valley did something it had never done before: It opened up three classes, including CS221, to anyone with a web connection. People around the world have gone crazy for this opportunity. Aside from computer-programming AI-heads, my classmates range from junior-high school students and humanities majors to middle-aged middle school science teachers and seventysomething retirees. Solid understanding? That stuff’s all easier said than done.
Exclusive: How Google's Algorithm Rules the Web | Wired Magazine Want to know how Google is about to change your life? Stop by the Ouagadougou conference room on a Thursday morning. It is here, at the Mountain View, California, headquarters of the world’s most powerful Internet company, that a room filled with three dozen engineers, product managers, and executives figure out how to make their search engine even smarter. This year, Google will introduce 550 or so improvements to its fabled algorithm, and each will be determined at a gathering just like this one. The decisions made at the weekly Search Quality Launch Meeting will wind up affecting the results you get when you use Google’s search engine to look for anything — “Samsung SF-755p printer,” “Ed Hardy MySpace layouts,” or maybe even “capital Burkina Faso,” which just happens to share its name with this conference room. Udi Manber, Google’s head of search since 2006, leads the proceedings. You might think that after a solid decade of search-market dominance, Google could relax.
Conflicting Codes and Codings How Algorithmic Trading Is Reshaping Financial Regulation Abstract Contemporary financial markets have recently witnessed a sea change with the ‘algorithmic revolution’, as trading automats are used to ease the execution sequences and reduce market impact. Being constantly monitored, they take an active part in the shaping of markets, and sometimes generate crises when ‘they mess up’ or when they entail situations where traders cannot go backwards. algorithmic trading codes of conduct codings financial markets practices regulation SAGE, Los Angeles, London, New Delhi, and Singapore
Liberty Classroom Creativity A New Algorithmic Identity Soft Biopolitics and the Modulation of Control Abstract Marketing and web analytic companies have implemented sophisticated algorithms to observe, analyze, and identify users through large surveillance networks online. SAGE, Los Angeles, London, New Delhi, and Singapore