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10 Beliefs That Undercut Your Happiness

10 Beliefs That Undercut Your Happiness | Psychology and Psychiatry | Scoop.it
These deeply-held beliefs and faulty assumptions prevent us from putting ourselves out there and taking risks, strategies that happy people rely on to succeed.
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Psychology and Psychiatry
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10 Beliefs That Undercut Your Happiness

10 Beliefs That Undercut Your Happiness | Psychology and Psychiatry | Scoop.it
These deeply-held beliefs and faulty assumptions prevent us from putting ourselves out there and taking risks, strategies that happy people rely on to succeed.
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Rescooped by question everything from Knowmads, Infocology of the future
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Combining computer science, statistics creates machines that can learn

Combining computer science, statistics creates machines that can learn | Psychology and Psychiatry | Scoop.it
Learning a subject well means moving beyond the recitation of facts to a deeper knowledge that can be applied to new problems. Designing computers that can transcend rote calculations to more nuanced understanding has challenged scientists for years.

Only in the past decade have researchers' flexible, evolving algorithms—known as machine learning—matured from theory to everyday practice, underlying search and language-translation websites and the automated trading strategies used by Wall Street firms.

 

These applications only hint at machine learning's potential to affect daily life, according to John Lafferty, the Louis Block Professor in Statistics and Computer Science. With his two appointments, Lafferty bridges these disciplines to develop theories and methods that expand the horizon of machine learning to make predictions and extract meaning from data.

"Computer science is becoming more focused on data rather than computation, and modern statistics requires more computational sophistication to work with large data sets," Lafferty says. "Machine learning draws on and pushes forward both of these disciplines."

Lafferty's work focuses on the theories and algorithms that power machine learning. The goal is to develop computer programs that, with little or no human input, can extract knowledge from large amounts of numbers, text, audio or video and make predictions and decisions about events that haven't been coded in its instructions.

"The classical areas of applied mathematics, including partial differential equations, developed from the study of physical processes such as fluid flow," Lafferty says. "What we're seeing now is that entirely new directions in applied mathematics are opening up from the study of modern large data sets."


Via Wildcat2030
question everything's insight:

Amazing what we are learning to do with computers.  Now if we could just learn how to live and let live, we might make real progress.

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These Amazing Twitter Metadata Visualizations Will Blow Your Mind | #dataviz #bigdata #visualization

These Amazing Twitter Metadata Visualizations Will Blow Your Mind | #dataviz #bigdata #visualization | Psychology and Psychiatry | Scoop.it
Metadata in Twitter posts lets readers in on your geographic location the language you speak the phone you use and more. They're also a mapmaker's...

Via Aaron Balick, luiy, Wildcat2030
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luiy's curator insight, June 20, 2013 5:32 AM

Twitter's full data stream--their “firehose”--is a very detailed thing. Access to raw tweet upon raw tweet lets brands know what customers think and allows first responders to instantly tabulate hurricane damage. The firehose is also full of metadata which discloses personal, geographic, and technological information on Twitter's tens of millions of users. Gnip, one of the best known Twitter firehose resellers, just turned a raw sample of metadata from 280 million tweets into an amazing example of data visualization.

 

The fully scalable and searchable visualizations, created by Eric Fischer and MapBoxfor Gnip, uses metadata from 280 million tweets collected from a data sample going back to 2011. Gnip's Ian Cairns told Fast Company in a phone conversation the sample was pruned to remove multiple tweets from the same geographic location in order to emphasize geographic distribution rather than tweet frequency. Gnip and MapBox only selected tweets with location metadata attached, which ranged from 2% to 4% of the total tweets in Twitter's firehose. When posting messages to Twitter, users can choose whether to embed geographic location metadata. According to Cairns, the percentage of tweets with location metadata attached is decreasing over time.