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http://www.ibertic.org Tenemos que comprender que la inteligencia colectiva existe desde siempre y la especie humana es un ejemplo maravilloso de ello. Per 
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Scoop.it!
Les utilisateurs de Twitter sont, en moyenne, plus diplômés et plus jeunes que l'ensemble des internautes présents sur les réseaux sociaux, selon une étude réalisée conjointement par Twitter, l'American press institute et la société d'études DB5, publiée mardi. L'étude a comparé un échantillon de 3713 utilisateurs actifs de Twitter avec un autre échantillon de 1000 personnes présentes sur les réseaux sociaux, représentatif au niveau national, dont 469 n'étaient pas utilisateurs de Twitter. Toutes les personnes sondées étaient majeures et ont été interrogées en ligne.
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The choices we make are our own. We’re as smart as we want to be.
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From
hbr
Our traditional obsessions—success, taking action, fitting in, and relying on experts—undermine continuous improvement. Virtually all leaders believe that to stay competitive, their enterprises must learn and improve every day. But even companies revered for their dedication to continuous learning find it difficult to always practice what they preach. Consider Toyota: Continuous improvement is one of the pillars of its famed business philosophy. After serious problems in late 2009 led Toyota to recall more than 9 million vehicles worldwide, its leaders confessed that their quest to become the world’s largest automobile producer had compromised their devotion to learning. Why do companies struggle to become or remain “learning organizations”? Through research conducted over the past decade across a wide range of industries, we have drawn this conclusion: Biases cause people to focus too much on success, take action too quickly, try too hard to fit in, and depend too much on experts. In this article we discuss how these deeply ingrained human tendencies interfere with learning—and how they can be countered. Bias Toward Success
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The buzz is all about 'Big Data' and how best to use it to generate actionable intelligence. To do this, companies will need to hire loads of highly trained, highly paid data scientists -- or will they?
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