E-Learning-Inclusivo (Mashup)

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Howard Gardner es un extraordinario ideólogo que está convencido de la individualidad de las capacidades humanas, que no pueden medirse en ningún test de inteligencia.

"How can people and computers be so connected - collectively - that they act more intelligently than any individuals, groups or computers have ever done before?"

Two articles in PDF:

Managing Collective Intelligence

An Introduction to Collective Intelligence

An interesting looking kind of annotated bibiliography about collective intelligence.

In the coming years, there will be a shift toward what is now known as contextual computing, defined in large part by Georgia Tech researchersAnind Dey and Gregory Abowd about a decade ago. Always-present computers, able to sense the objective and subjective aspects of a given situation, will augment our ability to perceive and act in the moment based on where we are, who we’re with, and our past experiences. These are our sixth, seventh, and eighth senses.

Hints of this shift are already arriving. Mobile devices with GPS deliver location-based services, which sets a baseline for the many ways your phone can gather information it will use to make your life easier down the line. Amazon’s and Netflix’s recommendation engines, while not magnificently intuitive, feed you book and video recommendations based on your behavior and ratings. Facebook’s and Twitter’s valuations are premised on the notion that they can leverage knowledge of your acquaintances and interests to push out relevant content and market to you in more effective ways.

This is the set of data relating to a person’s deepest held beliefs, core values, and personality. It’s what makes a person unique in the world, just as the social graph helps to show what makes her similar to others. The data set is under-developed at the moment, and it’s quite difficult to design for, even conceptually.

Given that psychology still struggles to explain exactly how our personal identities function, it’s not surprising that documenting such information in a computable form is slow to emerge. There are early indicators that this will change, however. For example, Proust.com, a relatively new (and struggling) social-networking service, asks users to document intimate details of their lives and their beliefs based on the idea of the famed Proust Questionnaire. People have, quite reasonably so, been reluctant to share such information in a publicly viewable social network.

A more successful example is Evernote, which has built a large business based on making it incredibly easy and secure to document both recently consumed information and your innermost thoughts. Scraping such intimate files for data is currently the questionable realm of the NSA, however. Entirely new solutions will need to be created if the potential of the personal graph is to be reached.

It’s easy for data to depict what you actually do instead of what you claim to do. Sensors do the job. So do, if less elegantly, self-reporting mechanisms. This data can sit in pivotal contrast to the interest graph, allowing computers to know, perhaps better than you, how likely you are to go for a jog. It would be useful, too, for a travel site that notes how you tell friends you’d like to visit China but records that you only vacation in Europe. Rather than uselessly recommending vacation deals to Beijing, a smart travel app would instead feed you deals to Paris or Berlin. The behavior graph provides the foundation, to some extent, of Google Search, Netflix recommendations, Amazon recommendations, iTunes Genius, Nike+ run tracking, FourSquare, FitBit, and the entire "quantified self" movement. When mashed against the other three graphs, there’s a potential for real insight.

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