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People who have already sifted through online information to make sense of a subject can help strangers facing similar tasks without ever directly communicating with them, researchers at Carnegie Mellon University and Microsoft Research have demonstrated. This process of distributed sensemaking, they say, could save time and result in a better understanding of the information needed for whatever goal users might have, whether it is planning a vacation, gathering information about a serious disease or trying to decide what product to buy.

"If you've ever had to make a snap decision between two unfamiliar choices, you may want to thank your subconscious for making it possible. According to new research, the brain's memory areas link new memories to old associations, providing a roadmap for decision-making we don't even realize we have. The research, published in the Oct. 12 issue of the journal Science, focuses on the hippocampus, a region nestled deep in the brain that helps consolidate memories. Scientists have long known the hippocampus links memories and integrates them together, but the new study is the first to look at the region's role in biasing the brain toward certain choices."

There's no doubting the cloud invasion. But the research firm Gartner believes the personal cloud will replace the PC as the center of our digital lives sooner than you might think: 2014. "Major trends in client computing have shifted the market away from a focus on personal computers to a broader device perspective that includes smartphones, tablets and other consumer devices," Steve Kleynhans, research vice president at Gartner, said in a statement on Monday. "Emerging cloud services will become the glue that connects the web of devices that users choose to access during the different aspects of their daily life."

"There are many explanations for today's uncertain economy. But Nobel economist Joseph Stiglitz of Columbia University has advanced an analysis that's starting to resonate. In a recent article, Stiglitz says that our problem is "rooted in the kinds of jobs we have, the kind we need, and the kind we're losing, and rooted as well in the kind of workers we want, and the kind we don't know what to do with." To advance our economy, Stiglitz believes that wrenching, fundamental change is required - no less dramatic than the shifts experienced by an earlier generation during the Great Depression. While Stiglitz and I work in different worlds, I see evidence in all types of organizations that we need to better prepare, train, and inspire successful self-directed learners to meet today's challenges. As I see it, there are two big questions to consider. First, what are the critical 21st century skills that the workforce of tomorrow needs to develop and master today? Secondly, how can we improve our learning methods to enable the self-directed learner to thrive in this new environment?"

For the most part, the biological metaphor has long been just that - a simplifying analogy rather than a blueprint for how to do computing. Engineering, not biology, guided the pursuit of artificial intelligence. As Frederick Jelinek, a pioneer in speech recognition, put it, "airplanes don't flap their wings." Yet the principles of biology are gaining ground as a tool in computing. The shift in thinking results from advances in neuroscience and computer science, and from the prod of necessity.

What is creativity? Where does it come from? The workings of the creative mind have been subjected to intense scrutiny over the past 25 years by an army of researchers in psychology, sociology, anthropology and neuroscience. But no one has a better overview of this mysterious mental process than Washington University psychologist R. Keith Sawyer, author of the new book Explaining Creativity: The Science of Human Innovation (Oxford; 336 pages). He's working on a version for the lay reader, due out in 2007 from Basic Books. In an interview with Francine Russo, Sawyer shares some of his findings and suggests ways in which we can enhance our creativity not just in art, science or business but in everyday life.

 Tim Berners Lee recognized the problematics of the messy web early on and proposed the Semantic Web to overcome messiness and apply a semantic structure to bring order (read: computer logic) in the chaos (read: human expression). Pierre Levy, French philosopher and leading expert on collective intelligence, is on a similar mission. While driven by, arguably, a similar set of goals, his approach takes a step further. The problem with Berners Lee semantic structure is that implies a universal ontology, which might prove out to be the Achilles heel of the protocol. Levy's approach overcomes these problems. Levy is currently working on a research program, called IEML (Information Economy Meta Language). IEML is a metalanguage and proposes itself as the language of collective intelligence. 

"Two things are clear. The first is that smart machines are evolving at breakneck speed. Moore's law-that the computing power available for a given price doubles about every 18 months-continues to apply. This power is leaping from desktops into people's pockets. More than 1.1 billion people own smartphones and tablets. Manufacturers are putting smart sensors into all sorts of products. The second is that intelligent machines have reached a new social frontier: knowledge workers are now in the eye of the storm, much as stocking-weavers were in the days of Ned Ludd, the original Luddite. Bank clerks and travel agents have already been consigned to the dustbin by the thousand; teachers, researchers and writers are next. The question is whether the creation will be worth the destruction."

"Today, we are at the dawn of another epochal shift in the evolution of technology. At IBM Research, we call it the era of cognitive systems. This is a big deal. The changes that are coming over the next 10 to 20 years-building on IBM's Watson technology-will transform the way we live, work and learn, just as programmable computing has transformed the human landscape over the past 60+ years. You could even call this the post-computing era."

"Over the past few decades, Moore's Law, processor speed and hardware scalability have been the driving factors enabling IT innovation and improved systems performance. But the von Neumann architecture-which established the basic structure for the way components of a computing system interact-has remained largely unchanged since the 1940s. Furthermore, to derive value, people still have to engage with computing systems in the manner that the machines work, rather than computers adapting to interact with people the way they work."

Design thinking is basically "a way of approaching a challenge" - it is not a radical new methodology. Design Thinking encapsulates prototyping, convergent and divergent thinking, customer research, and is a holistic approach to management and leadership. It enlivens the gift of curiosity along with the power of observation. It takes data and places it in a given point of time.

Results. Statistically significant findings suggest that millennial generation Web searchers proceed erratically through an information search process, make only a limited attempt to evaluate the quality or validity of information gathered, and may perform some level of 'backfilling' or adding sources to a research project before final submission of the work.

AMHERST, Mass. - As computer scientists this year celebrate the 100th anniversary of the birth of the mathematical genius Alan Turing, who set out the basis for digital computing in the 1930s to anticipate the electronic age, they still quest after a machine as adaptable and intelligent as the human brain. Now, computer scientist Hava Siegelmann of the University of Massachusetts Amherst, an expert in neural networks, has taken Turing's work to its next logical step. She is translating her 1993 discovery of what she has dubbed "Super-Turing" computation into an adaptable computational system that learns and evolves, using input from the environment in a way much more like our brains do than classic Turing-type computers. She and her post-doctoral research colleague Jeremie Cabessa report on the advance in the current issue of Neural Computation. "This model is inspired by the brain," she says. "It is a mathematical formulation of the brain's neural networks with their adaptive abilities." The authors show that when the model is installed in an environment offering constant sensory stimuli like the real world, and when all stimulus-response pairs are considered over the machine's lifetime, the Super Turing model yields an exponentially greater repertoire of behaviors than the classical computer or Turing model. They demonstrate that the Super-Turing model is superior for human-like tasks and learning.

Online insights are as liquid as currency, their velocity of circulation - incredible. Insights are no plaything, forming and acting upon them effects industry structures at the core. Their mere utterance gels prior assumptions and triggers a succession of indirect and direct mimicry. Why? People are genuinely curious and suffer from time pressure to be accurate and objective. Which has resulted in an insights market and industry that rewards accuracy over ingenuity. Insights by nature are descriptive and reflective - retrospective of lived experiences. Insights are the past. They have been separated from life. To exist as unpredictable patterns the may or may not fully characterize the "real world" or persist as perceived. The list of problems and challenges organizations and consultancies face in managing and applying insight continue to grow. This list is by no means definitive but intends to describe what we have been seeing around us lately.

Howard Gardner of Harvard has identified seven distinct intelligences. This theory has emerged from recent cognitive research and "documents the extent to which students possess different kinds of minds and therefore learn, remember, perform, and understand in different ways," according to Gardner (1991). According to this theory, "we are all able to know the world through language, logical-mathematical analysis, spatial representation, musical thinking, the use of the body to solve problems or to make things, an understanding of other individuals, and an understanding of ourselves. Where individuals differ is in the strength of these intelligences - the so-called profile of intelligences -and in the ways in which such intelligences are invoked and combined to carry out different tasks, solve diverse problems, and progress in various domains." Gardner says that these differences "challenge an educational system that assumes that everyone can learn the same materials in the same way and that a uniform, universal measure suffices to test student learning. Indeed, as currently constituted, our educational system is heavily biased toward linguistic modes of instruction and assessment and, to a somewhat lesser degree, toward logical-quantitative modes as well." Gardner argues that "a contrasting set of assumptions is more likely to be educationally effective. Students learn in ways that are identifiably distinctive. The broad spectrum of students - and perhaps the society as a whole - would be better served if disciplines could be presented in a numbers of ways and learning could be assessed through a variety of means."

In the world of technology companies, the "labs" concept and nomenclature found a friendly home. Microsoft Research has FUSE Labs, there's HP Labs, and Mozilla Labs, and let's not forget the once-mighty Google Labs (R.I.P.), among many others. Digging into the history of big tech "labs" would be the subject worthy of a book, of course, but in the context of this narrow post, it's worth briefly noting that for those that make things and are builders, every big company needed to have something like this for branding, recruiting, and to keep the innovation engine humming as their corporate parents grew larger and more bureaucratic.  Perhaps each one wasn't referred to as a "lab" explicitly in name, as Amazon has A9 and Google now has Google X. No matter the name, there's something powerful in the word that reminds us of the old chem lab and that spirit of experimentation.

Much hype surrounds the topic of gamification. Often seen as a technique to add engagement to existing tasks, projects, marketing campaigns, and initiatives, the term gamification unfortunately lacks the seriousness it deserves. This report seeks to change the point of view and demonstrate where gamification plays a role in the enterprise. More importantly, executives will discover how gamification can drive behavior and outcomes through both monetary and non-monetary incentives in enterprise class settings.

To what extent are you responsible for innovation in your company? The reality is that unless they're in research or product development, most people in organizations don't think of themselves as innovators. In fact, many managers discourage their people from inventing new ways of doing things - pushing them instead to follow procedures and stay within established guidelines.

Cognitive computing, as the new field is called, takes computing concepts to a whole new level.  Earlier this week, Dharmendra Modha, who works at IBM's Almaden Research Center, regaled a roomful of analysts with what cognitive computing can do and how IBM is going about making a machine that thinks the way we do.  His own blog on the subject is here.

Businesses are using collective intelligence to speed up company growth, improve efficiency, enhance products and services, and strengthen the employee environment, according to new research from IBM.