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"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?"

"This talk explores how we can use game mechanics to facilitate more engaging and inspiring learning experiences for our students. The talk was presented during Learning Innovation Talks 02 held at Taylor's "

"Yesterday, I was inspired by a great post by Chris Wilson on How to Survive the Teacher Apocalypse.  I thought about it all afternoon especially as it touched upon thoughts I had had on the cost of knowledge.  The interesting turn for me came when I started reflecting more on the Value of Knowledge because realistically the costs of knowledge have and will continue to drop rapidly (despite the efforts of gatekeepers) due to digitalization and other market factors, but something is rising in counter weight…"

"For more than half a century, computers have been little better than calculators with storage structures and programmable memory, a model that scientists have continually aimed to improve. Comparatively, the human brain-the world's most sophisticated computer-can perform complex tasks rapidly and accurately using the same amount of energy as a 20 watt light bulb in a space equivalent to a 2 liter soda bottle. Cognitive computing: thought for the future Making sense of real-time input flowing in at a dizzying rate is a Herculean task for today's computers, but would be natural for a brain-inspired system. Using advanced algorithms and silicon circuitry, cognitive computers learn through experiences, find correlations, create hypotheses, and remember-and learn from-the outcomes. For example, a cognitive computing system monitoring the world's water supply could contain a network of sensors and actuators that constantly record and report metrics such as temperature, pressure, wave height, acoustics and ocean tide, and issue tsunami warnings based on its decision making."

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.

For most of us, it usually occurs at the most inopportune times; never when we're searching for it. To Archimedes, it happened in the bathtub. Newton experienced it while wandering an apple orchard. Arthur Fry: church. Each encountered an epiphany, that powerful moment of spontaneous insight. Archimedes shouted Eureka! upon realizing how to calculate density and volume; to Newton came the law of universal gravity; to Arthur Fry, Post-it notes.

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.