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"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."

"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."

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.

"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."

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."

"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?"