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In cognitive systems, performance improvements will derive from scaling in: moving key components, such as storage, memory, networking and processing onto a single chassis, closer to the data.

The volume of data produced today isn't just increasing—it's getting faster, taking more forms and is increasingly uncertain in nature.

Uncertainty arises from such sources as social media, imprecise data from sensors and imperfect object recognition in video streams. IBM experts believe that by 2015, 80 percent of the world's data will be uncertain.

They also observed three consistent factors that impact how effective a group is: The average social perceptiveness of the group members The evenness of conversational participation The proportion of women in the group

All three factors were linked - the women in the group were shown to be more socially perceptive and conversation was more even, as a result, the groups with a higher number of women were more collectively more productive. 

Several biologically inspired paths are being explored by computer scientists in universities and corporate laboratories worldwide. But researchers from I.B.M. and four universities — Cornell, Columbia, the University of Wisconsin, and the University of California, Merced — are engaged in a project that seems particularly intriguing. The project, a collaboration of computer scientists and neuroscientists begun three years ago, has been encouraging enough that in August it won a $21 million round of government financing from the Defense Advanced Research Projects Agency, bringing the total to $41 million in three rounds. In recent months, the team has developed prototype “neurosynaptic” microprocessors, or chips that operate more like neurons and synapses than like conventional semiconductors.

The technology produced, according to the guidelines, should have the characteristics of being self-organizing, able to “learn” instead of merely responding to conventional programming commands, and consuming very little power.

The human brain integrates memory and processing together, weighs less than 3 lbs, occupies about a two-liter volume, and uses less power than a light bulb.  It operates as a massively parallel distributed processor.  It is event driven, that is, it reacts to things in its environment, uses little power when active and even less while resting.  It is a reconfigurable, fault-tolerant learning system.  It is excellent at pattern recognition and teasing out relationships.

A computer, on the other hand, has separate memory and processing.  It does its work sequentially for the most part and is run by a clock.  The clock, like a drum majorette in a military band, drives every instruction and piece of data to its next location — musical chairs with enough chairs.  As clock rates increase to drive data faster, power consumption goes up dramatically, and even at rest these machines need a lot of electricity.  More importantly, computers have to be programmed.  They are hard wired and fault prone.  They are good at executing defined algorithms and performing analytics.

Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE)

Now when I’m asked, “What's the most effective way for people to share their tacit knowledge?” I always think of Hans and the answer I give is: “Tacit knowledge needs to be shared through conversation.” My reasoning is as follows. Our tacit knowledge is drawn from our experience as well as our years of study and is stored in bits and pieces in our brain, that is, it is not stored as answers or explanations but as fragments. What we call “tacit knowledge” is the human ability to draw on those fragments to construct a response to a new problem or question. Tacit knowledge is particularly useful when we are faced with a complex problem. By complex I mean a problem that does not have a factual, right or wrong answer, for example, "What architectural design would best fit this physical space and meet the needs of the client?" or “How would you stop an oil leak 5000 feet under water?” When an expert like Joachim faces a complex problem he brings together those bits and pieces of his experience and study that are relevant to that specific problem situation and puts those together to form a solution. Because he is embedded in the situation he knows the context and the end goal. In bringing together those bits and pieces that are in his head, he conducts, what Don Schon would call, a “reflective conversation with the situation.”

Tacit knowledge, then, is constructed in response to a question or to a problem at a specific moment in time. It is a magnificent human capability we have to be able to continually reconstruct what we know into new forms to face new situations.