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"Knowledge can only be volunteered it cannot be conscripted. You can't make someone share their knowledge, because you can never measure if they have. You can measure information transfer or process compliance, but you can't determine if a senior partner has truly passed on all their experience or knowledge of a case. We only know what we know when we need to know it. Human knowledge is deeply contextual and requires stimulus for recall. Unlike computers we do not have a list-all function. Small verbal or nonverbal clues can provide those ah-ha moments when a memory or series of memories are suddenly recalled, in context to enable us to act. When we sleep on things we are engaged in a complex organic form of knowledge recall and creation; in contrast a computer would need to be rebooted. In the context of real need few people will withhold their knowledge. A genuine request for help is not often refused unless there is literally no time or a previous history of distrust. On the other hand ask people to codify all that they know in advance of a contextual enquiry and it will be refused (in practice its impossible anyway). Linking and connecting people is more important than storing their artifacts. Everything is fragmented. We evolved to handle unstructured fragmented fine granularity information objects, not highly structured documents. People will spend hours on the internet, or in casual conversation without any incentive or pressure. However creating and using structured documents requires considerably more effort and time. Our brains evolved to handle fragmented patterns not information. Tolerated failure imprints learning better than success. When my young son burnt his finger on a match he learnt more about the dangers of fire than any amount of parental instruction cold provide. All human cultures have developed forms that allow stories of failure to spread without attribution of blame. Avoidance of failure has greater evolutionary advantage than imitatio

" Daniel Pink has done it again. Last time, he wrote about left and right brains and this time it's motivation. He neatly sums up recent advances in science and gives lots of practical examples of how these play out. Fascinating reading and highly applicable."

"Daniel Pink has done it again. Last time, he wrote about left and right brains and this time it's motivation. He neatly sums up recent advances in science and gives lots of practical examples of how these play out. Fascinating reading and highly applicable."

How do we learn the meaning of text from data? In other words, how can a machine truly understand the phrases that human beings blab into its search fields and microphone? The researchers at Google and elsewhere have settled on an answer to that question: machine learning; specifically, a form of artificial intelligence called neural networks-self-organising systems modelled on the way the brain works.

Misconceptions about the brain are embedded in corporate training programs and could be sabotaging their effectiveness. Companies should reevaluate them in light of the latest scientific insights.

Scientists have discovered how to "read" minds by scanning brain activity and reproducing images of what people are seeing - or even remembering.

"The "big data, little brain" phenomenon is defined as managers who rely excessively on data to guide their decisions, abdicating their knowledge and experience."

Excellent short article on the difference between learning and knowing

Recommended by Luc Glasbeek at the 2014 Autumn workshop

" Report of recent US survey of HR Managers reveals lack of preparation for demographic changes and lack of knowledge strategies to address them… "In the USA only 12% of HR Managers see Knowledge Retention as a priority despite the fact that 20% of the workforce will be eligible for retirement over the next few years…""

In the cycle of accelerating computing power, we've gone from the slate to the paper, from the paper to mechanical systems, mechanical systems to the vacuum tube, vacuum tubes to silicon, and now we are moving to neurons.