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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

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.

"Smart Wikis™ combine Artificial Intelligence and knowledge concepts with your existing IT to provide integrated collaborative working environments that anticipate each user's personal information needs and surround that user with seamless and non-obtrusive forms of assistance."

The world's biggest artificial intelligence companies, including Facebook and Google, have joined forces to mould the ethical rules that will govern how robots and computer programs behave in the future.

About time and welcome. I hope that those with experience of medical ethics are also involved. For two reasons; medicine is likely to be a major player and beneficiary of AI (eg personalised drugs) and medical ethics is a mature field that is used to responding to rapid innovation.

Google answers these questions with the help from deep neural networks, a form of artificial intelligence rapidly remaking not just Google's search engine but the entire company and, well, the other giants of the internet, from Facebook to Microsoft.

When it comes down to it, knowledge is worthless until it is converted into some form of action.

" Interesting article form TMA"

" Innovation Culture - MIT Sloane talk by the charismatic CEO, Terri Kelly. The top things I took from this inspiring talk are: - Staff turnover only 5% (the new hire process is lengthy and rigorous to ensure cultural fit) - CEO is elected by staff (CEO is one of the few job titles in the organisation - Costs are regarded as 'investments' - Every individual has a sponsor or coach - Leaders get there through others wanting to follow, not their power - Innovation culture is the MAIN driver of business results - Business units are no larger than 250 people (the founder talked about divide to multiply) - 'Give them the right tools, minimal bureaucracy, responsibility for P&L, expect people to lattice (network), organise around small teams'. Lastly, the culture at Gore has evloved of 50 years - it takes huge effort (equal to strategy and business development) and a lot of time to change culture I screen grabbed some of the culture survey questions that staff fill out about their leaders (not the other way round) http://members.ki-network.org/innovation/Innovation%20SIG%20Picture%20Library/Forms/DispForm.aspx?ID=1"

"Discussion led by Michael Mol of proposed KIN Innovation SIG framework. This should be used in conjunction with Michael's slides available at http://members.ki-network.org/innovation/SIG%20Document%20Library/Forms/DispForm.aspx?ID=18&Source=http%3A%2F%2Fmembers.ki-network.org%2Finnovation%2FSIG%2520Document%2520Library%2FForms%2FAllItems.aspx&RootFolder=%2Finnovation%2FSIG%20Document%20Library"

"From an excerpt form a recent book by Chip Heath "

" Interesting article form Tom Davenport about why he feels that social media won't change the workplace"

" Link to KIN group on face-book - interesting articles on here form Jenny Ambrozec. For more information about how to join this group - please contact Phil Ridout"

In 2009, the Center for Marketing Research at the University of Massachusetts Dartmouth released one of the first studies of the Fortune 500's adoption and usage of one of the best-known forms of social media - blogging. This new study revisits and refreshes that prior in-depth study and expands to look at the Fortune 500's usage of the most dramatically growing new social media site - the microblogging service Twitter.

A new form of Web content that is meaningful to computers will unleash a revolution of new possibilities

The theme for the forthcoming KIN Workshop (1st December 2015) is "Knowledge in Action". Hopefully a title that will resonate with anyone who practices knowledge management, since it reminds us that knowledge without some form of action is worthless

AI is (currently) very good at specialist/single tasks that require brute force computational effort or training algorithms, but we are a long way from developing generalised AI, that requires some form of unsupervised deep learning. There are many things that humans understand but are well beyond the reach of AI, and this will remain the case for many years to come - if it ever happens.

Virtual assistants get infuriating when they fail to do something we expect to be within their capabilities. Researchers are interested in studying how people form expectations about what such systems can and can't do-and how virtual assistants themselves might be designed to nudge us toward only asking things that won't lead to disappointment. One of the research questions is how do you reset a user's expectations on the fly when they're interacting with a virtual assistant.

The debate between artificial intelligence (AI) and intelligence augmentation (IA) has been raging for decades. Will computer systems replace humans with a faster, smarter form of intelligence (AI) or will they augment our existing human intelligence and work alongside us (IA)?

Zoom announced its first flirtation with artificial intelligence in the form of automated meeting transcripts, eliminating the need for a note taker and creating a searchable transcript. Using AI, it converts all the speech from a meeting into text and even identifies each speaker, plus tighter integration with Slack.