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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

"Tullow Oil, a London-based independent oil and gas exploration and production company, regularly wins awards for its innovative approach to problem solving. Its business culture is based on investing in the best people and then trusting them to work together to keep Tullow on the leading edge of the industry. Tullow's CIO recently challenged his team to develop an approach to devolve control of IT project prioritization to non-IT leaders within the company. This article explains the approach developed and how it is working to keep the business's IT strategy aligned with Tullow's entrepreneurial spirit and commitment to collaborative decision making."

Jaron Lanier - who pioneered virtual reality - stressed that we need to divide "artificial intelligence" into two different things: -the engineering and the science on the one hand -the storytelling about it, the narrative that we have about it, the fantasy life of it - perhaps the religion of it. It doesn't mean one is good and one is bad, but they're just different sorts of beasts.

Twitter has struck a chord among sufficient numbers that it needs corporate attention and policies. Employees need to understand its potential for misuse and corporations need to understand its potential value...

"The company of the future - and indeed the company of the present - needs new instruments to adapt to a changing reality. The new generation of digital natives is progressively being incorporated to the world of work. We are talking about a generation that has lived most of its life within the technological revolution that has occurred in the last two decades. It has connected people, who spend more time on the Internet than in front of the television and who have lived with the emergence of video games. It is not to judge whether that is good or bad, it's simply real and nothing will change it."

Looking at the HiSocial offering, I can't help but wonder about unintended consequences. The digital natives are savvy and will naturally find ways of 'gaming' the system. If you simple reward actions such as visiting intranet pages or 'downloading corporate material', you are in no way increasing the sum total knowledge, helping efficiency or decision making. What's needed is reward that stimulates participation and qualitative contribution, not just transactions.

The After Action Review (AAR) is a simple tool pioneered by the US army which is now widely used in organisations to capture lessons learned. It is a structured review after an activity or stage in a project, including handover or close, that analyses what happened, why it happened, and how it can be done better. The key to successful AAR's is that it helps you turn mistakes and poor performance into learning opportunities.

Stephen Wolfram and his team have created an astonishingly powerful collection of information. As he puts it on the Wolfram blog, the dream is to make this "computable knowledge" available to immediately enhance any program that's connected to the service. Today's announcement is a big step forward to opening it up to far more developers, but it will need much more computer-readable results before it will really fulfill that promise. Do you agree, or am I misunderstanding the power of the API as it is right now? Are there existing applications beyond the handful that Wolfram highlight?

"Google subsidiary DeepMind has accomplished some amazing things over the past couple of years, from beating humans at their own game to saving its parent company money on its electricity bill. Now, however, it's coming under major scrutiny because of the specifics of a deal with the United Kingdom's National Health Service."

Our local hospital is part of the Royal Free Trust (the body that gave Deepmind MY data). The trust has never consulted me on this. I don't care whether it has a legal duty to do so or not, I believe it has a moral obligation to tell me what it is doing and why. Deepmind may have altruistic public health motives (hmmm), but this silence is destroying trust (pardon the pun). It's about time the data Ombudsmen did their job.

"Thinking is hard - yet barely a waking moment passes when we're not labouring away at it. A few of us may be natural geniuses, able to work through the toughest tangles in an instant; others, blessed with reserves of willpower, stay the course in the dogged pursuit of truth. Then there's the rest of us. Not prodigies and a little bit lazy, but still aspiring to understand the world and our place in it. What can we do? In Intuition Pumps, Daniel Dennett, one of the world's most original and provocative thinkers, takes us on a profound, illuminating and highly entertaining philosophical journey. He reveals a collection of his favourite thinking tools, or 'intuition pumps', that he and others have developed for addressing life's most fundamental questions. Along with new discussions of familiar moves - Occam's Razor, reductio ad absurdum - Dennett offers cognitive tools built for the most treacherous subject matter: evolution, meaning, consciousness and free will. In his genial style, Dennett guides readers around the pitfalls in arguments, and reveals easier ways to better understand the world around us and our place in it. An enlightening and practical store of knowledge, Intuition Pumps and Other Tools for Thinking will teach you to think truly independently and creatively."

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

IBM has created a 'Visual Recognition Demo' to showcase Watson's latest trick, which allows users to feed Watson an image before it tells you what it believes it sees.

This is a journey to explore the latest innovations in AI and, most importantly, its human element, to ultimately answer the controversial questions: What physical human characteristics and emotions must a robot have to make people react to it? And, obversely, Can AI recognize human emotions?

The knowledge map is a canvas, it is a way to model how knowledge is spread inside an organization and how it is used. The canvas is composed by 12 blocks. The right side describes the available knowledge, the left side describes the needed knowledge.

Social search - is Google missing a trick?

Steve, have you tried Greplin and if so what's your experience? Does it negate the need for other search tools (I don't want a proliferation of search tools)? Does the indexing slow up your machine?

Gary, it does a better search of your social networks than Google, probably because you're giving it permission to index them. You still need a general search engine (such as Google) for the broader internet content. Indexing has no impact on your machine. I haven't used it long enough to determine whether or not it's features are useful enough to make it my first choice search engine for social media/social network content.

"Ban social media as a distraction? No, it boosts productivity" Any manager who thinks staff should be banned from using social media at work is seriously misguided and could be doing grave damage to the business.

In the communities that we choose to belong to (online and offline), we have to do our part in feeding it. It is only when we are generous about sharing our gifts that we build credibility to receive anything meaningful in return, build influence, thought leadership and learn.

Although we'll be writing a lot here about the potential of social media to help people tell their stories, share ideas, start and continue conversations, it is seldom enough on its own. In fact, it is still very much a minority medium in the field of local community action - however powerful it can be, as shown by the work of hyperlocal bloggers (examples here, and we'll be mapping more).

Social media is having an increasingly pervasive influence on both personal and professional life. But there is still widespread ignorance about the risks associated with using it. The Snowden revelations gave us the first insight into how our personal data is being "harvested" by both companies and governments, and not all of it for benign purposes. This (preliminary) declaration of digital human rights is something that perhaps we should all give serious consideration to supporting.

Recommended by Luc Glasbeek at the 2014 Autumn workshop

Google Search itself provides one of the most familiar examples of predictive intelligence. When you enter keywords in the search box, Google predicts what you are interested in and then presents you with results that match that intent. Since it released the first version of its Prediction API in 2010, Google has made some of these methods available to developers. Adoption among developers has not been high because machine learning requires a lot of infrastructure and validation to produce accurate results. Developers have also reported discomfort with basing products on black box APIs.