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"Thinking, Fast and Slow is a 2011 book by Nobel Prize winner in Economics Daniel Kahneman which summarizes research that he conducted over decades, often in collaboration with Amos Tversky.[1][2] It covers all three phases of his career: his early days working on cognitive bias, his work on prospect theory, and his later work on happiness. The book's central thesis is a dichotomy between two modes of thought: System 1 is fast, instinctive and emotional; System 2 is slower, more deliberative, and more logical. The book delineates cognitive biases associated with each type of thinking, starting with Kahneman's own research on loss aversion. From framing choices to substitution, the book highlights several decades of academic research to suggest that we place too much confidence in human judgment."

BM's Watson Artificial Intelligence System is capable of searching across vast repositories of unstructured data and returning answers to natural language queries, but it won't replace humans. Instead, the system will augment humans and help us to make better decisions.

This paper introduces Dervin's Sense-Making Methodology (SMM) as an approach to KM system design using Web2.0. SMM is a philosophically derived approach which allows knowledge management (KM) researchers and practitioners to more fully understand and listen to user's needs so as to inform the design of dialogic KM practices and systems to promote knowledge sharing.

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.

A great introduction to Systems Thinking. Uses healthcare interventions as an example.

"The current scandal about the mis-use of Facebook data to manipulate elections feels like a pivotal moment in the recent history of the internet and its growing power over societies around the world. "

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.

Researchers trained an algorithm using more than 1,500 photos of Chinese citizens, hundreds of them convicts.They said the program was then able to correctly identify criminals in further photos 89% of the time. But the research, which has not been peer reviewed, has been criticised by criminology experts who say the AI may reflect bias in the justice system. #

A future in which human workers are replaced by machines is about to become a reality at an insurance firm in Japan, where more than 30 employees are being laid off and replaced with an artificial intelligence system that can calculate payouts to policyholders.

If we are going to make systems that are going to be more intelligent than us, it's absolutely essential for us to understand how to absolutely guarantee that they only do things that we are happy with.

The DoNotPay artificial intelligence service, created by a 19-year-old student, is called the "world's first robot lawyer" and uses a step-by-step chat-like system that asks a series of questions to find out the details of the issued ticket and then highlight areas where you can appeal the fine.

The use of machine learning, expert systems and analytics in combination with big data, is the natural evolution of what has been two different disciplines. They are converging.

The image of a robot stepping into the shoes of a human worker couldn't be more wrong. When it comes to technology's most significant applications, we are neither usurped or seduced - because the systems involved are nothing like us in either their function or faculties. As a species, we are not in competition with information technology at all: we are, rather, busily adapting the fabric of our world into something machines can comprehend.

Amazon is pursuing an open-systems approach that allows quick development of many features, while Apple is taking a slower route, asserting more control over the technology in order to assure security and ease-of-use.

"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

"Value Networks and the true nature of collaboration meets this challenge head on with a systemic, human-network approach to managing business operations and ecosystems. Value network modeling and analytics provide better support for collaborative, emergent work and complex activities."

the Diigo team aims to evolve Diigo into the best personal knowledge management system (PKM) on the market, providing unsurpassed capabilities for the collection, compilation, organization, digestion, presentation and collaboration of knowledge and information.

Most intranets are an absolute and utter joke. Enterprise search is pathetic. Why? Because today's management culture has no interest in making the work lives of -particularly its knowledge workers - easier and more productive. In fact, management practice often heaps more complexity and awful, unusable systems on top of frustrated, overwhelmed employees.

Systems of insight are the business discipline and technology to harness insights and turn data into action.