Who Really Suffers When You Don't Share Your Ideas at Work Worried that someone at work might be stealing your good ideas? Relax. It doesn't happen as often as you think. A study in the current issue of the Academy of Management Journal discovered employees have nothing to gain from hiding their insights from co-workers, and just end up hurting themselves by doing so. The study's authors said employees should reconsider and be careful about hiding knowledge from their peers, because what goes around comes around. "More specifically, employees who intentionally hide more knowledge seem bound to receive such selfish behavior in return from their co-workers, which will ultimately hurt them and decrease their creativity," the researchers wrote in the study. One of the paper's authors, Matej Cerne of Ljubljana University in Slovenia, said certain workplaces encourage this behavior. "But, given the lack of emphasis on individual rewards in such settings, there is little incentive to hide knowledge," he said.
The Fifth Discipline The Fifth Discipline: The Art and Practice of the Learning Organization (Senge 1990) is a book by Peter Senge (a senior lecturer at MIT) focusing on group problem solving using the systems thinking method in order to convert companies into learning organizations. The five disciplines represent approaches (theories and methods) for developing three core learning capabilities: fostering aspiration, developing reflective conversation, and understanding complexity. The Five Disciplines[edit] The five disciplines of what the book refers to as a "learning organization" discussed in the book are: "Personal mastery is a discipline of continually clarifying and deepening our personal vision, of focusing our energies, of developing patience, and of seeing reality objectively. Senge describes extensively the role of what it refers to as "mental models," which he says are integral in order to "focus on the openness needed to unearth shortcomings" in perceptions. The Learning Disabilities[edit]
The Programmer Behind Heartbleed Speaks Out: It Was an Accident The Internet bug known as Heartbleed was introduced to the world on New Year's Eve in December 2011. Now, one of the people involved is sharing his side of the story. Programmer Robin Seggelmann says he wrote the code for the part of OpenSSL that led to Heartbleed. Seggelmann told the Sydney Morning Herald that the actual error was "trivial," but that its impact was clearly severe. Heartbleed is a vulnerability in the encryption that many sites use to ensure that your communications can't be intercepted. As the name suggests, OpenSSL is open-source, which makes it attractive to many services, big and small, as an easily implemented security tool. Although anyone can contribute to OpenSSL — either by contributing code or reviewing it to spot vulnerabilities like Heartbleed — few actually do. Although anyone can contribute to OpenSSL — either by contributing code or reviewing it to spot vulnerabilities like Heartbleed — few actually do. For now, most sites affected have patched the bug.
Peter Senge Peter Michael Senge (born 1947) is an American systems scientist who is a senior lecturer at the MIT Sloan School of Management, co-faculty at the New England Complex Systems Institute, and the founder of the Society for Organizational Learning. He is known as the author of the book The Fifth Discipline: The Art and Practice of the Learning Organization (1990, rev. 2006). Life and career[edit] Peter Senge was born in Stanford, California. He received a B.S. in Aerospace engineering from Stanford University. While at Stanford, Senge also studied philosophy. He is the founding chair of the Society for Organizational Learning (SoL). He has had a regular meditation practice since 1996 and began meditating with a trip to Tassajara, a Zen Buddhist monastery, before attending Stanford.[3] He recommends meditation or similar forms of contemplative practice.[3][4][5] Work[edit] An engineer by training, Peter was a protégé of John H. Organization development[edit] Publications[edit] See also[edit]
A Closer Look at Transformation: Collective Intelligence | Frank Diana's Blog Next up in this transformation series is the seventh enabler: Collective Intelligence. One of the key themes throughout this transformation series is the clear movement from an enterprise entity to an extended enterprise of stakeholders. This extended enterprise – or what I alternatively call value ecosystem – increases complexity and requires a new management approach to be effective. I use the term collective intelligence as an umbrella phrase that combines the critical need for both collaboration and analytic excellence. Collective intelligence allows us to harness the efforts, knowledge and brainpower of a community. Thanks to advances in technology, individuals, groups and computers can collectively act more intelligently than ever before. Value ecosystems complicate collaboration and exacerbate the diffusion of knowledge – I described the drivers of value ecosystems as part of this transformation series in an earlier Post. Extended Enterprise Value Ecosystems Forcing Functions: Mr.
peter senge and the learning organization contents: introduction · peter senge · the learning organization · systems thinking – the cornerstone of the learning organization · the core disciplines · leading the learning organization · issues and problems · conclusion · further reading and references · links Peter M. Senge (1947- ) was named a ‘Strategist of the Century’ by the Journal of Business Strategy, one of 24 men and women who have ‘had the greatest impact on the way we conduct business today’ (September/October 1999). While he has studied how firms and organizations develop adaptive capabilities for many years at MIT (Massachusetts Institute of Technology), it was Peter Senge’s 1990 book The Fifth Discipline that brought him firmly into the limelight and popularized the concept of the ‘learning organization’. Since its publication, more than a million copies have been sold and in 1997, Harvard Business Review identified it as one of the seminal management books of the past 75 years. Peter Senge The core disciplines
The Rise of the Sharing Economy- PapyrusEditor By Lonnie Shekhtman Governments have their work cut out for them in keeping pace with innovation, especially as mobile, social and cloud technologies allow for new business models that, in the eyes of regulators, threaten consumer safety and incumbent industries. The most poignant current-day example of the tug-of-war between government and technology entrepreneurs is the legal quagmire many “sharing,” or “collaborative consumption,” companies face in the cities they operate. The problem, at least for home- and car-sharing services, is multifaceted: they’re agitating dozens of stakeholders, operating in uncharted territories, and legally indefinable. And indefinable is hard to regulate. You can’t talk about legal issues surrounding ‘sharing’ without talking about the industry’s ‘800-pound gorilla’: home rental service Airbnb. “Government is usually the last one to pick up on innovations,” Turner said. Or is it?
What if Universities were like Wikipedia? – Managing Turbulence A recent session at Educause apparently invoked Wikipedia and spoke to universities as agile organizations. The speaker wasn’t really suggesting that Wikipedia should be the model for the university of the future, but the abstracted concept was a little intriguing. Of course, Peter Drucker foretold the knowledge economy built with knowledge workers long before some of us were born, and I suspect his agile brain had glimmers of the knowledge management implications of Wikipedia around the same time. And, understandably, most academics keep their distance and steer toward more critically-accepted and stringently peer-reviewed resources. But Wikipedia made me think about knowledge in different ways. Knowledge as co-generative: maybe this is crowdsourcing on steroids. Knowledge for the sake of itself may be a penultimate goal. So can the University be a place of realized potential?
May the Best Model Win WIKIMEDIA, W.REBELA little friendly competition never hurt anyone, right? But can a healthy dose of rivalry actually solve major medical conundrums and, ultimately, spur innovation? That’s the motivating idea behind a series of open-source, Big Data computational challenges hosted by Sage Bionetworks and DREAM (Dialogue for Reverse Engineering Assessments and Methods) and an ever-increasing number of other companies looking to crowdsource the brightest minds in statistics, machine learning, and computational biology to develop better predictive models of disease. Though teams are pitted against each other in individual competitions, organizers say the challenges promote the kind of collaboration necessary to solve massive biological quandaries. Though teams from computational big hitters like IBM were early leaders, the winners were a small group from Columbia University’s School of Engineering led by electrical engineer turned computational biologist Dimitris Anastassiou.
Cultural Creatives 1.0: The (R)evolution | Watch the Full Documentary Online Featuring many key figures from Europe and the U.S., this is the first documentary film to look with scientific thoroughness at the world of Cultural Creatives. It shows that a great mass of people think differently from the way propagated by the media and promoted by the establishment. By the end of the film it becomes evident that this huge mass, were it to become aware of its power, could change the world. Because Cultural Creatives are unstoppable and their number is continuously rising, the values they champion could soon become core values for human civilization generally. Cultural Creatives are emerging without anybody organizing their presence, without anyone seeking to create political power from their existence, and without any group having any interest in them. So they are all here, among and around us: 80 million Cultural Creatives in the United States and 120 million in Europe, all with a similar mindset — the citizens of a new world.
Chinese Search Giant Baidu Thinks AI Pioneer Andrew Ng Can Help It Challenge Google and Become a Global Power Punk bands from Blondie to the Ramones once played in Broadway Studios, an age-worn 95-year-old neoclassical building surrounded by strip clubs in San Francisco’s North Beach. But early on this bright June morning, a different sort of rock star arrives. A small crowd attending a tech startup conference swarms around a tall, soft-spoken man in a blue dress shirt and navy suit who politely poses for photos. Andrew Ng, newly appointed chief scientist at Baidu, China’s dominant search company, is here to talk about his plans to advance deep learning, a powerful new approach to artificial intelligence loosely modeled on the way the brain works. The avid reception helps explain why Baidu has made Ng, 38, the linchpin of an effort to transform itself into a global force. Andrew Ng hopes to lure AI talent to Baidu’s new Silicon Valley research lab. As they look beyond China, Baidu and other Chinese companies find themselves on a collision course with the established U.S. Cool Things
Collaborative Intelligence – Knowledge Visualization, IBM Manay Eyes, visual analytics, Katy Borner, Zann Gill Collaborative Intelligence in Ecosystem Forecasting Ecosystem forecasting is supported by information visualization, e.g. Visualization of Data, Indicators, and Thresholds Collaborative Problem-Solving — Process Visualization & Management Navigation and Search — User Interface & Knowledge Management Frameworks Geospatial Visualization — Spatio-Temporal Representations Visualization of Data, Indicators, and Thresholds Outstanding visualization is the key to understanding how components interact in a complex system. Tim Nyerges reviews the challenge of visualizing sustainability in his paper: “Linked Visualizations in Sustainability Modeling: An Approach Using Participatory GIS for Decision Support.” Three visualizations representing sustainability issues: 1. Example of visual conceptual models developed for indicator analysis. In the directed graph above, nodes represent: Imagery in a Knowledge Framework.