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Developing Performance Support Products
Published in James R. Williams, Developing Performance Support for Computer Systems, 2004
Since more and more organizations are using, or planning to use, some type of knowledge management system, it is important to design documents with knowledge management requirements in mind. Knowledge management systems are used to categorize, store and retrieve various kinds of knowledge within an organization. Such systems are intended to capture organizational best practice, share lessons learned and make use of the collective intelligence within the organization about the organization itself, customers and competitors. It is important to note that moving to a knowledge management system is a huge undertaking that requires a comprehensive knowledge content model as well as software that can categorize and retrieve knowledge.
Next-Generation Portals and Portal Trends
Published in Shailesh Kumar Shivakumar, and User Experience Platforms, 2015
Hypercollaboration platforms: Customer-facing websites such as B2C and retail websites are actively involved in providing social and collaboration features such as social marketing, social listening, and targeted campaigns. They enable and encourage users to author content, share content, co-create content, and rate content through various collaboration features such as blogs, wikis, communities, forums, etc. This harnessing of collective intelligence not only improves productivity but also keeps the users actively engaged. Social media is the new distribution channel as word-of-mouth/peer influence is preferred over a paid advertisement.
Similarity-Based Artificial Intelligence
Published in Mark Chang, Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare, 2020
Ensemble methods can be sequential (e.g., AdaBoost in Chaper 8) or parallel (e.g., random forest in Chapter 8) approaches. A sequential method is, through exploiting the dependence between the base learners, to improve the overall performance by weighing previously mislabeled cases with higher weight. A parallel method can reduce the error dramatically by averaging the independent base learners. In terms of the difference of base learners, an ensemble method can be homogeneous ensembles (the same learner) or heterogeneous ensembles (different learners). Collective intelligence is an ensemble of intelligence from members of a society or group.
Intelligent Collectives: Impact of Independence on Collective Performance
Published in Cybernetics and Systems, 2022
Van Du Nguyen, Hai Bang Truong
Collective intelligence has been widely applied to solve a wide range of complex problems. In general, collective intelligence is often considered as intelligence emerging from the collaboration of many individuals in a collective, and such intelligence can outperform individual intelligence (Malone and Bernstein 2015). The underlying mechanism behind this notion is a group of individuals as a whole can display abilities not shown by individuals. In addition, the main aim of Collective Intelligence is to maximize groups’ potential. Referring to Centola (2022), one can classify collective intelligence into two broad categories: collective problem-solving and the wisdom of the crowd. The first category typically focuses on optimally designing communication networks within a group. The latter is often used for representing the case in which the aggregated solutions of a crowd are superior to individual solutions (Surowiecki 2005).
A vision for design in the era of collective computing
Published in Journal of Engineering Design, 2022
Jiwon Jung, Maaike Kleinsmann, Dirk Snelders
Abowd (2016) introduced a new era of modern computing called collective computing. This era describes a new stage in modern computing where many people interact with one another through many computing devices, with a prevalent influence on the physical world, and on economic and social values. Based on past and current developments in shareable information systems of collective intelligence (Malone and Bernstein 2015) and combined with recent observations by design scholars (e.g. Chan, Wong, and Kwong 2018; Cooper 2019; Coulton and Lindley 2019; Giaccardi and Redström 2020; Höök and Löwgren 2021) about the new complex forms of computing which designers engage with (e.g. the economic and social structure changes from the various technological development), collective computing can be expected to influence the content and the organisation of the design process. Therefore, this article explores the transformations of design activities during collective computing to establish a future vision of the role of design in the collective computing era, with practical and actionable guidance for designers.
Becoming a resilient organisation: integrating people and practice in infrastructure services
Published in International Journal of Sustainable Engineering, 2020
To build on Stary’s model – which focuses on an organisation’s knowledge generation processes, we present a model of Work Integrated Learning Design (WILD) which complements this from the viewpoint of the learning individual or teams who are navigating the process of change. It overlays four flows of human learning processes onto a learning journey framework – i.e. a project which moves reflexively from the articulation of purpose, through problem structuring and planning, prototyping and testing to the arrival at the presentation of ‘new knowledge’ as practical wisdom (Nonaka 2014). The processes are (i) developing learning power for self-leadership and personal resilience (ii) implementing and embedding learning relationships (iii) working with data and information to generate new knowledge and (iv) harnessing, curating and sharing collective intelligence. The WILD model is presented in Figure 10.