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Collaborative intelligence

Collaborative intelligence
Collaborative intelligence characterizes multi-agent, distributed systems where each agent, human or machine, is uniquely positioned, with autonomy to contribute to a problem-solving network. Collaborative autonomy of organisms in their ecosystems makes evolution possible. Natural ecosystems, where each organism's unique signature is derived from its genetics, circumstances, behavior and position in its ecosystem, offer principles for design of next generation social networks to support collaborative intelligence, crowd-sourcing individual expertise, preferences, and unique contributions in a problem-solving process.[1] Overview[edit] Collaborative intelligence is a term used in several disciplines. History[edit] In the late 1980s, Eshel Ben-Jacob began to study bacterial self-organization, believing that bacteria hold the key to understanding larger biological systems. Contrast with collective intelligence[edit] Application[edit] See also[edit] References[edit] Related:  Intelligence Forms

Intelligence distribuée Un article de Wikipédia, l'encyclopédie libre. Un vol d'étourneaux maintient sa cohérence avec un ensemble de règles simples au niveau individuel. L'intelligence distribuée désigne l'apparition de phénomènes cohérents à l'échelle d'une population dont les individus agissent selon des règles simples. L'interaction ou la synergie entre actions individuelles simples peut de façons variées permettre l'émergence de formes, organisations, ou comportements collectifs, complexes ou cohérents, tandis que les individus eux se comportent à leur échelle indépendamment de toute règle globale. C'est l'indépendance entre d'une part les actions et règles qui régissent les individus et de l'autre la forme ou la dynamique collective, qui est au cœur du concept d'intelligence distribuée : comment cette cohérence globale apparaît-elle alors qu'elle n'est ni inscrite au niveau de l'individu, ni le résultat de "décisions communes", ni encore "commandée" par une "intelligence" centrale ? — [réf. nécessaire]

Valentin Turchin Valentin Fyodorovich Turchin (Russian: Валенти́н Фёдорович Турчи́н, 1931 – 7 April 2010) was a Soviet and American cybernetician and computer scientist. He developed the Refal programming language, the theory of metasystem transitions and the notion of supercompilation. As such he can be seen as a pioneer in Artificial Intelligence and one of the visionaries at the basis of the Global brain idea. Biography[edit] Turchin was born in 1931 in Podolsk, Soviet Union. In the 1960s, Turchin became politically active. He came to New York where he joined the faculty of the City University of New York in 1979. His son, Peter Turchin, is a world renowned specialist in population dynamics and mathematical modeling of historical dynamics. Work[edit] The philosophical core of Turchin's scientific work is the concept of the metasystem transition, which denotes the evolutionary process through which higher levels of control emerge in system structure and function. Major publications[edit] Valentin F.

Intelligence ambiante Un article de Wikipédia, l'encyclopédie libre. L'évolution des ordinateurs : la course à la miniaturisation et à la diffusion dans le milieu ambiant L'intelligence ambiante est ce que pourrait devenir l'informatique dans la première moitié du XXIe siècle en repoussant les limites technologiques qu'elle avait à la fin du XXe siècle [réf. nécessaire]. Ce concept semble pouvoir tenir lieu de traduction non littérale aux concepts nés en Amérique du Nord sous le vocable initial d'informatique ubiquitaire, systèmes pervasifs ou encore ordinateur évanescent [réf. nécessaire]. Dans cette approche, le concept même de système d’information ou d'ordinateur change : d’une activité de traitement exclusivement centrée sur l’utilisateur, l'informatique devient interface entre objets communicants et personnes, et entre personnes [réf. nécessaire]. Facteurs en jeu[modifier | modifier le code] Vers une informatique diffuse[modifier | modifier le code] Perspectives économiques[modifier | modifier le code]

Ambient intelligence An (expected) evolution of computing from 1960–2010. In computing, ambient intelligence (AmI) refers to electronic environments that are sensitive and responsive to the presence of people. Ambient intelligence is a vision on the future of consumer electronics, telecommunications and computing that was originally developed in the late 1990s for the time frame 2010–2020. In an ambient intelligence world, devices work in concert to support people in carrying out their everyday life activities, tasks and rituals in an easy, natural way using information and intelligence that is hidden in the network connecting these devices (see Internet of Things). As these devices grow smaller, more connected and more integrated into our environment, the technology disappears into our surroundings until only the user interface remains perceivable by users. A typical context of ambient intelligence environment is a Home environment (Bieliková & Krajcovic 2001). Overview[edit] History[edit] Criticism[edit]

Category:Animal intelligence Animal intelligence is the study about the origins of animal intelligence by studying the mental processes of other species. The basic premise of this research is that we need to understand the processes of association and learning in other animals in order to understand how human culture, art, religion, mathematics and more may have developed. Subcategories This category has the following 3 subcategories, out of 3 total. Pages in category "Animal intelligence" The following 39 pages are in this category, out of 39 total.

Group intelligence Group intelligence is a term used in a subset of the social psychology literature to refer to a process by which large numbers of people converge upon the same knowledge through group interaction. The term is not commonplace in the mainstream academic study of human intelligence. Social psychologists study group intelligence and related topics such as decentralized decision making and group wisdom, using demographic information to study the ramifications for long-term social change. Marketing and behavioral finance experts use similar research to forecast consumer behavior (e.g. buying patterns) for corporate strategic purposes. Definition[edit] The term group intelligence describes how, under the best circumstances, large numbers of people simultaneously converge upon the same knowledge. James Surowiecki, in The Wisdom of Crowds, claims that, counterintuitively, group intelligence requires independence of thought as well as superior judgment. History[edit] See also[edit]

Multi-agent system Simple reflex agent Learning agent Concept[edit] Multi-agent systems consist of agents and their environment. Typically multi-agent systems research refers to software agents. However, the agents in a multi-agent system could equally well be robots,[5] humans or human teams. Agents can be divided into different types: Very simple like: passive agents[6] or agent without goals (like obstacle, apple or key in any simple simulation)Active agents[6] with simple goals (like birds in flocking, or wolf–sheep in prey-predator model)Or very complex agents (like cognitive agent, which contain complex calculations) Environment also can be divided into: Virtual EnvironmentDiscrete EnvironmentContinuous Environment Characteristics[edit] The agents in a multi-agent system have several important characteristics:[10] Self-organization and self-steering[edit] Systems paradigms[edit] Many M.A. systems are implemented in computer simulations, stepping the system through discrete "time steps". First a "Who can?"

Systems thinking Impression of systems thinking about society[1] A system is composed of interrelated parts or components (structures) that cooperate in processes (behavior). Natural systems include biological entities, ocean currents, the climate, the solar system and ecosystems. Designed systems include airplanes, software systems, technologies and machines of all kinds, government agencies and business systems. Systems Thinking has at least some roots in the General System Theory that was advanced by Ludwig von Bertalanffy in the 1940s and furthered by Ross Ashby in the 1950s. Systems thinking has been applied to problem solving, by viewing "problems" as parts of an overall system, rather than reacting to specific parts, outcomes or events and potentially contributing to further development of unintended consequences. Systems science thinking attempts to illustrate how small catalytic events that are separated by distance and time can be the cause of significant changes in complex systems.