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Artificial Intelligence Depot

Artificial Intelligence Depot
Related:  Artificial Intelligence

Noam Chomsky on Where Artificial Intelligence Went Wrong - Yarden Katz An extended conversation with the legendary linguist Graham Gordon Ramsay If one were to rank a list of civilization's greatest and most elusive intellectual challenges, the problem of "decoding" ourselves -- understanding the inner workings of our minds and our brains, and how the architecture of these elements is encoded in our genome -- would surely be at the top. In 1956, the computer scientist John McCarthy coined the term "Artificial Intelligence" (AI) to describe the study of intelligence by implementing its essential features on a computer. Some of McCarthy's colleagues in neighboring departments, however, were more interested in how intelligence is implemented in humans (and other animals) first. Skinner's approach stressed the historical associations between a stimulus and the animal's response -- an approach easily framed as a kind of empirical statistical analysis, predicting the future as a function of the past. Noam Chomsky, speaking in the symposium, wasn't so enthused.

Automates Intelligents: Introduction au livre "Un modèle constructible de système psychique, par Jean-Paul Baquiast et Christophe Jacquemin 23 janvier 2011 par Jean-Paul Baquiast et Christophe Jacquemin Introduction à la lecture de l'ouvrage d'Alain Cardon «Un modèle constructible de système psychique» Les «processus coactivés» et la nouvelle maîtrise du monde Par Jean-Paul Baquiast et Christophe Jacquemin - 23/01/2011 La planète semble être entrée depuis quelques siècles dans une nouvelle ère (aire) géologique marquée par l'empreinte omniprésente des humains sur les phénomènes naturels. Comment définir les «processus coactivés» ? Ce système devient un système-méta (une sorte de superorganisme). On verra ainsi «émerger» ou s'auto-construire au sein du système-méta une couche haute dotée de l'aptitude à agir intentionnellement sur toutes les informations produites par les agents et par conséquent sur toutes les actions de ceux-ci. C'est ce qui commence à se produire dans les sociétés dites technologiques. Quelques exemples de processus coactivés Les places de marché électroniques Comment opère un trader humain ? ifférentes.

Artificial General Intelligence in Second Life  Virtual worlds are the golden path to achieving Artificial General Intelligence and positive Singularity, Dr Ben Goertzel’s, CEO of Novamente LLC and author of “The Hidden Pattern: A Patternist Philosophy of Mind” explained in his presentation “Artificial General Intelligence in Virtual Worlds” given at the Singularity Summit 2007 earlier this month. According to Goertzel, Singularity is no longer a far future idea. About a year ago Goertzel gave a talk “Ten Years to a Positive Singularity — If We Really, Really Try.” The slide that opens this post was in Goerzel’s presentation. What is singularity? Singularity is the creation of the kind of “massively intelligent machines” Hugo de Garis discusses in his book “The Artilect War.” — “machine mega brains that may end up being smarter than human brains by not just a factor of two or even ten times but by a factor of trillions of trillions of time i.e. truly godlike.” Second Life Insider cracks “Do you want your pet whispering “Dave?

Category:Artificial intelligence From Wikipedia, the free encyclopedia Subcategories This category has the following 32 subcategories, out of 32 total. Pages in category "Artificial intelligence" The following 200 pages are in this category, out of 256 total. (previous 200) (next 200)(previous 200) (next 200) On Intelligence Outline[edit] Hawkins outlines the book as follows: The book starts with some background on why previous attempts at understanding intelligence and building intelligent machines have failed. A personal history[edit] The first chapter is a brief history of Hawkins' interest in neuroscience juxtaposed against a history of artificial intelligence research. Hawkins is an electrical engineer by training, and a neuroscientist by inclination. The theory[edit] The hierarchy is capable of memorizing frequently observed sequences (Cognitive modules) of patterns and developing invariant representations. Hebbian learning is part of the framework, in which the event of learning physically alters neurons and connections, as learning takes place. Vernon Mountcastle's formulation of a cortical column is a basic element in the framework. Predictions of the theory of the memory-prediction framework[edit] An Appendix of 11 Testable Predictions: Enhanced neural activity in anticipation of a sensory event[edit]

Autonomous agent An autonomous agent is an intelligent agent operating on an owner's behalf but without any interference of that ownership entity. An intelligent agent, however appears according to a multiply cited statement in a no longer accessible IBM white paper as follows: Intelligent agents are software entities that carry out some set of operations on behalf of a user or another program with some degree of independence or autonomy, and in so doing, employ some knowledge or representation of the user's goals or desires. Non-biological examples include intelligent agents, autonomous robots, and various software agents, including artificial life agents, and many computer viruses. References[edit] External links[edit] See also[edit]

Autonomic Computing The system makes decisions on its own, using high-level policies; it will constantly check and optimize its status and automatically adapt itself to changing conditions. An autonomic computing framework is composed of autonomic components (AC) interacting with each other. An AC can be modeled in terms of two main control loops (local and global) with sensors (for self-monitoring), effectors (for self-adjustment), knowledge and planner/adapter for exploiting policies based on self- and environment awareness. Driven by such vision, a variety of architectural frameworks based on “self-regulating” autonomic components has been recently proposed. Autonomy-oriented computation is a paradigm proposed by Jiming Liu in 2001 that uses artificial systems imitating social animals' collective behaviours to solve difficult computational problems. Problem of growing complexity[edit] Self-management means different things in different fields. Autonomic systems[edit] Control loops[edit] Automatic Adaptive

Artificial consciousness Artificial consciousness (AC), also known as machine consciousness (MC) or synthetic consciousness (Gamez 2008; Reggia 2013), is a field related to artificial intelligence and cognitive robotics whose aim is to "define that which would have to be synthesized were consciousness to be found in an engineered artifact" (Aleksander 1995). Neuroscience hypothesizes that consciousness is generated by the interoperation of various parts of the brain, called the neural correlates of consciousness or NCC. Proponents of AC believe it is possible to construct machines (e.g., computer systems) that can emulate this NCC interoperation. Artificial consciousness can be viewed as an extension to artificial intelligence, assuming that the notion of intelligence in its commonly used sense is too narrow to include all aspects of consciousness. Philosophical views of artificial consciousness[edit] As there are many designations of consciousness, there are many potential types of AC. 61. Awareness Learning

Evolvable hardware Evolvable hardware (EH) is a new field about the use of evolutionary algorithms (EA) to create specialized electronics without manual engineering. It brings together reconfigurable hardware, artificial intelligence, fault tolerance and autonomous systems. Evolvable hardware refers to hardware that can change its architecture and behavior dynamically and autonomously by interacting with its environment. Introduction[edit] Each candidate circuit can either be simulated or physically implemented in a reconfigurable device. Typical reconfigurable devices are field-programmable gate arrays (for digital designs) or field-programmable analog arrays (for analog designs). The concept was pioneered by Adrian Thompson at the University of Sussex, England, who in 1996 evolved a tone discriminator using fewer than 40 programmable logic gates and no clock signal in a FPGA. Why evolve circuits? In many cases, conventional design methods (formulas, etc.) can be used to design a circuit. Literature[edit]

Hugo de Garis Hugo de Garis (born 1947, Sydney, Australia) was a researcher in the sub-field of artificial intelligence (AI) known as evolvable hardware. He became known in the 1990s for his research on the use of genetic algorithms to evolve neural networks using three-dimensional cellular automata inside field programmable gate arrays. He claimed that this approach would enable the creation of what he terms "artificial brains" which would quickly surpass human levels of intelligence.[1] He has more recently been noted for his belief that a major war between the supporters and opponents of intelligent machines, resulting in billions of deaths, is almost inevitable before the end of the 21st century.[2]:234 He suggests AIs may simply eliminate the human race, and humans would be powerless to stop them because of technological singularity. De Garis originally studied theoretical physics, but he abandoned this field in favour of artificial intelligence. Evolvable hardware[edit] Current research[edit]

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