Life Science Technologies: Sanger Who? Sequencing the Next Generation In November 2008 Elaine Mardis of Washington University in St. Louis and colleagues published the complete genome sequence of an individual with acute myeloid leukemia. Coming just a few years after the decade-long, multibillion dollar Human Genome Project, the paper was remarkable on several levels. For one thing, the team sequenced two human genomes, both cancerous and normal, some 140 billion bases in all. More impressive, though, was what the study omitted: the 50 human genomes Mardis sequenced that year (albeit not as deeply) for the 1,000 Genomes Project. "It's like a whole new world," she says. By Jeffrey M. Inclusion of companies in this article does not indicate endorsement by either AAAS or Science, nor is it meant to imply that their products or services are superior to those of other companies. The instruments in question, Illumina Genome Analyzers, are one of a cadre of so-called next-generation DNA sequencers. Such is life on genomics' bleeding edge. Out with the Old±
Evolutionary algorithm Evolutionary algorithms often perform well approximating solutions to all types of problems because they ideally do not make any assumption about the underlying fitness landscape; this generality is shown by successes in fields as diverse as engineering, art, biology, economics, marketing, genetics, operations research, robotics, social sciences, physics, politics and chemistry[citation needed]. In most real applications of EAs, computational complexity is a prohibiting factor. In fact, this computational complexity is due to fitness function evaluation. Fitness approximation is one of the solutions to overcome this difficulty. However, seemingly simple EA can solve often complex problems; therefore, there may be no direct link between algorithm complexity and problem complexity. A possible limitation [according to whom?] Implementation of biological processes[edit] Evolutionary algorithm types[edit] Related techniques[edit] Swarm algorithms, including: [edit] See also[edit] References[edit]
Gestion des connaissances Un article de Wikipédia, l'encyclopédie libre. La gestion des connaissances (en anglais knowledge management) est une démarche managériale pluridisciplinaire qui regroupe l'ensemble des initiatives, des méthodes et des techniques permettant de percevoir, identifier, analyser, organiser, mémoriser, partager les connaissances des membres d'une organisation – les savoirs créés par l'entreprise elle-même (marketing, recherche et développement) ou acquis de l'extérieur (intelligence économique) – en vue d'atteindre un objectif fixé. Définition[modifier | modifier le code] Actuellement, nous sommes submergés d'informations. Les entreprises, les scientifiques ou même les particuliers sont maintenant obligés d'appliquer une stratégie dans le traitement et la transmission de l'information dans les activités de tous les jours : voter, travailler, chercher un emploi, gagner des marchés, etc. D'après des praticiens et des académiciens tels que R. Historique[modifier | modifier le code] SI MARIÉ(?
Alien From Earth Alien From Earth PBS Airdate: November 11, 2008 NARRATOR: It is the dream of every archaeologist who slogs through backbreaking days of excavation, the find that changes everything. ABC NEWS REPORTER (Archival Footage):A team of Australian and Indonesian archeologists has discovered the remains of what's believed to be a new species of human. HENRY GEE (Nature Magazine): This is a major discovery. CHRIS STRINGER (Natural History Museum, United Kingdom): It implies we are missing a huge amount of the story of human evolution. NARRATOR: Paradoxically, the discovery is huge because its pieces are not: a skeleton of an adult, the size of a three-year old child; a skull one-third the size of a modern human's. To many, the evidence is irrefutable. BILL JUNGERS (Stony Brook University): This is not a little person. NARRATOR: But some scientists just aren't buying it. RALPH HOLLOWAY (Columbia University): It just invites tremendous skepticism. NARRATOR: An astonishing discovery, a bitter controversy.
Human-based computation Human-based computation (HBC) is a computer science technique in which a machine performs its function by outsourcing certain steps to humans. This approach uses differences in abilities and alternative costs between humans and computer agents to achieve symbiotic human-computer interaction. In traditional computation, a human employs a computer[1] to solve a problem; a human provides a formalized problem description and an algorithm to a computer, and receives a solution to interpret. Human-based computation frequently reverses the roles; the computer asks a person or a large group of people to solve a problem, then collects, interprets, and integrates their solutions. Early work[edit] Human-based computation (apart from the historical meaning of "computer") research has its origins in the early work on interactive evolutionary computation. A concept of the automatic Turing test pioneered by Moni Naor (1996) is another precursor of human-based computation. Alternative terms[edit]
Machine learning Machine learning is a subfield of computer science[1] that evolved from the study of pattern recognition and computational learning theory in artificial intelligence.[1] Machine learning explores the construction and study of algorithms that can learn from and make predictions on data.[2] Such algorithms operate by building a model from example inputs in order to make data-driven predictions or decisions,[3]:2 rather than following strictly static program instructions. Machine learning is closely related to and often overlaps with computational statistics; a discipline that also specializes in prediction-making. It has strong ties to mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. Example applications include spam filtering, optical character recognition (OCR),[4] search engines and computer vision.
Collective intelligence: Ants and brain's neurons CONTACT: Stanford University News Service (415) 723-2558 Collective intelligence: Ants and brain's neurons STANFORD - An individual ant is not very bright, but ants in a colony, operating as a collective, do remarkable things. A single neuron in the human brain can respond only to what the neurons connected to it are doing, but all of them together can be Immanuel Kant. That resemblance is why Deborah M. "I'm interested in the kind of system where simple units together do behave in complicated ways," she said. No one gives orders in an ant colony, yet each ant decides what to do next. For instance, an ant may have several job descriptions. This kind of undirected behavior is not unique to ants, Gordon said. Gordon studies harvester ants in Arizona and, both in the field and in her lab, the so-called Argentine ants that are ubiquitous to coastal California. Argentine ants came to Louisiana in a sugar shipment in 1908. The motions of the ants confirm the existence of a collective. -jns/ants-
Artificial neural network An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Here, each circular node represents an artificial neuron and an arrow represents a connection from the output of one neuron to the input of another. For example, a neural network for handwriting recognition is defined by a set of input neurons which may be activated by the pixels of an input image. Like other machine learning methods - systems that learn from data - neural networks have been used to solve a wide variety of tasks that are hard to solve using ordinary rule-based programming, including computer vision and speech recognition. Background[edit] There is no single formal definition of what an artificial neural network is. consist of sets of adaptive weights, i.e. numerical parameters that are tuned by a learning algorithm, andare capable of approximating non-linear functions of their inputs. History[edit] Farley and Wesley A. Recent improvements[edit] Models[edit] and .
Applying machine learning or bio-inspired learning techniques to communication networks: Firestation <p class="box warning">JavaScript is disabled in your browser. Please enable to use all features of this website.</p> FIRE News subscription E-mail address: In accordance with EU data protection laws, your e-mail address will only be used for the purpose of the project and will not be forwarded to third parties. Applying machine learning or bio-inspired learning techniques to communication networks Setup of an Internet Research Task Force group Further information can be obtained at: groups.google.com/group/lccn/ and reading material at tools.ietf.org/html/draft-tavernier-irtf-lccn-problem-statement-01. Let us know if you have ideas or use cases on the ideal learning network on the open LCCN mailing list: groups.google.com/group/lccn/.
Cellular automaton The concept was originally discovered in the 1940s by Stanislaw Ulam and John von Neumann while they were contemporaries at Los Alamos National Laboratory. While studied by some throughout the 1950s and 1960s, it was not until the 1970s and Conway's Game of Life, a two-dimensional cellular automaton, that interest in the subject expanded beyond academia. In the 1980s, Stephen Wolfram engaged in a systematic study of one-dimensional cellular automata, or what he calls elementary cellular automata; his research assistant Matthew Cook showed that one of these rules is Turing-complete. Wolfram published A New Kind of Science in 2002, claiming that cellular automata have applications in many fields of science. The primary classifications of cellular automata as outlined by Wolfram are numbered one to four. Overview[edit] The red cells are the von Neumann neighborhood for the blue cell, while the extended neighborhood includes the pink cells as well. A torus, a toroidal shape History[edit]
Principaux projets et réalisations en intelligence artificielle Un article de Wikipédia, l'encyclopédie libre. Cette liste indique les principaux projets et réalisations marquants dans le domaine de l’intelligence artificielle. La quasi-totalité de ces travaux ont été accomplis aux États-Unis, et il est à noter que nombre d’entre eux ont été financés par l’armée américaine. La liste est organisée par ordre chronologique. Logic Theorist (Théoricien de la logique) (1956)[modifier | modifier le code] IPL (Langage de traitement de l’information) (1956)[modifier | modifier le code] Dans le cadre de la réalisation de Logic Theorist, le résultat le plus important pour le développement de l’intelligence artificielle, a été l’invention d’un langage de programmation spécifique nommé IPL. GPS (Système général de résolution de problèmes) (1957)[modifier | modifier le code] Sad Sam (1957)[modifier | modifier le code] Créé par Robert K. LISP (LISt Processing ou traitement de listes) (1958)[modifier | modifier le code] Perceptron (1958)[modifier | modifier le code]
Bees algorithm In computer science and operations research, the Bees Algorithm is a population-based search algorithm which was developed in 2005.[1] It mimics the food foraging behaviour of honey bee colonies. In its basic version the algorithm performs a kind of neighbourhood search combined with global search, and can be used for both combinatorial optimization and continuous optimization. The only condition for the application of the Bees Algorithm is that some measure of topological distance between the solutions is defined. The Bees Algorithm is inspired by the foraging behaviour of honey bees. Honey bees foraging strategy in nature[edit] A colony of honey bees can extend itself over long distances (over 14 km) [4] and in multiple directions simultaneously to harvest nectar or pollen from multiple food sources (flower patches). The Bees Algorithm[edit] The Bees Algorithm [2][6] mimics the foraging strategy of honey bees to look for the best solution to an optimisation problem. Applications[edit]
Seven Aspects of Strategy Formation Exploring the Value of Planning Abstract It has been widely argued that the planning approach that dominates entrepreneurial training does not represent either actual or good strategic decision-making. Studies examining the impact of planning on performance have had inconclusive results and have been subject to considerable methodological problems.
Why the Theory of Evolution Exists Introduction to the Mathematics of Evolution Chapter 1 Why the Theory of Evolution Exists "In the preface to the proceedings of the [Wistar] symposium, Dr. 's Enigma, Luther D. Introduction Many times students hear that the theory of evolution is a "proven fact of science." The reality is that the theory of evolution is NOT a proven fact of science. For example, the theory of evolution requires that life be created from simple chemicals. Such a conversion has never been demonstrated and such a conversion has never been proven to be possible. Even the simplest life on earth, which does not require a host, is far too complex to form by a series of accidents. The theory of evolution also requires massive amounts of new genetic information form by totally random mutations of DNA. New genetic information, including at least one new gene, has never been observed in nature, nor has new genetic information, created by random mutations of DNA, ever been accomplished in a science lab. Science Mr.