Swarm intelligence is a relatively new discipline that deals with the study of self-organizing processes both in nature and in artificial systems. Researchers in ethology and animal behavior have proposed many models to explain interesting aspects of social insect behavior such as self-organization and shape-formation. Recently, algorithms inspired by these models have been proposed to solve difficult computational problems. An example of a particularly successful research direction in swarm intelligence is ant colony optimization, the main focus of which is on discrete optimization problems. Ant colony optimization has been applied successfully to a large number of difficult discrete optimization problems including the traveling salesman problem, the quadratic assignment problem, scheduling, vehicle routing, etc., as well as to routing in telecommunication networks. Another interesting approach is that of particle swarm optimization, that focuses on continuous optimization problems. Here too, a number of successful applications can be found in the recent literature. Swarm robotics is another relevant field. Here, the focus is on applying swarm intelligence techniques to the control of large groups of cooperating autonomous robots.
ANTS 2014 will give researchers in swarm intelligence the opportunity to meet, to present their latest research, and to discuss current developments and applications.
The BSR Conference 2013 will celebrate and explore the power of networks, bringing together more than 1,000 leaders in business and sustainability from around the world to share their expertise and knowledge.
I'm just finishing up a 24-lecture series titled Understanding the Mysteries of Human Behavior by Professor Mark Leary of Duke University. I've thoroughly enjoyed the series, finding myself curious to learn more about the ...
Over the course of a career, an actor may develop a lexicon of human behavior, allowing him or her to produce portraits of incredible nuance. Bringing stories to life is one of the most ancient forms of knowledge creation and ...
The article focuses on those aspects of the reductive explanation that provide insight into unique human characteristics, specifically in relation to social behaviours, emotion, the philosophy of language, and creativity.
A new robot unveiled this week highlights the psychological and technical challenges of designing a humanoid that people actually want to have around.
Like all little boys, Roboy likes to show off.
He can say a few words. He can shake hands and wave. He is learning to ride a tricycle. And - every parent's pride and joy - he has a functioning musculoskeletal anatomy.
But when Roboy is unveiled this Saturday at the Robots on Tour event in Zurich, he will be hoping to charm the crowd as well as wow them with his skills.
"One of the goals is for Roboy to be a messenger of a new generation of robots that will interact with humans in a friendly way," says Rolf Pfeifer from the University of Zurich - Roboy's parent-in-chief.
As manufacturers get ready to market robots for the home it has become essential for them to overcome the public's suspicion of them. But designing a robot that is fun to be with - as well as useful and safe - is quite difficult.
I am utterly astonished. We have heard of the DaVinci code, but there is a MONOPOLY code. The secret rules of the game from the early 1900s show how, by changing the rules, everyone can live in prosperity.
Back in the early 1900s a woman called Elizabeth J. Magie patented the Landlord’s game to be a “practical demonstration of the present system of land-grabbing with all its usual outcomes and consequences.” Elizabeth was not against capitalism, rather defeating monopoly in all its forms (but, particularly, monopoly of nature). Lizzie continued to work on the design of The Landlord’s Game as a way to explain how the political economy system of Henry George would work in real life. Henry George was against all forms of taxation except from those who owned land.
Parker Brothers purchased Elizabeth’s patent in 1932 for $500, on condition that Parker Brothers would continue to publish The Landlord’s Game as well as Monopoly. In the third edition, published in 1939, and consistent with the agreement with Elizabeth, the game came with two sets of rules. However, only the rules copyrighted by Parker Brothers were actually sold with the game. Purchases were required to contact Elizabeth Magie Phillips to obtain the alternative rules. Remarkably, Elizabeth’s rules were made available by Hasbro on the company’s website.
Belting (or vocal belting) is a specific technique of singing by which a singer produces a loud sound in the upper middle of the pitch range. It is often described as a vocal register, although some dispute this since technically the larynx is not oscillating in a unique way. Singers can use belting to convey heightened emotional states.
The term "belt" is sometimes mistakenly described as the use of chest voice in the higher part of the voice. The chest voice is a very general term for the sound and muscular functions of the speaking voice, singing in the lower range, and the voice used to shout. Still, all those possibilities require help from the muscles in the vocal folds and a thicker closure of the vocal folds. The term "chest voice" is therefore often a misunderstanding, as it describes muscular work in the chest-area of the body, but the "sound" described as "chest voice" is also produced by work of the vocal folds. However, the proper production of the belt voice according to some vocal methods involves minimizing tension in the throat and change of typical placement of the voice sound in the mouth, bringing it forward into the hard palate.
Atif Aslam, a Pakistani singer is an expert and well known for this. It is possible to learn classical vocal methods like bel canto and also to be able to belt; in fact, many musical roles now require it. The belt sound is easier for some than others, but the sound is possible for classical singers, too. It requires muscle coordinations not readily used in classically trained singers, which may be why some opera singers find learning to belt challenging.
Medicine is not the only profession that seems suited to literary creativity. Lawyers often make good writers too, with their forensic approach to human behaviour, their concern with judgment and with justice, and their use of ...
These aspirations of understanding human behaviour rely on the ability to analyse the activities and information captured as people interact with systems and each other through the Web. Therefore Web Science is intimately ...
Jeffrey Pfeffer, Thomas D. Dee II Professor of Organizational Behaviour, Stanford Graduate School of Business. Organization behavior expert and Stanford Professor Jeffrey Pfeffer on the poor state of human sustainability in ...
The school is intended for postdocs, lecturers and predocs with a background in computer science (artificial intelligence) or computational linguistics (corpus linguistics or natural language processing) and a strong interest in music and the origins of language. There will be background lectures that introduce concepts from biology, anthropology, psychology, music theory and linguistics that are helpful to understand the nature of creativity, the role and intimate relations between language and music, and the mechanisms underlying cultural evolution. It contains technical lectures on the fundamental computational components required for language processing and technical ateliers to learn how to set up evolutionary linguistics experiments. Participants have the opportunity to present their latest research in a poster session. The school also features artistic ateliers in which participants create new creative works and engage in performance.
Music and the Origins of Language International Summer School on Agent-based Computational Models of Creativity, 15 – 20 September 2013 in Cortona (Italy)
Workshop on Information Processing in Cognition (IPCog-2013) -
Complex systems approaches to computational neuroscience and artificial neural systems
27-28 February, 2013
This workshop seeks to examine the intersection of the studies of computation in biological cognition, and the design of artificial cognitive systems, from the perspective of information processing in complex systems.
Computational neuroscience has produced statistically robust tools to analyse brain imaging data, revealing much about how different brain regions interact to create outcomes. A topical area is investigating mechanisms that give rise to complex information processing, in terms of how information is stored and transferred across brain networks. Certainly it is well understood that biological cognition is vastly different from the Von Neumann computing paradigm, involving an enormous number of distributed, simple units. From this perspective, there is much scope for complex systems science to provide insights here, including areas such as: measures of information dynamics, network structure and inference, and synchronization.
From another perspective, traditional computation faces the challenge of matching the performance of biological computation. The challenge must be met in order to deliver next-generation leaps in performance, and to be able to handle future problems. There are several approaches towards these challenges, with much hype around "Big Data", and the large-scale Blue Brain project. Again however, there is certainly much scope for complex systems science to provide insights to further the field, e.g., regarding biologically inspired hardware and software, combinations of distributed computing and data-processing together, and principled approaches to guiding the emergence of intelligence.
This workshop seeks to bring together active researchers from these communities to consider these issues, discuss current research in the area and future directions and challenges.
In late January, The Human Brain Project—an attempt to create a computer simulation of the brain at every scale from the nano nano to the macro biotic—announced that it had successfully arranged a billion Euro funding package for a 10-year run.
And then on Feb. 18, an article in The New York Times took the wraps off a plan to spend perhaps billions of dollars for an effort to record large collections of brain cells and figure out what exactly they are doing.
Is this the Large Hadron Collider vs. the Superconducting Supercollider redux?
Not yet. The billions for the Brain Activity Map, the U.S. project, are still a wish that has yet to be granted.
But, despite as-always hazy government finances, brain researchers are thinking large as they never have before, and invoking the attendant rhetoric of moon shots, next-generation Human Genome Projects and the need for humankind to muster the requisite visionary zeal to tackle one of science’s “last frontiers.” Oy, spare me that last part.
The challenges these projects have set for themselves, though, illustrate the challenge of going from today’s crude profiles of a biological machine of incomprehensible complexity to an accurate rendering of the goings-on of some 100 billion neurons woven together by a pulsating tapestry of 100 trillion electrical interconnections.