The Global Brain Institute The GBI uses scientific methods to better understand the global evolution towards ever-stronger connectivity between people, software and machines. By developing concrete models of this development, we can anticipate both its promises and its perils. That would help us to steer a course towards the best possible outcome for humanity. Objectives (for more details, check our strategic objectives and activities) Assumptions We see people, machines and software systems as agents that communicate via a complex network of communication links. Challenges that cannot be fully resolved by a single agent are propagated to other agents, along the links in the network. The propagation of challenges across the global network is a complex process of self-organization.
Swarm intelligence Swarm intelligence (SI) is the collective behavior of decentralized, self-organized systems, natural or artificial. The concept is employed in work on artificial intelligence. The expression was introduced by Gerardo Beni and Jing Wang in 1989, in the context of cellular robotic systems.[1] The application of swarm principles to robots is called swarm robotics, while 'swarm intelligence' refers to the more general set of algorithms. 'Swarm prediction' has been used in the context of forecasting problems. Example algorithms[edit] Particle swarm optimization[edit] Ant colony optimization[edit] Artificial bee colony algorithm[edit] Artificial bee colony algorithm (ABC) is a meta-heuristic algorithm introduced by Karaboga in 2005,[5] and simulates the foraging behaviour of honey bees. Bacterial colony optimization[edit] Differential evolution[edit] Differential evolution is similar to genetic algorithm and pattern search. The bees algorithm[edit] Artificial immune systems[edit] Bat algorithm[edit]
How to Burst the "Filter Bubble" that Protects Us from Opposing Views The term “filter bubble” entered the public domain back in 2011when the internet activist Eli Pariser coined it to refer to the way recommendation engines shield people from certain aspects of the real world. Pariser used the example of two people who googled the term “BP”. One received links to investment news about BP while the other received links to the Deepwater Horizon oil spill, presumably as a result of some recommendation algorithm. This is an insidious problem. This is the filter bubble—being surrounded only by people you like and content that you agree with. And the danger is that it can polarise populations creating potentially harmful divisions in society. Today, Eduardo Graells-Garrido at the Universitat Pompeu Fabra in Barcelona as well as Mounia Lalmas and Daniel Quercia, both at Yahoo Labs, say they’ve hit on a way to burst the filter bubble. They found over 40,000 Twitter users who had expressed an opinion using the hashtags such as #pro-life and #pro-choice.
Craig Reynolds (computer graphics) Craig W. Reynolds (born March 15, 1953), is an artificial life and computer graphics expert, who created the Boids artificial life simulation in 1986.[1] Reynolds worked on the film Tron (1982) as a scene programmer, and on Batman Returns (1992) as part of the video image crew. Reynolds won the 1998 Academy Scientific and Technical Award in recognition of "his pioneering contributions to the development of three-dimensional computer animation for motion picture production."[2] He is the author of the OpenSteer library.
US Military Scientists Solve the Fundamental Problem of Viral Marketing Viral messages begin life by infecting a few individuals and then start to spread across a network. The most infectious end up contaminating more or less everybody. Just how and why this happens is the subject of much study and debate. Network scientists know that key factors are the rate at which people become infected, the “connectedness” of the network and how the seed group of individuals, who first become infected, are linked to the rest. It is this seed group that fascinates everybody from marketers wanting to sell Viagra to epidemiologists wanting to study the spread of HIV. So a way of finding seed groups in a given social network would surely be a useful trick, not to mention a valuable one. These guys have found a way to identify a seed group that, when infected, can spread a message across an entire network. Their method is relatively straightforward. This process finishes when there is nobody left in the network who has more friends than the threshold. Expect to hear more!
The Wisdom of Crowds The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, published in 2004, is a book written by James Surowiecki about the aggregation of information in groups, resulting in decisions that, he argues, are often better than could have been made by any single member of the group. The book presents numerous case studies and anecdotes to illustrate its argument, and touches on several fields, primarily economics and psychology. The opening anecdote relates Francis Galton's surprise that the crowd at a county fair accurately guessed the weight of an ox when their individual guesses were averaged (the average was closer to the ox's true butchered weight than the estimates of most crowd members, and also closer than any of the separate estimates made by cattle experts).[1] Types of crowd wisdom[edit] Surowiecki breaks down the advantages he sees in disorganized decisions into three main types, which he classifies as
New research to uncover nuances of networks Feb. 20, 2013 9:01 a.m. When a species disappears from a region, the rest of the ecosystem may flourish or collapse, depending on the role that species played. When a storm rolls across the coast, the power grid might reconfigure itself quickly or leave cities dark for days. Each of these systems is a kind of network, with thousands of members and relationships linking them. With current network theory, scientists can predict a few simple trends, such as which web pages are likely to get more hits over time. A new four-year, $2.9 million grant from the Defense Advanced Research Projects Agency is supporting SFI research that will, the researchers hope, propel their understanding of networks to the next level. Nodes and links in real networks are cloaked in details, he says. “We’re at a very early stage of understanding how structure is related to dynamics,” Moore says. They have already made progress.
Boids From Wikipedia, the free encyclopedia Artificial life program Separation Alignment Cohesion As with most artificial life simulations, Boids is an example of emergent behavior; that is, the complexity of Boids arises from the interaction of individual agents (the boids, in this case) adhering to a set of simple rules. separation: steer to avoid crowding local flockmatesalignment: steer towards the average heading of local flockmatescohesion: steer to move towards the average position (center of mass) of local flockmates More complex rules can be added, such as obstacle avoidance and goal seeking. The basic model has been extended in several different ways since Reynolds proposed it. The movement of Boids can be characterized as either chaotic (splitting groups and wild behaviour) or orderly. The Boids model can be used for direct control and stabilization of teams of simple unmanned ground vehicles (UGV)[5] or micro aerial vehicles (MAV)[6] in swarm robotics. See also[edit] References[edit]
1+1=1 : la formule des réseaux On parle de réseau lorsque des éléments interagissent entre eux au sein d’un groupe. Ils sont étudiés aussi bien par les sciences humaines et sociales, les sciences du vivant et de la terre, que les sciences et techniques de l'information et de la communication. De façon étonnante, tous ces réseaux apparemment distincts ont certains points communs. Ce texte cherche à proposer une synthèse accessible à des non-spécialistes de ce qui est commun (mais aussi différent) dans les différents types de réseaux. Il fait donc appel autant que possible à un vocabulaire usuel. La triade « constituants, règles, réseau » Souvent nous tentons d’aborder un réseau social, technique ou autre par le réductionnisme : en étudiant le comportement de ses constituants. Deux hommes veulent se confronter au tir à la corde. Les constituants plus les règles donnent le réseau Le réseau peut être déterminé par ses constituants et ses règles. La perte (partielle) de notre capacité à prévoir Le réseau global