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Artificial Intelligence is not yet HAL from the 2001: The Space Odyssey…but we are getting awfully close. Sure enough, one day it could be as similar to the sci-fi potboilers being churned out by Hollywood. If that's your idea of what artificial intelligence is all about, then you aren't far off the mark. In layman's terms, artificial intelligence is about creating intelligent machines through the use of intelligent computer programs. Most, if not all of artificial intelligence (AI) tries to mimic human behavior. The scale of ambition is different, but artificial intelligence programming is a full-fledged field in the cutting edge of science today. If you have some interest in the world of tomorrow, check out these websites to grasp what artificial intelligence is all about.

"What does collective intelligence mean? It's important to realize that intelligence is not just something that happens inside individual brains. It also arises with groups of individuals. In fact, I'd define collective intelligence as groups of individuals acting collectively in ways that seem intelligent. By that definition, of course, collective intelligence has been around for a very long time. Families, companies, countries, and armies: those are all examples of groups of people working together in ways that at least sometimes seem intelligent."

"Collective intelligence has been around for a long time. What is different today, however, is how collective intelligence, combined with technology, has the power to create what has been described as a "global brain." Technology optimists like Thomas Malone, who heads MIT's Center for Collective Intelligence, argue that this global brain will develop into an awesome problem solving tool that will be able to tackle seemingly insurmountable problems. "

Howard Gardner of Harvard has identified seven distinct intelligences. This theory has emerged from recent cognitive research and "documents the extent to which students possess different kinds of minds and therefore learn, remember, perform, and understand in different ways," according to Gardner (1991). According to this theory, "we are all able to know the world through language, logical-mathematical analysis, spatial representation, musical thinking, the use of the body to solve problems or to make things, an understanding of other individuals, and an understanding of ourselves. Where individuals differ is in the strength of these intelligences - the so-called profile of intelligences -and in the ways in which such intelligences are invoked and combined to carry out different tasks, solve diverse problems, and progress in various domains." Gardner says that these differences "challenge an educational system that assumes that everyone can learn the same materials in the same way and that a uniform, universal measure suffices to test student learning. Indeed, as currently constituted, our educational system is heavily biased toward linguistic modes of instruction and assessment and, to a somewhat lesser degree, toward logical-quantitative modes as well." Gardner argues that "a contrasting set of assumptions is more likely to be educationally effective. Students learn in ways that are identifiably distinctive. The broad spectrum of students - and perhaps the society as a whole - would be better served if disciplines could be presented in a numbers of ways and learning could be assessed through a variety of means."

"Intelligence is not just something that happens inside individual minds.   It also arises within groups of individuals and this phenomenon can be quite powerful.  For a number of months Imagination for People has been working on software that will facilitate the creation of what is called 'collective intelligence.' In preparation for the launch of this software, we thought we would take some time to introduce the I4P community to collective intelligence (CI) and show off some of its benefits.  "

"The emerging science of 'collective intelligence' - and the rise of the global brain"

WeKnowIt is a 3 year Integrated Project developing novel techniques for exploiting multiple layers of intelligence from user-generated content, which together constitute Collective Intelligence, a form of intelligence that emerges from the collaboration and contributions of many individuals.

Artificial intelligence began with an ambitious research agenda: To endow machines with some of the traits we value most highly in ourselves-the faculty of reason, skill in solving problems, creativity, the capacity to learn from experience. Early results were promising. Computers were programmed to play checkers and chess, to prove theorems in geometry, to solve analogy puzzles from IQ tests, to recognize letters of the alphabet. Marvin Minsky, one of the pioneers, declared in 1961: "We are on the threshold of an era that will be strongly influenced, and quite possibly dominated, by intelligent problem-solving machines."

 Tim Berners Lee recognized the problematics of the messy web early on and proposed the Semantic Web to overcome messiness and apply a semantic structure to bring order (read: computer logic) in the chaos (read: human expression). Pierre Levy, French philosopher and leading expert on collective intelligence, is on a similar mission. While driven by, arguably, a similar set of goals, his approach takes a step further. The problem with Berners Lee semantic structure is that implies a universal ontology, which might prove out to be the Achilles heel of the protocol. Levy's approach overcomes these problems. Levy is currently working on a research program, called IEML (Information Economy Meta Language). IEML is a metalanguage and proposes itself as the language of collective intelligence. 

"Collective intelligence is a growing trend that seeks to exploit the computational power of millions of users You have probably done it but maybe you didn't realise. Or maybe you did it on purpose, but it was a game. What is it? Collective intelligence, or "human computation", is a growing trend that looks to harness the wisdom of the crowd to solve problems. Today, enormous computational power is distributed among millions of users, and the internet offers a means to connect it, explains Prof Barry Smyth, professor of computer science at University College Dublin."

"Thomas Malone, director of the MIT Center for Collective Intelligence,  is one of the leading thinkers in the realm of anticipating how new technologies will transform the way work is done and leaders lead. His 2004 book, The Future of Work: How the New Order of Business Will Shape Your Organization, Your Management Style, and Your Life, helped thousands of executives and would-be executives see their organizations, and themselves, in startling new ways. As a result, many organizations are becoming more collaborative and democratic. Now, Malone is exploring how social business, data analytics and cognitive computing will transform organizations once again. Here, he talks about the revolution that is coming."

Businesses are using collective intelligence to speed up company growth, improve efficiency, enhance products and services, and strengthen the employee environment, according to new research from IBM.

"Fascinating insight from the MIT Centre for Collective Intelligence at the IBM Think Forum. The video is 40mins long but worth watching if you're interested in what makes groups effective in solving complex problems. It also shows how (and why) the idea of 'distributed leadership' is becoming more widely seen as the future model for managing organisations and complexity."

For the most part, the biological metaphor has long been just that - a simplifying analogy rather than a blueprint for how to do computing. Engineering, not biology, guided the pursuit of artificial intelligence. As Frederick Jelinek, a pioneer in speech recognition, put it, "airplanes don't flap their wings." Yet the principles of biology are gaining ground as a tool in computing. The shift in thinking results from advances in neuroscience and computer science, and from the prod of necessity.

Can computing and science fiction collide to create a true Artificial Intelligence? A.I has been part of our computing landscape for a long time, first as an idea, then taking baby steps, thing started to move in the early days of computers. After that, there was a period of disillusion and with the rise of cloud computing and massively parallel consumer-level chips A.I is more than ever on our lips and in our minds - but how far are we really from the awakening of a digital form of consciousness?

""Under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them.""

Perhaps the biggest announcement at Apple's iPhone event on Tuesday will be Assistant, Apple's evolution of the Siri Personal Assistant Software. Siri, you'll remember, is the company Apple picked up for a rumored $200 million in April of last year for, in Steve Jobs' words, its "Artificial Intelligence", not search or speech recognition.

"Two things are clear. The first is that smart machines are evolving at breakneck speed. Moore's law-that the computing power available for a given price doubles about every 18 months-continues to apply. This power is leaping from desktops into people's pockets. More than 1.1 billion people own smartphones and tablets. Manufacturers are putting smart sensors into all sorts of products. The second is that intelligent machines have reached a new social frontier: knowledge workers are now in the eye of the storm, much as stocking-weavers were in the days of Ned Ludd, the original Luddite. Bank clerks and travel agents have already been consigned to the dustbin by the thousand; teachers, researchers and writers are next. The question is whether the creation will be worth the destruction."

"The sensational rise of Big Data within the reporting and analytics realm resembles an uncanny likeness to the escalating popularity of bothersome big-haired British boy 'band', One Direction."

AMHERST, Mass. - As computer scientists this year celebrate the 100th anniversary of the birth of the mathematical genius Alan Turing, who set out the basis for digital computing in the 1930s to anticipate the electronic age, they still quest after a machine as adaptable and intelligent as the human brain. Now, computer scientist Hava Siegelmann of the University of Massachusetts Amherst, an expert in neural networks, has taken Turing's work to its next logical step. She is translating her 1993 discovery of what she has dubbed "Super-Turing" computation into an adaptable computational system that learns and evolves, using input from the environment in a way much more like our brains do than classic Turing-type computers. She and her post-doctoral research colleague Jeremie Cabessa report on the advance in the current issue of Neural Computation. "This model is inspired by the brain," she says. "It is a mathematical formulation of the brain's neural networks with their adaptive abilities." The authors show that when the model is installed in an environment offering constant sensory stimuli like the real world, and when all stimulus-response pairs are considered over the machine's lifetime, the Super Turing model yields an exponentially greater repertoire of behaviors than the classical computer or Turing model. They demonstrate that the Super-Turing model is superior for human-like tasks and learning.