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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.

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."

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?

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.

The most frequently asked question about Primal is, Where do you get the interests data? It's a fair question. If Primal is the most comprehensive source of open interests data in the world, it begs the question of where we get this valuable data. (1)

"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."

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.