The concept of artificial intelligence began as pure fiction, something to be imagined but never actually existing. Today, we know that that's no longer the case. Artificial Intelligence is real and there are already real-world applications where artificial intelligence is helping us solve some of the biggest problems facing humanity. Here's a look at how artificial intelligence has developed through the years. Greek myths The earliest known reference to something we could term artificial intelligence dates back to the ancient Greeks.

The concept of artificial intelligence began as pure fiction, something to be imagined but never actually existing. Today, we know that that's no longer the case. Artificial Intelligence is real and there are already real-world applications where artificial intelligence is helping us solve some of the biggest problems facing humanity. Here's a look at how artificial intelligence has developed through the years. The earliest known reference to something we could term artificial intelligence dates back to the ancient Greeks.

In the 1950s when computer science was in its infancy and artificial intelligence was nothing but science fiction, Alan Turing, a pioneer in theoretical computer science felt that there needed to be a way to determine whether a machine could be said to be intelligent. So he created a test called the Turing Test, or Imitation Game. His test was very simple. A human and a machine would be separated so they could not see each other and an evaluator would listen as the human and the machine converse. If the evaluator, a third party that isn't part of the conversation, can't determine whether both parties are human or if one is a machine, then the machine is said to have passed the Turing Test.

In this special guest feature, Ross Andrew, CEO and Chairman of Maropost, suggests that by embracing AI and machine learning, marketers can turn big data into food that their tools use to accomplish business objectives. Ross is responsible for the direction of Maropost. Since its founding in 2011, he has consistently doubled the growth of the company annually by devising innovative solutions to the industry's biggest challenges. Once upon a time, a famous author imagined a great machine called the Multivac. While the name is cheesy, the concept is not: the technological behemoth was an artificial intelligence powered by machine learning that enabled it to process all the data of the universe and answer the last question at the end of time – marketing has more modest goals in mind for their own artificial intelligence platforms.

Can you imagine taking a cab which is driverless or conducting all basic banking transactions with the help of ATM without requiring a teller. Let's go one step further, your medical treatment being done by a robot or robots teaching you in the classrooms. Well, all of this is not a dream anymore but possible and achievable because of Artificial Intelligence. Artificial Intelligence, though in a nascent stage, has already started to enter a lot of fields and is trying to make a mark. AI has already started to automate some type of jobs and it's not far when AI will replace all the repetitive type of jobs in the analytics space and perform it solely without any human intervention.

Nasa has announced that it has found evidence of flowing water on Mars. Scientists have long speculated that Recurring Slope Lineae -- or dark patches -- on Mars were made up of briny water but the new findings prove that those patches are caused by liquid water, which it has established by finding hydrated salts. Several hundred camped outside the London store in Covent Garden. The 6s will have new features like a vastly improved camera and a pressure-sensitive "3D Touch" display

The government plans to promote the development of artificial intelligence technology in cooperation with other entities for use in medical, manufacturing and other sectors to mitigate the expected labor shortages that will result from an aging society, officials said Friday. The public-private initiative will involve 20 firms and research institutions such as the government-backed Riken research institute, Toyota Motor Corp. and NEC Corp. The science ministry, which oversees Riken, is planning to seek 10 billion ( 99.7 million) in the fiscal 2017 budget for costs related to the project, which is expected to run for 10 years. In the medical field, AI technology is expected to be used in diagnosing the symptoms of patients and advising doctors on optimal treatments by analyzing electronic medical records and huge amounts of data on similar cases. In the manufacturing sector, for example, AI technologies could be used to detect signs of impending machinery glitches and failures at factories more efficiently than humans to reduce output disruptions.

On the day after Intel announced its acquisition of San Diego machine learning startup Nervana Systems, investor Steve Jurvetson told me he was feeling a sense of satisfaction about a call he made three years ago, and how it has been playing out. In a 2013 panel discussion at Silicon Valley's Churchill Club, the DFJ partner said "machine learning" was his pick as the most important tech trend to watch for the next three to five years. "Just about anything you've heard [about] at Google that sounds interesting and new is based on machine learning," Jurvetson said at the time. "Everywhere, technology is starting to percolate into an otherwise prosaic, non-tech industry--apply big data, apply machine learning--and revolutionize it." Just over a year later, Jurvetson led the Series A round of venture funding for Nervana Systems.

Organizations often conduct door-to-door surveys to identify people living in poverty, but the downside is that these surveys are often time-consuming and expensive. Indeed, locating impoverished environments is still a challenging process for researchers, and the availability of accurate information is still lacking. Now, in a new study, scientists from Stanford University propose a more reliable method to map poverty in areas previously void of data -- by combining satellite images and making use of machine learning. Led by Stanford computer science doctoral student Neal Jean, researchers sought to determine whether the combination of high-satellite imagery and machine learning -- the science of designing algorithms that learn from data -- could predict estimates of areas where impoverished people lived. Specifically, they extracted information about poverty from these satellite images, and built upon previous machine learning algorithms to detect impoverished areas across five countries in Africa.

Each time analyst group Gartner unveils a new edition of its Hype Cycle chart, it inspires either schadenfreude or a sinking feeling. Your competitor has banked on a technology that is mired in the Trough of Disillusionment, and you were wise enough to cash out on the Slope of Enlightenment -- or maybe it is the other way around. The most curious detail about the 2016 edition of the Hype Cycle is not where any one technology shows up. It is how multiple incarnations of one underlying technology -- machine intelligence -- are spread out across several points on the infamous trough-and-plateau chart. Perceptual smart machines Gartner's label for the rise of machine intelligence is "the perceptual smart machine age," and it predicts that such machines will be "the most disruptive class of technologies over the next 10 years." The benefits of what Gartner calls "radical computational power, near-endless amounts of data, and unprecedented advances in deep neural networks" are on the rise, but none has yet ripened to the point where it is boringly useful.