Last year Microsoft and Google both showed that their image-recognition algorithms had learned to best humans. They independently created software that could exceed the average human score on a standard test that challenges software to recognize images of a thousand different objects, from mosques to mosquitoes. But to get good enough to defeat humanity, each company's software scrutinized 1.2 million labeled images. A child can learn to recognize a new kind of object or animal using only one example. Startup Geometric Intelligence said Monday that it has developed machine-learning software that is a much quicker study.

The conversations around artificial intelligence continue to evolve. From customer service to friendly social robots, AI can be used for a variety of practical uses. But can an AI system learn how to be creative? Google's new project Magenta wants to investigate just that. According to Popular Science, Google is launching a "research project to explore using artificial intelligence to create art, and make that process easier for TensorFlow users."

Zebra Medical Vision, an Israeli startup that uses machine learning to teach computers to read and diagnose imaging data, raised 12 million in its latest funding round, including a re-investment from Salesforce.com Inc. co-founder Marc Benioff. Zebra has been building a database of millions of files such as CT-scans and MRIs of real patients over the past three years, offering enough data so that machines can learn to accurately detect illnesses including breast cancer, and health problems with bones, the liver and lungs, said President and co-founder Eyal Gura. Company developers are writing specific algorithms for each health issue and three have been approved by the U.S. Food and Drug Administration, according to Gura. The company says its product can help the medical industry deal with a growing shortage of radiologists amid more chronic diseases, an aging population and an expanding middle class seeking more advanced health care. According to the World Bank, the middle class in low and middle income countries will jump from 5 percent in 2005 to 25 percent in 2030.

Just how worried should we be about killer robots? To go by the opinions of a highly regarded group of scholars, including Stephen Hawking, Max Tegmark, Franz Wilczek, and Stuart Russell, we should be wary of the prospect of artificial intelligence rebelling against its makers. "One can imagine (AI) outsmarting financial markets, out-inventing human researchers, out-manipulating human leaders, and developing weapons we cannot even understand," Hawking wrote in a 2014 article for The Independent. "Whereas the short-term impact of AI depends on who controls it, the long-term impact depends on whether it can be controlled at all." The fear that our irresponsible creations might bring about the end of humanity is a common one.

"Machine learning" has graduated into the upper echelon of business buzzwords. That hallowed level where we've all used it, but few up us really know what it means. We sort of get it, but what are the real world applications and ramifications? The impacts of machine learning are far reaching. Just look at everything IBM's Watson, a cognitive technology, has done in the past year.

Deep learning is a topic that is making big waves at the moment. It is basically a branch of machine learning (another hot topic) that uses algorithms to e.g. Scientists have used deep learning algorithms with multiple processing layers (hence "deep") to make better models from large quantities of unlabeled data (such as photos with no description, voice recordings or videos on YouTube). It's one kind of supervised machine learning, in which a computer is provided a training set of examples to learn a function, where each example is a pair of an input and an output from the function. Very simply: if we give the computer a picture of a cat and a picture of a ball, and show it which one is the cat, we can then ask it to decide if subsequent pictures are cats.

By 2018, China shall build platforms for fundamental AI resources and innovation and make breakthroughs on basic core technology, said the three-year implementation program for "Internet Plus" artificial intelligence. The plan is formulated jointly by the National Development and Reform Commission, the Ministry of Science and Technology, the Ministry of Industry and Information Technology, and the Cyberspace Administration of China. According to the website, the country shall be in line with global AI technology and industries by 2018. At key regions, the country will cultivate some global leading AI enterprises and set up an innovative, open, cooperative, green and safe AI industrial ecology. The country will cultivate and develop emerging artificial intelligence industries, promote innovation in intelligent products and enhance the intelligence level of terminal products.

These days it's often proclaimed to be the next big thing in fraud management. The only bit of that you'll find me disagreeing with is the word'next'. Machine learning's been at the heart of the CyberSource approach to fraud management nearly since the beginning of its development. Machine learning underpins fraud scores produced by Decision Manager, our fraud management platform. In this three-part blog series, I'll first provide a quick primer on what machine learning is.

Just as electricity transformed everything we do, artificial intelligence -- think really, really smart machines -- will upend industries from retail to finance to transportation. And that will reshape our world and change our lives, said a panel of experts Monday discussing "The State of AI" at the EmTech Digital Conference in San Francisco. The transformation, though, will rest on humbler underpinnings. In much the same way that all companies learned to make use of the Internet, they'll start to adapt to AI by expanding their data teams. Three of the biggest experts in artificial intelligence, Andrew Ng, Peter Norvig and Oren Etzioni, say despite its recent boom, AI still has a long way to go.

Big data is the next big thing, but so far all it seems to do is deliver marginally better spam. And by "marginally better" I really mean "no different from the spam we got a decade ago." Commenting on this disconnect between the potential and reality of big data, former Facebook executive (and current co-founder at Hadoop vendor Cloudera) Jeff Hammerbacher said, "The best minds of my generation are thinking about how to make people click ads. SEE Are you being exploited by online marketers using "tricks for clicks"? What particularly "sucks" is that these "best minds" don't seem to be very good at it.