This is the first chapter of a five-part series about machine learning. Machine learning is a very hot topic for many key reasons, and because it provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming instructions. Despite the popularity of the subject, machine learning's true purpose and details are not well understood, except by very technical folks and/or data scientists. This series is intended to be a comprehensive, in-depth, and non-technical guide to machine learning, and should be useful to everyone from business executives to machine learning practitioners. It covers virtually all aspects of machine learning (and many related fields) at a high level, and should serve as a sufficient introduction or reference to the terminology, concepts, tools, considerations, and techniques of the field.

In the airy, loft-like Microsoft Research lab in New York City, five computer scientists are spending their days trying to get a Minecraft character to climb a hill. That may seem like a pretty simple job for some of the brightest minds in the field, until you consider this: The team is trying to train an artificial intelligence agent to learn how to do things like climb to the highest point in the virtual world, using the same types of resources a human has when she learns a new task. That means that the agent starts out knowing nothing at all about its environment or even what it is supposed to accomplish. It needs to understand its surroundings and figure out what's important – going uphill – and what isn't, such as whether it's light or dark. It needs to endure a lot of trial and error, including regularly falling into rivers and lava pits.

Google's AlphaGo program recently defeated the world's second-ranked player of Go -- a vastly more complex game than chess -- marking an important milestone in the development of artificial intelligence (AI). But equally important was Google's revelation that one of its robots has developed the ability to pick up objects in ways that had previously only been identified in cognitive life forms. Both advances were brought about by the development of computational models based on the human central nervous system, which is particularly well suited to certain aspects of AI, such as pattern recognition and machine and adaptive learning. Research in this area will have significant implications for geopolitics in the future. Automation and AI are already being applied in nearly every economic sector.

Throughout that history, technology has brought comfort, ease, and prosperity. And all along it has taken jobs, disrupted lives, and changed the way people live and think. Each new age of innovation has brought a revolution. First, there was steam, then mass production, and late last century information technology. Now, researchers and thought leaders have declared a Fourth Revolution: an age of Artificial Intelligence (AI), advanced automation, and sophisticated robotics.

The Department of Defense announced in early February, in an address to the Economic Club of Washington by Defense Scretary Ashton Carter, that its Strategic Capabilities Office was innovating "new roles and game-changing capabilities to confound potential enemies." The Washington Post's Dan Lamothe wrote an exclusive piece on the SCO, a hitherto unknown agency within the DoD, on March 8. In that piece, Lamothe explained that the future of war is now- and the future is the SCO's artificial intelligence. The SCO's drone program, Perdix, originated at MIT in 2010- 2011. They fit easily in the hand and are surprisingly light-weight.

In August 2001, I was a telecoms analyst visiting investors in Tokyo. In one of these meetings, a portfolio manager at a Very Large Fund asked me what would happen now that GPRS meant that all mobile voice calls would be packet-switched and that therefore mobile operators' voice revenue would disappear within the next 18 months or so. This was a surprisingly hard question to answer well. It was nonsense, but to explain why it was nonsense you had to work out quite which things the person asking it didn't know, and what completely incorrect narrative he'd arrived at to think that this was going to happen. He'd heard'packet' and'mobile' and added 2 2 to get 22.

I recently finished reading "The Second Machine Age", which was recommended to me by one of our board advisors. The authors Eric Brynjolfsson and Andrew McAfee make a convincing argument that we are in the midst of a paradigm shift every bit as transformative as the industrial revolution. While the industrial revolution was about harnessing physical power, this new machine age is about harnessing cognitive power. The authors identify the steam engine as the key catalyst for change in the industrial revolution. Between 1765 and 1776, James Watt, in partnership with Matthew Bolton, made fundamental improvements to the efficiency of the existing steam engine that would see its widespread adoption across a range of industries.

Although computers are getting smarter and intelligent due to the advancement of artificial intelligence but these man-made machines will not overtake the humans in coming few years, says IBM Research Director Arvind Krishna. Krishna is also the Senior Vice-President of IBM and heads the arm that developed award-winning cognitive computing system named Watson that is currently being used in several complex tasks including treatment of cancer. Krishna said that a machine does what it is taught by humans and it just follows the given instructions. A machine cannot overtake humans until it starts doing things on its own and that day will not come at least in'our living lifetimes'. Greatest living Physicist Stephen Hawking and Engineer-entrepreneur Elon Musk have claimed the artificial intelligence as a demon that will bite us back someday in future.

Fresh supplies are due to ship out late Tuesday for the International Space Station, where the shelves finally are getting full after a string of failed deliveries. An unmanned Atlas V rocket is scheduled to blast off at 11:05 p.m. by the light of a nearly full moon. Orbital ATK's Cygnus capsule holds nearly 8,000 pounds of food, equipment and scientific research for NASA, including a commercial-quality 3-D printer anyone can rent and experimental robotic grippers modeled after the thousands of sticky hairs on geckos' feet. There's also a fire experiment that will remain on the Cygnus. Researchers will ignite a large-scale blaze, in a contained box, to see how it spreads in weightlessness. The fire will not be set until the Cygnus departs the space station in May, full of trash for a destructive re-entry.

So called predictive policing has long been thought of as a possibility, whether it was the Orwellian thought police or the technical wizardry highlighted in Hollywood films like Minority Report. Such capabilities are increasingly possible however. Last year I wrote about the ATHENA project that is being run by West Yorkshire police. The project is aiming to bring the public into policing a lot more than is currently the case by using social and mobile tools as a force for good. The project team concede that the public are usually first on the scene of an incident, and are often therefore well placed to relay crucial information to officials.