The robot maneuvered through the dark portals, which had been deprived of oxygen in an effort to smother the fire, aiming onboard lights and cameras in all directions, scanning for flames, monitoring for explosive methane gas, and looking for rocks cracked and loosened by the heat. The exercise marked the first time a robot was ever sent into a coal mine ahead of humans to make sure conditions were safe, said John Correll, assistant director of the U.S. Mine Safety and Health Administration. "The conditions could have been very, very hazardous," Correll said. "We didn't have to send humans in there, because we had the robot." Federal mine safety officials gathered at the Pikeville coal mine, operated by Alliance Resource Partners, on Thursday to tout the work of the robot and speculate about what the future of mining holds for such machines. Investigators still haven't determined the cause of the fire, which started on Dec. 25 and forced the mine to be closed for 33 days.

As you start to make robots smarter, you also in some ways make them less predictable, because they become more complex systems. Very complex systems tend to go wrong in unpredictable ways… If you're looking for a cast-iron guarantee that a robot is not going to hurt someone, it's like asking for the same guarantee with a person, and you just can't give it.

David Ferrucci's official title is "IBM Fellow and Leader of the Semantic Analysis and Integration Department at IBM's T.J. Watson Research Center." But to the world, he's the genius behind Watson, the question-answering supercomputer system that bested two humans in a nationally televised broadcast of the popular game show Jeopardy! On Monday, Ferrucci delivered a fantastic keynote at the ACM's 2011 Federated Computing Research Conference in San Jose, CA. Ferrucci walked the audience -- nearly 2,000 computer scientists from around the country -- through the creation of Watson, from its initial conception in 2004 to its nationally televised victory this past February. "The story goes," he began, "that an IBM vice president was dining at a restaurant" when, suddenly, everyone around him got up and rushed toward a TV.

Robots may hold the key to preventing an industrial crisis in a country whose geography makes many key jobs undesirable. I knew Australia was big, but it didn't really hit me till I stood on a viewing platform hanging over a valley in the Blue Mountains. As I watched the land fall away below me, giving way to a valley of forest that stretched to the horizon, I could feel thousands of miles of silence sucking me in like a vacuum. Part of Australia's beauty is also its problem. Its untamed, uninhabited interior contains rich pickings, but there are few who want to go and get them.

Carl Doersch, a doctoral student studying machine learning, and his colleagues have developed new graphical software capable of identifying stylistic features of cities. The team, composed of researchers from both Carnegie Mellon and the Institut National de Recherche en Informatique et en Automatique, has published its work in the computer graphics journal ACM Transactions on Graphics. The process of finding related patterns between images is known as visual data mining. Project collaborator Alexei Efros, an associate professor of robotics and computer science at Carnegie Mellon, pointed out that the science is still relatively new. "The field of visual data mining is still in its infancy, but I believe it holds a lot of promise," he said in a university press release.

If you want to creep past this new security bot, you'd better be good at holding your breath. TiaLinx's new Cougar20-H is a lightweight, remote-controlled surveillance robot that can detect human breathing and scan through concrete walls with its ultra-wideband radio frequency sensor array. The Cougar20-H moves around on tracks and can roll up to a building, extend its arm, and start scanning through the wall with its RF array, developed with funding from the U.S. Army. Operated from a laptop that can be more than 300 feet away, the robot can scan through reinforced concrete by detecting reflected radio waves. It can find people who are moving or even keeping still, so the operator can see them in real time.

What makes Paris look like Paris? It is instead the details woven into the urban fabric that form a pattern, according to a machine learning system that's part of a U.S.-French visual data mining project. Yes, computers are learning to ID your city just by looking at random photos. Researchers at Carnegie Mellon University and INRIA/Ecole Normale Superieure in Paris had the system look at 40,000 Google Street View images of Paris, London, New York, and Barcelona, as well as eight other cities to find frequent and unique elements. The machine learning program found features like the street signs, balconies, and lampposts of Paris to be distinct.

Juergen Schmidhuber taught a computer to park a car. He's also showing that same machine how to trade stocks and detect flaws in steel production. Unrelated as these tasks may appear, Schmidhuber thinks a seemingly random training regimen is key to creating artificial intelligence that can solve any problem. Schmidhuber's AI theories tend to carry weight. In 1997, he co-authored a seminal paper that laid the groundwork for modern AI systems.

While every single book in this list is provided for free, if you find any particularly helpful consider purchasing the printed version.

If you've stumbled upon this article, you may already be in this position. However, what's more likely is that this is going to become your situation in near future, and learning from someone else's experience is now needed to prepare. While there's a plethora of theory around business applications for data analytics; there is a significant lack of practical, real-life experience to draw on. This is largely due to the fact that adoption of these technologies, for many industries, is new and the results of pilots are just coming to light now. Machine learning technologies are successfully used in predictive and recommendation services.