Enterprise search leader Lucidworks is tapping into the IBM Watson Developer Cloud platform for its Fusion platform, an application framework that helps developers to create enterprise discovery applications so companies can understand their data and take action on insights. Tagged In USS Enterprise (NCC 1701 D) IBM Machine Learning Natural Language Processing Unstructured Data Knowledge Worker Web Application Framework Lucidworks Researchers from the US have developed an artificial intelligence system that surfs the internet to extract information and organise it for quantitative analysis. Enterprise search leader Lucidworks is tapping into the IBM Watson Developer Cloud platform for its Fusion platform, an application framework that helps developers to create enterprise discovery applications so companies can understand their data and take action on insights. Researchers from the US have developed an artificial intelligence system that surfs the internet to extract information and organise it for quantitative analysis.

Trained AI Can Beat "Expert" Human Players at Mortal Kombat A Terminator of American jobs went unnoticed on Trump's judgment day: artificial intelligence What Your CEO Is Reading: Baidu Explains AI; Exclusive Gym Offers Retinal Scans; McKinsey on ... Stay up-to-date on the topics you care about. We'll send you an email alert whenever a news article matches your alert term. It's free, and you can add new alerts at any time.

This space has been dedicated to exploring the ways in which robots are acting as change agents – of the category and of manufacturing. There's a lot to be said for the ways in which those transformations are taking place now, and it's all good. There's a case though, that what lies ahead for robotics in the Industry 4.0 or the Industrial Internet of Things (IIoT) is where real excitement takes place. In the IIoT, robots that combine mind and machine change the ways in which goods are produced, and the entire business of manufacturing. In the IIoT, agile factories are driven by "big data" – including quality metrics, market feedback and real-time demand signals.

Researchers, scientists and academics around the world publish roughly 2.5 million scientific papers each year, on top of a backlog of more than 50 million papers dating back to 1665. Plus, the rate at which researchers publish these academic papers keeps rising, a la Moore's Law. It's impossible for scientists to read every paper published in their fields, and searching for a specific study can be a daunting task. Enter: Paul Allen, Microsoft co-founder and leader of the non-profit Allen Institute for Artificial Intelligence. The Allen Institute's latest effort is Semantic Scholar, a scientific-paper search engine powered by machine learning and other artificial intelligence systems.

Roughly 20 km away from Hong Kong's slick, densely packed urban center lies the New Territories -- a suburban mishmash of rugged hills and scruffy villages, soaring new housing developments and vacant lots. This is where over half of the territory's 7.2 million people live. It could also be the resting place for your old PC or printer. Up to 20% of all U.S. electronic waste may be ending up in Hong Kong. Not in some scrapyard in the developing world, picked over by haggard children and wheezing laborers, but in the backyard of one of the world's most sophisticated financial capitals.

In the 1960s, the moral philosopher Philippa Foot devised a thought experiment that would revolutionize her field. This ethical puzzle, today known as the "trolley problem," has become so influential--not just in philosophy but also in neuroscience, behavioral economics, evolutionary psychology, and meme culture--that it's garnered its own tongue-in-cheek sub-discipline, called "trolleyology." That body of commentary, wrote one philosopher, "makes the Talmud look like Cliffs Notes." The person largely responsible for popularizing the trolley problem was the philosopher Judith Jarvis Thomson. Her 1976 paper, "Killing, Letting Die, and The Trolley Problem," tweaked the original scenario.

Push it, pull it, break it, maybe even give it a lick. Children experiment this way to learn about the physical world from an early age. Now, artificial intelligence trained by researchers at Google's DeepMind and the University of California, Berkeley, is taking its own baby steps in this area. "Many aspects of the world, like'Can I sit on this?' or'Is it squishy?' are best understood through experimentation," says DeepMind's Misha Denil. In a paper currently under review, Denil and his colleagues have trained an AI to learn about the physical properties of objects by interacting with them in two different virtual environments.

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We'll also be posting a weekly calendar of upcoming robotics events for the next two months; here's what we have so far (send us your events!): Let us know if you have suggestions for next week, and enjoy today's videos. This RHex dance contest was filmed in 2013, as part of the Philadelphia Science Festival. More isn't always better, but with glowy flying drones, it definitely is: If you do the math (and the math is simple and exponential), by 2020 Intel will be flying 312,500 drones, which is a VGA display.

International Business Machines Corp. showed off its artificial intelligence software this week for use as a foundation for programmers, as it welcomed about 1,300 software developers to the first Watson Developer Conference in San Francisco. Watson in 2011 famously beat human contestants on the television game show "Jeopardy," and IBM went on to apply the technology to helping doctors diagnose and treat cancer. Since then, the technology has evolved into a collection of software that dispenses human-like conversation,...

One of the latest trends in the world of technology and engineering is "machine learning" -- in fact, all of the big technology companies today have invested in artificial intelligence and machine learning projects. The term "machine learning" was first defined by Arthur Samuel, way back in 1959. He defined it as "the ability to learn without being explicitly programmed," which basically means that a machine could learn from its own mistakes and reprogram itself to improve its performance over time. The idea gained popularity in the 90s when the concept of data mining came into existence. Data mining uses algorithms to look for patterns in a given set of information, which led to data-driven predictions and decision making.