At the end of last week, a team of Virginia Tech researchers revealed the first look at a hefty new robotic jellyfish that it's building for the U.S. Navy. This is not the Navy's first robotic jellyfish. A year ago, America learned about the descriptively named Robojelly, a self-charging blob-bot built by the same team hailed or its deceptively natural-looking ability to swim through water and morph into new shapes much like a real jellyfish. Ultimately, it's supposed to be equipped with a stealthy engine that sucks hydrogen out of the water around it for fuel. The new robot is a lot like the old robot but bigger.

Will your next doctor be a human being--or a machine? Will you have a choice? If you do, what should you know before making it?This book introduces the reader to the pitfalls and promises of artificial intelligence (AI) in its modern incarnation and the growing trend of systems to "reach off the Web" into the real world. The convergence of AI, social networking, and modern computing is creating an historic inflection point in the partnership between human beings and machines with potentially profound impacts on the future not only of computing but of our world and species.AI experts and researchers James Hendler--co-originator of the Semantic Web (Web 3.0)--and Alice Mulvehill--developer of AI-based operational systems for DARPA, the Air Force, and NASA--explore the social implications of AI systems in the context of a close examination of the technologies that make them possible. The authors critically evaluate the utopian claims and dystopian counterclaims of AI prognosticators.

As data continues to increase at an evermore rapid pace, organizations struggle to deal with this data torrent, let alone use it to analyze and capture value. The ways used to understand this big data also is increasingly rapidly, which introduces myriad terms used to define these methods. The follow is an attempt to provide natural explanations to some of the significant terms and technologies you will come across when you're getting into big data. Algorithms: Mathematical and analytical formulas that also include statistical processes used to analyze data. Algorithms are implemented in software to analyze, process the input data and produce output or results.

Boasting one of the most advanced rail networks in the world, Japan is investigating the use of artificial intelligence and a machine learning technology approach that would attempt to add what promoters call a "train time delay" function intended to provide riders with up-to-date route-planning information. Fujitsu Ltd. (TYO: 6702) said this week it has begun field trials in Japan for a delay time prediction engine developed with partner SRI International of Menlo Park, Calif. The trial also leverages a route-planning app developed by partner Jorudan Co. that claims about 10 million monthly users. The partners said the time delay engine learns from past train delay information. Updates on train delays are delivered as a cloud service.

It's clear that the age of applied AI is at hand. Deep learning toolkits are becoming essential tools for software engineers and data scientists. Tech giants like Google, Microsoft, and Facebook have open-sourced their artificial intelligence frameworks. Applied AI developers now develop software that doesn't just do what it's told, but also has the ability to anticipate the needs of its users through a combination of pattern recognition, knowledge, planning, and reasoning. There is a growing--and urgent--need for information on applied AI, as opposed to the kind of research presented at academic conferences.

Everyone says we're in the first inning of AI (or even the first at-bat) but what does that mean specifically in regards to how AI is being utilized and is understood by the leading tech companies? You would think Google would have a major AI advantage baked into its business model, and it does. Search is a data scientist and machine learning professional's dream. Your entire core (search) business is data. Google Search has data on a perhaps unprecedented scale, because of the volume of its users and the volume of searches.

While we've previously seen crafty inventors fly drones with the assistance of devices like the Apple Watch or the Nintendo Power Glove, researcher Panagiotis Artemiadis is working on a technology that will take drone control to a whole new level. The scientist that heads the Human-Oriented Robotics and Control Lab at Arizona State University has come up with a way to control multiple drones using simply the power of your mind. To accomplish this, Artemiadis and his team have devised a special skullcap that connects your brain to a computer through 128 electrodes. The device then records electrical brain activity and proceeds to decipher and convert your thoughts to movements that the drone(s) can understand. Unlike joysticks that allow controlling only one drone at a time, this mind-control technology can handle up to 4 drones collectively.

Artificial intelligence and robotics have enjoyed a resurgence of interest, and there is renewed optimism about their place in our future. But what do they mean for us? You submitted your questions about artificial intelligence and robotics, and we put them – and some of our own – to The Conversation's experts. It is 100% plausible that we'll have human-like artificial intelligence. I say this even though the human brain is the most complex system in the universe that we know of. But there are also no physical laws we know of that would prevent us reproducing or exceeding its capabilities. Popular AI from Issac Asimov to Steven Spielberg is plausible. What the question doesn't address is: when will it be plausible?

For more than 65 years, computer scientists have studied whether robots' behavior could become indistinguishable from human intelligence. But while we've focused on machines, have we ignored changes to our own capabilities? In a book due to be published next year, Being Human in the 21st Century, a law professor and a philosopher argue that we've overlooked the equally important, inverse question: Are humans becoming more like robots? In 1950, computer scientist Alan Turing put forward what's now known as the "Turing Test." Essentially, Turing proposed that a key test of machine thinking is whether someone asking the same questions to both a human and a robot could tell which is which. This has since become an important method to evaluate artificial intelligence, with regular Turing Test competitions to determine the extent of robots' growing ability to mimic human behavior.

"Who was your favorite captain?" When this question was posed by a Comic-Con attendee to the members of the The 50th anniversary "Star Trek" panel -- with Capt. James Tiberius Kirk himself seated at the head of the table onstage in Hall H -- there was only one way for the panelists to answer. One among multiple celebrations around the seminal series, the panel brought together representatives from all five iterations of the television show: William Shatner (Captain Kirk), Brent Spiner (the android Data from "Star Trek: The Next Generation"), Michael Dorn (the Klingon Worf from "The Next Generation," and also holding it down for "Deep Space Nine"), Jeri Ryan (Seven of Nine from "Star Trek: Voyager") and Scott Bakula (Capt. "Is anyone legitimately not going to say Kirk," asked Ryan with a laugh when the favorite captain question emerged.