For die-hard fans of the Syfy drama "Battlestar Galactica," the closing night reunion panel at the ATX Television Festival was a treat-filled trip down memory lane. Seven key cast members -- Edward James Olmos (Admiral Adama), Mary McDonnell (President Laura Roslin), Katee Sackhoff (Starbuck), James Callis (Gaius Baltar), Tricia Helfer (Number Six), Grace Park (Boomer/Number Eight) and Michael Trucco (Sam Anders) -- and executive producer Ronald D. Moore came together to celebrate the groundbreaking series that was as much post-9/11 allegory as it was grand space adventure. The "BSG" gang still clearly enjoy each other's company and had both the audience and each other cracking up throughout the nearly two-hour Q&A session at Austin's Paramount Theatre. From awkward sex scenes (Callis and Helfer) to uncontrollable giggles (McDonnell and Sackhoff) to Olmos's Adama-like leadership qualities, the cast mates and Moore fondly recalled funny and touching moments from their time on the series, which ran from 2004 to 2009. Callis recalled a scene in which he fell and hit his head requiring a trip to an emergency room.

We alluded to the possibility of Deep Learning and IoT previously where we said that Deep learning algorithms play an important role in IoT analytics because Machine data is sparse and / or has a temporal element to it. Devices may behave differently at different conditions. Hence, capturing all scenarios for data pre-processing/training stage of an algorithm is difficult. Deep learning algorithms can help to mitigate these risks by enabling algorithms learn on their own. This concept of machines learning on their own can be extended to machines teaching other machines.

Artificial intelligence pioneer Alain Colmerauer passed away on May 12. Alain Colmerauer, a French computer scientist and a father of the logic programming language Prolog, passed away on May 15 at the age of 76. Alain Marie Albert Colmerauer was born in the French town of Carcassonne on Jan. 24, 1941. He earned a degree in computer science from the Institut polytechnique de Grenoble (Grenoble Institute of Technology) in 1963, and a doctorate in the discipline in 1967 from the École nationale supérieure d'informatique et de mathématiques appliquées de Grenoble, which is part of the Institut. The newly minted doctor spent 1967–1970 as assistant professor at the University of Montreal, where he created Q-Systems, a method of directed graph transformations according to given grammar rules. Colmerauer moved to the University of Aix-Marseille at Luminy in 1970 as Professeur 2ème classe (associate professor).

Imagine your favorite go-to recipe mutated to conform to the traditional methods and ingredients of any number of diverse regional food cultures. Consider, say, lasagne, but a sort of lasagne that's instead a naturally occurring part of Japanese or Ethiopian cuisine. Not "fusion," but something deeper--a whole rewriting of what a lasagne even is according to the culinary traditions of some other place. It's not necessarily an easy or natural thing to do, but a new machine learning algorithm developed by a team of French, American, and Japanese researchers offers an automated solution based on neural networks and large amounts of food data. The result, which is described in a paper published this month to the arXiv preprint server (via I Programmer), is a system that can take a given recipe and shift it into an alternative dietary style--sushi lasagne, say--as well as parse a recipe for its underlying style components.

Kevin Ashley is a Professor of Law and Intelligent Systems at the University of Pittsburgh, Senior Scientist, Learning Research and Development Center, and Adjunct Professor of Computer Science. He received a B.A. from Princeton University, New Jersey, a J.D. from Harvard Law School, Massachusetts and a Ph.D. in computer science from the University of Massachusetts. A visiting scientist at the IBM Thomas J. Watson Research Center, New York, NSF Presidential Young Investigator and Fellow of the American Association for Artificial Intelligence, he is co-Editor-in-Chief of Artificial Intelligence and Law and teaches in the University of Bologna Erasmus Mundus doctoral program in Law, Science and Technology.

Lauren Hayes, a 27-year-old model and entrepreneur, is famous at the automation software company IPsoft. At a recent conference the company hosted in New York, suited c-level executives stopped her in the hallway to take photos. An executive at one of the largest insurance companies in the United States told her that 65,000 of his employees loved her. And during his keynote presentation, the CEO of IPsoft, Chetan Dube, called Hayes on stage to guest star in a faux game show. Her opponent was Amelia, who is also the reason for her contextual fame.

It all goes wrong at 90 mph. Mid-turn, a slight bump from another car sends Parker Kligerman, professional Nascar racing and motorsports analyst, straight into the wall. "I just dumped Parker, LMAO!" the dumper in question crows. We're at Volusia Raceway, a dirt track I'm pretty sure is in Tennessee and Kligerman thinks is in Alabama. It's actually in Florida, and the two of us are really in Kligerman's Connecticut bedroom, where he's showing me what a pro can do in a round of iRacing.

These experts offer up a possible timescale, and some practical solutions. Every decade since artificial intelligence was first formed as its own discipline in 1956, there's been a prediction that artificial general intelligence (AGI) is just a few years away -- and so far we can safely say that most of them have been shy of the mark. A new survey, conducted by the University of Oxford and Yale University, draws on the expertise of 352 leading AI researchers. It suggests that there's a 50-percent chance that machines will be bettering us at every task by the year 2062. However, plenty more milestones will be hit before then.

In the fall of 2016, a pop song was released in Japan. "Daddy's Car," derivative of a Beatles tune, had a soothing beat and vaguely uplifting lyrics: "Good day sunshine in the backseat car / I wish that road could never stop." The ditty was distinctive for its authorship. Sony's Computer Science Laboratories in Paris produced the song, which was written by an artificial intelligence (AI) system called Flow Machines. The melody and harmony were composed by AI, and a human musician mixed the sound and wrote lyrics for the track. AI -- the new set of technologies that perform tasks that require human intelligence, such as speech recognition, decision making, and learning -- is rapidly working its way into business operations within many global industries. Some members of the entertainment and media (E&M) industry have downplayed its potential. After all, these are creative industries in which both the germ of the business and the value added to it stem from the contribution of human ingenuity and people exchanging ideas. The most successful E&M products and services rely on connecting creative content, brands, and experiences with audiences.

Every decade since artificial intelligence was first formed as its own discipline in 1956, there's been a prediction that artificial general intelligence (AGI) is just a few years away -- and so far we can safely say that most of them have been shy of the mark. A new survey, conducted by the University of Oxford and Yale University, draws on the expertise of 352 leading AI researchers. It suggests that there's a 50-percent chance that machines will be bettering us at every task by the year 2062. However, plenty more milestones will be hit before then. These include machines that are better than us at translating foreign languages by 2024, better at writing high school essays than us by 2026, better at driving trucks by 2027, better at working retail jobs by 2031, capable of penning a best-selling book by 2049, and better at carrying out surgery by 2053.