The smart parking industry continues to evolve as an increasing number of cities struggle with traffic congestion and inadequate parking availability. While the deployment of sensor technologies continues to be core to the development of smart parking, a wide variety of other technology innovations are also enabling more adaptable systems--including cameras, wireless communications, data analytics, induction loops, smart parking meters, and advanced algorithms. The future of the smart parking market is expected to be significantly influenced by the arrival of automated vehicles (AVs). Several cities around the world are already beginning to trial self-parking vehicles, specialized AV parking lots, and robotic parking valets. For example, in Boulder, Colorado, ParkPlus is working on deploying the first fully automated parking garage in the Western United States through Boulder's PearlWest mixed-use development.

In AI, is data more important than algorithms? In AI, is data more important than algorithms? I hope you are not expecting a simple black or white answer to this question. Whether data or algorithms are more important has been debated at length by experts (and non-experts) in the last few years and the short version is that it depends on many details and nuances that take some time to understand. I answered a pretty similar question some time ago in this Quora post: In machine learning, is more data always better than better algorithms?

Connected cars made big news at the Consumer Electronics Show in Las Vegas this month. It's not surprising with this year's buildup of buzz surrounding autonomous and connected vehicles, and the ecosystem of technology that is feeding them. Gartner forecasts 21 million new automobiles will be equipped with data connectivity, either through a built-in communications module or tethered to a mobile device in 2017. However, amidst the opportunities for a range of technologies to merge our various worlds of data and to sense both vehicular and human conditions, several hurdles remain before connected cars become mainstream. Ogi Redzic, senior vice president for connected vehicles at the Renault-Nissan Alliance, stands next to the Nissan Motor Co. Intelligent Mobility IDS Concept electric vehicle during the 2017 Consumer Electronics Show (CES) in Las Vegas, Nevada, U.S., on Thursday, Jan. 5, 2017.

One thousand people will have their minds monitored at the same time while watching the new sci-fi movie MindGamers on March 28. The one-night event, MindGamers: One Thousand Minds Connected LIVE will take place in select movie theatres nationwide, presented by Fathom Events and Terra Mater Film Studios. The event will be the "world's first mass mind collective" Fathom Events said. The minds of one thousand people will be simultaneously connected through cloud technology while watching the sci-fi movie by wearing a cognition headband. The headbands will allow scientist to track the audience members' minds (all at once) and create an image of the "mass-mind state."

IBM said it is offering support for Google's open source machine learning technology TensorFlow with its PowerAI software. The next wave of IT innovation will be powered by artificial intelligence and machine learning. We look at the ways companies can take advantage of it and how to get started. The move highlights ongoing collaboration between tech giants in the AI space including Google, Nvidia, IBM, and AMD. According to IBM, TensorFlow has become popular among enterprises looking to use deep learning frameworks.

This article is by Mark-Hans Richer, founder and president, Richer Inc., and former CMO of Harley-Davidson. It's post-CES season and naturally all the buzz is about cool, futuristic things that will change the world as we know it. And nothing is more buzzy than AI and Machine Learning (VR and AR are so 2016...). Most of those who strolled the wide alleys of Las Vegas' convention halls earlier this month--and especially the creative marketing community--buzzed about what they saw as cutting-edge cool, the zeitgeist that every creative person wants to be on the edge of. But what if the tech that is so attractive suddenly turned on its acolytes?

A week hasn't even passed since the inauguration, but television news is saturated with the flurry of activity from President Donald Trump's administration. Trump, via Twitter, promised to launch an investigation into illegal voting and threatened to "send in the Feds" if Chicago police can't fix the "carnage." And that was just between Tuesday and Wednesday. This heightened scrutiny compelled the Internet Archive, a repository of everything posted on the web, to launch its Trump Archive in early January. You, perhaps, digitally time-traveled with the Internet Archive's Wayback Machine, or checked out free books, movies and software.

Medical diagnosis has been identified as one of the areas where artificial intelligence and machine learning could have most impact – but how should the NHS proceed? The likes of University College London Hospitals NHS Foundation Trust, Royal Free NHS Trust and Moorfields Eye Hospital have teamed up with Google DeepMind to work on better patient outcomes for those suffering from certain cancers, or for those who have had sight loss. According to Orlando Agrippa, deputy CIO at Barts Health NHS Trust, the NHS could benefit tenfold if it leveraged AI. "It could be used to increase accuracy over things like prescriptions, interventions and early diagnosis," he told BusinessCloud. The Commons Science and Technology Committee suggested last year that supercomputers assisting doctors with medical diagnoses could be one of the key impact areas of AI, but that government leadership in the fields of robotics and AI had been lacking.

An illustrated artificial neural network (ANN) (CC BY SA 4.0 LearnDataSci via Wikimedia Commons) The system's'neural network' is advanced, but its abilities are being exaggerated by observers I have a fascination with translation, primarily because I have an interest in languages. I'm what I like to call "an aspiring polyglot," with the implication that I don't have time to practice (and reach complete fluency in) the few foreign languages I have some knowledge of, yet I give myself plenty of time to learn about said languages, how they are all different and by extension how they all work. As a technology- and startups-focused journalist, that makes the evermore popular topic of machine translation (MT) and "translation memory" fascinating, giving me the chance to cover companies like Austrian startup LingoHub (an essential service for apps) or Portuguese startup Unbabel (the next-level stuff they're doing is very cool). I can ask people how they communicate with lovers from other countries and report on developments like Google Translate's upgrade from "phrase-based machine translation" (PMT) with a "neural machine translation" (NMT). "Google Translate invented its own language to help it translate more effectively," wrote UX developer Gil Fewster on Medium, with the bold emphasis his own.