This week, Oculus removed its hardware DRM checks bringing rejoicing to the VR streets, Adobe Premiere now has tools for editing 360 and stereoscopic video, Twitter is starting an AR/VR division, Bluetooth 5 was announced, AI can now anticipate and beat human experts in combat simulations and a Russian robot keeps trying to escape the lab. Robots and AI can beat us in combat simulations and escape labs now. Looking to buy yourself some VR games? While this is gonna sound like a promo, it's more of a public service announcement… Steam and Oculus have a stack of sales on over 120 VR games till 4th July! Adobe Premiere Pro's latest update now includes new tools for editing 360 degree video and stereoscopic VR video!

It's not always easy to know when you're under attack, or when your security has already been breached. If you're capable of detecting a breach, you might find it in as few as 10 days, but survey after survey finds that breaches that are detected by someone outside the business typically take over 100 days to find. For one thing, between ecommerce, company websites, email, mobile users and overseas divisions, your company is doing business 24/7; however, your IT security team probably works business hours. That's one way 60 percent of attackers are able to compromise an organization in minutes, according to Verizon's 2015 Data Breach Investigations Report. But only a third of businesses can detect a breach within a few days.

That's the takeaway from a draft report on robotics produced by the European Parliament, which warns that artificial intelligence and increased automation present legal and ethical challenges that could have dire consequences. "Within the space of a few decades [artificial intelligence] could surpass human intellectual capacity in a manner which, if not prepared for, could pose a challenge to humanity's capacity to control its own creation and ... the survival of the species," the draft states. The report offers a series of recommendations to prepare Europe for this advanced breed of robot, which it says now "seem poised to unleash a new industrial revolution." The proposal suggests that robots should have to register with authorities, and says laws should be written to hold machines liable for damage they cause, such as loss of jobs. Contact between humans and robots should be regulated, with a special emphasis "given to human safety, privacy, integrity, dignity and autonomy." If advanced robots start replacing human workers in large numbers, the report recommends the European Commission force their owners to pay taxes or contribute to social security.

Artificial intelligence (AI) developed by a University of Cincinnati doctoral graduate was recently assessed by subject-matter expert and retired United States Air Force Colonel Gene Lee - who holds extensive aerial combat experience as an instructor and Air Battle Manager with considerable fighter aircraft expertise - in a high-fidelity air combat simulator. The artificial intelligence, dubbed ALPHA, was the victor in that simulated scenario, and according to Lee, is "the most aggressive, responsive, dynamic and credible AI I've seen to date." The application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. Retired United States Air Force Colonel Gene Lee, in a flight simulator, takes part in simulated air combat versus artificial intelligence technology developed by a team comprised of industry, US Air Force and University of Cincinnati representatives. In its earliest iterations, ALPHA consistently outperformed a baseline computer program previously used by the Air Force Research Lab for research.

Chance Dorland spoke with Bloomberg TV's Angie Lau, Bloomberg News' David Merritt, Heather Timmons of Quartz, the AP's Paul Cheung & drone photographer Seongjoo Cho after their Asian American Journalists Association "Journalism in the Age of Artificial Intelligence" panel discussion at this weekend's New.Now.Next media conference in Seoul.

Artificial brains are man-made machines that are just as intelligent, creative, and self-aware as humans. No such machine has yet been built, but it is only a matter of time. This website tracks the latest scientific and technological progress. SyNAPSE is a DARPA-funded program to develop neuromorphic microprocessor systems that match the intelligence, physical size, and low power consumption of animal brains. Their approach is to first test neural networks in simulation on a supercomputer.

He said, " today we're publishing a technical paper, Concrete Problems in AI Safety, a collaboration among scientists at Google, OpenAI, Stanford and Berkeley." The big deal is a very big deal for those alarmed over what limits may be over-stepped by AI systems in carrying out their actions, and whether we had better anticipate any event where an AI system does not behave according to a predesigned purpose engineered by humans and where the unintended consequences deliver great harm. Google believes it is time to move to another rung than just fretting. Said Cade Metz in Wired on Tuesday: "...that's kind of the point: Because no one has good answers, it's time to start looking for them." Olah said, "We believe it's essential to ground concerns in real machine learning research, and to start developing practical approaches for engineering AI systems that operate safely and reliably."

Gradient descent is one of the most popular algorithms to perform optimization and by far the most common way to optimize neural networks. At the same time, every state-of-the-art Deep Learning library contains implementations of various algorithms to optimize gradient descent (e.g. These algorithms, however, are often used as black-box optimizers, as practical explanations of their strengths and weaknesses are hard to come by. This blog post aims at providing you with intuitions towards the behaviour of different algorithms for optimizing gradient descent that will help you put them to use. We are first going to look at the different variants of gradient descent. We will then briefly summarize challenges during training. Subsequently, we will introduce the most common optimization algorithms by showing their motivation to resolve these challenges and how this leads to the derivation of their update rules. We will also take a short look at algorithms and architectures to optimize gradient descent in a parallel and distributed setting.

Since last fall, marketing platform Kahuna has been expanding its targeted channels beyond mobile, to include pop-up messaging on web sites and greater capabilities for email and in-app messaging. Now, the Palo Alto, California-based company is expanding again to focus on customer journeys instead of individual messaging campaigns, with the recent launch of Experiences for optimizing messages across a journey. This newest incarnation of its marketing platform, senior vice president for product Mihir Nanavati told me, allows the firm's RevIQ machine learning engine to be applied across all the paths taken by a customer toward the marketer's goal. Previously, he said, the platform focused on a marketer's ability to, say, send a email to a mobile user encouraging them to install a new travel app. Once installed, there might be an in-app message suggesting that the user search for airfare deals.

This week is the Computer Vision and Pattern Recognition conference in Las Vegas, and Google researchers have several accomplishments to present. They've taught computer vision systems to detect the most important person in a scene, pick out and track individual body parts and describe what they see in language that leaves nothing to the imagination. First, let's consider the ability to find "events and key actors" in video -- a collaboration between Google and Stanford. Footage of scenes like basketball games contain dozens or even hundreds of people, but only a few are worth paying attention to. The CV system described in this paper uses a recurrent neural network to create an "attention mask" for every frame, then track relevance of each object as time proceeds. Over time the system is able to pick out not only the most important actor, but potential important actors, and the events with which they are associated.