The video analytics industry is typically split into two distinct camps: (1) systems designed around rules and user-specified rules or models and (2) autonomous systems designed around machine learning. Supervised learning systems require heavy training and feedback to achieve the desired output, where unsupervised learning systems train themselves from the input data and require minimal human input. The video analytic solutions we saw in the market a decade ago seem rudimentary compared to today's offerings; partly due to the technology catching up with early promises and partly due to the industry's understanding and level-setting of expectations from the initial splash of analytics hyped as a panacea and the future of security. However, some of the extreme claims such as its ability to replace trained human operators, eliminate the need for well-designed camera placement, completely eliminate false positives, and determine a person's intent ahead of an action have proven to be more hype than reality for many end users.

The industry has come a long way from analog CCTV video surveillance systems. The days of a few low-resolution cameras being monitored by a security guard at a desk are becoming rarer in mid-sized to enterprise organizations. Putting the cameras on an enterprise network and treating the video like any other data gives us endless possibilities of what we can do with this powerful and complex information. We witnessed the rise of video content analysis (VCA) technology, or video analytics, in the early 2000s in response to the growth of cameras and general surveillance, spurred on by the emergence of IP cameras, the falling costs of data storage and IT infrastructure, a reactive security posture to a changing threat landscape, and the quick realization that traditional monitoring approaches couldn't keep pace with the growth in video data. The video analytics industry is typically split into two distinct camps: (1) systems designed around rules and user-specified rules or models and (2) autonomous systems designed around machine learning.

PsiberLogic is a completely free, open-source fuzzy logic controller package for Python 3. Psibernetix proudly supports the amazing Python community, and is happy to contribute to Python's open-source movement. This package is for anyone seeking a high-performance, python3-callable package for creating fuzzy logic controllers. Details on ALPHA – a significant breakthrough in the application of what's called genetic-fuzzy systems are published in the most-recent issue of the Journal of Defense Management, as this application is specifically designed for use with Unmanned Combat Aerial Vehicles (UCAVs) in simulated air-combat missions for research purposes. The tools used to create ALPHA as well as the ALPHA project have been developed by Psibernetix, Inc., recently founded by UC College of Engineering and Applied Science 2015 doctoral graduate Nick Ernest, now president and CEO of the firm; as well as David Carroll, programming lead, Psibernetix, Inc.; with supporting technologies and research from Gene Lee; Kelly Cohen, UC aerospace professor; Tim Arnett, UC aerospace doctoral student; and Air Force Research Laboratory sponsors. ALPHA is currently viewed as a research tool for manned and unmanned teaming in a simulation environment.

Stanford has undertaken an important effort: envisioning the implications of artificial intelligence over a 100-year span, to "anticipate how the effects of artificial intelligence will ripple through every aspect of how people work, live, and play." But there is a problem, potentially fundamental enough that the team may want to revisit its first report or adjust its approach as it goes forward. This is the report's relatively weak coverage of the urban, human security implications of AI. According to the purpose statement, this first study focuses on the implications of AI in 2030 in the "typical North American city." I suppose the thin treatment of security may derive from the huge assumption that North American cities will remain peaceful and secure, and thus AI and intelligent machines won't carry significant human security implications.

US lawmakers mull long and short-term CRs, and'minibus' appropriations packages. Out of the shadows, the SCO now needs to justify its long-term existence to a new president. Defense dollars are the big issue as the'Big Four' lawmakers met to negotiate the 2017 defense poli… In a recent visit, Lockheed Martin's proposed move of the F-16 production line to India was a subjec… Directed energy may be ready in the future, but Kendall is tempering excitement. In a recent visit, Lockheed Martin's proposed move of the F-16 production line to India was a subjec… The next administration will grapple with the F-35's move to full-rate production and the first set… Is MEADS Back in Running for Poland's Missile Defense Competition? The United States is set to approve the sale of Mk-48 heavyweight torpedoes for Taiwan.

Dmitry Malioutov can't say much about what he built. As a research scientist at IBM, Malioutov spends part of his time building machine learning systems that solve difficult problems faced by IBM's corporate clients. One such program was meant for a large insurance corporation. It was a challenging assignment, requiring a sophisticated algorithm. When it came time to describe the results to his client, though, there was a wrinkle. "We couldn't explain the model to them because they didn't have the training in machine learning." In fact, it may not have helped even if they were machine learning experts. That's because the model was an artificial neural network, a program that takes in a given type of data--in this case, the insurance company's customer records--and finds patterns in them. These networks have been in practical use for over half a century, but lately they've seen a resurgence, powering breakthroughs in everything from speech recognition and language translation to Go-playing robots and self-driving cars.

Artificial intelligence might be on its way Photo credit: David Molina Evan Roberts and David MolinaSeptember 10, 2016Filed under Opinion # Hang on for a minute...we're trying to find some more stories you might like. Close # Email This Story Send Email Cancel Siri was first introduced in 2011 on the iPhone 4S. When it was first introduced it was fun to play with and receive cute responses from, but it was not too useful. Since then, Siri and its competitors have become increasingly more use... U.S. – Google Inc. (NASDAQ: inGOOGL) is working with a third-party company called DeepMind to enhance their AI personal assistant.

Apologizing for delays and an "unusually difficult couple of weeks," Elon Musk tweeted Saturday that he finally will provide more details about promised updates to Tesla's Autopilot program. The Tesla CEO said he will do an hourlong Q&A with reporters, then publish information about Autopilot on Sunday to the company's blog. Musk had said on Aug. 31 that "major improvements" to Autopilot were coming and would be announced that day. But the next morning, a SpaceX rocket exploded in a fireball on the launch pad, putting the announcement on hold. Musk is the chief executive of both Tesla and SpaceX.

A cybersecurity startup is applying the same "deep learning" techniques that are used in modern image and voice recognition to detect malware. Deep Instinct launched late last year with a system that it says can go beyond typical antivirus programs by not only detecting known malware but also flagging dangerous software it's never encountered before. The company doesn't need to have security experts create digital rules specifying what kind of characteristics should trigger alerts, says Maya Schirmann, Deep Instinct's chief marketing officer. Instead, the system essentially trains itself by studying enormous numbers of applications, documents, images, and other common types of files, labeled simply for whether they contain malware or not. "The training phase happens on our own premises, at Deep Instinct," Schirmann says.