Security Business

JUN 2019

Find news and information for the executive corporate security director, CSO, facility manager and assets protection manager on issues of policy, products, incidents, risk management, threat assessments and preparedness.

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20 Security Business / / June 2019 Tech Trends BY BRIAN COULOMBE Going Deeper on Deep Learning The challenge for integrators is sifting through the marketing buzz to get down to real-life uses If you attended ISC West in April, you were almost certainly bombarded with the industry buzz term and marketing tactic du jour: artificial intelligence (AI). While the term itself and its use in the security industry is not new, at some point in the last 12 months manufacturers far and wide have adopted the term as a key selling point and major product differentiator. It seems as though every developer of video analytics, sensors, IoT devices, and limitless varieties of smart widget was claiming their product was “AI-enabled.” It got me wondering about the definition of the term, whether some folks were using it a bit too liberally, and whether we were potentially setting ourselves up for a failure scenario which seems all too familiar: Expand the definition of a technology too broadly, overpromise capabilities, underdeliver performance, and let it eventually become a difficult topic at industry cocktail parties. So we are all defining AI correctly – myself included – I have included definitions to help contextualize the use of AI in our industry, which generally falls into two categories: Machine Learning: At a simple level, machine learning involves teaching computers to use algorithms to deconstruct a bunch of data, learn from that data, and use it to make helpful predictions. Video analytics does this well today – we teach computers what an action looks like using algorithms and a large amount of sample data, it looks for that action in a video feed, and it predicts when that action has occurred. In order to improve the accuracy of the prediction, we need to provide the algorithms with more information about what to look for. Over time, manufacturers have done that, and the video analytics at our disposal have gotten more accurate. Deep Learning: The next evolution of AI, deep learning takes machine learning further by teaching computers to formulate their own rules and algorithms for improving accuracy. Deep learning machines use neural networks, which pass data through several rounds of processing – allowing them to “think” about data before drawing final conclusions. Instead of thousands of data points involved in a decision, there are millions. This requires some serious computing power, but the outcomes can be more accurate and the use cases more complex. My suspicion, confirmed by conversations with industry professionals and manufacturers, is that the promise of deep learning has led many machine learning companies to newly adopt “AI” as a primary marketing term. While still correct, it is important to recognize the difference, as deep learning represents a large step forward in technology. While deep learning is still in its early stages, it can potentially expand computing capabilities to look for complex behaviors in streaming video feeds rather than simple actions. This may help reduce strain on security staffing and make more effective use of video surveillance system investments; however, these systems will require additional education on how to design, specify and deploy them. Deep Learning Implementation I recently spoke with Jeff Gurulé, the Founder and Principal of Obsys Group, a security consulting firm based in California that has helped clients implement AI solutions. Gurulé is also on the Strategic Advisory Board for, one of a small handful of firms developing true deep learning solutions in the security industry. This puts him at a unique intersection of technology provider and implementer. ■ Deep learning will eventually help video surveillance systems look for complex behaviors in streaming video feeds. Photo: ISC Events

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