Security Business

MAR 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.

Issue link: https://sdi.epubxp.com/i/1095589

Contents of this Issue

Navigation

Page 41 of 109

40 Security Business / www.SecurityInfoWatch.com / March 2019 Last year, NVIDIA released its Tesla graphi- cal processing unit (GPU), which contains hundreds of processing cores that is expected to accelerate the deep learning training that is central to most AI applica- tions. Intel recently launched OpenVINO (Open Visual Inference & Neural Network Optimization), a toolkit for the quick deployment of computer vision for edge computing in cameras and IoT devices. e OpenVINO toolkit's open source soware works with Intel's traditional CPUs or chips specifically for AI calculations. Intel's Neural Compute Stick 2 con- tains the company's Movidius Myriad VPU (Vision Processing Unit), along with soware that offloads the deep learning processing to a USB stick. is enables soware development to be done using traditional PC and lap- top computers. Case in point for the security industry's development using these tools came at GSX 2018, when Avigilon announced its next genera- tion of advanced AI cameras – based on the Intel Movidius VPU. AI brings valuable capabilities to security technology – especially video surveillance – that go beyond the tra- ditional roles of security, such as retail analytics. As manufacturers like Avig- ilon continue to invest in AI-based product development, it makes sense for security service providers to start taking a much closer look at how these products can fuel revenue improvements for their current and future client markets. To do that, it is vital to understand the terminology, how the technologies work, and how they can be applied for security customers. Machine Learning and Deep Learning Machine learning is the science of getting computers to perform actions without specifically being pro- grammed to do so. For example, machine learning so- ware for email spam filtering would be "trained" on recognizing spam by being fed thousands of emails labeled either as spam or not spam, and the soware would analyze each email and determine from those examples how to identify spam. Deep learning is a type of machine learning that involves artificial neural networks, whose designs are inspired by the way that scientists believe the brain works. A neural network is built from pieces of soware called "nodes," which are organized into layers. Each layer performs a step in the data pro- cessing, passing along its results from one layer to the next. Deep learning soware typically contains three parts: an input layer, hidden layers and an output layer. Hidden layers are so named because there are no connec- tions to them from the neural net- work's input and output interfaces. e term "deep" refers to neu- ral network soware that has many hidden layers – the number of layers determining the depth. A simple neu- ral network has one or two hidden lay- ers between the input and output lay- ers; three or more hidden layers makes it a deep learning neural network, as shown in the above illustration. An object detection deep neural network, for example, has: An input layer (receive a still image of a scene); hidden layers (detect moving object, detect object parts, classify object parts, classify object); and output layer (provide information on object). A simple neural network has one or two hidden layers between the input and output layers; three or more hidden layers makes it a deep learning neural network Cover Story AI-based technologies are typically offered under an "as-a-service" monthly-subscription model – whether the AI computing is done on the cloud or on premises. Illustration: Ray Bernard

Articles in this issue

Links on this page

Archives of this issue

view archives of Security Business - MAR 2019