What Happens When Machines Can See?
The latest advances in Machine Learning enable a new class of intelligent video monitoring for commercial and corporate buildings. Surveillance video has historically been used only for after-the-fact forensic evidence or real-time viewing. If instead security cameras notify people of events that warrant attention, automate workflows and make it fast to share information across organizations, then new levels of security, efficiency, and public safety are possible.
The tech industry is racing to make machines understand what's happening in the real world. The unexpected success of Amazon Echo has accelerated R&D to change the way we interact with computers, and the potential of self-driving cars has accelerated R&D to change the way computers understand the real world. The key technical insight behind this innovation is Machine Learning – where deep layers of Neural Networks learn on their own by observing lots of training examples. The Machines learn without supervision. That is what's so significant about this new wave of intelligence, because so many kinds of problems can be solved so quickly.
Cloud computing has simultaneously connected this Machine Learning to existing cameras, DVRs, and NVRs. Old devices get a “new brain in the cloud” while new devices begin thinking on their own in collaboration with the cloud. As an example, Camio is a new smart video monitoring Software-as-a-Service that continuously filters, ranks and labels video events in real-time. Its interest-based video compression and natural language search are far beyond anything seen before in video surveillance. Neural Networks not only recognize what's happening at any given moment but also – and more importantly – learn which events are interesting for each individual camera. Two very different problems – 1) labeling objects in the video, and 2) learning which events are interesting – are solved by using these latest advances in Machine Learning.
Even before we reach the level of interactive ambient computing shown in the classic movie - A Space Odyssey (where astronauts plea, "open the pod bay doors, HAL"), it's already clear that adding intelligence to security cameras reduces operational costs. Even the mundane requirement to archive security video for long periods of time becomes cheaper and simpler when AI powers the storage decisions. By running Machine Learning on the local network – so that video streams are analyzed before uploading them to the cloud – storage is reduced by a factor of 10 by ranking the importance of each video event in real-time to vary its decisions about resolution, frame rate, storage, and bandwidth for each individual video event. The connection to the cloud then enables an unlimited amount of video to be stored for any length of time without any need to manage on-site servers, disc failures, and storage upgrades. So, one of the first implications of having machines that see is that they save us money.
Safety is often the primary motivation for video surveillance. But most security video is archived without review until after something bad happens. Machines are particularly good at being attentive and fast in noticing and notifying people. When machines can see, events in the real world programmatically trigger actions like dispatching security guards for potential security breaches, or recording on a spreadsheet every time a person has entered a secure room. Plus, any forensic review takes seconds rather than days when machines have already indexed all the video—in real-time—for fast search for people, objects, colors, direction of movement, zones, and time. That makes it easy to see that the man-in-black at 7:49am is the same man-in-black in the side lot at 9:26am across two different cameras. Security video is encrypted for, and controlled by, each building owner. However, the video can be shared instantly (and selectively, on an opt-in basis) with neighboring buildings and government agencies to help investigate and respond to criminal and terrorist activity. Computer Vision and Machine Learning make it practical to marshal a coordinated response to security concerns – even across cameras in different buildings.
But the security problems with video surveillance have been particularly bad. The Mirai virus took down the Internet with a DDoS attack made possible by insecure video surveillance equipment on networks with open inbound ports for remote viewing and configuration. The infected equipment simply overwhelmed DNS servers with requests. And the infections could be much worse. With smart video monitoring, local networks are closed and secure; the intelligence to manage video and notifications using encrypted communication with the cloud means that there's no need for open ports for inbound network requests. In Camio's case, the Machine Learning operates on video that's stored redundantly across multi-regional data centers that are at least 100 miles apart. So even in the case of natural disasters or large scale cyber-attacks, important video evidence remains maximally accessible.
Whether it's called Artificial Intelligence, Cognitive Computing, Deep Learning or Ambient Computing, the convergence of technologies enabling machines to understand the real world in real-time changes the way we secure and manage buildings. Now that cameras can learn, all those building entrances, loading docks, server rooms, reception desks, and garages can talk to us—and to the machines. “Open the door, HAL!”
This continually evolving technology will affect every aspect of smart buildings and cities. The future of AI and Machine Learning as well as the Next Generation of Surveillance and the effects on commercial real estate will be featured topics at Realcomm | IBcon 2017, which will be held in San Diego on June 14-15 (June 13: Precon | June 16: RE Tech Innovation Tours).
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RE Tech Innovation Tours | Registration Now Open
Registration for the RE Tech Innovation Tourson June 15& 16 is officially open. Join us as we visit eight extraordinary locations around San Diego that demonstrate the latest technologies and innovations impacting the commercial, corporate and institutional real estate industry.
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UPCOMING REALCOMM WEBINARS
Commercial Real Estate Digital Transformation - Managing the Change - 1/18/2018
The Commercial Real Estate and the Technology industries operate at very different speeds. Real estate is long-term, slow moving, and relies on practices that have been refined over the last 50 years. Tech, on the other hand, moves at lightning speed with constant updates and innovation. This webinar will focus on the extraordinary change management required to digitally transform a commercial real estate organization. Executive sponsorship, accelerated collaboration, dual strategies and other programs that will best enable this transformation will be addressed. In addition to operational changes impacted by tech, the webinar will also cover tech’s influences on other industries’ business models and how that affects the way we use space.
Founder of Realcomm Conference Group, an education organization that produces Realcomm, IBcon and CoRE Tech, the world's leading conferences on technology, automated business solutions, intelligent buildings and energy efficiency for the commercial and corporate real estate industry. As CEO, Jim interacts with some of the largest companies globally pertaining to some of the most advanced and progressive next generation real estate projects under development.
Bob Rybak Is the CIO at Morguard, a leading Real Estate Investment and Property Management firm based in Toronto, Canada. Bob has been an IT professional, entrepreneur and frequent consultant for almost three decades, working in both the public and private sectors. A graduate of the University of Toronto with a degree in Mechanical Engineering, Bob's professional experience has spanned many different companies in a wide range of industries.
Sandy Jacolow joined Silverstein Properties in 2011 as Chief Information Officer, a role in which he oversees the technology initiatives that support the company's financial, operations and development, including the World Trade Center, and Silver Suites activities. Sandy has been active in the real estate industry for nearly 35 years with a focus on the institutional advisory, property management and brokerage markets.
Alex Stanton has over 20 years working with in the real estate application space. Currently as VP of Solution Consulting for Yardi Systems, he leads the solution presales team, who work with customers and prospects to explore how to address business needs. Alex’s recent areas of focus has been to work with clients on the real estate specific applications of cloud, mobile, 'big data' and energy.
Marc Petock is a pioneer in leading the Intelligent/Smart Buildings and M2M movements pushing the industry forward and has contributed to transforming and changing the Intelligent Buildings and M2M (now IoT) industries. As VP, Marketing for both Lynxspring and its sister company, Connexx Energy, heleads corporate and product marketing, strategy, brand management, PR and communications that support the company’s strategic and growth initiatives. Previously, Marc was VP, Global Marketing and Communications at Tridium. Marc is also a contributing author, noted speaker and recognized industry leader having earned several industry accolades. He serves on the board of directors of Connexx Energy and Project Haystack; is an advisor to the Realcomm and a contributing editor to automatedbuildings.com.
Dave Clute has been AEC/FM/IT Design Professional for over 35 years. He spent 10 years at Cisco Systems and 5 years at Zurich Insurance before he joined ESD in 2017. Dave is currently leading the Intelligent Building Practice for ESD Global.
Scott Sidman has 14 years of CRE technology experience leading sales and marketing efforts. He is responsible for supporting company growth goals and assuring company and product direction aligns with market needs as well as leads. Scott is CRE tech evangelist and host of a CRE Tech Talks podcast.