Artificial Intelligence Archives - Security Buyer Magazine

Unlocking the full potential of edge-AI cameras

Artificial intelligence has revolutionised video security by providing unprecedented control over situations. Security cameras that were once reactive devices used for investigative purposes can now offer real-time alerts, allowing users to take proactive action against threats. Market analysts expect the penetration of AI in physical security to increase in the coming years. According to OMDIA’s 2023 Trends to Watch: Physical Security Technologies report, implementing AI applications will remain the key market focus this year. “In the field of video surveillance, the global market size for AI chips in security cameras will reach $1.3 billion in 2026, according to the latest data from Omdia’s forecast,” the report said. “Current estimates suggest that AI cameras will account for 42 percent of all network cameras shipped globally in 2026.” The need for edge AI High performance video analytics used to be server-based because they required more processing power and storage than a camera could offer. But algorithm development and increasing processing power of edge devices in recent years have made it possible to run advanced AI-based video analytics on the edge. There are benefits to using edge-based video analytics applications – while AI applications can run on the server or cloud, latency and bandwidth concerns limit their performance. In situations where an access control solution must provide instant results after analysing a face or a number plate, sending data to the server and waiting for its answer is not feasible. This is why edge analytics is becoming an area of significant investment for video security systems. It offers low bandwidth consumption as only necessary data is sent to the server or cloud. Limiting processes to the edge ensures quicker alerts in case of threat detection, allowing faster data-driven analysis and decision-making. Edge-based analytics also come with lower hardware and deployment costs as fewer on-premises server resources are needed for the security solution. This however brings unique challenges. Edge analytics applications need to work on edge devices that can have memory and processing power constraints. Seamless management is critical to smooth operations. Challenges when using cameras with AI on edge AI enabled cameras provide more value than their traditional counterparts. But they also demand more resources in terms of data and hardware. New video security cameras are now being designed to process multiple video streams and higher resolutions (4K and above) to provide AI algorithms with the large dataset of detailed images and videos required for them to analyse. In addition to this, increasing amounts of metadata are captured and stored on the device to enable operators to quickly search and find relevant video footage. Much of the processing now occurs on the device level, with more computing power from new chipsets enabling deep neural network processing on the camera itself for edge intelligence. Memory and storage technologies then need to keep up with these evolving changes in processing and workload requirements. “With edge devices generating critical insights for everything from public safety to vehicle autonomy to manufacturing operations, today’s smart applications cannot afford to compromise on latency or quality,” said David Henderson, Director of Industrial Segment, Micron Embedded Business Unit. “Micron’s high-performance, ruggedised solutions — our i400 microSD card and 1α node based DRAM — will unlock new value for businesses and drive the rapid innovation needed at the intelligent edge.” Get reliable performance for a variety of deployment scenarios with Micron’s memory and storage products – delivering high capabilities and industrial temperature options in several small form factors. Learn more about Micron Note: Sponsored advertisement content by Micron Technology. To read the full exclusive and other news stories and exclusives, see our latest issue here. Never miss a story… Follow us on: Security Buyer @SecurityBuyer @Secbuyer Media Contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922 Email: editor@securitybuyer.com

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Freshworks add AI integration to digital assistant

Middle East News Desk / Ghyslaine Cordrey

Freshworks Inc. today announced new GPT-based conversational enhancements to Freshworks’ natively-built AI powered assistant, Freddy. Using OpenAI’s ChatGPT and underlying large language models, the latest generative AI capabilities of Freddy help a wide range of customer-facing professionals work faster, smarter, and more effectively. Customer service agents respond quickly to customers and employees in the right tone, marketers compose more compelling copy in a fraction of the time, and salespeople craft powerful emails that hook in a prospect. “We’ve made significant investments in our AI strategy over the last five years to enhance agent productivity and their customers’ experience. The newest Freddy updates using the latest in GPT large language models bring even more value to these experiences,” said Prakash Ramamurthy, Chief Product Officer at Freshworks. “We are fundamentally transforming how Freshworks customers will interact with our products through more conversations and fewer clicks.” Conversational AI will be embedded via Freddy across Freshworks’ entire customer and employee suite of products. Customer support agents will deliver faster issue resolution and have higher quality conversations with customers. Marketers will receive smart customer segmentation and optimized email content to maximize campaign efficacy. Sellers will close more deals through recommendations on opportunities with highest potential. Today, Freshworks customers participating in the Freddy AI beta programs are able to: ● Summarize Conversations: Support agents using Freshchat can view an automatic summary of customer conversations to gain context, rather than reading through an entire conversation before responding. ● Rephrase Responses: Support agents can replace casual language with more formal and clear responses. ● Autocomplete Content: Support agents can save keystrokes and respond faster to customer inquiries, with predictive sentence completion. ● Generate Articles: Support agents can save time by creating contextual knowledge-base articles and FAQs using generative AI and simple prompts. ● Write Email Copy: Marketers using Freshmarketer, Freshworks’ marketing automation suite, can write better email copy in less time to improve open rates and engagement. Sellers can create personalized emails tailored to individual prospects’ specific needs and pain points. To read more news and exclusive features see our latest issue here. Never miss a story… Follow us on: Security Buyer @SecurityBuyer @SecbuyerME Media Contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922 Email: editor@securitybuyer.com

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ISE 2023 marks a new era of AI in AV

ChatGPT, the AI phenomenon that has taken the world by storm, has propelled artificial intelligence into the mainstream. It’s no surprise that AI was the hot topic when the AV industry gathered at ISE in Barcelona, with participants eager to examine the potential applications of AI in video conferencing and witness game-changing innovations like Huddly Crew in action. “This year’s ISE was the biggest to date, with 58,000 visitors on the trade show floor – the perfect venue for the public debut of Huddly Crew, the world’s first AI-directed multi-camera system. The interest was massive, with our team demonstrating this cutting-edge technology to more than 150 companies over the four days of the event,” said Vebjørn Boge Nilssen, Head of Marketing at Huddly. “We’re also very grateful for the opportunity to exhibit together with our friends at Sharp/NEC. They’re great people, and we make for a great bundle,” Nilssen added. Huddly’s intelligent camera solutions were also on display at the booths of several other partners, including Shure, Barco, Crestron, and Google. “In today’s flexible office environment, it’s challenging for IT managers and AV integrators to find the right solutions that enable teams to communicate efficiently. We work closely with Huddly to ensure our Stem Ecosystem devices work with Huddly cameras, enabling a full conferencing system, as we showed at ISE 2023”, said Chris Merrick, Senior Director, Conferencing, at Shure. “Huddly Crew will enhance the video experience while continuing to integrate seamlessly with Shure’s audio devices”. Marcus Haraldsson Boij, technical enthusiast and manager at Informationsteknik Scandinavia, commented enthusiastically on Huddly Crew after receiving a live demo: “The Huddly Multi-Camera Experience, now named Huddly Crew, was mind-blowing. The AI-driven director creates a TV production with three or more Huddly L1 cameras, amazing!” Participants at ISE were able to see the physical setup of the initial Huddly Crew kit – comprised of three Huddly L1 cameras – and experience how the built-in AI director enhances video meetings with professional production values inspired by TV and movies. The networked L1 cameras exchange information continuously, capturing visual and auditory data points that the AI director analyzes in real time to determine the best shot to display at any given time. By doing so, Huddly Crew creates optimal meeting dynamics and engagement throughout the whole session. “With the launch of Huddly Crew and a fantastic array of demos lined up, we were particularly excited to return to ISE this year. Even though we announced Huddly Crew last year, we’ve never shown it live in action. This is an incredible system that has to be experienced to understand its true capabilities and potential”, Nilssen at Huddly said. “The response has been fantastic, which is a joy to see. We’ve worked hard towards this moment for more than three years. Throughout the development of Huddly Crew, our goal has been to design an innovative system that takes inclusion and engagement to the next level. The overwhelmingly positive response from our audience tells us we’ve hit the mark,” Nilssen continued. “The market has been eagerly awaiting a game-changer in the world of video conferencing, and with Huddly Crew, we’re proud to say that we’re delivering on that promise. This is the beginning of a new era, and we’re excited to be leading the charge!” With the buzz surrounding Huddly Crew growing rapidly after the launch, Huddly is now gearing up for a large influx of new orders and is planning to make Huddly Crew available to customers by autumn this year. “We believe Huddly Crew represents the future of the hybrid workplace. Hybrid collaboration is here to stay; we can’t afford to waste time on static and lifeless video streams. By launching Huddly Crew, we want to contribute to increased productivity for the organizations that use the system and make the workday much more enjoyable for their employees. Now it’s time to make sure those benefits are realized,” Nilssen concluded. To read the full exclusive and other news stories and exclusives, see our latest issue here. Never miss a story… Follow us on: Security Buyer @SecurityBuyer @Secbuyer Media Contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922 Email: editor@securitybuyer.com

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KEYper Systems and iDter Exhibiting at NADA

News Desk / Rebecca Morpeth-Spayne

KEYper Systems, an ASSA ABLOY Company and global provider of innovative key and asset control management solutions, together with iDter, an award-winning developer and manufacturer of an artificial intelligence (AI) surveillance system that puts an end to loitering and property crime, will showcase their joint commercial security solution at NADA 2023. KEYper Systems and iDter will be at Booth 233 during NADA, the auto industry event of the year, January 26-29 in Dallas. The companies announced a strategic partnership earlier this year that has KEYper Systems deploying iDter solutions at automobile dealerships to provide intelligent intrusion detection and immediate deterrence. iDter uses AI embedded in Niō Guardian nodes located around a property to detect intrusion and take immediate and programmable deterrence actions against the intrusion. KEYper Systems and its network of agents have full access to iDter’s security solution and services, which have been proven to thwart 98% of crime without the delays or high costs associated with human intervention. KEYper has the rights to embed the iDter Niō Guardian system in its line of key and asset management products and sell the entire iDter commercial security system directly to KEYper clients. “Auto dealerships are under attack,” said Steve Baucom, President of KEYper Systems. “Nearly half a million vehicles were stolen the first half of 2022 according to the National Insurance Crime Bureau (NICB), equating to an estimated $4.5 billion. KEYper’s secure Elite series of key management cabinets only allow authorized users to check out keys and provide detailed records and photos of who is using which key and why. By adding the deterrence capabilities of iDter’s Niō Guardians, we are deploying a robust, integrated solution that stops key and automobile losses before they occur.” Niō Guardians are easy-to-install devices equipped with a 4K high-resolution HD camera, motion detectors, microphone and deep learning AI to automatically detect an intrusion. Upon detection, Niō Guardians immediately deter intruders using 10,000 lumens of LED flood light, red/blue LED strobes, and three-way loudspeakers for spoken messages, startling sound effects and ear-piercing alarms. The Niō nodes execute programmable scripts that escalate their response upon detection and have access to a myriad of playlist responses designed to defy predictability. Notifications of intrusion with video recordings are sent to property owners for self-monitoring on mobile phones or to the iDter monitoring personnel for 911 response. “We are excited to be partnering with KEYper Systems during NADA to demonstrate how AI can help protect and manage keys and dealer assets,” said Greg Ayres, iDter Vice President of Marketing and Business Development. “KEYper’s outdoor security system provides deterrence in seconds and is highly effective perimeter protection that’s more cost effective than monitored cameras or security guards.” To read the full exclusive see our latest issue here. Never miss a story… Follow us on: Security Buyer @SecurityBuyer @Secbuyer Media Contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922 Email: editor@securitybuyer.com

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Digitising oilfield operations with AI video analytics

Exclusives / Rebecca Morpeth-Spayne

Digitising oilfield operations with AI video analytics, by Leena Sudhakar, Technical Product Manager, Oil and Gas, Azena, and Technical Editor for Security Buyer Oil and gas organisations are grappling with a host of challenges, ranging from lack of skilled labor, supply chain disruptions and increasing regulations around environmental compliance and sustainability. To combat this, companies are moving towards remote site monitoring that enhances operational efficiency and provides real-time awareness of changing conditions onsite, while at the same time reducing costs and carbon footprints. A key new development in this trend is to deploy cameras that use Artificial Intelligence (AI) to analyze and collect insights at the camera level from the camera’s field of sight. The mainstreaming of AI technologies has spurred the transition towards further automation and digitization of oil field operations. Enabled by the advancements in camera technology, AI-enabled video analytics have been helping the oil and gas industry move away from the traditional, manpower-intensive approach of monitoring sites. Cameras powered with AI can automatically monitor a range of different facilities around the clock, helping to achieve operational excellence and adherence to environmental regulations. Energy organizations have historically used single purpose solutions to monitor flares on oil derricks, tank levels and facility assets for gas leaks. When AI-enabled analytics are coupled with new smart cameras, all of these functions can be monitored simultaneously by a single camera. The secret sauce is the ability for cameras to run several applications that are trained by data scientists to perform specific functions. The applications can simply be called AI apps. AI apps that run directly on the camera provide a cost-effective yet flexible alternative to traditional monitoring technologies. Unlike other existing solutions, AI apps offer a high degree of automation, allowing real-time alerts on potential issues and speeding up response. This helps to achieve zero downtime and minimize environmental and occupational health and safety fines. The camera’s ability to make intelligent, autonomous decisions about what is detected by the video analytics apps means that only the most necessary video footage, as opposed to all video captured by the camera, needs to be transmitted to cloud server/storage for recording. This dramatically reduces storage and bandwidth costs. Eliminate environmental violations and fines Oil and gas operations are under increasing pressure to be even more environmentally conscious and to comply with an evolving slate of regulatory requirements. Operational mishaps can result not only in environmental incidents, but also in massive fines from environmental regulators. As such, automated monitoring can help organizations to remain compliant with regulations by ensuring clean flaring, detecting high tank levels and gas leaks early, and reliably controlling remote onshore and offshore facilities. Smart cameras equipped with smoke detection and flare monitoring will pan the facility, identify venting or smoking in gas flares and notify operators about anomalies, which results in much faster response times. AI-enabled apps can also detect gas leaks, which are notoriously hard to spot with the human eye. Furthermore, liquid gas can leak in a variety of ways, including forming a puddle, dripping, or spraying. In such situations, smart thermal cameras equipped with AI analytics can reliably detect leaks and send instant alerts to responsible teams. With this, operation teams can then expedite the clean up or rectify the malfunction that is causing the leak. The level of liquids in tank batteries cannot be measured by the human eye and requires special sensors to read. For this particular use case, AI-driven thermal imaging technology is a good, low-maintenance supplement to conventional monitoring sensors. Upon installation, these thermal cameras can continuously monitor the liquid levels and alert operations staff when the level is above/below the recommended threshold. It is also possible to install an AI-app that identifies leaks using the same camera, thus enabling a single camera to both monitor liquid levels and identify liquid leaks. Enhance operational safety and security Among the many responsibilities of a modern site operator, the first and foremost priority is to continually ensure operational safety and security. When a safety hazard occurs, it is important to protect employees by mitigating the hazard quickly. Monitoring for safety hazards manually is not only inefficient, but also highly prone to error. When safety regulations are not followed, it endangers the overall security of the site and can result in huge occupational health and safety fines. This is where AI-enabled video analytics can help detect non-compliant behavior and alert the site operator so that hazards can be mitigated quickly. To ensure operational safety, regulations require that oil and gas facilities enforce employees’ use of mandated personal protective equipment (PPE). AI-enabled video analytics can identify the appropriate usage of hard hats, goggles, vests and flash-resistant gear and subsequently alert staff to take action if these items are not detected. Similarly, AI-enabled video analytics can help enhance operational safety by proactively detecting falls and/or abnormal movement which could otherwise result in an injury if left unattended. Control access and manage facilities remotely Controlling access to facilities, particularly those in remote sites with very few personnel is critical to ensure that operations are not disrupted. Smart camera technology can monitor visitors to identify intruders and can pinpoint unauthorized open doors. When combined with gate control devices, these smart cameras are capable of reading license plates and opening the gate only for authorized vehicles. Facility managers can also assess the functionality of their equipment. Using AI-enabled analytics, predictive monitoring can help identify when machinery is not running properly and replace it before it stops working as well as detect early signs of failure for tank levels and water levels. By reducing operational downtime, the facility avoids profitability losses caused by unforeseen closures. Update existing surveillance infrastructure For oil and gas facilities, the two key challenges in upgrading their existing infrastructure to support new technologies are budget allocation and the need for seamless installations. Depending on the individual site, the installation of new equipment such as cameras could potentially disrupt operations and cause unwanted downtime. Organizations looking to avoid these challenges

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Ethical AI in the public domain

UK Exclusives / Editor

AI in the public domain is vastly improving with use in transportation, video surveillance, fraud prevention, and many more areas, so what are the ethical challenges? Artificial Intelligence (AI) has the potential to change the way we live and work. Embedding AI across all sectors has the potential to create thousands of jobs and drive economic growth. By one estimate, AI’s contribution to the United Kingdom could be as large as 5% of GDP by 2030. A number of public sector organisations are already successfully using AI for tasks ranging from fraud detection, video surveillance and even answering customer queries. The potential uses for AI in public are significant, but have to be balanced with ethical, fairness and safety considerations. At its core, AI is a research field spanning philosophy, logic, statistics, computer science, mathematics, neuroscience, linguistics, cognitive psychology and economics. AI can be defined as the use of digital technology to create systems capable of performing tasks commonly thought to require intelligence. AI is constantly evolving, but generally it involves machines using statistics to find patterns in large amounts of data and has the ability to perform repetitive tasks with data without the need for constant human guidance. There are many new concepts used in the field of AI and you may find it useful to refer to a glossary of AI terms. AI guidance mostly discusses machine learning. Machine learning is a subset of AI, and refers to the development of digital systems that improve their performance on a given task over time through experience. Machine learning is the most widely-used form of AI, and has contributed to innovations like selfdriving cars, speech recognition and machine translation. So, what are the most common uses of AI in the public domain, and how can we ensure public safety with ethical use? DVLA Each year, 66,000 testers conduct 40 million MOT tests in 23,000 garages across Great Britain. The Driver and Vehicle Standards Agency (DVSA) developed an approach that applies a clustering model to analyse vast amount of testing data, which it then combines with day-to-day operations to develop a continually evolving risk score for garages and their testers. From this the DVSA is able to direct its enforcement officers’ attention to garages or MOT testers who may be either underperforming or committing fraud. By identifying areas of concern in advance, the examiners’ preparation time for enforcement visits has fallen by 50%. Video surveillance We usually think of surveillance cameras as digital eyes, watching over us or watching out for us, depending on your view. But really, they’re more like portholes: useful only when someone is looking through them. Sometimes that means a human watching live footage, usually from multiple video feeds. Most surveillance cameras are passive, however. They’re there as a deterrence, or to provide evidence if something goes wrong. Did your car get stolen? Check the CCTV. But this is changing — and fast. Artificial intelligence is giving surveillance cameras digital brains to match their eyes, letting them analyse live video with no humans… To read the rest of this feature, check out our latest issue here. Media contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922

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Greater intelligence, safer ports

UK Exclusives / Editor

How can artificial intelligence and machine learning improve maritime and port security both on land and sea? AI has the potential to revolutionise the global economy and is an area of substantial investment across the world by companies big and small. For the maritime industry it is no different as ports, terminals, carriers and other supply chain stakeholders look to utilise the near-exponentially increasing amount of data.However, new technologies also bring risk. It must now be considered how best to implement cybersecurity and how to keep the digital and automated programmes ports increasingly rely on safe. The maritime industry is not unique in needing to be hyper-aware of the threat hackers pose; in 2018 alone, there were 10.5 billion malware attacks globally. Companies across the world, from heavy industry to finance are all spending big money to keep their data and operations safe, to the point where the Cloud segment alone will rise from $5 billion in 2018 to $12.6 billion in 2023. The number of data points in the maritime chain – vessel navigation, cargo handling, container tracking systems on shore and at sea, automated processing, etc – make it particularly important that decision-makers make their AI-powered systems as resilient as possible. In fact, AI itself may prove to be the solution. Machine learning AI is a broad term to describe a machine’s mimicking of a human brain, but to do that it needs machine learning, its most indispensable subset. In ordinary AI-driven operations, the Internet-of-Things (IoT) collects data and builds a decision making support system, from which AI then can spot patterns and make subsequent preparations. When it comes to improving cyber-security machine learning can be used to process information from previous hacking attempts. When combined with AI-decision making, threats can be spotted quicker and acted upon before operations are harmed. The drawback is that AI and machine learning algorithms needs months of event-specific data to set baseline, at which point it can detect anomalies and sophisticated threats. While undeniably valuable, this doesn’t necessarily help prepare for all future attacks because the cyber-threat is getting bigger and more advanced all the time. How can ports use Artificial Intelligence? Cyber-criminals themselves utilise AI systems to get around security processes, which means ports and all stakeholders have to constantly adapt and update their defences. This is particularly true at a time when ports are investing heavily in smart technologies to connect fleets of machines and automate processes. A possibility might be a different subset of AI called machine reasoning, which is a deeper layer of machine learning. It does all the things machine learning does but is much broader and flexible, and allows the critical human oversight needed to prevent malicious activity. Whereas machine learning will ingest mountains of data, machine reasoning, or automated reasoning as it is also known, will analyse it within a certain set of rules which are encoded and overseen by people. For more news updates and exclusive features, check out our latest issue here. Media contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922

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Greater intelligence, safer ports

Exclusives / Ghyslaine Cordrey

How can artificial intelligence and machine learning improve maritime and port security both on land and sea? AI has the potential to revolutionise the global economy and is an area of substantial investment across the world by companies big and small. For the maritime industry it is no different as ports, terminals, carriers and other supply chain stakeholders look to utilise the near-exponentially increasing amount of data.However, new technologies also bring risk. It must now be considered how best to implement cybersecurity and how to keep the digital and automated programmes ports increasingly rely on safe. The maritime industry is not unique in needing to be hyper-aware of the threat hackers pose; in 2018 alone, there were 10.5 billion malware attacks globally. Companies across the world, from heavy industry to finance are all spending big money to keep their data and operations safe, to the point where the Cloud segment alone will rise from $5 billion in 2018 to $12.6 billion in 2023. The number of data points in the maritime chain – vessel navigation, cargo handling, container tracking systems on shore and at sea, automated processing, etc – make it particularly important that decision-makers make their AI-powered systems as resilient as possible. In fact, AI itself may prove to be the solution. Machine learning AI is a broad term to describe a machine’s mimicking of a human brain, but to do that it needs machine learning, its most indispensable subset. In ordinary AI-driven operations, the Internet-of-Things (IoT) collects data and builds a decision making support system, from which AI then can spot patterns and make subsequent preparations. When it comes to improving cyber-security machine learning can be used to process information from previous hacking attempts. When combined with AI-decision making, threats can be spotted quicker and acted upon before operations are harmed. The drawback is that AI and machine learning algorithms needs months of event-specific data to set baseline, at which point it can detect anomalies and sophisticated threats. While undeniably valuable, this doesn’t necessarily help prepare for all future attacks because the cyber-threat is getting bigger and more advanced all the time. How can ports use Artificial Intelligence? Cyber-criminals themselves utilise AI systems to get around security processes, which means ports and all stakeholders have to constantly adapt and update their defences. This is particularly true at a time when ports are investing heavily in smart technologies to connect fleets of machines and automate processes. A possibility might be a different subset of AI called machine reasoning, which is a deeper layer of machine learning. It does all the things machine learning does but is much broader and flexible, and allows the critical human oversight needed to prevent malicious activity. Whereas machine learning will ingest mountains of data, machine reasoning, or automated reasoning as it is also known, will analyse it within a certain set of rules which are encoded and overseen by people. Ultimately, once machine reasoning software detects an unfamiliar attempt to access data, it will send a notification to the security team, which will have the final say over whether or not to grant it. It isn’t only cybersecurity where AI can help ports be more resilient, they also need to consider non-cyber health and safety. For carriers, it can also help with voyage optimisation, maintenance and vessel tracking, which can help a port’s operations by keeping cargo traffic flowing and increasing supply chain visibility. For more news updates, check out our June issue here. Media contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922

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Big Interview – Pauline Norstrom

Exclusives / Ghyslaine Cordrey

Security Buyer has the pleasure of sitting down with AI veteran and inspirational woman, Pauline Norstrom to discover the future landscape of security Please introduce yourself and tell us what you do? I’m the CEO of Anekanta Consulting an AI innovation and strategic advisory company for the ethical application of AI technology in defence, security, manufacturing and smart cities. I am also an advisor to boards and organisations focused on the ethical development of AI including the Digital Catapult Machine Intelligence Garage and Archangel Imaging. I am a Former Chair, honorary member and voluntary strategic advisor to the British Security Industry Association (BSIA) on ethical high-risk AI (automated facial recognition) policy. As well as a fellow and board member of several other organisations including IoD, ERP/Digital and SIA USA. I also engage in a wide range of other activities including contributing to the standards and regulation needed to ensure AI is developed and used safely. How did you start out in the industry? With over 10 years prior technology and business experience, I joined a video surveillance system manufacturer which was developing new digital recording products. I developed my career through commitment and 70-hour weeks, in strategic and international roles including on the board, and COO of a group company with multiple entities innovating in video and analytics. In addition, I shaped the acceptance of digital evidence in court which developed industry relationships leading to becoming Chair of the BSIA in 2014. What were some of the challenges that came with being a woman in a male-dominated industry? At times, it has been difficult to ignore evidence that my gender is an issue for some. Issues such as blocking, or sabotage are rare, and more common are barely detectable behaviours and minimising language. Sadly some women work against other women. Social conditioning and segregation of roles may play their part in fuelling this behaviour. These factors, if not addressed, can conspire to diminish the value of women’s contributions, which reduces access to opportunities. To counter the behaviour, I have understood the motivation and called it out directly. Those who cause inertia specifically for women are in the minority, and overall, the industry is full of professionals who are a pleasure to work with, and many of whom have been extremely supportive over the years. All my business mentors have been male, and I have learned a great deal from them at every stage of my career. I believe role models are very important. Someone can imagine themselves in a particular job because they see someone like them doing it. To read the rest of this interview, as well as other exclusives and news, see our latest issue here. Media contact Rebecca Morpeth Spayne, Editor, Security Portfolio Tel: +44 (0) 1622 823 922

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AI at the edge

News Desk / Ghyslaine Cordrey

Hanwha Techwin’s Head of Product and Marketing for Europe, Uri Guterman, explores how implementing AI into your business will put you above the competition We’ve become used to seeing artificial intelligence (AI) in almost every aspect of our lives. It used to be that putting AI at work involved huge server rooms and required vast amounts of computing power and, inevitably, a significant investment in energy and IT resources. Now, more tasks are being done by devices placed across our physical world, “at the edge.” By not needing to stream raw data back to a server for analysis, AI at the edge, or “edge AI”, is set to make AI even more ubiquitous in our world. It also holds huge benefits for the video surveillance industry. Sustainability benefits AI at the edge has several benefits compared to server-based AI. Firstly, there are reduced bandwidth needs and costs as less data is transmitted back to a server (also, security benefits that are outlined below). Cost of ownership decreases, and there can also be important sustainability gains as a large server room no longer has to be maintained. Energy savings in the device itself can also be realised, as it can require significantly less energy to carry out AI tasks locally instead of sending data back to the server. Cost efficiencies With edge AI devices, compared to a cloud-based computing model, there isn’t usually a recurrent subscription fee, avoiding the price increases that can come with this. Focusing on edge devices also enables end-users to invest in their own infrastructure. Greater scalability Cameras using edge AI can make a video installation more flexible and scalable, which is particularly helpful for organisations that wish to deploy a project in stages. More AI cameras and devices can be added to the system as and when needs evolve, without the end-user having to commit to large servers with expensive GPUs and significant bandwidth from the start. Improved operational performance and security Because video analytics is occurring at the edge (on the device) only the metadata needs to be sent across the network, and that also improves cybersecurity as there’s no sensitive data in transit for hackers to intercept. Processing is done at the edge so no raw data or video streams need to be sent over a network. As analysis is done locally on the device, edge AI eliminates delays in communicating with the cloud or a server. Responses are sped up, which means tasks like automatically focusing cameras on an event, granting access, or triggering an intruder alert, can happen in near real-time. Additionally, running AI on a device can improve the accuracy of triggers and reduce false alarms. People counting, occupancy measurement, queue management and more, can all be carried out with a high degree of accuracy thanks to edge AI using deep learning. This can improve the efficiency of operator responses and reduce frustration as they don’t have to respond to false alarms. AI cameras can also run multiple video analytics in the same device — another efficiency improvement that means operators can easily deploy AI to alert for potential emergencies or intrusions, detect safety incidents, or track down suspects, for example. Video quality improvements What’s more, using AI at the edge the quality of video captured is improved. Noise reduction can be carried out locally on a device and using AI, can specifically reduce noise around objects of interest like a person moving in a detected area. Features such as BestShot ensure operators don’t have to sift through lots of footage to find the best angle of a suspect. Instead, AI delivers the best shot immediately, helping to reduce reaction times and speed up post-event investigations. It has an added benefit of saving storage and bandwidth as only the best shot images are streamed and stored. AI-based compression technology also works to apply a low compression rate to objects and people which are detected and tracked by AI, whilst applying a high compression rate to the remaining field of view — this minimises network bandwidth and data storage requirements. Using the metadata Edge AI cameras can provide metadata to third party software through an API (application programming interface). This means that system integrators and technology partners can use it as a first means of AI classification, then apply additional processing on the classified objects with their own software — adding another layer of analytics on top of it. Resilience There is no single point of failure when using AI at the edge. The AI can continue to operate even if a network or cloud service fails. Triggers can still be actioned locally, or sent to another device, with recordings and events sent to the back-end when connections are restored. AI is processed in near real-time on edge devices instead of being streamed back to a server or on a remote cloud service. This avoids potentially unstable network connections from delaying analytics. Benefits for installers For installers specifically, offering edge AI as part of your installations helps you stand out in the market, by offering solutions for many different use cases. Out-of-the-box solutions are extremely attractive to end-users who don’t have the time or resources to set up video analytics manually. AI cameras like the ones in the Wisenet X Series and P Series work straight from the box so there’s no need for video analytics experts to fine tune the analytics. Installers don’t have to spend valuable time configuring complex server-side software. Of course, this also has a knock-on positive impact on training time and costs. Looking ahead The future of AI at the edge looks bright too, with more manufacturers looking at ways to broaden classification carried out by AI cameras and even move towards using AI cameras as a platform, to allow system integrators and software companies to create their own AI applications that run on cameras. Right now, it’s an area definitely worth exploring for both end-users and installers because of the huge efficiency, accuracy and sustainability

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