Going Green: Occupant Count-Based Demand Control Ventilation

Posted on by Micah Kaplan

Imagine trying to plan a dinner party, but not knowing how many guests will arrive. It would be impossible to prepare for. This is the daily challenge faced by facilities managers implementing demand control ventilation (DCV) without access to real-time occupant-count (RTO) data.

Demand control ventilation optimizes HVAC usage for energy efficiency based on a building’s current air quality and temperature needs. But older approaches to DCV usually rely on CO2 sensors—which lack the granularity of data needed for optimum efficiency.

“CO2-based systems can determine when a space is in ‘occupied mode,’ meaning that someone is in the room, but they don’t have access to real-time occupant count data,” explains David Whalley, co-founder and CEO of Feedback Solutions, an energy efficiency specialist that offers occupant count-based DCV solutions. “Because of this, they’re forced to default to a level of ventilation that approaches 100% of system capacity, even if there are only a handful of people present. For example, we have worked with clients with office areas built for 4,000 employees that at times have occupant levels dip to below 300 people.”

This kind of over-ventilation is costly. But it also hampers greenhouse gas (GHG) reduction efforts at universities, government facilities, commercial office buildings, and other large-scale venues where sustainability is a high priority—and in many cases, a compliance requirement.

But now flexible edge computing platforms enable occupant count-based demand control ventilation solutions. Far more effective than older systems that use CO2 sensors, occupant count-based DCV is already achieving impressive real-world results. The increased energy efficiency is a result of the decreased kWh required by the ventilation fans along with thermal savings driven by the reduced amount of outside air required to be heated or cooled.

Realizing Green Building Benefits

Case in point is Feedback Solutions deployment with the New York State Energy Research and Development Authority (NYSERDA) at New York University (NYU).

Globally, building operations account for 28% of GHG emissions, but in New York, where extremes of temperature and older facilities are common, that number is even higher. Both the State of New York and NYU’s leadership were understandably concerned about energy efficiency on campus.

Far more effective than older systems that use CO2 sensors, occupant count-based DCV is achieving impressive real-world results. Feedback Solutions via @insightdottech

Working with NYU, Feedback Solutions engineers installed an occupant count-based DCV system in the College of Dentistry at the Washington Square campus in Manhattan. People-counting sensors monitored the exact occupant counts of lecture halls and other large rooms in real time, with the data processed at the edge using Feedback Solutions software running on an Intel device. The resulting information was then sent to the university’s building management system (BMS) via the Building Automation Control Network (BACnet) protocol so the level of ventilation could be adjusted automatically based on actual current demand.

This resulted in a significantly more efficient HVAC strategy compared to the previous solution, which required ventilation equipment to run at more than 80% capacity when the system was in occupied mode. The new occupant count-based DCV solution was able to maintain air quality and temperature set points in lightly used rooms or rooms with fluctuating occupant levels at as little as an average of 30-40% capacity, resulting in an overall GHG emission reduction of 18%. Feedback’s system enables ventilation rates to go up and down with the population of an HVAC zone automatically in the background without manual intervention.

The university was so pleased with the results that it rolled out the technology to 15 other buildings on campus—a decision made even easier by financial incentives the new solution delivered. Beyond direct OPEX savings, NYU also offset penalties related to New York Local Law 97, a municipal sustainable-building mandate. In addition, the university was able to reduce its payback period on its investment by taking advantage of the incentive programs implemented by local power companies.

“Local utility providers in New York and many other sustainability-focused regions offer some extremely generous green-building incentives,” says Whalley. “Those incentives can cut an already fast three-year payback in half.”

Future-Proofing Sustainable Buildings

Ability to convert aging infrastructure into something greener attracts large organizations and governments around the world. It’s also of great interest to utility providers that need to find ways to reduce the load on existing building stock to enable electrification of new construction.

But a major stumbling block when undertaking retrofits is that each facility will have its own existing BMS solution—as well as its unique needs and concerns. To address this challenge, solutions providers turn to flexible designs that can be adapted to different kinds of buildings.

Feedback Solutions, for example, offers a sensor- and BMS-agnostic software platform. If an end user has unique sensor requirements, or runs multiple BMS solutions in its IT environment, it’s still straightforward to help the building operator and energy team implement a DCV system that will suit their needs (Figure 1).

Chart of Feedback Solution’s demand control ventilation architecture
Figure 1. A flexible DCV solution architecture that is sensor- and BMS-agnostic, designed to optimize energy consumption while enhancing occupant comfort. (Source: Feedback Solutions)

The company’s technology partnership with Intel has played an integral role in developing such a versatile DCV platform.

“The Intel edge device we use is powerful and highly flexible,” says Whalley. “It allows us to offer many different configurations for our end users, from architectures that send all usage data to the cloud to solutions that are entirely on-premises.”

Creating a Holistic Data Analytics Strategy

Occupant count-based DCV systems are crucial in their own right. But access to real-time occupant data from buildings has far-reaching implications, making these solutions part of a much larger story.

When organizations know how their spaces are being used, tremendous value can be unlocked. Offices and universities can rationalize their post-Covid real estate footprints. Facility management teams can schedule more effectively, reducing operating hours at underused buildings and allocating cleaning and maintenance staff more logically. In the long term, it’s possible to make data-driven decisions about repurposing or consolidating buildings based on actual usage patterns.

This isn’t just a case of adding value in disparate areas. When building occupancy data is treated as a common fabric, it enables an integrated approach to solving some of the toughest problems of the coming decades.

“By breaking down data silos, it’s going to be possible to implement far more sophisticated optimization strategies,” says Whalley. “We see ourselves as part of a future in which the world meets its energy efficiency, space utilization, and sustainability challenges through holistic solutions.”

 

This article was edited by Christina Cardoza, Editorial Director for insight.tech.

Bosch Digital Twin Industries: Advancing Industrial AI

Posted on by Micah Kaplan

Manufacturers, energy companies, and other enterprises dependent on heavy equipment do everything they can to keep their expensive machines up and running. Many would like to use IoT and predictive AI analytics to ward off trouble before it leads to more serious problems.

But applying AI analytics to industrial equipment is not easy to do. Large businesses have thousands of sensors in machines operating in plants across the globe, all of them rapidly generating performance data in a dozen different formats. Just collecting this information can be a nightmare, and it is often filled with errors, omissions, and inconsistencies. Predictive-analytics models must have reliable data to produce good results. If the data is wrong, incomplete, or too slow to arrive, the models may fail—leading to costly breakdowns.

Modern digital-twin solutions can overcome these problems, quickly cleaning and validating machine data before subjecting it to AI analysis. Digital twins can provide companies an accurate dashboard replica of machine operations everywhere—and send alerts that help solve problems before they get out of hand.

#DigitalTwins can provide companies an accurate dashboard replica of #machine operations everywhere—and send alerts that help solve problems before they get out of hand. @prescientPDI via @insightdottech

Harnessing Machine Data for AI Predictive Maintenance

An industrial digital twin requires several technologies to function together like clockwork. To help one of its manufacturing customers predict machine behavior, German engineering technology company Bosch GmbH began working on a digital twin solution, using its expertise in industrial machinery to create AI algorithms that can spot significant deviations in pressure, temperature, vibration, and other important metrics.

But Bosch Digital Twin Industries realized it needed help to develop another crucial part of the solution—corralling the company’s vast array of machine sensor data and preparing it for AI use. A customer recommendation led Bosch to Prescient Devices, Inc., a firm that specializes in data engineering and IoT solutions.

“AI is critically dependent on data quality—if it’s bad, the AI outcome is going to be bad,” says Prescient Devices CEO Andy Wang.

Industrial data is notoriously challenging to manage, in part because the large quantity of machines and sensors provide more opportunities for error. Sensors can become disconnected or turn off unexpectedly, or a network can go down, creating information gaps that give AI algorithms a false picture of operations. And faulty sensors sometimes send duplicate data.

“You have to correct for these problems for the data quality to be high,” Wang says. “And the corrections must be accomplished quickly, on large data sets that follow different protocols and are transmitted at high speed. Our platform supports a very high-speed data rate. We were able to collect the customer’s high-speed sensor data, clean it, format it, and deliver it to Bosch in time to meet their time-to-market.”

The two companies continued honing the solution, which was integrated into the Bosch Digital Twin IAPM, or integrated asset performance management system. It is now used by companies in many industries to monitor machines made by Bosch and other manufacturers. Access to timely, accurate machine data enables industrial businesses to stop potential problems before they happen.

“The data may tell you there’s a small machine component that’s getting old and not working properly. You could solve the problem by replacing it for $1,000,” Wang says. But without such advance knowledge, the degrading part could lead to a cascading set of failures.

For example, if the component gets burned through, it can damage the next component, which can damage a bigger component. Eventually the engine can get damaged. When a million-dollar machine goes bust, it can cost thousands or millions of dollars to fix.

Worse yet, a defective machine can cause the entire production line to shut down, costing factories enormous amounts of time and money. “If machines go down unexpectedly, they can take multiple days to fix. With predictive AI analytics, managers can fix them during preplanned maintenance windows, so the production line would never go down,” Wang says.

Implementing Digital Twins for Manufacturing

Companies can obtain the Bosch Digital Twin IAPM by purchasing a starter kit containing sensors, an on-premises industrial PC, and a sensor master to transfer the sensor data to the computer for processing before it is sent to the Bosch cloud for AI analysis.

Prescient’s software is installed on the Intel-powered computer to automatically recognize different sensor types and quickly clean and validate their incoming data. Intel is known for its reliable and long-lifetime processors—a key value for businesses with equipment in remote areas.

“For example, one of Bosch’s customers is an oil and gas pipeline company with computers deployed in locations that are difficult to access. Technicians have to apply for permission to enter and schedule appointments weeks in advance,” Wang says.

The Digital Twin IAPM also allows companies to reduce the amount of data they send to the cloud, transferring only the kinds of information they deem useful. That eases cloud data ingestion problems and saves money.

For companies that prefer not to use the cloud, a newer version of the solution—the Bosch IAPM Digital Twin in-a-box—is like having a data center at the edge. It runs the Bosch AI model on-premises, using a high-performance computer that contains both Intel CPU and GPU processors for advanced AI analytics.

“Many companies do not want to ship their data to the cloud for security and privacy reasons, and running AI directly at the plant is also less expensive. This solution is gaining a lot of traction from customers across the globe,” Wang says.

Prescient’s software can also save money—and time—for builders of AI-enabled machines. “The majority of data scientists spend only about 20% of their time building and working with AI models. They spend the other 80% preparing data to go into the models,” Wang says. “We have the technology to prepare data very quickly, speeding their production of AI solutions.”

Improving Operations with Industrial AI

Whether they operate in the cloud or at the plant, industrial digital twins create an indelible record of machine performance. By analyzing this information, companies can adjust machine settings to changing conditions and make other tweaks to optimize their processes. Historical data can also help them predict spending on equipment and repairs, and make informed decisions about vendors and service providers. These capabilities can give companies an important competitive advantage, Wang believes.

“I predict that in the near future, every company that has large, expensive physical assets will be using a digital twin solution,” says Wang.

 

This article was edited by Georganne Benesch, Editorial Director for insight.tech.

SASE Solutions Enable Secure Digital Transformation

Posted on by Micah Kaplan

Businesses everywhere are experiencing the benefits of rapid digital transformation. But this change has given rise to new cybersecurity challenges.

“Organizations today have access to a rich ecosystem of cloud computing applications that boost productivity,” says Akarsh Jain, Product Marketing Manager at Sangfor Technologies, an IT infrastructure provider that specializes in cloud computing and network security. “However, this has blurred the physical boundaries of network security, resulting in coverage gaps and complicating secure access management.”

Similarly, mobile computing has made the modern workforce more agile and efficient. But this in turn has expanded the attack surface available to bad actors—and made it difficult and costly for defenders to guarantee cybersecurity using traditional solutions.

In the face of these challenges, cloud security providers like Sangfor have embraced a new approach: the secure access service edge (SASE). An SASE is a way to deliver core networking and cybersecurity capabilities as an integrated, cloud-native service. SASE solutions help IT and cybersecurity teams manage networking and access control in a secure way that is effective and scalable, while at the same time reducing costs and complexity.

SASE Solutions: Convergence of Networking and Security

Cloud providers like Sangfor offer their SASE as a holistic solution, making it tempting to consider SASE platforms as a single end-to-end solution rather than managing and integrating standalone software applications or security tools.

But it’s more helpful to think of SASE as an architectural approach that attempts to solve fundamental problems of networking, performance, and security in a cloud-based world.

“SASE delivers several key network and security technologies via the cloud, including software-defined wide-area networks (SD-WAN), secure web gateways (SWG), and firewall-as-a-service (FWaaS) applications,” says Jain. “They also make it easier for IT and security groups to adopt a zero-trust network access model of security—and enforce security policies consistently for all users and applications.”

This delivers a number of significant benefits to businesses. Use of SD-WAN reduces data backhaul and latency issues—improving overall network traffic efficiency and helping workers access their mission-critical data and applications without unnecessary delays. This is a tremendous operational advantage for businesses with multiple branches or a distributed workforce with remote users.

Beyond the modern software-defined networking capabilities it delivers, SASE also helps organizations protect sensitive data, mitigate cyber threats, and enforce security policies no matter where an employee is—or what device they’re using. SASE accomplishes this by bringing together several security technologies in a single service. For example, Sangfor’s SASE solution, Sangfor Access Secure, includes:

  • Next-generation firewall (NGFW) to extend the protections of a traditional firewall solution with deep-packet inspection and a custom AI-enabled threat intelligence integration.
  • Secure web gateway to filter web traffic and enforce internet security policies.
  • Intrusion prevention system (IPS) that helps detect cyber threats in real time.
  • Zero-trust network access model that guarantees secure remote access by requiring all users to verify themselves and their devices before they can access network resources.

The upshot is that SASE solutions allow companies to embrace digital transformation—SaaS and cloud-based tools, mobile computing, hybrid, and distributed workforces. At the same time, SASE ensures the same level of security that they enjoyed when data and applications were housed in secure data centers and employees worked in a central office behind the safety of the corporate firewall. 

Next-Generation Hardware Improves SASE Performance

The engineering behind SASE solutions can be challenging, in part because they are a relatively new technology, and in part because they perform so many different functions.

But SASE developers are finding ways to take advantage of recent improvements to edge processors and associated technologies to make their solutions more effective and performant. Sangfor, for instance, bases its SASE offering on next-generation Intel hardware:

  • 4th Generation Intel® Xeon® Scalable Processors form the basis of SASE’s distributed edge nodes and provide a stable and powerful processing platform for the solution.
  • Intel® Advanced Matrix Extensions (Intel® AMX) accelerates the inferencing workloads required by SASE’s AI analytics and behavioral detection features.
  • Intel® QuickAssist Technology (Intel® QAT) is used to speed encryption and compression workloads and improve overall performance of the solution.
  • Intel® Software Guard Extensions (Intel® SGX) helps protect sensitive data even in highly distributed use cases.

Sangfor’s partnership with Intel has been instrumental in bringing its SASE solution to market. “Intel processors provide excellent performance and security,” says Jain. “They offer a powerful foundation for our SASE applications running at distributed edge topology points.”

Digital Transformation Without the Tradeoffs

SASE solutions are still a fairly new idea (even the term “SASE” was coined only in 2019), but they should see significant uptake in the future.

For one thing, almost every business is looking to foster digital transformation. SASE solutions make it possible to do that safely by alleviating secure network access issues and offering a better way to manage cybersecurity challenges of the modern workplace.

In addition, SASE helps eliminate complexity and reduce costs for IT teams. Multiple networking and security functions are managed through unified, cloud-based technology. There’s no longer any need to buy and deploy separate security equipment for each new branch office. It’s not necessary to juggle multiple security tools to accommodate different types of users in a hybrid office. And similar to other cloud-based services, enterprises can easily scale their SASE deployment up or down as business needs dictate.

“SASE represents a new model of network-security integration that is perfectly suited for our current age of digital transformation,” says Jain. “These solutions will deliver excellent value to all kinds of organizations over the coming years.”

 

This article was edited by Georganne Benesch, Editorial Director for insight.tech.

Beyond the Hype: Real-World Edge AI Applications

Posted on by Micah Kaplan

The power of edge AI transforms the embedded systems industry with new levels of performance, energy efficiency, memory optimization, and accuracy. But how can businesses leverage these capabilities to build applications that deliver real value?

In this episode, we explore where and how the latest technology is deployed and how companies should approach their next-generation solutions. We dive into real-world edge AI applications, highlight their impact on various industries, and showcase their potential to solve complex challenges.

Listen Here

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Our Guest: Avnet Embedded/Tria Technologies

Our guest this episode is Alex Wood, Global Marketing Director at Tria Technologies, formerly known as Avnet Embedded. Alex is joined by guest co-host Brandon Lewis, a longtime contributor to insight.tech. Before joining Avnet and Tria, Alex was Head of Digital Communications at Canon and an Account Manager for JD Marketing. In his current role, he focuses on the company’s global go-to market, digital presence, and content marketing strategy.

Podcast Topics

Alex answers our questions about:

  • 1:47 – Current state of the embedded systems industry
  • 2:58 – Driving factors behind edge AI applications
  • 5:08 – Real-world use cases from customers
  • 10:11 ­– When to leverage the latest technologies
  • 17:41 ­– Meeting the different technology demands
  • 21:37 – Processor advancements and benefits
  • 28:39 ­– Final thoughts and key takeaways

Related Content

To learn more about the latest edge AI innovations, see what Intel partners across the global doing in their industries. For the latest innovations from Tria Technologies, follow them on LinkedIn.

Transcript

Christina Cardoza: Hello, and welcome to “insight.tech Talk,” where we explore the latest IoT, edge, AI, and network technology trends and innovations. I’m your host, Christina Cardoza, Editorial Director of insight.tech. And today we’re going to be exploring the embedded systems industry with two special guests.

First, we have Brandon Lewis, longtime friend and contributor to insight.tech, who will be guest hosting the podcast today. And joining him is Alex Wood, Global Marketing Director of Avnet. But as always, before we get started, let’s get to know our guests. Brandon, I’ll start with you. What can you tell us about what you’ve been up to these days?

Brandon Lewis: Sure. So, I’ve been doing a lot of coverage still of the embedded and IoT space. I’ve been getting a lot closer with developers recently, seeing what they’re working with, what they’re working on, from a tools and chipsets perspective, which is always a lot of fun, getting closer and closer to the action.

Christina Cardoza: Yeah, absolutely. And looking forward to the conversation today with Alex. Alex, welcome to the podcast. What can you tell us about what you do at Avnet and the company itself?

Alex Wood: At Avnet, we’ve just launched our new compute brand, Tria, which is born out of the old Avnet Embedded business, which used to be Avnet Integrated, which used to be MSC Technologies. So it’s a bit of an evolution over the last few years, but we’ve got a really good, strong brand now.

So I’ve been focusing entirely on launching that new brand for the last six months. It’s nice to take a step back and have a cup of coffee with you and talk a little bit more about technology and less about branding guidelines.

Christina Cardoza: Absolutely. Before I throw it over to Brandon, Alex, I wanted to start the conversation, especially since you have this new product coming out. What are the technology trends? What’s going on in this space that you guys launched a new line, or especially with edge and AI becoming more prevalent in the industry? What are some things that you see going on?

Alex Wood: I think we’re at a nexus point, really, in the industry. With AI there’s a lot of emphasis on putting things into the cloud, and there’s a lot of pushback from people that want to put things on the edge as well. So you’ve got one half going to the cloud, the other half going to the edge, and both of them have their own challenges and potential setbacks.

So that’s really what we’re seeing at the moment, is customers are saying, “We want to leverage this, but we’re not entirely sure how we can leverage this.” And it really is a sort of—there’s no perfect silver bullet. So we’ve got to find the right path for our customers.

Brandon Lewis: It’s interesting you bring that up, because obviously a lot of people who are going to the cloud are really looking for things like more performance, working with bigger data sets usually. And on the edge you tend to think that you have a need for lower power, quick inferencing. But then we see all these GPUs and stuff coming out, these huge, powerful GPUs.

And I wonder, number one, what are some of the applications that are driving things at the edge that you see. And then, do they really need the performance of a GPU? Can they get away with something else? Are you seeing more low power at the edge, more performance requirements? What is it?

Alex Wood: Yeah, I think power is the key thing, right? That’s going to be the make-or-break for AI. At the moment, AI is super power hungry. It’s consuming a vast amount of data. It’s really putting Bitcoin—it’s making Bitcoin look almost power efficient right now, with the amount of power it’s consuming.

And I think, for a lot of businesses, people don’t realize how much power AI applications consume, because they don’t see it. They’ve sort of outsourced the demand. Like, you run an AI application at the edge, it’s hugely power hungry, and you have to deal with that problem—the power and the heat, at the edge. If you’re sending it off to a data center, you don’t see the challenges that it brings up. So it’s easy for people to forget about that.

So I think reducing the power requirements of performing these applications is going to be a key challenge. And that’s going to make or break whether or not AI sticks around in this hype cycle—depending on how you define AI and how it works. And accessing those large data models, being able to process things and also absorb data and processes in real time. The applications all require more efficient—more energy efficient, more heat efficient processing. And I think that’s going to be the challenge.

Brandon Lewis: You’re a marketing guy. By the way, thanks for the rebrand.

Alex Wood: Sorry.

Brandon Lewis: No, no, no—I was going to say, I love Avnet. It was sometimes confusing which Avnet I was referring to, right? So I think the rebrand was great with Tria.

So, this push of a lot of these super—what we would consider embedded—like super big or super high-performance processors—based on what you said, is this marketing? Are we just marketing to get—to sell more units? Do we really need that? And what are some use cases that you’re seeing in real-world use cases? We all hear about computer vision and stuff like that, but what’s the reality like from your customers?

Alex Wood: There is that speeds-and-feeds element of marketing. So, it can perform an extra amount of tops; it can clock at this frequency; it’s got even more RAM. And if I’m building my gaming PC, then that’s a sort of like, “Oh, this is great. I want to be able to get this extra amount of frame rates. I want to be able to render videos much faster.” But at the same time, you then have to—like, upgrading graphics card at my last upgrade, I had to get a PSU that was twice the size of the previous PSU. And you’re just like, “Wow, I’m pushing a thousand watts now to run a proper PC rig” when it used to be, like, 300 watts was a lot. That’s triple the amount.

For customers, that’s the issue. We had the energy crisis recently; that brought it to the top of the agenda. And now it’s eased off a little bit for now, but it’s not so long before I think it’s going to come back up again—the energy consumption, the power, is going to be critical. So it’s not so much about getting a more powerful processor, the most powerful processor; it’s about balancing consumption, longevity, capability, specific to the application.

For customers like that, okay, there is a marketing element. You want to buy the absolute top of the range, the flagship processor, when actually you might not need that. But sometimes you do. And it depends on the application for what you’re going to do. I’m the least marketing-y marketing guy in that respect. I’m kind of like, I’d rather sit down with the customer and say, “Okay, tell me what you’re actually building.” Rather than just say, “Yes, you need the top of the range. You need the i9 immediately.”

Brandon Lewis: What are they building? What have you seen?

Alex Wood: There’s loads of different things that we’re working with customers on at the moment. And a lot of the applications, I mean, there’s a crazy amount. Everything from new farming applications—I was reading about an opportunity, mentioning no names, I mean, there’s a lot of articles at the moment about more efficient farming and artificial intelligence being used as an alternative to things like dangerous forever chemicals that are being put into the soil.

So can you train an AI robot to move around fields and identify weeds, being able to tell weeds and pests apart from crops and non-harmful animals, and to be able to organize accordingly. One of my friends works in the farming industry here in the UK, and he works in a farming-management industry—so, crop checking. And he has to walk through fields taking photos of the different plants and then educate people working in the fields to tell the difference between the different varieties of the plant and which one to select for breeding to build the best crop.

And you can create an AI application in the field that does that for you. You don’t necessarily want to put all of that content into a data center; you want to be able to program the robot at the edge to be able to do that. So we’re seeing applications like that in agriculture. And those are edge-based applications. You don’t necessarily have a reliable cell data connection all of the time. You want to be able to do that edge-based AI recognition.

And then at the opposite end of that spectrum—so, you’ve got the massive industrial agriculture use case, and then we’ve got automatic lawnmowers for people at home and being able to map the best path around the lawn, but then also being able to spot hazards and deal with hazards around the lawn as well. So one is a sort of great future-facing altruistic solution; the other one is a more practical, real-life solution. But it’s those practical challenges in the real world that really put the technology to the test.

Brandon Lewis: Are both of those vision applications, I’m assuming? Like camera vision?

Alex Wood: Yeah, yeah. So both of those—I mean, both customers—one is vision, one can be more radar-sensor application, but vision is where the jump is in terms of the processing requirements. So that live-vision AI, so being able to understand what it’s looking at as quickly as possible, identify it reliably, and act on that identification instead of having to send signals back to a data center for crunching and then get it back again. It’s being able to do that in a short amount of space and a short amount of time.

Brandon Lewis: So this is exactly where it’s like—okay, well, you got your trade-off time. It’s decision time, right? Because now you’re saying—all right, well, you’ve got vision out there, and these are probably both mobile, I’m assuming, or semi-mobile, right? And you have to send, at least in the industrial-ag use case, you’re sending that back somewhere, right? So is this the place where you’re like, how many GPU execution units can I fit into this? Or are you really, with Tria now, are you taking it case by case and saying, look—I mean, from a cost perspective—let’s figure out form, fit, and function here. And it’s not top-of-the-line. Is that the case?

Alex Wood: A lot of customers will have several different tiers of the product that they’re creating, especially for different markets where there’s a different appetite and also different sizes of the amount of things that they need to crunch. So for agriculture you’ll see that there’s the top of the range, where they want to have mass-scale farming—say in America with the giant fields, and they want to be able to do things at speed—they’ll have the top-of-the-range solution. You buy something really big; it will work in the field; It’s going to cover a huge amount of distance in a huge amount of time for a giant farm. So they have the money, they have the ability to invest in that.

And then you’ll want to have a slightly slower, slightly cheaper, mid-range application as well. And then you want the lower-end range as well for the market, and then let the consumer decide. Obviously you want to sell them the best solution, but sometimes it’s not going to be an option. And it’s balancing—most customers will have various different levels of capability and sell that to the end user based on their application.

And for me that’s where the industry is driven forward by the actual application and whether or not the end user feels the need for that amount of use. I’m always reminded of the picture that does the rounds on the internet of the field and the path that leads around the corner, like an L-shaped corner. And then there’s a trodden path across the field where people have just walked across diagonally, and it’s like design versus user experience.

And I think that, like, the last cycle of AI, there was all of this sort of exciting talk about what was possible, but at the end of the day what was successful and wasn’t successful was defined by people actually using it and finding it useful. So the applications that were created—some of them stuck around, some of them didn’t. It was the same with blockchain when blockchain was skyrocketing in usefulness, and the same with NFTs, Bitcoin, that kind of thing. People actually finding it useful as an application and being able to use it every day decided what stuck around and what didn’t.

Brandon Lewis: The same thing seems to have happened in the IoT space. There were a bunch of different use cases that were really pushed hard, like smart-home stuff. And there’s a point at which as a consumer—not just a B2C consumer but any kind of consumer—where you just either don’t need any more of that or it’s just not really practical. It was a great proof of concept, but it’s not useful at the scale that it’s being promoted.

And I think we run into that danger zone here with AI too, where it’s like there’s a lot of vision-type stuff that’s getting pushed, and it’s cool, and I know that the margins are bigger there. But ultimately a lot of the actual deployments aren’t going to be exactly what you see out in the media. And it sounds like what you’re talking about with the trodden path across the fields, right? It’s like the use cases, the demand in the market is going to start driving exactly where this technology goes and then how it evolves.

Alex Wood: Yeah. You knew that the IoT concept had reached the top of its hype cycle when there were IoT toasters on the market. And, okay, like we were saying before, there’s different tiers of the products that’s available. Some people will go for that top tier and some people will just be like, “I want my toast to be slightly more toasted. I just turn a knob on it, same as I did back in the 1950s. It doesn’t need to be any more smart than that.”

I do—like, I recently upgraded my aging fridge to a semi-IoT fridge that tells me if the door is open or if the temperature’s too high or too low. And for me, I don’t need a fridge with a screen on the front that gives me information about the weather, because I’ve got a separate display in my kitchen. I don’t need something where you knock on the door and it shows me the products behind it. I don’t need a camera in there, but I do like it if it warns me if the door’s been left open and it beeps on my phone. And that’s usually because my partner’s been loading food into the fridge and forgot to close the door. And then I’m in here in my room and I’m just like, “You left the fridge door open.” Those real life applications are what sticks around.

So IoT is now quite a mature market, where the businesses that are investing in that level of technology, they put all of the technology into the device. The consumer demand for that sort of technology cools off a little bit to a level where the consumers understand what’s beneficial to them in their everyday life.

We’ve got another customer that we’re working with that makes industrial cookers. So, for cooking consistently huge amounts of the same identical foodstuffs over and over and over again. There’s an IT model—an IoT model there, because you want to be able to control all of the different ovens and also manage a hundred different ovens at the same time and know if one of them is over temperature or under temperature—that kind of thing. There’s applications there that we’re working with where that is a requirement, where it might not have been 50 years ago when cookers were being used at an industrial scale like that.

Brandon Lewis: So, unfortunately what happens with this hype cycle, like you mentioned, is that everybody has these huge ideas, these grandiose visions of what the future is going to be like. Where with IoT, for example, it was everything is going to be connected, and your toast is going to be ready in the morning and your car is going to be sitting there waiting to drive you off to work and it’s going to be perfectly climate controlled, and by the time the market starts to mature and people realize it’s going to cost you a quarter of a million dollars per consumer to realize that vision and it’s not going to happen everyone experiences a sort of letdown—that’s the trough of disillusionment, right?

But that doesn’t mean that the technology is actually dead or even unsuccessful, right? It just means that it’s evolved in some different way, and I think what you’re describing with AI here and even that last industrial-ovens example is like, hey, there are a lot of use cases out there that aren’t necessarily the biggest, baddest processor-RAM combination that you could potentially have, but the volume’s there and it exists.

Alex Wood: That connects up with what we were talking about with power efficiency—so, understanding you get all of the innovation, the excitement, all the things we could add and then you say, yeah, but I need realistically, practically, to run it with this amount of power draw in order to get what I want. So you’ve got to sacrifice something in order to get something else.

Sort of like with an electric car: you add loads of bells and whistles to it, it gets heavier and heavier to the point that the range drops. And then you’re—well, I want a long-range model, so I’ve got to increase the aerodynamics, which means making it look a little bit less attractive and strip out things like power seats in order to reduce the weight as well. So you’ve got to find that middle space, that sweet spot in these sorts of applications.

Brandon Lewis: How does the portfolio—Tria—expand or develop in order to meet that range of requirement?

Alex Wood: I think we’ve got a pretty good range that goes from tiny little low-power compute applications all the way up to the COM-HPCs with the server-grade Intel processors in them. So the COM-HPCs with the Intel processors are designed for edge-based image processing and AI applications, but they’re larger as well. So you have to have a balance between size and power consumption and what they’re capable of.

So a lot of the larger, the COM-HPC modules, are this sort of size—they’re sort of motherboard sized, which means that you’ve got to put them inside a dedicated case. You couldn’t just embed them directly into a product unless it was a really big product. So for things like edge security or public transportation—so AI applications and public transportation is another thing that we’re working on at the moment—being able to take data from a huge number of sensors from a train or other vehicle or train station, analyze them all, react to them in real time—that pretty much requires an on-location server, because sometimes you can’t rely on the data network being reliable. And that means that we’re using those for those sorts of applications in standalone servers.

But a lot of the requirements—we’ve got ones for industrial automation. So, again, we’re working with Intel on cobotics with one of our customers, building real-time image sensors into a cobotics—a cooperative robotics—environment. A robot can operate in the same space as a human, safely. So if the human moves into that space, the robot arm stops moving, can move around. If the human picks something up, the robot knows where it is and can take it off them again.

We were demonstrating an early example of that at Embedded World in Nuremberg this year. That was built around a combination of the Intel-based ComExpress modules that we have and the Intel-based—actually no, it’s Intel-based SMARC modules—and then our Intel-based COM-HPC modules for the image processing. And those two things communicating with each other, so, getting the signals from the cameras analyzed and then communicating with the robot in real time as well.

So there is that, sort of, how useful is the environment that you’re creating, the application that you’re creating there versus the amount of power, the amount of processing that you need, the amount of space you need in that environment as well? For some customers, that’s a pinnacle. So it’s giving them the option to say, “Okay, well, I need cobotics; I need to have a reliable environment, and therefore I need that extra processing power and the associated costs that come with setting that up and developing and installing it.”

Whereas other manufacturers, they might want to just have an enclosed robotic space, no cobotics required. You got to—like you were saying before—you have to create the potential for innovation. So you have to inspire the customers with that new technology, that new possibility, and then let the customer then build that application around it. And if it works for them, then that creates the foothold for that technology to develop further.

And sometimes you’ll create that new technology, like a lot of the AI applications that we’re seeing at the moment, where the customer, the user, can’t really find that killer app point that becomes a foothold for the technologies to develop.

Brandon Lewis: Tria—I almost used the bad “A” word now, the old “A” word. Within the Tria portfolio obviously it’s pretty expansive, right? I mean, there’s lots of options. What does the Intel portfolio look like? I mean, are you offering Atom, Core, Xeon You know, the sort of the gamut, or what does that look like in terms of scale?

Alex Wood: Yeah, pretty much the full gamut. I think within the mobile-processor space, like I said, up to the COM-HPC level we can put server-grade processors onto those. But at that point you may as well have an actual server. So it depends on the size, the shape that you need to put it into. So, yeah, we typically offer the Atom® and the Core™ series and the Xeon® series at the server end—we have those. It’s really cool to see what the product team does, putting things into such a small space.

I’ve been working with motherboards and processes for motherboards for years and years and years, so to see that sort of computing application in such a small package with heat management, thermal management, is a fine art. And watching the team develop those sorts of applications in the environment that the product’s going to be used in is a fascinating challenge. So being able to deploy an Intel processor and its capabilities and the new AI-based processes we’re working on as well, to bake those into a small product to be able to use at the edge is pretty exciting.

Brandon Lewis:  Well, cool. I mean, it’s really exciting to see more of the AI in action than AI in advertisement, right? So, really looking forward to seeing how this continues.

Christina Cardoza: Brandon, I actually wanted to ask you, because you covered Embedded World for us this year—which feels like it was last year at this point already—but there was a lot of next-generation edge processors that came out, that Intel launched. Intel—new core processors called Ultra Intel® Arc™ GPU. So I’m curious, what have you been seeing around the industry, especially as we’ve talked about all these use cases and constraints of how the latest processors and technology advancements are helping some of the partners in this space?

Brandon Lewis: There’s obviously the software side and the silicon side. On the software side you’ve got DevCloud, and OpenVINO™ has got a really good foothold, really helping streamline and accelerate the development of models. And there’s even Intel® Getty™, which is even further back on the training side and just making it easier there.

On the silicon side—man, the Core Ultras, like the AI PCs, I think that they’re a really nice fit in this sort of spectrum that Alex is talking about, because they enable you to scale up and scale down even with inside the same skew, right? Because you’ve got a lot of different compute that’s available to you. These heterogeneous processors where you can say, “Look, I want a performance core; I want an efficiency core from a CPU standpoint.” But then also they’ve got graphics-execution units built, integrated GPUs, where you can do acceleration there. And then you bring in neural accelerators.

So you can get this sort of ability to move your application in one direction or the other based on what is available on the SoC or chip set. And that just gives you so much flexibility, because at that point—to Alex’s point about efficiency and power consumption—you’re using the right core for the right workload, right? And that’s really ultimately what it’s all about, because that allows something that would traditionally have been a smaller or less expensive processor to accomplish more. And really that’s kind of the name of the game here.

Alex Wood: That’s a really good point, Brandon. I was at Intel’s AI event recently. They had that global event where they showcased all of their latest AI technologies. The applications there—to look at some of the partners that were showcasing—were fascinating for how you can take AI to accelerate an application at the edge. There were things like supermarket-checkout applications which were automatic checkouts that recognize what it is you’re holding, and queue management automating supermarket management as well.

But it was really cool to see the applications that Intel was developing at the Olympics. So, the athlete applications that they developed there—that’s a really great way of taking the technology and showing a real-life use case to capture the imagination of potential developers of the technology. And the case study video that they showed of the technology being used in Africa to sort of scout a huge number of potential athletes and then find potential future Olympians based on image processing using that platform—that was really cool, that really captured my imagination. It really stuck with me.

But being able to take that AI processing to the edge and in a laptop as well, it goes back to what we were saying at the beginning—taking that high power today, it’s hugely—they’re large units, they’re very powerful—compressing it, making it smaller, making it more energy efficient, being able to put an AI application into a laptop, a laptop-sized device that can be used in the field is really exciting. I think it was Dell that was up on stage that was showing the laptops that they’re going to be releasing with built-in AI applications. So it’s an AI device instead of a computing device and really leaning into that collaborative AI-application environment.

You’ve got a great example from the Olympics that Intel’s done, but it’s a blank slate. I’m really excited to see what developers do with that amount of AI-processing technology at the edge, instead of having to depend on sending stuff back to a huge data center and back again. And I think that’s going to be a turning point, really, for the future for AI at the edge.

Brandon Lewis: Honestly, I think a lot’s going to be about sustainability, and something I forgot to bring up was—man, have you ever put a farmer’s market piece of produce next to a supermarket piece of produce? It’s weird.

Alex Wood: I don’t know where you’re going with that, Brandon.

Brandon Lewis: Well, you were talking about not using chemicals, right? Not having to use as many chemicals. And when you put the farmer’s market versus the supermarket—like, something is not right here. But sustainability in the future I think is really important, and I think all the things that you’ve been talking about and we’ve discussed today will help us on that path.

Alex Wood: Yeah, for sure.

Christina Cardoza: It’s amazing, all the different use cases and everywhere you can go with these AI applications. I can’t wait to see where else we go, especially with partners like Avnet.

So, it’s been a great conversation, guys. Thank you for joining. Before we go, Alex, I just want to throw it back to you one last time, if there’s any final thoughts or key takeaways you want to leave with us today.

Alex Wood: Like I said at the beginning, and like we were kind of leading back into at the end there, I think that AI is at a nexus point at the moment, and I think edge computing is at a nexus point as well. So that advancement in edge-based AI applications—so, being able to take it away from the cloud and onto the device, that’s the nexus point. If you’re watching this, find those applications and tell us about them if you’ve got them. I think it’s a really exciting time to be working in computing on a small-form factor with AI in this space.

Christina Cardoza: Yeah, and I invite all of our listeners to visit the Avnet website, check out their new product line, see how they can help you take some of your AI efforts and initiatives off the ground.

So, thank you both again. Brandon, it’s always great connecting with you. You’ve always been our embedded-systems expert. And thanks to our listeners. And thanks, Alex, from Avnet. Until next time, this has been “insight.tech Talk.”

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel® trademark information.

This transcript was edited by Erin Noble, copy editor.

AI Joins the Fight Against Retail Loss Prevention

Posted on by Micah Kaplan

Retailers face a myriad of challenges, from competing with online sellers, attracting and retaining workers, to creating seamless customer experiences. But one issue continues to escalate: retail loss prevention, particularly shoplifting.

“I’ve heard everything from shoplifting doubling over the last 12 months to quadrupling over the last month,” says Matt Redwood, Vice President of Retail Technology for Diebold Nixdorf, a retail and banking technology company. “Regardless of the magnitude, it’s a really complex problem because the types of offenders and their methods vary widely.”

Some thefts are opportunistic, motivated by rising inflation and the impulse to steal small items. Then there are professional shoplifters targeting specific stores and brands. And some are driven by desperation, with people stealing to feed themselves or their families due to financial hardship. It’s also important to remember that not all losses are intentional; customers might accidentally forget to scan items or experience payment processing errors.

With such a diverse range of factors contributing to retail shrink, how can retailers address the issue? “Retailers are having to level up in regard to the technology that they use, the sophistication of that technology, and optimization of that technology across the store and its employees,” Redwood explains.

Video 1. Listen to Matt Redwood, VP of Retail Technology for Diebold Nixdorf, explore the use of AI in reducing retail shrink and streamlining checkout processes, in this insight.tech Talk episode. (Source: insight.tech)

Combating Retail Shrink at Self-Checkout

One of the most obvious places to start is at the self-checkout area. Here, it is presumed the easiest place to steal since it is technically unstaffed.

To protect this area, technology solutions like Diebold Nixdorf’s Vynamic Smart Vision come to the forefront. This AI-powered system detects potential theft or unintentional errors by identifying behaviors such as missed scans, item manipulation, and product concealment.

Once an incident is flagged, the system can alert store staff or directly prompt the customer to correct the issue. This proactive approach is particularly effective in addressing unintentional errors where a customer may be distracted and leave items under their cart.

“What we’re finding is that with the use of customer nudging—such as audio or visual alert that says, ‘There’s items left in your basket, do you want to scan those?’—about 85% to 95% of customers will self-rectify,” Redwood explains.

If the customer does not do so, the solution provides store staff with detailed information about the incident, including what happened, how it happened, and where it happened so they can decide how best to intervene.

To effectively implement a system like the Vynamic Smart Vision, Diebold Nixdorf recommends installing a dedicated fixed camera above each self-checkout device rather than using existing CCTV networks.

“Technology is constantly evolving, with equipment being moved around stores, new units being added, and the layout of POS lanes and self-service areas changing,” says Redwood. “Every time the camera’s position relative to the checkout area changes, the AI requires retraining. By using fixed cameras, deployment is much quicker and easier, even in dynamic store environments.”

“#Retailers are having to level up in regard to the #technology that they use, the sophistication of that technology, and optimization of that technology across the store and its employees.” – Matt Redwood, @DieboldNixdorf via @insightdottech

CCTV networks can still play a valuable role by providing a secondary viewpoint of the self-checkout area and the overall solution. This additional data can enhance the solution’s accuracy through data triangulation.

Intel is crucial to the success of this solution. According to Redwood, whether a business opts for an attended POS, self-checkout, or kiosk, each operates on a single Intel platform. By harnessing Intel technology, devices not only gain sufficient computing power but can also execute several use cases directly on the solution itself.

“Our partnership with Intel unlocks many retail benefits. Not only does it help retailers reduce the expense and maintenance associated with large, space-consuming servers but it also enhances energy-efficiency through lower power consumption,” says Redwood.

Retail Loss Prevention Across the Store

Redwood envisions a broader application for Vynamic Smart Vision beyond self-checkout areas.

“We are only seeing 30% of total store shrink happening at self-service checkout, which means the rest of the shrink is happening elsewhere in the store,” he explains. “This is a ‘whack-a-mole’ type journey where we must explore one loophole and then try to limit that loophole as much as possible with technology. That will either stop shrink from happening or it will drive malicious theft to other areas of the store, and then we will have to deploy the technology elsewhere.”

Diebold Nixdorf is developing a comprehensive AI-powered solution that tracks shrink and customer behavior throughout the store, including aisles, entry, and exit points. This technology can be integrated into any device with a camera, creating a connected store where data from various sources feeds into a central AI model.

“We can identify suspicious actions in the aisles, follow the customer to the checkout, and intervene at the appropriate moment, such as when we detect concealed items,” Redwood emphasizes.

In the future, a scalable solution will be essential for retailers to experiment and learn while building a robust, store-wide AI system capable of handling multiple endpoints and use cases.

 

This article was edited by Georganne Benesch, Editorial Director for insight.tech.

Blueprint for Smart, Sustainable Buildings

Posted on by Micah Kaplan

Steel, concrete, wiring, plumbing—these things make a building. But what makes a smart building? And how does a smart building evolve to be a smart and sustainable building? These questions have grown more pressing with the current push to bring workers back into an office, increasing occupancy and subsequent pressure on building systems. New buildings have an advantage here of being able to incorporate sustainability and smart systems from the get-go, but what about older buildings? Are they beyond the help of technological solutions?

Answering some of these questions are Lauren Long, VP of Brand and Marketing at Nantum AI (formerly known as Prescriptive Data), and Maciej Labuszewski, Onboarding Specialist at Blue Bolt, a cutting-edge solutions provider (Video 1). They discuss the role that smart buildings can play in fighting the climate change crisis, the reason to incorporate AI and data into smart building solutions, and the benefits of partnering with the best technology to power the best solutions, because these are complicated systems and ambitious, important goals, and it all involves so much more than just remembering to turn off the lights.

Video 1. Nantum AI’s Lauren Long and Blue Bolt’s Maciej Labuszewski discuss the journey toward smart, sustainable buildings. (Source: insight.tech) 

What actually defines a smart, sustainable building?

Lauren Long: Every building is different—when it was built, the style it was built in, the materials used, the climate it’s in. So there is no one answer to what sustainability looks like. But all sustainable buildings do share common characteristics, and many of those revolve around intent. You want to avoid waste: So you turn off lights in empty rooms, you use low-flush toilets, you seal the windows, etc. There are many things you can do on that sort of level.

But if you take it a step further and think about the technology aspects, that’s how you really make a building smarter and more sustainable. And it’s because of the data being collected and, hopefully, acted on. But you can’t improve without a baseline, so it’s important to understand what’s happening today and then to make an improvement plan.

The built world is said to be responsible for 40% of global carbon emissions, and 28% of that is from operational processes—so everything it takes to make a building comfortable and keep it operating as it should. If we even made building operations 50% more efficient than they are currently, that would decrease global emissions by 14% alone, and that’s a pretty sizable impact.

A 2024 real estate outlook report from Deloitte says that only 5% of buildings are fully modernized, which means that they have core systems that can easily incorporate emerging digital technologies. However, the report also says that 34% of buildings are mostly converted to these modernized systems, and 30% are currently transitioning from legacy models.

As sensors feed into building systems and give operators more data, change can actually happen. So while buildings are definitely part of the problem, they also have a unique ability to be part of the solution and be role models for other industries.

What do building owners and managers need to focus on in their sustainability efforts?

Maciej Labuszewski: In recent years a lot has been done in areas such as recyclable construction materials and streamlining building processes. But at the same time there has been a shift in the focus around how a building can not only be built sustainably but also exploited sustainably throughout its lifetime. And with more and more aging properties accumulating on the market, achieving a net-zero goal and decreasing a carbon and plastic footprint is an absolute necessity in extending the lifetime of buildings that will otherwise be considered obsolete very soon.

In practice, there are two vectors that building owners focus on. The first is reducing inefficiencies arising from the use of old systems and incorrect assumptions around patterns of heating, light, air conditioning. These incorrect assumptions often stem from a lack of data—data that could help identify targets, set performance indicators, and pinpoint unnecessary costs. But when that data is not available, it becomes essentially impossible to formulate a strategy, let alone implement it successfully.

The second vector is engaging tenants in changing their own consumption patterns. However, in a building with many diverse tenants this is a very difficult task. It requires appropriate communications, appropriate rewards, and of course the time for it to come into effect.

Blue Bolt addresses these issues by considering sustainability factors as integral to every capability that we provide to a building. One example is switching from classic access-control tools—plastic cards and remote controls—to access that is fully stored on a mobile phone. In a big office building, plastic consumption just on access cards can amount to almost 200 kilos per year.

What recent technological innovations make these solutions possible?

Maciej Labuszewski: I think here it’s important to start off by stating clearly that just having a building app is not a game changer in the property market anymore. Many of those mobile solutions only deliver functionalities that are tailored to certain, isolated, very narrow problems and target very specific property portfolios. They simply lack the scope and scale to accommodate the needs of owners, administrations, and tenants.

Building managers now have their sights set on more complex solutions, the ones that can deliver measurable results and have universal capabilities across an entire portfolio with uniform ESG standards, guest management, and resources that don’t require a lot of time to effect the switch from the traditional methods—the access cards and remote controls—so that results can be felt immediately. A strong focus is also placed on the cost-effectiveness of the solution.

This is something that Blue Bolt excels in, because our system employs hardware with excellent flexibility and adaptability that allows us to install it on essentially any building and deploy the entire system within a few days no matter how old the building is. This ties to my previous point about this accumulating pile of aging properties.

What is the role of AI in making buildings smart and sustainable?

Lauren Long: AI and machine learning are definitely tools to help us reach our goals. Data can be consolidated into a unified user interface, and then you can dig into it to find patterns, correlations, or causational relationships between different types of data. It’s almost impossible to try to keep tabs on all that data as a human; AI is the only way to make it scalable. And there’s always the element of human error to factor in.

Where we are in the industry in terms of using all this data is really unique. I often recommend the book Crossing the Chasm by Geoffrey Moore from a marketing perspective. But the building space is in a really similar situation, so you could say we need to cross the engineering chasm. Let me build the situation for you.

In building operations there are sustainability managers, and they are the goal-setters. They collaborate to set goals, find solutions, and track progress around building sustainability. There are also the asset managers, who are the cost-optimizers. They analyze, invest in, and manage real estate for long-term value and sustainability.

Think about the #technology aspects, that’s how you really make a building smarter and more #sustainable. And that’s because of the #data being collected and, hopefully, acted on. @nantumai via @insightdottech

But then there’s a third group—the building operators, who are the change-makers. They maintain efficient operations and optimize building performance for sustainability. And the chasm I mentioned is between the first two groups and the building operators when there isn’t any real-time performance data.

But without actually using the data when you have it, what’s the point? Using AI to make actionable insights is really important for creating real-time accountability. This is something Nantum AI does with daily AI engineering recommendations and also our compliance analysis, where we’re able to assess the number of recommendations that our system sends to the engineers, the number of them that are actually acted upon, and then the resulting success of that.

Do you have any examples of how owners can make sustainability changes?

Maciej Labuszewski: One of our very first clients was a coworking-space brand and the owner of commercial real estate with offices located in five countries. One of the Blue Bolt features that really caught their attention and that proved really successful was switching off elevators during off-peak hours. This is something that is helped by our AI system, which aggregates the data from the building’s access control and the elevator systems and combines it into easily navigable information that can be viewed by property owners and asset managers. It is this kind of solution that guarantees that we are not just another gadget but a tool that helps make informed decisions on a daily basis.

Lauren Long: One of our favorite focal points at Nantum AI is helping companies meet their energy conservation measures, or ECMs. One of our customers is Jamestown Properties; they have the Waterfront Plaza in San Francisco. They wanted to use Nantum AI to generate savings with a smart shutdown during the day, changing the building system’s operations based on real-time occupancy. Against a 2019 baseline they have saved over $71,000 and almost 285,000 kilowatts of energy. Just that small change in building operations can make a difference.

Talk about the technology partners that help create these solutions.

Lauren Long: We focus mostly on the software aspect, and we partner with a lot of companies that have access to accurate and precise data. The better the data in, the better the data out, right? But we also rely heavily on hardware, and we have found that buildings that are powered by Intel chips have the most capacity to become smart.

All of our buildings operate on an Intel Gateway, and this makes us a perfect partner with Intel. Our goal is to become the smarter-building provider for every building in the world—and I believe that every building could become a smart building. This is made possible and powered by Intel.

What else should people think about in the smart building journey?

Maciej Labuszewski: We’ve been talking about technology, but something very important to mention in the context of sustainability is that it must also be used to unite people over a common goal. We are talking about something that isn’t just a business decision but also an ethical decision that may impact the collective future.

The solutions that we are working on at Blue Bolt—it’s not just a business model to realize but also a higher concept that is good to have in mind when thinking about the needs of our stakeholders, our users, and the way we can maximize the collective goodness.

Lauren Long: I completely agree with what Maciej just said. There’s this urgency to creating and maintaining sustainable buildings. We really need to work together to eliminate data silos and challenges across different departments, but we also need to implement the technology we have and realize what technology we need so that we’re able to hit our goals. AI is a huge tool and a huge asset to have in our toolbox, and it can make reaching our global decarbonization goals possible.

Related Content

To learn more about sustainable smart buildings, listen to AI Innovations: The Foundation for Sustainable Buildings and read Mobile Access Control Promotes Sustainable Buildings and From Smart Buildings to Intelligent Ones. For the latest innovations from Nantum AI, follow them on X/Twitter at @nantumai and on LinkedIn. For the latest innovations from Blue Bolt by NTT, follow them on LinkedIn.

 

This article was edited by Erin Noble, copy editor.

Building the Sustainable Smart City

Posted on by Micah Kaplan

When we talk about the Internet of Things and the high-tech, precision machinery involved, it’s easy to limit our view to things that happen only inside—in factories, hospitals, and other buildings. But plenty of IoT applications are for the built world outside as well, and some of the most compelling involve smart cities. Smart cities provide a new way of thinking about traffic, utilities, transportation, and a host of other complex systems.

But step out into the urban environment, and another factor comes into play, one that we’ve become more and more aware of in recent years—sustainability. Cities are major producers of pollution and carbon emissions, so it’s critical that the transition to smart city can also deliver sustainability solutions, even taking the burden off human efforts to improve city livability.

Experts Jody Cheng, Product Solution Manager, and Manny Hicaro, Application Engineer Supervisor, at Axiomtek, an industrial PC field expert, speak to challenges and rewards of the sustainable smart city (Video 1). They discuss crucial roles of AI and edge computing to urban sustainability solutions, infrastructure needed to implement those solutions, and partnerships that can leverage this technology to make it work for the benefit of everyone who lives or works in a city.

Video 1. Learn why smart cities need to become more sustainable and discover how they can start achieving their goals in this insight.tech Talk episode. (Source: insight.tech)

Why is sustainability a goal of smart cities today?

Jody Cheng: First off, more and more people are becoming aware of climate change and its effects. Urban areas can contribute a lot to greenhouse gas emissions, and with the global population becoming increasingly urbanized, the pressure is on cities to tackle these environmental issues. According to a recent study done by the World Bank, 56% of the world population lives in cities, which is about 4.4 billion people. It’s expected that by 2050 seven out of 10 people will be city dwellers.

This has really pushed cities to be more proactive about reducing their carbon footprints, adopting practices to combat climate change and improve air quality that will boost the overall quality of life for their residents. At Axiomtek, we’re really excited to see this trend towards sustainability growing. It’s all about making sure that our environment stays healthy and thrives in the long run.

What are some of the technology solutions that can help sustainable smart cities?

Jody Cheng: Sustainable smart cities are cities that work smarter, not just harder, for both people and the planet. This is achieved by using smart grids and IoT data collection, along with innovations in buildings, transportation, and resource management.

Smart grids are the foundation for sustainable smart cities; an advanced electric grid employs monitoring tools to efficiently manage electricity use. This will also help integrate renewable resources, like solar, and reduce reliance on fossil fuels. For example, traffic lights that can adjust based on the real-time traffic can ease congestion and lower emissions.

But the possibilities are endless. Edge computing and AI make things even better by processing data right where it’s collected—think of a network of IoT sensors around the city tracking air quality, water usage, and traffic. This means quicker decisions and more efficient operations, helping city managers optimize resources and reduce waste and making smart cities even smarter.

Another big driver of sustainability is smart buildings and homes; LEED-certified buildings that follow strict sustainability standards are becoming more common. These buildings have energy-efficient systems—like automated lighting that adjusts based on the occupancy or HVAC systems that optimize heating and cooling.

Transportation is another key focus for cities aiming to reduce emissions. Cities are developing EV-charging infrastructure and promoting public transit systems with passenger tracking and traffic optimization. These innovations help cut emissions, ease congestion, and offer eco-friendly travel options for residents.

Managing resources like waste and water is also key to sustainability. Automated waste collection and energy-to-waste conversion help reduce landfill use and promote recycling. As for water—smart irrigation and advanced treatment processes optimize usage and cut down waste.

How do you see edge AI being implemented across smart cities?

Manny Hicaro: One example that Jody mentioned, and I can talk more about, is transportation. While EV charging and promoting public transportation support sustainability, edge AI can offer many other applications. Automated public transportation can optimize routes and manage passenger flow. In another instance, real-time sensors could optimize public parking. Also, improved autonomous vehicles in the future might even coordinate with each other and with traffic infrastructure to improve efficiency and safety. Edge AI can enhance traffic management, too; more cameras in place can allow for immediate accident detection and traffic rerouting.

Moreover, AI-powered surveillance can boost public safety by detecting unusual activity and predicting incidents like floods or fires. Infrastructure monitoring can detect anomalies that require prompt maintenance, including of road conditions and utility lines. This kind of monitoring can even extend to resources like water and air quality.

Talk more about the contributions of AI and edge computing to these solutions.

Jody Cheng: The advancement of AI and edge computing has elevated these sustainable solutions to a level that we haven’t seen before. AI has the ability to enable energy-efficiency improvements in all city environments, from buildings and factories to transportation systems and more.

“#Sustainable smart cities are cities that work smarter, not just harder, for both people and the planet. This is achieved by using smart grids and #IoT #data collection.” @Axiomtek via @insightdottech

Edge computing processes data locally onto devices such as IoT sensors, routers, or gateways. This proximity to the data source allows for quick decision-making and reduces the need for data transmission to centralized servers. It enhances responsiveness within the system while cutting down on the energy usage and resource consumption associated with transferring large amounts of data.

When combined, edge computing and AI bring together real-time data analysis and decision-making at the network’s edge, minimizing latency and bandwidth constraints. This decentralized approach enhances system responsiveness, reduces network congestion, and cuts costs. For city operations, this can optimize everything from traffic flow to energy-consumption patterns, reducing energy waste and increasing overall efficiency, ultimately improving quality of life while addressing environmental challenges.

What types of infrastructure investment are necessary to make this possible?

Manny Hicaro: Creating smart cities isn’t cheap. IoT sensors and cameras need to be integrated throughout the city in order to gather information on things like traffic, public safety, and environmental conditions. But it’s not just about the devices. Having a solid network infrastructure is crucial, too, and there needs to be a big investment in advanced hardware—powerful GPU-based edge computers that can handle a variety of processors. These machines are essential for real-time data processing and AI tasks.

A network of edge data centers strategically placed around the city can boost the efficiency and reliability of the edge computing while contributing to sustainability goals. These centers not only reduce latency and speed up the processing of real-time information—as Jody mentioned—they also provide redundancy, support the quick deployment of new applications, and improve disaster-recovery capabilities.

So implementing these technologies in existing urban infrastructures isn’t easy. It comes with a high initial cost and the challenge of ensuring compatibility with older legacy systems. This is where collaboration between the public and private sectors is crucial. These partnerships help align technology deployments and public policies, making sure that the solutions are sustainable and effective in the long run.

How has your partnership with Intel and its technology enabled the Axiomtek solutions?

Manny Hicaro: Our partnership with Intel has been key to the success of our smart city solutions; its dedication to advancing technologies aligns perfectly with our goals at Axiomtek. This partnership allows us to use cutting-edge technologies to develop robust and reliable solutions and has helped us stay ahead in AI and edge-computing advancements.

Intel processors provide the high-performance computing power needed to handle complex AI and data-processing tasks. And over time they’ve been fine-tuned to boost efficiency and performance, making sure that they meet the tough demands of city applications.

Another benefit of our partnership is scalability. There’s a wide range of products that Intel lets us customize to our solutions, ensuring that our systems can scale efficiently to meet the growing demands of urban areas. Whether it’s expanding the network of IoT sensors or adding more advanced AI capabilities, Intel technology supports the seamless scaling of our solutions.

Overall, the Intel technology and support have empowered us to develop advanced smart-city solutions and enhanced our ability to implement them effectively. This partnership keeps our systems at the cutting edge of technology and helps to provide reliable and scalable solutions for cities around the world.

Do you have any use cases that highlight the effectiveness of this technology?

Manny Hicaro: Absolutely. One of our standout projects involves an AI-enabled recycling bin that has pretty much transformed waste management in several urban areas so far. These bins use advanced AI algorithms to efficiently sort through the recyclable materials, making the recycling programs much more effective and significantly cutting down the frequency of waste collection.

Here’s how it works. People can easily deposit recyclable waste, like cans and plastic bottles, into these bins. The system then autonomously sorts through those materials, ensuring that they are correctly categorized for optimal resource recovery. This boosts sorting accuracy and overall recycling rates.

Sensors continuously monitor the fill levels in the bins in real time and send notifications when the bin is nearly full. Plus, the system maximizes physical storage space by automatically compressing the waste materials. These AI-powered recycling bins also take a proactive approach to maintenance: They can notify cleaners when they need attention. This streamlines operations and eliminates the need for constant manual monitoring. It results in improved accuracy, higher recycling rates, and greater operational efficiency.

Any final thoughts to inspire people on the road to creating sustainable smart cities?

Jody Cheng: Many people have traditionally viewed economic and environmental concerns as conflicting interests. And in the past, maintaining sustainable operations often demanded significant human resources for management and close oversight. However, we anticipate that the introduction of edge AI will significantly alter this dynamic for urban environments.

We see examples of this in recycling and resource management, where many of those operations that now incorporate edge AI operate more efficiently and autonomously, reducing the need for human intervention. So with edge AI advancement, these activities will become more cost-effective, more achievable, or more efficient.

At Axiomtek, we’re really excited to contribute to the sustainability journey by offering improved methods to fight the greenhouse effect, reduce carbon emissions in the long term, and maybe leave a cleaner environment and brighter future for the next generation.

Manny Hicaro: The possibilities are only limited by our imagination.

Related Content

To learn more about the latest sustainable smart city efforts, listen to Empowering Sustainable Smart Cities with Edge AI and check out our smart cities page. For the latest innovations from Axiomtek, follow them on Twitter/X at @Axiomtek and on LinkedIn.

 

This article was edited by Erin Noble, copy editor.

AI Innovations: The Foundation for Sustainable Buildings

Posted on by Micah Kaplan

Buildings account for a staggering 40% of global carbon emissions. With mounting regulatory pressures, escalating energy costs, and growing corporate sustainability goals, the demand for greener buildings has never been more urgent.

AI is emerging as a game changer in this space with its ability to deliver precise insights into a building’s performance—enabling businesses to optimize energy consumption, enhance occupant well-being, and reduce their environmental impact.

In this podcast, we explore practical applications of AI, revealing exactly how businesses and building owners can accelerate their sustainability journey and achieve tangible green building objectives.

Listen Here

[Podcast Player]

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Our Guests: Nantum AI and NTT Blue Bolt

Our guests this episode are:

Podcast Topics

Lauren and  Maciej answer our questions about:

  • 3:25 – The role of buildings in the fight against climate change
  • 5:31 – Sustainable building goals for building owners and managers
  • 9:20 – Recent smart building technology innovations
  • 13:01 – Leveraging AI to create insights and track performance
  • 16:15 – Inside a sustainable smart building
  • 19:36 – Overcoming challenges of implementation
  • 21:37 – Real-world examples and smart building use cases
  • 27:26 – Valuable technology partnerships

Related Content

To learn more about sustainable smart buildings, read Mobile Access Control Promotes Sustainable Buildings and From Smart Buildings to Intelligent Ones. For the latest innovations from Nantum AI, follow them on X/Twitter at @nantumai and on LinkedIn. For the latest innovations from Blue Bolt by NTT, follow them on LinkedIn.

Transcript

Christina Cardoza: Hello and welcome to “insight.tech Talk,” formerly known as “IoT Chat” but with the same high-quality conversations around Internet of Things, technology trends, and the latest innovations you’ve come to know and love. I’m your host, Christina Cardoza, Editorial Director of insight.tech. And today I’m joined by two special guests to talk about this idea of smart and sustainable buildings.

But as always before we get started, let’s get to know our guests. Lauren from Nantum AI, I’ll start with you. What can you tell us about yourself and the company?

Lauren Long: Thank you for having me here today. It’s a pleasure to talk to you all. So I am Lauren. I’m the VP of Brand and Marketing at Nantum AI, formerly known as Prescriptive Data. We are the creator of the award-winning platform called Nantum. I’ve been in the CRE space for about 15 years now, mostly working at data-driven companies but also on the media side.

And at Nantum AI we empower buildings with actionable building insights. So we focus on saving energy, reducing carbon emissions, and lowering costs without sacrificing any occupant health or comfort. And we also help buildings hit their energy targets through real-time and prescriptive data as well as intelligent recommendations. We’re based out of New York. I’m actually outside of DC. But that’s pretty much us in a nutshell.

Christina Cardoza: Great. Looking forward to getting into how AI and data insights are helping move along this idea of sustainable buildings. But before we get there, I’d like to also introduce Maciej from Blue Bolt. What can you tell us about yourself and your company?

Maciej Labuszewski: Hi, Christina. Also, thank you for the chance to be here today. Like you said, my name is Maciej Labuszewski. I’m a Customer Success Specialist over at Blue Bolt. We are a European-based company offering a versatile tenant-experience app and dedicated self-assembled hardware. Our system combines contactless access within a building’s common areas via mobile app; a reservation panel that allows for bookings and tracking of all key building resources, such as parking spots, meeting rooms, desks; and most importantly for our topic today, a plethora of both short- and long-term solutions that help building owners develop their sustainability strategy.

And as for my role in the company, it is to cooperate with our principal stakeholders—so property owners, managers, tenants—in learning their needs, which are always unique for each type of building, and leading Blue Bolt’s implementation process so that our technology can best address those needs and translate into tangible everyday advantages.

Christina Cardoza: Absolutely. And the reason why I wanted to have this conversation on the podcast today is I feel like 2023, we had a lot of companies more focused on sustainability. They have a lot of corporate goals, a lot of legislations and regulations that they have to meet, as well as this idea of climate change to really make more sustainable operations and transform the way that they’re doing things.

So I wanted to have this conversation because I heard an astronomical number about the amount that buildings are actually contributing to some of these issues that we’re having. So Lauren, I want to start the conversation there. If you can talk about, with this urgent need to mitigate climate change, what is the role of buildings that they play to solving this problem?

Lauren Long: Yeah. So the built world is said to be responsible for 40% of global carbon emissions, and 28% of that is from operational processes—so, everything that it takes to make a building comfortable and healthy and operate as it should. That’s something that we focus on at Nantum AI, is bringing together all the data from HVAC and other building systems and integrating it with other real-time data—like occupancy or people counting; as well as third-party data, including water, gas, electrical demand among others.

So, back to your point about that number, even if we made building operations 50% more efficient than currently, then that would decrease global emissions by 14% alone, and that’s a pretty sizable impact. And since today 90% of our lives are inside buildings and climate change can radicalize weather for the future, we’re likely going to spend more time in buildings in the future.

So buildings have a unique responsibility to create these healthy and comfortable environments for us today and tomorrow, and it’s going to evolve. The needs for how those buildings make these environments comfortable are going to change as the outside environment changes too. So while buildings are definitely part of the problem, they also have a unique ability to be part of the solution and kind of be a role model for other industries.

Christina Cardoza: Yeah, that’s great, and especially a problem that we want to start tackling now that everybody has been working from home. But there’s a push to get back into the offices, so a lot more people are going to be occupying these buildings. So, definitely a priority that we need to focus on now. And when we think about these buildings, it’s not just the buildings themselves. These are large buildings, and there’s multiple different businesses within those buildings. There’s the building owners, there’s managers of business, there’s the business owners.

So Maciej, I’m curious, from your perspective—because you do a lot around the building owners and the managers there—what are their goals when it comes to sustainability? And what do they need to be focused on to really make a dent in these efforts?

Maciej Labuszewski: So, undeniably in recent years a lot has been done in the areas such as recyclable construction materials and streamlining building processes to improve the real estate market. But, at the same time, there has been a shift in the focus when discussing how a building can not only be built sustainably but also exploited in this manner throughout its lifetime. And we have to remember that with more and more aging properties accumulating on the market with each year and tenant expectations continuing to rise, achieving this net-zero goal and decreasing the carbon and plastic footprint is an absolute necessity to extend the lifetime of buildings that will simply be considered obsolete very soon. And this is also actively signaled by the tenants, who are prioritizing environmentally conscious brands over those who put the environmental strategy aside.

So, in practice, there are two vectors that building owners sort of focus on. The first is to reduce inefficiencies arising from the use of old systems and incorrect assumptions on the patterns of heating, light, air conditioning—all basic resources such as those. But the incorrect assumptions also often stem from a lack of data. This data could help identify targets, set performance indicators, pinpoint unnecessary costs. But when it is not available, it becomes essentially impossible to formulate a strategy, let alone implement it successfully.

And the second vector is the attempt to engage tenants in changing their own consumption patterns and launching incentives from the bottom up for the creation of an environmentally responsible community in the building. However, in a building with many diverse tenants of course this is a very difficult task, since it requires appropriate communications, appropriate rewards, and of course the time for it to come into effect.

So Blue Bolt addresses these issues by considering sustainability factors as integral to every capability that we provide to a building. No matter how extensive the level of implementation is, the client can always enjoy the benefits of a more sustainable building. For example, switching from plastic cards and remote controls—which are the classic access-control tools—to access fully stored on a mobile phone. It’s an important step to becoming plastic-free in the building. And in a big office building this plastic consumption just on access cards can amount to almost 200 kilos per year.

Another example is the booking history on our resource booking panel, which can be viewed and analyzed by building administration to make adjustments in real time and adjust the availability of other resources as well.

Christina Cardoza: It sounds like not only do these buildings have to be sustainable, but they also have to be smart, which I talked about in the introduction here. And a couple things come to mind. Because you mentioned using a mobile phone, I assume that the building needs to have adequate network services in order for people to use their mobile phone for access. And then the resource booking—that involves some software and some technology to process to be able to give accurate results and get all that data and see those bookings.

So, Maciej, I’m curious, because things are always changing, what have been the recent technological innovations that are making all of these things that we just talked about possible? And then with those innovations, what is the expectation, not only from the building owners, but from the employees and the consumers to bring some of these into the building?

Maciej Labuszewski: I think here it’s important to start off by stating clearly that just having a building app is not a game changer anymore on the property market. A few years ago this market was only just realizing the potential of introducing PropTech Solutions to the common areas of a building, instead mostly choosing to focus on the smart home apps. Now the market has become quite saturated with the former.

But many of those mobile solutions only deliver functionalities that are tailored to certain, very narrow, isolated problems and target very specific property portfolios. And therefore they simply lack the scope and scale to accommodate the needs of the owner, the administration, the tenants, all the end users, the actual people who reside in the building.

And because of that building managers now have their sights set on all the complex solutions, the ones that can deliver measurable results and ones that have universal capabilities across the entire portfolio. Something they value a lot is a product that ensures uniform ESG, guest management, and the resource aspect at a similar standard or the same standard in all locations, where users have the same interface that is easily navigable, that doesn’t require a lot of time to switch from all those traditional methods that I mentioned earlier—so the cards and remote controls.

And a strong focus is also placed on the cost-effectiveness of the solution, and rapid implementation so that the results can be felt immediately. And this is something that Blue Bolt excels in, because our system employs hardware with excellent flexibility and adaptability that allows us to install essentially on any building and deploy the entire system within a few days.

So, the ability to integrate with all the various access control, parking, elevator systems, in addition to sensors installed in the building itself—all this can be completed within just days, no matter how old the building is. Which ties to my previous point about this pile of aging property that keeps accumulating. So the system is deployable whether in residential, office, logistical, commercial properties—this makes no difference for us, and it’s this universality that truly led to our success so far.

Christina Cardoza: Yeah, that’s a great point that you make about older buildings, because of course newer buildings have the advantage of being able to implement all of this technology in the beginning. But it’s good to know that we can utilize some existing infrastructure, and we can still make some of these things happen in the existing buildings and older buildings that we have, which are part of the problem.

You mentioned there’s also been a problem with lack of data that has made business owners struggle to make some of these things happen. But all of these things we’re talking about, as these things come online, I think we have then the problem of too much data or what to do with that data. How do we really see those consumption patterns? How do we get insight? How do we derive value that is going to make us be able to have actionable insight and actionable decision making?

So, Lauren, I’m curious because, since you do a lot of work with AI, how AI can help create these insights, show us how the building is performing, and then make efforts there?

Lauren Long: Yeah. So, AI is definitely a tool to help us reach our goals using the different types of data. Using artificial intelligence and machine learning, you can consolidate data into a unified user interface and then dig into that data to find patterns, correlations, or causational relationships between different types of data. And it takes a lot less time than it would for a human to do so.

And, like you said, there’s so much data, so it’s almost impossible to try to keep tabs on everything as a human. You would need an entire team to do that. And there’s still the human element or error that can happen there. So when AI does this on a repeatable basis, AI is the only way to make it scalable.

But where we are in the industry using all of this information is really unique. I often recommend the book Crossing the Chasm by Geoffrey Moore. And I normally do that from a marketing perspective, but the building space is in a really similar situation, so you could say we need to cross the engineering chasm.

Let me build the situation for you. So, in building operations there are sustainability managers, and they’re the goal-setters of the group. They collaborate to set goals, find solutions, and track progress around building sustainability. But there’s also the asset managers, who are the cost-optimizers. They analyze, invest in, and manage real estate for long-term value and sustainability. But then there’s the third group, and these are the building operators, who are the change-makers. They maintain efficient operations and optimize building performance for sustainability. So the chasm I talked about is between the first two groups and the building operators, where there isn’t any real-time performance data.

So, why am I talking about all this? Because AI has the ability to create real-time accountability. And that’s something Nantum AI does with our daily AI engineering recommendations and also our compliance analysis, where we’re able to assess the number of recommendations that our system sent to these engineers, and the number that were actually acted upon, and then the resulting success of that.

But there’s also—AI can assist in fault detection, where you can discover that the building isn’t actually running to its full savings potential. So, like you said, there’s so much data and there’s so much insight that you can derive from those. But without actually using the data, what’s the point? So using AI to make actionable insights is really important.

Christina Cardoza: I love how you described the building owners or the building operators as the change-makers, because I think that’s very powerful. They are the ones that have the ability to make these changes. And I think not only in addition to all of the data being overwhelming, all the changes that they can make are a little overwhelming, and they might not know where to start. We’re talking about heating, lighting, occupancy, mobile networks—all of these different things.

So I’m curious, Lauren, if you can talk a little bit more about what a sustainable building actually looks like. What are the things that they should be focused on? And is there one thing that they should start and then grow? Or is it happening everything at once? Can you talk a little bit more about that?

Lauren Long: Yeah, sure. So, there’s a saying that everyone uses that says “Every building is a snowflake.” And that phrase is used so much that it’s no longer a snowflake—it’s very common. But every building is different, from construction—including when it was built, the style it was made in, materials that were used, the climate it’s in. So it’s hard to say that there’s—I mean, there is no one answer to what sustainability looks like for a building.

But to that point, all sustainable buildings do share common characteristics, and many of those revolve around intent. You want to avoid waste, so you turn off lights in empty rooms, which is super easy to do with sensor integration these days. You use low-flush toilets, well seal your windows, use revolving doors, the outside. There’s many things that you can do on that sort of a level.

But if you take it a step further and think about the technology aspects, that’s how you really make a building smarter and more sustainable. Sustainable buildings use technology, and they’re smarter than the average building because of the data they’re collecting and hopefully acting on. You can’t improve without a baseline, so it’s important to understand what’s happening today and make an improvement plan so that you can become better. And that’s the only way to do it. This extends to space utilization and making spaces more comfortable and ready to support productivity for occupants.

But I also want to talk about a report from Deloitte that came out this year called the “2024 Real Estate Outlook Survey.” In the report, they say that only 5% of buildings are fully modernized, which mean they have core systems that are easy to incorporate emerging digital technologies. So that’s only 5% of all buildings, which doesn’t help us really address that 40% number of carbon emissions that we’re trying to go after.

However, the report also says that 34% are mostly converted to these modernized systems, and 30% are currently transitioning from legacy models. So as these buildings continue to move towards modernization, the 5% will become more like a 40%, and building sustainability targets will be easier to hit. So as these sensors feed into the building systems and give operators more data, change can actually happen.

Christina Cardoza: Yeah, that’s great. It sounds like there are a lot of things that building owners and operators can be doing, and a lot of technology or sensors that they can be adding to be making a dent in some of these goals.

But one thing I want to talk about, Maciej, and I’ll point this question to you, is I know a lot of businesses when they are adopting a new technology or they’re working with a partner or company, there’s that fear that they’re going to have that vendor lock-in or that they’re not going to be able to future-proof their investments. They want to make sure that they can continue to scale, continue to innovate. Because I think, even if you just look at AI, the world is changing every day, every week, every year, so we want to make sure we can stay on top of it.

So what would you recommend for building owners? How can they avoid some of those challenges or issues that they have and make sure that they can scale in the future?

Maciej Labuszewski: Some of the fears, the main fears that building owners face when it comes to making this big decision—whether we should invest, innovate, try a new product—it’s a fear of the change bringing more chaos into the process than the benefits that carries with it. And that pertains to both the process itself and the product that’s being evaluated.

And within the business environment that Blue Bolt revolves in, we most often notice this dependency on existing technology in terms of access-control systems. Many property owners are absolutely aware, they are fully conscious, that their existing systems do not offer a satisfactory level of user experience, but they believe that changing to another system will be synonymous with installation works that drag on forever, create problems for tenants, and, in the end, do not contribute in any way directly to improving the sustainability metrics of a building.

So when they encounter Blue Bolt, they’re quite often surprised that a superior level of comfort and security is available with so little effort and, importantly, on average with 60% lower costs, which is very important to those asset managers or building owners who are working on tight budgets. Additionally, there’s also a question of the wiring and cabling that the hardware requires, which is minimal in our case. Whereas with some of our traditional competitors this process can literally last for several months.

Christina Cardoza: So do you have any customer examples that you can share with us of how Blue Bolt came in, how you guys were able to streamline those changes, put them on a path to innovation, and really help existing build building owners make changes?

Maciej Labuszewski: Actually, one of our very first clients is a co-working space brand and the owner of commercial real estate with offices located in five countries. So at first they decided to launch Blue Bolt in just one out of seven locations, here in Warsaw, Poland. But the ease of access and the modernity that a simple building app brought to the entire building made them actually extend the cooperation, first onto all buildings in Poland and then allowing us to expand into the other four countries where they exist.

Some of the features that really caught their attention and that proved really successful was switching off elevators during off-peak hours. This is actually something that’s helped by our AI system that aggregates the data from both the building’s access control and the elevator systems and combines it into easily navigable information that can be viewed by property owners and asset managers straight from their phone or from their computer. And this is what guarantees that we are not just another gadget but a tool that helps make informed decisions on a daily basis.

And another example that I would give is one of the largest property holdings in central Eastern Europe, with a portfolio of almost 20 buildings in Poland alone, who wanted to solve several key problems—one of them being the need for building guests to drive around the building and find a parking spot because they couldn’t access the underground garage. So with our data analytics we were able to learn—first of all to provide access through the mobile-access part of our solution, and then learn from the patterns of occupancy when a certain amount of spaces should be left free how the building management can optimize their processes. And furthermore, by ordering their integration of Blue Bolt with their external sensors within the offices, the client was always up to date with data on reducing wastage—the factors I mentioned before, so, lighting, heating, air conditioning, HVAC, and so on.

Christina Cardoza: Those are a great example of how businesses and building owners, they can make changes. Change doesn’t have to be hard. If you partner with the right partner, like Blue Bolt, they can help you implement some of these things and make it a lot smoother.

So, since you got to talk about your sweet spot, I want to hear about Nantum AI’s sweet spot; if you guys have any customer examples or use cases that you can provide—how you helped, where you came in, and where really Nantum AI works best.

Lauren Long: Sure. So, one of our favorite focal points is to help companies meet their energy-conservation measures, or widely known as ECMs. One of our customers is Jamestown Properties, and they have the Waterfront Plaza in San Francisco, California. And they wanted to generate savings using a smart shutdown—or smart startup, smart shutdown, and midday ramps during the day. So, that would be changing the building system’s operation based on real-time occupancy.

And this year they released a report about the accomplishments of using Nantum AI within their properties, and against a 2019 baseline they have saved over $71,000 and almost 285,000 kilowatts of energy. That’s really great tangible information to have about just that small change of building operations can make a difference.

And I’d also like to share the US General Services Administration’s (or GSA’s) Green Proving Ground report. It was done in partnership with the National Renewable Energy Labs, and they completed a large pilot study on energy-management-information systems and automated system optimization—and they call this EMIS and ASO—the government and acronyms, you know; it’s a thing.

But the report concluded that using these systems, Nantum AI, they can result in 5%–11% whole building savings, which is pretty significant. And that by automating the government’s real estate operations they could save the federal government $28.7 million in energy costs a year, while also significantly reducing carbon emissions.

Christina Cardoza: Yeah, thanks for sharing those numbers. Because it’s one thing just to talk about it, but when you actually see the numbers and the impact that it can have, it makes it a little bit more powerful and makes these initiatives that we’re working towards something greater.

So I’m curious, though, because you mentioned that partnership, but we’re talking about a lot of different technologies and sensors and software and hardware that go into doing some of these things and making those use cases successful for your customers. I’m curious, are there any technology partnerships that you guys used to make some of this happen?

Lauren Long: We focus mostly on the software aspect of what we do today. And we partner with a lot of companies who have access to accurate and precise data. And the best way to get that is often through API integration. And, you know, the better data in, the better data out, right?

So there’s definitely that component of our partnerships, but we also rely heavily on hardware. At Nantum AI, I believe that every building could become a smart building. And we have found that buildings that are powered by Intel chips have the most capacity to become smart. So all of our buildings operate on an Intel Gateway, and this combination kind of makes us a perfect partner with Intel. Our goal is to become the smarter-building provider for every building in the world, which is made possible and powered by Intel.

Christina Cardoza: Yeah, that’s great to hear. Obviously, insight.tech and the insight.tech Talk, we are sponsored by Intel. But I think what’s great about using Intel chips—and even they have the AI toolkits—is that they’re always updating based on the innovations and the trends that they see are happening. So we talked about vendor lock-in, we talked about being able to scale and being able to innovate—they’re making sure that that’s all happening. So it’s great to see that you guys are partnering with them.

We are running out of time. I know we could probably talk about this space for another hour or so. We’ve only scratched the surface. But before we go, I want to throw it back to each of you guys, if there’s any final thoughts or final takeaways that you want to leave our listeners with. This is such a big topic, so if there’s anything that you think that they should get out of this conversation or that they should start doing to make some of these goals. Maciej, I’ll start with you.

Maciej Labuszewski: So, we’ve been talking about technology, but something very important to mention in context of this entire wide topic of sustainability and attaining net-zero goals is that it must be used to also unite people over a common goal. At points we are talking about something that isn’t just a business decision but also an ethical decision, and a decision that may impact the collective future. Delivering the solutions that we are working on here in Blue Bolt, it’s not just a business model to realize but also a higher concept that is good to have in mind when thinking about the needs of our stakeholders, when thinking about the needs of our users, and the way we can maximize the collective goodness, I suppose, if you can call it that, of our solution and the change that it brings into the world.

Christina Cardoza: Great. Anything from you, Lauren?

Lauren Long: I completely agree with what you just said. And it’s this urgency in creating and maintaining sustainable buildings has never been stronger. Climate is not improving, and we really need to work together to eliminate data silos and challenges across different departments, but we also need to implement the technology we have and realize what technology we need so that we’re able to hit our goals. AI is a huge tool and a huge asset that we can have in our toolbox, and it can make reaching our global decarbonization goals possible.

Christina Cardoza: Well, I just want thank you both again for joining the podcast. It’s been a great conversation seeing how data can really help make buildings more sustainable and smarter. It’s not just about turning off the lights. You need access to data, you need AI so you can get some of these insights, you can uncover some of these patterns, and start making changes today to better tomorrow. So this has been a great conversation. Thank you guys again.

I invite all of our listeners, please go to the Nantum AI and the Blue Bolt websites to see how they can help you start making some changes and start reaching your goals. As well as follow us on insight.tech. We’ll continue to cover these partners as well as other partners in this space, keep you up to date of all the latest and greatest happening. So until next time, this has been “insight.tech Talk.”

The preceding transcript is provided to ensure accessibility and is intended to accurately capture an informal conversation. The transcript may contain improper uses of trademarked terms and as such should not be used for any other purposes. For more information, please see the Intel® trademark information.

This transcript was edited by Erin Noble, copy editor.

Unlock New Revenue with Hyper-Personalization Marketing

Posted on by Micah Kaplan

When we’re online—doing a search, reading an article, or simply scrolling social media—our feeds are filled with product offers and topics most likely to grab our attention. If you’re like me, you find that these hyper-personalized experiences are becoming more relevant, convenient, informative, and even entertaining.

Wouldn’t it be nice if our in-store experiences were the same way? We not only could more easily find items we might like or are looking for, but businesses would be able to gain more insights. When shopping center operators, retailers, and brands can visualize a shopper’s journey—from the parking lot to product purchases and everywhere in between—the door is wide open to new revenue opportunities.

With edge AI computer vision, the next wave of physical retail is expected to offer the same kinds of highly targeted customer interactions you find online, but in an anonymized way.

Businesses are discovering that their digital-signage displays can be more lucrative when they know their audience behavior and patterns better and match content based on a rich data set. One company making retail hyper-personalization marketing a reality is meldCX, a provider of AI and intelligent edge technologies and solutions.

“We have found many of our customers have a basic understanding of how shoppers look at screens and match the content to play at key target types in descriptive and predictive cycles,” says Joy Chua, Executive Vice President, Strategy and Development at meldCX. “What’s missing is the idea of automation across these two, as well as a proactive approach to be able to trigger content based on multiple factors.”

With #EdgeAI #ComputerVision, the next wave of physical #retail is expected to offer the same kinds of highly targeted customer interactions you find online, but in an anonymized way. meldCX via @insightdottech

Hyper-Personalization Marketing: Timing Is Everything

meldCX helps shopping malls and other venue operators further monetize their digital screens with COATRO (Content at the Right Opportunity), a module of its Viana vision analytics platform.

COATRO integrates seamlessly with existing signage systems, enabling them to dynamically trigger content based on a myriad of predefined factors such as demographics, vehicles, behavior patterns, product and zone engagement, journey profiling, and even clothing worn within the camera’s field of view.

The software supports multiple triggers per screen and aggregates data across multiple audiences or vehicle types to determine the most effective content approach, allowing for highly customized content delivery. Additionally, every advertisement is assessed against multiple attraction indicators to generate an effectiveness score, giving advertisers insights into how individual content is performing.

At the same time, Viana is trained using synthetic data, ensuring that any data collected is anonymous and secure. This power to understand shopper trends in such detail uncovers actionable insights for highly targeted marketing strategies and campaigns that increase conversion rates and ROI.

For example, one longtime meldCX customer—a shopping center network operator with multiple large venues—had clear business goals it wanted to achieve. The operator deployed COATRO to gain a deeper understanding of how its digital assets perform and use these insights to develop new revenue opportunities. From there the objective was to formulate a broader story across all venues.

The deployment includes use of cameras and screens both indoors and outdoors. The Viana COATRO platform provides far more insights than usual, including the number and types of cars that come into the center, vehicle traffic mapping in major intersections, and even which digital assets—such as ads—result in people turning into the parking lot. Inside the mall, the system measures where and how long people dwell, their attention time at a display, and the conversion ratio.

With this depth of data, the operator can provide trend metrics that advertisers want to see before funding signage-based marketing campaigns. “They can combine historical data collected from an aggregation of data points with our visual technology across anonymized individuals, vehicles, and targeted logos or products,” says Chua. “They’re able to use real-time information to have a clearer view on who’s actually looking at the screen and when.”

The project is part of a multi-step process the operator takes to deliver more information about how different centers perform to the broader group and apply that to their other buildings or centers—creating more growth opportunities going forward.

“COATRO today is playing a key role in making sure that conversion and attraction are in line with what both the business and the customer wants and needs,” says Chua.

Low-Code Platform Simplifies AI-Driven Personalization

Viana COATRO integrates five elements that work together as a single solution: an Intel® processor-powered edge computing device, media player, camera or sensor, software license, and managed services.

As a low-code platform, users don’t have to be AI or machine vision experts to deploy the solution. In fact, meldCX designs its software for domain experts like marketing specialists or brand managers versus data analysts. In addition to COATRO, the Viana solution offers a range of use cases that can be combined to enhance the depth and breadth of the insights captured. All data is visualized as a comprehensive data story on the Viana dashboard.

meldCX works closely with Intel to deliver on a shared goal of making AI more accessible for customers. “This is especially close to our hearts because with COATRO we’re integrating AI to existing infrastructure for the first time in physical spaces,” says Chua. “Our technology enables seamless alignment of messaging between the physical and virtual, enhancing targeting strategies and customer experience in an ongoing improvement loop. And we are continuously working with Intel to make AI as small and compact as possible, yet powerful.”

Going Beyond Personalization in Retail

Hyper-personalization is not just for retail. Content at the right opportunity certainly applies to sporting and entertainment venues. Take the Olympic and Paralympic Games, for example. “When we see an Australian contingent wearing yellow and gold, the content triggered on the screen might be engaging information about Australian swimmers,” says Chua. “Then we could do the same at the aquatic center when we see red, white, and blue for the American team.”

Beyond these use cases—from advertising to entertainment—digital-signage displays and dynamic content have the potential for applications such as enhancing public safety and citywide experiences in a myriad of ways. It’s never been truer that a picture is worth a thousand words.

Listen to our conversation on the power of omnichannel experiences with meldCX and Intel to dive deeper into how technology can transform physical spaces.

 

Edited by Christina Cardoza, Editorial Director for insight.tech.

Bolster Perimeter Protection with Video Analytics

Posted on by Micah Kaplan

A sheep straying into a guarded facility could just be an animal that is lost or separated from its herd. But there have been very real cases of people dressed like sheep walking into critical infrastructure facilities and stealing equipment.

A human can easily tell the actual animal from a fake. But a tired human can slip up. While critical infrastructure facilities might be fully equipped with video cameras, watching hours upon hours of footage can be mind-numbing. In such instances, humans can make expensive mistakes.

It’s why perimeter protection using video analytics and a network of cameras is a job that’s ripe for automation. “Using computer vision and applying analytics makes a lot of sense because machines can analyze video 24 hours a day without getting bored or losing attention, especially in areas where nothing happens most of the time,” says Eduardo Cermeño, CEO of Vaelsys, a company that specializes in AI vision solutions.

Security Automation for Intruder Detection

Vaelsys offers Deepwall, a perimeter protection solution that provides intruder detection, which is more advanced than simply identifying and detecting humans. “We can detect people that are in places where they should not be and we’re very accurate at doing so. We can detect not only walking but also running and crawling,” Cermeño says.

And yes, Deepwall can deal with humans disguised as sheep. “It’s not just about humans or human behavior, it’s about the behavior of suspicious elements. When human intelligence tries to trick artificial intelligence, you need something beyond person recognition, we detect people that don’t want to be detected. We analyze behavior, we analyze how elements are moving, how critical an area is, a lot of information goes into our algorithm,” Cermeño says. Sometimes, an umbrella moves around in strange ways and in unexpected places, and this suspicious activity Deepwall detects.

The Deepwall algorithm is a potent combination of deep neural networks integrated with computer vision and applied to a network of cameras. The cameras can be standard definition (SD), high-definition (HD), or thermal. The thermal imaging equivalent of the solution is called Deepwall Thermai. The kind of camera used depends on the distance that needs patrolling. “If you’re talking about perimeter protection, the farther out you’re able to detect, the better,” Cermeño says. Vision cameras can perform up to 80 meters before losing accuracy while thermal equivalents can cover several hundred meters.

The combination of #VideoAnalytics and camera imaging is particularly attractive in #remote and expansive locations. @Vaelsys via @insightdottech

Perimeter Protection for Widespread Operations

The combination of video analytics and camera imaging is particularly attractive in remote and expansive locations. For example, solar farm operators face theft of resources like copper, a common component of photovoltaic panels.

When such incidents happen, it’s not just the loss of copper that’s a problem but also the downtime during which the farm does not generate electricity. “To prevent such incidents, you place cameras on the perimeter, connect those cameras to our solution. And the Deepwall system will analyze the video feed. When it detects an intruder, it’s going to generate an alarm and call the monitoring station to take action,” Cermeño says.

High Performance and Low-Power Computing

Vaelsys works with an extensive range of Intel® Core™ Processors to accommodate banks of cameras. One of the advantages of Intel CPUs is that they deliver processing power without having to rely on energy-consuming GPUs. Being energy efficient saves money and helps companies achieve sustainability goals.

Plus, the Intel® OpenVINO™ toolkit helps companies like Vaelsys test-drive AI and computer vision solutions, Cermeño says.

Use Cases for Video Analytics

Beyond perimeter protection, Cermeño sees video analytics as a powerful security solution, able to detect people or vehicles in restricted areas, but also as a perfect support tool for safety supervision. Computer vision can be helpful to detect someone who has fallen or a person not wearing the proper safety gear.

For its part, Vaelsys hopes that the robust shell it has created with the Deepwall solution—metadata generation for video and video optimizations for the Intel platform—can readily transfer to a variety of computer vision applications.

Instead of recognizing intruders, for example, solutions could detect special ambulances. Companies interested in a specific object could piggyback on the Vaelsys computer vision platform V4 and plug in the recognition engine for the particular use case. Then “you’ve got a complete solution that’s going to be able to work with any camera on the market and that’s easy to integrate with any software,” Cermeño says. The process can work with Vaelsys vision analytics developed in-house or other third-party implementations.

The packaged solution Vaelsys delivers stems from a market need to turn a proprietary AI model into a viable implementation. After all, simply having a model is not enough; you need to use it with a web interface and integrate with a bank of CCTV cameras, Cermeño says.

Such a plug-and-play approach to computer vision and object detection dramatically reduces the cost of product development. And that’s a good thing, whether video analytics technology ensures worker safety by detecting helmets or protects perimeters by detecting fake sheep.

 

This article was edited by Georganne Benesch, Editorial Director for insight.tech.