Edge AI and Visual Inspection Cut Product Defects

Posted on by Micah Kaplan

In industries where accuracy is nonnegotiable—like high-precision metal processing—manual quality inspection often falls short. It’s time-consuming, error-prone, and ill-suited to the demands of modern manufacturing. At the same time, companies are pressured to design, build, and ship products free from defects, all while lowering operating costs.

This is a problem Schlote, a classic automotive supplier with very strict customers like Volkswagen and BMW, unfortunately knows all too well. Schlote struggled with costly product quality problems that kept making their way to customers.

The manufacturer faced serious consequences when it failed to detect flaws in its turbocharger production, leading to repeated shipments of defective products to a global automobile maker. Not surprisingly, the customer lost trust in the company, and Schlote faced a serious risk to its reputation.

To overcome these challenges, Schlote turned to Intel partners Bechtle, a leading European IT service provider, and Vivaldi, an AI specialist.

“This problem did not just happen once. It happened twice, three times, four times, and so on,” says Uwe Siegward, CEO and Cofounder of Vivaldi.

Schlote was forced to implement a multi-tiered quality process with its own inspection operators checking each and every turbocharger. In addition, it hired a third-party company to double-check its work at the automaker’s plant. This became very expensive for Schlote.

Manufacturing AI: Technology That Quality Teams Need

Luckily, today’s advanced technologies like manufacturing AI and computer vision can help hardware builders solve these challenges. Real-time AI quality inspection, for example, augments human visual inspection to enable inspection teams to uncover product flaws across the entire supply chain with great speed and accuracy.

Bechtle, Vivaldi, and Intel partnered to deliver the end-to-end defect detection Schlote needed. With extensive industry knowledge and deep expertise, they provide AI quality assurance to some of the largest manufacturers of high-precision metals products.

“You have to find digital solutions to overcome product anomalies. That’s not only a problem of one company, but it’s also a problem for a whole industry,” says Stefen Schweiger, IoT Solutions Business Manager at Bechtle. “Partnering with Vivaldi is a perfect combination which exactly fits our approach in going to market with IoT AI solutions.”

AI Image Recognition Produces Results

The solution—a combination of an Intel® processor-powered edge server, computer vision cameras, and Vivaldi software—was supplied and serviced by Bechtle. Pictures are sent to the server that runs the Vivaldi AI algorithms with Intel-optimized software that analyzes inspection results.

If there is an anomaly that falls inside the acceptable parameters, then production can continue as normal. But if it is outside of these parameters, the information is sent to a robot that designates the part for manual inspection. From there, the part undergoes human inspection to determine if the AI was correct and close the inspection process as appropriate: approved or rejected.

The deployment enables Schlote to document every detail of the test procedures. Quality managers have constant visibility into inspection data, like which machine a specific part was built on, the list of tests it passed, and the operator who did the final inspection.

As a result, Schlote gained a reliable and scalable inspection solution that no longer needed third-party testing at its customer site—an immense cost savings for the company. And the customer was no longer receiving faulty product, which was crucial to restoring a positive relationship.

With the quality issues resolved, Bechtle’s ongoing services continue to be essential to sustaining overall operations.

“With all the inspection automated, if something happens to the hardware or software, they can’t ship product anymore because the people are not there,” says Schweiger. “That is where you really need to think about the service concept, how you can minimize the downtime of the whole thing.”

AI Quality Assurance Benefits the Entire Supply Chain

Across every market segment digitizing defect detection enables companies to gain new cost efficiencies, increase sustainability, improve customer satisfaction, and stay ahead of the competition.

As the products like Schlote’s turbocharger—and its components—get smaller and more complex, forward-thinking manufacturers will integrate AI and computer vision into their quality inspection processes.

In the end, the entire supply chain achieves better results. “Not only do you improve the efficiency of your own company but also the overall ecosystem,” says Schweiger. “And if you can save money with your supplier, your company, and provide cost benefits to your customer, you have good quality for a good price with correct information at the right time, your overall business running better as well.”

 

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

Serving Up the Future of the Foodservice Industry

Posted on by Micah Kaplan

Are long queues and slow service costing your foodservice business time, money, and customer loyalty? The foodservice industry is under constant pressure to serve fast and efficiently while delivering a great experience—but traditional solutions often fall short.

In this episode, we uncover how AI-powered self-service technology revolutionizes foodservice operations. Learn how cutting-edge innovations can slash checkout times, enhance customer satisfaction, and empower operators to streamline workflows like never before.

Whether you run a busy quick-service restaurant, manage a bustling cafeteria, or strategize for the future of dining, this episode will show you how AI reshapes the industry to meet the demands of modern consumers—and how your business can stay ahead of the curve.

Listen Here

[Podcast Player]

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Our Guest: Autocanteen

Our guest this episode is Sergii Khomenko, Founder of Autocanteen, a touchless self-checkout solution provider.  Sergii founded Autocanteen in 2018, where he works to provide a solution that addresses checkout challenges and increases efficiency. He is also the owner of NMedia Systems, a team of eCommerce and technology experts.

Podcast Topics

Sergii answers our questions about:

  • 2:30 – Foodservice industry challenges
  • 5:35 – Customer checkout expectations
  • 10:57 – Bringing automation to foodservice
  • 13:06 – Leveraging technology partners
  • 15:20 – Autocanteen in the real world
  • 18:54 – The changing foodservice industry

Related Content

To learn more about self-checkout, read AI Self-Checkout Redefines Food Service Efficiency. For the latest innovations from Autocanteen, follow them on Twitter at @autocanteen and 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. As always, I’m your host, Christina Cardoza, Editorial Director of insight.tech, and today we’re joined by Autocanteen to talk about AI-powered self-service solutions. But before we get started, let’s get to know our guest, Sergii, from Autocanteen. What can you tell us about yourself and the company?

Sergii Khomenko: Hi, it’s a pleasure to be here. Thank you, Christina, for the invite. Yeah, Autocanteen is an AI-powered self-checkout solution. And so our solution mainly caters for food-service industry, and we use computer vision and machine learning to identify meals or retail items very, very quickly and efficiently.

As of today, we process millions of transactions for guests per month, reducing first of all the queuing, because it’s a very fast experience and very fast checkout, and that empowers staff members. We pretty much give the team more eyes, if you like, to do the job, and processing power. And Autocanteen terminals are fully localized and live in the UK, and Ireland, and Canada, US, Czech Republic, Lithuania, and Estonia. So that’s our brief introduction!

Christina Cardoza: Great. I love how you guys are focused on the food-service industry, because when we talk about self-service solutions, sometimes we’re thinking retail, to self-checkout in healthcare, self-check-in—things like that. But I haven’t really seen it too much in the food-service industry. And this is not going through the drive-through and having AI talk to you through the drive-through; this is something that if you’re online buying food, it’s recognizing the food, it’s using all this technology to help make the food-service industry run a lot more efficiently.

So that’s where I want to start the conversation today—talking about the challenges that are facing food-service operators in terms of the efficiency, labor cost, customer experience—why self-service solutions was a great opportunity for this space.

Sergii Khomenko: Self-service solutions—they help to obviously maximize and enhance the capacity of teams during peak times. And previously it wasn’t—before an AI self-service solution like Autocanteen—it wasn’t possible to do self-service for transactions within hospitality because those products, they’re not labeled and there are no barcodes on them. But with the technologies of computer vision and machine learning it’s possible to identify them very, very quickly in a very similar fashion that human beings do and present the total and to automate that process.

The food-service operators, they face the challenges, because like I said that operationally the main challenge is that people tend to get hungry at a very similar time, and they all turn up for lunchtime or for breakfast all at the same time and it’s hundreds or thousands of people. And obviously the challenge here, you need to serve them quickly with food, with meals, and then also to transact very quickly so that the food doesn’t get cold and people don’t waste time.

So that’s the challenge, number one. And also imagine if you’re short on stuff that day and then it might be even challenging to cater. We can talk about the labor costs as well, but it’s always a present factor for any operation, for any team, but it’s important to think how you can utilize that capacity in the best possible way to enhance the speed and customer service. So that’s, I think, the main challenges that the food-service operators, they tackle, during service times, and we, with our solution, we help them to remove some of that load on their shoulders and help them to get through that service quicker and more efficiently.

Christina Cardoza: So, to set the stage—because we’re talking about sometimes in retail environments there’s no barcodes in some of these items that people are picking up—so, I’m thinking it’s lunchtime, I run into a store to grab a sandwich. I make myself a salad or grab a cup of soup; there’s no barcodes or anything on that, and then I go to checkout and pay for the meal that I just created and having to tell the food-service operator at the end what is it that I’m eating, how much of it have I got, and that will bog up the line for other people then. It doesn’t make it a quick grab-lunch-and-go type of situation. Is that correct?

Sergii Khomenko: Absolutely; that’s exactly the process that you described. And actually, like the traditional, manned checkout points—we know these metrics because we measure them—and traditional checkout points, they take up to 30 seconds per transaction, and that is not very fast. So that means you get two customers per minute checking out via one checkout point. And we can do it with an automated solution times faster. So it’s three- to four-times faster; we can see that one checkout point will process four to six customers per minute.

And that’s where the automation really, really shines, and that’s where the Autocanteen solution really can bring the benefit, because otherwise it’s tricky to get the same speed and efficiency. If we talk about the times and if we talk about the performance, our terminals, if we look at the transaction, they will identify everything within a second. So as you place the tray or retail items, the algorithm will take the inputs and within a second it’ll prompt you the total on the screen. And then there is a couple of seconds to acknowledge the total and then pay for it. So, a few seconds to process transaction and so on, and we end up with a sub-10-second transaction from start to finish and with the receipt. It’s the components within that transaction time.

And some of the sites that we support, they process 2,000 people—or, sorry, not “process”; they cater for 2,000 people—and imagine what kind of operation it is to feed everyone within one hour or two hours for that sort of capacity, and on those sites we can see 20 to 30 people checking out per minute with our terminal. We can see that 20 to 30 transactions land within the minute at peak time, and to achieve the same speed of service it would require a lot more manned till points. So the numbers just talk for themselves.

And also another point of view here and additional benefit is that the customer experience in this journey is if you can get through the checkout process so much quicker, it only increases your satisfaction, because you do it in a very quick and efficient way and your food doesn’t get cold and it’s a nice magic—as some people see it—magic machine that processes your transactions and the friction in this way. So that’s definitely a benefit to the customer service in there. From a staff point of view, imagine how powerful they can feel when previously they could only process two people per minute, but now they’re just keeping an eye on the process that actually processes 20 to 30 people, and they can do it all by themselves, just keeping an eye on the flow. So there are multiple benefits of such implementations.

Christina Cardoza: Absolutely. I can imagine if I wanted to grab something to eat and it took a long line for me to get my food, next time I grab something to eat—especially when you’re coming for lunch on a rush hour and you have limited time—I’m probably going to go somewhere else if it takes too long at one place. So I can see the benefits, both from the customer side and both from the business side: the customer getting their food in a timely manner, in a manner that it’s not cold by the end of it, and then the business being able to retain those customers for their next purchases.

So that definitely sounds like a great solution in the food-service industry. I’m wondering if you can tell us about some of the technology that’s implemented in the Autocanteen solution that makes this automation possible.

Sergii Khomenko: Yes, absolutely. Thank you for that question. We rely fundamentally on, like I mentioned, computer vision and machine learning. And so computer vision feeds the input into the algorithms and then we do the analysis, the decomposition, and then classification and learning, and so on. And the terminals, they are all interconnected—I mean, within the same site, within the same account—and once you’ve taught one machine, your other machines obviously follow the same knowledge. And all the terminals, they’re managed via a web-based admin panel, so you can make any changes to add products, pricing amendments, or view reporting, see how your sales are going—that’s all available at your fingertips, and you get the information synchronized pretty much instantly. That’s how the technological offering looks like, and those are fundamental components of the offering.

Christina Cardoza: How do you work with technology partners in the ecosystem to make this happen? And I should mention, “inside.tech Talk” and inside.tech as a whole, we’re sponsored by Intel. But I’m curious how—because I know Intel has done a lot of work when it comes to being able to bring AI into these different solutions, and their hardware is making it possible to do this all in real time, make sure that it’s high performant, high efficiency. So, what is the value of leveraging partners and technology from partners like Intel?

Sergii Khomenko: Yeah, we are super thankful to Intel and to this partnership, because we have been relying on their components within our software and hardware. And one of the components that we use is OpenVINO™; it’s blazing fast, and we can pretty much make decisions and those computations on the fly, and it really shows the difference, comparing it to other—this toolkit, OpenVINO toolkit—to other frameworks that we tried before, and we can only recommend it highly. So, yeah, OpenVINO has been an integral part of our solution, for sure.

And apart from this partnership, we have others that help us with payment processing, and on the hardware side work we with vendors such as Elo Touch they have also great products and a great platform that we run on, which also has Intel processors, and we prefer to run our applications on those processors. So, yeah, this Intel partnership has been super helpful during our journey.

Christina Cardoza: Yeah, that’s great to hear. One thing that I love about the partners that you just mentioned is that they’re always looking at what’s on the forefront, so they’re always staying on top of the latest trends. This AI-technology space is changing so fast that they’re always making sure that they’re providing technologies and capabilities that allow their customers or their partners to future-proof and to scale and be flexible to any innovations that are happening in the industry.

I’m wondering if you can share any customer examples with us that highlight how Autocanteen works in the food-service industry. You don’t have to name any names if you can’t, but just to give us a picture of the real benefits and the value and the impact that it’s brought to industries and to customers.

Sergii Khomenko: Yeah, absolutely; I can share some case studies. So, one site, for instance, in central London, it processes about a million pounds a year and serving hundreds of thousands of customers, and that site alone showed the dynamics, like I said before, that it processes 20 to 30 customers per minute. And also it saved a lot of labor efforts needed for the transactional function so they can focus on better customer service or be really helping elsewhere apart from being on the tills and doing their routine job. And also we can confidently say it was measured that the queuing—so the solution saved thousands of hours in queuing time. So, yeah, that’s a great KPI that we also rely on.

And in terms of case studies and names, actually I would be proud to share the name and also the award they won. So, Restaurant Associates, part of Compass Group, and they are a great partner in the UK, and they have just won awards at the Cateys, a prestigious award ceremony in the UK within the food-service industry. And the award was “Best Use of Technology” by a food-service operator, and that was for the implementation of Autocanteen and of our solution. So that was fantastic recognition, obviously, for within the industry, and it was the best solution on the markets.

Christina Cardoza: Yeah, all the benefits that we’ve been talking about, all the improvements, it’s no surprise to me that they would win an award for best use of innovative technology, because that’s something that’s really solving a problem in their industry, solving pain points. We see so many times companies try to implement technology for the sake of implementing technology, and this is more of implementing technology for the sake of easing pain points and improving customer experiences. So that’s great to hear. Congratulations to Restaurant Associates and Autocanteen as well for making that happen.

Sergii Khomenko: Thank you, thank you, Christina. Yeah, that was great, great news for all of us, for sure, and we just welcome those awards to keep coming.

Christina Cardoza: So, how do you envision then, this food-service industry changing? How will you continue to stay up on any of the latest trends and bring more innovations to the forefront that these awards and these innovations and these recognitions just continue on your journey?

Sergii Khomenko: Well, the food-service industry—I mean, if we look at it through the prism of automation and what’s going to happen in the next years and how these things are going to be optimized, of course we cannot say that every function is going to be automated. Because hospitality, it cannot run without people and it shouldn’t; I’m a big believer about it. However, those functions that are very repetitive and are monotonous, where they can be automated, they will, and we are bringing this for the function of transactional functions and we’re bringing automation, and we can also see these efforts already in the kitchen, back of house.

So, for example, for analysis for waste management. And there is actually some robotic companies that are challenging, not only challenging, but already offering the solutions how to make meals and prepare pizzas and so on. So the repetitive tasks, they will be automated, for sure, but to a certain extent technology is not going to replace people in hospitality, that’s for sure.

Christina Cardoza: Yeah, I think that’s a really powerful statement too, because hospitality and these experiences, we still need that human element and that human interaction, but this is just enhancing those experiences to make the outcome and the solution better. So it’s all exciting things.

I know you were talking about how this technology is being used in the back of the house too, and some robotic company is trying to figure out how they can use technology to make meals, but I’m curious, beyond food service, are there any other industries that Autocanteen’s self-service capabilities are going to be applied to or any other areas that we can expect to see Autocanteen automate some of these tasks?

Sergii Khomenko: Absolutely, absolutely. We are already helping on some sites within retail and micromarkets to also enable, first of all, either fast transactions or just enable 24/7 capability for unattended transactions. So, for example, micromarkets where you just have some products around you, and there is a couple of terminals that people or guests, customers, can always use, and those are the environments that we also help, that we bring the benefits with our solution.

Our journey, it started in 2020, and it was during pandemic year, and we started working with Aramark, and they, at the time, they were looking for a fast and touchless AI self-checkout solution, and we had just that. Fast forward four years, and our terminals, they’re helping operators such as Compass Group and Dussmann and Delirest and others to enhance their food service—within banks, insurance companies, Ministry of Defense, factories, warehouses, entertainment parks to handle peaks—and I think that’s 24/7 capability and it impresses customers. So it’s been a great journey.

Christina Cardoza: Great, well I can’t wait to see how Autocanteen continues to transform other industries, as well as see some of this technology come—see it out in the wild and come to my own stores and retail places. So, exciting to see you guys continue your journey. I recommend our listeners visit the Autocanteen website; get in touch to see how they can help you solve some of the pain points that you’re having.

This has been a great conversation. Before we go, 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 our listeners with today.

Sergii Khomenko: Oh, I just wanted to say thanks to Intel for the support on the journey and for being such an amazing partner and to contribute to our solution in a way. So, thank you.

Christina Cardoza: Great. Well, thank you for joining us and for giving us some insight into Autocanteen and how you guys are serving up the future of the food industry. So appreciate your time. Thanks to our listeners for tuning in with us. 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 Success: AI Accelerator Program for VARs and SIs

Posted on by Micah Kaplan

When it comes to business transformation these days, artificial intelligence is always at the center of the conversation. Organizational leaders want to use AI to move faster, lower costs, and stay ahead of the competition. But it’s not clear where and how to start, go beyond proofs of concept, and generate tangible value.

Systems Integrators (SIs) and Value-Added Resellers (VARs) can play a pivotal role in helping their customers deploy AI-enabled solutions that scale. But few have the set of skills across the organization to do it on their own.

That’s why TD SYNNEX, a leading distributor and solutions aggregator, launched its Destination AI Accelerator Program. This program is designed to help VARs and SIs leverage technology partners to develop an AI practice that solves customers’ specific business problems in a repeatable, scalable, and profitable way.

“There’s a lot of noise around AI, and our partners need help in recognizing its value and building something that allows them to leverage its benefits,” says Jessica Yeck, VP of the TD SYNNEX AI Data and Business Applications organization. “And that’s what this AI practice accelerator does.”

Systems Integrators (SIs) and Value-Added Resellers (VARs) can play a pivotal role in helping their customers deploy #AI-enabled solutions that scale. @TDSYNNEX via @insightdottech

Community of Partners Profit from AI Collaboration

VARs and SIs turn to TD SYNNEX to help cut through the noise, identify the real business problems their customers face, and deploy the technologies, solutions, and services needed to solve them.

TD SYNNEX recently partnered with Intel to co-sponsor the first Destination AI Accelerator, which brought together 20 carefully selected partners and solutions providers—known as a cohort—for a three-month curriculum. TD SYNNEX and Intel led a cross-functional group of participants—practice leaders, sales and marketing executives, and solution architects—through a step-by-step training that guided them from conception to revenue creation.

From the June 2024 kickoff to the October 2024 graduation, the cohort participated in video-meetings and webinars, culminating in a two-day workshop and graduation. Participants essentially become part of a community where they also learn from one another by sharing business challenges, strategies, and opportunities.

Technology Partners Deliver Market-Ready Solutions

Each accelerator is built around a specific vertical use case or cross-segment business opportunity. The first one led the cohort through the business value and opportunities when retail customers have a more personalized shopping experience. TD SYNNEX collaborated with AI ISV meldCX, data analytics leader SAS, and Intel to build an AI computer vision solution that can be deployed in a repeatable way.

“We put together what is essentially a market-ready solution with the various elements that each of us brought to the table,” says Yeck. “So you look at the SAS analytics piece, edge device cameras, and meldCX integrated to deliver a hyper-personalized shopping experience at scale.”

There’s a clear value proposition for retailers when they can augment traditional analytics with real-time data and insights into consumer behavior. Retailers can gain tangible benefits with information that wasn’t available before. And highly targeted customer interactions drive more sales and promote return business.

In providing a value-add solution like this, partners are positioned to increase their own profitability. They’re bringing together a complex solution that offers differentiation and a higher-margin engagement. The program opens new revenue streams for partners as discussions with customers move away from low-margin hardware refreshes to business outcomes.

“The idea is that we’re helping our partners come up with a methodology within their own practice and quickly be able to put together these solutions by leveraging TD SYNNEX and Intel,” says Yeck.

AI Collaboration with Lighthouse Customers

The Destination AI Accelerator includes a novel approach to foster success—both for TD SYNNEX and participants. Throughout the program each partner engages a lighthouse customer with whom there is mutual respect and understanding. In this way, each partner has a customer that’s with them from day one and throughout the process.

It’s a safe place for participants to practice what was learned and receive feedback to determine what adjustments are needed in their approach and engagement, Yeck explains. Partners build a practice with real customer feedback that naturally leads to taking a more consultative approach and create more stickiness as the conversation is more about solutions versus endpoint products.

New Skills Lead to New Revenue Opportunities

The AI accelerator proves its success as participants in this first cohort share their results.

Three partners indicated they had customers in the pipeline even before the three-month program ended. It’s a noteworthy outcome given that the combined solution isn’t something the average individual partner could not easily do on their own. Leveraging the power of the ecosystem and everything that TD SYNNEX has to offer makes it possible.

Yeck reports that one partner reworks its organization around AI to ensure that it has a structure that supports success. Another essentially merges its IT and MSP (Managed Service Provider) business to create better alignment and efficiencies.

Participants come away from this program with a clear path to predictable, repeatable, and scalable revenue. They also have proven credentials with Certificates of Achievement such as Artificial Intelligence Business Professional and Certified Computer Vision Partner.

“It’s really been through our partnership with Intel that we’ve been able to bring this program to market,” says Yeck. “Their collaboration, funding, and support has been absolutely instrumental to its success.”

TD SYNNEX is launching new accelerators throughout 2025, with the next focusing on generative AI for workplace productivity, covering all verticals. Find out how you can participate in an upcoming Destination AI Accelerator and register for the next Cohort. And for even more, listen to the insight.tech Talk podcast: AI for All: The Power of Democratization and Collaboration.

 

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

Top 10 Tech Influencers to Follow in 2025

Posted on by Micah Kaplan

As technology and innovation continue to evolve at a rapid pace, influencers have become pivotal in shaping conversations, trends, and the future of the various industries. Every year, insight.tech highlights influential voices driving change and innovation across the tech world.

We strategically selected these tech influencers to help you stay ahead of the curve in 2025. Whether it’s understanding the latest digital transformation efforts or navigating the complexity of AI security, these thought leaders deliver essential insights and unique perspectives.

Start the new year off right by staying on top of the trends—follow these influencers across their digital platforms. And while you are at it, be sure to follow along with insight.tech on X (formerly Twitter) and LinkedIn as we bring you exclusive content, expert interviews, and more.

Antonio Grasso

Expertise: AI, Cybersecurity, and Digital Transformation

Why you should follow Antonio: Antonio Grasso is a LinkedIn “Top Voice” (a select group of global experts, leaders, and innovators recognized for consistently demonstrating expertise and leadership that educates, inspires, and informs the community) as well as the Founder and CEO of Digital Business Innovation Srl, a startup driving innovation in AI, IoT, blockchain, and cybersecurity. In 2024, he was awarded a Generative AI patent for a “method and system for the autonomous and cognitive generation of infographics from text using a combination of artificial neural networks.”

LinkedIn   X

Elitsa Krumova

Expertise: AI, IoT, and IIoT

Why you should follow Elitsa: With more than five years as a globally recognized B2B technology influencer and thought leader, Elitsa is dedicated to helping businesses and audiences explore and harness the power of emerging technologies.

LinkedIn   X

Glen Gilmore

Expertise: AI and Tech for Good

Why you should follow Glen: Glen was recently named a Forbes Top 20 “Social Media Influencer” and dubbed a “Man of Action” by TIME magazine. In addition, he is an international speaker, author, marketing strategist, and prominent influencer in emerging technology, travel, and digital transformation. He is also the founding faculty member of Rutgers University School of Business Digital Marketing Executive Program.

LinkedIn   X

Harold Sinnott

Expertise: 5G, AI, and IoT

Why you should follow Harold: As an author, coach, and speaker, Harold is determined to stay at the forefront of developments in AI, 5G, IoT, and beyond. With his extensive knowledge, he brings valuable industry insights to drive technological innovation.

LinkedIn   X

Kevin Jackson, CISSP, CCSP

Expertise: AI, Data, and Digital Transformation

Why you should follow Kevin: Kevin is a global business technology thought leader, video podcast producer, and best-selling author. He is a recognized authority in the digital transformation space. He offers expert commentary on the integration of AI, machine learning, and data analytics—always providing valuable insights and a roadmap for organizations navigating the challenges of the digital age.

LinkedIn   X

Stay ahead of the curve in 2025! Explore @insightdottech’s top tech influencers shaping conversations in #AI, #DigitalTransformation, and more

Kirk Borne, Ph.D.

Expertise: AI, Big Data, and IoT

Why you should follow Kirk: Kirk Borne, another LinkedIn “Top Voice,” is a renowned expert in big data, IoT, and AI. His extensive expertise across these domains offers a holistic perspective on the intersection of data and technology.

LinkedIn   X

Linda Grasso

Expertise: AI and IoT

Why you should follow Linda: Linda Grasso, a LinkedIn “Top Voice,” is recognized as the #1 female creator from Italy in the CEOs, Tech Leaders, and AI & Machine Learning categories by influencer marketing platform Favikon. She is also the CEO of DeltalogiX, a blog dedicated to innovations in digital transformation.

LinkedIn   X

Dr. Monika Sonu

Expertise: AI and Healthcare

Why you should follow Dr. Sonu: With more than 18 years of experience in healthcare, Dr. Sonu has honed her expertise in designing and developing consumer-facing digital health technologies that seamlessly integrate emerging innovations, advancing the future of healthcare.

LinkedIn   X

Peggy Smedley

Expertise: IoT, Sustainability

Why you should follow Peggy: Peggy Smedley is a podcaster, influencer, and futurist dedicated to educating audiences about IoT and emerging technologies, inspiring the next generation of innovators.

LinkedIn   X

Ronald van Loon

Expertise: AI, Big Data, and IoT

Why you should follow Ronald: Ronald is a top-10 global influencer and thought leader to leading institutions. He is dedicated to advancing education and thought leadership in big data, IoT, AI, and beyond. He is also the CEO of Intelligent World, where he empowers businesses, experts, and influencers to collaborate, innovate, and share groundbreaking content.

LinkedIn   X

 

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

Simplify Data Retrieval with GenAI Tools and AI GPUs

Posted on by Micah Kaplan

When business customers need answers to their questions, they usually need them fast, especially with product designers and developers looking for critical information needed to complete a project. This is a challenge that sales reps often face.

More often than not, core technical know-how and product data is in the hands—and on the servers—of product managers and sales engineers. This means salespeople must make requests to get the data their customer wants, which often results in time wasted with a lot of back and forth between both parties, not to mention unhappy customers.

Removing IT/OT information silos, commonly found in manufacturing, engineering, and similar organizations, helps solve this challenge. No-code generative AI (GenAI) tools, large language models (LLMs), and chatbot solutions make it possible, by putting data in the hands of those who need it when they need it. Technical teams are freed up to focus on their work while sales can respond to customers faster and more effectively.

A collaboration between Tallgeese AI, an on-premises, privacy-first GenAI software company, and hardware manufacturer ADLINK Technology Inc. shows how it’s done.

Together, the two companies make a “perfect match,” says Jeffrey Lai, cofounder and CEO of Tallgeese AI. “We can run our software on ADLINK’s robust hardware smoothly, and together provide our customers full control of the data and AI capability on-premises, reducing cybersecurity risks.”

No-Code AI Speeds Chatbot Training

The ADLINK hardware and Tallgeese AI software form a cohesive orchestration where even nontechnical users can ask an AI chatbot to find information that exists locally and get a response within moments.

The Tallgeese AI turnkey workstation solution performs the data ingestion, converts documents stored on the server, and puts them in a vector database. The LLMs access the files from the database and run AI inference and training on-site, while creating an offline record system for data retrieval.

“We take a no-code #AI approach. You turn on your computer, select the file directory, click ‘train the chatbot’, and you’ve basically gone through the entire chatbot training process.” — Jeffrey Lai, Tallgeese. @ADLINK_Tech via @insightdottech

“We take a no-code AI approach. You turn on your computer, select the file directory, click ‘train the chatbot’, and you’ve basically gone through the entire chatbot training process,” says Lai. “We parse PDF, Excel, PowerPoint, and other file types, turning all the tags, elements, and images into vectors. We store it locally, essentially preparing it for AI access—all in a matter of minutes.”

Authorized users simply login to the Tallgeese AI portal, choose the on-premises server, and see all AI chatbots created on that machine. They can drag and drop files from databases, file directories, and even web pages for external crawling.

With a plug-and-play model, no IT support is needed. As a rule, Tallgeese AI software comes pre-loaded and ready to run on a network-connected PC. In this case, it’s the ADLINK AI GPU Server—a new product based on 4th Generation Intel® Xeon® processors and Intel® ARC A770 GPUs (Video 1).

Video 1. ADLINK AI GPU Server overview. (Source: ADLINK)

Secure, Fast Data Access with AI GPU Servers

ADLINK knows firsthand about this challenge. It faced the same knowledge management challenges as its own customers, making the company a great example of how deploying a GenAI-powered solution makes mundane tasks easier.

The development of almost any product requires a massive amount of documentation throughout its lifecycle—from concept to end-of-life. Product requirements documents, component specifications, design documentation, engineering change notices (ECNs), and much more are created and updated continually. Many documents contain proprietary IP and need to be securely accessible for a variety of purposes by a range of non-engineering personnel. But gaining access to this information is time-consuming and frustrating. 

Hank YH Lin, Product Manager in ADLINK’s Networking and Communication Department, describes a typical problem: “Our customers have a huge number of regulatory compliance data, and if their quality team goes to the factory for an on-site audit, they need very experienced subject matter experts to answer questions.”

The Tallgeese GenAI solution, running on an ADLINK AI GPU Server, can improve the quality of customer interactions—even with new and less knowledgeable employees.

Another challenge for engineering companies is tracking historical documentation such as the many product engineering change notices (ECN) and associated FAQs. “The problem is once an ECN is issued, people will forget about it,” says Lman Chu, CSO of Tallgeese AI. “I think the key is that AI doesn’t forget. All the records are inside the AI’s head, so to speak, helping everyone do a better job.”

The Tallgeese no-code AI solution enables project teams to quickly, seamlessly, and securely integrate GenAI into their existing workflow—without having to rely on the IT department. They can benefit from new productivity gains almost immediately. And when information is easily found and shared, product development can be more agile, creating the potential to bring new solutions to market faster.

GenAI Tools Democratize AI

It’s inevitable that businesses across every vertical will invest in GenAI-powered transformation to increase operational efficiency, lower costs, and stay ahead of the competition.

“From the top down, organizations that are adopting AI help their employees focus more on meaningful work, maximize their productivity, and allow for a better work-life balance,” says Lai. “I think it’s executives’ responsibility and the outcome I see for the future.”

Technologies like GenAI, LLMs, and chatbots are in use today and are where we’ll see true democratization of AI for the future.

 

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

Intelligent Traffic Management Gets the Green Light

Posted on by Micah Kaplan

It’s happened to us all. It might be late at night; it might be out in the country. You’re sitting at a lonely red light minute after minute, with no cross traffic in sight. It seems like the signal will never go green! Isn’t there some way—you wonder, tapping your fingers on the steering wheel with impatience—to trigger the light to change in response to the actual conditions on the ground?

Intelligent traffic management (ITS) could provide a solution. And not only for the impatient country driver. Commuters, city planners, even passengers and pedestrians have an interest in the use of up-to-the-minute technologies, including AI, to improve conditions on the road. Joseph Harvey, ITS Market Sector Lead from video analytics company Intelligent Security Systems (ISS), talks to us about the challenges and opportunities of making roadways smarter, safer, and more efficient for us all (Video 1).

Video 1. Joe Harvey from ISS explains what traditional traffic management solutions are no match for today’s modern roadways. (Source: insight.tech)

Talk to us about intelligent traffic management.

For a very long time, I’d say that ITS has been grounded in pushback to technology—wrongly and rightly, because there have been some great solutions that were already working in this space. But as AI and neural networking come into everyday consciousness more, we are seeing more and more technology feed into the ITS area.

Now end-user agencies—the customers for ITS technologies—can see what solutions like the one from ISS can offer from a safety standpoint and from a data standpoint because of neural networking and video intelligence. So we’re seeing things change rapidly. The excitement in ITS now is in how the interconnected realm is really going to work with everyday motorists. And companies like ISS can be at the forefront of that conversation.

What are some ways you can integrate AI into traffic management?

AI, neural networking, video intelligence—they are at the core of every single one of our vast portfolio of products. Devices are gathering data from everyday motorists—urban, arterial, out on the freeway—in a manner that is 95%, 96%, 97% accurate. It’s in real time and it’s automated, so operators only respond to specific needs, which is valuable for an end-user agency.

For example, one of the products that we have here at ISS is a pedestrian-safety device: a dynamic illumination for pedestrians within crosswalks. When you think about the more traditional measures, you’re asking a driver to react to, say, that static yellow sign in a school zone that indicates: This is a school crossing. A pedestrian may be in the crosswalk.

What we have done is leveraged AI and camera technology to dynamically illuminate at dusk or nighttime hours a pedestrian, a child, a mobility device that is within the crosswalk, and actually show where they are. This was a revolutionary thing in the industry for our company to do; no one had done anything like it before.

How does an AI solution for ITS improve on traditional means of gathering traffic data?

The traditional road measure at a signalized intersection is something like magnetic loops in the ground. Or if you look up you might see a number of devices. But with the development of cameras along with AI we’re really seeing the ability for the controller, the smarts, the brains behind those intersections to have greater understanding of the environment and be able to react in real time if there is an incident.

The original adoption of cameras was a little hit-or-miss because of their unreliability at times, especially during weather events. But with the advancements in cameras and then with this neural network, the system is able to understand the environment and make adjustments on the fly. Instead of getting only partial data and outliers, instead of making assessments on ones and zeros that an engineer might be looking at, you can go back and actually pull video feeds and really drive true meaning and understanding from the data that you’re looking at.

With the development of cameras along with #AI, we’re really seeing the ability to have a greater understanding of an environment and react in real time if there is an incident. @isscctv via @insightdottech

Near miss is a really big topic for us right now. If someone calls in and reports that a car has gone through a red light at 50 miles per hour, traditionally an engineer would have to go out to the field and take a look at what had happened on the cameras. Now, with AI and with video intelligence, all of that is constantly at the fingertips of operators, and they’re able to react much more quickly. Or they can look at the data sets long term to effect change on the roadways when they’re considering design or considering reshaping the roadway itself.

What about that scenario of the seemingly endless red light and no cross traffic?

I think there are 300,000 to 400,000 signalized intersections in the United States, and somewhere around half of those are still without detection or else have some sort of outdated detection method. But absolutely: There is the ability to add the visual aids, the algorithms, and the video intelligence to advise the traffic light that a vehicle or vehicles are waiting there, and basically to place a call into the signalized controller in order to effect the signal phase and timing—or SPAT. That is something that we are presently doing, and we’re seeing it in the marketplace.

It limits congestion; it limits the environmental impact of a car sitting and waiting two, three minutes at some of these intersections. But it also then gathers all of that data: What time of day is it? What type of vehicles are there? Are there pedestrians? All of these data points are really helping engineers continue the ITS conversation.

And as more and more devices get connected and as more and more end user agencies are able to take the inputs of an alerted event and interconnect everything that they have at their fingertips, they’re making the road safer; they’re having a real impact at time of event. This is honestly the reason a lot of us are in this industry, because we can see that change, and right now we are seeing it happen very quickly.

What are the challenges to implementing these solutions?

What ISS is looking to do is leverage the infrastructure an end-user agency might have in the field already—say, the cameras—and build on top of that. That’s what scalability or flexibility means to us. We are able to take the camera input and just leverage our video intelligence and our neural network to provide whatever outcome the customer may be looking for: Is it for pedestrian safety? Is it just incident detection?

Ultimately, the funding comes from our tax dollars, and we need to make sure that we make the best use of those dollars as spent by an agency. Ultimately, the traveling public is who is funding what is going out onto the road space.

Can you give any real-world examples of the ISS technology in action?

I would say the biggest-scale project we’ve done was in Mexico City. We implemented our operating system, SecurOS®, within their entire transit agency. So we were the interconnected network for somewhere north of 65,000 cameras and other physical devices.

And we were able to then leverage that neural network to open up the possibilities. If you think about the amount of personnel they would have needed in order to review that number of cameras or to look at them live—allowing our system to be the point of the spear for them, while everything else lives behind it, that was transformational for Mexico City as a smart city.

So that’s the feather in our hat. That was a very large project for us, but it allows you to understand what the scale of the need is in some of these very large cities—in the world or here in the US—when reviewing just inbound video into their system.

But in some places we’re just doing flow estimation: speed, volume, gap, occupancy. This is the data that engineers hold dear and need constantly in order to make decisions about reshaping where we’re driving for the future.

We also have a few examples with tolling agencies, where we provide our LPR solution to do license plate capturing. If you’ve ever driven in a heavily populated area and been on a toll road, there are generally a number of cameras and devices up on the gantries that are already in use by that agency. So we’re able to be a part of the totality of what that governing body may be doing.

Does making sure personal data is protected factor into your solutions?

It absolutely does. From the privacy standpoint the industry has taken a branched approach: There’s what we are actually capturing out on the roadway where we can blur faces, blur license plates; but then we also have the intelligence within the cameras to help us remove any of that personal data.

ISS had its grounding in what physical security meant for real-world applications, and we have continued to build on that. We work with each end-user agency individually on what its standards are. And we also make sure that, for any advancement we bring to market, there is a parallel path concerned with security and privacy. Because trust and understanding from our users is paramount to our success, so it has to be a part of what we do.

Are you partnering with other technology companies, such as Intel, to make this happen?

From a performance standpoint, a company like Intel is almost invaluable for companies like ISS. More and more of our end-user agencies are asking us to include different data points and just to push the capabilities of the physical hardware and software.

Intel partners with us to react to and solve the problems we see in the real world. But the understanding and forethought from a company like that of what the marketplace is going to need—that has a huge impact on us generally, and ultimately the industry is relying on Intel and companies like it to continue to push that forward for us.

At ISS, we are trying to solve the real-time, actual problems and events that you are seeing out on the roadway. But we are also looking to solve problems that we don’t even understand yet. And we believe we are up for that challenge.

Related Content

To learn more about intelligent transportation systems, listen to Intelligent Traffic Management: Keeping Your AI on the Road and read Video Intelligence Illuminates Path to Pedestrian Safety. For the latest innovations from ISS, follow them on X at @isscctv and on LinkedIn.

 

This article was edited by Erin Noble, copy editor.

Intelligent Traffic Management: Keeping Your AI on the Road

Posted on by Micah Kaplan

Tired of gridlock and traffic jams? Smart cities are the future, but their roads often feel stuck in the past. Traditional traffic management solutions are no match for the complexities of modern urban life.

In this episode, we explore the future of transportation with AI and visual data taking the front seat. We discuss the importance of real-time analytics combined with historical data, and gain some insights into the critical role of visual capabilities in traffic management, how AI-driven insights aid city planners, and the ways these technologies promote sustainability.

Listen Here

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Our Guest: Intelligent Security Systems

Our guest this episode is Joseph Harvey, Intelligent Transportation Systems (ITS) Market Sector Leader at ISS, an AI-driven video analytics company. Joe joined ISS last year to help drive solutions that enable safer and more-efficient roadways with the power of real-time data and analytics.

Podcast Topics

Joe answers our questions about:

  • 2:00 – State of traffic management
  • 4:11 – AI’s power in city planning
  • 8:43 – Empowering traffic flow
  • 11:34 – Opportunities for improvement
  • 13:39 – Traffic technology implementations
  • 16:52 – Taking security into consideration
  • 19:38 – Partnerships making it possible
  • 21:14 – Final thoughts and takeaways

Related Content

To learn more about intelligent transportation systems, read Video Intelligence Illuminates Path to Pedestrian Safety. For the latest innovations from ISS, follow them on X at @isscctv and on LinkedIn.

Transcript

Christina Cardoza: Hello and welcome to “insight.tech Talk,” where we explore the latest IoT, AI, edge, and network-technology trends and innovations. As always, I’m your host, Christina Cardoza, Editorial Director of insight.tech, and today we’re taking it to the road, talking about intelligent traffic management with Joe Harvey from ISS, which stands for Intelligent Security Systems. Hey, Joe, how’s it going?

Joe Harvey: It’s going well. How about yourself?

Christina Cardoza: Not too bad. I’m excited to dive into this conversation. But first I want to know a little bit more about yourself and what you do at ISS.

Joe Harvey: So, here at ISS I’m the ITS Market Sector Lead. What I’m doing is taking our leaders, our founders, what their visions are for the analytics and the neural networking efforts that we do here at our company, and bringing those into the ITS market space.

ISS has a background of 25-plus years in being a security and safety company, and we’ve developed this vast network of analytics and just really safety-solving solutions. In the last three to four years that has now been my responsibility to bring that into the ITS market space and allow those solutions to really take fruit and take hold in the marketplace.

Christina Cardoza: Yeah, absolutely. It’s amazing to see the advancements and what these solutions can do to improve industries. A lot of times on the podcast we’re talking about how they can help manufacturing plants or retail areas, and today we’re talking about traffic management, which I think is something that a lot of people—whether you’re the driver, commuter, passenger—they struggle with. So it’s amazing to see a part of the technology and advancements be able to be applied to everyday life.

So, I wanted to start the conversation just looking at the state of traffic management today, and where are some of the improvements where a company like ISS can come in and help?

Joe Harvey: Yeah, that’s a really great question. I think you hit on something in the lead-up there, too, that these products and what ISS has done have been in the marketplace for 25-plus years. As AI and neural networking become a little more buzzwordy, or just even a little more in the conscious of everyday space, we are seeing them applied in a lot of different areas, specifically ITS. Being able to take that fundamental understanding and growing from a really grassroots side where we control the build of all of the products, all of the solutions, and kind of taking an à la carte approach when you’re looking at ITS—to apply those specifically into the solution space.

ITS for a very long time has had a lot of traditional measures, has been grounded on a lot of just, I’d say, pushback to technology at times—right and wrong—because we do have great solutions that work in this space. But as more and more technology feeds into that area, and they can see just from a safety-saving-device, a data-rich standpoint, what a solution like ISS can offer—or several of the companies in the space that are pushing into it now with neural networking, video intelligence, and pushing that forefront—we’re seeing things rapidly change.

That’s where the excitement really is in ITS now, how the interconnected realm is going to really work with everyday motorists and how companies like ISS can be at the forefront of that conversation.

Christina Cardoza: Yeah. So let’s dig deeper into that a little bit—AI’s role in all of this. How can AI start to be integrated into something like traffic management that drivers, city planners, government officials—that we start seeing some improvements there?

Joe Harvey: Yeah, absolutely. We have a vast portfolio of products that AI, neural network, video intelligence are at the core of every single one of those. For an end-user agency, when you’re taking a look at either data gathering from a standpoint of vulnerable road users, from your traveling everyday motorist—urban, arterial, out on freeway—being able to use devices that are gathering this data in a manner that is at a 95%, 96%, 97% accuracy and being able to do it real time, automated, where an operator is then only responding to a specific need.

One of the specific products that we have here at ISS is a pedestrian-safety device: It has a dynamic illumination for pedestrians within crosswalks. When you think about the more traditional measures, you’re asking a driver to react to a notification that—Hey, a pedestrian may be in the crosswalk. When you think about driving in a school zone, you come up, you have that yellow static sign that says—Hey, this is a school crossing.

What we have done is leveraged AI and the camera technology in order to dynamically illuminate at dusk or nighttime hours a pedestrian, a child, a mobility device that is within the crosswalk and actually show where they are. This was a revolutionary thing for our company in the industry, just because no one has done anything like that before.

But if you also take a look at signalized intersection—more of the traditional road measures—or if you ever come up and you see just a cutout in the road and you wonder what construction or what may have happened, there’s magnetic loops in the ground. If you look up at the intersection, you may see a number of devices up there. But with the development of cameras along with AI, we’re really seeing the ability for the controller, the smarts, the brains behind those intersections being able to greater understand its environment, be able to react real time if there is an incident.

So, if you have collisions—near miss is a really big topic for us right now—having an operator take a look at that. Or traditionally someone has picked up the phone and called and said, “Wow, you just had someone go through that intersection at 50 miles per hour on a red light. You have an issue here,” an engineer would have to go out to the field, take a look at what’s going on with cameras.

With AI and with video intelligence, all of that is at the fingertips constantly of operators, and they’re able to react much quicker and or look at these data sets long term to affect change on the roadways when they’re looking at design or reshaping of the roadway itself.

Christina Cardoza: Yeah, I can imagine those visual analytics just become even that more important. I’m thinking about sometimes I see workers on the side of the road, they have their radar guns out. They’re making sure—testing the speed of everybody going—making sure the speed is correct there. But you can have different things happening at different times, and one person going really fast at some area could mess up the entire sample data.

So if you’re able to visually see what happened at that time, it can help provide deeper insights beyond that real-time analytics that you were talking about. I’m thinking, being able to improve overall city planning and being able to improve these traffic areas like the lights and how often things happen there.

Joe Harvey: The original adoption of cameras was a little hit or miss because of just their inability at times, especially during weather events. But with the advancements there, and then along with you have this neural network that is able to understand those environments and make adjustments on the fly—exactly what you said. Instead of getting partial data outliers and making assessments on ones and zeros that an engineer might be looking at, they’re able to go back and actually pull those video feeds and really drive true meaning and understanding to the data that they’re looking at.

Christina Cardoza: I’m curious, since we’re using AI, are you guys able to implement any models or automatic triggers that say: if one event happens, this event will happen? I’m thinking, just from my personal experience, my parents live a mile down the road, and there’s just one light between us and them. And that light can be five minutes long to get to their house, and there will be no cars coming on either way. So I’m just wondering if there could be, like, an AI trigger that says: Okay, a car has pulled up, there’s no other cars coming, we’re going to make it green, and I can get to my parents’ house faster.

Joe Harvey: Yeah, absolutely. Something as simple as, yes, at the given intersection where you’re able to visually take a look, basically place a call into the signalized controller in order to affect the signal phase and timing—or SPAT—absolutely. That is something that we are presently doing and we’re seeing in the marketplace.

There’s a statistic that anywhere from—I think there’s 300,000 to 400,000 intersections within the United States, signalized intersections—somewhere around half of those still either are without detection or some sort of outdated or historical methods. So, like the loops that I had made mention of and being able to add these visual aids that, again, yes, the algorithms and the video intelligence is able to advise that a vehicle is there and limit the congestion, limit the environmental impact of a car sitting and waiting two, three minutes at some of these intersections, but also then gather all of that data. What time of day? What type of vehicles? Are there other pedestrians? Do you have a heavy bike or scooter population that might be an alternative method that you were unaware of during certain times of day?

All of these data points are really helping engineers continue that conversation. And, honestly, long term, as more and more devices get connected and more and more of these end-user agencies are able to take inputs of an alerted event and interconnect everything that they have at their fingertips, they’re making the road safer and having a real impact at time of event to affect that change. And it is honestly the reason a lot of us are in this industry, because we can see that change, but we are seeing it right now happen very quickly.

Christina Cardoza: Yeah, all of these benefits—it would seem to me a no-brainer to start implementing this intelligent technology at these intersections on the roads. But, like you mentioned, a lot of these intersections still have outdated or traditional technology. So how can they start making these improvements? What are the challenges to implementing it? Can they leverage any existing infrastructure? How does that work?

Joe Harvey: Yeah, that’s, at least from our standpoint here at ISS, one of the benefits to the end-user agency that we are taking a look at with the just pure amount of capital that is spent on infrastructure. What we are looking to do is leverage what customers might have in field already and being able to build on top of that. When you hear “scalability” or “flexibility” from a manufacturer, what that means from ISS is the cameras that are already out within the traveling motorist, within the infrastructure of an end-user agency, we are able to take that input and just leverage our video intelligence and our neural network in order to provide whatever outcome they may be looking for.

If that is something like the intersection technology, if that is for pedestrian safety, is it just incident detection? Use what you’ve already had in place and leverage that, and then allow ISS as a manufacturer to continue to build on top of that and give the scalability to agencies.

Funding is our tax dollars, and we understand that much of the ITS market space is exactly that. We need to make sure that those dollars being spent—even if new technology and advancements are being made—we can make best use of those dollars pre-spent by an agency and ultimately the traveling public that is funding what is going out on their road space.

Christina Cardoza: Yeah, absolutely. So, I want to be able to give our listeners a clearer picture of how this works, so I’m curious if you have any real-world examples or customer case studies—anything that you can share with us of how ISS came in, whether you’re working with a city official, government official, how you guys came in and implemented the technology and what the result of that was.

Joe Harvey: Yeah, there’s a number off the top of my head. A few are specifically in tolling agencies, where if you’ve ever driven in heavily populated areas and been on a toll road, there are a number of cameras and devices that are up on the gantries already in use by that agency. Being able to go in and provide, in this case, our LPR solution, where we’re able to be a part of the totality of what that governing body may be doing, so in that case, if we are doing license plate capturing.

In some places we’re just doing flow estimation. So, your speed, volume, gap, occupancy—different things like that, again, the data that engineers just hold dearly, need constantly in order to make these decisions about where they’re going to bring different roads into, how they’re going to reshape where we might be driving. So that is one aspect.

I would say the biggest scale we’ve done was actually in Mexico City, as a global company founded here in the US. But as a global company, we actually have implemented in Mexico City our SecurOS®—which is our operating system—within their entire agency. And we are that operating system, the end point for their operators to use, and have somewhere north of 65,000 cameras, along with alarming devices, horns, and really have taken their smart city, allowed the interconnection between all of these physical devices to live on our network.

And we are able to then leverage, again, that neural network to really just open up the possibilities. If you think about that number of cameras and the number of personnel you would have to have in order to even review or take a look at live, allowing our system to really be that point of the spear for them and everything else to just kind of live behind was transformational for Mexico City.

So that’s our feather in the hat. That was a very large project for us, but allows you to kind of understand the scale to which some of these very large cities in the world or here in the US have, and kind of what their need is when reviewing just inbound video into their system.

Christina Cardoza: Those are awesome use cases to hear. And I imagine with SecurOS®—obviously, Intelligent Security Systems, security is in your name—so when we think of that, sometimes it’s a thought about, like, safety and surveillance and protecting what the cameras are capturing. But also on the backend, the security we’re talking about—collecting license plate data, collecting videos of drivers—so I assume that SecurOS® or any other technology and solutions that you guys have, privacy and security of keeping that data safe and making sure that personal data is protected is something that you guys are also on top of.

Joe Harvey: Absolutely, it is. At the forefront of the digital age has been both a privacy aspect but then a security aspect. Yes, as our name implies, ISS, Intelligent Security Systems, as you may have mentioned, too, we had our grounding in what physical security meant for real-world applications and have continued to build on that. From the privacy standpoint the industry has taken a branched approach in what they are looking at, from the standpoint of what we are actually capturing out on the roadway where we can blur faces, blur license plates, actually have the intelligence within the cameras understand and help us remove any of that personal data.

But from the standpoint of then what is captured, working with each agency individually on what their standards are, and then from a security standpoint here at ISS being able to follow all the major outlines for your security, for your privacy, and making sure that any advancement that we might make, that is the parallel path that we are making sure we are following. Because trust and understanding from our users is paramount to our success. It has to be a part of what we do and for us here at ISS has been kind of a driving parallel path to what we bring to market.

Christina Cardoza: Absolutely. And that’s great to hear, because technology and all these things, no matter how big the benefits that they do bring, there’s always going to be those privacy or data concerns. So it’s great to be able to have a solution that gives you both. You can take advantage of all these benefits and ensure that data and sensitive information is protected.

And I also, since we’re talking about AI, I wanted to ask—and I should mention insight.tech and the “insight.tech Talk,” we are sponsored by Intel—but I can imagine being able to apply AI to these different areas, you need it to be high performance. You’re collecting real-time analytics, so that needs to happen at the edge. So I can imagine that you are using and partnering with Intel in all of this. So, I’m just curious what the value of that partnership and that technology use from Intel has been for ISS.

Joe Harvey: Yes, I would say almost invaluable when you’re going to try to really put into a box what companies like ISS need from a performance standpoint and the partnership we need with a company like Intel. As advancements continually get made, more and more of our end-user agencies are asking us to include different data points and just push the capabilities of what the physical hardware and software are. Without a company like Intel understanding and the forethought they have of what the marketplace is going to need and how they can be a benefit to manufacturers to instantly react, to have solutions, and truly partner with us to solve problems that we are seeing in the real world, you can’t understand how great of an impact that has from our standpoint, and ultimately the rest of the industry that is relying on an Intel to really continue to push that forward for us.

Christina Cardoza: Awesome. Well, I can’t wait to see some of these technologies and advancements come to my area and be just more widely adopted and more spread out—you know, get to my parents’ house a little faster. But I appreciate this conversation. It’s been very interesting to hear.

Before we go, Joe, I just wanted to turn it back to you one last time. Any final thoughts or key takeaways you want to leave our listeners with today?

Joe Harvey: Really just understanding what is possible from an Intel, from a video-intelligence company, and what the traveling motorists are going to see. We are trying to solve problems that are either real-time, today, actual events that you are seeing out on the roadway, but also looking to partner and solve problems that we don’t even understand yet.

As that connected realm continues to build itself, more and more challenges will be brought to us and asked of us to solve. And we believe, here at ISS, that we are up for that challenge. But we look forward to continuing conversations with all parties to see how we can leverage the strength of what we’ve built here over 25-plus years, and everybody else that is interconnected within solving these problems, to transform the traveling public.

So, we look forward to it. We appreciate being a part of it. ITS is in our lifeblood, here at ISS, so we’re just happy to be a part and appreciate the time to just share a little bit of our knowledge and the excitement behind these products, the solutions, and the entire space itself.

Christina Cardoza: Great. And I would urge all of our listeners, like Joe said, see how you can partner with ISS, visit the website, have any real-world problems you’re looking to solve. We’ve talked about intelligent traffic management today, but ISS offers many different solutions across many different industries. So, have a conversation with Joe and the team and see how they can help you out, solve your problems.

Also, keep up with us on insight.tech as we continue to cover partners like ISS in this space. So, thank you, Joe, again for joining us. Thank you to our listeners. 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.

Educational Technology Preps Students for an AI World

Posted on by Micah Kaplan

As AI becomes more deeply integrated in today’s society, the same is true in education—offering extraordinary benefits for the future. Teaching AI skills in the classroom fosters critical thinking, creativity, ethical awareness, personalized learning, collaboration, and much more. At the same time, AI provides many benefits to educators—from automating repetitive tasks, helping teachers better prepare lesson plans, and have more personalized interactions with students.

This is why school districts across South Korea are dedicated to promoting an educational technology culture that enables students to develop technical skills they need to succeed. One example is the Jeonbuk State Office of Education, which has the overriding goal of “nurturing global citizens with future capabilities.”

To do so, Jeonbuk seeks to develop active student participation in the classroom and encourage teachers to also grow along with their students by:

  • Building a learning environment for digital transformation that links to students’ lives
  • Cultivating global competencies and global citizenship education
  • Promoting a curriculum to foster future capabilities
  • Developing student-centered, personalized education

A big reason Jeonbuk selected the AI PC is its high-performance computing, combined with #AI capabilities, which helps students learn across various platforms. @LGUS via @insightdottech

Intel® AI PCs Key to Effective Educational Technology

An essential element to Jeonbuk achieving its mandate is the deployment of Intel® Core™ Ultra™ processor-powered AI PCs alongside Intel® AI Education programs. A big reason Jeonbuk selected the AI PC is its high-performance computing, combined with AI capabilities, which helps students learn across various platforms.

“Jeonbuk’s highest goal for the AI PC is to upgrade students’ skills in areas like digital literacy, AI, and coding,” says Rosie Kang, Education AE at Intel.

Another driving factor was the inclusion of AI development tools like Intel® OpenVINO™—an open-source toolkit that accelerates AI inference—which comes installed on each PC. Taking it one step further, Intel created 10 simple OpenVINO models, making it easy for students to experience AI for the first time. For example, students can use AI to identify their age based on facial ID or change the background image the camera sees.

“Each student has their own PC, enabling them to learn AI software by themselves, and grow as self-directed learners,” says Kang.

Educational Programs Focus on AI Skill Development

Maximizing the classroom AI experience requires more than just hardware and software. In-depth training programs enhance learning outcomes for everyone.

For educators, the right hardware, software, and tools in the classroom must go hand in hand with empowering teachers to succeed. Education programs like Intel® Skills for Innovation (SFI) and Intel® AI for Youth are essential for teachers to improve their own AI skills and develop curricula that provide students with more engaged learning.

The SFI initiative helps leaders not just adopt educational technology but also create leading-edge learning experiences. It guides teachers in building new skills that can prepare students for a technology-dominated future. Jeonbuk selected Intel SFI partner BrainAI to deliver localized content and workshops that instruct teachers on topics like using AI in education to solve everyday challenges, AI ethics, and more.

And while SFI targets educational professionals, Intel AI for Youth is designed for students. It helps them develop the skills they need to become AI-ready, learn how to use technology and tools, and create solutions with positive social impact. The 200-hour program covers computer vision, natural language processing, and statistical data, and presents children the opportunity to develop applications that solve real-world problems with AI.

Jeonbuk Educational Technology Bidding Process

To obtain the hardware, software, cost, and service it needed for a successful deployment, Jeonbuk released an RFP with precise and detailed requirements. Competing against a number of suppliers, ultimately Intel partner LG Electronics, a digital transformation specialist, was selected.

With a project estimate of $73,000, Jeonbuk’s requirements included:

  • Supply and distribution of one smart device per student who can freely use educational technology to realize distance learning, blended learning, and competency-based education by the end of 2024.
  • Device management to maintain a stable system that supports student-centered computer use and teacher workload reduction.
  • Content management system that supports teacher learning, such as how to install educational apps and content, and block harmful apps.
  • Stable operation of a defect management system and sustainable management through a service level management (SLA) support system.

“Jeonbuk and Intel agreed on the partnership to provide solutions on how to use the deployed devices effectively in class and integrate them into their lessons with the AI PC and SIC program,” says Kang.

Educational Technology Investments Prepare Students for the Future

With a strong emphasis on STEM, South Korea invests significantly in education, making it a global leader in the field. For example, initiatives like the Korean Edutech Contents Deployment Project provide funding to school districts across the country. “This project holds significant value for Jeonbuk, as it empowers teachers to independently choose the applications that best suit their lessons,” says Kang. “Jeonbuk also believes that this flexibility allows teachers to create more efficient, creative lessons and provide students with a more engaging learning experience.”

Technology that places students at the center of all educational activities fosters global citizens who will lead a sustainable future society. And using AI in education—by students and teachers—provides a big step forward for today’s youth to become the leaders of tomorrow.

 

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

Smart Building Technology Leads to Efficiency Gains

Posted on by Micah Kaplan

Buildings are an essential part of everyday life—we live, sleep, play, and work in them. But they’re also one of the largest asset classes and most inefficient sectors of the entire economy. Forward-thinking building owners and operators are turning to data-driven management to save money and energy while increasing sustainability.

These are just a few reasons why CEO Deb Noller cofounded Switch Automation, a global real estate software company.

Coming from a logistics background, where Noller tracked freight and containers in distributed purchasing environments, she saw firsthand the challenges with manual tracking. Companies were losing visibility of their procurement purchases—causing projects to run behind schedule. She and her cofounder developed solutions for automating everything from tracking of materials to customs integration—keeping projects on time and within budget.

“When manual, paper-driven processes become digital, it leads to enormous efficiency gains,” says Noller. “Not just money savings but time, and making people’s jobs easier. And when I looked at the world of buildings, it was the same opportunity.”

Building management for retail stores, bank branches, and fleets of office buildings have traditionally relied on a large workforce performing low-skill—and likely unenjoyable—tasks. Noller foresees an entirely data-driven way of managing buildings: “I think there’s an opportunity not just to save money but to save energy, make buildings more efficient, and lower their impact on the planet.”

When manual, paper-driven processes become #digital, it leads to enormous efficiency gains. Not just money savings but time, and making people’s jobs easier. @SwitchHQ via @insightdottech

Data-Driven Retail Banks Become Digital Facilities

A great example of this is within financial institutions, which tend to be early adopters of smart building technology for everything from energy savings to visitor comfort. Switch Automation worked with a banking customer that operates and manages hundreds of retail branches in-house. The bank wanted a data-driven solution that highlighted problems and opportunities in every building, while having this data available in a central operational center. In deploying the Switch Automation Building Optimization Platform the bank continually collected actionable information about each branch that enabled significant efficiencies and cost savings.

Noller found this customer to be a great standout, with a forward-thinking operational team that understood from the start that hardware and software should be decoupled. This allowed them to take advantage of the pre-existing hardware deployed in all of their buildings—such as smart thermostats, access control, and lighting systems—and just put a software layer over the top—leveraging earlier investments. As a result, the bank can start a digital program immediately, select different vendor solutions, upgrade hardware more gradually, and not be locked into decisions they made in the past.

For another banking client, Switch Automation partnered with the bank’s cybersecurity provider and integrated its software onto the solution. “The bank has remarkable high-end headquarters in New York and London,” says Noller. “These are modern, state-of-the-art buildings, with all the bells and whistles, and some really advanced use cases.”

For example, the company deployed on-demand climate control for conference rooms, which increased overall efficiency and energy savings. Instead of heating or cooling every room all day, the system activates the meeting room thermostat only when it’s booked. If the occupants don’t show up, climate control is turned off after 10 minutes. This one technology investment expanded to demand-driven maintenance for smart cleaning, and even space utilization planning, like closing floors on low-use days, which led to increasing ROI over time.

While it may seem relatively simple, data integration is broad and complex—including both IT and OT systems. On the IT side, room booking systems integrate with email and calendars. On the OT side are occupancy sensors, HVAC, and lighting systems.

The Building Optimization Platform software takes all of this disparate information, then aggregates, normalizes, and transforms it, empowering the teams responsible for maintaining and operating buildings with insights they can act on.

In general, the payback on data-driven buildings is impressive. “One of our banking clients saved in excess of 20% in total energy across their entire portfolio. Over the last five years, that has amounted to $12 million,” Noller says.

Building Automation Depends on Reliable Performance

The value Switch Automation brings to its customers is not only the software and data that uncover cost-saving opportunities but also the company’s partnerships, which allows it to do so at scale. With clients operating anywhere from 100 to 5,000 buildings, Switch Automation relies on Intel and its OEM partners to develop high-performance and affordable systems that are reliable, inherently secure, and scalable.

The Building Automation software runs on an Intel-powered IoT gateway that controls sequences and runs the analytics, with computations performed in the cloud and then deployed to the edge.

System installations are also automated, with gateways being discovered and centrally commissioned at remote sites. “We discover all the systems, we discover all the data points, and then we start to ingest and tag that data, and map it into our system,” Noller says. “It has to be this way to scale, but of course behind all of that there’s 10 years of R&D work to get to this point.”

Building Automation Is the Future, Today

While sustainability is an important motivating factor for organizations to embrace building automation, it’s equally about efficiency gains and saving money. Market leaders deploying automation at scale run their buildings at a fraction of the cost per square foot, unlike those who do not.

Going forward, Noller expects data-driven automation to become mainstream and one of the big technology growth sectors over the next decade: “There’s just so much opportunity to save money and save energy, and I’m driven at the end of the day by the fact that we need to resolve, and fix things for climate change.”

 

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

Smart Manufacturing Goes Green

Posted on by Micah Kaplan

Automation has been key to improving business operations in so many ways—from increasing manufacturing speed to enhancing the accuracy of error-prone manual processes. But what about improving sustainability practices? And do we even think about factories having sustainability practices? We should. But it’s a bit more complicated than automating the lights in an office building when no one’s there; in manufacturing, downtime isn’t always part of the picture.

Our guest Anna Kiseleva, Product Manager from NEXCOM International subsidiary NexAIoT, paints the green-manufacturing picture for us. She talks about why going green is important, technologies that companies should have in their automation tool sets to get there, and the future of manufacturing sustainability (Video 1). Because going green is only going to become more important going forward—not just for factories, but for everyone. And manufacturers may start their journeys down this path because of regulations, but they may just find that becoming energy efficient can lead to the optimization of all kinds of resources in the end.

Video 1. Anna Kiseleva from NexAIoT talks about what it means to become a green manufacturer. (Source: insight.tech)

What does green manufacturing mean, and why is it important for Industry 4.0?

Let’s talk about what it means to be environmental-friendly as a company. First and foremost, reducing power consumption is a fundamental aspect of environmental conservation, because excessive energy use contributes to depletion of natural resources and increased carbon emissions, leading to climate change and ecological degradation. By adopting greener practices, industries can mitigate their ecological footprints, preserving ecosystems and biodiversity.

Therefore, the first step in being an environmentally friendly manufacturer is reducing power consumption. The second is automated combustion control, which also helps to reduce emissions. The third part of it is minimizing waste and promoting recycling, such as using automation to sort and recycle factory waste materials. Fourth, sustainable supply chain management to track and manage commodities sourcing. Fifth is green logistics and transportation by adopting hybrid and green vehicles and autonomous or smart logistics systems. And the last part of the whole sustainability effort is worker safety, to reduce the risk of accidents and improve efficiency.

Right now, green manufacturing and industrial sustainability focus a lot on carbon-emission reduction. We need to monitor power consumption in the first place, because this is where most waste comes from. If power consumed is not needed for the factory, it will lead to increased expenses and, of course, not very sustainable practices. So we need to monitor every device; we need to connect them, monitor them, and track where improvements can be made.

It’s important to mention that many traditional manufacturers face challenges that slow down this transition toward sustainability. This includes lack of networking capabilities in old production equipment, incomplete production-management systems, low equipment-utilization rates, long product-delivery times, and insufficient information transparency.

“By adopting greener practices, industries can mitigate their ecological footprints, preserving ecosystems and biodiversity.” —Anna Kiseleva, NexAIoT via @insightdottech

How does NexAIoT help factories meet that checklist?

NexAIoT provides a one-stop service to help, including industrial IoT and automation products, customer–systems integration projects, and consulting services for building whole-factory smart manufacturing.

Basically, we can divide our tools into three main categories: One is smart meters and controllers to collect data from equipment locally. The second is gateways to collect data from various controllers and upload the collected data to the cloud. And the third is servers to store a huge amount of data. NexAIoT provides all industrial equipment needed for this solution. With these tools we can reduce carbon emissions and impact the carbon footprint accordingly.

We’re talking first about tools that will help us collect data from the bottom of the factory—from the production line—upload it to the cloud, and then visualize that data so that managers can make more strategic decisions. These are our NexDATA and NexWall platforms for digitalization and visualization. They will also track the data in the cloud for future AI-model training and predictions. Once the data is collected, we also help our customers with the software to visualize it.

To achieve green manufacturing, all the equipment should be connected. That is the second thing. Our AI-enabled edge getaway tools for analog and digital inputs and outputs—called nDAS and nPAC—can be used as data collectors, as well as edge monitoring devices for more efficient connectivity performance. We also have a NISE series that redirects data and uploads it to the cloud. Basically, these tools help collect all the information from sensors and send it to the cloud so more data and decision-making can be done at the edge side for better communication from machine to machine.

Once machine-to-machine communication is established, we can move to the next level. And while monitoring the operation status of the entire factory’s equipment, our solution will also start researching ways to reduce carbon emissions and prepare for carbon-tax policies.

Here’s the core: Our industrial computers will help collect data from the field, upload the data to the cloud, visualize the data for managers, and will also help with AI models to do some predictions.

Can you give us any real-world examples from NexAIoT?

Recently we have been working on some factory-automation projects in Taiwan. The government there has regulations regarding power consumption and sustainability indicators. One of the main KPIs for factories is their total yearly power consumption. Typically, a factory will apply for a certain electricity capacity and pay for it accordingly. Then, at the end of the year, the factory will have to report its actual power-consumption numbers to the government.

Ideally, the amount of electricity applied for will equal what was used during the year. If what is used exceeds the amount applied for, the factory will have to pay a fine. If the value is lower than expected, then the factory will have ended up paying for some amount of electricity that it did not need. So the question from our clients is how can the factory managers know that KPI for the factory at the beginning? How can the factory know its baseline?

Typically, what we see is that manufacturers will apply for a higher power-consumption limit and end up paying more than what they really need. But after installing our automation solutions and smart meters in the factory, our clients can estimate that baseline for the factory; then they can apply for only the amount of power that they really need.

Also in Taiwan, once you apply for a certain power limit, then every year you will have to reduce that power consumption by 1%. But how do you achieve this goal? The factory manager faces the problem of not knowing what to turn off: “Should I stop the production line? Should I turn off the AC system?”

One of our customers, an aluminum manufacturer, received penalties from the government every year. But the factory managers couldn’t understand why. What equipment was the most power consuming? Many different electric panels were connected to the power meters, so there was no way to trace the power consumption through the whole system.

We will work with a factory—either with a new factory or with an old factory that requires modernization—to install all the required automation solutions and then help them trace their power consumption. With the aluminum manufacturer we installed our nDAS smart meters and the whole automation solution so that we could see where the main power consumption was and which equipment was consuming the most energy. Then they could do the adjustment—maybe turn off the equipment when it wasn’t in use.

Eventually these practices will lead to green manufacturing. Even if green manufacturing was not the manufacturer’s goal in the beginning, by adopting these practices—as well as by lowering their power consumption by 1% each year—companies will start to adopt more and more green manufacturing practices.

Talk about the partnerships NexAIoT has established to help provide these solutions.

Basically, NexAIoT solutions are based on Intel technologies that have been used in factory automation to bring unparalleled precision, efficiency, and flexibility to manufacturing processes. Of course, we have other providers, but Intel is the main one.

The new generation of Intel CPU has a lot of enhanced features—for example, the low-power Intel Atom® or Celeron processors with Intel Time-Sensitive Networking, or the high-performance Intel® Core™ processors that include Intel Time Coordinated Computing to reduce latency and provide real-time control. This is all very crucial in our applications, because we need real-time data to be uploaded to the cloud and to do the calculations and predictions. Intel processors also support multi-display outputs, and this allows them to work on different workloads at the same time for better efficiency.

We are very grateful to have Intel as our partner so that we can achieve our automation-project goals and bring our customers the best solutions we can.

How do you foresee these technologies evolving to address green manufacturing?

Future advancements will focus on reducing energy consumption, optimizing resource usage, and integrating more sustainable practices. NexAIoT is looking at ways to use AI to optimize factory operations and achieve ESG goals. We are also investigating the use of AI to identify and mitigate production-process bottlenecks. AI-model optimization can also help carbon-emission optimizations.

Coming up, we are preparing an AI system that is all Llama-based GPT for manufacturers’ CO2 management. Other advancements will include AI automation, real-time training, and AI models to help water treatment plants.

Another trend is AI-model customization, because you cannot use one AI model for all factories. They are all different and require different procedures and different data tracking. But whether their goals are to reduce power consumption or to recycle or to reduce waste in the factory, there will be more and more customized AI models.

The incorporation of edge computing and cloud technologies into manufacturing processes aligns with green manufacturing objectives by promoting resource efficiency, reducing waste, optimizing energy consumption, and enhancing overall sustainability in production operations.

There will undoubtedly be many challenges along the way—as well as new possibilities. This is to be expected and embraced. Green manufacturing is not just another solution; it’s a long-term journey to continue improving manufacturing practices for greater efficiency, sustainability, and profitability.

Related Content

To learn more about green manufacturing, listen to A Greener Path for Manufacturers and read Smart Factory Tech Proves Data Is Power. For the latest innovations from NexAIoT, follow them on LinkedIn.

 

This article was edited by Erin Noble, copy editor.