Touchless Technology Enhances the Self-Service Experience

Posted on by Micah Kaplan

From checking out at the grocery store, to ordering food at a restaurant, or finding their way at an airport, people want fast and convenient self-service options.

In recent years, Olivier Raulot, founder and CEO at iNUI Studio, observed how innovation leaders across many industries have been searching for hygienic self-service options. “The need for touchless technology solutions is real and increasing,” Raulot shares. “Businesses successfully delivering upon this—not only for health and safety but to provide delightful experiences—will establish their brands as people-centric and forward-thinking.”

Advancements in computer vision and touchless technology are changing the game, giving users the safe and simple experience they’re looking for. And businesses gain all sorts of new opportunities to grow sales, reduce costs, and streamline operations.

The potential use cases for interactive, touchless screen solutions are boundless, and many organizations around the world are taking the leap.

The potential use cases for interactive, #touchless #screen solutions are boundless, and many organizations around the world are taking the leap. @airxtouch via @insightdottech

Smart Self-Service Options from North America to Europe

One example is the Bergen, Norway Airport and Tourist Information Center, where iNUI Studio, a Luxembourg-based deep tech company, has deployed its AIRxTOUCH KIOSKs throughout the terminal and visitors center. The interactive touchless screen solution is engaging locals and international travelers alike as they plan their visit.

Anders Nyland, CEO of Visit Bergen, believes that good digital technology is synonymous with good hospitality, which is achieved by delivering new and engaging visitor experiences. Nyland saw an opportunity to make helpful and promotional information more accessible by positioning iNUI’s interactive displays in high-traffic tourist locations.

Now Bergen visitors can safely and easily use these touchless kiosks to navigate the area confidently, book a fjord tour, or find the best restaurants nearby. And the Bergen tourism bureau can more effectively promote businesses and destinations today and into the future (Video 1).

Video 1. Travelers can access helpful information touchless digital screens at the Bergen Airport and Tourist Information Center in Norway. (Source: iNUI Studio)

Infinite Options with Touchless Technology

The flexibility and scalability of iNUI Studio’s touchless kiosk solutions also offer great experiences in other use cases, including foodservice and the corporate office.

For example, St-Hubert, a Canadian restaurant chain famous for their spit-roast chicken and legendary BBQ sauce, was the first North American quick-serve restaurants (QSRs) to roll out the AIRxTOUCH solution. The company’s goal was to offer customers a self-service experience through interactive digital screens. The result is a highly hygienic and engaging experience where diners can order their favorite menu items without physical contact, through intuitive gestures. St-Hubert wins by improving customer satisfaction, creating repeat business, and upselling menu items.

Another shift the world has experienced as a result of the pandemic is the adoption of hybrid and remote work. Maintaining social cohesion for employees in that context can be quite challenging. For that reason, fast-growing law firm Arendt decided to implement 29 AIRxTOUCH kiosks throughout their corporate offices in Luxembourg.

This solution enables Arendt to seamlessly communicate valuable of information to more than 1,000 employees such as corporate news and events, real-time traffic conditions, the cafeteria menu, respond to surveys, and more. Employees can interact with informative and social content through the touchless screen experience.

The company sees strong enthusiasm from their staff in using the new communication tools. The usage statistics are promising, and this new format allows them to communicate more creatively on a wide variety of subjects.

Easing Touchless Interactive Screen Adoption

Because interacting with digital screens with hand gestures is not always intuitive, Raulot believes adoption requires non-intrusive technology that doesn’t require a learning curve. “We intentionally preserve the gesture behaviors users have when interacting with touchscreen devices, with our gesture recognition technology,” says Raulot. “For touchless adoption to grow, it’s critical to have well-designed software aligned with specific usage context and taking users’ psychology into account—especially avoiding cognitive biases.”

Enabling this is iNUI’s proprietary computer vision engine that processes images in real time with low CPU/GPU consumption. It also delivers an impressive touchpoint accuracy of less than 3mm when interacting with the kiosk, preventing erroneous clicks and delivering an intuitive user experience.

According to Raulot, partnerships with Intel and Samsung provide both essential technology and marketing opportunities for iNUI Studio and its solutions. “Intel brings the computing performance and reliability with its compact Intel® NUC mini PC and the technical support we need,” says Raulot. “Samsung, our digital display provider, not only elevates the experience of our solution but also the international visibility of our product with demos in their showroom.”

Interactive touch-free digital screen technology is the next wave in self-service, delivering safe and delightful user experiences and new ways for businesses to grow and thrive.

Smart Rail Technology Keeps Trains Safe

Posted on by Micah Kaplan

Everybody wants the trains to run on time, but more important than keeping to a tight schedule is traveling without incident. And to do so, you need to be collecting real-time information from outside a train as it rolls down the tracks. This level of insight is critical to being able to alert train operators and drivers of any issues so that they can make intelligent decisions and avoid what could be potential catastrophes.

If there’s a problem up ahead, the faster the engineer knows about it, the faster they can apply the train brakes and stop. Even a split second can make a difference between disaster and getting the passengers safely to their destination.

Thankfully, with the use of artificial intelligence in transportation and 5G connectivity, data has never been more readily available. There are two types of data systems in specific that are critical to keeping railroad operations on track: Automatic Train Protection (ATP) and pantograph monitoring systems.

An ATP system is designed to continuously check how fast a train travels against the allowable speed along any stretch of track. Pantograph monitoring can sense unusual vibrations and capture video of sparks, among other issues around the train.

Getting AI in Transportation on the Right Track

One example of this is a rail system in Taiwan. The system recently started using AI and 5G for real-time ATP and pantograph monitoring, using hardware technology from MiTAC Computing Technology Corporation (MCT), which has developed a railway-focused solution—the MiAIOT Train Protecting Monitoring System (MTPMS)—based on its AI platform MiAIOT.

“With the solution, train drivers get help from new detection and alert technologies. Once there is an equipment failure, drivers no longer need to download and check the pantograph video when they get back to the train office. Instead, videos are transferred by the 5G channel,” says Russell Lo, Marketing Manager of MiTAC Information Technology Corp.

With the use of #ArtificialIntelligence in transportation and #5G connectivity, #data has never been more readily available. @MitacComputing via @insightdottech

This can accelerate delivery of information to train drivers by up to 98%, allowing them to handle alerts properly and more efficiently, Lo explains.

MTPMS transmits information not only to the driver but also the railway control center and operation rooms, thus combining information that previously was available in different streams and required more work and time to put together.

“It makes previously scattered information readable and synchronized, and improves the overall efficiency and safety of railway transportation,” says Lo.

Besides safety data, the system handles schedules and logs. “Drivers no longer need to carry USB media with the schedule when they get to the train or carry back the same USB that stores the log data from the train when they’re back. Instead, all data is transferred by the 5G channel,” says Lo.

So far, MTPMS has seen positive results when used in the Taiwan Railway Hsinchu Locomotive Depot and is expected to be expanded to Taiwan’s entire railway system, according to Lo.

Developing AI Safety Solutions

Providing an AI safety solution for trains has been a natural fit for MiTAC. The company has a long history in the IT industry, providing hardware and software solutions, systems integration services, and—through its distribution arm, Synnex—logistics and fulfillment. Around 2016 is when the company decided to make a significant investment in AI.

Its Artificial Intelligence of Things (AIOT) solutions like MiAIOT are already available to a wide range of industries, such as smart cities and government, transportation, education, security, enterprise, and manufacturing. The MiAIOT platform has even been used in various settings, including preventing the spread of disease by mosquitoes, illegal event, water resource management, and tourism.

The platform leverages seven main systems—data storage, real-time streaming, AI analysis, visualization toolkits, API, platform management, and alarm notification—to convert non-standardized data from different systems to a standard format to deliver actionable insights.

“MiAIOT is a powerful and yet simple tool that provides governments and enterprises with the insight to make the informed decisions for municipal administration, city construction, process control, asset management, and even epidemic prevention,” Lo says.

For example, leveraging MiAIOT in its MTPMS enabled the solution to predict eight train system failures about two minutes before they occurred just in the third quarter of 2022—making it possible for train operators to take immediate action, Lo explains.

The Future of AI Management

Besides expanding the use of MTPMS in Taiwan, MiTAC has other plans. Among them is a plan to offer monitoring capabilities inside train cars for safety purposes.

MiTAC eventually hopes to offer its AI technology to railway systems outside Taiwan. To do so, MiTAC plans to leverage its partnership with Intel, which would help improve the ongoing global digital transformation, Lo says.

The key to having a successful digital transformation and eliminating data silo problems is to have a unified platform where data can be allocated, aggregated, and integrated across enterprise departments, vendors, and customers, Lo explains. To make that possible, Intel is helping MiTAC develop edge-to-cloud solutions by taking out some of the complexity involved in the process and providing streamlined workflows and accelerated deployment.

Specifically for MTPMS, MiTAC plans to continue refining its AI technology to make it more predictive, giving railway operators real-time data to improve the safety of their trains and the people who ride them.

 

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

AI-as-a-Service: Inspection Accuracy and Rapid ROI

Posted on by Micah Kaplan

For manufacturers, machine vision provides tangible, immediate benefits. Human oversight of quality control is only about 80% accurate—and in many cases, product defects or problems can be too small for the human eye to catch. AI provides a much more reliable solution, empowering manufacturers to reach 99% accuracy, with the ability to catch tiny abnormalities—less than 1 mm in size.

But the steep up-front cost of a machine vision solution can be a barrier for manufacturers, especially those who need to adapt the same solution on different production lines. To solve this dilemma, BlueSkies.AI, a provider of AI Vision as a Service, worked with Lenovo, a global leader in computing devices, and Intel. The combination of best-in-class computing performance, open-source software, and vision AI expertise provides secure and scalable quality inspection solutions. With a worker shortage facing many areas of the world, solutions like these yield rapid ROI by enabling companies to move humans from manual inspection and train them for more value-add roles.

AI-as-a-Service at Work

BlueSkies.AI and Lenovo partnered closely with Intel to pilot the solution at a large pharmaceutical company during the constraints of the COVID-19 pandemic. On the hardware side, they deployed the Intel® processor-powered Lenovo ThinkEdge SE30 industrial PC, built to withstand harsh environments.

“The appliance is designed to snap onto a conveyor belt and the camera can be chosen based on the product and size of the potential defect,” says Ted Connell, Founder and CEO of Blueskies.AI. In this case, computer vision cameras needed to look at full images and detect defects as small as 0.1 mm on the pharmaceutical tablet line.

The client does not need any AI or IoT skills. BlueSkies.AI developed AInspect, an edge machine vision appliance with an integrated PC, camera, and light that fits on top of conveyors to inspect products. All the client needs to do is to show the system 30 to 50 examples of each defect type, a similar number of good examples, and the system trains its own AI models.

“That’s all the data you need to start with, and we can achieve mid- to high-90% accuracy,” says Connell. “We’ve figured out how to train models with a small amount of data and quickly reach a better level of accuracy than humans can provide.” If the client needs higher accuracies, all we need to do is show the system additional samples of each defect type and the AI models improve with each interaction. As part of its AI-as-a-Service model, BlueSkies.AI oversees initial training to reach the pre-chosen level of accuracy, and provides ongoing support.

“#Security is front and center for #manufacturers when they’re considering implementation and deployment of a solution at scale” – Blake Kerrigan, @Lenovo via @insightdottech

Edge Compute Provides Data Security Plus Scalability

Keeping data secure is of utmost importance to manufacturers. In many companies, it’s against protocol for data to leave the factory, making cloud solutions an impossible choice.

“Security is front and center for manufacturers when they’re considering implementation and deployment of a solution at scale,” explains Blake Kerrigan, Senior Director of ThinkEdge Business Group at Lenovo. “This is especially true when you’re blending IT and OT in organizations where there’s been a lot of bifurcation in the past.”

The solution answers that concern with powerful on-premises compute that keeps data at the edge and behind the customer’s firewall. Manufacturers can deploy Lenovo hardware confidently, knowing it meets all their security criteria and quality standards. They also benefit from a trusted supplier program supported in more than 180 markets around the world. “We’re able to leverage that economy of scale and service large global companies,” says Kerrigan.

The Future: Open Source at the Edge

Proprietary machine protocols have a long history in manufacturing, creating interoperability challenges and data bottlenecks that can slow down ROI for connected solutions.

Kerrigan believes an open-source approach is the way forward. “The thing I love about AI, and especially computer vision, is that it’s basically a single language,” he says. “BlueSkies.AI and Intel are leading the way by adopting and embracing this open-source community, which is the way of the future and will lead us to better horizontal strategies from the IT-down perspective.” The Intel® OpenVINO™ Toolkit, for example, provides an extensive development framework that runs on standards and allows for new innovations in deep-learning applications.

Latency is another challenge manufacturers face, especially as they adopt high-bandwidth solutions like AI machine vision. Processing data at the edge is one way to minimize latency—and alleviate security concerns—as AI and connected things become ubiquitous.

“There’s been a massive consolidation of workloads in the cloud and movement of enterprise applications to the cloud,” Kerrigan says, “and now we’re talking about moving them back to the edge for efficiency and speed.”

Ultimately, though, the future of connected manufacturing will take a holistic view of computing. “It will be about managing data across the entire plane, from edge to cloud,” says Connell. “AI is going everywhere. To support those applications, we’ll need a homogenous environment and network to minimize latency and maximize security between the distributed compute centers.”

With robust edge compute and data security in place, imagine what AI-as-a-Service could do for your business.

 

Edited by Georganne Benesch, Associate Editorial Director for insight.tech.

The Power of the 4th Gen Intel® Xeon® Scalable Processors

Posted on by Micah Kaplan

Intel has recently released some of the most versatile processors available to IoT hardware engineers, AI software developers, and systems architects today. The 4th Gen Intel® Xeon® Scalable processors, codenamed Sapphire Rapids, offer a secure and powerful foundation for workload consolidation, edge AI, deep learning, and long life needed in demanding environments.

In this podcast, we take a closer look at what this release means for the industrial and federal space, and explore advantages of these processors over previous generations, including increased versatility, security, and power. We also discuss how these new processors can help industrial organizations optimize their operations and improve efficiency.

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Our Guest: Intel

Our guest this episode is Christine Boles, Vice President of the Network & Edge Group and General Manager of Federal & Industrial Solutions at Intel. Christine has been with the company for more than 30 years in various roles such as Smart Building Solutions Director, Retail Solutions Product Marketing Director, and Program Management Office Director. In her current role, she focuses on helping the federal and industrial space transform its operations with the latest capabilities, products, and technologies from Intel.

Podcast Topics

Christine answers our questions about:

  • (1:44) What the release means to the federal and industrial space
  • (6:58) How the latest processors compare to previous generations
  • (12:22) Top use cases to benefit from the 4th Gen Xeon processors
  • (18:23) The role these processors play in edge adoption

Related Content

To learn more about the 4th Gen Intel® Xeon® Scalable processors, read Intel Boosts Edge Productivity with Processor Innovations. For the latest innovations from Intel, follow them on Twitter and LinkedIn.

Transcript

Christina Cardoza: Hello and welcome to the IoT Chat, where we talk about the latest developments in the Internet of Things. I’m your host, Christina Cardoza, Editorial Director of insight.tech. And today we’re going to take an inside look at the latest 4th Gen Intel® Xeon® Scalable processors, code name “Sapphire Rapids,” with Christine Boles from Intel. Christine, welcome to the show.

Christine Boles: Hello, Christina. It’s great to be here.

Christina Cardoza: Before we jump into the conversation, I would love to learn a little bit more about you. And you have quite the impressive career over there at Intel. So, what can you tell me about it?

Christine Boles: Yes, Christina. Yes. It has been a long and prosperous career here. So, today I am the leader of the Federal Industrial Solutions Division within the Network and Edge group, where I’m a Vice President within that Network and Edge organization. And I like to say I have one of the best jobs at Intel, and frankly in the industry, where I have the opportunity to utilize incredible assets that Intel has from products and technologies that are really helping many in the industry utilize the capabilities they need for this transforming space that is really rapidly transforming in both the federal and industrial areas.

Christina Cardoza: Great. And of course one of those technologies we’re talking about today—the 4th Gen Xeon Scalable processors, which Intel announced at the beginning of the year. I’d love to start off with that release. What can you tell me about these latest processors? And, as it relates to the federal and industrial space, what makes it so exciting for them?

Christine Boles: Yes, it is exciting to see these processors that really have been designed to deliver incredible capabilities for very demanding workloads. And many of those workloads are in the industrial and federal space. And if you really look at what’s been happening in the—and I’ll focus on the industrial space—in the industry space, the Industry 4.0 transformation that’s been underway, this sector and these areas have really been looking for technologies that help deliver capabilities that are really going to extend and increase the business value and address some real challenges that manufacturers or utilities in other areas are looking for.

And so this 4th Generation Intel Xeon Scalable processor, with all of the new capabilities that it’s brought in, particularly in the areas of acceleration for AI and machine learning and data analytics types of capabilities, in addition to networking and storage, really are exciting areas. And Intel really stepped back and re-architected the microarchitecture in order to really address some of these dynamic workloads—whether it is in the networking space or in the area that I focus on: what is happening in the industrial edge. And really extending the processing capability, but within a great performance-to-power performance area, while also extending the memory capacity in the IO that you need in these industrial kinds of capabilities.

So it really is an exciting kind of solution that is made possible with this 4th Generation processor. And perhaps, Christina, I can extend this a bit more and talk about some of the specific areas where we have added some capabilities in. One of the areas is really around the deep learning/machine learning areas, by putting some additional acceleration into the CPUs versus what we’ve had in the past from our Xeon Scalable processors. And two of those areas are the Advanced Matrix Extensions—the AMX extensions—as well as the Intel® Data Streaming Accelerator.

These AMX extensions are really what is that addition for accelerating the AI capabilities in the workloads that you will see in the industrial space, such as machine vision, defect detection, or even quality assessment of what is happening in the equipment as well as of the products moving down the line. And as you can imagine, when you’re in the industrial space there’s a lot of data that manufacturers or oil and gas providers really need to deal with. And having the Data Streaming Accelerator to really prioritize and manage the data through the virtualized environments, as well as presenting the information, is really a benefit that the industrial-solution providers can tap into to meet the demanding needs that the industrial space has.

And I guess one other area that I’d call attention to—and I’m really excited to see how this will be utilized by the industrial-solution providers—is the Intel Speed Select Technology. And this is SST technology that really will help with the concepts of where the industry has been going to consolidate workloads onto form factors that really have multiple workloads running. And the ability to select where you’re going to be processing and optimizing that performance where you need it in some of the virtual machines, and versus, say, other workloads that might not need as much, and really balancing that overall load.

So, there’s some really exciting technologies that we’ve built into this 4th Gen Scalable Xeon processor, that really are going to address some of the needs that the industrial and federal space is looking for.

Christina Cardoza: Absolutely. It sounds like it’s jam-packed with new and great features, especially all the AI stuff. Manufacturers, they want to become more intelligent. They want to leverage AI capabilities, and this sounds like it’s not going to only help them perform better, but perform faster and improve their operations. And you mentioned that the microarchitecture in this release was sort of reinvented also.

So, I think all of these capabilities sound great. And a lot of the things that people want to hear when Intel has new releases like this is what makes it different from the last release? Why should I move now from the release I’m on onto this one? And so you mentioned a couple of the new capabilities there, but what would you say are the main takeaways of how this latest Xeon Scalable processor performs or is compared to the previous generations?

Christine Boles: Yes, Christina. As I mentioned, there’s a myriad of improvements that we brought in in new capabilities. And there’s probably more than we can cover in a short conversation. But I guess four areas that I really see that are really going help the IoT edge, and what customers are looking for, and what solution providers will develop around.

The first is really in the area of performance and memory and IO. So, in the overall architecture we have a higher per-core performance than previous generations, and with up to 52 cores or different sockets for the range of IoT-edge use cases that customers will be looking for. We also have extended the capabilities in the area of memory, with eight channels of DDR5. Now, DDR5 allows for an overall 1.5x improvement in bandwidth over the DDR4 generation, and really will ultimately improve the performance and the capacity for memory utilization.

Now, when we talk about industrial use cases, one of the areas that really pushes limits is IO capabilities. And this generation has up to 80 lanes of PCI Express Gen 5, and test and measurement really pushes the limits of the IO capabilities. And having this range of IO is definitely a great area. And I guess the final IO area that I’ll mention is we do also provide the ability—we have great acceleration of our AI capabilities in the Xeon processor—but if you need additional CPUs or accelerators external, we do have the CXL 1.1 connectivity for interconnect to external devices. So one big category is that memory and IO and overall performance.

Now I did mention the AI acceleration that is the biggest—one of the biggest—additions to this product in the 4th Gen with those AMX—those Advanced Matrix Extensions—that we’ve added in. And of course we take it one step further and make sure you have the right toolkits to take advantage of that capability for workload inferencing and optimization utilizing Intel’s distribution of the OpenVINO™ toolkit. And having both that improved AI acceleration with the toolkits will really help customers have the right support for deep learning and overall training of workloads utilizing this Xeon Scalable processor product line.

And the next area is what I mentioned a bit ago about that bringing together workloads utilizing the Intel Speed Select Technology. Now, this is really to allow for better control over the CPU performance and how you’re utilizing that performance and the compute power across the Xeon processor. And so we also do make available tools that allow for that monitoring control with the Intel Resource Director Technology toolkit that really enables the control and the sharing of the resources and managing the overall environment.

And then the fourth big area is of course resiliency and security—particularly in when you’re putting solutions into operations such as the manufacturing or federal types of environments. Having that efficiency and resiliency that Intel is known for with our processors, allowing for that trusted compute for the life of the product the deployment—so that’s a big part of what we continue to provide. And then of course we have the security extensions with the Software Guard Extensions to really allow for secure enclaves of execution of different applications, and really monitoring and assessing for security support in the platforms that we provide. So, a lot of great areas of improvements over the previous generation.

Christina Cardoza: That definitely sounds like it. And you guys are hitting all of the areas for Industry 4.0 success—security, reliability, flexibility, scalability—everything that manufacturers want to see. And so you mentioned a little bit about how this is being used in federal environments and manufacturing environments. I would love to dig a little bit deeper into this release, and talk about some use cases that you really think are going to benefit from the Xeon Scalable processors.

Christina Boles: Yes, there’s quite a few areas where the Xeon processors have really been set up for a range of edge IoT use cases in the federal and industrial, but actually additional areas, and I’ll touch on a few. The first big area within the federal and industrial spaces—really those use cases that have a high demand for compute, whether that’s machine vision kinds of applications or detecting defects that might be happening and then taking action upon them.

Of course there’s an emerging area of digital twin capabilities, of having both visibility and ability to have a representation of what is happening within the factory area. And then it’s expanding into evolving areas of automation—whether that’s within the utility space, with the modernization of the grid and bringing greater levels of software-defined capability and management of the utility and the electric-grid infrastructure or process automation.

And the capabilities that have been built into the next generation—one of the big areas will be in this machine vision area. How do manufacturers really improve the detection of defects, as well as quality inspection from a range of cameras, gathering that information in, accurately analyzing, and then acting upon the images that it’s bringing in. And those capabilities that we’ve built into the Xeon processor with the AMX extensions will really allow for these workloads to be processed and managed with the amount of information flowing through. And that’s inclusive of also the Data Streaming Accelerator area.

Now one area I’ve mentioned quite a bit is the manufacturing space; you’ll also see those same benefits in the use cases in the fed aerospace, where there they really need to utilize those capabilities of those new instructions and the vector extensions that we’ve integrated in for doing signal processing on algorithms—that signal processing algorithm on workloads that really need that additional capability for an analysis.

Now, those same kind of areas that we have in the federal and industrial—you can think about some of those improvements being utilized in more of those consumer-focused industries of the retail environment, the hospitality types of spaces. And really over the last few years you’ve really seen a change in what is made available as you’re going into your stores or into your hotel locations—with self-checkout kiosks, or even robotics for assessing what is happening with the logistics, say, in the warehouse and the back room and managing the overall inventory.

So, one area that the Xeon processors will really help is in that—that front end of the store, and as well as of course the back end as well. But we’ll focus on the front end, where these storefronts have really been changing, and they’ve been utilizing new capabilities to have interaction with consumers as they’re coming in, as well as assessing what is happening and preferences that the customers may have. So, having that 4th Gen Intel Xeon Scalable processor–based solution with the additional AI and analytics capabilities will really allow utilities to offer new products, offer new service positions for customers as they’re coming in, offer new service revenue, as well as overall assessment of what is happening in the environment in those commercial-storefront areas. So, great opportunities to utilize the capabilities of these Xeon-based solutions.

And I guess the last area—if you extend that use of visualization and gathering of information—there isn’t an area that has more of an opportunity than most than the health and healthcare and life sciences area. And how they might utilize the AI extensions and support that has been built into this 4th Generation Xeon processor. And really utilizing the information to assess images; to do advanced analysis on genomics, sequencings and other areas; and really utilizing the improvements that we’ve put into the products. So it’s going to be exciting to see how medical-equipment manufacturers utilize some of the new capabilities we’ve put into these processors.

Christina Cardoza: Yeah, absolutely. That is such a wide range of use cases. So, it’s great to see how every industry can start taking advantage of this. And one of the things you were talking about with all of these improvements and opportunities out there for these industries is they want those real-time insights, that real-time visibility, so that they can start making these informed-based decisions to really improve everything and move faster.

And one thing that I just want to talk about quickly is, as they start adding these new and advanced solutions into their operations, it starts to put some constraints on the network, and then they want to start moving closer to the edge so that they can utilize all this data much faster and perform much better. So I’m curious what role do you see the 4th Gen Intel Xeon processors playing as all these opportunities start adding more network workloads and start moving closer to the edge?

Christine Boles: Yes. This is one of the exciting parts of the next-generation platform that we’ve been working on with these Xeon processors, is a range of workloads that it enables. And one of those areas is really this shift that is happening from what have traditionally been more fixed-function network architectures, to what is evolving to be more of a software-based, virtualized network environment, particularly in the radio and access-network area. And ultimately these communication-service providers can utilize these capabilities for the optimizations that we put in, not just for the examples I’ve given, but also for the network workloads in how we need to optimize and manage the range of data, as well as the overall load balancing that you need within the network.

And so the Intel Xeon processor capability that we’ve built in really allows for the solutions providers to have more of a software-defined network environment, while at the same time having the AI and machine learning capability for the information that’s flowing through the network and optimizing it. So it’s a very interesting time, as more of the network becomes software and managing. And then you have this range of use cases that you can process alongside them in a converged, network-edge kind of capability. And the Xeon processor really helps make that possible, while at the same time really optimizing the overall performance-power kind of measurement that the overall network solutions are looking for. And at the same time having the right latency, low-latency capability, that you need in these network capabilities. So it’s an exciting place for us to see how the Xeon processors are going to help the industry move along.

Christina Cardoza: Yeah, absolutely. I can’t wait to see how industries just continue to transform and leverage these 4th Generation Xeon Scalable processors, and see what else they come out with, because, like you’ve mentioned in this call, there’s lots to take advantage of in this latest release. Unfortunately we are running out of time. But before we go, I just want to throw it back to you one last time. We talked about a lot of different features and capabilities here; so are there any final takeaways or thoughts you want to leave our listeners with today?

Christine Boles: Christina, I’ve talked a lot about the new features, but there’s—I’ll call it at its roots—this Intel Xeon Scalable processor that has brought capabilities to the edge really helps with not only the things I mentioned on the performance, the security, etc., but we’ve also kept in mind what you need going into more ruggedized kinds of environments, where you need to ensure that you have the reliability and the support for that.

And so we offer a broad range of SKUs of these processors. And the SKUs have really been specifically built catering for the long-life needs, as well as the reliability needs, of 10-year reliability and availability that industrial-commercial offerings need. And it’s also available in a more industrial temperature range of 0°C–84°C, such that they really can be used in the industrial environments.

In addition we offer the range of SKUs—so, where I mentioned the range of cores and performance and how it can be up to the numerous number of cores that the Xeon processor brings—it also can be scalable to really bring, “What does the edge workload really need?” and really balancing the power-performance needs, as well as bringing them into the specific environment. So, bottom line, we really have redefined and ensured that the solutions providers that are utilizing this 4th Gen Xeon processor can really have the right capabilities that they need in performance acceleration for AI workloads or networking workloads and analytics, but also have the range of power and performance that they need for the environments they’re going into. So, really excited to see the applications coming to market based on this new generation.

Christina Cardoza: Yeah, and some great final points there, especially when you think about the manufacturing industry. They operate in harsh environments, like you mentioned, sometimes, and their equipment has to last a long time. They’re not one to really “rip and replace” every couple of years; they sometimes have their equipment there for decades. So these 4th Generation Intel Xeon processors are really helping them future-proof their investments and meet the needs that they have of the factory today, while also looking forward to tomorrow. So, this has been a great conversation. I just want to thank you again so much for joining us and for the thoughtful conversation.

Christine Boles: Thank you, Christina.

Christina Cardoza: And thanks to our listeners for tuning in. Until next time, this has been the IoT Chat.

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.

Transforming Industrial Operations on the Factory Floor

Posted on by Micah Kaplan

There are so many shiny new toys to play with on the factory floor—from edge computing to digital twins—that have the potential to lead to faster, safer, more sustainable industrial processes. But with those benefits come the challenges, like bridging the IT/OT divide, and pairing advanced technologies with legacy infrastructure.

Rainer Brehm, CEO of Factory Automation at industrial manufacturing solution provider Siemens, discusses these industrial trends and transformations. He talks about standardization, autonomous AI, and use cases that include Siemens itself. He’s been at the company since 1999, and has seen firsthand how the space has evolved, as well as where it might be going next.

What trends can we expect in 2023 and beyond for the industrial space?

The trends we are seeing are combining the digital—which is the simulation module, digital twins—and the real worlds. You basically simulate everything up front and then you implement it. Now you have a feedback loop. You get real-time data out of the operation and feed it back to the digital twin, then you can further optimize it. Leveraging of data is significantly important, because AI isn’t yet really a big thing on the shop floor, but it will become a big thing as data becomes more and more available.

We will also see software-defined control, or software-defined automation. Currently, everything is very much bundled and tied with hardware, and it’s going to be more decoupled, more virtualized.

And, last but not least, especially when we look at the shop floor, the users of these more complex technologies are still the people operating the machines. These are not IT experts, but they still need to be capable of operating and maintaining those lines, those machines, those infrastructure plants. Therefore, we have the topic of human-centric automation: How can we make it as easy as possible?

Leveraging of #data is significantly important, because #AI isn’t yet really a big thing on the shop floor, but it will become a big thing as data becomes more and more available. @Siemens via @insightdottech

What are the challenges to reaching those Industry 4.0 goals?

I think a lot of the technologies are there. But the reason why they aren’t scaling is that OT and IT people, they simply speak different languages. I experience that even within our own organization, where I’m more the OT guy. When I talk about connectivity, I think about connectivity to the real world, to the equipment, to the sensors, to their drives, and so on. The IT person, when he talks about connectivity, he is thinking about connectivity to databases, to cloud, to data lakes. And what we experience in our company our customers experience as well. There is still a gap between the IT department and the OT people, who are the ones defining how you’re going to automate something, how you set up the equipment, how you set up the lines, how you maintain it all to optimize it.

So how do you bring the languages together? This could be about terms, but it could also be about, for example, how you program the OT landscape. We have introduced a new programming environment called SIMATIC AX (Automation Xpansion) extension. It’s called an extension because it makes the OT world more accessible to the IT people.

The landscape is also very, very heterogeneous. A lot of the machines don’t speak the same language because they’re from different vendors. There aren’t standards, so you can’t really scale. You need a standard for that. And this also applies even to new machines, to greenfield, but it applies even more to brownfield. A factory normally runs a minimum of 10 years—most are 20 years or 30 years. If you go to the energy sector or the chemical sector, it might run 40 years.

How has the emergence of the edge and AI complicated factory automation?

When you talk about edge computing on a shop floor, there are more requirements. And if you talk about real time, maybe it’s a jitter of microseconds. If you imagine a very fast process, in a microsecond a lot of things could happen. And if you’re not reacting fast enough, then you might question a machine, or you might get to different results. So the topic of real time is very important.

Secondly, if you want to deploy AI workload on a shop floor and you want it to react very fast, it’s important that this AI workload has an inference close to the machine, simply because of the speed of light. The other aspect is that you want the AI to interact frequently with your real process. Basically, you’re going to interfere with the process, so you want to have that kind of close allocation, close to the machine or to the line. You also want to take data out of the process and feed it back into the AI.

I can give you one example. In our factory in Hamburg, we produce about 10 terabytes of data every day. You don’t want to send those 10 terabytes of data into a cloud; you want to have it there where the source of the data is. That is different, maybe, to a classical IT landscape. But we need to add not only real-time capabilities, we also need to add the safety.

It’s a little bit like autonomous driving, where safety is also a very important aspect. You could imagine that, when you do autonomous driving in the car industry, you don’t want the cloud to be defining whether you stop or not if a child is running into the street. You want that reaction being executed as fast as possible directly in the car. The same is true on a machine. If a press is going down and somebody has his finger there, it should stop immediately. So you need to have that kind of fast reaction.

But why not think ahead? When I started at Siemens in 1999, what you automated, basically, were very repetitive tasks. And mass production was perfect for that because mass production has a lot of repetitive tasks. Or you automated something that was predictable. You could basically only automate what you knew.

Now there’s leveraging AI for optimizing processes, but couldn’t we also use AI for a more autonomous factory? How could we use AI so that a machine, a robot, could decide itself what to do? That means AI is not only optimizing the process, optimizing, and enhancing the engineering, but really steering the robot, the machine, and the line. And that application for AI is really, really exciting because it opens up new fields for automation.

What are some Siemens use cases that show these solutions in action?

Let’s start with our own factories; what we apply to our customers, we apply it to ourselves here also. One example of a use case of IT/OT leveraging AI is, again, in our plant in Hamburg. There’s a very high throughput of PCB lines, and a complex process of how you put the components on the circuit board. In the past we normally did an X-ray inspection of the PCB at the end, and there was always a bottleneck there. So, by leveraging AI, we now predict whether each individual PCB has a high quality or not, and every machine with a very, very high probability of having no quality issue, we don’t send it to the X-ray machine anymore; it bypasses the X-ray machine and goes to the final assembly.

Another example is in infrastructure, doing tunnel automation. If you drive through a tunnel in the Alps or in the Rocky Mountains, there’s a high probability that those tunnels are automated and controlled by our PLCs. We are now using AI more and more in order to detect emergency situations in those tunnels—if there’s a traffic jam, if there’s a fire. If you need to react fast, how do you evacuate the tunnel? How do you switch on or off vents or lights?

Going back to the factory again—we’re doing real-time flexible grasping where something is taken out of a box. The AI tells a robot where to grasp an aspect without having to train or program that robot on the thing that needs to be picked up. We train the robot on the skill: to pick up. Basically, the robot can pick up anything that is necessary—as long as the gripper allows it. So, with that skill of grasping, we can start automating something unknown or unpredictable.

And my last use case, which is not currently reality, but it’s where I invest money: Can you in the future automate repair? If you take, for example, a car battery. You, maybe, in the future can take a car to a workshop; the battery is taken out, there’s a defect, and a system can automatically detect where the problem is and can autonomously repair the battery cell. That is also automating the unknown, because every battery is a unique thing. Can you automate that leveraging AI? So, some of the use cases where I’m really excited really will make a difference in the future.

Tell us about the value of your partnership like the one with Intel.

We have worked with Intel probably for four decades. But I know that we started in 2012 with TAP, the Technology Accelerator Program, to enable the processes of low-latency functionality—especially for those workloads where you need to act in microseconds. So that was very, very fruitful, and helped us to use the Intel chips in our controllers.

We’re currently working with Intel on the supply chain crisis. So—also thanks to Intel—I think we have been capable of fulfilling, maybe not all the demands of our customers, but as much as possible.

Machine-vision application is a workload that consumes a lot of compute power. And for that we will bring out a new portfolio leveraging the 4th Gen Intel® Xeon® Scalable processors. We’re looking forward to introducing that in the market in the middle of 2023. So, very excited to have that new portfolio element, which is addressing exactly that need we see on the shop floor.

Any final thoughts for us?

First of all, I strongly believe there will be no sustainable future without automation, electrification, and digitalization. And, therefore, what we do together with Intel really is a significant contribution for our future. Number two, I believe the area of automation will expand more and more as we automate workload that is unpredictable and individualized. And, third, we need to make this technology as user-friendly as possible so that OT people can handle this complex technology.

Related Content

To learn more about factory automation, watch Achieving Factory Automation: With Siemens. For the latest innovations from Siemens, follow them on Twitter at @Siemens and LinkedIn.

 

This article was edited by Erin Noble, copy editor.

Telemedicine Solutions: Closing Healthcare’s Digital Divide

Posted on by Micah Kaplan

In many parts of the world, addressing the digital divide isn’t just about advancing timely access to information—it’s truly a matter of life and death.

In remote and rural areas, the lack of a robust digital communications infrastructure greatly impacts healthcare access and the ability to deliver effective telemedicine solutions. Healthcare facilities in countries like Brazil, for example, face challenges administering a large volume of medical exams, efficiently transmitting this information to providers for evaluation, and then getting exam results to patients as quickly as possible so they can seek the follow-up treatment they need. In some cases, delays in this process have been tragic and even resulted in a patient’s death, according to Leonardo Melo, Founder and Executive Director of Diagnext, one of the first providers to specialize in telemedicine in Brazil.

That’s why Melo has set out on a mission to change this. In places where using advanced technologies to support sophisticated medical activities would be almost impossible, Diagnext is enabling medical care without technological or geographic boundaries.

Bringing Telemedicine Solutions to Brazil

Brazil lacked any formal telemedicine regulation until the pandemic. Melo explains that is because the telemedicine experience in Brazil has been traditionally impersonal and inefficient.

“Conventional distance medical care, at least in Brazil and Latin America, has proven to be far from the patient—taking away a necessary empathy between the doctor and the patient,” he says. “We try to maintain the form and humanity of a conventional service, inserting a dash of technology to make it closer and more efficient.”

Diagnext’s solutions allow healthcare facilities to deliver any medical exam right at the point of care. The process mimics traditional medical care at the beginning: A patient is taken to an exam room where a healthcare provider administers their test, such as an X-ray, ultrasound, or electrocardiogram. From there, exams are processed and sent to Diagnext’s equipment, which uses artificial intelligence, low-cost 3D modems, and satellite technology to compress and efficiently transmit information. The data is compressed to the extreme—up to 97% lossless compression is reached in environments where the maximum is always close to 50%, Melo says. It is then securely forwarded to remote healthcare professionals in the most efficient and optimized way possible.

“We try to maintain the form and humanity of a conventional service, inserting a dash of #technology to make it closer and more efficient” – Leonardo Melo, @diagnext via @insightdottech

Diagnext relies on Intel®-powered servers in its remote operations center to redistribute and funnel the data from medical exams to healthcare professionals for analysis. With the help of AI-based algorithms, Diagnext automatically decides the best path to route information so it can get to its intended destination as rapidly as possible—even in places with limited communications infrastructure. The company’s solutions are particularly critical in rural and remote areas in Brazil and Latin America that have limited access to technology or high-speed internet.

“The environment as a whole automatically evaluates the conditions of the communication structures based on the urgency of delivering the exam,” Melo says, adding that “the process can even be thought of as conventional telemedicine, but the difference is centered on the performance time,” which is up to 240 times faster compared to market standards. Employing this approach is also 60% cheaper for healthcare facilities to execute compared to traditional healthcare administration processes. It allows them to handle a much larger volume of consultations—the equivalent of thousands of exams a month, Melo says. 

Advancing Healthcare Equity

Melo already sees Diagnext’s telemedicine solutions making a meaningful difference.

Working with government hospitals and clinics in the Amazon rainforest, Diagnext has delivered healthcare without borders and reduced exam transmission times from six hours to just two hours, he explains. As a result, clinicians provided mammograms to 52,000 women in a single year and administered 48,000 X-rays, contributing to more than 4,500 reassessments and nearly 150 cancer surgeries that have contributed to saving many lives.

The company’s solutions were also critical during the pandemic. When a major hospital system in São Paulo was overwhelmed and lacked the telecommunications infrastructure to meet demand, it worked with Diagnext and used Intel®-powered mobile phones to securely transmit exam data. Diagnext worked with the Red Cross in Brazil to efficiently process data after its systems broke down.

Melo says some large hospitals have experienced a similar situation because their systems lacked the capacity to handle a surge of medical data during the COVID-19 pandemic. In these situations, Diagnext has been able to step in and provide intelligent, AI-driven data compression capabilities to reorganize and deploy the data and free up capacity for mission-critical systems.

Diagnext is on a mission for everyone—regardless of their environment or location—to receive quality medical care, according to Melo. The company already is making significant strides to reshape the future of telemedicine. With the State of Amazonas, Diagnext plans to build the first integrated clinical exam data environment and use AI to analyze even more patient exams. In their first collaboration, government hospitals in the Amazonas were able to process 100,000 exams a year, but now the goal is to exceed that number. Melo says he hopes Diagnext will create an enduring legacy of expanding healthcare access to those who need it most.

“We are aiming to take our techniques, processes, project management, and methodologies, among others, to other corners of the world,” he says. “We are studying countries that have large populations and extreme public health needs. The idea is to bring health to those who need it wherever they are—with efficiency, effectiveness, quality, and affordable costs.”

 

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

Development Tools Put AI to Work Across Industries

Posted on by Micah Kaplan

It’s no secret that AI promises to transform industries and solve real-world problems.

“The power of AI has the potential to do all kinds of great things. It makes existing use cases and applications better, but it also opens up the door for new possibilities,” says Bill Pearson, VP of the Network and Edge Group, and General Manager of Solutions Engineering at Intel.

But while the benefits and opportunities are known, it’s not always clear how one can go about developing and deploying applications that meet expectations. Up until recently, AI has been limited to developers and data scientists with very specialized skill sets. And it hasn’t always been easy accessing the proper data to build and deploy models for AI applications.

“We’re finally at the point in the industry where we: one—have the data, two—have the computer power to process the data, and three—have the technology and tools to make it accessible for any business to use AI in unique, impactful ways,” says Pearson.

This enables all kinds of developers to start building edge AI applications for their business. But while it’s tempting to want to dive right in, developers should understand the foundation of AI as well as the tools and technologies available to them and then build from there.

AI Building Blocks, Get Started with Optical Character Recognition (OCR)

One of the early steppingstones is OCR. This is a very simple machine learning capability that has been around since 1965. It enables the ability to extract, convert, and repurpose data from documents and images through computer vision.

For instance, users can upload a document from an image or even upload a book and create an eBook. Or you may be more familiar with scanning and depositing a bank check from your mobile device. The postal service even uses OCR to automate the process of sending letters and packages, eliminating error-prone and time-consuming manual tasks. 

Creating innovative and powerful #AI solutions goes beyond understanding the fundamentals. #Developers and businesses can leverage the right development tools in their toolbox to make it all possible. @Inteliot via @insightdottech 

Enhance Businesses with Object Detection and Recognition

Going beyond OCR, object detection and recognition expands the capabilities and the problems that can be solved.

For instance, many shipping ports use OCR systems to automate container check-in, but then expand the solution to do other computer vision tasks like identifying free space and determining space utilization.

This real-time visibility is made possible with the AI capability known as object detection, which is one of the most commonly known AI applications today because it allows users to do things like:

  • Defect detection on the manufacturing line
  • Predictive maintenance on equipment
  • Inventory management in warehouses
  • Weed detection in agriculture fields

With object detection and recognition, systems can also prevent car accidents before they happen and even alert pedestrians of oncoming traffic. Medical professionals use object detection to provide better care and accurate diagnosis to patients. “This idea of taking images—and a lot of medical technology today relies on imaging—and being able to leverage AI to help identify and detect disease early on is really powerful,” says Pearson.

The food service industry is beginning to use AI and object detection to improve the quality of service for customers. For instance, PreciTaste, a smart software automation provider, uses edge AI and object detection to improve the quality of service for customers. The company does this by not only leveraging images but also video data. The ability to find and categorize activities within a recorded or live video is known as human action recognition.

PreciTaste’s software can leverage video feeds to detect whether a quick-service restaurant worker properly packaged a takeout order and alert them of any potential errors. It also observes restaurant inventory and predicts customer demand throughout the day to help match their production based on that information. “There’s major demand for reliability, which is fantastic to see. Not to mention it has its own benefits. One being less waste—users can more accurately determine what level of resources or ingredients they need to meet their demand. It also makes for faster service for customers. Who doesn’t like getting their meal quickly?” says Pearson.

Vision recognition applications also extend to other industries such as smart cities for safety and security as well as manufacturing for monitoring the factory floor. For example, Vulcan AI, an AI workplace solution provider, has a solution called WorkSafe, where they leverage their workplace cameras to analyze video data and identify safety hazards. “Now manufacturers can flag safety hazards very, very quickly—allowing them to make major improvements,” says Pearson.

Empower AI Developers with the Right Tools

Creating innovative and powerful AI solutions goes beyond understanding the fundamentals. Developers and businesses can leverage the right development tools in their toolbox to make it all possible—and with the right software, they can speed up their edge AI development.

For instance, the Intel® Edge Software Hub allows developers to leverage prevalidated software, including use cases and reference implementations based on real-world applications, to experiment, test, create, and optimize their edge AI solutions.

The OpenVINO™ Toolkit becomes a powerful tool for edge AI developers. Because of its “write once, play anywhere” approach, they can create an application or algorithm once and deploy it across a variety of hardware architectures, according to Pearson. For a curated OpenVINO experience, developers can take advantage of the Intel® Developer Cloud for the Edge, which allows them to evaluate, benchmark, and protype their AI and edge solutions on Intel® hardware—no matter where they are in their development process.

“Once you have a new capability like AI, and you put it in the hands of developers, they’re going to add their magic to it and do fascinating things that we never thought of,” Pearson says.

But they can’t create every solution alone. In manufacturing, for example, developers can create a defect detection solution, but they may not be the subject matter experts in this area, and often need to leverage the expertise of the operations team to create a solution that solves a problem.

To make it easier to bring domain users, subject matter experts, and business users into the AI development lifecycle, Intel offers the Intel® Geti™ Platform.

“We’ve been on a journey to make a developer’s life easier, to give them more tools, more foundational software, and more capability to figure out the problems they’re trying to solve more effectively and efficiently,” says Pearson.

Intel Geti is designed to simplify the AI training model process by enabling collaboration with developers and subject matter experts to label, train, optimize, and deploy computer vision models, according to Pearson. “Now, developers and non-developers can come into this easy-to-use interface, identify the images with defects, identify the good images, and very, very quickly build a model that’s been trained, modified and updated,” he says. “It simplifies labor-intensive tasks, ultimately freeing those individuals up to focus on new areas.”

Intel Geti also integrates with OpenVINO so once the domain experts or business users develop high-quality models, developers can leverage the AI toolkit to deploy them into the real world.

Beyond Computer Vision

As you can see, AI is solving all kinds of real-world problems, but computer vision is just the start. There are also many AI-based audio and speech solutions and opportunities out there today.

For example, speech-to-text recognition has become an important part of our daily lives. It’s helping drivers safely read and send messages while they are on the road. AI-based audio solutions are also helping to remove or reduce background noises in phone calls and virtual meetings, which is extremely important in today’s hybrid and remote workplace.

“We’re using technology to solve real-world challenges and to help make our livelihoods safer. It’s the heart of the work developers are doing that makes these solutions a reality. Give them the right tools, and they’ll develop the use cases, models, and apply the technology in meaningful ways,” says Pearson. “When tools can bring AI development and the business together, that’s when we really get life-changing, world-changing results.”

MWC 2023: Where IoT Networking Meets the Intelligent Edge

Posted on by Micah Kaplan

The Internet of Things has become so pervasive it’s hardly discernible from non-IoT tech. Now it’s just tech. The same is true for IoT suppliers, which have evolved solutions from the ground up to emphasize the inextricable link between data, compute, and the connectivity that binds them to value-generating applications and services.

Since IoT technology and its creators have matured, ecosystems are following suit—which you can see on display at Mobile World Congress (MWC) in Barcelona from February 27th to March 2nd. There, Intel and dozens of Intel® Partners Alliance members will demonstrate how software-defined solutions from the company’s Network and Edge Group (NEX) facilitate convergence of communications and compute from edge to cloud.

Across Intel’s 10 demo stations at Mobile World Congress, partners like Wistron NeWeb Corporation (WNC), Silicom, Kontron, HPE, Ericsson, and Nokia will exhibit the advantages of blending fully programmable edge networks using virtualized Radio Access Network (vRAN) technology. Meanwhile, Dell, Red Hat, Capgemini, and others will demonstrate how this reimagining of the network infrastructure can be combined with capabilities like 5G to deliver game-changing applications and services to the intelligent edge.

Read on to discover exactly what you can expect for this event.

vRAN Bridges Cloud-Native, the Intelligent Edge, and Everything in Between

The rigidity of legacy network architectures has been one of the biggest obstacles to IoT scalability to date, as proprietary infrastructure, different protocol stacks, and a step-down in resources made it difficult if not impossible for telecom and network operators to deliver the same services to the edge that they offer in the cloud. The emergence of vRAN technology has turned that equation upside down.

vRANs abstract network functions that previously ran on specialized hardware into software, making it possible for providers to scale their offerings on top of standard server hardware, like what you’d find in a data center. With a more homogeneous end-to-end architecture, it’s possible to adopt fully programmable, software-defined, and container-based approaches to application development and delivery, which opens a world of possibilities for IoT services at the edge. Ericsson and HPE will be demonstrating these benefits using 4G/5G vRANs at the event.

With vRAN, telcos and #network operators can design and deploy high-performance, cost-effective, and power-optimized networks that scale with demand, then deploy #cloud-native apps all the way to the intelligent #edge. @MWCHub via @insightdottech

WNC, Silicom, Juniper Networks, and Kontron will also be showing the possibilities of vRAN technology in an accelerated virtual cell site router built on Intel® Xeon® processor technology and the Intel® N6000 Acceleration Development Platform (ADP). An off-the-shelf, Agilex FPGA-based Smart Network Interface Card (SmartNIC), the N6000 ADP supports programmable acceleration of communications workloads. In the demo, it will be deployed in an Open RAN-compliant platform alongside an Intelligent RAN controller from Juniper Networks, collectively netting ample performance and flexibility to meet the demands of private 5G and IoT edge use cases.

Proving that this open networking concept can still be secure, Intel partners Nokia, Rafay, and Zscaler have developed a Secure Access Service Edge Points of Presence (SASE PoP) demo that implements zero-trust handoffs between multiple locations and a private 5G network.

New Networks Have a New Edge to Deliver New Use Cases

With vRAN, telcos and network operators can design and deploy high-performance, cost-effective, and power-optimized networks that scale with demand, then deploy cloud-native apps all the way to the intelligent edge. Partners like Dell, Red Hat, and Capgemini will bring all of this to life at the event in a joint 5G-enabled smart manufacturing demo built with capabilities like 5G network slicing on top of an open RAN.

At their booths, Red Hat and Capgemini will showcase sustainability and metaverse use cases. And elsewhere, UST Global will display the importance of the intelligent edge in the retail industry with its demo “Intuitive Touchless Checkout for Retail Convenience Stores,” where it will showcase how vision AI-powered checkout solutions can help improve customer experiences while addressing labor shortage issues. Meanwhile, China Mobile, ZTE, and Thundersoft will trial an edge video platform, both built on the Intel® Data Center GPU Flex Series.

And don’t miss the demo from Microsoft where it will be displaying its work with AT&T to show how operators can manage distributed edge services like these through the Azure cloud platform.

These are just a handful of the partners and activities at MWC 2023 leveraging Intel NEX technology. You can also expect a multi-radio access technology (RAT) demonstration from Wind River, Samsung, Vodafone, and Dell that enables 2G/4G/5G communications on a single platform. And there will be exhibits from F5 Networks, HCL, TATA, VMWare, Rakuten, and many more.

All of them are focused on enhancing new services and enabling new revenue streams for network operators and IoT end users at the new converged network edge—and all of them are built on Intel technology. Attend Mobile World Congress and see it all for yourself.

 

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

AI in Restaurants: The Missing Ingredient for QSRs

Posted on by Micah Kaplan

Today’s quick-serve restaurants (QSRs) operate in a pressure cooker. Harried workers scramble to keep customer wait times down, burgers hot, and fries crisp—crossing their fingers that nothing goes wrong with the equipment. Perpetually short-staffed, they must manage a barrage of new online orders, in addition to those arriving in the restaurant and the drive-through.

So why don’t these restaurants just hire more staff? On top of everything, QSR owners face higher costs for food, supplies, and labor—eliminating any option of bringing in extra help.

“Restaurants can’t afford to add one more shift manager and hire six more people to handle the influx of orders. They have to learn new ways to be more efficient with their existing staff,” says Atif Kureishy, founder and CEO of Vistry, a company that develops AI and automation solutions for retailers.

That’s one of the main reasons why Vistry developed the Discrn platform, an AI solution for restaurant automation. By analyzing operations with edge AI and computer vision technology, restaurants can gain real-time insights that enable them to do more with less, improving efficiency, service, and food quality without breaking the bank.

AI in Restaurant Kitchens

QSRs must maintain a delicate balance, preparing enough food in advance to meet customer demand without allowing it to get cold or soggy. But staffers are often too busy to monitor pre-prepared burgers or fries. They need to coordinate order volume with preparation time, a tricky business as new orders flow in.

That’s where Vistry’s Discrn platform comes in. It uses computer vision cameras to record the exact time food leaves the broiler or deep fryer, sending staff alerts through voice bots to prevent them from sitting around too long. That keeps food fresher and saves restaurants money by reducing waste.

“With operational complexity growing and restaurant staffing shortages likely to persist, #edge #AI will continue to gain popularity in quick-serve restaurants.” – Atif Kureishy, @vistryai via @insightdottech

The system can also help detect when food preparation isn’t keeping up with order demand.

“The voice bots will say, ‘Hey, manager on duty, please go over to the fry station and prepare two more batches,’” Kureishy says.

Using AI in Restaurants at Scale

QSR owners who have multiple restaurant locations can also gain valuable insights by analyzing operations and patterns across all their sites.

For example, when a nationwide chicken restaurant was switching potato suppliers, it was able to use Discrn to parse data from its 2,000 stores and ensure quality was maintained.

“If they saw an increase in complaints, they could determine whether it was because of the product or the food preparation,” Kureishy explains.

The restaurant is also testing drive-through express lanes for customers who order by mobile phone with the help of Vistry. By crunching incoming data, the Discrn platform can predict wait times within a four-minute window, with 90% accuracy. Customers appreciate the information, and managers can use it to spot and resolve holdups.

“The more cars you get through, the more money you make,” Kureishy says.

Depending on the restaurant and operations, information can vary. The chicken chain uses metrics to enforce its rules, such as not holding fries for more than five minutes. Other shops attach AI sensors to equipment to warn staff of breakdowns and measure factors such as oil quality and yield. Discrn uses the Intel® Distribution of OpenVINO™ Toolkit to tailor solutions to chain and store requirements.

“We use Intel-based devices that can run edge AI workloads very cost-effectively,” Kureishy says. “We know restaurants aren’t ready to spend hundreds of millions on technology.”

Dealing with Increased Complexity

But it’s not just the restaurant staffing shortage adding complexity to operations. QSRs have to deal with new mobile and online ordering options and delivery services.

“You used to have an 18-year-old kid making food and giving it to someone at the counter. It’s not like that now,” Kureishy says.

The increase of online orders comes with hyper-personalized requests, throwing a monkey wrench in streamlined operations.

“Customers have all these options online, like, ‘I want exactly two pickles on my sandwich, with ketchup and no mustard.’ That puts more burdens on the restaurant staff to deliver,” Kureishy says.

And since the third-party delivery service providers such as DoorDash and Uber Eats are unfamiliar with the restaurant and its products, restaurant owners have to deal with human error and omissions.

“A high percentage of these orders tend to have missing or incorrect items, and restaurants have very limited visibility when things go wrong. It’s a big problem in the industry right now,” Kureishy says.

Thankfully, edge analytics can help owners manage these challenges. Computer vision cameras can analyze and track orders, validating that staffers prepare food according to customer specifications and packers place the correct items in the right bags. Analytics can also help restaurants with the increasingly complex calculus of prioritizing orders.

“Maybe you make more money per order with one delivery service. With another, you make less money, but they have a highly loyal customer base. How do you make sense of all that? Edge analytics can help you make the right business decisions,” Kureishy says.

QSR Solutions for Other Retailers

With operational complexity growing and restaurant staffing shortages likely to persist, Kureishy believes edge AI will continue to gain popularity in quick-serve restaurants.

“I think we’re on the frontier of doing this at scale, in a cost-effective way,” he says.

The Discrn platform can easily be tweaked to help other kinds of retailers operate more efficiently. For example, a hardware store could deliver a lawn mower ordered online to the parking lot just as the customer arrives. A convenience store could measure the effectiveness of digital-sign promotions. An auto shop could learn whether employees do retreads or oil changes, how long these tasks take, how many materials are used, and whether workers adhere to safety rules.

“Ultimately, these systems are going to work seamlessly with human beings to improve capabilities,” Kureishy says. “They’re going to make businesses more efficient and effective so that they can do more with the same or fewer resources.”

 

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

Smart Cities Extend Their Reach

Posted on by Micah Kaplan

Twenty-eight miles east of Cairo, Egypt, a new administrative capital is arising—a smart city showpiece that will transform operations with IoT technologies—managing everything from public transit to water meters to parking. Administrators will be able to respond to changing conditions in real time, dispatching crews to fix power outages before they’re reported, redirecting traffic on clogged highways, and sending emergency responders along the most efficient routes available. More than 6 million new residents will be able to use a mobile app for needs such as turning on their water service, booking a parking spot, or reporting a pothole to the right department—making city life nearly hassle-free.

Egypt is a great example of how government leaders are going all-in on IoT and connected infrastructure—changing the game in automating controls for complex systems and responding to problems.

IoT and Smart Cities

Cities of all sizes are looking at smart city technologies and solutions to deploy services and gain actionable insights more effectively. For example, sensors on buses and trains allow officials to track their location and gauge load and fuel consumption. Smart streetlights detect pedestrians and activate at full or partial capacity, depending on the weather and time of day. Advanced camera systems can detect accidents, alert first responders, capture data for investigators, and generate reports.

Smart infrastructure implementation varies greatly by region. In the high-growth areas of the Middle East and Asia, central governments often combine information from a vast array of smart devices. Egypt’s yet-to-be-named capital will send data from 32,000 cameras, a quarter-million smart meters, and 10,000 parking spaces to the Honeywell City Suite platform, where administrators can view metrics in any combination, set controls, and receive real-time alerts.

Improving Safety and Security

In the United States and Europe, cities are often more likely to adopt smart technology piecemeal, and systems typically don’t communicate with one another.

“Cities may not get information they need because their traffic camera system doesn’t talk to their public safety system or vice versa,” says Matthew Britt, general manager of Smart Cities and Communities for Honeywell.

By moving data feeds onto a single platform, officials can orchestrate a faster, better-coordinated response to emergencies and large events. This means, for example, the potential to see significant reduction in emergency response times.

Coordinating #infrastructure isn’t just for emergencies. It also helps #cities support citizen #safety and manage operations for large public gatherings, such as concerts, parades, or sports events. @honeywell via @insightdottech

Coordinating infrastructure isn’t just for emergencies. It also helps cities support citizen safety and manage operations for large public gatherings, such as concerts, parades, or sports events.

“If you’re hosting a large sporting event and your camera network shows crowds gathering and closing off streets, you can reroute vehicle traffic so that everyone will get to the stadium safely—and faster,” Britt says.

With Honeywell City Suite, all data stays on city servers, where it is processed on Intel-powered, high-performance hardware. Officials can analyze information on the platform with machine learning and artificial intelligence algorithms, making predictions about future demand and shoring up resources before conditions deteriorate.

“Cities are trying to make long-term planning decisions, and the need for data has never been so strong,” Britt says.

With data insights, administrators may change the timing of stoplights to reduce congestion as traffic patterns change, or do predictive maintenance to prevent costly, time-consuming transit breakdowns. They can also collect additional revenue from parking garages by charging more based on higher demand.

The Future: Sustainable Smart Cities

As smart infrastructure expands, some cities are offering new services through mobile apps. Citizens can report problems, such as potholes, downed power lines, or vandalism without having to navigate the city bureaucracy. Commuters can purchase transit tickets with contactless payments and receive real-time updates on arrival times. Residents can obtain alerts about natural disasters and other safety issues.

Smart operations and analytics can also help cities make progress toward environmental goals. For example, drivers can receive directions to parking spaces using the most efficient route, not only saving time but also using less energy.

“If you know exactly where you’ll park from the second you leave the house, you’ll spend less time in traffic, waste less fuel, and emit fewer greenhouse gases. Many cities have put aggressive targets in place without devising any way to get there. By aggregating and analyzing their data, they can find ways to improve efficiency and sustainability,” Britt says.

As 5G connectivity spreads, Britt expects to see cities expand their use of smart infrastructure: “With 5G, you can connect millions of sensors on widely distributed assets without the expense and potential latency of other types of connectivity.”

Further economic incentives are on the way as funding from the U.S. Infrastructure Investment and Jobs Act and the Inflation Reduction Act becomes available. “There is more money currently aimed at upgrading infrastructure than we are likely to see again in our lifetimes,” says Britt.

The boon could encourage the development of innovative technologies to help cities create a safer, more sustainable future.

For example, Honeywell is working on municipal microgrids that will help cities to continue vital services during sustained outages, such as those that occurred during 2021’s Winter Storm Uri in Texas. With microgrids, cities can store energy in batteries, instead of relying on fuel deliveries that may not arrive when they’re needed most. Batteries can store energy from any source, including solar.

“If you can generate solar energy on-site, you could theoretically have an infinite amount of power at your disposal,” Britt says.

He expects to see many such ideas flourish in the near future: “I think you will see the concept of a smart city take hold much faster. Governments will implement technologies that have a wider impact on people’s lives.”

 

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