PODCAST

Christina Cardoza: Hello and welcome to the IoT Chat, where we explore the latest developments in the Internet of Things. I’m your host, Christina Cardoza, Associate Editorial Director of insight.tech, and today we’re talking about creating innovative solutions without compromise with Scott Chmiel from Arrow, and Steen Graham from Scalers AI. But before we jump into the conversation, let’s get to know our guests. Scott, I’ll start with you. Welcome to the show. Please tell us more about yourself and your role at Arrow.

Scott Chmiel: Thank you very much, Christina. With Arrow for more than 17 years, multiple roles starting at field sales and engineering, moving through my current roles: business development for cloud and edge development, helping customers realize their solutions on edge with Arrow orchestrating and helping where we can. Multiple roles, very focused on Microsoft and other software platforms.

Christina Cardoza: Great. Looking forward to digging in more on where Arrow fits into this IoT, innovative landscape. But let’s introduce Steen as well. Welcome to the show. What can you tell us about yourself and Scalers AI?

Steen Graham: Christina, thanks so much for having me, and I must admit you guys produce some great content and have had some great guests. So, just really thrilled to be here with Scott showcasing some of the great work we’ve done together. And I’m the CEO and co-founder of Scalers AI, and we’re an enterprise AI company that’s focused on deploying AI in the physical world to drive industry transformation and ultimately enrich our lives.

Christina Cardoza: Great. And now something you both sort of mentioned in your introduction is really helping customers navigate through this new digital transformation, intelligent IoT world, and it certainly is a lot easier said than done. There’s a lot of benefits that companies want to get to, but a lot of challenges that are standing in the way. So, Scott, I want to start with you to jump into this conversation. What can you tell us about the current industry transformation efforts? How well have they been applied, and what are the challenges that businesses are coming across?

Scott Chmiel: Well, the first thing that comes to mind is the challenges have changed quite a bit over time. The complexity of solutions has just increased. From days in the past where we were talking about simple appliances or devices that we’re doing, everything contained in the single piece of hardware or software, we’re adding cloud, we’re adding complexity, we’re adding new technologies which not only require more from the technology standpoint, but different skill sets from the development. As you create these solutions, you have to integrate them and deploy them in existing environments or customer environments that differ from one to another. There’s just a complexity of those environments. And, for instance, connected devices now require that additional operational technology security be applied and looked at to make sure that not only is the solution working but it’s secure, it’s not creating vulnerabilities, and, obviously, we can do new things that weren’t possible for the advancement to machine learning and AI, it’s possible to solve new business problems that we couldn’t even address in the past.

Christina Cardoza: Definitely seeing a lot of complexity and challenges on our side too, as we talk to partners on insight.tech, and I think what I see from my standpoint is that this IoT ecosystem, it’s really a partner ecosystem and there’s a lot of players involved. So it’s not only if a business is failing or a business is struggling that can have ripple effects to other issues in the industry or in society. I’m thinking about the supply chain, for instance. So, Steen, I’m wondering if you can expand on the impact that you’ve seen those challenges being, both on a business level, but a society level too.

Steen Graham: Absolutely, Christina, and I think Scott said it well as he kind of outlined the challenges that we have in this physical world in deploying artificial intelligence in the IoT in the physical world to drive industry transformation. It’s really challenges across development and developing these new unique technologies. Deployment and data is incredibly important, and when you look at something like a port, obviously these ports and the infrastructure for ports has been around for decades, and so what you get is you get a mix of existing applications that are working just fine, just fantastic in a port environment, but then you want to implement some technologies that Scott’s really familiar with, like those cloud and native technologies, and how do we actually deploy these cloud-native methodologies, including artificial intelligence, on existing infrastructure, and so can cohabitate with the existing infrastructure and provide these added enhancements so we can do things like analyze the efficiency of the ports, monitor the CO₂emissions so we reduce the fossil fuel burn associated with the ports as well. And so this combination of existing infrastructure and new infrastructure, both from a hardware perspective and from a software perspective, and being able to acquire the right data so you can make these near real-time AI decisions, are all very critical in driving these industry transformations and solving some of the challenges we face, for example, in our supply chain crisis.

Christina Cardoza: Great, and I want to get into more about how businesses and companies can actually apply new and innovative capabilities and technologies to some of their existing and legacy technology. But one thing that stands out to me when we’re talking about the ports—this is the part in the supply chain where you’re delivering product and containers, and trucks have to come and offload those from ships, and it’s a huge bottleneck to do that right now, which is causing a lot of delays in getting product out for businesses and end users. And there’s not one single company or business that owns a port like that. So, I’m interested with all of these different players involved in something like a port, which is just one aspect of a supply chain, how can you make innovative and impactful changes when you have multiple people and businesses involved?

Steen Graham: So, I think contextually maybe we’ll start at the macro level. The US federal government administration has been fantastic, and supporting port modernization. They recently passed the infrastructure bill, which is about $17 billion allocated to modernizing our ports. In addition to that, in the newly approved inflation reduction act, there was another—a few billion dollars around monitoring CO₂emissions at ports. But one of the really unique things about ports is they’re actually managed by the local municipalities, which is quite interesting. Insofar as you can look at the ports of Long Beach or Los Angeles and see that 30% of traffic goes through Los Angeles and Long Beach—and that’s managed or containerized traffic in the United States goes through these two ports managed by local municipalities. So, what those leaders do locally impacts all of us at a US scale, so that’s where not only those local municipalities and their leadership is critically important, unions are also critically important as well. And so, one of the job roles that is sustained in the United States is crane operations, and what we’ve automated is the front end, actually removing the containers from the ship. But where we really have heavy, invested, human, union-based roles is in loading and unloading those trucks at ports, and so that’s one of the key bottlenecks that you find as well, but those three critical parties: the federal government on igniting the opportunity financially and creating the frameworks, the local municipalities are really the leaders in this front, and then finally the unions are incredibly important. They do occasionally go on strike too, so you can imagine the impact it would have if the unions went on strike in our current challenge as well. And so, that’s really the key players, and obviously I didn’t talk about technology at all, because I think those three angles are tough enough to navigate. Deploying the technology performantly. We’ll talk a little bit more about that, but that’s a broad challenge in itself as well.

Christina Cardoza: So, Scott, I’m interested from your perspective when you take all the challenges that Steen just talked about, as well as the industry business challenges that you spoke about in the beginning, how do you see businesses making impactful transformations and changes?

Scott Chmiel: Well, it’s obviously the challenge to understand what business outcome they’re seeking. That’s often the first step, is what are they trying to accomplish and who are the stakeholders. As Steen pointed out the port of Los Angeles—there’s not just one company, there’s the municipality, there’s the people handling the containers, there are the truck drivers—there are dozens, if not hundreds of subcontractors who all run that and have to dance around to move the ports. When one of those things breaks down, the whole system breaks down. So our solution focuses a little bit on one of the challenges they have there, around safety and just tracking in and out.

Christina Cardoza: Great. And seeing you mentioned tools and technologies as being a part of this, we haven’t gotten too deep into it yet, but we were talking about the ports. We have crane operations, we have different loading and offloading technology already in place—has been in place for years—but at the same time, these governing bodies and businesses want to start adding new and innovative solutions to start tackling and addressing these problems, and that can be challenging when you’ve had these other things in place for years, if not decades. So how do you go about deciding what to replace, what to upgrade, and get over challenges where you just can’t add a new technology or capability because the tools in place aren’t allowing you to do so?

Steen Graham: I think what Scott and I have looked at is how to deploy in a no-compromise way, and from a simplistic operating system perspective, which is foundational to technology, we know that there’s two pervasive operating systems in the world: notably Windows and Linux. And those cloud-native workloads in the modern AI workloads are written in Linux, whereas a lot of existing workloads and applications have been written in Windows. For example, one of the common operating systems for cranes is Siemens SIMOCRANE, which is a Windows-based operating system. For example, there’s Windows 32 applications that are alarm managers to notify the crane operators when it’s safe to proceed based on sensor-based data proximity around them. And so, with that kind of foundational element of no compromise between Windows and Linux, existing applications and new applications, we’re able to retrofit these modern AI applications on existing infrastructure and make sure that they work better together. That avoids the long conversation, Christina, that you alluded to, of when to upgrade our infrastructure, what to keep, what to remove. All of the retraining associated with employees, as well, on that digital transformation effort ultimately becomes a multiyear effort if you start replacing existing applications that are working just fine today. So layering on the modern, cloud-native attributes and AI capabilities was really the approach we used in this solution.

Christina Cardoza: So, when we’re looking at adding cross-platform capabilities to some of these technologies and looking at the port-modernization efforts, as an example, getting Linux on cranes instead of the traditional Windows, or being able to use both together—how do you actually go about that? What is driving that cross-platform interoperability? Scott, if you want to answer that one.

Scott Chmiel: Well, a lot of times it’s the existing hardware, and I’m going to let Steen answer it a little bit after. The example we use for EFLOW was a specific issue we identified, or challenge that can be addressed, but the technology, the infrastructure—whether it’s the hardware or it’s the codebase—can be applied to many different solutions depending on where you are in the port, where you are—whether it’s a retail application or within a smart port—there’s many different places—or in a warehouse. It’s all the same types of challenges or same technology can be used and can be customized or repackaged. And retraining AI, they have different Windows applications, and the integration that’s being done—that’s the opportunity for SIs or the people building the solution, is bringing this off-the-shelf technology and building solutions they couldn’t do anymore for the specific vertical, for the specific problem they have or identify. It’s bringing additional value to the existing hardware they have, adding value with things they couldn’t do. And in the particular example we did with the smart port was adding safety. One of the most significant things is the amount of people and how things fast things have to move. A mistake is critical. It’s devastating if a mistake is made. If somebody’s standing where they’re not supposed to be. And that that’s an example we try to illustrate. Obviously, in retail it might not be as critical as a fault. But then again, some people aren’t supposed to stand something—or stand someplace, or before a crane moves through a warehouse making sure this place is void. Anything you can attribute to safety is value, and ultimately a cost benefit for the customers and their customers. Steen, would you like to add a little bit to that?

Steen Graham: I think just to expand upon that is the gift that we were given, notably by Microsoft and Intel, is that underlying technology, which we use the acronym EFLOW, which is Edge for Linux on Windows. It’s now accurately described as, I think, Azure IoT Edge for Linux on Windows. Quite a mouthful, but the reason the mouthful is important is it gives us that no-compromise capability across Windows and Linux. I mean, that’s something that’s really unique to running applications that are just running great—existing applications and these modern applications. And so that’s been fantastic, and maybe the hidden gem from Intel is that Intel invested in hardware-acceleration capabilities via its integrated-graphics capability that allows us to do these workloads on deployed Intel-based CPUs today without having upgrade to expensive GPS. Why that’s so unique is typically you’re abstracted from access to the integrated graphics if you’re running an AI model and Linux via Windows, but that was a really hidden gem that Intel produced, and now we can run multiple AI models, multiple camera feeds on affordable, off-the-shelf technologies like Intel’s net platform, and Windows and Linux as well. So, an incredible array of technology that allows us to deploy these modern workloads and make sure they’re interoperable with existing infrastructure.

Scott Chmiel: And I’ll add one more thing too, is the investment companies have done in their Windows architecture, their infrastructure, they have people who understand Windows. They have people who manage those Windows devices. When you bring something into an existing application OT, there usually is an IT element there managing those devices. The value is you’re still utilizing that skill set, that expertise you bring to the table, and now you’re adding that modern, cloud-based, machine learning AI, so they’re leveraging additional value on top of their skill set. So, once again, all the investments that would’ve been made can be reused and can be built on top of.

Christina Cardoza: Now, I should mention the IoT Chat, and insight.tech as a whole, is owned by Intel, so always love hearing about what Intel is doing and how they’re helping others in the industry. But I want to expand a little bit more on EFLOW—what the process was getting this into the port system, how hard, or where the initiative came from, and then the benefits that we’ve seen in the supply chain because of EFLOW. Steen, I’ll throw that one to you.

Steen Graham: So, in regards to the port system, it’s quite an extensive, multiyear process of RFIs and RFPs. So, this was technology—the EFLOW technology—was released just late last year, so we really built this solution within the past year and are still in the engagement phase on a number of opportunities with ports to demonstrate in RFP and RFI mode. So, even though the technology’s highly ready to solve some of these challenges, as you alluded to earlier, the multilayered decision making process is what you kind of run into on these sophisticated situations, and so that’s certainly something we’re working through. From a business-outcome perspective, the problem that we were trying to address is the bottleneck associated with the turn times: the operational-technology metric of how fast the containers can be loaded and unloaded. You may have seen at the height of the crisis you would find many times the truck drivers blaming the crane operators for being unfamiliar with their workstation because the union assigns them a workstation based on their seniority on a daily basis, and as COVID was in place as well, it’s even more dynamic union positioning. Meanwhile, the crane operators were not happy with the truck drivers because they were no-showing about 50% of their appointments, and because of the goods shift in US consumption from services to physical goods, we had peak traffic of containers as well. So you’ve got more people than ever on the port. And so the business problem that we’re trying to solve is how do we optimize those turn times of those cranes? How fast can they be unloaded and loaded? How do we make sure the truck’s in the right place at the right time, and efficiently does that while providing enhanced safety experiences for the workers on site as well. And we also are tracking CO₂ emissions based on the fossil fuel consumption of those cranes. Although, I should say there is a mix between hybrid cranes as well as diesel cranes, so many ports have a nice mix—like, for example, the Port of Houston has about one-third hybrid cranes and two-thirds diesel cranes. And so that’s another metric that we’re tracking, is how efficient are those hybrid cranes? Are the operators trained and familiar with that as well?

Christina Cardoza: So, a lot of the things that we’ve been talking about—this problem between Linux versus Windows, and legacy technology versus new technology—we’ve been talking about these in regards to ports and smart ports. But these sound like challenges that every industry deals with in some shape or form. So, Scott, I’m wondering where else do you see EFLOW being used outside of ports, and what other challenges or benefits is EFLOW looking to solve and giving to businesses?

Scott Chmiel: Well, I think there’s, depending on who the audience is, it’s going to be different verticals they’re focused on. I know there’s a strong focus on the retail from both Microsoft and Intel: the opportunities there to do workload consolidation. I think the example we’ve shared or talked about it a little bit is a consolidation of surveillance and point of sale, where one machine could do both or, once again, new services you couldn’t do before. Now what you have, a visual element with a transaction, what kind of value can you generate out of that? What kind of benefit to the business, benefit to the customer? Obviously the transportation, when we talk about smart ports, we can expand on that and say transportation in general—whether it’s warehouse, workflow management, the smart port, we’re talking crane—but there’s a lot going on in ports of entry and warehouses, and if you think about just a logistical hub where trucks are coming and going, and how much volume of material is exchanged between trucks or put on shelves. There’s the accelerator—we did both talk about efficiencies—but also safety using AI—Visual AI to, for instance, detect where people shouldn’t be—giving warnings: things are moving fast; things are big that are moving; people and those machines don’t always interact very well if they’re in the wrong place.

So there’s lots of opportunities, and I think transportation, industrial and retail are a lot of them. I’m sure somebody innovative in a different vertical, like medical, has applications as well. But the great thing is the people who understand those industries, the people who have the IP and the investment in those industries, they understand the solutions they’re trying to solve, and a lot of that—the code, the underlining technology—can be repurposed for those verticals. And, once again, a lot of the work’s already done for them with the accelerators and the tools that Microsoft and Intel with OpenVINO have provided to be leveraged for their use. It’s pretty exciting, what they can do, and I think if you get Steen and I going, we can talk about and get pretty excited about, and start making up solutions, but I think the end customers, the people who live and breathe in those industries, know the kind of problems, and, like I said, being aware of the new technologies and what EFLOW can do, they’re going to start coming up with some pretty exciting ways to apply it from very simple, solving simple problems, to more complex problems they might have on their sites or locations or their vertical.

Steen Graham: Just to maybe extend Scott’s thoughts on a couple of these industries, and maybe starting with healthcare where he left it, is if you look at medical-imaging equipment, such as ultrasound, a lot of ultrasound vendors are Windows-based applications, but they’re looking to add new, AI-based features. And so, for example, to make sure that mother and child are safe through pregnancy, you can look at the fetal position of the baby as it exits the womb. And that’s something that you can take an existing Windows-based ultrasound equipment with, and then overlay modern deep-learning capability as well. Another example is anesthesiologists occasionally have challenges finding the veins on folks, and it could be a material difference if you obviously don’t hit the vein correctly. And so you can use ultrasound equipment—again, Windows-based with modern deep-learning skills—to determine the accuracy of the vein, and then pinpoint the associated anesthesiology inputs with that one as well. So that’s a couple of use cases in medical where you have this demand to run—my existing ultrasound works great on Windows, and now I’d like to overlay some modern deep-learning capability as well.

And so that’s a good use case in factories—I think in manufacturing Windows is pervasive, but we’ve also seen just an incredible demand around things like computer vision to do defect detection in line in the manufacturing-process flow, and I think it’s an incredible use case. And I think perhaps kind of the hidden cool factor in that computer vision for quality detection use case, if you do in-line AI defect detection, you can actually find products that are essentially having quality issues earlier in the manufacturing flow, which is nice because of course you can address product-quality issues. But simultaneously, if you address that earlier in the flow, you use less fossil fuels to actually run through the rest of the process; you use less raw commodities as well, and so there’s a sustainability effort that’s quite impressive in doing some of those use cases as well. And again, huge install base of Windows and manufacturing facilities as well, so across transportation, healthcare, manufacturing, there’s really some incredible opportunities to pair modern Linux-based, cloud-native apps and AI on top of Windows via this technology. So, kudos again to the teams developing it and continuously updating it at Microsoft.

Christina Cardoza: Absolutely. I love those examples because it really shows you these benefits go beyond businesses and really have a ripple effect to society. Now, you guys have mentioned Intel and Microsoft ,and you’re from Scalers AI and Arrow, so, I’m curious about the partnership that goes into this. Typically businesses, you don’t think that they synergize or work well together, but really this IoT landscape—and when you look at supply chain as another example—it really takes a team effort. So how are you guys working together, and working with some of those other partners you mentioned: Microsoft and Intel?

Steen Graham: I think Arrow is kind of a natural fit for working across partners and solving these multipartner solutions, because they’re one of the leading solution providers in the industry. And Arrow is always, I think, looking to figure out how they can make one plus one equal three across their partnerships. And that’s really where Scott actually came to us with an incredible idea about showcasing the value of this underlying technology, which is quite primitive relative to deployment with a real business outcome, and we were able to take those technologies from Intel and Microsoft, a number of open source projects as well. So there’s many other we could modern—we would call them modern microservices in many cases: full open source projects as well, so I have a hard time calling them a microservice because they’re a macro set of code. But we were really building upon numerous open source projects, numerous technologies from Intel and Microsoft and others, and building that solution code and where Scalers fits in is really understanding how to fit all those things together into a solution and providing that high-fidelity enterprise AI solution, as well as building the custom AI models for deployment. And so Scott, would you like to add anything on?

Scott Chmiel: Arrow’s one of those companies that, unless you’re in electronics, a lot of people don’t know who we are. We’re over $30 billion in 2021, and we cover quite a wide breadth of different things Arrow does, from the enterprise to components and everything in between. The thing that Arrow focuses on, and the intelligent solutions of Pacific group that I’m in, is we call ourself an orchestrator and aggregator, bringing the different technologies—the trusted advisor role orchestrating different partners. Because, once again, we talked about the complexity—it’s hard for one company who has a vision or a challenge to be able to necessarily have the resources, the skill sets in house to do everything for an end-to-end solution. Obviously they might have a component—that might be their IP, their technology, a device, a sensor, something that does something really well—but, as I said, the solutions, especially in operational technology, are wide and deep within the end use.

So what Arrow looks to do is work with the end user, the prime whoever’s coming up with the solution, and bring in appropriate partners. Those could be technology partners, they can be existing technology-solutions systems, the compute from Intel, different form factors, the IO—and if the customer has something which doesn’t exist in the market, help them build it, help them pick the right solutions, not only for their end use, but looking at the longevity, the overall life cycle of that solution. Smart ports—that’s not something that’s going to be deployed and done in a couple of years. And, once again, you don’t want something on each crane being different. That’s the important point, something that’s repeatable. And, obviously if you can do it in smart port—Port of Los Angeles—I’m sure you want to move it down to the Port of LA—move it to, excuse me, Port of Houston, other locations, if you can reuse that. There’s even inland port. We think of ports as next to water—no, there’s a lot of hubs for transporting and redoing containers throughout the US, and where they distribute from the rail systems, from the trucking systems—if you can reuse it, the company who’s developing that solution or who is bringing these pieces together can reuse it, and, once again, create more scale, create more value across the ecosystem.

So Arrow works on orchestrating, bringing the appropriate partners when a company deploys a solution. I’m going to go back to operational technology—security’s a huge thing. There are a lot of options in what you need to do to deploy into a system—making sure the sensors, the gateways, all the devices are secure in that operational technology. Because, keep in mind, it might be not just their network, it might be on a facilities network. How are you securing those devices? How are you making sure that data’s safe? That the devices aren’t vulnerable to people with devious intentions. And, of course, just making sure it works. That’s very important. So, once again, Arrow is orchestrating—whether it’s services, whether it’s components, whether it’s helping them with design—helping them make the right technology solutions. That’s really important. Talking about Intel—the long relationship we have with Intel, there’s a lot of products to look at. There’s a lot of ways to do things. We want to make sure that the customers are educated and selecting the right product for their solution, for their usage model.

Christina Cardoza: Great. Well, I can’t wait to see what else comes out of your partnerships with each other and Intel and Microsoft, as well as where else EFLOW is going to make some of these innovative transformations for the industry. But, unfortunately, we are running out of time on the podcast. Before we go, I know we’ve covered a lot, I just want to throw it back to each of you quickly if there’s anything that we didn’t go over that you think it’s important for our listeners to know, or any final thoughts or key takeaways you want to leave them with today. So, Steen, I’ll throw it to you first.

Steen Graham: Thank you, Christina. Well, it’s been a pleasure talking with you and Scott about this transformative technology. I think, to the listeners, I think as we talk about the cost of development and software engineering and really solutioning these, it’s incredibly important that we write the code to integrate these partnerships. And there’s so many incredible companies with great technologies, but what many times is missing is the single line of code that connects the APIs to really drive transformation as well. And so I think that’s really important that we take that step to fortify our partnerships so we can drive these industry transformations. And, as an industry, we really got to come together on the deployment challenge, because building capabilities in the cloud is fantastic, and it’s really affordable and easy to do these days. And so, Applied AI is affordable as it’s ever been, it’s democratized as it’s ever been. But where challenges occur is deploying it in the physical world, and the continuous learning, the transfer learning, the continuous annotation requirements to do that. And so, I think as an industry we’ve got to continuously focus on deployment and DevOps capabilities as well. And finally, I think, although, we’re getting really good at synthetic data and creating AI models with small data sets, if we want really want to move society forward, we have to be able to build models with high fidelity on good data sets and do it in a way with explainable AI so we know why the AI is making its determinations, which is very frequently required for mini RFPs these days because we want to make sure we know why the artificial intelligence is coming to its conclusions to make sure it’s as inclusive as possible and accurate as well. So those are certainly the three areas that I would emphasize here. You’re fantastic listeners here, Christina, and it’s been a pleasure. Thanks for having me.

Christina Cardoza: Great points, yeah, absolutely. And, Scott, any final thoughts or takeaways you want to leave today?

Scott Chmiel: I’m always amazed when I talk to companies in specific verticals—whether it’s somebody running a warehouse, somebody in a port, somebody in surveillance or whatever, the medical industry—the amount of knowledge they have with what they do, their particular industry—their particular solutions are amazing. And as these solutions get more complex, I want to make sure people understand there’s no need to go alone on some of the more complex solutions. It’s no longer the days of building a device that does one thing. It’s not an MRI which just does visioning; it’s how it integrates with that hospital. It’s how additional leveraging, additional technology—whether helping the visual inspectors for all that vision scanning, finding—being zeroed in certain things. And let’s talk honestly, with machine learning AI you’re not only bringing the expertise of one person; it’s how you train that and the thousands of models and thousands of things you can leverage. Companies don’t need to do it alone. They really can’t do it alone when they do more complex solutions, and Arrow has a lot to do that they can leverage to help them kit that end-to-end solution. And, once again, learning about the technology, learning about them, them learning about what can be done, what they hadn’t thought about doing, or the thought was outside the scope of what they can actually accomplish in their solution. The bar is moving down for what can be done, and it’s amazing—business solutions that couldn’t be solved in the past can be. It’s just getting the right partners, technology, hardware, ready-made solutions to be available for you to look at and to leverage, get your time-to-market faster, and it can save you money on—once again, why reinvent the wheel if there’s a piece of your solution that you can reuse from somebody else? It’s the final solution that’s brought together, that’s the important thing, and most likely the customer that knows that vertical is trying to solve the problem. They’re the experts on that, but let’s help them get that, and solve those problems with what’s out there.

Christina Cardoza: Yeah. I love that, that last thought: the bar is getting low for what we can do, but expectations are high for what others think we should do. So having the right partners is definitely going to help you be successful creating these new and innovative apps. So with that, I just want to thank you both for joining the podcast today. It’s been a pleasure talking to you. And thanks to our listeners for tuning in. If you liked this episode, please like, subscribe, rate, review, all of the above, on your favorite streaming platform. 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.