AI for All: The Power of Democratization and Collaboration

Christina Cardoza: Hello, and welcome to the IoT Chat, where we explore the latest technology advancements and trends. Today we’re talking about the power of AI collaboration and democratization with Mario Lopez from Bravent and Michael Nelson from TD SYNNEX. So before we get into the conversation, as always, let’s get to know our guests. Mario, I’ll start with you. What can you tell us about yourself and Bravent?

Mario Lopez: Hello, Christina, I’m very good, thank you. Thank you for this opportunity and thank you for participating in this podcast. I’m Mario Lopez. I’m working as Chief Innovation Officer at Bravent. I am normally leading the most innovative projects where we are using the latest technologies such as AI, mixed reality, digital twins, and those things.

My colleagues told me that I have probably the best job in my company because I’m working always with all the toys that we have in our company, also with the most exciting customers. And I’m very proud of that, because sometimes I have very good opportunities to meet those customers, also working directly with them in their locations. And that’s something very, very good. So, thank you. It’s a pleasure to be here.

Christina Cardoza: Absolutely And I would agree with your coworkers: technology is always changing and it’s always advancing. There’s always so many new cool things that you can do with it. So, very excited to get into a little bit about what you guys are doing and how you are working with partners like Intel and TD SYNNEX and make some of this happen.

But before we get into that, Michael—want to welcome you to the show also. What can you tell us about yourself and TD SYNNEX?

Michael Nelson: Well, I’m Michael Nelson, as you said. I’m a Field Solutions Architect here at TD SYNNEX. I’ve been covering Intel and supporting all their initiatives now for 26 years here. I support both our internal customers as well as the resellers and their customers. So, I cover the gambit from end user all the way up to our salespeople and anywhere in that area.

As far as TD SYNNEX: I mean, they’re a leading channel supplier and have been for over 40 years. They’re a behemoth worldwide distributor. We provide products and services for every level of the IT supply chain.

Christina Cardoza: Great. Look forward to getting into it. And from that technical aspect, getting a little bit deeper into what you guys are doing and how, with Mario, how you guys are making some of these solutions happen. So, thanks both for joining.

Mario, I want to start the conversation with you. You mentioned how you get to work with AI and all of these transformative technologies. So, wanted to start off the question, because obviously we are talking about the power of AI collaboration and making it accessible to more people and to more businesses. So why do you think AI specifically and computer vision—they’ve become so transformative and so impactful across all of these different kinds of industries?

Mario Lopez: That’s a very interesting question, to be honest, because AI, in my humble opinion, I think that is transforming all the things that we have around us, all the companies, the process, whatever we have around our lives. It’s true that AI—I think that in the last year in my opinion—is starting to be the main topic in every conversation. Because probably with appearance of the generative AI, we are democratizing even more AI in all the companies. And also—I always talk about my family—for example, my parents never used AI, and now with the appearance of the generative AI with GPT and those things, for example, I was explaining to them how they were able to use ChatGPT, asking some questions and getting answers in just few seconds. And the right answer—that’s probably the most important thing.

So I think that the AI is starting to be very important, because it’s able to give us the right answer and probably the right predictions or the right anticipation to the errors that we can face in our company. And that’s something very important.

And with computer vision it’s more or less the same, because computer vision is allowing us to interpret it and understand what is happening around us in our city, in our life, in our company, in our jobs—in whatever we have around us. With computer vision and with just a camera we are able to understand everything that is happening and obviously do the right task that we need after we see that it’s happening, something, through the cameras.

I remember when I was starting to study my degree, I was investigating some technologies about computer vision. And I remember that it was very complicated to use it, because just to do something very simple we needed to program a very long software with functions that were very complex. And now everything has changed; now with just a normal camera, even with my laptop or whatever I have, and with just a software and two clicks I can get a model running and understand, for example, what is happening in my room or, as I said, in every space.

So that’s something that in my opinion is changing everything, because now it’s very easy to use AI or computer vision in my company and also very cheap to implement it.

Christina Cardoza: Absolutely. I’m sure it’s a little frustrating seeing all the new tools out today, that if you had that just a couple of years ago would’ve made your job and your life a little easier. But it is exciting to see how much we can adopt this, now that it is becoming easier to implement. And you make some great points about the generative AI and how it’s becoming more accessible to everybody in their everyday lives. Consumers can start playing around with it and experimenting with it.

And I think even before generative AI we’ve been having AI and computer vision impact our lives without even noticing. I’m just thinking about my car, for example, like backing out of the driveway or merging. There’s always those computer vision alerts saying you’re getting too close to a car or your car is going to be hitting something on the way out. Things like that, and I rely on it a lot.

But I’m sure even with these new technologies coming out, making it easier for us to implement and play around with it, there’s still some challenges to building these AI and advanced computer vision solutions. Can you talk about some of the complexities of the limitations and challenges that businesses still face today?

Mario Lopez: Well, one of the main problems that we sometimes have with customers or with projects that we are working on is probably the data. Always when we are talking with a customer, we explain that the most important thing to start working with AI is the data. If you don’t have data, you cannot do AI. And that’s the first thing that you must know, because you need to prepare everything: you need to prepare your company, your process and everything. And when you have the data ready, you can start working on AI.

It’s true that now with some of the services that we have available on the market, and even more from Intel, we can start working on AI, but you need to adapt your company, your process, and those things to be able to use it. So this is one, in my opinion, one of the main challenges that we face.

The other one is how we need to change the process with the technology and the hardware that is needed to do the AI. Again, that’s something that is changing, and it’s much easier than I remember from two years ago, for example, because we have new services that are making our life easier. But some of the clients, when they need to install or they need to put some new hardware in their factory, for example, in their company—sometimes it’s a bit complicated.

And the last one that I think it improved a lot in the last years, in my opinion, I think that is the cloud computing. Some of our customers, when we start talking with them, they don’t want to use the cloud. They don’t want to send any data to the cloud; they want to process everything on the edge. And that’s something that is normal, because I understand that their data is part of them and they don’t trust how the data moving around to the cloud or moving through the data centers. And that’s something that has changed a lot in the last years, because now with the hardware that we have available to do edge computing it’s enough, and probably it’s even better than what we have on the cloud.

For example, we have in our company a solution to do computer vision. And when we started with this solution, when we started making some tests with our customers, we were doing everything on the cloud, and it was impossible to use the solution on the cloud because of the delay sending information to the cloud, getting the answers, and those things. And when we started working with edge computing, everything changed. And after that we started to have good results, real-time results. And that’s something that in my opinion is very important. And that’s how we are now implementing those solutions—using AI on the customers with edge computing.

Christina Cardoza: Yeah, I’ve seen edge computing be closely coupled with AI now for that low latency you mentioned—that performance at real-time analysis so that businesses can be more informed about the decisions that they make and then also be able to make these decisions in real time. So it’s really exciting to see all of these things happening.

You mentioned a couple of things that you need or that businesses need to implement AI and computer vision solutions. We’re talking about the edge, cloud; there’s hardware, there’s software that goes into it. And then not to mention the security aspects of all of this. So there’s a lot that I think it can feel overwhelming or that it looks like it’s too complex or complicated to make some of these initiatives and efforts towards AI and computer vision in your business.

But I think maybe what we forget sometimes is that we don’t have to go about it alone. The right partnership can help empower some of our innovations and help deploy some of these solutions. So, Michael, coming from a TD SYNNEX standpoint, I’m curious how you guys work together with other companies, how that partnership can really bring some of these technologies together and support businesses’ and clients’ AI initiatives.

Michael Nelson: Well, that’s kind of the specialty of distribution these days, is we have a large breadth of product specialists that our resellers can use to fill in the knowledge gaps that they may have. So when they go to deploy these solutions, they’re very rarely one vendor all the way through, it’s typically multi-vendor.

And that’s where we come in as the shining white knight, is we can, again, fill in those knowledge gaps and guide them to an actual solution—as opposed to: here’s a bunch of products that kind of work together and then you figure it out, we will get you to your end result on the first try. That’s really the strength of what we bring to the table.

As far as with Intel specifically—again, Intel has a very far reaching goal here of democratizing AI and putting it everywhere. And part of that approach using their oneAPI and then some of their other software platforms that are coming out very soon—you’re going to be able to write your model, your platform; you’re going to write it once and you’re going to run it everywhere. So it won’t matter which hardware you decide to use. If you develop with the Intel ecosystem of software, it’s going to work everywhere.

Christina Cardoza: I love that relationship that TD SYNNEX provides. You mentioned there’s a lot of products and solutions that go into this, and it can feel a little piecemeal or overwhelming to manage. And some of these things can become siloed when you are working with AI and data is really important. You can’t have these things be siloed or not talk to each other.

Michael Nelson: Yeah. In the past, if you’re going to deploy some sort of visual-AI solution, it would’ve been one vendor, usually a camera manufacturer. And it was going to do whatever one job it was designed to do, and it would be siloed. That’s all it did. Now you’re going to use off-the-shelf cameras, the servers you already own, the infrastructure, networking infrastructure you already own; and you’re going to bring all that together and do multiple solutions. So it’s powerful. It really democratizes AI.

Christina Cardoza: Absolutely. And I think you mentioned that in the past it’d be one vendor, and now we have multiple different vendors. I think sometimes businesses, they get worried about who they choose to work with or what solutions they choose to bring in, because now that it’s such this open ecosystem they don’t want to be stuck with a vendor that they can’t innovate or they can’t move forward.

So I’m curious, when companies and businesses are looking for these partners or looking to bring in these solutions, what should they be considering? Like, what should they be evaluating, especially when it comes from that collaboration aspect?

Michael Nelson: When evaluating a potential partner for collaboration, there’s a lot of critical factors that come into play. I’d say some of the top ones are shared vision and goals. You also need to have complementary strengths. There needs to be a level of trust and reliability, open communication. And probably in my field the most important is measurable outcomes and success metrics. Because if you don’t have that, you can’t guarantee the result. You need to remember that successful partnerships are built on mutual respect and shared goals, effective communications. Choose your partners wisely, and together you can achieve remarkable results.

Christina Cardoza: That’s all great, and I think it highlights the value of TD SYNNEX, the expertise that you bring, that you can help businesses select these products or help businesses deploy and bring these together and make the right decisions for them. So I think that’s really powerful and important.

Mario, I’m curious, because we were talking about some of the ways that you guys are helping businesses bring this to market, so if you had any customer examples or case studies that you can share of how partnerships like TD SYNNEX and Bravent can really support AI initiatives and efforts for businesses.

Mario Lopez: Yes, absolutely. As I was saying before, one of the solutions that we created and also we are offering to our customers is a solution where we are using computer vision to do quality control, quality inspection, and ensure that the quality of the products is fine. That’s a solution that we created around two years ago, more or less. And we started working with John Deere.

It’s important, because we started with them just working on a POC, on a pilot, just to see if with the technology that we had in that moment we were able to solve the quality controls or to ensure the quality of the products at the factory in the production line. That’s something that, before using this solution, it was taking a lot of time for the customer before starting with the manufacturing process, because they needed to prepare all the parts, all the different parts, that they were using in the production line. And also after putting together all the different parts, they were spending or using a lot of time just to ensure the quality of the product.

So we created a solution that—only using a camera in the production line and also Intel software and Intel hardware—we are running a model that is able to analyze what is happening in real time and is able to provide feedback in real time to the operator to ensure that every part and every step was done in the right way. So that’s something very important for them—and obviously very important for most of the customers that we are offering this kind of solution to—because it’s saving a lot of money and a lot of time. But at the end it’s improving the customer success, the customer experience. Because if you don’t need to use your warranty or if you don’t have any problem with the product at the end, you will be very happy with the brand, with the product that you are getting.

So that’s, in my opinion, I think a perfect example of how we are applying AI and how we are improving a real process just using computer vision and just using AI with a very simple solution. Because, as I said, this is just a camera with a PC, with a computer, a normal computer that is running an AI model and just getting the results and, at the end, improving the quality of the products.

Christina Cardoza: Great. And, Michael, since you are an engineer, I assume you’re a little bit closer to the ground, so to speak, of getting these systems implemented or making sure everything is running smoothly. So I’m curious if you have any examples, or if you can walk us through the process a little bit of what it takes to bring AI initiatives to market, and if there’s anything that goes wrong in the process or anything that you can highlight.

Michael Nelson: Yeah. I think we mentioned earlier the Geti software platform that Intel has just launched. I was shown a demonstration of that at one point, and then two months later they sent me a license so I could install it and give it a try myself. I worked with their sample data for about an hour and a half, trying to recreate the demo that they showed me where they built a model in about 10 minutes. Like I said, I was working an hour and a half and it still wasn’t happening.

And what it taught me was that, even though the tool was very easy to use, you still have to really understand the data, make sure you have the right data, right pictures. Basically, I picked the wrong task. So, I chose, with the images they gave me, to do identification, when I really should have been just doing anomaly detection. And so I had the wrong sample data.

Once I changed the type of model I was building, I was up and running in 15 minutes. I was literally able to take the exact same model I was already working on, make a couple adjustments, and all of a sudden it just worked. So the Geti platform is really powerful in that it allows you to take your work and then to optimize it. So even though I completely started out wrong because I didn’t know what I was doing because I’m not a data scientist—I’m literally just reading all the menu options and the little help tips trying to figure out how to build my first AI model—but it allowed me to do that once I got past my knowledge gap of what I’m actually trying to accomplish; it took me 15 minutes to have a model that I could deploy.

And the reason why that’s so important is over 40% of models never get that far. And 70% of AI projects die at this stage. And I was past it in 15 minutes—an hour and 45, technically. But again, most people jumping into AI aren’t going as cold as I was. They typically hire people that have done it at least one time before they start. So that’s my personal experience with AI, as far as getting started, that having the right tools makes a huge difference.

Christina Cardoza: Yeah. And I think your personal experience, that’s a great example of we’re democratizing AI; you’re not going to get it on the first try. And that’s why it’s important that we have these agile environments that give you an opportunity to experiment, to fail, and then learn from those mistakes and really implement this.

So I think that’s a powerful point, is that it’s not going to happen on the first time and you need to work out some kinks and it’s not just the tools—and that’s the power of partnerships too. You can have all the best tools in the world, but if you don’t know how to work them or you don’t have a partner like TD SYNNEX or Bravent helping you along the way, the tools aren’t really going to do that much for you. So I think that’s a great example, Michael, so thanks for sharing that.

And obviously we talked about Intel Geti—you have an Intel polo on right now, and this is an Intel-sponsored podcast, as well as insight.tech publication. We’re owned and sponsored by Intel. But I think, in spite of that, they do provide those tools and that partnership and that expertise to really work with the tools.

And so I’m wondering, from the Bravent side, what has been the value for your team leveraging technologies like Intel Geti and being able to have some of your use cases like the John Deere example become a reality?

Mario Lopez: Well, to be honest, for us it was key to do a partnership with Intel, and also in this case with TD SYNNEX. But regarding the solution that I was explaining before, one of the problems that I was mentioning that we were facing is the real-time process. And that’s something that it changed everything when we started to use Intel technology. Because, as I was saying, we were using before the cloud computing, but changing to the edge what we started to use is OpenVINO^™. And OpenVINO is what allowed us to do the inference on CPU as Michael was explaining. And that’s something that, as I said, changed completely our solution, because with just a normal computer or just a normal laptop with an Intel CPU, we were able to do the inference and run the models and get the resource in real time in a very easy way.

And the other thing is with Intel Geti—you were explaining and Michael was explaining his own story with using Geti—and for us was completely the same, because one of the problems that we were facing with our customers is that if you want to train your own AI models you need, as I said at the beginning, you need a lot of data, but also you need some experts doing the labeling of the images and also some data scientists to prepare the AI models.

And that’s something that could be very complex. It will require time to do it; it will require some employees in your company with the right knowledge. And that’s something that sometimes is not very easy to get. Before using Geti, that’s something that we were offering to our customers: to do the labeling, to do the training of the models, and doing everything. But with Intel Geti it changed everything, because now with just a very simple knowledge of using Intel Geti and just doing some clicks—to me, it is something very similar to PowerPoint, because it’s just: move your mouse, select the images, put the label, and that’s everything.

And when you know how to use Intel Geti, you can prepare your own AI models and in just a few minutes or just a few hours you can get all the AI models ready to use. And that’s something that it changed completely also in our solution, because now we can provide the tools to our customers. We can offer training, for example, in how to use Intel Geti, and they will be able on their own to just prepare the AI models and integrate in the solution.

Because the other thing that we have done is that we prepare our solution. We created a pipeline that allows us to prepare the AI models in a very easy way and deploy it. And the customer, in just few minutes, as I said, they will have available the model to use it in their company. So, to conclude, for us the partnership between Bravent and Intel was perfect, because we had the right solution, but Intel provided us the right software and hardware to do a perfect solution.

Christina Cardoza: Yeah. It’s amazing to me to see how, with Intel Geti, somebody like Michael—who’s an engineer and is not a data scientist and hasn’t worked with AI—can start building AI models or start training different things. And even though it took almost two hours, some people in the past have taken years of training to be able to do this. And now with a couple of clicks and a little bit of experimenting it can be really easy to do. So I can’t wait to see how else democratizing AI—and with Intel Geti—how that’s going to change the way that the businesses deploy and build AI solutions.

So, Mario, is there anything else that you can talk about, about the importance of democratizing AI and how it’s really going to—because AI is not going anywhere—so how it’s really going to help bring some of these solutions and advancements and innovations into the future?

Mario Lopez: Yeah. No, as I said, everything has changed—I think that in the last one or two years—because now AI is much more accessible, is very easy to use. As I was explaining, with Geti, now our customers that are not data scientists, they are not probably an engineer, they just have some knowledge about how to use the computer. And here—probably the most important thing is the knowledge about their own company and their own business scenarios—they are able to train the AI models. And it is the same as I was explaining at the beginning with generative AI—now just using the chat window or just using our voice we can access to the information.

Here I think that the most important thing is that the machine or the process are able to understand what we want. That’s, for me, the key of AI, and at the end we are able to get the right answer to the questions that we are doing. So I think that everything is changing, and I’m sure that in the next coming months and next coming years it will change even more, because now we are seeing that the software is evolving a lot, the technology is evolving. Now we have more intelligent machines around us. So I think that the AI will be even more accessible. As I was explaining, my parents now are able to access, to use, the AI, and I didn’t imagine anything like that just two years ago. So what we are expecting for the next coming months or years I think that it will blow our mind.

Christina Cardoza: Yeah, absolutely. My parents can’t even send an email, but now they can write their emails using AI. So it is amazing to see. I can’t wait to see what else Bravent and TD SYNNEX do in this space.

Before we go, I just want to throw it back to each of you, if you have any final thoughts or key takeaways of where you think this space is going, the importance of democratizing AI, and just collaborating with partners like yourself. So, Michael, I’ll start with you.

Michael Nelson: AI is definitely not going away. We have these little buzzwords that come through our industry: big data, and then IoT, and right now it’s definitely AI. I feel like AI is going to be here for more than just the usual sales cycle. It’s really in its infancy, and it’s very exciting to see how it’s going. Intel’s approach—again, with that “write once, run everywhere,” their AI-edge suite of software, it’s amazing.

Between the Geti to create the model, you deploy it to OpenVINO, and now they have another product we’re bringing to market called Scenescape that taps into cameras, uses that model, and then it can interact with—you know it can send messages, it can interact through MQTT protocols to other devices. It’s pretty amazing. It’s making it very actionable. So it’s not just the model running and giving us clever answers like you see with all these large language models; it’s real-world solutions to solve real problems.

Christina Cardoza: Yeah, absolutely. Totally agree. It’s another buzzword, but it’s not just another buzzword.

Michael Nelson: No, it’s just it’s so applicable in so many places. I mean, there’s not just one place we’re going to deploy AI; it’s going to be everywhere. And that’s not hyperbole; it’s literally going to be everywhere.

Christina Cardoza: It’s going to change the way we work and move for real. So, Mario, what about you? Is there anything else you want to leave our listeners with today before we go?

Mario Lopez: Yeah. No, Christina; I think that AI, in my opinion, is starting to be a commodity, because now everybody can access AI in every company. And as we were explaining, it’s more accessible. So I think that it will be changing in the next coming years even more. And I think that right now we have AI everywhere—probably we don’t know, but AI is everywhere, because in our cars, in our computers, in our TVs, in all the devices that probably we use every day, we have AI.

But for me, I think that it will be a commodity. All the companies are including AI in their process. And it’s going to be even easier, because now probably we are seeing that medium, medium-large companies are investing a lot of money in AI. But what they think from my point of view is that, in the next coming years, very small companies or even a self-employee is able to access AI, for example, just using Copilot or just using ChatGPT.

And that’s something that will change everything and will improve all the process that we have in our companies. To be honest, I think that that’s something that will be another revolution. We are seeing that everything is changing. We need to prepare our work, we need to prepare our life, because we are just seeing the beginning of a new era, a new revolution of the technology, and also in the companies.

Christina Cardoza: Absolutely. And the fact that we have AI everywhere and we may not even notice where it is or that we’re using it, I think is a testament to working with partners like TD SYNNEX and Bravent that businesses are able to seamlessly integrate this and transform our lives in meaningful and impactful ways, but also in ways that aren’t intrusive. So I think that that’s very important.

And just want to thank you guys again for the insightful conversation. I invite all of our listeners to visit the TD SYNNEX and Bravent websites, see how you can partner with them and how you can make some of your AI dreams a reality. So, thank you guys again, and thank you 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.