Take a second to think about every single item in your home. Your television, your refrigerator, your desk, lamp, your laptop, even the smartphone you might be using to hear my voice right now. All of these things, and so many more items in our lives, began in a factory. There are more than six hundred and twenty thousand manufacturing businesses in the United States right now, responsible for nearly twelve percent of the total US economic output. The numbers are even more staggering in China, which makes up nearly twenty nine percent of the total global output. For manufacturing. Factories have been around since the late eighteenth century, and today they're used everywhere from South Korea to southern California to make cars, airplanes, textiles, and even space vehicles, and each one depends on a carefully choreographed system of steps, each one as essential as the next before the final product rolls off the production line. Mistakes, however, are also an unavoidable part of this process. Manufacturers simply can't check every piece of every product, and it's nearly impossible to achieve perfection when some manufacturing plants produce thousands of items a day. So how can technology help an industry so crucial to our daily lives, how can factories use AI to reduce and even prevent defective products? Welcome to Technically Speaking, an Intel podcast produced by iHeartMedia's Ruby Studio in partnership with Intel. In every episode, we explore how AI innovations are changing the world and revolutionizing the way we live. Hey there, I'm gram class, and today we're headed into the world of manufacturing, an expansive and essential industry that drives the global economy and both the history dating back nearly two hundred and fifty years, we've seen manufacturing create a revolution, resurrect nation's economies, connect people around the globe, and even send mankind into space. But what's next at the intersection of manufacturing and technology. In this episode, we'll be focusing on how AI technology can help optimize manufacturing and improve quality thanks to no small part to the minds at Intel and at Eigen Innovations, a company committed to helping organizations unlock the power of machine vision to automate quality inspections. Before we go any further, let's welcome our guest joining us today is John Weiss, the chief revenue officer at Eigen innovations. John oversees all revenue generation activities at Eigen, including driving sales in Eigen's machine vision software and engineering services.

Take a second to think about every single item in your home. Your television, your refrigerator, your desk, lamp, your laptop, even the smartphone you might be using to hear my voice right now. All of these things, and so many more items in our lives, began in a factory. There are more than six hundred and twenty thousand manufacturing businesses in the United States right now, responsible for nearly twelve percent of the total US economic output. The numbers are even more staggering in China, which makes up nearly twenty nine percent of the total global output. For manufacturing. Factories have been around since the late eighteenth century, and today they're used everywhere from South Korea to southern California to make cars, airplanes, textiles, and even space vehicles, and each one depends on a carefully choreographed system of steps, each one as essential as the next before the final product rolls off the production line. Mistakes, however, are also an unavoidable part of this process. Manufacturers simply can't check every piece of every product, and it's nearly impossible to achieve perfection when some manufacturing plants produce thousands of items a day. So how can technology help an industry so crucial to our daily lives, how can factories use AI to reduce and even prevent defective products? Welcome to Technically Speaking, an Intel podcast produced by iHeartMedia's Ruby Studio in partnership with Intel. In every episode, we explore how AI innovations are changing the world and revolutionizing the way we live. Hey there, I'm gram class, and today we're headed into the world of manufacturing, an expansive and essential industry that drives the global economy and both the history dating back nearly two hundred and fifty years, we've seen manufacturing create a revolution, resurrect nation's economies, connect people around the globe, and even send mankind into space. But what's next at the intersection of manufacturing and technology. In this episode, we'll be focusing on how AI technology can help optimize manufacturing and improve quality thanks to no small part to the minds at Intel and at Eigen Innovations, a company committed to helping organizations unlock the power of machine vision to automate quality inspections. Before we go any further, let's welcome our guest joining us today is John Weiss, the chief revenue officer at Eigen innovations. John oversees all revenue generation activities at Eigen, including driving sales in Eigen's machine vision software and engineering services.

Welcome to the show, John, thanks for having me. Graham's great to be here.

Welcome to the show, John, thanks for having me. Graham's great to be here.

Let's start with a bit of background on manufacturing and the role it plays in our society. I mean it's fair to say that I phone, our car, laptop, even the food we eat involves some sort of manufacturing process. I'd like to get your thoughts on just the importance and scale of manufacturing plants around the world.

Let's start with a bit of background on manufacturing and the role it plays in our society. I mean it's fair to say that I phone, our car, laptop, even the food we eat involves some sort of manufacturing process. I'd like to get your thoughts on just the importance and scale of manufacturing plants around the world.

Yeah, sure, Well, like you said, just about everything in our daily lives comes from factories or plants. But sure, depending on if you commute on a train or in a car, lots of those components are coming from factories. Very little these days are really kind of hand crafted and handmade and smile batch, especially large scale consumer items. And there's many different types of processes and many different types of ways things are made.

Yeah, sure, Well, like you said, just about everything in our daily lives comes from factories or plants. But sure, depending on if you commute on a train or in a car, lots of those components are coming from factories. Very little these days are really kind of hand crafted and handmade and smile batch, especially large scale consumer items. And there's many different types of processes and many different types of ways things are made.

And look, I know there's a multitude of ways and types of manufacturing processes. Like a Volkswagen built in Germany is going to be very different from an iPhone built in China. Do you find common things, reads or similarities across manufacturing industries.

And look, I know there's a multitude of ways and types of manufacturing processes. Like a Volkswagen built in Germany is going to be very different from an iPhone built in China. Do you find common things, reads or similarities across manufacturing industries.

Yeah, definitely, so, I guess maybe as a kind of a starting point, it's important to understand there are two types of manufacturing processes or approaches. One is called process manufacturing. This is things like chemicals, plastics, things that can't really be broken down or deconstructed easily. Or you have discrete manufacturing, which is much more of the process of putting stuff together. Think about a watch or a car. Now, both of those processes, discrete and process manufacturing, they're quite different, but there are certainly similarities. And between the two methods, you basically have all of the things that we use every day, right then oftentimes actually they kind of bleed into one another. Most things have a little bit of process manufacturing involved and then a little bit of discrete manufacturing as well. However, I would say the commonalities across both are really heavily reliant on technology. We see a very large push for data driven decision making. We see large patterns or trends in both realms of manufacturing around empowering the workforce, trying to opt skill workers via technology to get them to be focused on more mission critical tasks or higher value activities while letting some of the technology do more of the mundane tasks.

Yeah, definitely, so, I guess maybe as a kind of a starting point, it's important to understand there are two types of manufacturing processes or approaches. One is called process manufacturing. This is things like chemicals, plastics, things that can't really be broken down or deconstructed easily. Or you have discrete manufacturing, which is much more of the process of putting stuff together. Think about a watch or a car. Now, both of those processes, discrete and process manufacturing, they're quite different, but there are certainly similarities. And between the two methods, you basically have all of the things that we use every day, right then oftentimes actually they kind of bleed into one another. Most things have a little bit of process manufacturing involved and then a little bit of discrete manufacturing as well. However, I would say the commonalities across both are really heavily reliant on technology. We see a very large push for data driven decision making. We see large patterns or trends in both realms of manufacturing around empowering the workforce, trying to opt skill workers via technology to get them to be focused on more mission critical tasks or higher value activities while letting some of the technology do more of the mundane tasks.

Yeah. In a previous job that I had, we were doing consumer electronics, and we struggled quite a bit with the quality side of things and being able to ensure a good product can you manufactured in our China plant. And one thing that struck me was that there was a very manual process in terms of the quality inspection, and it will take samples in one out of every ten and the test that and then if that worked, then okay, and then we assume the rest kind of work. I'm wondering if you could share any stories or examples of I guess problems with quality or defective products that stick in your mind.

Yeah. In a previous job that I had, we were doing consumer electronics, and we struggled quite a bit with the quality side of things and being able to ensure a good product can you manufactured in our China plant. And one thing that struck me was that there was a very manual process in terms of the quality inspection, and it will take samples in one out of every ten and the test that and then if that worked, then okay, and then we assume the rest kind of work. I'm wondering if you could share any stories or examples of I guess problems with quality or defective products that stick in your mind.

Yeahsolutely. I mean that's all we do at Eigen, right. All we do is industrial machine vision for inline quality inspection. So a couple that stick into my mind. Actually very relevant to what you said, sample testing. Lots of manufacturers do this, right if they're a high volume shop or a high volume process. For example, we have some customers that use our technology to inspect upwards of forty thousand units a week per facility. The challenge is if you do find a problem, now you're kind of scratching your head wondering how many in between the last one hundred or last fifty also had a problem right, And unfortunately you tend to find out the hard way when you get returns or warranty claims that maybe something wasn't right in that process. And so technology is a great way, especially the arena that we operate in computer vision, we're helping customers actually get away from that. A great example is one of our manufacturing customers who makes fuel tanks for a variety of different vehicles, and they do what's called destructive testing. They don't just test, they actually break the fuel tank, that cut it up, and they look at all of the plastic components inside and they see was it molded correctly, was it welded correctly? And if they have a problem, well, now they have to reverse engineer a whole bunch of stuff and try to figure out, holy cow, what went wrong right? And how do we ensure that no bad fuel tank gets on a truck. They started the journey with us about three years ago, and fast forward today, we're builds back on every new machine that gets put into those plants for fuel tank inspection. So they know unequivocally, every single product that they ship out the door is of the highest quality standard and if it's not. If something happens, now they have complete traceability on everything they've made, so they can figure out exactly what went wrong in the process.

Yeahsolutely. I mean that's all we do at Eigen, right. All we do is industrial machine vision for inline quality inspection. So a couple that stick into my mind. Actually very relevant to what you said, sample testing. Lots of manufacturers do this, right if they're a high volume shop or a high volume process. For example, we have some customers that use our technology to inspect upwards of forty thousand units a week per facility. The challenge is if you do find a problem, now you're kind of scratching your head wondering how many in between the last one hundred or last fifty also had a problem right, And unfortunately you tend to find out the hard way when you get returns or warranty claims that maybe something wasn't right in that process. And so technology is a great way, especially the arena that we operate in computer vision, we're helping customers actually get away from that. A great example is one of our manufacturing customers who makes fuel tanks for a variety of different vehicles, and they do what's called destructive testing. They don't just test, they actually break the fuel tank, that cut it up, and they look at all of the plastic components inside and they see was it molded correctly, was it welded correctly? And if they have a problem, well, now they have to reverse engineer a whole bunch of stuff and try to figure out, holy cow, what went wrong right? And how do we ensure that no bad fuel tank gets on a truck. They started the journey with us about three years ago, and fast forward today, we're builds back on every new machine that gets put into those plants for fuel tank inspection. So they know unequivocally, every single product that they ship out the door is of the highest quality standard and if it's not. If something happens, now they have complete traceability on everything they've made, so they can figure out exactly what went wrong in the process.

What John is talking about here is the output of the manufacturing process. How can we ensure every fuel tank that leaves the plant will work as designed? Just as importantly, we need to consider the quality of the input components. Everything from the greater steel to the precision of the fuel gate, These need to be expected to ensure that these are up to the manufacturer standard. I asked John for his thoughts about this.

What John is talking about here is the output of the manufacturing process. How can we ensure every fuel tank that leaves the plant will work as designed? Just as importantly, we need to consider the quality of the input components. Everything from the greater steel to the precision of the fuel gate, These need to be expected to ensure that these are up to the manufacturer standard. I asked John for his thoughts about this.

We don't often look at raw material although it's possible in some cases, but more often than not our inputs that we're looking at it's actually process inputs or parameters. So we're looking at feed rates of raw materials, temperatures of raw materials, things like this. In the process that become more of a scientific look of what's happening on the assembly line and ensuring that everything is inspect We don't just look at the output of you know, did you make a good or bad product? But we'll actually show you all of the process data that went into making that product. The other side is on the discrete world where you're actually assembling things. In this instance, what we do is we'll actually monitor the assembly, so we'll look at how people are placing door panels into a doorframe for example, on to motif asset, or look at tail lamps for lighting purposes right the way that they're assembled and put together. And what we can do in real time is tell folks, hey, what you're putting together is misconfigured, or it's missing components, or it has too many components. Those are defect types that are pretty common in the assembly world.

We don't often look at raw material although it's possible in some cases, but more often than not our inputs that we're looking at it's actually process inputs or parameters. So we're looking at feed rates of raw materials, temperatures of raw materials, things like this. In the process that become more of a scientific look of what's happening on the assembly line and ensuring that everything is inspect We don't just look at the output of you know, did you make a good or bad product? But we'll actually show you all of the process data that went into making that product. The other side is on the discrete world where you're actually assembling things. In this instance, what we do is we'll actually monitor the assembly, so we'll look at how people are placing door panels into a doorframe for example, on to motif asset, or look at tail lamps for lighting purposes right the way that they're assembled and put together. And what we can do in real time is tell folks, hey, what you're putting together is misconfigured, or it's missing components, or it has too many components. Those are defect types that are pretty common in the assembly world.

And what are some of the technology that is used for that? Is it vision? Is it sensors? Is it combination?

And what are some of the technology that is used for that? Is it vision? Is it sensors? Is it combination?

Everything we do is vision based, so we don't make cameras. By the way, we are a software provider, we also act as a system integrator, so a large part of our business is actually delivering turnkey solutions, not just the software. But we don't make hardware, which is actually really cool for us because that means we get to use tons of different types of options that are available for our customers and it helps us really find the perfect design and configuration that is definitely going to solve problems, and so having the flexibility is really nice, and of course that's a large and why we partner with Intel. We're built on the open Veno tech stack, and that means we can run our software really on any device that leverages an Intel chip, which gives us tons of options for deployments. What's really cool about this though, from a quality perspective, is that it means you now have one vision system that can integrate with different types of sensors. So if you want to do, say an optical inspection for surface defects like scratches and dents, but you also want to look at perhaps inside that product in a thermal application, if it's a molded part or something like that, Well you can look at all those different types of sensor in one easier to use screen, right, So it removes the headache of having to have five six different vision systems to do a variety of inspections.

Everything we do is vision based, so we don't make cameras. By the way, we are a software provider, we also act as a system integrator, so a large part of our business is actually delivering turnkey solutions, not just the software. But we don't make hardware, which is actually really cool for us because that means we get to use tons of different types of options that are available for our customers and it helps us really find the perfect design and configuration that is definitely going to solve problems, and so having the flexibility is really nice, and of course that's a large and why we partner with Intel. We're built on the open Veno tech stack, and that means we can run our software really on any device that leverages an Intel chip, which gives us tons of options for deployments. What's really cool about this though, from a quality perspective, is that it means you now have one vision system that can integrate with different types of sensors. So if you want to do, say an optical inspection for surface defects like scratches and dents, but you also want to look at perhaps inside that product in a thermal application, if it's a molded part or something like that, Well you can look at all those different types of sensor in one easier to use screen, right, So it removes the headache of having to have five six different vision systems to do a variety of inspections.

And I'm also interested in the deployment of these sorts of new technologies. I'd like to get your thoughts and experiences around what's some of the tips and tricks for people out there trying to deploy not just for manufacturing quality, but technology and AI in general into a workforce that maybe is a little bit hesitant.

And I'm also interested in the deployment of these sorts of new technologies. I'd like to get your thoughts and experiences around what's some of the tips and tricks for people out there trying to deploy not just for manufacturing quality, but technology and AI in general into a workforce that maybe is a little bit hesitant.

Yeah, humans don't like change, that's for sure. I know I don't. I'm guilty of that. And it's certainly like that when you go into a factory and you've got folks that have been on the same line or in the same steel plant for twenty five thirty years, and you show up and you've got this bright, new shiny software and you say, hey, don't worry, data is going to solve everything. Naturally, people can be quite apprehensive. We don't often run into technology challenges anymore now, it's really we run into people challenges and organizational challenges. So first and foremost, I'll give the advice that I give on most of the times I'm asked this question. But it's so true. Is you don't ever start adopting technology just for the sake of adopting it, just because competitors are using something, or just because somebody way up the chain says, hey, we need an AI strategy. Go invest in AI boom, spend some time and really think about the problems that you're trying to tackle in my world, in the quality world, in manufacturing, it's looking at things you can do to increase yields, increase your throughput, reduce your waste, reduce your rework, and ultimately lower what's called the cost of quality. Start with that, find a way that you can or process that you can optimize by using some of this newer technology, and then of course do a cost assessment or a return on your investment analysis, and ensure that the business justification is there. My experience, that's where a lot of these projects fall short, and where folks get stuck in these pilots and pocs is because they get really excited to try something, but there is no proven business value or business justification behind it, and naturally then you don't get the executive sponsorship you need, your budget falls through, and the project goes nowhere.

Yeah, humans don't like change, that's for sure. I know I don't. I'm guilty of that. And it's certainly like that when you go into a factory and you've got folks that have been on the same line or in the same steel plant for twenty five thirty years, and you show up and you've got this bright, new shiny software and you say, hey, don't worry, data is going to solve everything. Naturally, people can be quite apprehensive. We don't often run into technology challenges anymore now, it's really we run into people challenges and organizational challenges. So first and foremost, I'll give the advice that I give on most of the times I'm asked this question. But it's so true. Is you don't ever start adopting technology just for the sake of adopting it, just because competitors are using something, or just because somebody way up the chain says, hey, we need an AI strategy. Go invest in AI boom, spend some time and really think about the problems that you're trying to tackle in my world, in the quality world, in manufacturing, it's looking at things you can do to increase yields, increase your throughput, reduce your waste, reduce your rework, and ultimately lower what's called the cost of quality. Start with that, find a way that you can or process that you can optimize by using some of this newer technology, and then of course do a cost assessment or a return on your investment analysis, and ensure that the business justification is there. My experience, that's where a lot of these projects fall short, and where folks get stuck in these pilots and pocs is because they get really excited to try something, but there is no proven business value or business justification behind it, and naturally then you don't get the executive sponsorship you need, your budget falls through, and the project goes nowhere.

And in your experience, what industries do you find actually a little bit more advanced in terms of adopting these new technology both on a technical level but also at an organizational level that it seems like the teams are actually involved and successfully deploying these sorts of techniques.

And in your experience, what industries do you find actually a little bit more advanced in terms of adopting these new technology both on a technical level but also at an organizational level that it seems like the teams are actually involved and successfully deploying these sorts of techniques.

Yeah, that's a great question. We see pretty advanced deployments in the automotive world as far as discrete manufacturing goes, they tend to be far ahead of the curve compared to say, steel manufacturers or something like that, or concrete manufacturers. There's a lot of very advanced technology and those automotive facilities that make sure what you buy is actually perfect. Similarly, in the process world, pharmaceuticals tends to be on the continuous process side that tends to be pretty advanced. They have a lot of vision systems in place looking at the vaccine vials to ensure the integrity of vile caps and seals and things like that. Some of the laggers would be metals, some of the plastics organizations. But there's also a kind of a bigger dynamic in manufacturing that I think folks don't really understand that also contributes to who's advanced and who's which is the sheer size of these organizations, right, Manufacturers are not all large. Folks tend to think about John Deere and three M and you know, the largest players in the world, and the reality is that makes up such a small fraction of the manufacturing pool, especially in America, most manufacturing facilities have you know, twenty people or less. Small to medium manufacturers anywhere from say like the twenty to two hundred range of employees. That's who makes up the vast majority of our products. Even when you buy something really big, you know, whether it's a whirlpool dishwasher or a hot tub or whatever it might be, all those little components that make up that consumer good, Well, it came from probably many different suppliers, and most of those are small.

Yeah, that's a great question. We see pretty advanced deployments in the automotive world as far as discrete manufacturing goes, they tend to be far ahead of the curve compared to say, steel manufacturers or something like that, or concrete manufacturers. There's a lot of very advanced technology and those automotive facilities that make sure what you buy is actually perfect. Similarly, in the process world, pharmaceuticals tends to be on the continuous process side that tends to be pretty advanced. They have a lot of vision systems in place looking at the vaccine vials to ensure the integrity of vile caps and seals and things like that. Some of the laggers would be metals, some of the plastics organizations. But there's also a kind of a bigger dynamic in manufacturing that I think folks don't really understand that also contributes to who's advanced and who's which is the sheer size of these organizations, right, Manufacturers are not all large. Folks tend to think about John Deere and three M and you know, the largest players in the world, and the reality is that makes up such a small fraction of the manufacturing pool, especially in America, most manufacturing facilities have you know, twenty people or less. Small to medium manufacturers anywhere from say like the twenty to two hundred range of employees. That's who makes up the vast majority of our products. Even when you buy something really big, you know, whether it's a whirlpool dishwasher or a hot tub or whatever it might be, all those little components that make up that consumer good, Well, it came from probably many different suppliers, and most of those are small.

It's nice that you mentioned that because my father has a small manufacturing facility here. And just to talk a little bit more of the technology stack that you're using with open Vino and Intel's edge devices. I'm really interested to see how some of the smaller guys can actually use this sort of technology so that it can actually be more competitive.

It's nice that you mentioned that because my father has a small manufacturing facility here. And just to talk a little bit more of the technology stack that you're using with open Vino and Intel's edge devices. I'm really interested to see how some of the smaller guys can actually use this sort of technology so that it can actually be more competitive.

Sure, well, leveraging open Veno helps us have a real wide range of how on the hardware side, how we can install our software. What that means for smaller manufacturers is that we can be quite flexible in the design of a system and can accommodate just about any budget, which that alone is pretty significant to understand. I still think there's a misconception that it's too expensive or too cumbersome for the little guys, so to speak to really innovate in their plants, and it's simply not true. You know, we have customers that make as little as twenty parts of shift, and even for them, having the flexibility of how we design and configure these systems, it ensures that even they can embrace newer technology and provide the highest amounts of quality to their customers.

Sure, well, leveraging open Veno helps us have a real wide range of how on the hardware side, how we can install our software. What that means for smaller manufacturers is that we can be quite flexible in the design of a system and can accommodate just about any budget, which that alone is pretty significant to understand. I still think there's a misconception that it's too expensive or too cumbersome for the little guys, so to speak to really innovate in their plants, and it's simply not true. You know, we have customers that make as little as twenty parts of shift, and even for them, having the flexibility of how we design and configure these systems, it ensures that even they can embrace newer technology and provide the highest amounts of quality to their customers.

Part of the reason I can can design and configure of those systems is because the company uses Intel's central processing units or CPUs, as opposed to GPUs or graphics processing units. GPUs are specialized processes are originedly designed to accelerate graphics rendering. The key difference in the manufacturing world is that CPUs, like the ones Intel provides for Ogen, are able to perform under harsher or hotter conditions like the ones you might find in a factory or manufacturing plant. GPUs, meanwhile, are prone to overhitting without the use of a fan to cool it down, and most factories won't use fans so they can avoid spreading dust and debris. There's always a trade off between designing software optimized for CPUs or GPUs and a manufacturing plant. I asked John about this, and I found his answer to be quite illuminating.

Part of the reason I can can design and configure of those systems is because the company uses Intel's central processing units or CPUs, as opposed to GPUs or graphics processing units. GPUs are specialized processes are originedly designed to accelerate graphics rendering. The key difference in the manufacturing world is that CPUs, like the ones Intel provides for Ogen, are able to perform under harsher or hotter conditions like the ones you might find in a factory or manufacturing plant. GPUs, meanwhile, are prone to overhitting without the use of a fan to cool it down, and most factories won't use fans so they can avoid spreading dust and debris. There's always a trade off between designing software optimized for CPUs or GPUs and a manufacturing plant. I asked John about this, and I found his answer to be quite illuminating.

It's always an interesting discussion when people ask, why don't you just go on GPUs and what's the real difference? And from a manufacturing perspective, just logically thinking about what happens in a plant. If you remember, like late nineties, you remember you had your COMPAC or your Gateway PC, this big old white box on the floor, and every so often you'd take the front panel off and it would just be totally caked in dust. Right, you'd hear the fans humming, and well, this is what happens to GPUs and factories. This is why we don't use fans, because factories are dirty. There's dust everywhere. And what we found is that when we explored using various types of mediums to do our processing, what we found is that fanless intel boxes were not only just as performant and in some instances probably even more beneficial to use, but on the maintenance side of it, we didn't have to worry about dirt and debris, which exists in every single plant that we deploy these in. We also didn't have to worry about heat. GPUs generate tons of heat. Had this discussion with somebody who did deploy GPUs in a manufacturing environment and they were looking at in tens of millions of dollars in HVAC improvements just to keep the factories cool enough to operate effectively. Right. And then the flexibility, like I mentioned, being able to very easily scale the hardware for more advanced use cases, if we need two or three different edge boxes, it's really easy to do, and also be able to scale down for the smaller applications where we want to make it a bit more cost effective for the smaller manufacturers as well.

It's always an interesting discussion when people ask, why don't you just go on GPUs and what's the real difference? And from a manufacturing perspective, just logically thinking about what happens in a plant. If you remember, like late nineties, you remember you had your COMPAC or your Gateway PC, this big old white box on the floor, and every so often you'd take the front panel off and it would just be totally caked in dust. Right, you'd hear the fans humming, and well, this is what happens to GPUs and factories. This is why we don't use fans, because factories are dirty. There's dust everywhere. And what we found is that when we explored using various types of mediums to do our processing, what we found is that fanless intel boxes were not only just as performant and in some instances probably even more beneficial to use, but on the maintenance side of it, we didn't have to worry about dirt and debris, which exists in every single plant that we deploy these in. We also didn't have to worry about heat. GPUs generate tons of heat. Had this discussion with somebody who did deploy GPUs in a manufacturing environment and they were looking at in tens of millions of dollars in HVAC improvements just to keep the factories cool enough to operate effectively. Right. And then the flexibility, like I mentioned, being able to very easily scale the hardware for more advanced use cases, if we need two or three different edge boxes, it's really easy to do, and also be able to scale down for the smaller applications where we want to make it a bit more cost effective for the smaller manufacturers as well.

Coming up next on Technically Speaking and Intel Podcast.

Coming up next on Technically Speaking and Intel Podcast.

Computer vision specifically for quality is becoming more and more common. I think this will become completely commonplace over the next twelve years.

Computer vision specifically for quality is becoming more and more common. I think this will become completely commonplace over the next twelve years.

We'll be right back after a brief message from our partner.

We'll be right back after a brief message from our partner.

Is that Intel?

Is that Intel?

Welcome back to Technically Speaking an Intel podcast. I'm here now with John Weiss. I'd actually like to get you to talk a little bit about Eigen Innovations, if you could tell us a little bit about the company and its mission.

Welcome back to Technically Speaking an Intel podcast. I'm here now with John Weiss. I'd actually like to get you to talk a little bit about Eigen Innovations, if you could tell us a little bit about the company and its mission.

Sure so, Iigen Innovations has been around for twelve years. We started in academia out of the University of New Brunswick. It was founded by a PhD student and a professor. We started as a system integrator, so we were going into factories actually installing vision systems, and over the course of about a decade, we developed our own software to make our job as a system integrator easier. And about I don't know, two and a half years ago or so, we realized there's actually a ton of value in IP and the software we created, and so we reinvented the company and moved away from leading as a system integrator to actually leading as a software SaaS based company. We really only do one thing. We do inline quality inspection and actually, to be more specific, our specialty thermal applications that leverage AI. So when you think of like injection molding, blow molding, metal welding, plastic welding, void detection, and plastic goods, anything that has a heated process that the human eye can't easily see defects, we do really really well there.

Sure so, Iigen Innovations has been around for twelve years. We started in academia out of the University of New Brunswick. It was founded by a PhD student and a professor. We started as a system integrator, so we were going into factories actually installing vision systems, and over the course of about a decade, we developed our own software to make our job as a system integrator easier. And about I don't know, two and a half years ago or so, we realized there's actually a ton of value in IP and the software we created, and so we reinvented the company and moved away from leading as a system integrator to actually leading as a software SaaS based company. We really only do one thing. We do inline quality inspection and actually, to be more specific, our specialty thermal applications that leverage AI. So when you think of like injection molding, blow molding, metal welding, plastic welding, void detection, and plastic goods, anything that has a heated process that the human eye can't easily see defects, we do really really well there.

And we talked a little bit about AI, and I think we've also talked about the software that utilizes machine vision. Where do you see AI models and the CPU based technology being able to compete with machine vision use cases?

And we talked a little bit about AI, and I think we've also talked about the software that utilizes machine vision. Where do you see AI models and the CPU based technology being able to compete with machine vision use cases?

Yeah, it's a good question. Look, I think there are pros and cons of both approaches. We actually have not yet come across a project that we had any kind of processing limitation on being CPU based. We have applications in production running yet thirty inferences a second across cameras. Right, that's quite quite fast. There are definitely higher demand applications. But in our world of process in discrete manufacturing and the types of projects we typically focus on, speed has actually not been a problem for us with CPUs, even at quite aggressive speed. I see the tools getting easier and easier to use, more and more self service, if you will. Years ago, we had this phrase of democratizing data if you remember that, around the days of big data, kind of empowering everybody to be a data scientist, and I see the same movement happening in the AI world. In fact, actually we're a good example of that. You can use our tool to build deploy train models across factories and you don't have to touch a line of code. So I think that's the future. I think the tools get easier and easier to use, so that my good friend Jimmy, who's down in Texas at one of our customer plants, who's been in that same plant for over thirty years, that he can blow me away with how he can build a model that does thermal inspection on metal welding. And years ago, oh, somebody that didn't have that kind of training from a data science perspective or a programming perspective, they would never be able to do that. And today they're building dashboards and building models that are literally redefining the way these manufacturers operate. It's amazing.

Yeah, it's a good question. Look, I think there are pros and cons of both approaches. We actually have not yet come across a project that we had any kind of processing limitation on being CPU based. We have applications in production running yet thirty inferences a second across cameras. Right, that's quite quite fast. There are definitely higher demand applications. But in our world of process in discrete manufacturing and the types of projects we typically focus on, speed has actually not been a problem for us with CPUs, even at quite aggressive speed. I see the tools getting easier and easier to use, more and more self service, if you will. Years ago, we had this phrase of democratizing data if you remember that, around the days of big data, kind of empowering everybody to be a data scientist, and I see the same movement happening in the AI world. In fact, actually we're a good example of that. You can use our tool to build deploy train models across factories and you don't have to touch a line of code. So I think that's the future. I think the tools get easier and easier to use, so that my good friend Jimmy, who's down in Texas at one of our customer plants, who's been in that same plant for over thirty years, that he can blow me away with how he can build a model that does thermal inspection on metal welding. And years ago, oh, somebody that didn't have that kind of training from a data science perspective or a programming perspective, they would never be able to do that. And today they're building dashboards and building models that are literally redefining the way these manufacturers operate. It's amazing.

You heard John say earlier that eigen has been around for more than a decade and this technology has been implemented across a variety of manufacturing spaces to thermally inspect items like metal paper, cardboard, box adhesive, automotive windshields, and high glass plastics. With such a lengthy track record of achievements, John spoke about one specific company success story that stuck out for him.

You heard John say earlier that eigen has been around for more than a decade and this technology has been implemented across a variety of manufacturing spaces to thermally inspect items like metal paper, cardboard, box adhesive, automotive windshields, and high glass plastics. With such a lengthy track record of achievements, John spoke about one specific company success story that stuck out for him.

A couple that come to mind. I mentioned we inference about thirty images per second in this one process. This is a paper process, so it's continuous, very high speed, and it's for a high glass specialty paper. And what happens is this high glass coding goes on paper very rapidly as it's going down the line, and unfortunately there's a problem where this coding can build up and if it's not caught in about eight seconds, it will do roughly one hundred and twenty thousand dollars worth of damage to the equipment. This can happen multiple times as shift. This is a very expensive problem if it's not caught. And so this one's a great example of a thermal application. It's a heated coating where we look at that we inference, like I mentioned about thirty images a second, and in just about one second, we look at all of those images, we make a determination is there a problem or not, is it good or is it bad? And we actually do close loop automation as well. We'll send a signal back there and trigger a stoppage on the line to avoid equipment failure. All of that happens in less than one second. So that's a really good example of speed. Another good example, I'll give you just one more in the interest of time, how we can help see things that folks can't see. Well, I mentioned fuel tanks, and I mentioned some plastic components and things like that earlier. Naturally we use thermal vision for that humans can't see. In thermal patterns of course, so we're able to show quality engineers inconsistencies in the product that they would never be able to see with the human eyes. One of our customers manufacturers the front plates for a dishwasher company, very large dishwasher manufacturer. And so if you've recently gotten a new appliance, you probably remember you had to peel all that film off, right. Well, what you might not know is that film is on from the raw material phase and what happens is as it goes down the process, it gets stamped like a cookie cutter. But that film is on it the whole time to protect it. So what's really tough is for the quality engineers to actually see through the blue film or whatever tint it might be, to see if there's a scratcher dent. And so this is one problem we solved for one of our customers where they were missing the dents, they were missing the scratches because the humans simply couldn't see through the protective film. Fast forward to today again, another customer that inspects one hundred percent of their production on our tooling and gives them indicators in real time through that blue film if they have any kind of service defect.

A couple that come to mind. I mentioned we inference about thirty images per second in this one process. This is a paper process, so it's continuous, very high speed, and it's for a high glass specialty paper. And what happens is this high glass coding goes on paper very rapidly as it's going down the line, and unfortunately there's a problem where this coding can build up and if it's not caught in about eight seconds, it will do roughly one hundred and twenty thousand dollars worth of damage to the equipment. This can happen multiple times as shift. This is a very expensive problem if it's not caught. And so this one's a great example of a thermal application. It's a heated coating where we look at that we inference, like I mentioned about thirty images a second, and in just about one second, we look at all of those images, we make a determination is there a problem or not, is it good or is it bad? And we actually do close loop automation as well. We'll send a signal back there and trigger a stoppage on the line to avoid equipment failure. All of that happens in less than one second. So that's a really good example of speed. Another good example, I'll give you just one more in the interest of time, how we can help see things that folks can't see. Well, I mentioned fuel tanks, and I mentioned some plastic components and things like that earlier. Naturally we use thermal vision for that humans can't see. In thermal patterns of course, so we're able to show quality engineers inconsistencies in the product that they would never be able to see with the human eyes. One of our customers manufacturers the front plates for a dishwasher company, very large dishwasher manufacturer. And so if you've recently gotten a new appliance, you probably remember you had to peel all that film off, right. Well, what you might not know is that film is on from the raw material phase and what happens is as it goes down the process, it gets stamped like a cookie cutter. But that film is on it the whole time to protect it. So what's really tough is for the quality engineers to actually see through the blue film or whatever tint it might be, to see if there's a scratcher dent. And so this is one problem we solved for one of our customers where they were missing the dents, they were missing the scratches because the humans simply couldn't see through the protective film. Fast forward to today again, another customer that inspects one hundred percent of their production on our tooling and gives them indicators in real time through that blue film if they have any kind of service defect.

And you've talked a little bit about the journey twelve years ago to now. I want to get you to cast your mind ahead twelve years in the future. Where do you think Igen will be and in general, where do you think manufacturing and quality control technology will be in the next twelve years.

And you've talked a little bit about the journey twelve years ago to now. I want to get you to cast your mind ahead twelve years in the future. Where do you think Igen will be and in general, where do you think manufacturing and quality control technology will be in the next twelve years.

That's a pretty far horizon. I don't even know if I could guess the next twelve months, to be honest with you, just because the industry moves so fast. But let's say over the course of the next decade, I would definitely see some of the more innovative technologies becoming mainstream. So computer vision, there's no doubt about it. Computer vision specifically for quality is becoming more and more common. I think this will become completely commonplace over the next twelve years.

That's a pretty far horizon. I don't even know if I could guess the next twelve months, to be honest with you, just because the industry moves so fast. But let's say over the course of the next decade, I would definitely see some of the more innovative technologies becoming mainstream. So computer vision, there's no doubt about it. Computer vision specifically for quality is becoming more and more common. I think this will become completely commonplace over the next twelve years.

Often ask this of our guess, but if you could have AI solve one thing in your field that is manufacturing, what would it be.

Often ask this of our guess, but if you could have AI solve one thing in your field that is manufacturing, what would it be.

I would like to use AI to clone the entire Eigen team, because these are some of the most talented people I've ever worked with, and I just need like three to four times more of them so I can go take over the world.

I would like to use AI to clone the entire Eigen team, because these are some of the most talented people I've ever worked with, and I just need like three to four times more of them so I can go take over the world.

Yeah. Well, we did have an episode on digital twins and have a human digital twin, so yeah, you never know. With that. I'll leave it there. Thank you John for your time.

Yeah. Well, we did have an episode on digital twins and have a human digital twin, so yeah, you never know. With that. I'll leave it there. Thank you John for your time.

Well, thank you, this was great. Thanks for having me.

Well, thank you, this was great. Thanks for having me.

Thank you to John Weiss for his quality insights in today's episode of Technically Speaking.

Thank you to John Weiss for his quality insights in today's episode of Technically Speaking.

In a world where we.

In a world where we.

Are somewhat preoccupied with virtual and digital goods, I love hearing stories about the production of real world physical products. I think we take for granted how much time, effort, and brain power it takes not only to conceive of new products, but to design the whole manufacturing process and get them into the hands of you, the customer. John highlighted that quality is now non negotiable for consumers and that manufacturers need to continually reinvent the new technology and methods to keep producing high quality products as economically as possible. A common theme in all of our episodes, and one that I'm always exploring, is whether these new advances in AI, like the machine and computer vision discussed today, will help all businesses, regardless of size. So it's pleasing to hear John say that their technology can help the smaller niche manufacturers to use the same quality control software and hardware that the big players have. This is why I'm so bullish about AI and technology in general, the ability to lift all people and businesses up, no matter what stage of life they are in. In our next episode, we will look at how we can close the AI workforce gap through education. So join us on July second for the next edition of Technically Speaking and Intel podcast. Technically Speaking was produced by Ruby Studio from iHeartRadio in partnership with Intel, and hosted by me Class. Our executive producer is Molly Sosher, our EP of Post Production is James Foster, and our supervising producer is Nika Swinton. This episode was edited by Sierra Spreen and written by Nick Firshall.

Are somewhat preoccupied with virtual and digital goods, I love hearing stories about the production of real world physical products. I think we take for granted how much time, effort, and brain power it takes not only to conceive of new products, but to design the whole manufacturing process and get them into the hands of you, the customer. John highlighted that quality is now non negotiable for consumers and that manufacturers need to continually reinvent the new technology and methods to keep producing high quality products as economically as possible. A common theme in all of our episodes, and one that I'm always exploring, is whether these new advances in AI, like the machine and computer vision discussed today, will help all businesses, regardless of size. So it's pleasing to hear John say that their technology can help the smaller niche manufacturers to use the same quality control software and hardware that the big players have. This is why I'm so bullish about AI and technology in general, the ability to lift all people and businesses up, no matter what stage of life they are in. In our next episode, we will look at how we can close the AI workforce gap through education. So join us on July second for the next edition of Technically Speaking and Intel podcast. Technically Speaking was produced by Ruby Studio from iHeartRadio in partnership with Intel, and hosted by me Class. Our executive producer is Molly Sosher, our EP of Post Production is James Foster, and our supervising producer is Nika Swinton. This episode was edited by Sierra Spreen and written by Nick Firshall.