Speaker 1: Take a second to think about every single item in
Speaker 1: your home. Your television, your refrigerator, your desk, lamp, your laptop,
Speaker 1: even the smartphone you might be using to hear my
Speaker 1: voice right now. All of these things, and so many
Speaker 1: more items in our lives, began in a factory. There
Speaker 1: are more than six hundred and twenty thousand manufacturing businesses
Speaker 1: in the United States right now, responsible for nearly twelve
Speaker 1: percent of the total US economic output. The numbers are
Speaker 1: even more staggering in China, which makes up nearly twenty
Speaker 1: nine percent of the total global output. For manufacturing. Factories
Speaker 1: have been around since the late eighteenth century, and today
Speaker 1: they're used everywhere from South Korea to southern California to
Speaker 1: make cars, airplanes, textiles, and even space vehicles, and each
Speaker 1: one depends on a carefully choreographed system of steps, each
Speaker 1: one as essential as the next before the final product
Speaker 1: rolls off the production line. Mistakes, however, are also an
Speaker 1: unavoidable part of this process. Manufacturers simply can't check every
Speaker 1: piece of every product, and it's nearly impossible to achieve
Speaker 1: perfection when some manufacturing plants produce thousands of items a day.
Speaker 1: So how can technology help an industry so crucial to
Speaker 1: our daily lives, how can factories use AI to reduce
Speaker 1: and even prevent defective products? Welcome to Technically Speaking, an
Speaker 1: Intel podcast produced by iHeartMedia's Ruby Studio in partnership with Intel.
Speaker 1: In every episode, we explore how AI innovations are changing
Speaker 1: the world and revolutionizing the way we live. Hey there,
Speaker 1: I'm gram class, and today we're headed into the world
Speaker 1: of manufacturing, an expansive and essential industry that drives the
Speaker 1: global economy and both the history dating back nearly two
Speaker 1: hundred and fifty years, we've seen manufacturing create a revolution,
Speaker 1: resurrect nation's economies, connect people around the globe, and even
Speaker 1: send mankind into space. But what's next at the intersection
Speaker 1: of manufacturing and technology. In this episode, we'll be focusing
Speaker 1: on how AI technology can help optimize manufacturing and improve
Speaker 1: quality thanks to no small part to the minds at
Speaker 1: Intel and at Eigen Innovations, a company committed to helping
Speaker 1: organizations unlock the power of machine vision to automate quality inspections.
Speaker 1: Before we go any further, let's welcome our guest joining
Speaker 1: us today is John Weiss, the chief revenue officer at
Speaker 1: Eigen innovations. John oversees all revenue generation activities at Eigen,
Speaker 1: including driving sales in Eigen's machine vision software and engineering services.
Speaker 2: Welcome to the show, John, thanks for having me. Graham's
Speaker 2: great to be here.
Speaker 1: Let's start with a bit of background on manufacturing and
Speaker 1: the role it plays in our society. I mean it's
Speaker 1: fair to say that I phone, our car, laptop, even
Speaker 1: the food we eat involves some sort of manufacturing process.
Speaker 1: I'd like to get your thoughts on just the importance
Speaker 1: and scale of manufacturing plants around the world.
Speaker 2: Yeah, sure, Well, like you said, just about everything in
Speaker 2: our daily lives comes from factories or plants. But sure,
Speaker 2: depending on if you commute on a train or in
Speaker 2: a car, lots of those components are coming from factories.
Speaker 2: Very little these days are really kind of hand crafted
Speaker 2: and handmade and smile batch, especially large scale consumer items.
Speaker 2: And there's many different types of processes and many different
Speaker 2: types of ways things are made.
Speaker 1: And look, I know there's a multitude of ways and
Speaker 1: types of manufacturing processes. Like a Volkswagen built in Germany
Speaker 1: is going to be very different from an iPhone built
Speaker 1: in China. Do you find common things, reads or similarities
Speaker 1: across manufacturing industries.
Speaker 2: Yeah, definitely, so, I guess maybe as a kind of
Speaker 2: a starting point, it's important to understand there are two
Speaker 2: types of manufacturing processes or approaches. One is called process manufacturing.
Speaker 2: This is things like chemicals, plastics, things that can't really
Speaker 2: be broken down or deconstructed easily. Or you have discrete manufacturing,
Speaker 2: which is much more of the process of putting stuff together.
Speaker 2: Think about a watch or a car. Now, both of
Speaker 2: those processes, discrete and process manufacturing, they're quite different, but
Speaker 2: there are certainly similarities. And between the two methods, you
Speaker 2: basically have all of the things that we use every day,
Speaker 2: right then oftentimes actually they kind of bleed into one another.
Speaker 2: Most things have a little bit of process manufacturing involved
Speaker 2: and then a little bit of discrete manufacturing as well. However,
Speaker 2: I would say the commonalities across both are really heavily
Speaker 2: reliant on technology. We see a very large push for
Speaker 2: data driven decision making. We see large patterns or trends
Speaker 2: in both realms of manufacturing around empowering the workforce, trying
Speaker 2: to opt skill workers via technology to get them to
Speaker 2: be focused on more mission critical tasks or higher value
Speaker 2: activities while letting some of the technology do more of
Speaker 2: the mundane tasks.
Speaker 1: Yeah. In a previous job that I had, we were
Speaker 1: doing consumer electronics, and we struggled quite a bit with
Speaker 1: the quality side of things and being able to ensure
Speaker 1: a good product can you manufactured in our China plant.
Speaker 1: And one thing that struck me was that there was
Speaker 1: a very manual process in terms of the quality inspection,
Speaker 1: and it will take samples in one out of every
Speaker 1: ten and the test that and then if that worked,
Speaker 1: then okay, and then we assume the rest kind of work.
Speaker 1: I'm wondering if you could share any stories or examples
Speaker 1: of I guess problems with quality or defective products that
Speaker 1: stick in your mind.
Speaker 2: Yeahsolutely. I mean that's all we do at Eigen, right.
Speaker 2: All we do is industrial machine vision for inline quality inspection.
Speaker 2: So a couple that stick into my mind. Actually very
Speaker 2: relevant to what you said, sample testing. Lots of manufacturers
Speaker 2: do this, right if they're a high volume shop or
Speaker 2: a high volume process. For example, we have some customers
Speaker 2: that use our technology to inspect upwards of forty thousand
Speaker 2: units a week per facility. The challenge is if you
Speaker 2: do find a problem, now you're kind of scratching your
Speaker 2: head wondering how many in between the last one hundred
Speaker 2: or last fifty also had a problem right, And unfortunately
Speaker 2: you tend to find out the hard way when you
Speaker 2: get returns or warranty claims that maybe something wasn't right
Speaker 2: in that process. And so technology is a great way,
Speaker 2: especially the arena that we operate in computer vision, we're
Speaker 2: helping customers actually get away from that. A great example
Speaker 2: is one of our manufacturing customers who makes fuel tanks
Speaker 2: for a variety of different vehicles, and they do what's
Speaker 2: called destructive testing. They don't just test, they actually break
Speaker 2: the fuel tank, that cut it up, and they look
Speaker 2: at all of the plastic components inside and they see
Speaker 2: was it molded correctly, was it welded correctly? And if
Speaker 2: they have a problem, well, now they have to reverse
Speaker 2: engineer a whole bunch of stuff and try to figure out,
Speaker 2: holy cow, what went wrong right? And how do we
Speaker 2: ensure that no bad fuel tank gets on a truck.
Speaker 2: They started the journey with us about three years ago,
Speaker 2: and fast forward today, we're builds back on every new
Speaker 2: machine that gets put into those plants for fuel tank inspection.
Speaker 2: So they know unequivocally, every single product that they ship
Speaker 2: out the door is of the highest quality standard and
Speaker 2: if it's not. If something happens, now they have complete
Speaker 2: traceability on everything they've made, so they can figure out
Speaker 2: exactly what went wrong in the process.
Speaker 1: What John is talking about here is the output of
Speaker 1: the manufacturing process. How can we ensure every fuel tank
Speaker 1: that leaves the plant will work as designed? Just as importantly,
Speaker 1: we need to consider the quality of the input components.
Speaker 1: Everything from the greater steel to the precision of the
Speaker 1: fuel gate, These need to be expected to ensure that
Speaker 1: these are up to the manufacturer standard. I asked John
Speaker 1: for his thoughts about this.
Speaker 2: We don't often look at raw material although it's possible
Speaker 2: in some cases, but more often than not our inputs
Speaker 2: that we're looking at it's actually process inputs or parameters.
Speaker 2: So we're looking at feed rates of raw materials, temperatures
Speaker 2: of raw materials, things like this. In the process that
Speaker 2: become more of a scientific look of what's happening on
Speaker 2: the assembly line and ensuring that everything is inspect We
Speaker 2: don't just look at the output of you know, did
Speaker 2: you make a good or bad product? But we'll actually
Speaker 2: show you all of the process data that went into
Speaker 2: making that product. The other side is on the discrete
Speaker 2: world where you're actually assembling things. In this instance, what
Speaker 2: we do is we'll actually monitor the assembly, so we'll
Speaker 2: look at how people are placing door panels into a
Speaker 2: doorframe for example, on to motif asset, or look at
Speaker 2: tail lamps for lighting purposes right the way that they're
Speaker 2: assembled and put together. And what we can do in
Speaker 2: real time is tell folks, hey, what you're putting together
Speaker 2: is misconfigured, or it's missing components, or it has too
Speaker 2: many components. Those are defect types that are pretty common
Speaker 2: in the assembly world.
Speaker 1: And what are some of the technology that is used
Speaker 1: for that? Is it vision? Is it sensors? Is it combination?
Speaker 2: Everything we do is vision based, so we don't make cameras.
Speaker 2: By the way, we are a software provider, we also
Speaker 2: act as a system integrator, so a large part of
Speaker 2: our business is actually delivering turnkey solutions, not just the software.
Speaker 2: But we don't make hardware, which is actually really cool
Speaker 2: for us because that means we get to use tons
Speaker 2: of different types of options that are available for our
Speaker 2: customers and it helps us really find the perfect design
Speaker 2: and configuration that is definitely going to solve problems, and
Speaker 2: so having the flexibility is really nice, and of course
Speaker 2: that's a large and why we partner with Intel. We're
Speaker 2: built on the open Veno tech stack, and that means
Speaker 2: we can run our software really on any device that
Speaker 2: leverages an Intel chip, which gives us tons of options
Speaker 2: for deployments. What's really cool about this though, from a
Speaker 2: quality perspective, is that it means you now have one
Speaker 2: vision system that can integrate with different types of sensors.
Speaker 2: So if you want to do, say an optical inspection
Speaker 2: for surface defects like scratches and dents, but you also
Speaker 2: want to look at perhaps inside that product in a
Speaker 2: thermal application, if it's a molded part or something like that,
Speaker 2: Well you can look at all those different types of
Speaker 2: sensor in one easier to use screen, right, So it
Speaker 2: removes the headache of having to have five six different
Speaker 2: vision systems to do a variety of inspections.
Speaker 1: And I'm also interested in the deployment of these sorts
Speaker 1: of new technologies. I'd like to get your thoughts and
Speaker 1: experiences around what's some of the tips and tricks for
Speaker 1: people out there trying to deploy not just for manufacturing quality,
Speaker 1: but technology and AI in general into a workforce that
Speaker 1: maybe is a little bit hesitant.
Speaker 2: Yeah, humans don't like change, that's for sure. I know
Speaker 2: I don't. I'm guilty of that. And it's certainly like
Speaker 2: that when you go into a factory and you've got
Speaker 2: folks that have been on the same line or in
Speaker 2: the same steel plant for twenty five thirty years, and
Speaker 2: you show up and you've got this bright, new shiny
Speaker 2: software and you say, hey, don't worry, data is going
Speaker 2: to solve everything. Naturally, people can be quite apprehensive. We
Speaker 2: don't often run into technology challenges anymore now, it's really
Speaker 2: we run into people challenges and organizational challenges. So first
Speaker 2: and foremost, I'll give the advice that I give on
Speaker 2: most of the times I'm asked this question. But it's
Speaker 2: so true. Is you don't ever start adopting technology just
Speaker 2: for the sake of adopting it, just because competitors are
Speaker 2: using something, or just because somebody way up the chain says, hey,
Speaker 2: we need an AI strategy. Go invest in AI boom,
Speaker 2: spend some time and really think about the problems that
Speaker 2: you're trying to tackle in my world, in the quality world,
Speaker 2: in manufacturing, it's looking at things you can do to
Speaker 2: increase yields, increase your throughput, reduce your waste, reduce your rework,
Speaker 2: and ultimately lower what's called the cost of quality. Start
Speaker 2: with that, find a way that you can or process
Speaker 2: that you can optimize by using some of this newer technology,
Speaker 2: and then of course do a cost assessment or a
Speaker 2: return on your investment analysis, and ensure that the business
Speaker 2: justification is there. My experience, that's where a lot of
Speaker 2: these projects fall short, and where folks get stuck in
Speaker 2: these pilots and pocs is because they get really excited
Speaker 2: to try something, but there is no proven business value
Speaker 2: or business justification behind it, and naturally then you don't
Speaker 2: get the executive sponsorship you need, your budget falls through,
Speaker 2: and the project goes nowhere.
Speaker 1: And in your experience, what industries do you find actually
Speaker 1: a little bit more advanced in terms of adopting these
Speaker 1: new technology both on a technical level but also at
Speaker 1: an organizational level that it seems like the teams are
Speaker 1: actually involved and successfully deploying these sorts of techniques.
Speaker 2: Yeah, that's a great question. We see pretty advanced deployments
Speaker 2: in the automotive world as far as discrete manufacturing goes,
Speaker 2: they tend to be far ahead of the curve compared
Speaker 2: to say, steel manufacturers or something like that, or concrete manufacturers.
Speaker 2: There's a lot of very advanced technology and those automotive
Speaker 2: facilities that make sure what you buy is actually perfect. Similarly,
Speaker 2: in the process world, pharmaceuticals tends to be on the
Speaker 2: continuous process side that tends to be pretty advanced. They
Speaker 2: have a lot of vision systems in place looking at
Speaker 2: the vaccine vials to ensure the integrity of vile caps
Speaker 2: and seals and things like that. Some of the laggers
Speaker 2: would be metals, some of the plastics organizations. But there's
Speaker 2: also a kind of a bigger dynamic in manufacturing that
Speaker 2: I think folks don't really understand that also contributes to
Speaker 2: who's advanced and who's which is the sheer size of
Speaker 2: these organizations, right, Manufacturers are not all large. Folks tend
Speaker 2: to think about John Deere and three M and you know,
Speaker 2: the largest players in the world, and the reality is
Speaker 2: that makes up such a small fraction of the manufacturing pool,
Speaker 2: especially in America, most manufacturing facilities have you know, twenty
Speaker 2: people or less. Small to medium manufacturers anywhere from say
Speaker 2: like the twenty to two hundred range of employees. That's
Speaker 2: who makes up the vast majority of our products. Even
Speaker 2: when you buy something really big, you know, whether it's
Speaker 2: a whirlpool dishwasher or a hot tub or whatever it
Speaker 2: might be, all those little components that make up that
Speaker 2: consumer good, Well, it came from probably many different suppliers,
Speaker 2: and most of those are small.
Speaker 1: It's nice that you mentioned that because my father has
Speaker 1: a small manufacturing facility here. And just to talk a
Speaker 1: little bit more of the technology stack that you're using
Speaker 1: with open Vino and Intel's edge devices. I'm really interested
Speaker 1: to see how some of the smaller guys can actually
Speaker 1: use this sort of technology so that it can actually
Speaker 1: be more competitive.
Speaker 2: Sure, well, leveraging open Veno helps us have a real
Speaker 2: wide range of how on the hardware side, how we
Speaker 2: can install our software. What that means for smaller manufacturers
Speaker 2: is that we can be quite flexible in the design
Speaker 2: of a system and can accommodate just about any budget,
Speaker 2: which that alone is pretty significant to understand. I still
Speaker 2: think there's a misconception that it's too expensive or too
Speaker 2: cumbersome for the little guys, so to speak to really
Speaker 2: innovate in their plants, and it's simply not true. You know,
Speaker 2: we have customers that make as little as twenty parts
Speaker 2: of shift, and even for them, having the flexibility of
Speaker 2: how we design and configure these systems, it ensures that
Speaker 2: even they can embrace newer technology and provide the highest
Speaker 2: amounts of quality to their customers.
Speaker 1: Part of the reason I can can design and configure
Speaker 1: of those systems is because the company uses Intel's central
Speaker 1: processing units or CPUs, as opposed to GPUs or graphics
Speaker 1: processing units. GPUs are specialized processes are originedly designed to
Speaker 1: accelerate graphics rendering. The key difference in the manufacturing world
Speaker 1: is that CPUs, like the ones Intel provides for Ogen,
Speaker 1: are able to perform under harsher or hotter conditions like
Speaker 1: the ones you might find in a factory or manufacturing plant. GPUs, meanwhile,
Speaker 1: are prone to overhitting without the use of a fan
Speaker 1: to cool it down, and most factories won't use fans
Speaker 1: so they can avoid spreading dust and debris. There's always
Speaker 1: a trade off between designing software optimized for CPUs or
Speaker 1: GPUs and a manufacturing plant. I asked John about this,
Speaker 1: and I found his answer to be quite illuminating.
Speaker 2: It's always an interesting discussion when people ask, why don't
Speaker 2: you just go on GPUs and what's the real difference?
Speaker 2: And from a manufacturing perspective, just logically thinking about what
Speaker 2: happens in a plant. If you remember, like late nineties,
Speaker 2: you remember you had your COMPAC or your Gateway PC,
Speaker 2: this big old white box on the floor, and every
Speaker 2: so often you'd take the front panel off and it
Speaker 2: would just be totally caked in dust. Right, you'd hear
Speaker 2: the fans humming, and well, this is what happens to
Speaker 2: GPUs and factories. This is why we don't use fans,
Speaker 2: because factories are dirty. There's dust everywhere. And what we
Speaker 2: found is that when we explored using various types of
Speaker 2: mediums to do our processing, what we found is that
Speaker 2: fanless intel boxes were not only just as performant and
Speaker 2: in some instances probably even more beneficial to use, but
Speaker 2: on the maintenance side of it, we didn't have to
Speaker 2: worry about dirt and debris, which exists in every single
Speaker 2: plant that we deploy these in. We also didn't have
Speaker 2: to worry about heat. GPUs generate tons of heat. Had
Speaker 2: this discussion with somebody who did deploy GPUs in a
Speaker 2: manufacturing environment and they were looking at in tens of
Speaker 2: millions of dollars in HVAC improvements just to keep the
Speaker 2: factories cool enough to operate effectively. Right. And then the flexibility,
Speaker 2: like I mentioned, being able to very easily scale the
Speaker 2: hardware for more advanced use cases, if we need two
Speaker 2: or three different edge boxes, it's really easy to do,
Speaker 2: and also be able to scale down for the smaller
Speaker 2: applications where we want to make it a bit more
Speaker 2: cost effective for the smaller manufacturers as well.
Speaker 1: Coming up next on Technically Speaking and Intel Podcast.
Speaker 2: Computer vision specifically for quality is becoming more and more common.
Speaker 2: I think this will become completely commonplace over the next
Speaker 2: twelve years.
Speaker 1: We'll be right back after a brief message from our partner.
Speaker 2: Is that Intel?
Speaker 1: Welcome back to Technically Speaking an Intel podcast. I'm here
Speaker 1: now with John Weiss. I'd actually like to get you
Speaker 1: to talk a little bit about Eigen Innovations, if you
Speaker 1: could tell us a little bit about the company and
Speaker 1: its mission.
Speaker 2: Sure so, Iigen Innovations has been around for twelve years.
Speaker 2: We started in academia out of the University of New Brunswick.
Speaker 2: It was founded by a PhD student and a professor.
Speaker 2: We started as a system integrator, so we were going
Speaker 2: into factories actually installing vision systems, and over the course
Speaker 2: of about a decade, we developed our own software to
Speaker 2: make our job as a system integrator easier. And about
Speaker 2: I don't know, two and a half years ago or so,
Speaker 2: we realized there's actually a ton of value in IP
Speaker 2: and the software we created, and so we reinvented the
Speaker 2: company and moved away from leading as a system integrator
Speaker 2: to actually leading as a software SaaS based company. We
Speaker 2: really only do one thing. We do inline quality inspection
Speaker 2: and actually, to be more specific, our specialty thermal applications
Speaker 2: that leverage AI. So when you think of like injection molding,
Speaker 2: blow molding, metal welding, plastic welding, void detection, and plastic goods,
Speaker 2: anything that has a heated process that the human eye
Speaker 2: can't easily see defects, we do really really well there.
Speaker 1: And we talked a little bit about AI, and I
Speaker 1: think we've also talked about the software that utilizes machine vision.
Speaker 1: Where do you see AI models and the CPU based
Speaker 1: technology being able to compete with machine vision use cases?
Speaker 2: Yeah, it's a good question. Look, I think there are
Speaker 2: pros and cons of both approaches. We actually have not
Speaker 2: yet come across a project that we had any kind
Speaker 2: of processing limitation on being CPU based. We have applications
Speaker 2: in production running yet thirty inferences a second across cameras. Right,
Speaker 2: that's quite quite fast. There are definitely higher demand applications.
Speaker 2: But in our world of process in discrete manufacturing and
Speaker 2: the types of projects we typically focus on, speed has
Speaker 2: actually not been a problem for us with CPUs, even
Speaker 2: at quite aggressive speed. I see the tools getting easier
Speaker 2: and easier to use, more and more self service, if
Speaker 2: you will. Years ago, we had this phrase of democratizing
Speaker 2: data if you remember that, around the days of big data,
Speaker 2: kind of empowering everybody to be a data scientist, and
Speaker 2: I see the same movement happening in the AI world.
Speaker 2: In fact, actually we're a good example of that. You
Speaker 2: can use our tool to build deploy train models across
Speaker 2: factories and you don't have to touch a line of code.
Speaker 2: So I think that's the future. I think the tools
Speaker 2: get easier and easier to use, so that my good
Speaker 2: friend Jimmy, who's down in Texas at one of our
Speaker 2: customer plants, who's been in that same plant for over
Speaker 2: thirty years, that he can blow me away with how
Speaker 2: he can build a model that does thermal inspection on
Speaker 2: metal welding. And years ago, oh, somebody that didn't have
Speaker 2: that kind of training from a data science perspective or
Speaker 2: a programming perspective, they would never be able to do that.
Speaker 2: And today they're building dashboards and building models that are
Speaker 2: literally redefining the way these manufacturers operate. It's amazing.
Speaker 1: You heard John say earlier that eigen has been around
Speaker 1: for more than a decade and this technology has been
Speaker 1: implemented across a variety of manufacturing spaces to thermally inspect
Speaker 1: items like metal paper, cardboard, box adhesive, automotive windshields, and
Speaker 1: high glass plastics. With such a lengthy track record of achievements,
Speaker 1: John spoke about one specific company success story that stuck
Speaker 1: out for him.
Speaker 2: A couple that come to mind. I mentioned we inference
Speaker 2: about thirty images per second in this one process. This
Speaker 2: is a paper process, so it's continuous, very high speed,
Speaker 2: and it's for a high glass specialty paper. And what
Speaker 2: happens is this high glass coding goes on paper very
Speaker 2: rapidly as it's going down the line, and unfortunately there's
Speaker 2: a problem where this coding can build up and if
Speaker 2: it's not caught in about eight seconds, it will do
Speaker 2: roughly one hundred and twenty thousand dollars worth of damage
Speaker 2: to the equipment. This can happen multiple times as shift.
Speaker 2: This is a very expensive problem if it's not caught.
Speaker 2: And so this one's a great example of a thermal application.
Speaker 2: It's a heated coating where we look at that we inference,
Speaker 2: like I mentioned about thirty images a second, and in
Speaker 2: just about one second, we look at all of those images,
Speaker 2: we make a determination is there a problem or not,
Speaker 2: is it good or is it bad? And we actually
Speaker 2: do close loop automation as well. We'll send a signal
Speaker 2: back there and trigger a stoppage on the line to
Speaker 2: avoid equipment failure. All of that happens in less than
Speaker 2: one second. So that's a really good example of speed.
Speaker 2: Another good example, I'll give you just one more in
Speaker 2: the interest of time, how we can help see things
Speaker 2: that folks can't see. Well, I mentioned fuel tanks, and
Speaker 2: I mentioned some plastic components and things like that earlier.
Speaker 2: Naturally we use thermal vision for that humans can't see.
Speaker 2: In thermal patterns of course, so we're able to show
Speaker 2: quality engineers inconsistencies in the product that they would never
Speaker 2: be able to see with the human eyes. One of
Speaker 2: our customers manufacturers the front plates for a dishwasher company,
Speaker 2: very large dishwasher manufacturer. And so if you've recently gotten
Speaker 2: a new appliance, you probably remember you had to peel
Speaker 2: all that film off, right. Well, what you might not
Speaker 2: know is that film is on from the raw material
Speaker 2: phase and what happens is as it goes down the process,
Speaker 2: it gets stamped like a cookie cutter. But that film
Speaker 2: is on it the whole time to protect it. So
Speaker 2: what's really tough is for the quality engineers to actually
Speaker 2: see through the blue film or whatever tint it might be,
Speaker 2: to see if there's a scratcher dent. And so this
Speaker 2: is one problem we solved for one of our customers
Speaker 2: where they were missing the dents, they were missing the
Speaker 2: scratches because the humans simply couldn't see through the protective film.
Speaker 2: Fast forward to today again, another customer that inspects one
Speaker 2: hundred percent of their production on our tooling and gives
Speaker 2: them indicators in real time through that blue film if
Speaker 2: they have any kind of service defect.
Speaker 1: And you've talked a little bit about the journey twelve
Speaker 1: years ago to now. I want to get you to
Speaker 1: cast your mind ahead twelve years in the future. Where
Speaker 1: do you think Igen will be and in general, where
Speaker 1: do you think manufacturing and quality control technology will be
Speaker 1: in the next twelve years.
Speaker 2: That's a pretty far horizon. I don't even know if
Speaker 2: I could guess the next twelve months, to be honest
Speaker 2: with you, just because the industry moves so fast. But
Speaker 2: let's say over the course of the next decade, I
Speaker 2: would definitely see some of the more innovative technologies becoming mainstream.
Speaker 2: So computer vision, there's no doubt about it. Computer vision
Speaker 2: specifically for quality is becoming more and more common. I
Speaker 2: think this will become completely commonplace over the next twelve years.
Speaker 1: Often ask this of our guess, but if you could
Speaker 1: have AI solve one thing in your field that is manufacturing,
Speaker 1: what would it be.
Speaker 2: I would like to use AI to clone the entire
Speaker 2: Eigen team, because these are some of the most talented
Speaker 2: people I've ever worked with, and I just need like
Speaker 2: three to four times more of them so I can
Speaker 2: go take over the world.
Speaker 1: Yeah. Well, we did have an episode on digital twins
Speaker 1: and have a human digital twin, so yeah, you never know.
Speaker 1: With that. I'll leave it there. Thank you John for
Speaker 1: your time.
Speaker 2: Well, thank you, this was great. Thanks for having me.
Speaker 1: Thank you to John Weiss for his quality insights in
Speaker 1: today's episode of Technically Speaking.
Speaker 2: In a world where we.
Speaker 1: Are somewhat preoccupied with virtual and digital goods, I love
Speaker 1: hearing stories about the production of real world physical products.
Speaker 1: I think we take for granted how much time, effort,
Speaker 1: and brain power it takes not only to conceive of
Speaker 1: new products, but to design the whole manufacturing process and
Speaker 1: get them into the hands of you, the customer. John
Speaker 1: highlighted that quality is now non negotiable for consumers and
Speaker 1: that manufacturers need to continually reinvent the new technology and
Speaker 1: methods to keep producing high quality products as economically as possible.
Speaker 1: A common theme in all of our episodes, and one
Speaker 1: that I'm always exploring, is whether these new advances in AI,
Speaker 1: like the machine and computer vision discussed today, will help
Speaker 1: all businesses, regardless of size. So it's pleasing to hear
Speaker 1: John say that their technology can help the smaller niche
Speaker 1: manufacturers to use the same quality control software and hardware
Speaker 1: that the big players have. This is why I'm so
Speaker 1: bullish about AI and technology in general, the ability to
Speaker 1: lift all people and businesses up, no matter what stage
Speaker 1: of life they are in. In our next episode, we
Speaker 1: will look at how we can close the AI workforce
Speaker 1: gap through education. So join us on July second for
Speaker 1: the next edition of Technically Speaking and Intel podcast. Technically
Speaker 1: Speaking was produced by Ruby Studio from iHeartRadio in partnership
Speaker 1: with Intel, and hosted by me Class. Our executive producer
Speaker 1: is Molly Sosher, our EP of Post Production is James Foster,
Speaker 1: and our supervising producer is Nika Swinton. This episode was
Speaker 1: edited by Sierra Spreen and written by Nick Firshall.