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Speaker 1: Take a second to think about every single item in

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Speaker 1: your home. Your television, your refrigerator, your desk, lamp, your laptop,

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Speaker 1: even the smartphone you might be using to hear my

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Speaker 1: voice right now. All of these things, and so many

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Speaker 1: more items in our lives, began in a factory. There

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Speaker 1: are more than six hundred and twenty thousand manufacturing businesses

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Speaker 1: in the United States right now, responsible for nearly twelve

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Speaker 1: percent of the total US economic output. The numbers are

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Speaker 1: even more staggering in China, which makes up nearly twenty

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Speaker 1: nine percent of the total global output. For manufacturing. Factories

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Speaker 1: have been around since the late eighteenth century, and today

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Speaker 1: they're used everywhere from South Korea to southern California to

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Speaker 1: make cars, airplanes, textiles, and even space vehicles, and each

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Speaker 1: one depends on a carefully choreographed system of steps, each

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Speaker 1: one as essential as the next before the final product

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Speaker 1: rolls off the production line. Mistakes, however, are also an

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Speaker 1: unavoidable part of this process. Manufacturers simply can't check every

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Speaker 1: piece of every product, and it's nearly impossible to achieve

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Speaker 1: perfection when some manufacturing plants produce thousands of items a day.

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Speaker 1: So how can technology help an industry so crucial to

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Speaker 1: our daily lives, how can factories use AI to reduce

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Speaker 1: and even prevent defective products? Welcome to Technically Speaking, an

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Speaker 1: Intel podcast produced by iHeartMedia's Ruby Studio in partnership with Intel.

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Speaker 1: In every episode, we explore how AI innovations are changing

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Speaker 1: the world and revolutionizing the way we live. Hey there,

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Speaker 1: I'm gram class, and today we're headed into the world

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Speaker 1: of manufacturing, an expansive and essential industry that drives the

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Speaker 1: global economy and both the history dating back nearly two

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Speaker 1: hundred and fifty years, we've seen manufacturing create a revolution,

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Speaker 1: resurrect nation's economies, connect people around the globe, and even

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Speaker 1: send mankind into space. But what's next at the intersection

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Speaker 1: of manufacturing and technology. In this episode, we'll be focusing

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Speaker 1: on how AI technology can help optimize manufacturing and improve

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Speaker 1: quality thanks to no small part to the minds at

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Speaker 1: Intel and at Eigen Innovations, a company committed to helping

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Speaker 1: organizations unlock the power of machine vision to automate quality inspections.

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Speaker 1: Before we go any further, let's welcome our guest joining

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Speaker 1: us today is John Weiss, the chief revenue officer at

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Speaker 1: Eigen innovations. John oversees all revenue generation activities at Eigen,

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Speaker 1: including driving sales in Eigen's machine vision software and engineering services.

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Speaker 2: Welcome to the show, John, thanks for having me. Graham's

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Speaker 2: great to be here.

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Speaker 1: Let's start with a bit of background on manufacturing and

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Speaker 1: the role it plays in our society. I mean it's

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Speaker 1: fair to say that I phone, our car, laptop, even

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Speaker 1: the food we eat involves some sort of manufacturing process.

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Speaker 1: I'd like to get your thoughts on just the importance

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Speaker 1: and scale of manufacturing plants around the world.

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Speaker 2: Yeah, sure, Well, like you said, just about everything in

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Speaker 2: our daily lives comes from factories or plants. But sure,

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Speaker 2: depending on if you commute on a train or in

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Speaker 2: a car, lots of those components are coming from factories.

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Speaker 2: Very little these days are really kind of hand crafted

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Speaker 2: and handmade and smile batch, especially large scale consumer items.

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Speaker 2: And there's many different types of processes and many different

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Speaker 2: types of ways things are made.

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Speaker 1: And look, I know there's a multitude of ways and

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Speaker 1: types of manufacturing processes. Like a Volkswagen built in Germany

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Speaker 1: is going to be very different from an iPhone built

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Speaker 1: in China. Do you find common things, reads or similarities

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Speaker 1: across manufacturing industries.

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Speaker 2: Yeah, definitely, so, I guess maybe as a kind of

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Speaker 2: a starting point, it's important to understand there are two

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Speaker 2: types of manufacturing processes or approaches. One is called process manufacturing.

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Speaker 2: This is things like chemicals, plastics, things that can't really

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Speaker 2: be broken down or deconstructed easily. Or you have discrete manufacturing,

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Speaker 2: which is much more of the process of putting stuff together.

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Speaker 2: Think about a watch or a car. Now, both of

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Speaker 2: those processes, discrete and process manufacturing, they're quite different, but

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Speaker 2: there are certainly similarities. And between the two methods, you

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Speaker 2: basically have all of the things that we use every day,

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Speaker 2: right then oftentimes actually they kind of bleed into one another.

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Speaker 2: Most things have a little bit of process manufacturing involved

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Speaker 2: and then a little bit of discrete manufacturing as well. However,

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Speaker 2: I would say the commonalities across both are really heavily

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Speaker 2: reliant on technology. We see a very large push for

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Speaker 2: data driven decision making. We see large patterns or trends

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Speaker 2: in both realms of manufacturing around empowering the workforce, trying

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Speaker 2: to opt skill workers via technology to get them to

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Speaker 2: be focused on more mission critical tasks or higher value

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Speaker 2: activities while letting some of the technology do more of

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Speaker 2: the mundane tasks.

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Speaker 1: Yeah. In a previous job that I had, we were

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Speaker 1: doing consumer electronics, and we struggled quite a bit with

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Speaker 1: the quality side of things and being able to ensure

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Speaker 1: a good product can you manufactured in our China plant.

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Speaker 1: And one thing that struck me was that there was

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Speaker 1: a very manual process in terms of the quality inspection,

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Speaker 1: and it will take samples in one out of every

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Speaker 1: ten and the test that and then if that worked,

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Speaker 1: then okay, and then we assume the rest kind of work.

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Speaker 1: I'm wondering if you could share any stories or examples

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Speaker 1: of I guess problems with quality or defective products that

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Speaker 1: stick in your mind.

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Speaker 2: Yeahsolutely. I mean that's all we do at Eigen, right.

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Speaker 2: All we do is industrial machine vision for inline quality inspection.

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Speaker 2: So a couple that stick into my mind. Actually very

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Speaker 2: relevant to what you said, sample testing. Lots of manufacturers

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Speaker 2: do this, right if they're a high volume shop or

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Speaker 2: a high volume process. For example, we have some customers

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Speaker 2: that use our technology to inspect upwards of forty thousand

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Speaker 2: units a week per facility. The challenge is if you

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Speaker 2: do find a problem, now you're kind of scratching your

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Speaker 2: head wondering how many in between the last one hundred

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Speaker 2: or last fifty also had a problem right, And unfortunately

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Speaker 2: you tend to find out the hard way when you

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Speaker 2: get returns or warranty claims that maybe something wasn't right

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Speaker 2: in that process. And so technology is a great way,

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Speaker 2: especially the arena that we operate in computer vision, we're

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Speaker 2: helping customers actually get away from that. A great example

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Speaker 2: is one of our manufacturing customers who makes fuel tanks

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Speaker 2: for a variety of different vehicles, and they do what's

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Speaker 2: called destructive testing. They don't just test, they actually break

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Speaker 2: the fuel tank, that cut it up, and they look

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Speaker 2: at all of the plastic components inside and they see

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Speaker 2: was it molded correctly, was it welded correctly? And if

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Speaker 2: they have a problem, well, now they have to reverse

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Speaker 2: engineer a whole bunch of stuff and try to figure out,

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Speaker 2: holy cow, what went wrong right? And how do we

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Speaker 2: ensure that no bad fuel tank gets on a truck.

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Speaker 2: They started the journey with us about three years ago,

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Speaker 2: and fast forward today, we're builds back on every new

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Speaker 2: machine that gets put into those plants for fuel tank inspection.

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Speaker 2: So they know unequivocally, every single product that they ship

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Speaker 2: out the door is of the highest quality standard and

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Speaker 2: if it's not. If something happens, now they have complete

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Speaker 2: traceability on everything they've made, so they can figure out

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Speaker 2: exactly what went wrong in the process.

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Speaker 1: What John is talking about here is the output of

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Speaker 1: the manufacturing process. How can we ensure every fuel tank

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Speaker 1: that leaves the plant will work as designed? Just as importantly,

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Speaker 1: we need to consider the quality of the input components.

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Speaker 1: Everything from the greater steel to the precision of the

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Speaker 1: fuel gate, These need to be expected to ensure that

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Speaker 1: these are up to the manufacturer standard. I asked John

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Speaker 1: for his thoughts about this.

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Speaker 2: We don't often look at raw material although it's possible

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Speaker 2: in some cases, but more often than not our inputs

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Speaker 2: that we're looking at it's actually process inputs or parameters.

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Speaker 2: So we're looking at feed rates of raw materials, temperatures

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Speaker 2: of raw materials, things like this. In the process that

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Speaker 2: become more of a scientific look of what's happening on

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Speaker 2: the assembly line and ensuring that everything is inspect We

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Speaker 2: don't just look at the output of you know, did

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Speaker 2: you make a good or bad product? But we'll actually

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Speaker 2: show you all of the process data that went into

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Speaker 2: making that product. The other side is on the discrete

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Speaker 2: world where you're actually assembling things. In this instance, what

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Speaker 2: we do is we'll actually monitor the assembly, so we'll

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Speaker 2: look at how people are placing door panels into a

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Speaker 2: doorframe for example, on to motif asset, or look at

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Speaker 2: tail lamps for lighting purposes right the way that they're

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Speaker 2: assembled and put together. And what we can do in

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Speaker 2: real time is tell folks, hey, what you're putting together

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Speaker 2: is misconfigured, or it's missing components, or it has too

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Speaker 2: many components. Those are defect types that are pretty common

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Speaker 2: in the assembly world.

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Speaker 1: And what are some of the technology that is used

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Speaker 1: for that? Is it vision? Is it sensors? Is it combination?

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Speaker 2: Everything we do is vision based, so we don't make cameras.

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Speaker 2: By the way, we are a software provider, we also

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Speaker 2: act as a system integrator, so a large part of

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Speaker 2: our business is actually delivering turnkey solutions, not just the software.

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Speaker 2: But we don't make hardware, which is actually really cool

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Speaker 2: for us because that means we get to use tons

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Speaker 2: of different types of options that are available for our

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Speaker 2: customers and it helps us really find the perfect design

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Speaker 2: and configuration that is definitely going to solve problems, and

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Speaker 2: so having the flexibility is really nice, and of course

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Speaker 2: that's a large and why we partner with Intel. We're

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Speaker 2: built on the open Veno tech stack, and that means

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Speaker 2: we can run our software really on any device that

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Speaker 2: leverages an Intel chip, which gives us tons of options

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Speaker 2: for deployments. What's really cool about this though, from a

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Speaker 2: quality perspective, is that it means you now have one

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Speaker 2: vision system that can integrate with different types of sensors.

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Speaker 2: So if you want to do, say an optical inspection

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Speaker 2: for surface defects like scratches and dents, but you also

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Speaker 2: want to look at perhaps inside that product in a

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Speaker 2: thermal application, if it's a molded part or something like that,

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Speaker 2: Well you can look at all those different types of

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Speaker 2: sensor in one easier to use screen, right, So it

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Speaker 2: removes the headache of having to have five six different

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Speaker 2: vision systems to do a variety of inspections.

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Speaker 1: And I'm also interested in the deployment of these sorts

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Speaker 1: of new technologies. I'd like to get your thoughts and

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Speaker 1: experiences around what's some of the tips and tricks for

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Speaker 1: people out there trying to deploy not just for manufacturing quality,

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Speaker 1: but technology and AI in general into a workforce that

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Speaker 1: maybe is a little bit hesitant.

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Speaker 2: Yeah, humans don't like change, that's for sure. I know

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Speaker 2: I don't. I'm guilty of that. And it's certainly like

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Speaker 2: that when you go into a factory and you've got

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Speaker 2: folks that have been on the same line or in

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Speaker 2: the same steel plant for twenty five thirty years, and

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Speaker 2: you show up and you've got this bright, new shiny

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Speaker 2: software and you say, hey, don't worry, data is going

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Speaker 2: to solve everything. Naturally, people can be quite apprehensive. We

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Speaker 2: don't often run into technology challenges anymore now, it's really

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Speaker 2: we run into people challenges and organizational challenges. So first

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Speaker 2: and foremost, I'll give the advice that I give on

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Speaker 2: most of the times I'm asked this question. But it's

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Speaker 2: so true. Is you don't ever start adopting technology just

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Speaker 2: for the sake of adopting it, just because competitors are

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Speaker 2: using something, or just because somebody way up the chain says, hey,

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Speaker 2: we need an AI strategy. Go invest in AI boom,

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Speaker 2: spend some time and really think about the problems that

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Speaker 2: you're trying to tackle in my world, in the quality world,

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Speaker 2: in manufacturing, it's looking at things you can do to

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Speaker 2: increase yields, increase your throughput, reduce your waste, reduce your rework,

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Speaker 2: and ultimately lower what's called the cost of quality. Start

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Speaker 2: with that, find a way that you can or process

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Speaker 2: that you can optimize by using some of this newer technology,

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Speaker 2: and then of course do a cost assessment or a

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Speaker 2: return on your investment analysis, and ensure that the business

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Speaker 2: justification is there. My experience, that's where a lot of

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Speaker 2: these projects fall short, and where folks get stuck in

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Speaker 2: these pilots and pocs is because they get really excited

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Speaker 2: to try something, but there is no proven business value

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Speaker 2: or business justification behind it, and naturally then you don't

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Speaker 2: get the executive sponsorship you need, your budget falls through,

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Speaker 2: and the project goes nowhere.

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Speaker 1: And in your experience, what industries do you find actually

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Speaker 1: a little bit more advanced in terms of adopting these

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Speaker 1: new technology both on a technical level but also at

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Speaker 1: an organizational level that it seems like the teams are

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Speaker 1: actually involved and successfully deploying these sorts of techniques.

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Speaker 2: Yeah, that's a great question. We see pretty advanced deployments

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Speaker 2: in the automotive world as far as discrete manufacturing goes,

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Speaker 2: they tend to be far ahead of the curve compared

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Speaker 2: to say, steel manufacturers or something like that, or concrete manufacturers.

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Speaker 2: There's a lot of very advanced technology and those automotive

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Speaker 2: facilities that make sure what you buy is actually perfect. Similarly,

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Speaker 2: in the process world, pharmaceuticals tends to be on the

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Speaker 2: continuous process side that tends to be pretty advanced. They

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Speaker 2: have a lot of vision systems in place looking at

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Speaker 2: the vaccine vials to ensure the integrity of vile caps

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Speaker 2: and seals and things like that. Some of the laggers

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Speaker 2: would be metals, some of the plastics organizations. But there's

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Speaker 2: also a kind of a bigger dynamic in manufacturing that

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Speaker 2: I think folks don't really understand that also contributes to

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Speaker 2: who's advanced and who's which is the sheer size of

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Speaker 2: these organizations, right, Manufacturers are not all large. Folks tend

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Speaker 2: to think about John Deere and three M and you know,

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Speaker 2: the largest players in the world, and the reality is

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Speaker 2: that makes up such a small fraction of the manufacturing pool,

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Speaker 2: especially in America, most manufacturing facilities have you know, twenty

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Speaker 2: people or less. Small to medium manufacturers anywhere from say

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Speaker 2: like the twenty to two hundred range of employees. That's

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Speaker 2: who makes up the vast majority of our products. Even

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Speaker 2: when you buy something really big, you know, whether it's

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Speaker 2: a whirlpool dishwasher or a hot tub or whatever it

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Speaker 2: might be, all those little components that make up that

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Speaker 2: consumer good, Well, it came from probably many different suppliers,

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Speaker 2: and most of those are small.

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Speaker 1: It's nice that you mentioned that because my father has

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Speaker 1: a small manufacturing facility here. And just to talk a

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Speaker 1: little bit more of the technology stack that you're using

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Speaker 1: with open Vino and Intel's edge devices. I'm really interested

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Speaker 1: to see how some of the smaller guys can actually

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Speaker 1: use this sort of technology so that it can actually

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Speaker 1: be more competitive.

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Speaker 2: Sure, well, leveraging open Veno helps us have a real

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Speaker 2: wide range of how on the hardware side, how we

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Speaker 2: can install our software. What that means for smaller manufacturers

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Speaker 2: is that we can be quite flexible in the design

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Speaker 2: of a system and can accommodate just about any budget,

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Speaker 2: which that alone is pretty significant to understand. I still

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Speaker 2: think there's a misconception that it's too expensive or too

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Speaker 2: cumbersome for the little guys, so to speak to really

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Speaker 2: innovate in their plants, and it's simply not true. You know,

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Speaker 2: we have customers that make as little as twenty parts

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Speaker 2: of shift, and even for them, having the flexibility of

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Speaker 2: how we design and configure these systems, it ensures that

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Speaker 2: even they can embrace newer technology and provide the highest

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Speaker 2: amounts of quality to their customers.

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Speaker 1: Part of the reason I can can design and configure

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Speaker 1: of those systems is because the company uses Intel's central

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Speaker 1: processing units or CPUs, as opposed to GPUs or graphics

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Speaker 1: processing units. GPUs are specialized processes are originedly designed to

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Speaker 1: accelerate graphics rendering. The key difference in the manufacturing world

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Speaker 1: is that CPUs, like the ones Intel provides for Ogen,

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Speaker 1: are able to perform under harsher or hotter conditions like

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Speaker 1: the ones you might find in a factory or manufacturing plant. GPUs, meanwhile,

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Speaker 1: are prone to overhitting without the use of a fan

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Speaker 1: to cool it down, and most factories won't use fans

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Speaker 1: so they can avoid spreading dust and debris. There's always

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Speaker 1: a trade off between designing software optimized for CPUs or

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Speaker 1: GPUs and a manufacturing plant. I asked John about this,

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Speaker 1: and I found his answer to be quite illuminating.

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Speaker 2: It's always an interesting discussion when people ask, why don't

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Speaker 2: you just go on GPUs and what's the real difference?

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Speaker 2: And from a manufacturing perspective, just logically thinking about what

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Speaker 2: happens in a plant. If you remember, like late nineties,

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Speaker 2: you remember you had your COMPAC or your Gateway PC,

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Speaker 2: this big old white box on the floor, and every

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Speaker 2: so often you'd take the front panel off and it

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Speaker 2: would just be totally caked in dust. Right, you'd hear

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Speaker 2: the fans humming, and well, this is what happens to

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Speaker 2: GPUs and factories. This is why we don't use fans,

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Speaker 2: because factories are dirty. There's dust everywhere. And what we

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Speaker 2: found is that when we explored using various types of

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Speaker 2: mediums to do our processing, what we found is that

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Speaker 2: fanless intel boxes were not only just as performant and

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Speaker 2: in some instances probably even more beneficial to use, but

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Speaker 2: on the maintenance side of it, we didn't have to

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Speaker 2: worry about dirt and debris, which exists in every single

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Speaker 2: plant that we deploy these in. We also didn't have

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Speaker 2: to worry about heat. GPUs generate tons of heat. Had

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Speaker 2: this discussion with somebody who did deploy GPUs in a

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Speaker 2: manufacturing environment and they were looking at in tens of

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Speaker 2: millions of dollars in HVAC improvements just to keep the

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Speaker 2: factories cool enough to operate effectively. Right. And then the flexibility,

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Speaker 2: like I mentioned, being able to very easily scale the

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Speaker 2: hardware for more advanced use cases, if we need two

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Speaker 2: or three different edge boxes, it's really easy to do,

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Speaker 2: and also be able to scale down for the smaller

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Speaker 2: applications where we want to make it a bit more

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Speaker 2: cost effective for the smaller manufacturers as well.

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Speaker 1: Coming up next on Technically Speaking and Intel Podcast.

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Speaker 2: Computer vision specifically for quality is becoming more and more common.

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Speaker 2: I think this will become completely commonplace over the next

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Speaker 2: twelve years.

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Speaker 1: We'll be right back after a brief message from our partner.

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Speaker 2: Is that Intel?

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Speaker 1: Welcome back to Technically Speaking an Intel podcast. I'm here

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Speaker 1: now with John Weiss. I'd actually like to get you

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Speaker 1: to talk a little bit about Eigen Innovations, if you

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Speaker 1: could tell us a little bit about the company and

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Speaker 1: its mission.

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Speaker 2: Sure so, Iigen Innovations has been around for twelve years.

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Speaker 2: We started in academia out of the University of New Brunswick.

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Speaker 2: It was founded by a PhD student and a professor.

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Speaker 2: We started as a system integrator, so we were going

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Speaker 2: into factories actually installing vision systems, and over the course

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Speaker 2: of about a decade, we developed our own software to

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Speaker 2: make our job as a system integrator easier. And about

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Speaker 2: I don't know, two and a half years ago or so,

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Speaker 2: we realized there's actually a ton of value in IP

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Speaker 2: and the software we created, and so we reinvented the

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Speaker 2: company and moved away from leading as a system integrator

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Speaker 2: to actually leading as a software SaaS based company. We

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Speaker 2: really only do one thing. We do inline quality inspection

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Speaker 2: and actually, to be more specific, our specialty thermal applications

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Speaker 2: that leverage AI. So when you think of like injection molding,

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Speaker 2: blow molding, metal welding, plastic welding, void detection, and plastic goods,

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Speaker 2: anything that has a heated process that the human eye

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Speaker 2: can't easily see defects, we do really really well there.

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Speaker 1: And we talked a little bit about AI, and I

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Speaker 1: think we've also talked about the software that utilizes machine vision.

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Speaker 1: Where do you see AI models and the CPU based

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Speaker 1: technology being able to compete with machine vision use cases?

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Speaker 2: Yeah, it's a good question. Look, I think there are

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Speaker 2: pros and cons of both approaches. We actually have not

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Speaker 2: yet come across a project that we had any kind

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Speaker 2: of processing limitation on being CPU based. We have applications

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Speaker 2: in production running yet thirty inferences a second across cameras. Right,

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Speaker 2: that's quite quite fast. There are definitely higher demand applications.

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Speaker 2: But in our world of process in discrete manufacturing and

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Speaker 2: the types of projects we typically focus on, speed has

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Speaker 2: actually not been a problem for us with CPUs, even

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Speaker 2: at quite aggressive speed. I see the tools getting easier

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Speaker 2: and easier to use, more and more self service, if

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Speaker 2: you will. Years ago, we had this phrase of democratizing

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Speaker 2: data if you remember that, around the days of big data,

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Speaker 2: kind of empowering everybody to be a data scientist, and

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Speaker 2: I see the same movement happening in the AI world.

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Speaker 2: In fact, actually we're a good example of that. You

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Speaker 2: can use our tool to build deploy train models across

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Speaker 2: factories and you don't have to touch a line of code.

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Speaker 2: So I think that's the future. I think the tools

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Speaker 2: get easier and easier to use, so that my good

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Speaker 2: friend Jimmy, who's down in Texas at one of our

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Speaker 2: customer plants, who's been in that same plant for over

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Speaker 2: thirty years, that he can blow me away with how

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Speaker 2: he can build a model that does thermal inspection on

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Speaker 2: metal welding. And years ago, oh, somebody that didn't have

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Speaker 2: that kind of training from a data science perspective or

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Speaker 2: a programming perspective, they would never be able to do that.

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Speaker 2: And today they're building dashboards and building models that are

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Speaker 2: literally redefining the way these manufacturers operate. It's amazing.

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Speaker 1: You heard John say earlier that eigen has been around

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Speaker 1: for more than a decade and this technology has been

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Speaker 1: implemented across a variety of manufacturing spaces to thermally inspect

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Speaker 1: items like metal paper, cardboard, box adhesive, automotive windshields, and

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Speaker 1: high glass plastics. With such a lengthy track record of achievements,

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Speaker 1: John spoke about one specific company success story that stuck

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Speaker 1: out for him.

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Speaker 2: A couple that come to mind. I mentioned we inference

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Speaker 2: about thirty images per second in this one process. This

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Speaker 2: is a paper process, so it's continuous, very high speed,

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Speaker 2: and it's for a high glass specialty paper. And what

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Speaker 2: happens is this high glass coding goes on paper very

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Speaker 2: rapidly as it's going down the line, and unfortunately there's

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Speaker 2: a problem where this coding can build up and if

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Speaker 2: it's not caught in about eight seconds, it will do

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Speaker 2: roughly one hundred and twenty thousand dollars worth of damage

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Speaker 2: to the equipment. This can happen multiple times as shift.

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Speaker 2: This is a very expensive problem if it's not caught.

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Speaker 2: And so this one's a great example of a thermal application.

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Speaker 2: It's a heated coating where we look at that we inference,

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Speaker 2: like I mentioned about thirty images a second, and in

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Speaker 2: just about one second, we look at all of those images,

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Speaker 2: we make a determination is there a problem or not,

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Speaker 2: is it good or is it bad? And we actually

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Speaker 2: do close loop automation as well. We'll send a signal

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Speaker 2: back there and trigger a stoppage on the line to

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Speaker 2: avoid equipment failure. All of that happens in less than

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Speaker 2: one second. So that's a really good example of speed.

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Speaker 2: Another good example, I'll give you just one more in

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Speaker 2: the interest of time, how we can help see things

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Speaker 2: that folks can't see. Well, I mentioned fuel tanks, and

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Speaker 2: I mentioned some plastic components and things like that earlier.

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Speaker 2: Naturally we use thermal vision for that humans can't see.

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Speaker 2: In thermal patterns of course, so we're able to show

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Speaker 2: quality engineers inconsistencies in the product that they would never

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Speaker 2: be able to see with the human eyes. One of

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Speaker 2: our customers manufacturers the front plates for a dishwasher company,

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Speaker 2: very large dishwasher manufacturer. And so if you've recently gotten

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Speaker 2: a new appliance, you probably remember you had to peel

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Speaker 2: all that film off, right. Well, what you might not

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Speaker 2: know is that film is on from the raw material

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Speaker 2: phase and what happens is as it goes down the process,

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Speaker 2: it gets stamped like a cookie cutter. But that film

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Speaker 2: is on it the whole time to protect it. So

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Speaker 2: what's really tough is for the quality engineers to actually

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Speaker 2: see through the blue film or whatever tint it might be,

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Speaker 2: to see if there's a scratcher dent. And so this

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Speaker 2: is one problem we solved for one of our customers

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Speaker 2: where they were missing the dents, they were missing the

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Speaker 2: scratches because the humans simply couldn't see through the protective film.

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Speaker 2: Fast forward to today again, another customer that inspects one

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Speaker 2: hundred percent of their production on our tooling and gives

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Speaker 2: them indicators in real time through that blue film if

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Speaker 2: they have any kind of service defect.

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Speaker 1: And you've talked a little bit about the journey twelve

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Speaker 1: years ago to now. I want to get you to

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Speaker 1: cast your mind ahead twelve years in the future. Where

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Speaker 1: do you think Igen will be and in general, where

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Speaker 1: do you think manufacturing and quality control technology will be

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Speaker 1: in the next twelve years.

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Speaker 2: That's a pretty far horizon. I don't even know if

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Speaker 2: I could guess the next twelve months, to be honest

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Speaker 2: with you, just because the industry moves so fast. But

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Speaker 2: let's say over the course of the next decade, I

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Speaker 2: would definitely see some of the more innovative technologies becoming mainstream.

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Speaker 2: So computer vision, there's no doubt about it. Computer vision

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Speaker 2: specifically for quality is becoming more and more common. I

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Speaker 2: think this will become completely commonplace over the next twelve years.

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Speaker 1: Often ask this of our guess, but if you could

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Speaker 1: have AI solve one thing in your field that is manufacturing,

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Speaker 1: what would it be.

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Speaker 2: I would like to use AI to clone the entire

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Speaker 2: Eigen team, because these are some of the most talented

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Speaker 2: people I've ever worked with, and I just need like

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Speaker 2: three to four times more of them so I can

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Speaker 2: go take over the world.

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Speaker 1: Yeah. Well, we did have an episode on digital twins

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Speaker 1: and have a human digital twin, so yeah, you never know.

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Speaker 1: With that. I'll leave it there. Thank you John for

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Speaker 1: your time.

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Speaker 2: Well, thank you, this was great. Thanks for having me.

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Speaker 1: Thank you to John Weiss for his quality insights in

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Speaker 1: today's episode of Technically Speaking.

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Speaker 2: In a world where we.

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Speaker 1: Are somewhat preoccupied with virtual and digital goods, I love

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Speaker 1: hearing stories about the production of real world physical products.

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Speaker 1: I think we take for granted how much time, effort,

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Speaker 1: and brain power it takes not only to conceive of

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Speaker 1: new products, but to design the whole manufacturing process and

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Speaker 1: get them into the hands of you, the customer. John

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Speaker 1: highlighted that quality is now non negotiable for consumers and

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Speaker 1: that manufacturers need to continually reinvent the new technology and

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Speaker 1: methods to keep producing high quality products as economically as possible.

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Speaker 1: A common theme in all of our episodes, and one

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Speaker 1: that I'm always exploring, is whether these new advances in AI,

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Speaker 1: like the machine and computer vision discussed today, will help

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Speaker 1: all businesses, regardless of size. So it's pleasing to hear

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Speaker 1: John say that their technology can help the smaller niche

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Speaker 1: manufacturers to use the same quality control software and hardware

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Speaker 1: that the big players have. This is why I'm so

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Speaker 1: bullish about AI and technology in general, the ability to

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Speaker 1: lift all people and businesses up, no matter what stage

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Speaker 1: of life they are in. In our next episode, we

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Speaker 1: will look at how we can close the AI workforce

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Speaker 1: gap through education. So join us on July second for

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Speaker 1: the next edition of Technically Speaking and Intel podcast. Technically

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Speaker 1: Speaking was produced by Ruby Studio from iHeartRadio in partnership

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Speaker 1: with Intel, and hosted by me Class. Our executive producer

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00:28:02,240 --> 00:28:05,919
Speaker 1: is Molly Sosher, our EP of Post Production is James Foster,

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Speaker 1: and our supervising producer is Nika Swinton. This episode was

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Speaker 1: edited by Sierra Spreen and written by Nick Firshall.