0:00

Hi.

0:01

This is Graham Class, producer of the

0:03

Daily Dad Jokes podcast. You may

0:05

remember me from such episodes as Daily

0:08

Dad Jokes Explained Anyway,

0:11

coming up as an episode from my other

0:13

podcast, Technically Speaking and

0:15

Intel Podcast. I hope you enjoy

0:18

it. But before I go, here's a

0:20

dad joke I heard at the

0:22

local factory. The management is replacing

0:24

all their janitors with robots.

0:28

Talk about making sweeping changes.

0:35

Take a second to think about every single

0:38

item in your home, your television,

0:40

your refrigerator, your death lamp,

0:43

your laptop, even the smartphone

0:45

you might be using to hear my voice right

0:47

now. All of these things and

0:50

so many more items in our lives began

0:53

in a factory. There

0:55

are more than six hundred and twenty thousand manufacturing

0:58

businesses in the United States right

1:00

now, responsible for nearly twelve

1:02

percent of the total US economic

1:04

output. The numbers are even more

1:06

staggering in China, which makes up

1:09

nearly twenty nine percent of the total

1:11

global output. For manufacturing. Factories

1:14

have been around since the late eighteenth century,

1:17

and today they're used everywhere from

1:19

South Korea to southern California

1:21

to make cars, airplanes,

1:23

textiles, and even space vehicles

1:26

and each one depends on a carefully

1:29

choreographed system of steps, each

1:32

one as essential as the next before

1:34

the final product rolls off the production

1:37

line. Mistakes, however,

1:40

are also an unavoidable part of this process.

1:43

Manufacturers simply can't check every

1:45

piece of every product, and

1:48

it's nearly impossible to achieve perfection when

1:50

some manufacturing plants produce thousands

1:53

of items a day. So

1:55

how can technology help an industry so

1:57

crucial to our daily lives? How

1:59

canactories use AI to reduce

2:02

and even prevent defective products?

2:07

Welcome to Technically Speaking, an Intel

2:09

podcast produced by iHeartMedia's

2:12

Ruby Studio in partnership with Intel.

2:15

In every episode, we explore how

2:17

AI innovations are changing the world

2:19

and revolutionizing the way we live. Hey

2:23

there, I'm gram class, and

2:25

today we're headed into the world of manufacturing,

2:28

an expansive and essential industry that

2:30

drives the global economy and boast

2:32

the history dating back nearly two hundred

2:34

and fifty years, we've seen manufacturing

2:37

create a revolution, resurrect nation's

2:39

economies, connect people around the

2:41

globe, and even send mankind

2:44

into space. But what's

2:46

next at the intersection of manufacturing

2:48

and technology. In this episode,

2:51

we'll be focusing on how AI technology

2:53

can help optimize manufacturing and

2:55

improve quality thanks to no small

2:58

part to the minds at Intel and at our Innovations,

3:01

a company committed to helping organizations

3:03

unlock the power of machine vision to

3:05

automate quality inspections. Before

3:08

we go any further, let's welcome our guest

3:12

joining us today is John Weiss, the chief

3:14

revenue Officer at Eigen Innovations.

3:17

John oversees all revenue generation activities

3:19

at Eigen, including driving sales

3:21

in Eigen's machine vision software and engineering

3:24

services.

3:25

Welcome to the show, John, Thanks for having me, Graham,

3:27

it's great to be here.

3:32

Let's start with a bit of background on manufacturing

3:34

and the role it plays in our society.

3:36

I mean, it's fair to say that I phone, our car,

3:38

laptop, even the food we eat involves

3:41

some sort of manufacturing process. I'd

3:44

like to get your thoughts on just the importance

3:46

and scale of manufacturing plants around the world.

3:49

Yeah. Sure, Well, like you said, just

3:51

about everything in our daily lives comes

3:53

from factories or plants. But

3:56

sure, depending on if you commute

3:59

on a train or in a car,

4:01

lots of those components are coming from factories.

4:03

Very little these days are really kind

4:05

of handcrafted and handmade and small

4:08

batch, especially large scale

4:10

consumer items. And there's many

4:12

different types of processes and many

4:15

different types of ways things are made.

4:18

And look, I know there's a

4:20

multitude of ways and types

4:22

of manufacturing processes.

4:24

Like a volkswag And built in Germany is going

4:26

to be very different from an iPhone built in China.

4:30

Do you find common threads or

4:32

similarities across manufacturing

4:35

industries.

4:36

Yeah, definitely, so, I guess maybe

4:38

as a kind of a starting point, it's important to understand

4:41

there are two types of manufacturing processes

4:43

or approaches. One is called process manufacturing.

4:46

This is things like chemicals, plastics,

4:49

things that can't really be broken down

4:52

or deconstructed easily. Or

4:54

you have discrete manufacturing, which

4:56

is much more of the process of putting stuff together.

4:59

Think about a watch or a car. Now,

5:01

both of those processes, discrete and process

5:04

manufacturing, they're quite different,

5:06

but there are certainly similarities.

5:08

And between the two methods. You basically

5:10

have all of the things that we use every day,

5:13

right then oftentimes actually they kind of

5:15

bleed into one another. Most things have a little bit

5:17

of process manufacturing involved, and then a little bit

5:19

of discrete manufacturing as well. However,

5:22

I would say the commonalities across

5:24

both are really heavily reliant on technology.

5:27

We see a very large push for data

5:29

driven decision making. We

5:32

see large patterns or trends

5:34

in both realms of manufacturing around

5:36

empowering the workforce, trying

5:39

to upscale workers via technology

5:41

to get them to be focused

5:43

on more mission critical tasks or

5:46

higher value activities while

5:48

letting some of the technology do more of the mundane

5:51

tasks.

5:52

Yeah. In a previous job that I had, we

5:54

were doing consumer electronics, and

5:57

we struggle quite a bit with

5:59

the quality side of things, sure, and being

6:01

able to ensure a good product

6:03

can you manufactured in our China plant?

6:06

And one thing that struck me was

6:09

that there was a very manual process

6:11

in terms of the quality inspection, and

6:13

it'll take samples in a one out of every

6:16

ten and they'll test that and then if that worked

6:18

and okay, then we assume the rest kind

6:20

of work. Right. I'm wondering if you

6:22

could share any stories or examples

6:24

of I guess problems with quality

6:27

or defective products that stick in

6:29

your mind. Yeah.

6:31

Absolutely, I mean that's all we do at AIGEN, right,

6:33

all we do is industrial machine vision

6:35

for inline quality inspection. So

6:37

a couple that stick into my mind actually

6:39

very relevant to what you said, sample testing.

6:42

Lots of manufacturers do this right if there are a

6:44

high volume shop or a high volume

6:46

process. For example, we have some

6:48

customers that use our technology to inspect

6:50

upwards of forty thousand units a week per

6:53

facility. The challenge is if you do find

6:55

a problem, now you're kind of scratching your head

6:57

wondering how many in between the last

6:59

one hundred or last fifty also

7:01

had a problem right, And unfortunately

7:04

you tend to find out the hard way when

7:06

you get returns or warranty claims

7:08

that maybe something wasn't right in that process.

7:11

And so technology is a great

7:13

way, especially the arena that we operate in computer

7:15

vision, we're helping customers

7:17

actually get away from that. A great example

7:20

is one of our manufacturing customers

7:22

who makes fuel tanks for a variety

7:24

of different vehicles, and

7:27

they do what's called destructive testing. They

7:29

don't just test, they actually break the fuel

7:31

tank, that cut it up, and they look

7:33

at all of the plastic components inside

7:35

and they see was it molded correctly, was

7:37

it welded correctly? And if

7:39

they have a problem, well, now they have to reverse

7:41

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

7:44

holy cow, what went wrong? Right? And how do

7:46

we ensure that no bad fuel tank gets

7:48

on a truck. They started the journey with

7:50

us about three years ago, and fast forward today,

7:52

we're builts back on every new machine

7:54

that gets put into those plants for fuel

7:56

tank inspection. So they know unequivocally

7:59

every single product that they ship

8:01

out the door is of the highest quality standard

8:04

and if it's not, if something happens, now

8:06

they have complete traceability on everything

8:08

they've made, so they can figure out exactly

8:11

what went wrong in the process.

8:14

What John is talking about here is the output

8:16

of the manufacturing process. How can

8:18

we ensure every fuel tank that leaves

8:21

the plant will work as designed? Just

8:23

as importantly, we need to consider the quality

8:26

of the input components. Everything from

8:28

the greater steel to the precision

8:30

of the fuel gauges. These need

8:32

to be inspected to ensure that these are

8:34

up to the manufacturers standard. I

8:37

asked John for his thoughts about this.

8:43

We don't often look at raw material,

8:45

although it's possible in some cases, but

8:48

more often than not, are inputs that

8:50

we're looking at. It's actually process inputs

8:52

or parameters. So we're looking at feed

8:55

rates of raw materials, temperatures

8:57

of raw materials, things like this

8:59

and thes that become more of a scientific

9:02

look of what's happening on the assembly line

9:05

and ensuring that everything is inspect

9:07

We don't just look at the output of

9:10

you know, did you make a good or bad product, but we'll

9:12

actually show you all of the process data that

9:14

went into making that product. The

9:16

other side is on the discrete world,

9:18

where you're actually assembling things. In

9:21

this instance, what we do is we'll actually

9:23

monitor the assembly. So we'll look

9:26

at how people are placing door

9:28

panels into a doorframe,

9:30

for example on an automotive asset,

9:32

or look at tail lamps for

9:35

lighting purposes right the way that they're assembled

9:37

and put together. And what we can

9:39

do in real time is tell folks, hey,

9:41

what you're putting together is misconfigured, or

9:44

it's missing components, or it has too

9:46

many components. Those are defect

9:48

types that are pretty common in the assembly world.

9:51

And what are some of the technology that

9:54

is used for that? Is it vision? Is it sensors,

9:56

is it combination?

9:58

Everything we do is vision based so

10:00

we don't make cameras. By the way, we are a software

10:03

provider, we also act as a system integrator,

10:05

so a large part of our business is actually

10:07

delivering turnkey solutions,

10:09

not just the software. But we don't

10:11

make hardware, which is actually really

10:13

cool for us because that means we get to use

10:16

tons of different types of options that

10:18

are available for our customers and it

10:20

helps us really find the perfect

10:23

design and configuration that is

10:25

definitely going to solve problems. And

10:27

so having the flexibility is really nice, and of

10:29

course that's a large reason why we

10:31

partner with Intel. We're

10:33

built on the open Vino tech stack, and that

10:36

means we can run our software

10:38

really on any device that leverages

10:40

an Intel chip, which gives us tons

10:43

of options for deployments. What's

10:45

really cool about this though, from a quality perspective,

10:48

is that it means you now have one vision system

10:50

that can integrate with different types of

10:52

sensors. So if you want to do

10:55

say an optical inspection

10:57

for surface defects like scratches and dents,

11:00

but you also want to look at perhaps

11:02

inside that product in a thermal application,

11:05

if it's a molded part or something like that,

11:07

well you can look at all those different types

11:09

of sensor in one easier to use

11:11

screen right, So it removes

11:13

the headache of having to have five six different

11:16

vision systems to do a variety of inspections.

11:18

And I'm also interested in

11:20

the deployment of these sorts of new

11:23

technologies. I'd like to get your

11:25

thoughts and experiences around what's some of

11:27

the tips and tricks for people out there

11:29

trying to deploy not just for manufacturing

11:32

quality, but technology and AI in

11:34

general into a workforce that

11:36

maybe is a little bit hesitant.

11:38

Yeah, humans don't like change, that's for sure.

11:41

I know I don't. I'm guilty of that. And it's

11:44

certainly like that when you go into a

11:46

factory and you've got folks that have been

11:48

on the same line or in the

11:50

same steel plant for

11:53

twenty five thirty years, and

11:55

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

11:58

software and you say, hey, don't worry, data

12:00

is going to solve everything. Naturally, people

12:02

can be quite apprehensive. We don't

12:05

often run into technology challenges

12:07

anymore.

12:07

Now.

12:08

It's really we run into people challenges

12:10

and organizational challenges. So

12:12

first and foremost, I'll give the advice that I give

12:14

on most of the times I'm asked this question, but it's

12:17

so true. Is you don't ever

12:19

start adopting technology just

12:21

for the sake of adopting it, just because

12:23

competitors are using something, or just because

12:25

somebody wigh up the chain says, hey, we need an AI strategy,

12:28

Go invest in AI boom. Spend

12:30

some time and really think about the problems

12:33

that you're trying to tackle. In my world,

12:35

in the quality world in manufacturing, it's

12:37

looking at things you can do to increase yields,

12:40

increase your throughput, reduce your waste,

12:43

reduce your rework, and ultimately

12:45

lower what's called the cost of quality.

12:48

Start with that, find a way that you

12:50

can or process that you can optimize

12:53

by using some of this newer technology, and then

12:55

of course do a cost assessment or

12:58

a return on your investment analysis, and

13:00

ensure that the business justification is there.

13:03

My experience, that's where a lot of these projects

13:05

fall short, and where folks get stuck in

13:07

these pilots and pocs is because

13:09

they get really excited to try something,

13:12

but there is no proven business

13:14

value or business justification behind

13:16

it, and naturally then you don't get

13:19

the executive sponsorship you need. Your budget

13:21

falls through and the project goes nowhere.

13:23

And in your experience, what industries do

13:25

you find actually a

13:28

little bit more advanced in terms of adopting

13:30

these new technologies both on a

13:32

technical level but also at an organizational

13:34

level that it seems like the teams

13:36

are actually involved and successfully

13:39

deploying these sorts of techniques.

13:41

Yeah, that's a great question. We see pretty

13:44

advanced deployments in the automotive world

13:46

as far as discrete manufacturing goes. They

13:48

tend to be far ahead of the curve compared

13:51

to say steel manufacturers

13:53

or something like that, or concrete manufacturers.

13:56

There's a lot of very advanced technology and

13:58

those automotive facilities that make sure what you

14:00

buy is actually perfect. Similarly,

14:03

in the process world, pharmaceuticals

14:05

tends to be on the continuous process side

14:07

that tends to be pretty advanced. They have

14:09

a lot of vision systems in place looking at

14:12

the vaccine vials to ensure

14:14

the integrity of vile caps and seals

14:17

and things like that. Some of the laggers

14:19

would be metals, some of the plastics

14:22

organizations. But there's also

14:24

a kind of a bigger dynamic in manufacturing that

14:26

I think folks don't really understand

14:28

that also contributes to who's advanced

14:31

and who's not, which is the

14:33

sheer size of these organizations,

14:35

right, Manufacturers are not all large. Folks

14:38

tend to think about John Deere and three M

14:40

and you know, the largest players

14:43

in the world, and the reality is that makes

14:45

up such a small fraction

14:48

of the manufacturing pool. Especially

14:50

in America, most manufacturing facilities

14:53

have you know, twenty people or less, small

14:56

to medium manufacturers anywhere

14:58

from say like the twenty to two hundred range employees.

15:01

That's who makes up the vast majority

15:03

of our products. Even when you buy something

15:05

really big, you know, whether it's a whirlpool dishwasher,

15:08

or a hot tub or whatever it might be, all

15:10

those little components that make up

15:13

that consumer good, Well, it came from

15:15

probably many different suppliers, and most

15:17

of those are small.

15:18

It's nice that you mentioned that because my father

15:20

has a small manufacturing facility

15:23

here. And just to talk a little

15:25

bit more of the technology stack

15:27

that you're using with open Vino and Intel's

15:30

edge devices. I'm really interested

15:32

to see how some of the smaller guys can

15:34

actually use this sort of technology

15:37

so that it can actually be more competitive.

15:40

Sure, well, leveraging open Veno helps

15:42

us have a real wide range of how

15:45

on the hardware side, how we can install our

15:47

software. What that means for

15:50

smaller manufacturers is that we

15:52

can be quite flexible in the design

15:54

of a system and can accommodate just

15:57

about any budget, which that

15:59

alone is pretty significant to understand.

16:01

I still think there's a misconception that it's

16:03

too expansive or too cumbersome

16:06

for the little guys, so to speak, to really

16:08

innovate in their plants, and it's

16:10

simply not true. You know, we have customers

16:12

that make as little as twenty parts

16:14

of shift, and even

16:17

for them, having the flexibility of how

16:19

we design and configure these systems, it ensures

16:21

that even they can embrace newer technology

16:24

and provide the highest amounts of quality

16:26

to their customers.

16:29

Part of the reason i Can can design and configure

16:31

those systems is because the company uses

16:33

Intel's central processing units

16:36

or CPUs, as opposed to

16:38

GPUs, or graphics processing

16:40

units. GPUs are

16:42

specialized processes are allegedly

16:45

designed to accelerate graphics rendering.

16:47

The key difference in the manufacturing world is

16:50

that CPUs, like the ones Intel

16:52

provides for Iigen, are able

16:54

to perform under harsher or hotter conditions

16:57

like the ones you might find in a factory or factory

17:00

plant. GPUs, meanwhile,

17:02

are prone to overhitting without the use

17:04

of a fan to cool it down, and most

17:06

factories won't use fans so they

17:08

can avoid spreading dust and debris.

17:11

There's always a tradeoff between designing software

17:14

optimized for CPUs or GPUs

17:16

and a manufacturing plant. I

17:18

asked John about this, and I found his answer

17:20

to be quite illuminating.

17:24

It's always an interesting discussion when people ask

17:26

why don't you just go on GPUs and what's

17:29

the real difference? And from

17:31

a manufacturing perspective, just

17:34

logically thinking about what happens in

17:36

a plant. If you remember, like

17:38

late nineties, you remember you had your Compac or

17:40

your Gateway PC, this big old

17:42

white box on the floor, and every

17:44

so often you take the front panel off and it

17:46

would just be totally caked in dust.

17:49

Right, You'd hear the fans humming,

17:51

and well, this is what happens

17:53

to GPUs and factories. This is why

17:55

we don't use fans, because factories

17:58

are dirty. There's dust everywhere.

18:01

And what we found is that when

18:03

we explored using various types

18:05

of mediums, to do our processing. What we found

18:07

is that fanless Intel

18:10

boxes were not only

18:12

just as performant and in some instances probably

18:14

even more beneficial to use. But on

18:17

the maintenance side of it, we

18:19

didn't have to worry about dirt

18:22

and debris, which exists in every single

18:24

plant that we deploy these in. We also

18:26

didn't have to worry about heat. GPUs

18:29

generate tons of heat. I

18:31

had this discussion with somebody who did

18:33

deploy GPUs in a manufacturing environment,

18:35

and they were looking at in tens of millions

18:38

of dollars in HVAC improvements just

18:40

to keep the factories cool enough to

18:42

operate effectively. Right. And then the flexibility,

18:45

like I mentioned, being able to very easily

18:48

scale the hardware for more

18:50

advanced use cases. If we need two or three

18:52

different edge boxes, it's really easy to

18:54

do, and also be able to scale down

18:56

for the smaller applications where we want to make it

18:59

a bit more cost factor for the smaller

19:01

manufacturers as well.

19:04

Coming up next on technically speaking

19:06

and Intel podcast.

19:08

Computer vision specifically for quality is

19:10

becoming more and more common. I think this

19:12

will become completely commonplace

19:14

over the next twelve years.

19:16

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

19:18

partners at Intel.

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19:58

Welcome back to Technically Speaking, an

20:00

Intel podcast. I'm here now with

20:02

John Weiss. I'd

20:06

actually like to get you to talk a little bit about

20:08

Eigen Innovations. If you could tell

20:10

us a little bit about the company and

20:13

its mission.

20:14

Sure so, Iigen Innovations has been around

20:17

for twelve years. We

20:19

started in academia out of the University

20:21

of New Brunswick. It was founded by a PhD

20:24

student and a professor. We

20:26

started as a system integrator, so we were

20:29

going into factories actually installing vision

20:31

systems, and over the course of about

20:33

a decade we developed our own software

20:36

to make our job as a system integrator

20:38

easier and about I don't

20:40

know. Two and a half years ago or so, we realized

20:42

there's actually a ton of value in IP

20:45

and software we created, and

20:47

so we reinvented the company and moved

20:49

away from leading as a system integrator

20:52

to actually leading as a software SaaS

20:54

based company. We really only do

20:56

one thing. We do inline quality

20:58

inspection, and actually, to more specific,

21:00

our specialty is thermal applications

21:03

that leverage AI. So when

21:05

you think of like injection molding,

21:07

blow molding, metal welding, plastic

21:10

welding, void detection, and

21:12

plastic goods, anything that has

21:14

a heated process that the human eye

21:16

can't easily see defects. We

21:19

do really really well there.

21:21

And we talked a little bit about AI,

21:23

and I think we've also talked about

21:25

the software that utilizes

21:27

machine vision. Where do you see

21:30

AI models and the CPU based

21:32

technology being able to compete

21:35

with machine vision use cases?

21:37

Yeah, it's a good question. Look, I think there are pros

21:39

and cons of both approaches. We

21:42

actually have not yet come across

21:45

a project that we had

21:47

any kind of processing limitation on being

21:50

CPU based. We have applications

21:52

in production running yet thirty

21:55

inferences a second across cameras,

21:57

right, that's quite quite fast.

22:00

There are definitely higher demand applications,

22:03

but in our world a process in discrete manufacturing

22:06

and the types of projects we typically

22:08

focus on, speed has

22:10

actually not been a problem for us

22:12

with CPUs, even at quite aggressive

22:14

speed. I see the tools getting

22:16

easier and easier to use, more

22:19

and more self service, if you will.

22:21

Years ago, we had this phrase of democratizing

22:24

data, if you remember that, around the days of big data,

22:27

kind of empowering everybody to be a data scientist,

22:29

and I see the same movement

22:32

happening in the AI world. In fact,

22:34

actually we're a good example of that. You can use

22:36

our tool to build deploy train models

22:39

across factories and you don't

22:41

have to touch a line of code. So I think that's

22:43

the future. I think the tools get easier and easier

22:45

to use, so that my good

22:47

friend Jimmy who's down in Texas at

22:49

one of our customer plants, who's been in that same

22:52

plant for over thirty years, that

22:55

he can blow me away with how he can build

22:57

a model that does thermal inspection on metal

22:59

wellness, and years ago, somebody

23:02

that didn't have that kind of training from

23:04

a data science perspective or a programming

23:06

perspective, they would never be able to do that, and

23:09

today they're building dashboards

23:11

and building models that are literally

23:13

redefining the way these manufacturers operate.

23:15

It's amazing.

23:18

You heard John say earlier that Eigen has been

23:20

around for more than a decade and this technology

23:23

has been implemented across a variety of manufacturing

23:25

spaces to thermally inspect items

23:28

like metal paper, cardboard,

23:30

box adhesive, automotive windshields,

23:33

and high glass plastics. With

23:35

such a lengthy track record of achievements,

23:38

John spoke about one specific company success

23:40

story that stuck out for him.

23:45

A couple that come to mind. I mentioned

23:47

we inference about thirty images per

23:50

second in this one process. This is a paper

23:52

process, so it's continuous, very

23:54

high speed, and it's for a high

23:56

glass specialty paper. And

23:59

what happens is the high gloss coating

24:01

goes on the paper very rapidly as it's going

24:03

down the line, and unfortunately

24:05

there's a problem where this coding can

24:07

build up and if it's not caught in about

24:09

eight seconds, it will do roughly one hundred and

24:11

twenty thousand dollars worth of damage to the equipment.

24:14

This can happen multiple times as shift. This

24:17

is a very expensive problem if it's

24:19

not caught. And so this one's a great

24:21

example of a thermal application. It's a heated

24:23

coating where we look at that we inference, like

24:25

I mentioned about thirty images a second, and

24:28

in just about one second, we look at

24:30

all of those images, we make a determination is

24:32

there a problem or not, is it good or is it bad?

24:35

And we actually do close loop automation

24:37

as well. We'll send a signal back

24:39

there and trigger a stoppage on the line to avoid

24:41

equipment failure. All of that happens

24:44

in less than one second. So that's a really

24:46

good example of speed. Another good example,

24:48

I'll give you just one more in the interest of time. How

24:51

we can help see things that folks can't

24:53

see. Well, I mentioned fuel tanks,

24:55

and I mentioned some plastic components

24:57

and things like that earlier. Naturally we

24:59

use thermal vision for that humans

25:02

can't see in thermal patterns, of

25:04

course, so we're able to show quality

25:06

engineers inconsistencies in the product

25:08

that they would never be able to see with the human eyes.

25:11

One of our customers manufacturers the

25:13

front plates for a dishwasher

25:16

company. A very large dishwasher manufacturer. And

25:18

so if you've recently gotten a new appliance,

25:21

you probably remember you had to peel all that film

25:23

off, right. Well, what you might not know

25:25

is that film is on from the

25:27

raw material phase and what

25:29

happens is as it goes down the

25:31

process, it gets stamped like a cookie cutter.

25:34

But that film is on it the whole time

25:36

to protect it. So what's really tough

25:38

is for the quality engineers to actually see

25:41

through the blue film or whatever

25:43

tint it might be, to see if there's

25:45

a scratcher dent. And so this is one problem

25:48

we solved for one of our customers where

25:50

they were missing the dents. They were missing the scratches

25:52

because the humans simply couldn't see through the protective

25:54

film. Fast forward to today again,

25:57

another customer that inspects one hundred

25:59

percent of their production on our tooling and

26:01

gives them indicators in real time through

26:04

that blue film if they have any kind of service

26:06

defect.

26:08

And you've talked a little bit about the

26:10

Jenney twelve years ago to

26:12

now, I want to get you to cast

26:14

your mind ahead twelve years in the future.

26:17

Where do you think igen will be and

26:19

in general, where do you think manufacturing

26:22

and quality control technology

26:24

will be in the next twelve years.

26:27

That's a pretty far horizon. I

26:29

don't even know if I could guess the next twelve months,

26:32

to be honest with you, just because the industry

26:34

moves so fast. But let's say over

26:36

the course of the next decade, I would definitely see

26:38

some of the more innovative technologies becoming

26:40

mainstream. So computer vision, there's

26:42

no doubt about it. Computer vision, specifically

26:44

for quality is becoming more and more common.

26:47

I think this will become completely

26:49

commonplace over the next twelve years.

26:51

Often ask this of our guess, but if

26:54

you could have AI solve one thing

26:56

in your field that is manufacturing,

26:58

what would it be.

27:00

I would like to use AI to clone the

27:02

entire Eigen team because these are some

27:04

of the most talented people I've ever worked

27:07

with, and I just need like three to four

27:09

times more of them so I can go take over the world.

27:11

Yeah. Well, we did have an episode

27:14

on digital twins and have

27:16

a human digital twin, so yeah, you never

27:19

know. With that, I'll leave it there.

27:21

Thank you John for your time.

27:23

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

27:27

Thank you to John Weiss for his quality

27:29

insights in today's episode. Of technically

27:31

speaking, in

27:34

a world where we are somewhat preoccupied

27:36

with virtual and digital goods, I

27:39

love hearing stories about the production of real

27:41

world physical products. I

27:43

think we take for granted how much time, effort,

27:45

and brain power it takes not only to

27:47

conceive of new products, but to design

27:49

the whole manufacturing process and

27:52

get them into the hands of you, the customer.

27:55

John highlighted that quality is now non negotiable

27:57

for consumers and that manufacturers

28:00

need to continually reinvest in new technology

28:02

and methods to keep producing high quality

28:04

products as economically as possible.

28:07

A common theme in all of our episodes, and one

28:09

that I'm always exploring, is

28:11

whether these new advances in AI, like

28:14

the machine and computer vision discussed today,

28:16

will help all businesses, regardless

28:18

of size. So it's pleasing to hear John

28:21

say that their technology can help the smaller

28:23

niche manufacturers to use the same

28:25

quality control software and hardware that

28:28

the big players have. This is

28:30

why I'm so bullish about AI and technology

28:32

in general, the ability to lift all

28:34

people and businesses up, no matter

28:37

what stage of life they are in.

28:41

In our next episode, we will look at how we

28:43

can close the AI workforce. Gaut through

28:45

education. So join us on July second

28:48

for the next edition of Technically Speaking and

28:50

Intel podcast. Technically

28:55

Speaking was produced by Ruby Studio from

28:57

iHeartRadio in partnership with Intel

29:00

and hosted by me Graham Class. Our

29:02

Executive producer is Molly Sosher, our

29:05

EP of Post Production is James Foster,

29:08

and our Supervising producer is Nika Swinton.

29:11

This episode was edited by Sierra Spreen

29:14

and written by Nick Firshall.