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Hello and welcome to the Edge
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Tech podcast, where technology meets
the edge of innovation.
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I'm David Kosloski and today
we're talking about edge computing
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and really just the basics of
what is the edge.
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Alongside me today
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I've got the chief revenue officer, Ryan
McAnlis.
Hey, David.
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Hey, thanks for coming.
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And, also we have Jonny Smith,
who is the CEO of Simply Nuc.
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They're both from Simply Nuc,
I guess I didn’t
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bring up the company
that we all wonderfully work for.
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But, Jonny, thanks for being here. Thanks.
Good to have you here.
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Yeah, I am happy to be here.
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Thanks for having me.
Yeah,
so I think the first question is, truly.
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Let's talk about what exactly is edge
computing?
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And, you know, give me the basics of it.
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Yeah, I can take that. So
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there's
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lots of interpretation
of what is edge computing.
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If you say edge
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compute, depends who you're talking to
and what they will think it is.
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So they might think it's an enterprise
edge.
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They might think it's data center edge,
they might think it's something else.
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But I think what we're talking about
is what's at the very edge.
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So, so edge computing
being where people and things live.
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So a good example might be
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my smartwatch, my smartwatch.
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These things didn't exist
not that long ago.
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And now they give me insightful
information
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and analysis on my activity.
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They can, understand my walking, my
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heart rate.
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They can help me play golf better.
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They do so many things, with compute power
sitting right here on my wrist. So
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edge compute is where
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what we're talking about is where people
and things live.
Yeah.
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That's great. And what, outside of.
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Let's use an example here.
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I like the Apple Watch.
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What are some benefits though,
I guess, of edge
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computing versus if that device had
to communicate somewhere else?
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Yeah.
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And I think it does both. Right.
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So it communicates with the cloud.
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So cloud computing
obviously has been around for a long time.
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And it's really given us massive benefits
of having
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huge ability
to deliver compute at a great cost.
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Now the challenge with the cloud
for where people live
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is that kind of connected
device experience.
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So we're not always connected.
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And to be able to have
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the compute power on the analysis
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available
anytime, anywhere, means that we need
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that compute to, to live up at the edge,
not always in the cloud.
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The two together are very,
very complimentary.
Sure, sure.
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What's the benefits of,
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the edge, I guess.
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In like a.
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is there another example where, you know,
the watch is small, right?
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It's doing simple, simple calculations,
I would assume, versus
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something more complex, like an autonomous
vehicle.
Yeah.
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Look.
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Years ago, there was no need for the edge.
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But, you know, Ryan chatted about his
watch there.
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But if you think of all of the edge
devices that exist, whether it's a watch,
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whether it's stuff in your kitchen,
a smart fridge, whether it's a smart TV,
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the amount of data
that is being processed at the edge.
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Now is crazy.
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So, so even in the simplistic watch,
the amount of data
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that that is processing.
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Ryan chatted there about it
makes him a better golfer.
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I'm not sure how much it could make
him be a better golfer, but
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even in that, the amount of data
that it is processing
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and needs to process right here
and right now,
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you know, you're asking there
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what is the benefit?
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You know, autonomous vehicles,
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we're not quite there yet,
but we're certainly getting there.
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Whether that's autonomous vehicle
that is on the road
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or whether
that's autonomous vehicles in the air.
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You know, the amount of data
that needs to be processed there,
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but actually the latency that it takes
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for that data to be processed in
the cloud, is declared Irrelevant.
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No, it's not irrelevant.
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But I think as technology moves forward
and the,
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you know, the amount of data
that is being processed, it's all about
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latency, it's all about speed and
and the speed that it needs to be done.
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Now if you take.
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We load a web
page 1 or 2 seconds to load a web page.
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We can probably live with that.
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Sometimes we get a little frustrated,
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but an autonomous vehicle that is.
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That,
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that changes the whole dynamic.
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Yeah.
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Where we can’t
wait milliseconds and autonomous vehicles
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certainly autonomous vehicles
that that our children will be in,
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we can’t wait milliseconds for that data
to be processed,
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sent to the cloud for it
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to be given an answer to be sent
back to the autonomous vehicle. So,
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you know, for me,
you know, as technology moves
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and moves more into autonomous.
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Vehicles it's all about latency
and speed of that data.
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(5:19)
Yeah.
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So just to kinda reiterate,
I think what we're saying is that
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the cloud is essentially this, this
supercomputer that is making decisions
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based off of what it's seeing right
from the Internet of Things device.
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And, in this instance,
if something is happening
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it has to communicate to the cloud,
that's taking a lot of time.
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But on top of that,
there are also other benefits, right?
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Cost savings from a bandwidth perspective,
I mean, is there,
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what what other.
Well, well,
as as Jonny is saying, like the more.
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the more that we have analysis
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and information living at the edge,
the more data we create.And the more data
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we we create, the more it cost
to transfer that data from point to point.
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So actually, never mind the latency,
but actually the processing of the data
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to live at the edge.
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There's a lot of data then.
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The, the,
the forecast from smarter people than me
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is that there's going to be more data
at the edge over the, over the next
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five years,
than there is living in the cloud.
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And that, that requires more devices
to live at the edge, more compute power
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to live at the edge, just to process that
data and to store it, and to manage it.
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So, so edge compute is becoming more
and more critical, compared to
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what it might have
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been seen as and in years gone by,
whereas it's non-essential.
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So it's not critical.
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Hardware for the enterprise
is not critical hardware for individuals,
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but it's becoming more and more critical.
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The other example that I quite like,
that you were kind of touching on Jonny,
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you know, lack of patience as humans is a.
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The autonomous drive through.
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So, you know,
in, in quick serve restaurants,
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they're really moving towards,
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how do we automate all these processes?
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And if you've got.
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If somebody drives up and says, hey,
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can I order up a burger?
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Like, in your accent,
it's probably gonna understand you,
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in my accent, It might think a little, but
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but but if it takes a few seconds,
if it's not that, natural interaction
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between machine and human,
we're going to know.
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And we're also going to just think,
I don't like that,
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I'd rather go somewhere else
for that, that human interaction. So,
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that means that as computers
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got to deliver performance
and it's got to have that real experience.
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Otherwise we're going to go
and do something else.
Yeah.
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So it saves on bandwidth right.
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But it also only sends vital information
correct?
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to the cloud
because the cloud doesn't go away. Right?
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Correct. Cloud has to be there.
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And, the cloud can sort of send messages
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to the edge devices to update,
maybe the AI or the algorithm.
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Machine learning system
that's within the edge
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device, right?
Well,
so there's four main,
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there's four main reasons
why edge, edge compute is critical.
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So the latency is one of them.
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The availability of the bandwidth.
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There's places
where it's just not available.
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Middle of the ocean, middle of a desert,
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inside a led room.
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Yeah.
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So there's places
where it's just not available.
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So you need that compute part to live
where it's not.
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When it is available, you know?
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How available is it? Is it up and down?
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You know, people live with experiences
of the broadband going out, but you
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but you rely on it.
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But if you have to rely on it
for critical infrastructure out the edge,
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that's a problem.
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And then as we move more
and more into a world where security
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and cyber security
and physical security is a problem,
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we're using the edge
to deliver security answers.
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And we need the compute part
to live in that space.
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So there's a kind of the four,
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main and critical points
why edge computing is important.
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Do you think each area or every use case,
like when you talk about the four,
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is there a priority of
this is the most important
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or is it they're all important,
but in various sectors or applications?
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I think it depends who it is
and what what the use cases like
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Jonny's example of autonomous driving,
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latency is the most critical thing
there, for sure.
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So, but there's there's probably other
examples.
Yeah.
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Ryan touched on, on QSRs,
the autonomous drive through in QSRs.
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But if you look at QSRs as a whole,
they might have technology
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that's being used there,
whether it's in sensors or cameras,
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or the, the
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autonomous drive through every,
every vertical has has
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a different requirement to security
is probably less relevant in QSR,
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but the amount of data
that's being processed,
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certainly with the amount of cameras
that are in the QSRs
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and the amount of sensors
on the autonomous drive through.
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Kiosks,
they are pervasive in QSRs.
Yeah.
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So, like to answer your question,
all four are critical,
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but there's probably,
depending on the vertical,
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there's probably three that
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are more relevant
depending on what vertical
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it is.
Sure, how, how how
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widely has this been rolled out.
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Like when we talked about extreme edge
like is where,
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outside of your Apple Watch,
which is a great example
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in a consumer system
like are businesses adopting Extreme Edge.
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And is it a thing that we just are unable
to see that we're unaware
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of that's happening
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On a technology lifecycle curve of
where is edge computing?
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Yeah, it's
still for most of its infancy. So,
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we're seeing the leading technology
adopters across the verticals adopting
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or having an edge compute strategy
in place, executing well against it.
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I think others are trying to understand
what that means and how to do it
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so that the the companies
that really can see an opportunity
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for generating revenue
off the back of connected
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devices on edge compute, are adopting
that strategy.
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The ones there are
there are others who are laggards
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and will always be laggards
and waiting for others to prove it.
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That that they're thinking about it,
I would say.
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But but it'll happen fast.
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So over
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the next five years is probably a critical
time for edge computing.
Yeah.
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Do you think there's an alternative?
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So I don't
I don't see how, especially with AI,
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you know, I
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just just going to flow through society.
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So I don't see how you can deliver that
AI without compute at the edge.
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Yeah.
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It it seems
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so sometimes I think, you know,
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like we are in that era of, of the edge
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and it has been deployed.
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Yeah.
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Whether people are aware of it or not, it,
it already has been deployed
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and they may be aware of it,
they may not like if you take,
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you know, busses for example, there might
be sensors and cameras on a bus. Now.
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You know, when people getting on
and off a bus, are they automatically
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thinking, well, you know, there's edge
compute here that is processing the
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the reality that I'm getting on
and off the bus
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because the camera's tracking
to see how many people are on the bus
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to make the bus charities more efficient,
the right bus been used in the
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in the right journey.
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You know, so
I think is there is there an alternative?
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I don't think there's an alternative,
but I think it's being used
240
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more widely than people realize.
241
00:12:27,538 --> 00:12:30,207
You know, with sensors and data,
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it just provides companies
with the ability to be more efficient
243
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and drive efficiency
by collecting the data.
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They need to understand their business
and their business model.
245
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(12:38)
And you’re
also being tracked.
246
00:12:41,218 --> 00:12:42,553
Yeah,
247
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I think too, though, is is
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is the the fact
the size factor right is changing.
249
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Right?
250
00:12:48,684 --> 00:12:50,186
These AI models as they continue
251
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to get smaller, etc., like the devices
will begin to get smaller.
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00:12:54,273 --> 00:12:57,735
And so I think
even from the edge perspective, I've heard
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people explain edge
as like a server in a, in a building,
254
00:13:00,988 --> 00:13:04,742
but then that server essentially operates
all these different devices
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within the store.
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00:13:05,951 --> 00:13:08,579
However, it's possible that in the future,
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there will be solutions
that are much smaller that can actually
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be implemented into each of those devices
in itself natively,
259
00:13:14,835 --> 00:13:17,046
which I think is the probably
the most exciting part of it. Right?
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00:13:17,046 --> 00:13:22,009
Each camera has its own AI model built
into it, communicating and processing data
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versus one centralized location further
and making it
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00:13:25,763 --> 00:13:28,766
smaller.
Absolutely.
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00:13:28,849 --> 00:13:31,352
I think it's that,
what we talked about earlier,
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this ecosystem of solutions
coming together now.
265
00:13:35,314 --> 00:13:39,068
The complementary part of cloud
infrastructure and cloud compute,
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together with advancements in software
developments,
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together with the ability
to to have compute living at the edge,
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00:13:47,868 --> 00:13:53,499
and this ecosystem of being able
to manage the devices, being able to,
269
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virtualize the devices, being able
to have the complementary connectivity.
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This brings exponential possibility
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in the industry
as well as for us as individuals.
272
00:14:04,677 --> 00:14:06,178
Right, for sure.
273
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So one of the things that I wanted
to touch on, specifically,
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is that Ryan, you have a background
that is very unique.
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You were a dairy farmer back in the day,
and I, if I recall, we had a conversation
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00:14:17,815 --> 00:14:21,402
when I was originally interviewing
to, to work with you is, around
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how that process can not only
just be automated, but how edge computing,
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00:14:25,865 --> 00:14:28,826
sort of plays a part in it.
And it was
probably the most exciting conversation.
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00:14:28,826 --> 00:14:30,327
And it sounds weird
because it's dairy farming.
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But to me, I was like,
this is incredible. And it's exciting.
281
00:14:32,788 --> 00:14:36,542
Can you go over first, you have an
unconscious bias of like, knowledge.
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00:14:36,542 --> 00:14:39,503
Excuse me, of, like, competence
of what you know about dairy farming.
283
00:14:39,670 --> 00:14:42,089
Let's explain this process
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before the edge computing is involved,
and then let's talk
285
00:14:45,009 --> 00:14:46,844
about what edge computing
was able to do for it.
Okay.
286
00:14:46,844 --> 00:14:48,929
So I was never a dairy farmer.
We'll clear that up.
287
00:14:48,929 --> 00:14:51,765
Oh my. My my father was a dairy farmer.
288
00:14:51,765 --> 00:14:56,520
And I come from generations of, of dairy
farmers in County Armagh in Ireland.
289
00:14:56,687 --> 00:14:59,607
Yeah. The dairy farm is still there.
290
00:14:59,607 --> 00:15:02,276
It exists now.
291
00:15:02,276 --> 00:15:05,321
Like it's almost like a museum piece
compared to what modern dairy
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00:15:05,321 --> 00:15:07,031
farming is.
So,
293
00:15:08,490 --> 00:15:09,033
dairy is
294
00:15:09,033 --> 00:15:12,036
very, very important to the world. And,
295
00:15:12,828 --> 00:15:15,497
and what what we need from,
296
00:15:15,497 --> 00:15:18,500
from cattle is to produce milk.
297
00:15:18,542 --> 00:15:22,671
The most, that's gone
from sitting on a stool with a bucket
298
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under a cow, hand milking to then,
you know,
299
00:15:28,344 --> 00:15:31,889
industrial revolution, and technology
being able to do that
300
00:15:32,222 --> 00:15:35,726
through a farmer, being able to milk
multiple cows at once.
301
00:15:35,893 --> 00:15:38,896
That is the action of putting the,
302
00:15:38,979 --> 00:15:41,982
the the suction cups on the,
on the utter of the cow.
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00:15:42,274 --> 00:15:47,154
And, and now we've got a completely
automated process in, in agriculture.
304
00:15:47,404 --> 00:15:51,033
So it's, it's that end to end management
305
00:15:51,033 --> 00:15:54,036
of a dairy farm from,
306
00:15:54,536 --> 00:15:55,412
birth.
307
00:15:55,412 --> 00:15:56,664
How they're managed,
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00:15:56,664 --> 00:16:00,793
how the cattle are managed from birth
through their life cycle, the health care.
309
00:16:02,127 --> 00:16:03,837
And how that,
310
00:16:03,837 --> 00:16:08,425
automated management of the health care
for hoof health care or any other part
311
00:16:08,425 --> 00:16:12,554
of critical health care connected to the
the, the local veterinaries,
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00:16:14,098 --> 00:16:15,516
right through to,
313
00:16:15,516 --> 00:16:19,019
automated machines
that can milk and manage the,
314
00:16:19,019 --> 00:16:24,858
the the milk output, protein output,
fat content of the milk right through
315
00:16:25,359 --> 00:16:30,197
connecting that to the logistics
of how you get that milk from the farms
316
00:16:30,197 --> 00:16:34,410
and the network of farms or the co-ops
right through to on on the shelves
317
00:16:34,410 --> 00:16:35,536
of our supermarkets.
318
00:16:35,536 --> 00:16:39,581
So, so edge computing is living
right through that process.
319
00:16:39,915 --> 00:16:42,918
And it's fascinating. So,
320
00:16:44,003 --> 00:16:45,629
can I tell another quick story
about that? Yeah.
321
00:16:45,629 --> 00:16:50,676
So, one of our, colleagues, Matt Ford,
I think he might be on another episode.
322
00:16:51,844 --> 00:16:54,388
I used to work with him
when I was in Cisco Systems,
323
00:16:54,388 --> 00:16:57,850
and I remember talking to him
maybe 15 years ago about,
324
00:16:58,100 --> 00:17:01,103
hey, what if we put a rotor on a cow,
a router, I'm sorry.
325
00:17:01,437 --> 00:17:03,230
On a on a cow.
326
00:17:03,230 --> 00:17:05,399
And he was like, don't be ridiculous.
327
00:17:05,399 --> 00:17:08,193
But now, you know,
the amount of technology,
328
00:17:08,193 --> 00:17:13,073
machine vision and, and connectivity
that that, that the cattle even wearing
329
00:17:13,407 --> 00:17:16,118
to to manage their health care,
their food,
330
00:17:16,118 --> 00:17:20,914
their the, the, the entire process around
a farm is, is astronomical.
331
00:17:21,081 --> 00:17:24,084
So edge computing lives in agriculture
332
00:17:24,084 --> 00:17:27,838
and is really driving efficiencies
better for the cattle.
333
00:17:28,005 --> 00:17:30,924
And it's also better for
the earth because actually,
334
00:17:31,884 --> 00:17:34,261
a higher yield output per
335
00:17:34,261 --> 00:17:38,807
cow means lower emissions
from a, a herd of cattle.
336
00:17:38,807 --> 00:17:42,895
So it's that, you know, compute is helping
all of those things.
337
00:17:42,895 --> 00:17:44,104
It's quite hard
to connect all those things.
338
00:17:44,104 --> 00:17:48,567
But yeah,
But you give an example
of how it's being utilized,
339
00:17:48,692 --> 00:17:51,195
I guess, like what is the system,
the process of edge
340
00:17:51,195 --> 00:17:54,448
computing to be involved in dairy farming
today.
Yeah. So,
341
00:17:57,409 --> 00:17:58,368
I'll give you a quick example.
342
00:17:58,368 --> 00:17:59,536
So machine vision.
343
00:17:59,536 --> 00:18:03,582
So, a camera, that might live in the farm.
344
00:18:03,582 --> 00:18:05,793
It might be able to identify.
345
00:18:05,793 --> 00:18:07,294
Okay,
346
00:18:07,294 --> 00:18:08,212
what is that cow.
347
00:18:08,212 --> 00:18:11,215
What cow number is that? That I see.
348
00:18:11,215 --> 00:18:14,426
How is it walking today
compared to how it walked yesterday?
349
00:18:14,885 --> 00:18:17,888
I can use local compute
350
00:18:18,055 --> 00:18:21,517
together with a large language model
that might live in the cloud.
351
00:18:21,517 --> 00:18:23,018
To understand how that.
352
00:18:23,018 --> 00:18:26,021
How is that?
What is the health care of that,
353
00:18:26,522 --> 00:18:29,483
cow like today compared to yesterday?
354
00:18:29,483 --> 00:18:32,402
And if I can, if it
has a problem so that that,
355
00:18:34,113 --> 00:18:36,907
camera will do get analysis.
356
00:18:36,907 --> 00:18:39,660
You can see how to walk yesterday
compared to how it walks today.
357
00:18:39,660 --> 00:18:42,663
There's great stories of,
358
00:18:42,663 --> 00:18:47,584
cows that have hoof problems
when they walk past the farmer.
359
00:18:47,584 --> 00:18:50,087
They can walk naturally
and they walk around the corner,
360
00:18:50,087 --> 00:18:53,006
you know, they start limping again
so they they can know this.
361
00:18:53,006 --> 00:18:55,801
But they can't do that
for, for a camera. Right.
362
00:18:55,801 --> 00:19:00,806
So that is, you know, that,
that connected device, that, that camera
363
00:19:00,973 --> 00:19:06,145
connected to compute on the living it
with the camera connected to the cloud.
364
00:19:06,145 --> 00:19:09,690
And the large language model
can do analysis on that, on that cattle
365
00:19:09,982 --> 00:19:12,985
and have, you know, give it a better
lifespan.
366
00:19:13,735 --> 00:19:15,320
Better. Better.
367
00:19:15,320 --> 00:19:19,408
More healthy life, while it's
existing on the farm.
Yeah.
368
00:19:19,950 --> 00:19:21,493
And you're saying that. Just to
369
00:19:22,953 --> 00:19:24,830
reiterate, the farmer
370
00:19:24,830 --> 00:19:28,458
may not necessarily
always see the gate issue,
371
00:19:29,042 --> 00:19:32,171
even though they have 40 years
of experience doing it.
372
00:19:32,171 --> 00:19:35,174
The AI model might be able to pick it up
a little bit better.
Well,
373
00:19:35,716 --> 00:19:38,719
I'm not sure you'll ever meet a farmer
who will admit that,
374
00:19:38,886 --> 00:19:41,096
because, you know, they they
they know their, you know,
375
00:19:41,096 --> 00:19:44,933
farmers are so proud of
of knowing their, their livestock.
376
00:19:45,267 --> 00:19:46,977
And having a great relationship
with their livestock.
377
00:19:46,977 --> 00:19:50,689
And they do and they are, but a lot of,
378
00:19:51,148 --> 00:19:54,193
progressive farmers
are, are adopting technology
379
00:19:54,443 --> 00:19:57,446
to, to help their farm become,
380
00:19:57,613 --> 00:20:01,158
more efficient, can generate more cash,
but it's also better for,
381
00:20:01,366 --> 00:20:05,495
for their livestock and,
and we know farmers on the whole generally
382
00:20:05,495 --> 00:20:09,333
really care about their livestock.
The snippet I got is that farmers
383
00:20:09,333 --> 00:20:13,086
aren't as good as technology,
and that they're out of their job to
384
00:20:13,295 --> 00:20:13,712
say,tech is taking over.
385
00:20:13,712 --> 00:20:15,756
I will get in big trouble
with my dad for that.
386
00:20:15,756 --> 00:20:17,883
No, we're putting it out
there. Yeah, thanks. Yeah.
387
00:20:19,718 --> 00:20:20,344
This is great.
388
00:20:20,344 --> 00:20:23,847
So are there any other industries
that you could think of that benefit
389
00:20:23,847 --> 00:20:26,308
from edge technology?
Just kind of swinging in there.
390
00:20:26,308 --> 00:20:27,184
I'm sure they all do.
391
00:20:27,184 --> 00:20:30,103
But, I mean, is there anything off
the top of the mind.
392
00:20:30,103 --> 00:20:32,189
Yeah, I've got one. Okay.
393
00:20:32,189 --> 00:20:37,069
I think that I think
where a lot of people are looking for edge
394
00:20:37,069 --> 00:20:41,156
compute to, to interact
with us as consumers is in retail.
395
00:20:41,740 --> 00:20:44,743
So in that retail experience of
396
00:20:44,785 --> 00:20:47,663
at the minute,
a lot of people are buying from Amazon,
397
00:20:47,663 --> 00:20:52,876
right.
But actually the,
the as humans we like that relationship.
398
00:20:52,876 --> 00:20:54,795
So we, we want to shop local.
399
00:20:54,795 --> 00:20:56,755
But sometimes you find it difficult.
400
00:20:56,755 --> 00:21:00,759
I think that retail experience is changing
and it's changing fast.
401
00:21:00,759 --> 00:21:02,636
So out of out of quick service.
402
00:21:02,636 --> 00:21:05,722
Outside of quick serve restaurants,
edge compute for,
403
00:21:06,390 --> 00:21:09,518
an independent, independent retailer
or a chain of retailers
404
00:21:09,851 --> 00:21:11,603
is having a, a big impact.
405
00:21:13,313 --> 00:21:14,314
I'll give you a few examples.
406
00:21:14,314 --> 00:21:17,276
Digital signage used to be
407
00:21:17,276 --> 00:21:21,530
used to be outside
the realm of cost for most, retailers.
408
00:21:21,738 --> 00:21:25,242
And now the cost of digital signage
is, is lowering.
409
00:21:25,242 --> 00:21:28,245
So, so these retailers can run,
410
00:21:28,453 --> 00:21:32,165
special events or discount
events on the fly throughout the day,
411
00:21:32,291 --> 00:21:35,294
depending on what the weather's like,
depending on how many people
412
00:21:35,294 --> 00:21:39,673
are in the store.
So they can have
customizable events connected to
413
00:21:40,465 --> 00:21:44,011
door counters, camera counters, queue
counters, Wi-Fi accessibility.
414
00:21:45,470 --> 00:21:48,682
And it gives us as consumers
a different retail experience,
415
00:21:48,932 --> 00:21:52,394
which is really exciting
that that stock management for,
416
00:21:52,394 --> 00:21:55,397
can I get something?
417
00:21:55,564 --> 00:21:57,149
you know, if we're looking for something
418
00:21:57,149 --> 00:22:00,152
niche, how do we
how do we get that locally?
419
00:22:00,360 --> 00:22:04,114
That's going to start
to help the supply chain analysis
420
00:22:04,114 --> 00:22:07,909
and the, the logistics
for accessibility of
421
00:22:08,118 --> 00:22:11,580
of of hard to find, items.
422
00:22:11,580 --> 00:22:16,168
So for consumers, I think retail is going
to have a very, very short term
423
00:22:16,877 --> 00:22:20,922
impact, edge compute is going to deliver
some great impact for how we shop.
424
00:22:21,715 --> 00:22:24,718
And it already is with amazing
kiosks a number of times,
425
00:22:24,926 --> 00:22:27,929
but kiosks and self-checkout.
426
00:22:28,013 --> 00:22:28,347
Love it or luth it.
427
00:22:28,347 --> 00:22:34,436
It will get better.Retail is going to have
a great, transformation
428
00:22:34,686 --> 00:22:38,023
in the next few years because of edge
compute.
Awesome.
429
00:22:38,023 --> 00:22:40,734
Well,
thank you guys so much for taking time
430
00:22:40,734 --> 00:22:43,737
out of your day to talk about the basics
of edge computing.
431
00:22:43,862 --> 00:22:48,408
And thank you so much for tuning in
and listening to The Edge Tech podcast.
432
00:22:48,700 --> 00:22:49,993
Again, I'm David Kosloski.
433
00:22:49,993 --> 00:22:52,788
If you like this episode, please
like, share, subscribe or comment.
434
00:22:52,788 --> 00:22:53,997
It really helps out the show.
435
00:22:53,997 --> 00:22:56,583
It helps us produce more content.
Thank you so much for listening.
436
00:22:56,583 --> 00:22:57,000
Take care.
00:00:07,465 --> 00:00:08,508
Hello and welcome to the Edge
2
00:00:08,508 --> 00:00:11,720
Tech podcast, where technology meets
the edge of innovation.
3
00:00:12,012 --> 00:00:14,848
I'm David Kosloski and today
we're talking about edge computing
4
00:00:14,848 --> 00:00:18,476
and really just the basics of
what is the edge.
5
00:00:18,893 --> 00:00:19,728
Alongside me today
6
00:00:19,728 --> 00:00:23,023
I've got the chief revenue officer, Ryan
McAnlis.
Hey, David.
7
00:00:23,064 --> 00:00:24,315
Hey, thanks for coming.
8
00:00:24,315 --> 00:00:28,445
And, also we have Jonny Smith,
who is the CEO of Simply Nuc.
9
00:00:29,070 --> 00:00:30,488
They're both from Simply Nuc,
I guess I didn’t
10
00:00:30,488 --> 00:00:33,033
bring up the company
that we all wonderfully work for.
11
00:00:33,033 --> 00:00:35,702
But, Jonny, thanks for being here. Thanks.
Good to have you here.
12
00:00:35,702 --> 00:00:37,328
Yeah, I am happy to be here.
13
00:00:37,328 --> 00:00:42,333
Thanks for having me.
Yeah,
so I think the first question is, truly.
14
00:00:42,333 --> 00:00:45,336
Let's talk about what exactly is edge
computing?
15
00:00:45,795 --> 00:00:48,798
And, you know, give me the basics of it.
16
00:00:49,132 --> 00:00:51,009
Yeah, I can take that. So
17
00:00:52,469 --> 00:00:52,719
there's
18
00:00:52,719 --> 00:00:55,889
lots of interpretation
of what is edge computing.
19
00:00:55,889 --> 00:00:56,431
If you say edge
20
00:00:56,431 --> 00:00:59,517
compute, depends who you're talking to
and what they will think it is.
21
00:00:59,517 --> 00:01:01,728
So they might think it's an enterprise
edge.
22
00:01:01,728 --> 00:01:06,024
They might think it's data center edge,
they might think it's something else.
23
00:01:06,024 --> 00:01:09,486
But I think what we're talking about
is what's at the very edge.
24
00:01:09,486 --> 00:01:13,865
So, so edge computing
being where people and things live.
25
00:01:15,241 --> 00:01:17,786
So a good example might be
26
00:01:17,786 --> 00:01:20,580
my smartwatch, my smartwatch.
27
00:01:20,580 --> 00:01:22,832
These things didn't exist
not that long ago.
28
00:01:22,832 --> 00:01:25,627
And now they give me insightful
information
29
00:01:25,627 --> 00:01:28,630
and analysis on my activity.
30
00:01:29,672 --> 00:01:33,468
They can, understand my walking, my
31
00:01:35,011 --> 00:01:35,845
heart rate.
32
00:01:35,845 --> 00:01:37,472
They can help me play golf better.
33
00:01:37,472 --> 00:01:42,268
They do so many things, with compute power
sitting right here on my wrist. So
34
00:01:43,311 --> 00:01:44,854
edge compute is where
35
00:01:44,854 --> 00:01:48,233
what we're talking about is where people
and things live.
Yeah.
36
00:01:48,274 --> 00:01:50,401
That's great. And what, outside of.
37
00:01:50,401 --> 00:01:52,612
Let's use an example here.
38
00:01:52,612 --> 00:01:54,447
I like the Apple Watch.
39
00:01:54,447 --> 00:01:57,575
What are some benefits though,
I guess, of edge
40
00:01:57,575 --> 00:02:00,745
computing versus if that device had
to communicate somewhere else?
41
00:02:00,745 --> 00:02:01,621
Yeah.
42
00:02:01,621 --> 00:02:03,540
And I think it does both. Right.
43
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So it communicates with the cloud.
44
00:02:06,668 --> 00:02:10,630
So cloud computing
obviously has been around for a long time.
45
00:02:10,630 --> 00:02:14,968
And it's really given us massive benefits
of having
46
00:02:15,176 --> 00:02:19,055
huge ability
to deliver compute at a great cost.
47
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Now the challenge with the cloud
for where people live
48
00:02:22,934 --> 00:02:26,187
is that kind of connected
device experience.
49
00:02:26,396 --> 00:02:28,523
So we're not always connected.
50
00:02:28,523 --> 00:02:31,526
And to be able to have
51
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the compute power on the analysis
52
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available
anytime, anywhere, means that we need
53
00:02:38,241 --> 00:02:42,829
that compute to, to live up at the edge,
not always in the cloud.
54
00:02:42,871 --> 00:02:45,832
The two together are very,
very complimentary.
Sure, sure.
55
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What's the benefits of,
56
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the edge, I guess.
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In like a.
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is there another example where, you know,
the watch is small, right?
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It's doing simple, simple calculations,
I would assume, versus
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something more complex, like an autonomous
vehicle.
Yeah.
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Look.
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Years ago, there was no need for the edge.
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But, you know, Ryan chatted about his
watch there.
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But if you think of all of the edge
devices that exist, whether it's a watch,
65
00:03:14,068 --> 00:03:18,448
whether it's stuff in your kitchen,
a smart fridge, whether it's a smart TV,
66
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the amount of data
that is being processed at the edge.
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Now is crazy.
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So, so even in the simplistic watch,
the amount of data
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that that is processing.
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00:03:31,461 --> 00:03:34,505
Ryan chatted there about it
makes him a better golfer.
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00:03:34,505 --> 00:03:37,133
I'm not sure how much it could make
him be a better golfer, but
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00:03:38,176 --> 00:03:41,179
even in that, the amount of data
that it is processing
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and needs to process right here
and right now,
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you know, you're asking there
75
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what is the benefit?
76
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You know, autonomous vehicles,
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we're not quite there yet,
but we're certainly getting there.
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Whether that's autonomous vehicle
that is on the road
79
00:04:00,657 --> 00:04:03,660
or whether
that's autonomous vehicles in the air.
80
00:04:04,410 --> 00:04:07,622
You know, the amount of data
that needs to be processed there,
81
00:04:09,207 --> 00:04:11,834
but actually the latency that it takes
82
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for that data to be processed in
the cloud, is declared Irrelevant.
83
00:04:15,338 --> 00:04:16,381
No, it's not irrelevant.
84
00:04:16,381 --> 00:04:20,343
But I think as technology moves forward
and the,
85
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you know, the amount of data
that is being processed, it's all about
86
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latency, it's all about speed and
and the speed that it needs to be done.
87
00:04:29,185 --> 00:04:31,521
Now if you take.
88
00:04:31,521 --> 00:04:35,942
We load a web
page 1 or 2 seconds to load a web page.
89
00:04:36,526 --> 00:04:37,944
We can probably live with that.
90
00:04:37,944 --> 00:04:39,487
Sometimes we get a little frustrated,
91
00:04:40,613 --> 00:04:43,324
but an autonomous vehicle that is.
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That,
93
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that changes the whole dynamic.
94
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Yeah.
95
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Where we can’t
wait milliseconds and autonomous vehicles
96
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certainly autonomous vehicles
that that our children will be in,
97
00:04:55,211 --> 00:04:59,132
we can’t wait milliseconds for that data
to be processed,
98
00:04:59,424 --> 00:05:02,427
sent to the cloud for it
99
00:05:02,719 --> 00:05:05,722
to be given an answer to be sent
back to the autonomous vehicle. So,
100
00:05:06,055 --> 00:05:10,351
you know, for me,
you know, as technology moves
101
00:05:10,351 --> 00:05:13,354
and moves more into autonomous.
102
00:05:14,272 --> 00:05:17,942
Vehicles it's all about latency
and speed of that data.
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00:05:17,942 --> 00:05:18,568
(5:19)
Yeah.
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00:05:18,568 --> 00:05:21,696
So just to kinda reiterate,
I think what we're saying is that
105
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the cloud is essentially this, this
supercomputer that is making decisions
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based off of what it's seeing right
from the Internet of Things device.
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And, in this instance,
if something is happening
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it has to communicate to the cloud,
that's taking a lot of time.
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00:05:35,960 --> 00:05:38,963
But on top of that,
there are also other benefits, right?
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Cost savings from a bandwidth perspective,
I mean, is there,
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00:05:41,966 --> 00:05:45,887
what what other.
Well, well,
as as Jonny is saying, like the more.
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the more that we have analysis
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and information living at the edge,
the more data we create.And the more data
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we we create, the more it cost
to transfer that data from point to point.
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00:05:56,606 --> 00:06:00,777
So actually, never mind the latency,
but actually the processing of the data
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to live at the edge.
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There's a lot of data then.
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The, the,
the forecast from smarter people than me
119
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is that there's going to be more data
at the edge over the, over the next
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00:06:09,285 --> 00:06:12,288
five years,
than there is living in the cloud.
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00:06:12,663 --> 00:06:16,334
And that, that requires more devices
to live at the edge, more compute power
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00:06:16,584 --> 00:06:21,756
to live at the edge, just to process that
data and to store it, and to manage it.
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00:06:21,839 --> 00:06:27,261
So, so edge compute is becoming more
and more critical, compared to
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what it might have
125
00:06:29,097 --> 00:06:32,225
been seen as and in years gone by,
whereas it's non-essential.
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00:06:32,225 --> 00:06:33,684
So it's not critical.
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00:06:33,684 --> 00:06:36,813
Hardware for the enterprise
is not critical hardware for individuals,
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00:06:37,063 --> 00:06:40,066
but it's becoming more and more critical.
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00:06:40,525 --> 00:06:43,653
The other example that I quite like,
that you were kind of touching on Jonny,
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you know, lack of patience as humans is a.
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The autonomous drive through.
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So, you know,
in, in quick serve restaurants,
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they're really moving towards,
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how do we automate all these processes?
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And if you've got.
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If somebody drives up and says, hey,
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can I order up a burger?
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00:07:03,423 --> 00:07:06,509
Like, in your accent,
it's probably gonna understand you,
139
00:07:06,551 --> 00:07:09,554
in my accent, It might think a little, but
140
00:07:10,513 --> 00:07:16,144
but but if it takes a few seconds,
if it's not that, natural interaction
141
00:07:16,144 --> 00:07:20,231
between machine and human,
we're going to know.
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And we're also going to just think,
I don't like that,
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I'd rather go somewhere else
for that, that human interaction. So,
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that means that as computers
145
00:07:28,239 --> 00:07:32,201
got to deliver performance
and it's got to have that real experience.
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Otherwise we're going to go
and do something else.
Yeah.
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00:07:35,663 --> 00:07:38,249
So it saves on bandwidth right.
148
00:07:38,249 --> 00:07:40,918
But it also only sends vital information
correct?
149
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to the cloud
because the cloud doesn't go away. Right?
150
00:07:43,171 --> 00:07:45,006
Correct. Cloud has to be there.
151
00:07:45,006 --> 00:07:48,676
And, the cloud can sort of send messages
152
00:07:48,676 --> 00:07:53,097
to the edge devices to update,
maybe the AI or the algorithm.
153
00:07:53,222 --> 00:07:55,016
Machine learning system
that's within the edge
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00:07:55,016 --> 00:07:56,893
device, right?
Well,
so there's four main,
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there's four main reasons
why edge, edge compute is critical.
156
00:08:00,605 --> 00:08:03,566
So the latency is one of them.
157
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The availability of the bandwidth.
158
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There's places
where it's just not available.
159
00:08:09,864 --> 00:08:11,491
Middle of the ocean, middle of a desert,
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inside a led room.
161
00:08:14,702 --> 00:08:15,536
Yeah.
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00:08:15,536 --> 00:08:18,539
So there's places
where it's just not available.
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So you need that compute part to live
where it's not.
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00:08:21,417 --> 00:08:23,252
When it is available, you know?
165
00:08:23,252 --> 00:08:25,546
How available is it? Is it up and down?
166
00:08:25,546 --> 00:08:29,467
You know, people live with experiences
of the broadband going out, but you
167
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but you rely on it.
168
00:08:30,968 --> 00:08:34,263
But if you have to rely on it
for critical infrastructure out the edge,
169
00:08:34,472 --> 00:08:35,431
that's a problem.
170
00:08:35,431 --> 00:08:38,935
And then as we move more
and more into a world where security
171
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and cyber security
and physical security is a problem,
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we're using the edge
to deliver security answers.
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And we need the compute part
to live in that space.
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00:08:49,570 --> 00:08:51,656
So there's a kind of the four,
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main and critical points
why edge computing is important.
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00:08:54,992 --> 00:08:58,663
Do you think each area or every use case,
like when you talk about the four,
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00:08:58,663 --> 00:09:01,165
is there a priority of
this is the most important
178
00:09:01,165 --> 00:09:05,002
or is it they're all important,
but in various sectors or applications?
179
00:09:05,002 --> 00:09:09,590
I think it depends who it is
and what what the use cases like
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Jonny's example of autonomous driving,
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latency is the most critical thing
there, for sure.
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So, but there's there's probably other
examples.
Yeah.
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00:09:18,182 --> 00:09:22,520
Ryan touched on, on QSRs,
the autonomous drive through in QSRs.
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00:09:22,520 --> 00:09:25,856
But if you look at QSRs as a whole,
they might have technology
185
00:09:25,856 --> 00:09:30,486
that's being used there,
whether it's in sensors or cameras,
186
00:09:31,862 --> 00:09:33,406
or the, the
187
00:09:33,406 --> 00:09:36,784
autonomous drive through every,
every vertical has has
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00:09:36,784 --> 00:09:41,247
a different requirement to security
is probably less relevant in QSR,
189
00:09:41,247 --> 00:09:44,250
but the amount of data
that's being processed,
190
00:09:44,584 --> 00:09:46,919
certainly with the amount of cameras
that are in the QSRs
191
00:09:46,919 --> 00:09:50,047
and the amount of sensors
on the autonomous drive through.
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Kiosks,
they are pervasive in QSRs.
Yeah.
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00:09:53,426 --> 00:09:57,888
So, like to answer your question,
all four are critical,
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00:09:58,556 --> 00:10:01,976
but there's probably,
depending on the vertical,
195
00:10:02,518 --> 00:10:05,354
there's probably three that
196
00:10:05,354 --> 00:10:08,733
are more relevant
depending on what vertical
197
00:10:09,025 --> 00:10:11,819
it is.
Sure, how, how how
198
00:10:11,819 --> 00:10:13,696
widely has this been rolled out.
199
00:10:13,696 --> 00:10:16,699
Like when we talked about extreme edge
like is where,
200
00:10:16,699 --> 00:10:18,743
outside of your Apple Watch,
which is a great example
201
00:10:18,743 --> 00:10:22,288
in a consumer system
like are businesses adopting Extreme Edge.
202
00:10:22,496 --> 00:10:26,334
And is it a thing that we just are unable
to see that we're unaware
203
00:10:26,334 --> 00:10:27,001
of that's happening
204
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On a technology lifecycle curve of
where is edge computing?
205
00:10:32,590 --> 00:10:35,593
Yeah, it's
still for most of its infancy. So,
206
00:10:36,594 --> 00:10:41,641
we're seeing the leading technology
adopters across the verticals adopting
207
00:10:41,641 --> 00:10:46,145
or having an edge compute strategy
in place, executing well against it.
208
00:10:46,854 --> 00:10:50,608
I think others are trying to understand
what that means and how to do it
209
00:10:50,941 --> 00:10:54,654
so that the the companies
that really can see an opportunity
210
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for generating revenue
off the back of connected
211
00:10:57,782 --> 00:11:00,951
devices on edge compute, are adopting
that strategy.
212
00:11:00,951 --> 00:11:04,205
The ones there are
there are others who are laggards
213
00:11:04,205 --> 00:11:07,166
and will always be laggards
and waiting for others to prove it.
214
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That that they're thinking about it,
I would say.
215
00:11:10,670 --> 00:11:11,837
But but it'll happen fast.
216
00:11:11,837 --> 00:11:12,171
So over
217
00:11:12,171 --> 00:11:15,424
the next five years is probably a critical
time for edge computing.
Yeah.
218
00:11:15,508 --> 00:11:16,258
Do you think there's an alternative?
219
00:11:16,258 --> 00:11:20,179
So I don't
I don't see how, especially with AI,
220
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you know, I
221
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just just going to flow through society.
222
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So I don't see how you can deliver that
AI without compute at the edge.
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Yeah.
224
00:11:31,232 --> 00:11:33,651
It it seems
225
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so sometimes I think, you know,
226
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like we are in that era of, of the edge
227
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and it has been deployed.
228
00:11:43,285 --> 00:11:43,494
Yeah.
229
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Whether people are aware of it or not, it,
it already has been deployed
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and they may be aware of it,
they may not like if you take,
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00:11:51,919 --> 00:11:55,381
you know, busses for example, there might
be sensors and cameras on a bus. Now.
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You know, when people getting on
and off a bus, are they automatically
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thinking, well, you know, there's edge
compute here that is processing the
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00:12:04,890 --> 00:12:07,017
the reality that I'm getting on
and off the bus
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because the camera's tracking
to see how many people are on the bus
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to make the bus charities more efficient,
the right bus been used in the
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in the right journey.
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You know, so
I think is there is there an alternative?
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00:12:19,613 --> 00:12:23,492
I don't think there's an alternative,
but I think it's being used
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more widely than people realize.
241
00:12:27,538 --> 00:12:30,207
You know, with sensors and data,
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00:12:30,207 --> 00:12:34,128
it just provides companies
with the ability to be more efficient
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00:12:34,587 --> 00:12:37,131
and drive efficiency
by collecting the data.
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They need to understand their business
and their business model.
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(12:38)
And you’re
also being tracked.
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00:12:41,218 --> 00:12:42,553
Yeah,
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I think too, though, is is
248
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is the the fact
the size factor right is changing.
249
00:12:47,933 --> 00:12:48,684
Right?
250
00:12:48,684 --> 00:12:50,186
These AI models as they continue
251
00:12:50,186 --> 00:12:54,064
to get smaller, etc., like the devices
will begin to get smaller.
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And so I think
even from the edge perspective, I've heard
253
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people explain edge
as like a server in a, in a building,
254
00:13:00,988 --> 00:13:04,742
but then that server essentially operates
all these different devices
255
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within the store.
256
00:13:05,951 --> 00:13:08,579
However, it's possible that in the future,
257
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there will be solutions
that are much smaller that can actually
258
00:13:11,415 --> 00:13:14,543
be implemented into each of those devices
in itself natively,
259
00:13:14,835 --> 00:13:17,046
which I think is the probably
the most exciting part of it. Right?
260
00:13:17,046 --> 00:13:22,009
Each camera has its own AI model built
into it, communicating and processing data
261
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versus one centralized location further
and making it
262
00:13:25,763 --> 00:13:28,766
smaller.
Absolutely.
263
00:13:28,849 --> 00:13:31,352
I think it's that,
what we talked about earlier,
264
00:13:31,352 --> 00:13:35,022
this ecosystem of solutions
coming together now.
265
00:13:35,314 --> 00:13:39,068
The complementary part of cloud
infrastructure and cloud compute,
266
00:13:39,276 --> 00:13:43,155
together with advancements in software
developments,
267
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together with the ability
to to have compute living at the edge,
268
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and this ecosystem of being able
to manage the devices, being able to,
269
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virtualize the devices, being able
to have the complementary connectivity.
270
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This brings exponential possibility
271
00:14:01,549 --> 00:14:04,677
in the industry
as well as for us as individuals.
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Right, for sure.
273
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So one of the things that I wanted
to touch on, specifically,
274
00:14:09,265 --> 00:14:12,643
is that Ryan, you have a background
that is very unique.
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You were a dairy farmer back in the day,
and I, if I recall, we had a conversation
276
00:14:17,815 --> 00:14:21,402
when I was originally interviewing
to, to work with you is, around
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00:14:21,694 --> 00:14:25,197
how that process can not only
just be automated, but how edge computing,
278
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sort of plays a part in it.
And it was
probably the most exciting conversation.
279
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And it sounds weird
because it's dairy farming.
280
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But to me, I was like,
this is incredible. And it's exciting.
281
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Can you go over first, you have an
unconscious bias of like, knowledge.
282
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Excuse me, of, like, competence
of what you know about dairy farming.
283
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Let's explain this process
284
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before the edge computing is involved,
and then let's talk
285
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about what edge computing
was able to do for it.
Okay.
286
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So I was never a dairy farmer.
We'll clear that up.
287
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Oh my. My my father was a dairy farmer.
288
00:14:51,765 --> 00:14:56,520
And I come from generations of, of dairy
farmers in County Armagh in Ireland.
289
00:14:56,687 --> 00:14:59,607
Yeah. The dairy farm is still there.
290
00:14:59,607 --> 00:15:02,276
It exists now.
291
00:15:02,276 --> 00:15:05,321
Like it's almost like a museum piece
compared to what modern dairy
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00:15:05,321 --> 00:15:07,031
farming is.
So,
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00:15:08,490 --> 00:15:09,033
dairy is
294
00:15:09,033 --> 00:15:12,036
very, very important to the world. And,
295
00:15:12,828 --> 00:15:15,497
and what what we need from,
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00:15:15,497 --> 00:15:18,500
from cattle is to produce milk.
297
00:15:18,542 --> 00:15:22,671
The most, that's gone
from sitting on a stool with a bucket
298
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under a cow, hand milking to then,
you know,
299
00:15:28,344 --> 00:15:31,889
industrial revolution, and technology
being able to do that
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00:15:32,222 --> 00:15:35,726
through a farmer, being able to milk
multiple cows at once.
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That is the action of putting the,
302
00:15:38,979 --> 00:15:41,982
the the suction cups on the,
on the utter of the cow.
303
00:15:42,274 --> 00:15:47,154
And, and now we've got a completely
automated process in, in agriculture.
304
00:15:47,404 --> 00:15:51,033
So it's, it's that end to end management
305
00:15:51,033 --> 00:15:54,036
of a dairy farm from,
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00:15:54,536 --> 00:15:55,412
birth.
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How they're managed,
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00:15:56,664 --> 00:16:00,793
how the cattle are managed from birth
through their life cycle, the health care.
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00:16:02,127 --> 00:16:03,837
And how that,
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00:16:03,837 --> 00:16:08,425
automated management of the health care
for hoof health care or any other part
311
00:16:08,425 --> 00:16:12,554
of critical health care connected to the
the, the local veterinaries,
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00:16:14,098 --> 00:16:15,516
right through to,
313
00:16:15,516 --> 00:16:19,019
automated machines
that can milk and manage the,
314
00:16:19,019 --> 00:16:24,858
the the milk output, protein output,
fat content of the milk right through
315
00:16:25,359 --> 00:16:30,197
connecting that to the logistics
of how you get that milk from the farms
316
00:16:30,197 --> 00:16:34,410
and the network of farms or the co-ops
right through to on on the shelves
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00:16:34,410 --> 00:16:35,536
of our supermarkets.
318
00:16:35,536 --> 00:16:39,581
So, so edge computing is living
right through that process.
319
00:16:39,915 --> 00:16:42,918
And it's fascinating. So,
320
00:16:44,003 --> 00:16:45,629
can I tell another quick story
about that? Yeah.
321
00:16:45,629 --> 00:16:50,676
So, one of our, colleagues, Matt Ford,
I think he might be on another episode.
322
00:16:51,844 --> 00:16:54,388
I used to work with him
when I was in Cisco Systems,
323
00:16:54,388 --> 00:16:57,850
and I remember talking to him
maybe 15 years ago about,
324
00:16:58,100 --> 00:17:01,103
hey, what if we put a rotor on a cow,
a router, I'm sorry.
325
00:17:01,437 --> 00:17:03,230
On a on a cow.
326
00:17:03,230 --> 00:17:05,399
And he was like, don't be ridiculous.
327
00:17:05,399 --> 00:17:08,193
But now, you know,
the amount of technology,
328
00:17:08,193 --> 00:17:13,073
machine vision and, and connectivity
that that, that the cattle even wearing
329
00:17:13,407 --> 00:17:16,118
to to manage their health care,
their food,
330
00:17:16,118 --> 00:17:20,914
their the, the, the entire process around
a farm is, is astronomical.
331
00:17:21,081 --> 00:17:24,084
So edge computing lives in agriculture
332
00:17:24,084 --> 00:17:27,838
and is really driving efficiencies
better for the cattle.
333
00:17:28,005 --> 00:17:30,924
And it's also better for
the earth because actually,
334
00:17:31,884 --> 00:17:34,261
a higher yield output per
335
00:17:34,261 --> 00:17:38,807
cow means lower emissions
from a, a herd of cattle.
336
00:17:38,807 --> 00:17:42,895
So it's that, you know, compute is helping
all of those things.
337
00:17:42,895 --> 00:17:44,104
It's quite hard
to connect all those things.
338
00:17:44,104 --> 00:17:48,567
But yeah,
But you give an example
of how it's being utilized,
339
00:17:48,692 --> 00:17:51,195
I guess, like what is the system,
the process of edge
340
00:17:51,195 --> 00:17:54,448
computing to be involved in dairy farming
today.
Yeah. So,
341
00:17:57,409 --> 00:17:58,368
I'll give you a quick example.
342
00:17:58,368 --> 00:17:59,536
So machine vision.
343
00:17:59,536 --> 00:18:03,582
So, a camera, that might live in the farm.
344
00:18:03,582 --> 00:18:05,793
It might be able to identify.
345
00:18:05,793 --> 00:18:07,294
Okay,
346
00:18:07,294 --> 00:18:08,212
what is that cow.
347
00:18:08,212 --> 00:18:11,215
What cow number is that? That I see.
348
00:18:11,215 --> 00:18:14,426
How is it walking today
compared to how it walked yesterday?
349
00:18:14,885 --> 00:18:17,888
I can use local compute
350
00:18:18,055 --> 00:18:21,517
together with a large language model
that might live in the cloud.
351
00:18:21,517 --> 00:18:23,018
To understand how that.
352
00:18:23,018 --> 00:18:26,021
How is that?
What is the health care of that,
353
00:18:26,522 --> 00:18:29,483
cow like today compared to yesterday?
354
00:18:29,483 --> 00:18:32,402
And if I can, if it
has a problem so that that,
355
00:18:34,113 --> 00:18:36,907
camera will do get analysis.
356
00:18:36,907 --> 00:18:39,660
You can see how to walk yesterday
compared to how it walks today.
357
00:18:39,660 --> 00:18:42,663
There's great stories of,
358
00:18:42,663 --> 00:18:47,584
cows that have hoof problems
when they walk past the farmer.
359
00:18:47,584 --> 00:18:50,087
They can walk naturally
and they walk around the corner,
360
00:18:50,087 --> 00:18:53,006
you know, they start limping again
so they they can know this.
361
00:18:53,006 --> 00:18:55,801
But they can't do that
for, for a camera. Right.
362
00:18:55,801 --> 00:19:00,806
So that is, you know, that,
that connected device, that, that camera
363
00:19:00,973 --> 00:19:06,145
connected to compute on the living it
with the camera connected to the cloud.
364
00:19:06,145 --> 00:19:09,690
And the large language model
can do analysis on that, on that cattle
365
00:19:09,982 --> 00:19:12,985
and have, you know, give it a better
lifespan.
366
00:19:13,735 --> 00:19:15,320
Better. Better.
367
00:19:15,320 --> 00:19:19,408
More healthy life, while it's
existing on the farm.
Yeah.
368
00:19:19,950 --> 00:19:21,493
And you're saying that. Just to
369
00:19:22,953 --> 00:19:24,830
reiterate, the farmer
370
00:19:24,830 --> 00:19:28,458
may not necessarily
always see the gate issue,
371
00:19:29,042 --> 00:19:32,171
even though they have 40 years
of experience doing it.
372
00:19:32,171 --> 00:19:35,174
The AI model might be able to pick it up
a little bit better.
Well,
373
00:19:35,716 --> 00:19:38,719
I'm not sure you'll ever meet a farmer
who will admit that,
374
00:19:38,886 --> 00:19:41,096
because, you know, they they
they know their, you know,
375
00:19:41,096 --> 00:19:44,933
farmers are so proud of
of knowing their, their livestock.
376
00:19:45,267 --> 00:19:46,977
And having a great relationship
with their livestock.
377
00:19:46,977 --> 00:19:50,689
And they do and they are, but a lot of,
378
00:19:51,148 --> 00:19:54,193
progressive farmers
are, are adopting technology
379
00:19:54,443 --> 00:19:57,446
to, to help their farm become,
380
00:19:57,613 --> 00:20:01,158
more efficient, can generate more cash,
but it's also better for,
381
00:20:01,366 --> 00:20:05,495
for their livestock and,
and we know farmers on the whole generally
382
00:20:05,495 --> 00:20:09,333
really care about their livestock.
The snippet I got is that farmers
383
00:20:09,333 --> 00:20:13,086
aren't as good as technology,
and that they're out of their job to
384
00:20:13,295 --> 00:20:13,712
say,tech is taking over.
385
00:20:13,712 --> 00:20:15,756
I will get in big trouble
with my dad for that.
386
00:20:15,756 --> 00:20:17,883
No, we're putting it out
there. Yeah, thanks. Yeah.
387
00:20:19,718 --> 00:20:20,344
This is great.
388
00:20:20,344 --> 00:20:23,847
So are there any other industries
that you could think of that benefit
389
00:20:23,847 --> 00:20:26,308
from edge technology?
Just kind of swinging in there.
390
00:20:26,308 --> 00:20:27,184
I'm sure they all do.
391
00:20:27,184 --> 00:20:30,103
But, I mean, is there anything off
the top of the mind.
392
00:20:30,103 --> 00:20:32,189
Yeah, I've got one. Okay.
393
00:20:32,189 --> 00:20:37,069
I think that I think
where a lot of people are looking for edge
394
00:20:37,069 --> 00:20:41,156
compute to, to interact
with us as consumers is in retail.
395
00:20:41,740 --> 00:20:44,743
So in that retail experience of
396
00:20:44,785 --> 00:20:47,663
at the minute,
a lot of people are buying from Amazon,
397
00:20:47,663 --> 00:20:52,876
right.
But actually the,
the as humans we like that relationship.
398
00:20:52,876 --> 00:20:54,795
So we, we want to shop local.
399
00:20:54,795 --> 00:20:56,755
But sometimes you find it difficult.
400
00:20:56,755 --> 00:21:00,759
I think that retail experience is changing
and it's changing fast.
401
00:21:00,759 --> 00:21:02,636
So out of out of quick service.
402
00:21:02,636 --> 00:21:05,722
Outside of quick serve restaurants,
edge compute for,
403
00:21:06,390 --> 00:21:09,518
an independent, independent retailer
or a chain of retailers
404
00:21:09,851 --> 00:21:11,603
is having a, a big impact.
405
00:21:13,313 --> 00:21:14,314
I'll give you a few examples.
406
00:21:14,314 --> 00:21:17,276
Digital signage used to be
407
00:21:17,276 --> 00:21:21,530
used to be outside
the realm of cost for most, retailers.
408
00:21:21,738 --> 00:21:25,242
And now the cost of digital signage
is, is lowering.
409
00:21:25,242 --> 00:21:28,245
So, so these retailers can run,
410
00:21:28,453 --> 00:21:32,165
special events or discount
events on the fly throughout the day,
411
00:21:32,291 --> 00:21:35,294
depending on what the weather's like,
depending on how many people
412
00:21:35,294 --> 00:21:39,673
are in the store.
So they can have
customizable events connected to
413
00:21:40,465 --> 00:21:44,011
door counters, camera counters, queue
counters, Wi-Fi accessibility.
414
00:21:45,470 --> 00:21:48,682
And it gives us as consumers
a different retail experience,
415
00:21:48,932 --> 00:21:52,394
which is really exciting
that that stock management for,
416
00:21:52,394 --> 00:21:55,397
can I get something?
417
00:21:55,564 --> 00:21:57,149
you know, if we're looking for something
418
00:21:57,149 --> 00:22:00,152
niche, how do we
how do we get that locally?
419
00:22:00,360 --> 00:22:04,114
That's going to start
to help the supply chain analysis
420
00:22:04,114 --> 00:22:07,909
and the, the logistics
for accessibility of
421
00:22:08,118 --> 00:22:11,580
of of hard to find, items.
422
00:22:11,580 --> 00:22:16,168
So for consumers, I think retail is going
to have a very, very short term
423
00:22:16,877 --> 00:22:20,922
impact, edge compute is going to deliver
some great impact for how we shop.
424
00:22:21,715 --> 00:22:24,718
And it already is with amazing
kiosks a number of times,
425
00:22:24,926 --> 00:22:27,929
but kiosks and self-checkout.
426
00:22:28,013 --> 00:22:28,347
Love it or luth it.
427
00:22:28,347 --> 00:22:34,436
It will get better.Retail is going to have
a great, transformation
428
00:22:34,686 --> 00:22:38,023
in the next few years because of edge
compute.
Awesome.
429
00:22:38,023 --> 00:22:40,734
Well,
thank you guys so much for taking time
430
00:22:40,734 --> 00:22:43,737
out of your day to talk about the basics
of edge computing.
431
00:22:43,862 --> 00:22:48,408
And thank you so much for tuning in
and listening to The Edge Tech podcast.
432
00:22:48,700 --> 00:22:49,993
Again, I'm David Kosloski.
433
00:22:49,993 --> 00:22:52,788
If you like this episode, please
like, share, subscribe or comment.
434
00:22:52,788 --> 00:22:53,997
It really helps out the show.
435
00:22:53,997 --> 00:22:56,583
It helps us produce more content.
Thank you so much for listening.
436
00:22:56,583 --> 00:22:57,000
Take care.
