April 9, 2024 • 30 mins
Advancements in AI technology have made it possible to create virtual representations of our real-world environments, and these digital twins could change how we experience just about everything in our lives. These digital twins can create everything from more efficient workplaces to smarter traffic lights, and on the first episode of Season 2 of Technically Speaking: An Intel Podcast, digital twin expert Tony Franklin discusses what’s required to create a digital twin and how different industries can use this technology to create a safer future for everyone.
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Episode Transcript
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Speaker 1 (00:03):
Workplaces like factories or fulfillment centers are filled with many
moving parts and require constant supervision and alertness to ensure
worker safety. But what happens when errors or accidents happen
unexpectedly On the simplest level, it creates chaos and can
impact productivity. On a larger scale, can create a dangerous
(00:24):
environment for employees. How can technology like AI and the
creation of a digital twin help quickly correct errors and
prevent accidents before they occur? And could these virtual replications
of a physical space ensure workers go home to their
families safe and sound. Join us as we learn more
about the world of digital twins and the many ways
(00:47):
they can not only improve workplace safety, but also public safety.
Welcome to Technically Speaking, an Intel podcast, the show that
brings you the stories and insights of AI, presented by
iHeartMedia's Ruby Studio and Intel. Hey there, I'm gram class. Today,
(01:08):
we're exploring the spaces and places replicated by digital twins
for starters, what is a digital twin? We're going to
be examining digital spaces that represent an actual physical space
in our world. To discuss the topic further, We're joined
by Tony Franklin. Tony Franklin is the general manager of
(01:29):
the Federal and Aerospace Markets, which includes military, aerospace, and
Government within the Network and Edge Group. He has more
than twenty years of corporate entrepreneurial, business development and management experience,
focusing on starting and growing multiple businesses that apply to
the Internet of Things, intelligence systems, and communications technologies. Intel's
(01:51):
Network and Edge Group provide solutions that lead the industry
and transforming businesses and the way we live are making
it simple to create exciting new buyetis Welcome to the show.
Speaker 2 (02:01):
Tony, Thank you, Thank you, glad to be here.
Speaker 1 (02:06):
We've seen a lot of science fiction depictions of digital
twins over the years and movies and films. The one
that comes to mind is the matrix. But the entire
world is a digital twin. Although it's a useful sinister motives.
I'd like to get your definition of what a digital
twin is.
Speaker 2 (02:22):
Yeah. Sure, it's interesting you said the matrix.
Speaker 3 (02:24):
I had to laugh because of all the examples we've
joked about, that's one that hasn't come up, and it's
so obvious when you said it. I'll start to use
that in its simplest form from me a digital twin
is the digital replica of the real world.
Speaker 1 (02:38):
And in terms of the technologies that are out there
needed for digital twining, maybe you could describe a little
bit of how those sorts of systems are put together
and what are some of the technology that Intel has
that can help that.
Speaker 2 (02:56):
Yeah.
Speaker 3 (02:56):
Sure, it's really been an evolution of technologies that some
of them were all used to using, and some of them,
you know, if you're not in the field, maybe not
so much. And so Gartner, one of the well known analysts,
has this Emerging Technologies trending chart they do every year,
and they talk about edge AI, so artificial intelligence at
(03:16):
the edge, you know, the place where data is actually
being generated more so than in say the cloud or
data centers. That's happening, It continues to evolve, it's growing
more and more. We're pushing more and more intelligence to
the edge. And by the edge, it could be everything
from a cell phone or refrigerator or a car. Again,
where is the data actually being generated. And then they
talk about digital twins being sort of the now to
(03:39):
three years. I think one of their data points was
something like forty percent of businesses, large businesses in particular
plan to use digital twins over the next two to
three years to actually generate revenue. So that's happening now,
and then lastly over the next maybe six plus years,
they talk about the metaverse. Now, while we don't generally
talk about the metaverse as much, the name means different
things to different people, but it's the full extent of
(04:01):
how it does. Commerce and any other business really leverage
a fully digital space that has interaction with the real world.
So there's this spectrum that has been happening between sensors,
with cameras being the most obvious because we're all using
cameras today. Our phones have so many sensors, we take
them for granted. But all the sensors, one of the
(04:23):
key technologies that are needed an ability to replicate if
you're doing visual digital twins, so an ability to replicate
the real world, and of course physics modeling if you're
really doing analysis and you need to replicate the physical
asset in a digital world, So computing capability to be
able to replicate the behavior of the object, with AI
(04:47):
being a critical component to now actually apply intelligence and
analysis to the twin. So when you think about those
different areas well, intel, we don't make sensors. Everything else
along the way is where we tend to play clearly
muting technology the ability to apply AI, so both from
the software side and the various different computing technologies that
(05:08):
enable AI, whether it's processors or GPUs, are AI accelerators,
et cetera. And then of course we have a very broad,
really world class partnership and ecosystem that we work with
to enable the different industries.
Speaker 1 (05:24):
Okay, and in terms of trying to get like a
visual kind of representation of what this would look like.
So say if you're walking in a warehouse, for example,
and you're looking at the shelves around you, you might
see some conveyor belts. How would it actually look in
a digital twin? It depends on the use case. The
simplest version i'd use that many people should be able
(05:47):
to relate to. Obviously many people can relate to matrix.
But from an application standpoint, I would say think about
Google Earth or Google Maps. Even that is a type
of model right. Another example is many of the retail
applications allow you to basically embed their items that are
(06:08):
for sale into the digital replication of your particular space,
so it's a real world and digital combination. That's always
the key.
Speaker 3 (06:18):
So those are very basic, simple applications that people use
and don't even realize. They don't think about them as
digital twins, but they're already getting used to this relationship
between the real world and the digital world.
Speaker 2 (06:29):
Yes, I saw.
Speaker 1 (06:30):
Intel has a software platform called Scenscape that can transform
data from this world of senses to create a real
time digital twin of your physical space. Can you tell
me a little bit more of how that works?
Speaker 3 (06:41):
So there's really three basic steps. One is mapping your space.
The key though in the type of real time digital
twinning that we pursue is it is a coordinate, accurate space,
So it's not just taking a random space. We know
that that room is twenty feet by twenty five feet,
and we also know that the cameras are six feet
(07:02):
up on the wall. We know the actual XYZ of
the space, So that's the first step. Second step is
calibrate the space. So calibrate meaning I have the space,
where are my sensors? So my sensor is ten feet up,
four feet over in that corner, etc. Now you turn
on your sensors and you ingest that into scenescape. Notice
(07:22):
I haven't actually visualized necessarily anything. So the key for
people to realize in real time digital twinning. There are
some applications where someone's not actually going to be sitting
there watching the digital twin of the store. They don't
need to do that real time. The AI and computing obviously,
the AI tools and the actual AI models, the inferencing
(07:45):
capability of scenescape is doing the work. You are configuring
it so you could add things like heat maps or
trip wise so that you can actually have events based
on whatever policy you want to implement, so that you
don't have to actually monitor. So I'll know that over
in this area of the meat department, if there's more
(08:08):
than twenty people for twenty seconds, there's something that happens.
I don't even have to watch anything for that. The
intelligence is making it happen.
Speaker 2 (08:15):
Gotcha.
Speaker 3 (08:16):
Now, if what I want to do later is now
hit the rewind button. The founder and creator for this
particular product, he has a phrase I'd love to use.
It's called the DVR for the real world. AI is
happening real time, but if you want additional analysis afterwards,
you have that capability.
Speaker 1 (08:35):
I can imagine there's a lot of challenges trying to
come up with these digital twins. What are some of
the top challenges or issues that people who are looking
to try and deploy these sorts of systems would have
to consider.
Speaker 3 (08:49):
The most prominent one, to be honest with you, is
the mindset more than anything technical. You think about some
of the technology that's growing in our own home, Siri, Alexa,
are cars. There's so much technology and most people really
don't understand you're already using AI. In many cases, you're
(09:11):
already using some sort of digital twin technology. There was
one demo we had for Sea Escape and the executive
loved it. It's like, this is amazing. I can do
motion tracking. I see where people are. I can have
multiple cameras monitoring the same asset, our person or object,
but I only see one, so it deduplicates the person.
I can track withf somebody's been in a space. Maybe
(09:32):
I have a radiation sensor and I can actually track
how long that person has been in the space, and
I can set triggers. There's so much he saw that
can be done, and he was so excited and he said,
where's the AI, right, Well, it's the AI that's doing
everything you just described. That's right, you just like and
it actually set us back for a second. We're like, well, clearly,
(09:52):
we need to make sure we understand where people are
starting from. We can't assume they already know there's a
level of technology and integration of their technology.
Speaker 1 (10:03):
And that's one of the biggest challenges when it comes
to understanding digital twin technology. It's the messaging some of
the very tools that you're accustomed to right now, like
the cell phone or the smart speaker that you or
listening to this podcast with essential when we consider the
future of digital twinning. So when it comes to a
future that incorporates AI into our daily lives, we've actually
(10:25):
already taken the first steps down that path. One of
the things I like to examine is the way that
technology actually helps democratize. And maybe you have some sense
of the type of customers. Are they sort of large enterprises,
because I'm really keen to see this sorts of technology
(10:45):
really get pushed down to the smaller businesses and make
it affordable for them to adopt and use. Do you
have any thoughts about that particular trend?
Speaker 2 (10:54):
Yeah?
Speaker 3 (10:55):
Absolutely, I'd say they're all generally larger enterprises, but they
may be larger enterprises with smaller facilities, so they have
to think about the implementation at the store level, and
then they can step back and look at it at
an operational level for the entire business that they're trying
to run. When you think about physical security, well, physical
(11:15):
security can happen on a construction site, it can happen
in an office space, it can happen anywhere. But the
companies we're dealing with are generally the companies that one
have the actual technology, so they may be the camera vendors,
et cetera, but whereas actually being implemented. They're targeting a
broad range in particular segments like I mentioned, but the
actual implementation may happen at a different level. So it's
(11:37):
the companies that apply technology across specific segments and then
they actually tear those down. Cities can be large or
they can be smaller, but you're implementing generally starting at
an intersection level, so that could be maybe four cameras max.
But now I've got a thousand intersections, so it grows
(11:58):
in scales. And what we're saying, being back to that
early adopter, we see the big picture. Let's start with
three intersections and let's see and understand where technology can
be applied there. Because one of the ways I like
to explain to people, you need to understand the environment
the scene. That's what it's called scenescape the scene, the area,
your environment better. That's one way to think about digital
(12:19):
twin being able to enable that.
Speaker 1 (12:24):
Coming out next on Technically Speaking and Intel podcast.
Speaker 3 (12:28):
I am the ultimate digital twin that I want. I
don't care about an avatar that's fun and fancy. I
was something that helps improve my quality of life.
Speaker 1 (12:37):
We'll be right back after a brief message from our partner.
Is that Intel? Welcome back to Technically Speaking an Intel Podcast.
I'm here now with the Intel's own Tony Fenklin. Do
(12:58):
you have any other example of benefits that your customers
have seen, whether it be productivity, increase, revenue, better, safety.
Speaker 2 (13:07):
Yeah, I'll go on reverse ands.
Speaker 3 (13:09):
You said safety last, because that's one that is so
common yep to people. In fact one, I think it
was the university in Texas that is doing some pilots
with the cities and with smart vehicles, and it's a
device called a roadside unit. Again, most people don't even
realize that you pull up to an intersection there's normally
a smaller box on the side. You already have the
(13:30):
box that controls the lights, et cetera. Well, I want
to do more so you can make that unit more intelligent.
You can actually allow that roadside unit to communicate with cars.
As cars become more intelligent, they have five G they
have wireless communications. So they implemented a pilot where there
was a particular intersection. So as the car pulls up,
imagine an alley off to the left, so the car
(13:51):
can't see down the alley clearly, but there's a poll
on the right that has a camera. The camera can
see the car coming. The camera can see down the alley.
The camera has roadside unit with Intel processing equipment. It's
running scenescape. Again, they don't even need to visualize this.
The camera sees someone walking down the alley, the car
(14:12):
is coming forward. It can communicate to the car because
even the cars with the cameras can't see around corners,
so the camera can communicate there's somebody walking. You need
to slow down. Knowing the speed of the car is
great acceleration, we understand that, but knowing where that car
is at the speed of a car coming down the
highway is one thing. The speed of a car coming
down that street where there's an alley is a totally
(14:34):
different scenario. I need to know the location of that
car relative to the camera and relative to the person
around the corner, both coming at the same time. So
three D is also a key aspect and value of
digital twin that translates to end benefit like you're talking about.
So that's safety right there, and that safety translates to
insurance as an example.
Speaker 1 (14:53):
Yeah, just feeding off that safety theme, can technologies like
scenescape and having those sort of cameras help with worker safety,
say in a factory or warehouse, where they can detect
or even predict, you know, if something's going to go
wrong and actually warn a worker that something's going to happen.
Speaker 2 (15:16):
Yeah. Absolutely.
Speaker 3 (15:17):
Robot interaction is a common one also, so think about
robots with cameras and the cameras and sensors that are
around Mobile World Congress is going on right now, and
I think it was last year we did a Scenescape
demo there and it was purely an industrial We had
the robotic arms that were moving and they were building something,
and then you have a sensor doesn't even need to
(15:39):
be a camera that has a digital n scenescape to tripwire,
so we know if somebody crosses this point, then it's
a tripwire. So that was the actual demo. So there
was a safety zone and then there was crossing the
safety zone. So if you enter the safety zone, there
could be a warning light to go off. You don't
have to stop anything, but I know someone's in the
safety zone, I know how long they've been in the
safety zone, and if they cross past that, then I
(16:00):
know I can start to shut down automatically equipment if
that's the policy that that particular site chooses to use,
so they can execute another one in a more constrained environment.
Warehouse that we've seen is where there's actually a controlled space,
so radiation. Actually, the earlier example I talked about is
(16:21):
a real example where there's an area that it needs
to be climate controlled and it literally has radiation. So
they have a radiation sensor both inside outside and commnitor.
Do you have the equipment on how long has a
person been in this particular space, and I could set
timers and triggers so I know that they can only
be in for so long, and I can also track
that so that's real time action and control, and I
(16:43):
can also use that for later analysis and prediction. Maybe
I need to change the configuration of the room, maybe
I need to put more signs up. But you can
have real time action and decisions and also post analysis.
Speaker 1 (16:55):
Yeah, what you said there about the simulation is quite
interesting because you know, as you're talking else, you know,
it came back to the gaming side of things, playing
SimCity or roller Coaster Tycoon, being able to sort of
simulate you know, if I put this thing here, is
it going to be dangerous? If I put that over there?
Speaker 2 (17:13):
Does that help the workers? Does it help with productivity?
Speaker 1 (17:16):
Maybe talk a little bit of some examples of using
real world data to kind of do what if analysis
of various scenarios that management and workers together can can
simulate and potentially improve the workplace.
Speaker 3 (17:33):
So let's take something like a gaming site, I mean
like a football or soccer So clearly those are massive
events with a lot of people, a lot of insurances,
there's safety concerns, there's access to medical professionals that need
to get in and out. So can I take existing
data that has already been captured using existing cameras and
(17:57):
I can actually run simulations on that, I could also
ideally what I want and I need it. If I'm
going to do a digital twin, I need some sort
of digital twin of the environment. The level of depth
is just depending upon the level of analysis that you
want to conduct. Now, what I need is what's the
data that I've been able to collect, Because most of
these places they're already going to have some data, even
if it's just camera feed data. I could take that
(18:19):
and actually start to run models on Okay, where are
people congregating. I can actually post camera feed and apply
inference data to that, so I can use the AI
to identify, well, that's a person, and that's an animal,
that's a car over there. And now I can start
to look at, okay, how often are they in these spaces?
Where am I getting congregation? Where am I getting long
(18:40):
kelling lies? So I can do analysis all on existing data.
Now I can start to reconfigure whatever actions need to
be taken, so all of that can happen before I've
shown up physically at the space.
Speaker 1 (18:54):
Just think about all the personal identifiable information involved in
some of the tasks we're talking about today. Well, the
sheer amount of streaming data coming in from a host
of senses required to implement digital twining. Keeping that data
secure is paramount to the future of this industry. I'd
like to get your thoughts around the whole privacy side
of things, and you know, what can be done to
(19:16):
make sure that as individuals we don't feel like we're
our privacy is getting invaded.
Speaker 3 (19:22):
It's a very good topic and we thought about that
from the beginning. So one of the ways that we've
defined scenescape is we primarily work on metadata. And what
metadata simply means is, for instance, we don't do any
facial recognition. I need to know that that's a person,
or I need to know. In fact, we had an
actual scenario where customer had a particular area and they
knew people were around, but at night there were objects
(19:44):
and they didn't know what it was. They were animals,
and the model hadn't been trained for animals. So the
model can say, hey, there's something there. I can't say
that it's a deer versus or whatever, but it's not
a human, you know, And so think about the simplicity
of that. Now, I don't have to try trans every movement,
every aspect. I'm only transmitting what's critical to make the
(20:06):
decisions that are needed real time and for post analysis.
Speaker 1 (20:10):
We talked a little bit about fulfillment centers and warehouses. I,
like everyone else, use ecommace sites like Amazon Prime. I'm
just wondering if you could maybe paint a picture of
how from the time that I hit that buy now
button to the time that I get my pair of
socks at my doorstep. Perhaps take me through how digital
(20:32):
twins could be used. How would a system help that process,
both as an in consumer and also for the business.
Speaker 3 (20:41):
Actually to be honest, One of the first examples that
came to mind is the delivery truck and why location
intelligence is so important. All of us use location based
services today. There was a study I read I think
it was UPS is saving a lot of money per
truck because they realize the location intelligence they were getting more. Particularly,
(21:03):
this was when hotels were putting in their addresss because
they use Amazon Prime to and they're getting these packages
shipped to them. The location data of that hotel relative
to where the truck is coming from, and then mapping
the route were not good routes, so it was costing
the company so much money to get from point A
(21:25):
to point B. So now I can start to identify
where are the hotel. And if they discovered this and
they started taking copies which they have, this would go.
We can take copies of the maps, I can start
to locate where am I going. I can start to
figure out the routes. So they're using the twin of
the maps and the data they already have. Think about
they have tons of data on their routes and the locations,
(21:47):
and where are they normally congregating, and which truck should
they send, what time should they be. They did all
of that analysis to figure out just on the back
end of when I actually dropped the packets off to
you and what makes sense. That's saving money for them
completely and again it's location based.
Speaker 1 (22:04):
Yeah, and can you give me an example of how
digital twinning might already be in use for the consumer
on one of these sites such as Amazon Prime all similar.
Speaker 3 (22:13):
I have a chair behind me I just bought, so
now I can use digital twining right now to figure
out exactly where I want this, how does it look?
And they also have those clothing services which are digital twintying.
You're the real person and they have the digital where
you can apply the clothes to you. Yes, I mean again,
these are services that people are using today. But back
to my earlier comment, you're not actually thinking about the technology.
(22:37):
Are taking that now back to work, to your day job. Oh,
I do all of this at home. I should be
applying this to my business and saving money and getting
greater insights of my scene and of my environment. So
those are a couple of examples. Can I give you
a different example. Most people have some sort of ring
doorbell or type of It could be ring, it could
be simply safe whatever.
Speaker 1 (22:58):
Yeah, I'll have that.
Speaker 2 (22:59):
There you go.
Speaker 3 (23:01):
One of the ways we've gone to market is to
make sure what we're doing is standard based and open,
you know, maximum scalability and flexibility. So I have a ring.
One of my family members has simply safe. The challenge
is I have the ring camera at the door, I
have another ring camera. I can connect them. I can
see if something's walking by. That's great. It's not very open.
(23:23):
I'm somewhat siloed. What we've had someone do with scenescape
is they use scenescape. First of all, you don't need
to go to the cloud, so they're not paying anybody. Okay,
if you want to use the cloud, you can, but
you do not have to use the cloud. Everything can
be edge based. And by edge based, think about the
edge again at home, where's the data generated.
Speaker 2 (23:39):
That's my edge. So in this case, your edge is
your home. So my home.
Speaker 3 (23:44):
I already have a computer, and I already have one
or two cameras. But there's a particular type of camera
I want for the front yard, which is totally different
than the camera I want indoors, which is totally different
than the camera I want. But so three totally different brands.
Well it's called multi camera, multi brand from that sense.
So and by the way, I want a heat sensor
or something like in a particularly air in the backyard
because I don't know if there's something overheating. So now
(24:06):
I can add all different type of brand sensors and
I can connect that into scenescape. And now Scenescape party
has AI. So I've used different brands. I've used my
own computer, so I have standard based connectivity. And because
of standard based connectivity, I can connect it to my
phone every phone app. Now it's very easy to connect
to it and get alerts. So now I can start
(24:26):
to use the existing AI tools that are in scenescape,
but there are so many applications out there. With scenescape,
you can integrate it with other applications. So there was
one person that used it to identify the difference between
a car coming in the driveway versus a postal truck
that goes by and stops, and they set an alert.
So whenever the postal truck comes and stops for a
(24:48):
few minutes, the alert goes on the phone. He never
has to look at a camera. He knows when he
gets that alert. Mails here. You can't do that with ring,
you can't do that with these other applications. So that's
a common use use case that people know today where
standard digital twinning technology with AI, standard based communication, and
(25:08):
standard computing technologies can all be used to enable use
cases that we use every day.
Speaker 1 (25:14):
Yeah, we just have time for one more question. I
would like to get your number one. I guess area
of excitement for digital twins for me is healthcare. What
I want to see in my lifetime and we have
the technology to do it. In fact, we've have a
few use cases with scene skates where we're working with
(25:34):
the medical community. I want the digital twin of my
health I want for me the person all think about
all the data, all the medical records. First of all,
it's hard enough keeping all your medical records together.
Speaker 3 (25:46):
So not only my medical records, but the medications I've taken,
any reactions I've had. You have so much data from
blood work and positive reactions, negative reactions to medications, exercise
since I've done that may have improved weight or blood pressure.
So as I grow, and as all of us grow
(26:07):
an age, I should say, yes, I want all of
that history to follow my DNA, my person to maximize healthcare.
I am the ultimate digital twin that I want. I
don't care about an avatar that's fun and fancy. I
was something that helps improve my quality of life. That's
what I want.
Speaker 1 (26:25):
Out of interest, have you seen any companies or businesses
looking into this.
Speaker 3 (26:30):
I did meet a company or CEO of a company
that's working on the medical record side YEP, where they're
trying to tie all of that to the person so
that can follow them, so that now physicians and healthcare
workers can have all that. So it's a startup. It
seems to be much more challenging to get this done
than you think it would be. But we've engaged with
some companies and hospitals that are making their hospital smart.
(26:53):
You can see some areas called the smart operating room.
That's a particular area in a hospital that's obviously critical.
I mean, you think about something as basic as we're
in the operating room, we're starting the operation. I have
twelve high value instruments on my right. Those twelve high
value instruments need to be there when I finished, because
(27:14):
if they're not there, there's a very bad place they
could be. Yeah, that's right, And that is a real
example that I know personally somebody like that that's happened
to and they've had to go back and get one
of those instruments. So when you think about the seriousness
of the operating room, and that's before you even get
into intrusive sensors and I mean, you know, what's the
blood pressure and et cetera. Yes, you never go to
(27:35):
a hospital, are to a healthcare professional and they take
your blood pressure and that's it and you're good.
Speaker 2 (27:39):
Then they start talking to you.
Speaker 3 (27:40):
No, we don't even think about the fact that we
take blood pressure, we take temperature, we take weight, sometimes
we take blood. So we're already experiencing a multi modal environment.
To maximize our health, but we don't always think about
that when we bring that to work. So now I
need a temperature sensor, I need light, I need lie dar,
I need cameras, I need different brands. I need to
(28:00):
apply intelligence to that. So now I can perceive my
space and my environment, I can understand it with analysis
and AI and makes sense of it to make decisions.
And then I can also do prediction based on that.
So what should happen in the future.
Speaker 1 (28:14):
That's great, Tony. I think we'll leave it on that note.
Thanks so much.
Speaker 2 (28:18):
Now, thank you. This was fun, really enjoyed it.
Speaker 1 (28:22):
My deepest thanks to Tony Franklin for sharing his equities
with us. Today's chat about digital twins really opened my
eyes to the incredible potential. It's like stepping into a
simulation game where you can tweak maintenance schedules, production lines,
and even play around with the interaction between workers and
machinery using real world data. Yes, I'm letting my inner
(28:44):
geek shine through here, but the idea of managing a
supply chain with the ears of playing SimCity seems pretty
cool to me. Tony's closing thoughts on the future of
healthcare and the possibility of creating a human digital twin.
We're particularly striking. Imagine having clone of yourself in a sense.
I mean, we're already wearing watches that monitor heart rate,
(29:05):
physical activity, sleep quality, plus a range of other biometric data.
It's not too far fetched to dream about a future
where digital twins can forecast our health outcomes based on
our DNA, diet and exercise. It's an interesting idea and
I'm looking forward to seeing where this technology takes us,
(29:25):
and lucky for us, we'll get a chance to explore
this further on our next episode Tuesday, April twenty third,
on Technically Speaking and Intel Podcast, We'll be learning about
some of the revolutionary implementations of AI in the healthcare
space with team members from Intel and Siemens Health and
Ears See You then. Technically Speaking was produced by Ruby
(29:52):
Studio from iHeartRadio in partnership with Intel and hosted by
me Graham Class. Our Executive producer is my our EP
of Post production is James Foster, and our Supervising producer
is Nika Swinton. This episode was edited by Sierra Spreen
and was written by Molly Sosher and Nick Firshaw.
Workplaces like factories or fulfillment centers are filled with many
moving parts and require constant supervision and alertness to ensure
worker safety. But what happens when errors or accidents happen
unexpectedly On the simplest level, it creates chaos and can
impact productivity. On a larger scale, can create a dangerous
(00:24):
environment for employees. How can technology like AI and the
creation of a digital twin help quickly correct errors and
prevent accidents before they occur? And could these virtual replications
of a physical space ensure workers go home to their
families safe and sound. Join us as we learn more
about the world of digital twins and the many ways
(00:47):
they can not only improve workplace safety, but also public safety.
Welcome to Technically Speaking, an Intel podcast, the show that
brings you the stories and insights of AI, presented by
iHeartMedia's Ruby Studio and Intel. Hey there, I'm gram class. Today,
(01:08):
we're exploring the spaces and places replicated by digital twins
for starters, what is a digital twin? We're going to
be examining digital spaces that represent an actual physical space
in our world. To discuss the topic further, We're joined
by Tony Franklin. Tony Franklin is the general manager of
(01:29):
the Federal and Aerospace Markets, which includes military, aerospace, and
Government within the Network and Edge Group. He has more
than twenty years of corporate entrepreneurial, business development and management experience,
focusing on starting and growing multiple businesses that apply to
the Internet of Things, intelligence systems, and communications technologies. Intel's
(01:51):
Network and Edge Group provide solutions that lead the industry
and transforming businesses and the way we live are making
it simple to create exciting new buyetis Welcome to the show.
Speaker 2 (02:01):
Tony, Thank you, Thank you, glad to be here.
Speaker 1 (02:06):
We've seen a lot of science fiction depictions of digital
twins over the years and movies and films. The one
that comes to mind is the matrix. But the entire
world is a digital twin. Although it's a useful sinister motives.
I'd like to get your definition of what a digital
twin is.
Speaker 2 (02:22):
Yeah. Sure, it's interesting you said the matrix.
Speaker 3 (02:24):
I had to laugh because of all the examples we've
joked about, that's one that hasn't come up, and it's
so obvious when you said it. I'll start to use
that in its simplest form from me a digital twin
is the digital replica of the real world.
Speaker 1 (02:38):
And in terms of the technologies that are out there
needed for digital twining, maybe you could describe a little
bit of how those sorts of systems are put together
and what are some of the technology that Intel has
that can help that.
Speaker 2 (02:56):
Yeah.
Speaker 3 (02:56):
Sure, it's really been an evolution of technologies that some
of them were all used to using, and some of them,
you know, if you're not in the field, maybe not
so much. And so Gartner, one of the well known analysts,
has this Emerging Technologies trending chart they do every year,
and they talk about edge AI, so artificial intelligence at
(03:16):
the edge, you know, the place where data is actually
being generated more so than in say the cloud or
data centers. That's happening, It continues to evolve, it's growing
more and more. We're pushing more and more intelligence to
the edge. And by the edge, it could be everything
from a cell phone or refrigerator or a car. Again,
where is the data actually being generated. And then they
talk about digital twins being sort of the now to
(03:39):
three years. I think one of their data points was
something like forty percent of businesses, large businesses in particular
plan to use digital twins over the next two to
three years to actually generate revenue. So that's happening now,
and then lastly over the next maybe six plus years,
they talk about the metaverse. Now, while we don't generally
talk about the metaverse as much, the name means different
things to different people, but it's the full extent of
(04:01):
how it does. Commerce and any other business really leverage
a fully digital space that has interaction with the real world.
So there's this spectrum that has been happening between sensors,
with cameras being the most obvious because we're all using
cameras today. Our phones have so many sensors, we take
them for granted. But all the sensors, one of the
(04:23):
key technologies that are needed an ability to replicate if
you're doing visual digital twins, so an ability to replicate
the real world, and of course physics modeling if you're
really doing analysis and you need to replicate the physical
asset in a digital world, So computing capability to be
able to replicate the behavior of the object, with AI
(04:47):
being a critical component to now actually apply intelligence and
analysis to the twin. So when you think about those
different areas well, intel, we don't make sensors. Everything else
along the way is where we tend to play clearly
muting technology the ability to apply AI, so both from
the software side and the various different computing technologies that
(05:08):
enable AI, whether it's processors or GPUs, are AI accelerators,
et cetera. And then of course we have a very broad,
really world class partnership and ecosystem that we work with
to enable the different industries.
Speaker 1 (05:24):
Okay, and in terms of trying to get like a
visual kind of representation of what this would look like.
So say if you're walking in a warehouse, for example,
and you're looking at the shelves around you, you might
see some conveyor belts. How would it actually look in
a digital twin? It depends on the use case. The
simplest version i'd use that many people should be able
(05:47):
to relate to. Obviously many people can relate to matrix.
But from an application standpoint, I would say think about
Google Earth or Google Maps. Even that is a type
of model right. Another example is many of the retail
applications allow you to basically embed their items that are
(06:08):
for sale into the digital replication of your particular space,
so it's a real world and digital combination. That's always
the key.
Speaker 3 (06:18):
So those are very basic, simple applications that people use
and don't even realize. They don't think about them as
digital twins, but they're already getting used to this relationship
between the real world and the digital world.
Speaker 2 (06:29):
Yes, I saw.
Speaker 1 (06:30):
Intel has a software platform called Scenscape that can transform
data from this world of senses to create a real
time digital twin of your physical space. Can you tell
me a little bit more of how that works?
Speaker 3 (06:41):
So there's really three basic steps. One is mapping your space.
The key though in the type of real time digital
twinning that we pursue is it is a coordinate, accurate space,
So it's not just taking a random space. We know
that that room is twenty feet by twenty five feet,
and we also know that the cameras are six feet
(07:02):
up on the wall. We know the actual XYZ of
the space, So that's the first step. Second step is
calibrate the space. So calibrate meaning I have the space,
where are my sensors? So my sensor is ten feet up,
four feet over in that corner, etc. Now you turn
on your sensors and you ingest that into scenescape. Notice
(07:22):
I haven't actually visualized necessarily anything. So the key for
people to realize in real time digital twinning. There are
some applications where someone's not actually going to be sitting
there watching the digital twin of the store. They don't
need to do that real time. The AI and computing obviously,
the AI tools and the actual AI models, the inferencing
(07:45):
capability of scenescape is doing the work. You are configuring
it so you could add things like heat maps or
trip wise so that you can actually have events based
on whatever policy you want to implement, so that you
don't have to actually monitor. So I'll know that over
in this area of the meat department, if there's more
(08:08):
than twenty people for twenty seconds, there's something that happens.
I don't even have to watch anything for that. The
intelligence is making it happen.
Speaker 2 (08:15):
Gotcha.
Speaker 3 (08:16):
Now, if what I want to do later is now
hit the rewind button. The founder and creator for this
particular product, he has a phrase I'd love to use.
It's called the DVR for the real world. AI is
happening real time, but if you want additional analysis afterwards,
you have that capability.
Speaker 1 (08:35):
I can imagine there's a lot of challenges trying to
come up with these digital twins. What are some of
the top challenges or issues that people who are looking
to try and deploy these sorts of systems would have
to consider.
Speaker 3 (08:49):
The most prominent one, to be honest with you, is
the mindset more than anything technical. You think about some
of the technology that's growing in our own home, Siri, Alexa,
are cars. There's so much technology and most people really
don't understand you're already using AI. In many cases, you're
(09:11):
already using some sort of digital twin technology. There was
one demo we had for Sea Escape and the executive
loved it. It's like, this is amazing. I can do
motion tracking. I see where people are. I can have
multiple cameras monitoring the same asset, our person or object,
but I only see one, so it deduplicates the person.
I can track withf somebody's been in a space. Maybe
(09:32):
I have a radiation sensor and I can actually track
how long that person has been in the space, and
I can set triggers. There's so much he saw that
can be done, and he was so excited and he said,
where's the AI, right, Well, it's the AI that's doing
everything you just described. That's right, you just like and
it actually set us back for a second. We're like, well, clearly,
(09:52):
we need to make sure we understand where people are
starting from. We can't assume they already know there's a
level of technology and integration of their technology.
Speaker 1 (10:03):
And that's one of the biggest challenges when it comes
to understanding digital twin technology. It's the messaging some of
the very tools that you're accustomed to right now, like
the cell phone or the smart speaker that you or
listening to this podcast with essential when we consider the
future of digital twinning. So when it comes to a
future that incorporates AI into our daily lives, we've actually
(10:25):
already taken the first steps down that path. One of
the things I like to examine is the way that
technology actually helps democratize. And maybe you have some sense
of the type of customers. Are they sort of large enterprises,
because I'm really keen to see this sorts of technology
(10:45):
really get pushed down to the smaller businesses and make
it affordable for them to adopt and use. Do you
have any thoughts about that particular trend?
Speaker 2 (10:54):
Yeah?
Speaker 3 (10:55):
Absolutely, I'd say they're all generally larger enterprises, but they
may be larger enterprises with smaller facilities, so they have
to think about the implementation at the store level, and
then they can step back and look at it at
an operational level for the entire business that they're trying
to run. When you think about physical security, well, physical
(11:15):
security can happen on a construction site, it can happen
in an office space, it can happen anywhere. But the
companies we're dealing with are generally the companies that one
have the actual technology, so they may be the camera vendors,
et cetera, but whereas actually being implemented. They're targeting a
broad range in particular segments like I mentioned, but the
actual implementation may happen at a different level. So it's
(11:37):
the companies that apply technology across specific segments and then
they actually tear those down. Cities can be large or
they can be smaller, but you're implementing generally starting at
an intersection level, so that could be maybe four cameras max.
But now I've got a thousand intersections, so it grows
(11:58):
in scales. And what we're saying, being back to that
early adopter, we see the big picture. Let's start with
three intersections and let's see and understand where technology can
be applied there. Because one of the ways I like
to explain to people, you need to understand the environment
the scene. That's what it's called scenescape the scene, the area,
your environment better. That's one way to think about digital
(12:19):
twin being able to enable that.
Speaker 1 (12:24):
Coming out next on Technically Speaking and Intel podcast.
Speaker 3 (12:28):
I am the ultimate digital twin that I want. I
don't care about an avatar that's fun and fancy. I
was something that helps improve my quality of life.
Speaker 1 (12:37):
We'll be right back after a brief message from our partner.
Is that Intel? Welcome back to Technically Speaking an Intel Podcast.
I'm here now with the Intel's own Tony Fenklin. Do
(12:58):
you have any other example of benefits that your customers
have seen, whether it be productivity, increase, revenue, better, safety.
Speaker 2 (13:07):
Yeah, I'll go on reverse ands.
Speaker 3 (13:09):
You said safety last, because that's one that is so
common yep to people. In fact one, I think it
was the university in Texas that is doing some pilots
with the cities and with smart vehicles, and it's a
device called a roadside unit. Again, most people don't even
realize that you pull up to an intersection there's normally
a smaller box on the side. You already have the
(13:30):
box that controls the lights, et cetera. Well, I want
to do more so you can make that unit more intelligent.
You can actually allow that roadside unit to communicate with cars.
As cars become more intelligent, they have five G they
have wireless communications. So they implemented a pilot where there
was a particular intersection. So as the car pulls up,
imagine an alley off to the left, so the car
(13:51):
can't see down the alley clearly, but there's a poll
on the right that has a camera. The camera can
see the car coming. The camera can see down the alley.
The camera has roadside unit with Intel processing equipment. It's
running scenescape. Again, they don't even need to visualize this.
The camera sees someone walking down the alley, the car
(14:12):
is coming forward. It can communicate to the car because
even the cars with the cameras can't see around corners,
so the camera can communicate there's somebody walking. You need
to slow down. Knowing the speed of the car is
great acceleration, we understand that, but knowing where that car
is at the speed of a car coming down the
highway is one thing. The speed of a car coming
down that street where there's an alley is a totally
(14:34):
different scenario. I need to know the location of that
car relative to the camera and relative to the person
around the corner, both coming at the same time. So
three D is also a key aspect and value of
digital twin that translates to end benefit like you're talking about.
So that's safety right there, and that safety translates to
insurance as an example.
Speaker 1 (14:53):
Yeah, just feeding off that safety theme, can technologies like
scenescape and having those sort of cameras help with worker safety,
say in a factory or warehouse, where they can detect
or even predict, you know, if something's going to go
wrong and actually warn a worker that something's going to happen.
Speaker 2 (15:16):
Yeah. Absolutely.
Speaker 3 (15:17):
Robot interaction is a common one also, so think about
robots with cameras and the cameras and sensors that are
around Mobile World Congress is going on right now, and
I think it was last year we did a Scenescape
demo there and it was purely an industrial We had
the robotic arms that were moving and they were building something,
and then you have a sensor doesn't even need to
(15:39):
be a camera that has a digital n scenescape to tripwire,
so we know if somebody crosses this point, then it's
a tripwire. So that was the actual demo. So there
was a safety zone and then there was crossing the
safety zone. So if you enter the safety zone, there
could be a warning light to go off. You don't
have to stop anything, but I know someone's in the
safety zone, I know how long they've been in the
safety zone, and if they cross past that, then I
(16:00):
know I can start to shut down automatically equipment if
that's the policy that that particular site chooses to use,
so they can execute another one in a more constrained environment.
Warehouse that we've seen is where there's actually a controlled space,
so radiation. Actually, the earlier example I talked about is
(16:21):
a real example where there's an area that it needs
to be climate controlled and it literally has radiation. So
they have a radiation sensor both inside outside and commnitor.
Do you have the equipment on how long has a
person been in this particular space, and I could set
timers and triggers so I know that they can only
be in for so long, and I can also track
that so that's real time action and control, and I
(16:43):
can also use that for later analysis and prediction. Maybe
I need to change the configuration of the room, maybe
I need to put more signs up. But you can
have real time action and decisions and also post analysis.
Speaker 1 (16:55):
Yeah, what you said there about the simulation is quite
interesting because you know, as you're talking else, you know,
it came back to the gaming side of things, playing
SimCity or roller Coaster Tycoon, being able to sort of
simulate you know, if I put this thing here, is
it going to be dangerous? If I put that over there?
Speaker 2 (17:13):
Does that help the workers? Does it help with productivity?
Speaker 1 (17:16):
Maybe talk a little bit of some examples of using
real world data to kind of do what if analysis
of various scenarios that management and workers together can can
simulate and potentially improve the workplace.
Speaker 3 (17:33):
So let's take something like a gaming site, I mean
like a football or soccer So clearly those are massive
events with a lot of people, a lot of insurances,
there's safety concerns, there's access to medical professionals that need
to get in and out. So can I take existing
data that has already been captured using existing cameras and
(17:57):
I can actually run simulations on that, I could also
ideally what I want and I need it. If I'm
going to do a digital twin, I need some sort
of digital twin of the environment. The level of depth
is just depending upon the level of analysis that you
want to conduct. Now, what I need is what's the
data that I've been able to collect, Because most of
these places they're already going to have some data, even
if it's just camera feed data. I could take that
(18:19):
and actually start to run models on Okay, where are
people congregating. I can actually post camera feed and apply
inference data to that, so I can use the AI
to identify, well, that's a person, and that's an animal,
that's a car over there. And now I can start
to look at, okay, how often are they in these spaces?
Where am I getting congregation? Where am I getting long
(18:40):
kelling lies? So I can do analysis all on existing data.
Now I can start to reconfigure whatever actions need to
be taken, so all of that can happen before I've
shown up physically at the space.
Speaker 1 (18:54):
Just think about all the personal identifiable information involved in
some of the tasks we're talking about today. Well, the
sheer amount of streaming data coming in from a host
of senses required to implement digital twining. Keeping that data
secure is paramount to the future of this industry. I'd
like to get your thoughts around the whole privacy side
of things, and you know, what can be done to
(19:16):
make sure that as individuals we don't feel like we're
our privacy is getting invaded.
Speaker 3 (19:22):
It's a very good topic and we thought about that
from the beginning. So one of the ways that we've
defined scenescape is we primarily work on metadata. And what
metadata simply means is, for instance, we don't do any
facial recognition. I need to know that that's a person,
or I need to know. In fact, we had an
actual scenario where customer had a particular area and they
knew people were around, but at night there were objects
(19:44):
and they didn't know what it was. They were animals,
and the model hadn't been trained for animals. So the
model can say, hey, there's something there. I can't say
that it's a deer versus or whatever, but it's not
a human, you know, And so think about the simplicity
of that. Now, I don't have to try trans every movement,
every aspect. I'm only transmitting what's critical to make the
(20:06):
decisions that are needed real time and for post analysis.
Speaker 1 (20:10):
We talked a little bit about fulfillment centers and warehouses. I,
like everyone else, use ecommace sites like Amazon Prime. I'm
just wondering if you could maybe paint a picture of
how from the time that I hit that buy now
button to the time that I get my pair of
socks at my doorstep. Perhaps take me through how digital
(20:32):
twins could be used. How would a system help that process,
both as an in consumer and also for the business.
Speaker 3 (20:41):
Actually to be honest, One of the first examples that
came to mind is the delivery truck and why location
intelligence is so important. All of us use location based
services today. There was a study I read I think
it was UPS is saving a lot of money per
truck because they realize the location intelligence they were getting more. Particularly,
(21:03):
this was when hotels were putting in their addresss because
they use Amazon Prime to and they're getting these packages
shipped to them. The location data of that hotel relative
to where the truck is coming from, and then mapping
the route were not good routes, so it was costing
the company so much money to get from point A
(21:25):
to point B. So now I can start to identify
where are the hotel. And if they discovered this and
they started taking copies which they have, this would go.
We can take copies of the maps, I can start
to locate where am I going. I can start to
figure out the routes. So they're using the twin of
the maps and the data they already have. Think about
they have tons of data on their routes and the locations,
(21:47):
and where are they normally congregating, and which truck should
they send, what time should they be. They did all
of that analysis to figure out just on the back
end of when I actually dropped the packets off to
you and what makes sense. That's saving money for them
completely and again it's location based.
Speaker 1 (22:04):
Yeah, and can you give me an example of how
digital twinning might already be in use for the consumer
on one of these sites such as Amazon Prime all similar.
Speaker 3 (22:13):
I have a chair behind me I just bought, so
now I can use digital twining right now to figure
out exactly where I want this, how does it look?
And they also have those clothing services which are digital twintying.
You're the real person and they have the digital where
you can apply the clothes to you. Yes, I mean again,
these are services that people are using today. But back
to my earlier comment, you're not actually thinking about the technology.
(22:37):
Are taking that now back to work, to your day job. Oh,
I do all of this at home. I should be
applying this to my business and saving money and getting
greater insights of my scene and of my environment. So
those are a couple of examples. Can I give you
a different example. Most people have some sort of ring
doorbell or type of It could be ring, it could
be simply safe whatever.
Speaker 1 (22:58):
Yeah, I'll have that.
Speaker 2 (22:59):
There you go.
Speaker 3 (23:01):
One of the ways we've gone to market is to
make sure what we're doing is standard based and open,
you know, maximum scalability and flexibility. So I have a ring.
One of my family members has simply safe. The challenge
is I have the ring camera at the door, I
have another ring camera. I can connect them. I can
see if something's walking by. That's great. It's not very open.
(23:23):
I'm somewhat siloed. What we've had someone do with scenescape
is they use scenescape. First of all, you don't need
to go to the cloud, so they're not paying anybody. Okay,
if you want to use the cloud, you can, but
you do not have to use the cloud. Everything can
be edge based. And by edge based, think about the
edge again at home, where's the data generated.
Speaker 2 (23:39):
That's my edge. So in this case, your edge is
your home. So my home.
Speaker 3 (23:44):
I already have a computer, and I already have one
or two cameras. But there's a particular type of camera
I want for the front yard, which is totally different
than the camera I want indoors, which is totally different
than the camera I want. But so three totally different brands.
Well it's called multi camera, multi brand from that sense.
So and by the way, I want a heat sensor
or something like in a particularly air in the backyard
because I don't know if there's something overheating. So now
(24:06):
I can add all different type of brand sensors and
I can connect that into scenescape. And now Scenescape party
has AI. So I've used different brands. I've used my
own computer, so I have standard based connectivity. And because
of standard based connectivity, I can connect it to my
phone every phone app. Now it's very easy to connect
to it and get alerts. So now I can start
(24:26):
to use the existing AI tools that are in scenescape,
but there are so many applications out there. With scenescape,
you can integrate it with other applications. So there was
one person that used it to identify the difference between
a car coming in the driveway versus a postal truck
that goes by and stops, and they set an alert.
So whenever the postal truck comes and stops for a
(24:48):
few minutes, the alert goes on the phone. He never
has to look at a camera. He knows when he
gets that alert. Mails here. You can't do that with ring,
you can't do that with these other applications. So that's
a common use use case that people know today where
standard digital twinning technology with AI, standard based communication, and
(25:08):
standard computing technologies can all be used to enable use
cases that we use every day.
Speaker 1 (25:14):
Yeah, we just have time for one more question. I
would like to get your number one. I guess area
of excitement for digital twins for me is healthcare. What
I want to see in my lifetime and we have
the technology to do it. In fact, we've have a
few use cases with scene skates where we're working with
(25:34):
the medical community. I want the digital twin of my
health I want for me the person all think about
all the data, all the medical records. First of all,
it's hard enough keeping all your medical records together.
Speaker 3 (25:46):
So not only my medical records, but the medications I've taken,
any reactions I've had. You have so much data from
blood work and positive reactions, negative reactions to medications, exercise
since I've done that may have improved weight or blood pressure.
So as I grow, and as all of us grow
(26:07):
an age, I should say, yes, I want all of
that history to follow my DNA, my person to maximize healthcare.
I am the ultimate digital twin that I want. I
don't care about an avatar that's fun and fancy. I
was something that helps improve my quality of life. That's
what I want.
Speaker 1 (26:25):
Out of interest, have you seen any companies or businesses
looking into this.
Speaker 3 (26:30):
I did meet a company or CEO of a company
that's working on the medical record side YEP, where they're
trying to tie all of that to the person so
that can follow them, so that now physicians and healthcare
workers can have all that. So it's a startup. It
seems to be much more challenging to get this done
than you think it would be. But we've engaged with
some companies and hospitals that are making their hospital smart.
(26:53):
You can see some areas called the smart operating room.
That's a particular area in a hospital that's obviously critical.
I mean, you think about something as basic as we're
in the operating room, we're starting the operation. I have
twelve high value instruments on my right. Those twelve high
value instruments need to be there when I finished, because
(27:14):
if they're not there, there's a very bad place they
could be. Yeah, that's right, And that is a real
example that I know personally somebody like that that's happened
to and they've had to go back and get one
of those instruments. So when you think about the seriousness
of the operating room, and that's before you even get
into intrusive sensors and I mean, you know, what's the
blood pressure and et cetera. Yes, you never go to
(27:35):
a hospital, are to a healthcare professional and they take
your blood pressure and that's it and you're good.
Speaker 2 (27:39):
Then they start talking to you.
Speaker 3 (27:40):
No, we don't even think about the fact that we
take blood pressure, we take temperature, we take weight, sometimes
we take blood. So we're already experiencing a multi modal environment.
To maximize our health, but we don't always think about
that when we bring that to work. So now I
need a temperature sensor, I need light, I need lie dar,
I need cameras, I need different brands. I need to
(28:00):
apply intelligence to that. So now I can perceive my
space and my environment, I can understand it with analysis
and AI and makes sense of it to make decisions.
And then I can also do prediction based on that.
So what should happen in the future.
Speaker 1 (28:14):
That's great, Tony. I think we'll leave it on that note.
Thanks so much.
Speaker 2 (28:18):
Now, thank you. This was fun, really enjoyed it.
Speaker 1 (28:22):
My deepest thanks to Tony Franklin for sharing his equities
with us. Today's chat about digital twins really opened my
eyes to the incredible potential. It's like stepping into a
simulation game where you can tweak maintenance schedules, production lines,
and even play around with the interaction between workers and
machinery using real world data. Yes, I'm letting my inner
(28:44):
geek shine through here, but the idea of managing a
supply chain with the ears of playing SimCity seems pretty
cool to me. Tony's closing thoughts on the future of
healthcare and the possibility of creating a human digital twin.
We're particularly striking. Imagine having clone of yourself in a sense.
I mean, we're already wearing watches that monitor heart rate,
(29:05):
physical activity, sleep quality, plus a range of other biometric data.
It's not too far fetched to dream about a future
where digital twins can forecast our health outcomes based on
our DNA, diet and exercise. It's an interesting idea and
I'm looking forward to seeing where this technology takes us,
(29:25):
and lucky for us, we'll get a chance to explore
this further on our next episode Tuesday, April twenty third,
on Technically Speaking and Intel Podcast, We'll be learning about
some of the revolutionary implementations of AI in the healthcare
space with team members from Intel and Siemens Health and
Ears See You then. Technically Speaking was produced by Ruby
(29:52):
Studio from iHeartRadio in partnership with Intel and hosted by
me Graham Class. Our Executive producer is my our EP
of Post production is James Foster, and our Supervising producer
is Nika Swinton. This episode was edited by Sierra Spreen
and was written by Molly Sosher and Nick Firshaw.
Host
Graeme Klass
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