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