Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey they're
Speaker 1: tech Stuff fans. This is Jonathan Strickland speaking. I've got
Speaker 1: something special for y'all today. What follows is an episode
Speaker 1: from season two of our podcast technically Speaking. This particular
Speaker 1: episode is about digital twins, which I thought was a
Speaker 1: really fascinating topic. It was one that I wasn't actually
Speaker 1: really familiar with, and I learned a lot and I
Speaker 1: hope you will too. So check this out, and if
Speaker 1: you enjoy it, make sure you go and subscribe to
Speaker 1: technically Speaking. You've got two seasons of really cool material
Speaker 1: in those episodes, so check that out. And I hope
Speaker 1: you enjoy this episode.
Speaker 2: Where do world changing ideas get This start at Intel.
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Speaker 2: exceptional engineering, the quantum computing revolution, the next generation of
Speaker 2: AI experts, the renewable energy grid, liquid cooling, data centers,
Speaker 2: early diagnosis for cancer, water restoration, and even farmland protection.
Speaker 2: The examples are countless, the impacts are endless, but the
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Speaker 2: us in redefining what's achievable through the power of AI.
Speaker 2: Learn more at Intel Dot com slash stories. Workplaces like
Speaker 2: factories or fulfillment centers are filled with many moving parts
Speaker 2: and require constant supervision and alertness to ensure worker safety.
Speaker 2: But what happens when errors or accidents happen unexpectedly On
Speaker 2: the simplest level, it creates chaos and can impact productivity.
Speaker 2: On 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 twe in help quickly correct errors and prevent accidents
Speaker 2: before they occur? And could these virtual replications of a
Speaker 2: physical space ensure workers go home to their families safe
Speaker 2: and sound. Join us as we learn more about the
Speaker 2: world of digital twins and the many ways they can
Speaker 2: not only improve workplace safety, but also public safety. Welcome
Speaker 2: to Technically Speaking, an Intel podcast, the show that brings
Speaker 2: you the stories and insights of AI, presented by iHeartMedia's
Speaker 2: Ruby Studio and Intel. Hey then, I'm gram class. Today,
Speaker 2: we're exploring the spaces and places replicated by digital twins.
Speaker 2: For starters, what is a digital twin? We're going to
Speaker 2: be examining digital spaces that represent an actual physical space
Speaker 2: in our world. To discuss the topic further, We're joined
Speaker 2: by Tony Franklin. Tony Franklin is the general manager of
Speaker 2: the Federal and Aerospace Markets, which includes military, aerospace, and
Speaker 2: government within the Network and Edge Group. He has more
Speaker 2: than twenty years of corporate entrepreneurial, business development and management experience,
Speaker 2: focusing on starting and growing multiple businesses that apply to
Speaker 2: the Internet of Things, intelligence systems, and communications technologies. Intel's
Speaker 2: Network and Edge Group provide solutions that lead the industry
Speaker 2: and transforming businesses and the way we live. Are making
Speaker 2: it simple to create exciting new diet solutions.
Speaker 3: Welcome to the show, Tony, Thank you, thank you, glad
Speaker 3: 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, where 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 3: Yeah. Sure, it's interesting you said the matrix.
Speaker 4: I had to laugh because of all the examples we've
Speaker 4: joked about, that's one that hasn't come up, and it's
Speaker 4: so obvious when you said, I'll start to use that
Speaker 4: in its simplest form for me, a digital twin is
Speaker 4: 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 3: Yeah.
Speaker 4: Sure, it's really been an evolution of technologies that some
Speaker 4: of them were all used to using, and some of them,
Speaker 4: you know, if you're not in the field, maybe not
Speaker 4: so much. And so Gartner, one of the well known analysts,
Speaker 4: has this Emerging Technologies trending chart they do every year,
Speaker 4: and they talk about edge AI, so artificial intelligence at
Speaker 4: the edge, you know, the place where data is actually
Speaker 4: being generated more so than in say the cloud or
Speaker 4: data centers. That's happening, It continues to evolve, it's growing
Speaker 4: more and more. We're pushing more and more intelligence to
Speaker 4: the edge. And by the edge, it could be everything
Speaker 4: from a self phone or refrigerator or a car.
Speaker 3: Again, where is the data actually being generated?
Speaker 4: And then they talk about digital twins being sort of
Speaker 4: the now to three years. I think one of their
Speaker 4: data points was something like forty percent of businesses large
Speaker 4: businesses in particular plan to use digital twins over the
Speaker 4: next two to three years to actually generate revenue, So
Speaker 4: that's happening now, and then lastly over the next maybe
Speaker 4: six plus years, they talk about the metaverse. Now, while
Speaker 4: we don't generally talk about the metaverse as much, the
Speaker 4: name means different things to different people, but it's the
Speaker 4: full extent of how it was. Commerce and any other
Speaker 4: business really leverage a fully digital space that has interaction
Speaker 4: with the real world. So there's this spectrum that has
Speaker 4: been happening between sensors, with cameras being the most obvious
Speaker 4: because we're all using cameras today. Our phones have so
Speaker 4: many censers we take them for granted, but all the sensors.
Speaker 4: One of the key technologies that are needed an ability
Speaker 4: to replicate if you're doing visual digital twins, so an
Speaker 4: ability to replicate the real world, and of course physics
Speaker 4: modeling if you're really doing analysis and you need to
Speaker 4: replicate the physical asset in a digital world, So computing
Speaker 4: capability to be able to replicate the behavior of the object,
Speaker 4: with AI being a critical component to now actually apply
Speaker 4: intelligence and analysis to the twin. So when you think
Speaker 4: about those different areas well. Intel, we don't make sensors.
Speaker 4: Everything else along the way is where we tend to
Speaker 4: play clearly, computing technology, the ability to apply AI, so
Speaker 4: both from the software side and the various different computing
Speaker 4: technologies that enable AI, whether it's processors or GPUs, are
Speaker 4: AI accelerators, et cetera. And then of course we have
Speaker 4: a very broad, really world class partnership and ecosystem that
Speaker 4: we work with to enable the different industries.
Speaker 2: Okay, and in terms of trying to get like a
Speaker 2: visual kind of representation of what this would clients say, say,
Speaker 2: if you're walking in a warehouse, for example, and you're
Speaker 2: looking at the shelves around you, you might see some
Speaker 2: conveyor belts. How would it actually look in a digital twin.
Speaker 2: It depends on the use case. The simplest version i'd
Speaker 2: use 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.
Speaker 4: So those are very basic, simple applications that people use
Speaker 4: and don't even realize. They don't think about them as
Speaker 4: digital twins, but they're already getting used to this relationship
Speaker 4: between the real world and the digital world.
Speaker 3: 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? So
Speaker 2: there's really three basic steps. One is mapping your space.
Speaker 2: The key though in the type of real time digital
Speaker 2: twinning that we pursue is it is a coordinate, accurate space,
Speaker 2: so it's not just taking a random space. We know
Speaker 2: that that room is twenty feet by twenty five feet,
Speaker 2: and we also know that the cameras are six feet
Speaker 2: up on the wall. We know the actual XYZ of
Speaker 2: the space, So that's the first step. Second step is
Speaker 2: calibrate the space. So calibrate meaning I have the space,
Speaker 2: where are my sensors? So my sensor is ten feet up,
Speaker 2: four feet over in that corner, et cetera. Now you
Speaker 2: turn on your sensors and you ingest that into scenescape.
Speaker 2: Notice I haven't actually visualized necessarily anything. So the key
Speaker 2: for people to realize in real time digital twinning. There
Speaker 2: are some applications where someone's not actually going to be
Speaker 2: sitting there watching the digital twin of the store. They
Speaker 2: don't need to do that real time. The AI and
Speaker 2: computing obviously, the AI tools and the actual AI models,
Speaker 2: the inferencing capability of scenescape is doing the work. You
Speaker 2: are configuring it so you could add things like heat
Speaker 2: maps or trip wise so that you can actually have
Speaker 2: events based on whatever policy you want to implement, so
Speaker 2: that you don't have to actually monitor. So I'll know
Speaker 2: that over in this area of the meat department, if
Speaker 2: there's more than twenty people for twenty seconds, there's something
Speaker 2: that happens. I don't even have to watch anything for that.
Speaker 2: The intelligence is making it happen now. If what I
Speaker 2: want to do later is now hit the rewind button.
Speaker 2: The founder and creator for this particular product, he has
Speaker 2: a phrase I'd love to use. It's called the DVR
Speaker 2: for the real world. AI is happening real time, but
Speaker 2: 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 4: The most prominent one, to be honest with you, is
Speaker 4: the mindset more than anything technical. You think about some
Speaker 4: of the technology that's growing in our own home, siy
Speaker 4: alexa are cars. There's so much technology, and most people
Speaker 4: really don't understand you're already using AI. In many cases,
Speaker 4: you're already using some sort of digital twin technology. There
Speaker 4: was one demo we had for Scenescape and the executive
Speaker 4: loved it. It's like, this is amazing. I can do
Speaker 4: motion tracking. I see where people are. I can have
Speaker 4: multiple cameras monitoring the same asset, our person or object,
Speaker 4: but I only see one, so it deduplicates the person.
Speaker 4: I can track with somebody's been in a space. Maybe
Speaker 4: I have a radiation sensor and I can actually track
Speaker 4: how long that person has been in the space, and
Speaker 4: I can set triggers. There's so much he saw that
Speaker 4: can be done, and he was so excited and he said,
Speaker 4: where's the AI, right, Well, it's the AI that's doing
Speaker 4: everything you just described. That's right, you just and it
Speaker 4: actually set us back for a second. We're like, well, clearly,
Speaker 4: we need to make sure we understand where people are
Speaker 4: starting from. We can't assume they already know there's a
Speaker 4: 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: already taken the first steps down that path. One of
Speaker 2: the things I like to examine is the way that
Speaker 2: technology actually helps democratize. And maybe you have some sense
Speaker 2: of the type of customers. Are they sort of large enterprises,
Speaker 2: because I'm really keen to see this sort of technology
Speaker 2: really get pushed down to the smaller businesses, you know,
Speaker 2: and make it affordable for them to adopt and use.
Speaker 2: Do you have any thoughts about that particular trend.
Speaker 4: Yeah, absolutely, I'd say they're all generally larger enterprises, but
Speaker 4: they may be larger enterprises with smaller facilities, so they
Speaker 4: have to think about the implementation at the store level,
Speaker 4: and then they can step back and look at it
Speaker 4: at an operational level for the entire business that they're
Speaker 4: trying to run. When you think about physical security, well,
Speaker 4: physical security can happen on a construction site, it can
Speaker 4: happen in an office space, it can happen anywhere. But
Speaker 4: the companies we're dealing with are generally the companies that
Speaker 4: one have the actual technology, so they may be the
Speaker 4: camera vendors, et cetera. But whereas actually being implemented the
Speaker 4: targeting a broad range in particular segments like I mentioned,
Speaker 4: but the actual implementation may happen at a different level.
Speaker 4: So it's the companies that apply technology across specific segments
Speaker 4: and then they actually tear those down. Cities can be
Speaker 4: large or they can be smaller, but you're implementing generally
Speaker 4: starting at an intersection level, so that could be maybe
Speaker 4: four cameras max. But now I've got a thousand intersections,
Speaker 4: so it grows in scales, and what we're seeing back
Speaker 4: to that early adopter, we see the big picture.
Speaker 3: Let's start with.
Speaker 4: Three intersections, and let's see and understand where technology can
Speaker 4: be applied there. Because one of the ways I like
Speaker 4: to explain to people, you need to understand the environment
Speaker 4: the scene. That's what it's called scenes. Gape the scene,
Speaker 4: the area, your environment better. That's one way to think
Speaker 4: about digital twin being able to enable that.
Speaker 2: Coming out next on Technically Speaking and Intel podcast, I.
Speaker 4: Am the ultimate digital twin that I watch. I don't
Speaker 4: care about an avatar that's fun and fancy. I was
Speaker 4: something that helps improuse my quality of light.
Speaker 2: We'll be right back after a brief message from our
Speaker 2: partners at Intel. Where do world changing ideas get their start?
Speaker 2: At Intel? It starts with real solutions, and real solutions
Speaker 2: start with exceptional engineering. Empowering those with disabilities starts with
Speaker 2: assistive AI, and stopping crop loss from infestation starts with
Speaker 2: thermal imaging and open technology, while artificial intelligence that predicts
Speaker 2: depression starts with educational programs like Intel's AI for Youth.
Speaker 2: And that's just the start the quantum computing revolution. The
Speaker 2: next generation of AI experts the renewable energy grid, liquid cooling,
Speaker 2: data centers, radiation exposure prevention in space, water restoration, and
Speaker 2: early cancer detection. The examples are countless, the impacts are endless,
Speaker 2: but the foundation is always the same. It starts with Intel.
Speaker 2: Learn more at Intel dot com, Forward Slash Stories. Welcome
Speaker 2: back to Technically Speaking, an Intel podcast. I'm here now
Speaker 2: with Intel's own Tony Franklin. Do you have any other
Speaker 2: examples of benefits that your customers have seen, whether it
Speaker 2: be productivity, increase, revenue, better, safety.
Speaker 4: Yeah, I'll go on reverse since you said safety last,
Speaker 4: because that's one that is so common yep to people.
Speaker 4: In fact, I think it was the university in Texas
Speaker 4: that has doing some pilots with the cities and with
Speaker 4: smart vehicles. And it's a device called a roadside unit. Again,
Speaker 4: most people don't even realize that you pull up to
Speaker 4: an intersection, there's normally a smaller box on the side.
Speaker 4: You already have the box that controls the lights, etc. Well,
Speaker 4: I want to do more so you can make that
Speaker 4: unit more intelligent. You can actually allow that roadside unit
Speaker 4: to communicate with cars. As cars become more intelligent, they
Speaker 4: have five G, they have wireless communications. So they implemented
Speaker 4: a pilot where there was a particular intersection, so as
Speaker 4: the car pulls up. Imagine an alley off to the left,
Speaker 4: so the car can't see down the alley clearly, but
Speaker 4: there's a pole on the right that has a camera.
Speaker 4: The camera can see the car coming. The camera can
Speaker 4: see down the alley. The camera has a roadside unit
Speaker 4: with intel processing equipment. It's running scene skate again. They
Speaker 4: don't even need to visualize this. The camera sees someone
Speaker 4: walking down the alley, the car is coming forward. It
Speaker 4: can communicate to the car because even the cars with
Speaker 4: the cameras can't see around corners, so the camera can
Speaker 4: communicate there is somebody walking. You need to slow down.
Speaker 4: Knowing the speed of the car is great acceleration, we
Speaker 4: understand that, but knowing where that car is at the
Speaker 4: speed of a car coming down the highway is one thing.
Speaker 4: The speed of a car coming down that street where
Speaker 4: there's an alley is a totally different scenario. I need
Speaker 4: to know the location of that car relative to the
Speaker 4: camera and relative to the person around the corner, both
Speaker 4: coming at the same time. So three D is also
Speaker 4: a key aspect and value of digital twin That translates
Speaker 4: to end benefit like you're talking about. So that's safety
Speaker 4: right there, 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 3: Yeah. Absolutely.
Speaker 4: Robot interaction is a common one also, so think about
Speaker 4: robots with cameras and the cameras and sensors that are
Speaker 4: around Mobile World Congress is going on right now, and
Speaker 4: I think it was last year. We did a escape
Speaker 4: demo there and it was purely an industrial We had
Speaker 4: the robotic arms that were moving and they were building something.
Speaker 4: And then you have a sensor doesn't even need to
Speaker 4: be a camera that has a digital n scen escape
Speaker 4: the trip wire, so we know if somebody crosses this point,
Speaker 4: then it's a trip wire. So that was the actual demo.
Speaker 4: So there was a safety zone and then there was
Speaker 4: crossing the safety zone. So if you enter the safety zone,
Speaker 4: there could be a warning light to go off. You
Speaker 4: don't have to stop anything, but I know someone's in
Speaker 4: the safety zone, I know how long they've been in
Speaker 4: the safety zone and if they cross past that, then
Speaker 4: I know I can start to shut down automatically equipment
Speaker 4: if that's the policy that that particular site chooses to use.
Speaker 4: So I think can execute another one in a more
Speaker 4: constrained environment warehouse that we've seen is where there's actually
Speaker 4: a controlled space, so radiation. Actually, the earlier example I
Speaker 4: talked about is a real example where there's an area
Speaker 4: that it needs to be climate controlled and it literally
Speaker 4: has radiations. So they have a radiation sensor both inside
Speaker 4: outside and commodity or do you have the equipment on
Speaker 4: how long has a person been in this particular space,
Speaker 4: and I could set timers and triggers so I know
Speaker 4: that they can only be in for so long, and
Speaker 4: I can also track that. So that's real time action
Speaker 4: and control, and I can also use that for later
Speaker 4: analysis and prediction. Maybe I need to change the configuration
Speaker 4: of the room, maybe I need to put more signs up,
Speaker 4: But you can have real time action and decisions and
Speaker 4: 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?
Speaker 3: If I put that over there? Does that help the workers?
Speaker 3: 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: see mille and potentially improve the workplace.
Speaker 4: So let's take something like a gaming site, I mean
Speaker 4: like a football or soccer So clearly those are massive
Speaker 4: events with a lot of people, a lot of insurances,
Speaker 4: there's safety concerns, there's access to medical professionals that need
Speaker 4: to get in and out. So can I take existing
Speaker 4: data that has already been captured using existing cameras and
Speaker 4: I can actually run simulations on that. I could also
Speaker 4: ideally what I want and I need it. If I'm
Speaker 4: going to do a digital twin, I need some sort
Speaker 4: of digital twin of the environment. The level of depth
Speaker 4: is just depending upon the level of analysis that you
Speaker 4: want to conduct. Now, what I need is what's the
Speaker 4: data that I've been able to collect, because most of
Speaker 4: these places they're already going to have some data, even
Speaker 4: if it's just camera feed data. I could take that
Speaker 4: and actually start to run models on Okay, where are
Speaker 4: people congregating? I can actually post camera feed and apply
Speaker 4: inference data to that, so I can use the AI
Speaker 4: to it. Andy, well, that's a person, and that's an animal.
Speaker 4: That's a car over there, And now I can start
Speaker 4: to look at Okay, how often are they in these spaces?
Speaker 4: Where am I getting congregation? Where am I getting long
Speaker 4: killing live? So I can do analysis all on existing data.
Speaker 4: Now I can start to reconfigure whatever actions need to
Speaker 4: be taken, so all of that can happen before I've
Speaker 4: 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 twining, 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 4: That's a very good topic and we thought about that
Speaker 4: from the beginning. So one of the ways that we've
Speaker 4: defined scenescaper is we primarily work on meta data, and
Speaker 4: what metadata simply means is, for instance, we don't do
Speaker 4: any facial recognition. I need to know that that's a
Speaker 4: person or I need to know In fact, we had
Speaker 4: an actual scenario where a customer had a particular area
Speaker 4: and they knew people were around, but at night there
Speaker 4: were objects and they didn't know what it was. They
Speaker 4: were animals, and the model hadn't been trained for animals.
Speaker 4: So the model can say, hey, there's something there. I
Speaker 4: can't say that it's a deer versus or whatever, but
Speaker 4: it's not a human, you know. And so think about
Speaker 4: the simplicity of that. Now, I don't have to transmit
Speaker 4: every movement, every aspect. I'm only transmitting what's critical to
Speaker 4: make the decisions that are needed real time and for
Speaker 4: post analysis.
Speaker 2: We talked a little bit about fulfillment centers and warehouses.
Speaker 2: I like everyone else's ecomma 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: but to the time that I get my pair of
Speaker 2: socks at my doorstep, take me through how digital twins
Speaker 2: could be used. How would a system help that process,
Speaker 2: both as an in consumer and also for the business.
Speaker 4: Actually to be honest, One of the first examples that
Speaker 4: came to mind is the delivery truck and why location
Speaker 4: intelligence is so important. All of us use location based
Speaker 4: services today. There was a study I read I think
Speaker 4: it was UPS is saving a lot of money per
Speaker 4: truck because they realized that location intelligence they were getting more.
Speaker 4: Particularly this was when hotels were putting in their address
Speaker 4: because they use Amazon Prime to and they're getting these
Speaker 4: packages shipped to them. The location data of that hotel
Speaker 4: relative to where the truck is coming from, and then
Speaker 4: mapping the route were not good routes, so it was
Speaker 4: costing the company so much money to get from point
Speaker 4: A to point B. So now I can start to
Speaker 4: identify where are the hotel. And they discovered this and
Speaker 4: they started taking copies which they have.
Speaker 3: This would go.
Speaker 4: We can take copies of the maps, I can start
Speaker 4: to locate where am I going. I can start to
Speaker 4: figure out the routes. So they're using the twin of
Speaker 4: the maps and the data they already have. Think about
Speaker 4: they have tons of data on their routes and the
Speaker 4: locations and where are they normally congregating, and which truck
Speaker 4: should they send, what time should they be They did
Speaker 4: all of that analysis to figure out just on the
Speaker 4: back end of when I actually dropped the packets off
Speaker 4: to you and what makes sense that's saving money for
Speaker 4: them completely and again it's location based yep.
Speaker 2: And can you give me an example of how digital
Speaker 2: twinning might already be in use for the consumer on
Speaker 2: one of these sites such as Amazon Prime all similar.
Speaker 4: I have a chair behind me I just bought, so
Speaker 4: now I can use digital twining right now to figure
Speaker 4: out exactly where I want this, how does it look?
Speaker 4: And they also have those clothing services which are digital twining.
Speaker 4: You're the real person, and they have the digital where
Speaker 4: you can apply the clothes to you. Yes, I mean again,
Speaker 4: these are services that people are using today. But back
Speaker 4: to my earlier comment, you're not actually thinking about the technology.
Speaker 4: Are taking that now back to work, to your day job. Oh,
Speaker 4: I do all of this at home. I should be
Speaker 4: applying this to my business and saving money and getting
Speaker 4: greater insights of my scene and of my environment. So
Speaker 4: those are a couple of examples. Can I give you
Speaker 4: a different example. Most people have some sort of ring
Speaker 4: doorbell or type of it could be ring, it could
Speaker 4: be simply safe whatever.
Speaker 2: Yeah, I'll have that.
Speaker 3: There you go.
Speaker 4: One of the ways we've gone to market is to
Speaker 4: make sure what we're doing is standard based and open,
Speaker 4: you know, maximum scalability and flexibility. So I have a
Speaker 4: ring one of my family members has simply safe. The
Speaker 4: challenge is I have the ring camera at the door,
Speaker 4: I have another ring camera. I can connect them. I
Speaker 4: can see if something's walking by. That's great. It's not
Speaker 4: very open. I'm somewhat siloed. What we've had someone do
Speaker 4: with scenescape is they use scenescape. First of all, you
Speaker 4: don't need to go through the so they're not paying anybody. Okay,
Speaker 4: if you want to use the cloud, you can, but
Speaker 4: you do not have to use the cloud. Everything can
Speaker 4: be edge based, and by edge based, think about the
Speaker 4: edge again. At home, where's the data generated? That's my edge.
Speaker 4: So in this case, your edge is your home. So
Speaker 4: my home, I already have a computer, and I already
Speaker 4: have one or two cameras. But there's a particular type
Speaker 4: of camera I want for the front yard, which is
Speaker 4: totally different than the camera I want indoors, which is
Speaker 4: totally different than the camera I want, so three totally
Speaker 4: different brands. Well it's called multi camera multi brand from
Speaker 4: that sense. So and by the way, maybe I want
Speaker 4: a heat sensor or something like in a particularly air
Speaker 4: in the backyard because I don't know if there's something overheating.
Speaker 4: So now I can add all different type of brand
Speaker 4: sensors and I can connect that into scenescape. And now
Speaker 4: scenescape party has AI. So I've used different brands. I've
Speaker 4: used my own computer, so I have standard based connectivity.
Speaker 4: And because of standard based connectivity, I can connect it
Speaker 4: to my phone. Every phone app now it's very easy
Speaker 4: to connect to it and get alerts. So now I
Speaker 4: can start to use the existing AI tools that are
Speaker 4: in Scenescape. But there are so many applications out there.
Speaker 4: With scenescape, you can integrate it with other applications. So
Speaker 4: there was one person that used it to identify the
Speaker 4: difference between a car coming in the driveway versus a
Speaker 4: postal truck that goes by and stops, and they set
Speaker 4: an alert. So whenever the postal truck comes and stops
Speaker 4: for a few minutes, the alert goes on the phone.
Speaker 4: He never has to look at a camera. He knows
Speaker 4: when he gets that alert mails here. You can't do
Speaker 4: that with RING, you can't do that with these other applications.
Speaker 4: So that's a common use case that people know today
Speaker 4: where standard digital twinting technology with AI, standard based communication,
Speaker 4: and standard computing technologies can all be used to enable
Speaker 4: use cases that we use every day.
Speaker 2: Yep, we just have time for one more question. I
Speaker 2: would like to get your number one. I guess area
Speaker 2: of excitement for digital twins for.
Speaker 4: Me is healthcare. What I want to see in my lifetime,
Speaker 4: and we have the technology to do it. In fact,
Speaker 4: we've have a few use cases with scene skates where
Speaker 4: we're working with the medical community. I want the digital
Speaker 4: twin of my health. I want for me the person
Speaker 4: all think about all the data, all the medical records.
Speaker 3: First of all, it's hard enough keeping all your medical
Speaker 3: records together.
Speaker 4: So not only my medical records, but the medications I've taken,
Speaker 4: any reactions I've had. You have so much data from
Speaker 4: blood work and positive reactions, negative reactions to medications, exercises
Speaker 4: I've done that may have improved weight or blood pressure.
Speaker 4: So as I grow and as all of us grow
Speaker 4: and age, I should say yes, I want all of
Speaker 4: that history to follow my DNA, my person to maximize healthcare.
Speaker 4: I am the ultimate digital twin that I want. I
Speaker 4: don't care about an avatar that's fun and fancy. I
Speaker 4: want something that helps to improve my quality of life.
Speaker 4: That's what I want out of interest. Have you seen
Speaker 4: any companies or businesses looking into this. I did meet
Speaker 4: a company or CEO of a company that's working on
Speaker 4: the medical record side where they're trying to tie all
Speaker 4: of that to the person so that can follow them,
Speaker 4: so that now physicians and healthcare workers can have all that.
Speaker 4: So it's a startup. It seems to be much more
Speaker 4: challenging to get this done than you think it would be.
Speaker 4: But we've engaged with some companies and hospitals that are
Speaker 4: making their hospital smart. You can see some areas called
Speaker 4: the smart operating room. That's a particular area in a
Speaker 4: hospital that's obviously critical. I mean, you think about something
Speaker 4: as basic as we're in the operating room, we're starting
Speaker 4: the operation. I have twelve high value instruments on my right.
Speaker 4: Those twelve high value instruments need to be there when
Speaker 4: I finished, because if they're not there, there's a very
Speaker 4: bad place they could be. Ye, that's right, and that
Speaker 4: is a real example that I know personally somebody like
Speaker 4: that that's happened to and they've had to go back
Speaker 4: and get one of those instruments. So when you think
Speaker 4: about the seriousness of the operating room, and that's before
Speaker 4: you even get into intrusive sensors and I mean, you know,
Speaker 4: what's the blood pressure and et cetera. Yes, you never
Speaker 4: go to a hospital, are to a healthcare professional and
Speaker 4: they take your blood pressure and that's it and you're good.
Speaker 3: Then they start talking to you.
Speaker 4: No, we don't even think about the fact that we
Speaker 4: take blood pressure, we take temperature, we take weight, sometimes
Speaker 4: we take blood. So we're already experiencing a multi modal
Speaker 4: environment to maximize our health, but we don't always think
Speaker 4: about that when we bring that to work. So now
Speaker 4: I need a temperature sensor, I need light, I need
Speaker 4: lie dar, I need cameras, I need different brands. I
Speaker 4: need to apply intelligence to that. So now I can
Speaker 4: perceive my space and my environment, I can understand it
Speaker 4: with analysis and AI and make sense of it to
Speaker 4: make decisions, and then I can also do prediction based
Speaker 4: 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.
Speaker 3: Thank you, this was fun, really enjoyed it.
Speaker 2: My deepest thanks to Tony Franklin for sharing his equities
Speaker 2: with us. Today's chat about digital twins really our eyes
Speaker 2: to the incredible potential. It's like stepping into a simulation
Speaker 2: game where you can tweak maintenance schedules, production lines, and
Speaker 2: even play around with the interaction between workers and machinery
Speaker 2: using real world data. Yes, I'm letting my inner geek
Speaker 2: shine through here, but the idea of managing a supply
Speaker 2: chain with the ears of playing SimCity seems pretty cool
Speaker 2: to me. Tony's closing thoughts on the future of healthcare
Speaker 2: and the possibility of creating a human digital twin we're
Speaker 2: particularly striking. Imagine having a clone of yourself in a sense.
Speaker 2: I mean, we're already wearing watches that monitor heart rate,
Speaker 2: physical activity, sleep quality, plus a range of other biometric data.
Speaker 2: It's not too far fetched to dream about a future
Speaker 2: where digital twins can forecast our health outcomes based on
Speaker 2: our DNA, diet, and exercise. It's an interesting idea and
Speaker 2: I'm looking forward to seeing where this technology takes us,
Speaker 2: and lucky for us, we'll get a chance to explore
Speaker 2: this further on our next episode Tuesday, April twenty third,
Speaker 2: on technically Speaking, an Intel podcast, we'll be learning about
Speaker 2: some of the revolutionary implementations of AI in the healthcare
Speaker 2: space with team members from Intel and Siemens Healthineers See
Speaker 2: you then. Technically Speaking was produced by Ruby Studio from
Speaker 2: iHeartRadio in partnership with Intel and hosted by me Graham Class.
Speaker 2: Our executive producer is Molly Sosher, Our EP of Post
Speaker 2: Production is James Foster, and our supervising producer is Nikia Swinton.
Speaker 2: This episode was edited by Sierra Spreen and was written
Speaker 2: by Molly Sosher and Nick Firshall. Where do world changing
Speaker 2: ideas get their start? At Intel? It starts with real solutions,
Speaker 2: and real solutions start with exceptional engineering, the quantum computing,
Speaker 2: the next generation of AI experts, the renewable energy grid,
Speaker 2: liquid cooling, data centers, early diagnosis for cancer, water restoration,
Speaker 2: and even farmland protection. The examples are countless, the impacts
Speaker 2: are endless, but the foundation is always the same. It
Speaker 2: starts with Intel join us in redefining what's achievable through
Speaker 2: the power of AI. Learn more at intel dot com.
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