Speaker 1: Workplaces like factories or fulfillment centers are filled with many
Speaker 1: moving parts and require constant supervision and alertness to ensure
Speaker 1: worker safety. But what happens when errors or accidents happen
Speaker 1: unexpectedly On the simplest level, it creates chaos and can
Speaker 1: impact productivity. On a larger scale, can create a dangerous
Speaker 1: environment for employees. How can technology like AI and the
Speaker 1: creation of a digital twin help quickly correct errors and
Speaker 1: prevent accidents before they occur? And could these virtual replications
Speaker 1: of a physical space ensure workers go home to their
Speaker 1: families safe and sound. Join us as we learn more
Speaker 1: about the world of digital twins and the many ways
Speaker 1: they can not only improve workplace safety, but also public safety.
Speaker 1: Welcome to Technically Speaking, an Intel podcast, the show that
Speaker 1: brings you the stories and insights of AI, presented by
Speaker 1: iHeartMedia's Ruby Studio and Intel. Hey there, I'm gram class. Today,
Speaker 1: we're exploring the spaces and places replicated by digital twins
Speaker 1: for starters, what is a digital twin? We're going to
Speaker 1: be examining digital spaces that represent an actual physical space
Speaker 1: in our world. To discuss the topic further, We're joined
Speaker 1: by Tony Franklin. Tony Franklin is the general manager of
Speaker 1: the Federal and Aerospace Markets, which includes military, aerospace, and
Speaker 1: Government within the Network and Edge Group. He has more
Speaker 1: than twenty years of corporate entrepreneurial, business development and management experience,
Speaker 1: focusing on starting and growing multiple businesses that apply to
Speaker 1: the Internet of Things, intelligence systems, and communications technologies. Intel's
Speaker 1: Network and Edge Group provide solutions that lead the industry
Speaker 1: and transforming businesses and the way we live are making
Speaker 1: it simple to create exciting new buyetis Welcome to the show.
Speaker 2: Tony, Thank you, Thank you, glad to be here.
Speaker 1: We've seen a lot of science fiction depictions of digital
Speaker 1: twins over the years and movies and films. The one
Speaker 1: that comes to mind is the matrix. But the entire
Speaker 1: world is a digital twin. Although it's a useful sinister motives.
Speaker 1: I'd like to get your definition of what a digital
Speaker 1: twin is.
Speaker 2: 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 from me a digital twin
Speaker 3: is the digital replica of the real world.
Speaker 1: And in terms of the technologies that are out there
Speaker 1: needed for digital twining, maybe you could describe a little
Speaker 1: bit of how those sorts of systems are put together
Speaker 1: and what are some of the technology that Intel has
Speaker 1: that can help that.
Speaker 2: 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 does. 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 sensors, 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: muting 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 1: Okay, and in terms of trying to get like a
Speaker 1: visual kind of representation of what this would look like.
Speaker 1: So say if you're walking in a warehouse, for example,
Speaker 1: and you're looking at the shelves around you, you might
Speaker 1: see some conveyor belts. How would it actually look in
Speaker 1: a digital twin? It depends on the use case. The
Speaker 1: simplest version i'd use that many people should be able
Speaker 1: to relate to. Obviously many people can relate to matrix.
Speaker 1: But from an application standpoint, I would say think about
Speaker 1: Google Earth or Google Maps. Even that is a type
Speaker 1: of model right. Another example is many of the retail
Speaker 1: applications allow you to basically embed their items that are
Speaker 1: for sale into the digital replication of your particular space,
Speaker 1: so it's a real world and digital combination. That's always
Speaker 1: the key.
Speaker 3: So those are very basic, simple applications that people use
Speaker 3: and don't even realize. They don't think about them as
Speaker 3: digital twins, but they're already getting used to this relationship
Speaker 3: between the real world and the digital world.
Speaker 2: Yes, I saw.
Speaker 1: Intel has a software platform called Scenscape that can transform
Speaker 1: data from this world of senses to create a real
Speaker 1: time digital twin of your physical space. Can you tell
Speaker 1: 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: and we also know that the cameras are six feet
Speaker 3: up on the wall. We know the actual XYZ of
Speaker 3: the space, So that's the first step. Second step is
Speaker 3: calibrate the space. So calibrate meaning I have the space,
Speaker 3: where are my sensors? So my sensor is ten feet up,
Speaker 3: four feet over in that corner, etc. Now you turn
Speaker 3: on your sensors and you ingest that into scenescape. Notice
Speaker 3: I haven't actually visualized necessarily anything. So the key for
Speaker 3: people to realize in real time digital twinning. There are
Speaker 3: some applications where someone's not actually going to be sitting
Speaker 3: there watching the digital twin of the store. They don't
Speaker 3: need 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.
Speaker 2: Gotcha.
Speaker 3: Now, if what I want to do later is now
Speaker 3: hit the rewind button. The founder and creator for this
Speaker 3: particular product, he has a phrase I'd love to use.
Speaker 3: It's called the DVR for the real world. AI is
Speaker 3: happening real time, but if you want additional analysis afterwards,
Speaker 3: you have that capability.
Speaker 1: I can imagine there's a lot of challenges trying to
Speaker 1: come up with these digital twins. What are some of
Speaker 1: the top challenges or issues that people who are looking
Speaker 1: to try and deploy these sorts of systems would have
Speaker 1: 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, Siri, Alexa,
Speaker 3: are cars. There's so much technology and most people really
Speaker 3: don't understand you're already using AI. In many cases, you're
Speaker 3: already using some sort of digital twin technology. There was
Speaker 3: one demo we had for Sea Escape 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, our person or object,
Speaker 3: but I only see one, so it deduplicates the person.
Speaker 3: I can track withf 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 1: And that's one of the biggest challenges when it comes
Speaker 1: to understanding digital twin technology. It's the messaging some of
Speaker 1: the very tools that you're accustomed to right now, like
Speaker 1: the cell phone or the smart speaker that you or
Speaker 1: listening to this podcast with essential when we consider the
Speaker 1: future of digital twinning. So when it comes to a
Speaker 1: future that incorporates AI into our daily lives, we've actually
Speaker 1: already taken the first steps down that path. One of
Speaker 1: the things I like to examine is the way that
Speaker 1: technology actually helps democratize. And maybe you have some sense
Speaker 1: of the type of customers. Are they sort of large enterprises,
Speaker 1: because I'm really keen to see this sorts of technology
Speaker 1: really get pushed down to the smaller businesses and make
Speaker 1: it affordable for them to adopt and use. Do you
Speaker 1: have any thoughts about that particular trend?
Speaker 2: Yeah?
Speaker 3: Absolutely, I'd say they're all generally larger enterprises, but they
Speaker 3: may be larger enterprises with smaller facilities, so they have
Speaker 3: to think about the implementation at the store level, and
Speaker 3: then they can step back and look at it at
Speaker 3: an operational level for the entire business that they're trying
Speaker 3: to run. When you think about physical security, well, physical
Speaker 3: security can happen on a construction site, it can happen
Speaker 3: in an office space, it can happen anywhere. But the
Speaker 3: companies we're dealing with are generally the companies that one
Speaker 3: have the actual technology, so they may be the camera vendors,
Speaker 3: et cetera, but whereas actually being implemented. They're targeting a
Speaker 3: broad range in particular segments like I mentioned, but the
Speaker 3: actual implementation may happen at a different level. So it's
Speaker 3: the companies that apply technology across specific segments and then
Speaker 3: they actually tear those down. Cities can be large or
Speaker 3: they can be smaller, but you're implementing generally starting at
Speaker 3: an intersection level, so that could be maybe four cameras max.
Speaker 3: But now I've got a thousand intersections, so it grows
Speaker 3: in scales. And what we're saying, being back to that
Speaker 3: early adopter, we see the big picture. 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 scenescape the scene, the area,
Speaker 3: your environment better. That's one way to think about digital
Speaker 3: twin being able to enable that.
Speaker 1: Coming out 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: was something that helps improve my quality of life.
Speaker 1: We'll be right back after a brief message from our partner.
Speaker 1: Is that Intel? Welcome back to Technically Speaking an Intel Podcast.
Speaker 1: I'm here now with the Intel's own Tony Fenklin. Do
Speaker 1: you have any other example of benefits that your customers
Speaker 1: have seen, whether it be productivity, increase, revenue, better, safety.
Speaker 2: Yeah, I'll go on reverse ands.
Speaker 3: You said safety last, because that's one that is so
Speaker 3: common yep to people. In fact one, I think it
Speaker 3: was the university in Texas that is doing some pilots
Speaker 3: with the cities and with smart vehicles, and it's a
Speaker 3: device called a roadside unit. Again, most people don't even
Speaker 3: realize that you pull up to an intersection there's normally
Speaker 3: a smaller box on the side. You already have the
Speaker 3: box that controls the lights, et cetera. Well, I want
Speaker 3: to do more so you can make that unit more intelligent.
Speaker 3: You can actually allow that roadside unit to communicate with cars.
Speaker 3: As cars become more intelligent, they have five G they
Speaker 3: have wireless communications. So they implemented a pilot where there
Speaker 3: was a particular intersection. So as the car pulls up,
Speaker 3: imagine an alley off to the left, so the car
Speaker 3: can't see down the alley clearly, but there's a poll
Speaker 3: on the right that has a camera. The camera can
Speaker 3: see the car coming. The camera can see down the alley.
Speaker 3: The camera has roadside unit with Intel processing equipment. It's
Speaker 3: running scenescape. Again, they don't even need to visualize this.
Speaker 3: The camera sees someone walking down the alley, the car
Speaker 3: is coming forward. It can communicate to the car because
Speaker 3: even the cars with the cameras can't see around corners,
Speaker 3: so the camera can communicate there's somebody walking. You need
Speaker 3: to slow down. Knowing the speed of the car is
Speaker 3: great acceleration, we understand that, but knowing where that car
Speaker 3: is at the speed of a car coming down the
Speaker 3: highway is one thing. The speed of a car coming
Speaker 3: down that street where there's an alley is a totally
Speaker 3: different scenario. I need to know the location of that
Speaker 3: car relative to the camera and relative to the person
Speaker 3: around the corner, both coming at the same time. So
Speaker 3: three D is also a key aspect and value of
Speaker 3: digital twin that translates to end benefit like you're talking about.
Speaker 3: So that's safety right there, and that safety translates to
Speaker 3: insurance as an example.
Speaker 1: Yeah, just feeding off that safety theme, can technologies like
Speaker 1: scenescape and having those sort of cameras help with worker safety,
Speaker 1: say in a factory or warehouse, where they can detect
Speaker 1: or even predict, you know, if something's going to go
Speaker 1: wrong and actually warn a worker that something's going to happen.
Speaker 2: 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 to tripwire,
Speaker 3: so we know if somebody crosses this point, then it's
Speaker 3: a tripwire. So that was the actual demo. So there
Speaker 3: was a safety zone and then there was crossing the
Speaker 3: safety zone. So if you enter the safety zone, there
Speaker 3: could be a warning light to go off. You don't
Speaker 3: have to stop anything, but I know someone's in the
Speaker 3: safety zone, I know how long they've been in the
Speaker 3: safety zone, and if they cross past that, then I
Speaker 3: know I can start to shut down automatically equipment if
Speaker 3: that's the policy that that particular site chooses to use,
Speaker 3: so they can execute another one in a more constrained environment.
Speaker 3: Warehouse that we've seen is where there's actually a controlled space,
Speaker 3: so radiation. Actually, the earlier example I talked about is
Speaker 3: a real example where there's an area that it needs
Speaker 3: to be climate controlled and it literally has radiation. So
Speaker 3: they have a radiation sensor both inside outside and commnitor.
Speaker 3: Do you have the equipment on how long has a
Speaker 3: person been in this particular space, and I could set
Speaker 3: timers and triggers so I know that they can only
Speaker 3: be in for so long, and I can also track
Speaker 3: that so that's real time action and control, and I
Speaker 3: can also use that for later analysis and prediction. Maybe
Speaker 3: I need to change the configuration of the room, maybe
Speaker 3: I need to put more signs up. But you can
Speaker 3: have real time action and decisions and also post analysis.
Speaker 1: Yeah, what you said there about the simulation is quite
Speaker 1: interesting because you know, as you're talking else, you know,
Speaker 1: it came back to the gaming side of things, playing
Speaker 1: SimCity or roller Coaster Tycoon, being able to sort of
Speaker 1: simulate you know, if I put this thing here, is
Speaker 1: it going to be dangerous? If I put that over there?
Speaker 2: Does that help the workers? Does it help with productivity?
Speaker 1: Maybe talk a little bit of some examples of using
Speaker 1: real world data to kind of do what if analysis
Speaker 1: of various scenarios that management and workers together can can
Speaker 1: 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 post 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: kelling lies? 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 1: Just think about all the personal identifiable information involved in
Speaker 1: some of the tasks we're talking about today. Well, the
Speaker 1: sheer amount of streaming data coming in from a host
Speaker 1: of senses required to implement digital twining. Keeping that data
Speaker 1: secure is paramount to the future of this industry. I'd
Speaker 1: like to get your thoughts around the whole privacy side
Speaker 1: of things, and you know, what can be done to
Speaker 1: make sure that as individuals we don't feel like we're
Speaker 1: our privacy is getting invaded.
Speaker 3: It'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 try trans 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 1: We talked a little bit about fulfillment centers and warehouses. I,
Speaker 1: like everyone else, use ecommace sites like Amazon Prime. I'm
Speaker 1: just wondering if you could maybe paint a picture of
Speaker 1: how from the time that I hit that buy now
Speaker 1: button to the time that I get my pair of
Speaker 1: socks at my doorstep. Perhaps take me through how digital
Speaker 1: twins could be used. How would a system help that process,
Speaker 1: both as an in consumer and also for 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 realize the 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 locations,
Speaker 3: and where are they normally congregating, and which truck should
Speaker 3: they send, what time should they be. They did all
Speaker 3: of that analysis to figure out just on the back
Speaker 3: end of when I actually dropped the packets off to
Speaker 3: you and what makes sense. That's saving money for them
Speaker 3: completely and again it's location based.
Speaker 1: Yeah, and can you give me an example of how
Speaker 1: digital twinning might already be in use for the consumer
Speaker 1: 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 twining 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 which are digital twintying.
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. 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 1: Yeah, I'll have that.
Speaker 2: There you go.
Speaker 3: One of the ways we've gone to market is to
Speaker 3: make sure what we're doing is standard based and open,
Speaker 3: you know, maximum scalability and flexibility. So I have a ring.
Speaker 3: One of my family members has simply safe. The challenge
Speaker 3: is I have the ring camera at the door, I
Speaker 3: have another ring camera. I can connect them. I can
Speaker 3: see if something's walking by. That's great. It's not very open.
Speaker 3: I'm somewhat siloed. What we've had someone do with scenescape
Speaker 3: is they use scenescape. First of all, you don't need
Speaker 3: to go to the cloud, so they're not paying anybody. Okay,
Speaker 3: if you want to use the cloud, you can, but
Speaker 3: you do not have to use the cloud. Everything can
Speaker 3: be edge based. And by edge based, think about the
Speaker 3: edge again at home, where's the data generated.
Speaker 2: That's my edge. So in this case, your edge is
Speaker 2: your home. So my home.
Speaker 3: I already have a computer, and I already have one
Speaker 3: or two cameras. But there's a particular type of camera
Speaker 3: I want for the front yard, which is totally different
Speaker 3: than the camera I want indoors, which is totally different
Speaker 3: than the camera I want. But so three totally different brands.
Speaker 3: Well it's called multi camera, multi brand from that sense.
Speaker 3: So and by the way, I want a heat sensor
Speaker 3: or something like in a particularly air in the backyard
Speaker 3: because I don't know if there's something overheating. So now
Speaker 3: I can add all different type of brand sensors and
Speaker 3: I can connect that into scenescape. And now Scenescape party
Speaker 3: has AI. So I've used different brands. I've used my
Speaker 3: own computer, so I have standard based connectivity. And because
Speaker 3: of standard based connectivity, I can connect it to my
Speaker 3: phone every phone app. Now it's very easy to connect
Speaker 3: to it and get alerts. So now I can start
Speaker 3: to use the existing AI tools that are in scenescape,
Speaker 3: but there are so many applications out there. With scenescape,
Speaker 3: you can integrate it with other applications. So there was
Speaker 3: one person that used it to identify the difference between
Speaker 3: a car coming in the driveway versus a postal truck
Speaker 3: that goes by and stops, and they set an alert.
Speaker 3: So whenever the postal truck comes and stops for a
Speaker 3: few minutes, the alert goes on the phone. He never
Speaker 3: has to look at a camera. He knows when he
Speaker 3: gets that alert. Mails here. You can't do that with ring,
Speaker 3: you can't do that with these other applications. So that's
Speaker 3: a common use use case that people know today where
Speaker 3: standard digital twinning technology with AI, standard based communication, and
Speaker 3: standard computing technologies can all be used to enable use
Speaker 3: cases that we use every day.
Speaker 1: Yeah, we just have time for one more question. I
Speaker 1: would like to get your number one. I guess area
Speaker 1: of excitement for digital twins for me is healthcare. What
Speaker 1: I want to see in my lifetime and we have
Speaker 1: the technology to do it. In fact, we've have a
Speaker 1: few use cases with scene skates where we're working with
Speaker 1: the medical community. I want the digital twin of my
Speaker 1: health I want for me the person all think about
Speaker 1: all the data, all the medical records. First of all,
Speaker 1: it's hard enough keeping all your medical 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, exercise
Speaker 3: since 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: an 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: was something that helps improve my quality of life. That's
Speaker 3: what I want.
Speaker 1: Out of interest, have you seen any companies or businesses
Speaker 1: 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. Yeah, that's right, And that is a real
Speaker 3: example that I know personally somebody like that that's happened
Speaker 3: to and they've had to go back and get one
Speaker 3: of those instruments. So when you think about the seriousness
Speaker 3: of the operating room, and that's before you even get
Speaker 3: into intrusive sensors and I mean, you know, what's the
Speaker 3: blood pressure and et cetera. Yes, you never go to
Speaker 3: a hospital, are to a healthcare professional and they take
Speaker 3: your blood pressure and that's it and you're good.
Speaker 2: 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 environment.
Speaker 3: To maximize our health, but we don't always think about
Speaker 3: that when we bring that to work. So now I
Speaker 3: need a temperature sensor, I need light, I need lie dar,
Speaker 3: I need cameras, I need different brands. I need to
Speaker 3: apply intelligence to that. So now I can perceive my
Speaker 3: space and my environment, I can understand it with analysis
Speaker 3: and AI and makes sense of it to make decisions.
Speaker 3: And then I can also do prediction based on that.
Speaker 3: So what should happen in the future.
Speaker 1: That's great, Tony. I think we'll leave it on that note.
Speaker 1: Thanks so much.
Speaker 2: Now, thank you. This was fun, really enjoyed it.
Speaker 1: My deepest thanks to Tony Franklin for sharing his equities
Speaker 1: with us. Today's chat about digital twins really opened my
Speaker 1: eyes to the incredible potential. It's like stepping into a
Speaker 1: simulation game where you can tweak maintenance schedules, production lines,
Speaker 1: and even play around with the interaction between workers and
Speaker 1: machinery using real world data. Yes, I'm letting my inner
Speaker 1: geek shine through here, but the idea of managing a
Speaker 1: supply chain with the ears of playing SimCity seems pretty
Speaker 1: cool to me. Tony's closing thoughts on the future of
Speaker 1: healthcare and the possibility of creating a human digital twin.
Speaker 1: We're particularly striking. Imagine having clone of yourself in a sense.
Speaker 1: I mean, we're already wearing watches that monitor heart rate,
Speaker 1: physical activity, sleep quality, plus a range of other biometric data.
Speaker 1: It's not too far fetched to dream about a future
Speaker 1: where digital twins can forecast our health outcomes based on
Speaker 1: our DNA, diet and exercise. It's an interesting idea and
Speaker 1: I'm looking forward to seeing where this technology takes us,
Speaker 1: and lucky for us, we'll get a chance to explore
Speaker 1: this further on our next episode Tuesday, April twenty third,
Speaker 1: on Technically Speaking and Intel Podcast, We'll be learning about
Speaker 1: some of the revolutionary implementations of AI in the healthcare
Speaker 1: space with team members from Intel and Siemens Health and
Speaker 1: Ears See You then. Technically Speaking was produced by Ruby
Speaker 1: Studio from iHeartRadio in partnership with Intel and hosted by
Speaker 1: me Graham Class. Our Executive producer is my our EP
Speaker 1: of Post production is James Foster, and our Supervising producer
Speaker 1: is Nika Swinton. This episode was edited by Sierra Spreen
Speaker 1: and was written by Molly Sosher and Nick Firshaw.