Why Data and A.I. is at the Intersection of Sports and Science with Intel’s Jonathan Lee

Michael Rivo: Welcome back to blazing trails. I'm Michael Rivo from Salesforce studio. Today we're going to do something a little bit different. I am so excited and honestly, I don't know why it's taken so long for us to share a Salesforce sponsored show that we love called IT visionaries. It features leaders on the front lines of technological innovation and we've got the IT Visionaries host Albert Chu with us here today. Welcome Albert.

Albert Chu: Hey, great to be here, Michael.

Michael Rivo: Okay. So IT Visionaries, it's one of my personal favorites and you've been producing the podcast for three years now. It's been amazing to watch it grow and see the caliber of guests that you've been able to feature. But for those who don't know, Albert, tell me, what is IT Visionaries.

Albert Chu: Yeah, so IT Visionaries is a show with tech leaders. So we interview CIOs, CTOs CEOs, but everyone has to have a tech background. And what I love most about the show is we really dive into why people are doing the projects they're doing, what mistakes that they made. I think the most vulnerable guests have been the best listens. And I think that something we've done really well, at letting, giving an inside look really at some of these tech leaders, what they're up to, how they think about projects, what they do to try to solve them. Pretty fascinating stuff. I'll never forget Eric Muntz of MailChimp telling us that he nearly took down all of MailChimp. And he went from a single developer, developing Blackberry apps all the way to now he oversees the whole project and he was willing to say like," Hey, I nearly took down the whole thing." And I think it's that vulnerability to let people see the mistakes because they're building things that are not done. Right? So they are going to make mistakes. It is inevitable. So their ability to recover from that, really fascinating stuff. So whether it's security, whether it's internal IT, or whether it's chief technology officers building the next thing, the vulnerability, the inside stories of how they're doing this is what's I think giving it legs over these last three years.

Michael Rivo: Yeah. And I think it's such a great platform to share those stories that you just don't hear. And I think everybody in the trenches of building technology has been there. They've all been in that position that you're describing, and there's not a great place to hear those stories to learn from your colleagues, et cetera. So IT Visionaries has been amazing at creating that kind of opportunity.

Albert Chu: Yeah. We both, I have a tech background, you currently work at a tech company. You guys, some of your peers and colleagues are developers. You probably are aware of their late nights inaudible I mean, it's going to happen.

Michael Rivo: Right, right, right. So today we're going to hear an interview with Jonathan Lee, director of sports performance technology in the Olympic Technology Group at Intel. That's quite a title. I managed to get through that. So tell me a little bit about this interview.

Albert Chu: Yeah. So, when I first saw it too, I was like," I don't understand what they do." And so Jonathan broke it down and said," Intel, effectively, is continually inventing or investing in its own innovations by its technology and its hardware to different things," right, and they want to prove that their processing and their products are the best. So they spun up this group, specifically, he told me it had the mission of making the Olympics more interactive and interesting through AI, machine vision and all other types of components. And so they found out that not only could they capture stats in real time, but a by- product of this technology was that they could actually coach athletes in real- time as well. Coaches wanted to see the data, they wanted to see how the angle of the person's lean. They wanted to see how fast their first two steps were. They wanted to see breakdowns of how fast the person was accelerating between the first 20% of a race, the middle 50%, and the final leg of the race. So what started as a way to make TV more entertaining, became like," Oh, okay, this is an actual coaching tool." And it says, it was just able to break down in real time things that you and I, if we were coaching sports, we could never do without this type of technology.

Michael Rivo: Fascinating. Well, IT Visionaries is the place to get all this info. So be sure to subscribe wherever you get your podcast, that's IT Visionaries. So Albert, thank you so much for joining us today.

Albert Chu: Thank you.

Michael Rivo: Now let's listen to your conversation with Jonathan Lee, director of sports performance technology in the Olympic Technology Group at Intel.

Albert Chu: Welcome everyone, to another episode of IT visionaries. And today we have the director of sports performance technology at the Olympic Technology Group inside of Intel corporation, Jonathan Lee. Jonathan, welcome to the show.

Jonathan Lee: Hey, thanks Albert. Thanks for having me.

Albert Chu: All right. So I got to ask right out of the gate. So most of us, when we think of Intel, we just think of chips, things that are inside of a computer. What is the Olympic Technology Group at Intel?

Jonathan Lee: Yes, so Intel has actually been an Olympic sponsor since 2017. And as part of our sponsorship, we have different categories, including AI, sports performance, the group that I lead is responsible for putting technology into the games related to sports performance. So that could be for enhancing and broadcast or for coaching and training. So the focus of my team really is how do we highlight, showcase, and improve athlete performance?

Albert Chu: So give us an example of how this is playing a part. So, because I don't know where exactly your role sits, because you mentioned, for example, on your LinkedIn that you're 3D athlete tracking or 3DAT.

Jonathan Lee: Yeah.

Albert Chu: Talk about how that works. What is it?

Jonathan Lee: Yes. 3DDAT Is actually a technology that we developed that Intel for the games. In fact, it was originally for the Tokyo Olympics. And the way that 3DAT works is it's a platform that, that we've developed that allows us to take standard video of athletes and then extract form of motion from that video, but without the use of sensors or special inaudible just AI and computer vision. And so from that information, we can construct a 3D skeleton of the athletes and then extract metrics and insights that we use to, for example, enhance a broadcast. So when you watch the summer Olympics this July that are happening in Tokyo, you'll see sprinting events like the hundred meter, and after you see the race, you'll see graphics that are powered by Intel 3DAT that helped to bring out some of the stories that happen in the race. And there really are some really cool stories that you can't see until you sort of overlay the data onto the race and pull out some of these cool things that are happening.

Albert Chu: So give us an idea of what types of things you're going to be able to do, because I totally understand not using sensors, right. It makes total sense. Olympics, you're talking about fractions and upon fractions of a second, the difference between a gold medal and not placing at all. So no one's going to want to wear a sensor for you. When you say," Hey, add this microchip."" No." Right.

Jonathan Lee: That's right.

Albert Chu: "That's all it takes for me to slow down." So you're going to use the camera and it sounds like it's, is it a standard camera? Is it a standard camera or is it a special piece of equipment that you need to use? And you say you're going to record the athlete in motion. You're going to be able to measure... Give us an idea, what are you going to be measuring? Is it going to be angles of their arms and legs? Is it how fast they're pumping? What kind of heartbeats per minute that equates to? I didn't know... give us an... I'd love to hear a little bit more because I'm super fascinated. And I kind of have seen this kind of technology I feel like, I don't know if you've ever seen the sports science segments.

Jonathan Lee: Yeah.

Albert Chu: But those are usually done, I mean, there's quite a bit of research I'm assuming done with them, and I feel like there's graphics-

Jonathan Lee: In a lab somewhere.

Albert Chu: Yeah, yeah. It's not... The way you make it sound, it's I don't want to say on the fly, but it sounds close to on the fly, live, real- time. I don't know. Give us an idea of how fast this is processing.

Jonathan Lee: Yeah, absolutely. So the cameras that we use are standard cameras. So there's nothing actually special about the cameras. In fact, that was important for us as a technology to develop something that was camera agnostic. And we can talk about coaching later and just how useful it is to be able to use any camera that you bring. In the case of Tokyo for the broadcasts, we've got cameras that are about 200 meters away. So they're pretty far out there. They're out of the way of any fans and they are tracking the sprinters and we pull out a whole host of information, but what's useful from a broadcast standpoint are the metrics that everyone can understand. And so what's useful for a coach or athlete is a little different from what a fan might want to see. So for the broadcast, we pull out things like velocity, acceleration, when they hit their top speed. And then we look at that across the whole hundred meter stretch there. So you can see, for example, when an athlete hits her top speed, or we can see something that's really, really fast, which is true for every hundred meter sprinter, they actually, they hit a top speed and then they start to decelerate and every athlete does it, so even when you look at Usain Bolt, right, you see his race and he looks like he's pulling away.

Albert Chu: Yeah.

Jonathan Lee: And you think that he's kicking it up to another gear. He's actually not, he's actually slowing down, just not as much as his competitors. Really, really neat stories that you just can't see until you look at the data like that.

Albert Chu: No, that's super fascinating. Now I got to imagine. Let's talk, we'll stay on the subject of the consumer side first. There are certain sports that I feel like we as humans can't really tell what's going on to the naked eye. Let's use diving for example, or gymnastics, for example, figure skating. You'll sometimes hear the commentators say like," Oh, her angle was off." I'm like," What are you talking about? That looked pretty good to me." You know what I mean?

Jonathan Lee: Yeah.

Albert Chu: So are you able to capture in almost real time the angle of entry, are their legs perpendicular to each other? All those kinds of... Where they're holding their body, because I feel like when it comes to coaching, certainly that would be extremely useful.

Jonathan Lee: It is. Yeah. And we are, and the turnaround time for these can be... Really it all depends on what kind of horsepower we throw at it.

Albert Chu: Yeah.

Jonathan Lee: But we've tuned this technology to run super fast on Intel hardware. So when you, when you see the Games, that's going to be turned around in 20 seconds, right. Nine athletes, 100 meters of data. Every one of them, we're putting that through this deep learning model that we have. In terms of some of these things that you can't see with a naked eye, which is really one of the cool parts about the technology. I mentioned that the technology is camera agnostic, but it's also frame rate agnostic as well. So you can use a 60 FPS mobile phone camera, or 120, 240, right. Because it operates on a frame by frame basis, it's agnostic to the type of frame rate. So certain sports like gymnastics or diving, of course, those things happen so fast that you need to have that net really high frame rate, other activities less so. And so being able to pull out something that happens in the few milliseconds, right, it's super important for these athletes.

Albert Chu: You know, I'd love to hear, how did you get involved in this? Because this isn't something, I mean, maybe I'm aging myself, but this didn't seem to really an option when I was in school.

Jonathan Lee: It wasn't an option when I was in school either, so. Yeah. It's a really cool space, to be honest. I mean, when I was in school, I wanted to be a doctor and I was in the life science path and I volunteered at the hospital and then realized that I actually, I can't stomach seeing people in pain. So I said," Well maybe, maybe being a physician, isn't the right path for me." So then ended up moving into medical devices and worked on transdermal devices. So devices actually worn on skin. The first devices I worked on were continuous glucose monitors. So the idea of giving diabetics so much more information and data about their blood glucose levels than they ever had before. And so from there I transitioned into the wearables space and I worked on one of the first wearables to give continuous heart rate from the wrist and the same problem existed, which is, yeah, you have all this data, but what do you do with it?

Albert Chu: Right.

Jonathan Lee: Right. And so we developed things like sleep detection and improved calorie and energy expenditure algorithms, but still there's more. And so that's how I got into this space, where the thread of my career has been algorithms and AI. How do you capture data and then distill and provide that data to the end user in a way that is useful, provide some sort of insights. And so that's kind of how I ended up here, which is when we're thinking about things to do for Tokyo, we thought," What are some of the coolest new technologies that we can use to provide information that hasn't existed before?"

Albert Chu: So I got to ask, is this division of Intel or the specific application, is this more to market Intel's capabilities of real sensor compute or does the Olympic Technology Group, do you guys have, is this like investing in building technologies that other businesses are investing in as well? Because I can certainly see the utility of this in many applications, beyond sports performance, obviously physical therapy, it might be useful. I have no idea, behavioral, occupational therapy. There's certainly different things where people are always trying to help people, whether it's simply as your sitting posture, how long you've been sitting, there's probably tons of different things, checking and making sure I can imagine, we all obviously know warehouse workers, people that are working manual labor jobs, having sensors to help them say like," Hey, you're stooping much." I don't know. What are some of the use case applications or is this whole group centered on building these technologies to kind of showcase the power of Intel and then serves as a marketing function? Or is it actually a product services function where people are investing in buying these services?

Jonathan Lee: Yeah, that's a great question. And it's a bit of both, actually. I think that the broader Olympic Technology Group, we put technology into the games in a different, a bunch of different ways. For my team focused on sports performance, when we start developing 3DAT for Tokyo. And then we started getting, working with coaches and athletes to both help to collect some data to train our AI models, and then also start to work with these athletes. And the feedback was so positive. We heard from two or three different coaches that said that this is the holy grail of coaching. So when you get to get that kind of feedback, you think," Okay, maybe we're onto something too." And just as you said, Albert, once people start to hear about this technology, they think," Oh, my industry or my use case could use that to." And so it became almost a no brainer that," Okay, we do have something really cool here that we should use and make available to other developers, to partners and companies that want to build applications on top of 3DAT." And so that's actually the next stage here for 3DAT which is, let's make this available to others because you're right. There's applications we see in, of course, the sports science space, but also home fitness.

Albert Chu: Oh yeah. I didn't even think about that, home fitness. There's more people than ever doing workouts at home. It would make total sense if I'm in front of my mirror device or whatever, and I'm doing a movement that I've never done before for it to be able to say like," Hey, your angle is off. You need to lower your butt." Whatever the critique needs to be.

Jonathan Lee: Absolutely, yeah. And then we just talked about healthcare as well. Yeah, especially on the home fitness space, you hit it on the head, which is we're all working out at home here. Or even when we go back to hopefully normal life soon, I'm glad that people are working out more at home and hope that that keeps going on. And as you said, there's devices out there that help us to work out at home and many of them do have cameras on them. Many of them do have a need for things like form assessment or correction or some way to track the track the person working out in a way that's going to help benefit their training. And I think one of the cool things about 3DAT for that application is we actually, as part of our pipeline, have a way to extract or infer 3D information from a 2D video. So we talked about having multiple cameras and being able to look at 3D form there, but for us, we have another model that we use that allows us to infer that depth information from just a single camera.

Albert Chu: Okay. This is pretty impressive. So you could tell if I'm doing squats for example, and I've got a head on camera, you've developed a technology, just using a standard camera. Let's imagine I got the most beat up cell phone camera, because I'm doing like Beach Body or whatever. It's going to broadcast back to you and you have technology that can say," I know the depth of," for example," my squat. What's my angle of my back?" Because you're, I guess measuring distance between... I'll just make up two points, head and hips. Right? So if the head and hip angle is very small, then my back has to be bent or so on. I can't imagine what other, because if you're not seeing a full 3D picture, how do you know if my back straight or not? You're saying you guys have modeled this out.

Jonathan Lee: Yeah. It's really neat. And again, it's really the power of AI here. And being able to first start with true 3D data, but then use that data to train a 2D model or a single video model to be able to extract that 3D information. And you described it absolutely correctly too. I mean, it's being able to look at things like the depth of your squat, but not just from the front angle, but inferring it from a 360.

Albert Chu: This is super fascinating stuff. And the way that you describe it makes it sound very similar to the auto manufacturers and their involvement in racing. Why build a Formula one car, it costs like a billion dollars to run each year, but the reality is a lot of the technology that's developed on those teams does make it to consumer vehicles.

Jonathan Lee: Absolutely.

Albert Chu: And the adage in racing is win on Sunday, sell on Monday. And I guess it's a similar concept for what you guys are doing, is as you develop these technologies, it's not clear what their use case is, but then you'll soon find that the use case is actually quite useful for many industries.

Jonathan Lee: It really is. I mean, you look at some of the things that we've done with say elite sprinters, or even for the broadcasts where the Olympics coming up. That's the same technology that's going to be used for, and these other applications as well, and so a lot of innovation happens at those extremes, if you will, just as you described, right? So it's the race car, it's NASA, it's elite athletes. You develop for those, which are very challenging types of use cases. Giving feedback to and detecting small differences for elite athletes is much harder than it is for us.

Albert Chu: Yeah.

Jonathan Lee: Right. And I know you work out and I know that you're active, even a weekend warrior is going to have bigger things to correct or address then say an elite athlete.

Albert Chu: Yeah, they're at a level where they're changing, I don't know, inches off their steps to create more speed or, I don't know, give us an idea of, because you've probably heard some of the detail from the coaches because as you're developing this technology, certainly it's not right off the bat, so therefore a coach is going to be like," Hey, it's useful, but," and they say something where you guys on the sensor side are like," Wait." Or not sensor side, but on the Intel side, are like," Hey, I didn't think about that. So we have to develop a technology to answer that question." What are some of these," Yeah, that's nice, buts" that you've gotten from coaches to show the level of, I guess, exactness that they need to see and understand to further improve an elite athlete's performance.

Jonathan Lee: Yeah, especially when you go across different sports, how you look at something like jumpers, so tracking for the athletes.

Albert Chu: Jumpers. So high jump, long jump, triple jump.

Jonathan Lee: Yeah. High jump, long jump, triple... Exactly, exactly. Where you're looking at much higher frame rate captures, they're looking at angles and incoming and and take off velocities that are, again, there's such highly tuned athletes that you're looking at just really, really small differences in say how high a knee comes up or the angle in which they take off. But we're also looking at, we had worked with a company called EXOS that is a elite athlete training facility. We work with them to help train college football players who are entering the draft and preparing for the combine. This year there wasn't a combine, but there were individual pro days. And we worked with these athletes on how to get faster for the 40 yard dash. And in those cases some of the metrics that we use are actually on the relatively simple side, but still very impactful. I think that that's what's cool about the prospect for 3DAT, which is even what we gave EXOS and these combine athletes was just scratching the surface of what we can do. So examples would be velocity or their acceleration pattern, or when they hit their top speed, then we start to kind of peel away a little bit," Okay, so from run to run, this guy... This is a faster run." And we say," Oh, actually, something that we can tell is that their angle of attack." So how much they're leaning forward when they're running really affects their speed. So what we should tell this guy is," Don't get so upright so quickly, because you're losing speed that way." So those are relatively, to us, from a technology standpoint, relatively simple things to measure, but from a coach and athlete perspective, it's information that they just have never had before.

Albert Chu: Yeah. I remember when I was playing, I'm going to age myself, but in high school, one of the things that our coaches always tried to get out of us playing football, was false stepping. Most people step backwards to go forwards. It's a wasted step and it's very hard for most people. This is a good test. Anyone who's listening, go stand still put your feet planted and try to run fast forward as fast as possible. Have someone look at you and almost for most people, their first step is actually backwards. It's like the first thing you do is step back to push or propel yourself forward, because that first step going forward is so it doesn't feel right when you want to propel yourself. And anyways, people think it's slows them down. But those micro things are what people are adjusting for. And so when you're talking about this kind of stuff, probably I remember getting coached up to not take that false step and I thought that was the most annoying thing ever. So I got to imagine the Olympians are like, you're unlocking the coaching for them that they're like," Oh my gosh. Now he's talking about how high and my hand pump has to be at what angle to generate even," as you said," more speed," but that's the difference between gold and nothing.

Jonathan Lee: Yeah, it is. And it's really cool. There's things that have been developed in sprinting over the past several years that are outside of what we've done with 3DAT, but are things that we potentially could measure and get feedback on in the future. But even things like how the sprinter is starts. So in the past it was really this super powerful explode out of the blocks, knees up, try to get into full gear as soon as you can. Now, as you see, and it's something to watch out for is it's a little more like a shuffle step.

Albert Chu: Yeah.

Jonathan Lee: Interestingly. And so it's more of a shuffle step, which then allows them to get up to top speed faster, really fascinating things that you think over the course of decades of sprinting that we've reached this plateau, but there's always new things to learn.

Albert Chu: Well then here's the magic question. So Usain Bolt is the current world record holder for the 100 meter. I think he's the world record holder for the 200 as well, but let's go with the 100, because that's for sure. He holds that. Is there a person who has run a race, obviously they didn't beat the world record, but had they applied all of the proper techniques you guys have, with data said, you would have beaten Usain Bolt's time. Have you guys done studies into that?

Jonathan Lee: We have, that is a fascinating question, isn't it? If you could just assemble the perfect race from a technique standpoint with obviously the athlete that it's-

Albert Chu: Yeah. The existing athlete.

Jonathan Lee: Yeah. Yeah. That's a good question. That's a really good question. I don't know the answer to that, but it's really fascinating to think about. I was... So we actually have a, I have a former Olympian on the team.

Albert Chu: Okay.

Jonathan Lee: His name is his name is Ashton Eaton and he actually won the last two gold medals in the decathlon.

Albert Chu: Decathlon. I was going say.

Jonathan Lee: Yeah, really cool guy to have around. Yeah.

Albert Chu: I remember him now. Yeah.

Jonathan Lee: So he's actually a product engineer on the team and has worked on 3DAT for a couple of years now. Just super cool to have those insights. And one of the things that he was telling me is that actually, when it comes to sprinters, the women usually have better form, are technically better than the men. So I would be interested then, same question that you have, which is," Okay, if we could get these guys to," I mean, obviously they're super fast and already have great form, so I think that's something that only someone of that caliber could tell me. They all they'll look super fast to me, but yeah, if you could get the form to be that much better, could you shave off that extra 0. 2 seconds now, I think to beat Usain Bolt.

Albert Chu: Yeah. It's mind boggling to think that maybe... It was weird thinking like," Okay, Usain Bolt is the best, greatest athlete of all time, sprinter of all time." But with the question now, now everyone's going to wonder," I wonder if there's actually someone who could have broken his time with their current physical ability, had they applied all the right technique?" And then of course you would then say the same for Usain Bolt. What would his time have been had he applied all of this perfect technique? Because I believe when he set the world record, that was in Beijing. When he looked back before he crossed the finish line and had already put his arms out, like" I've won." Because he saw no one around him, he's like" I've already won this thing. I'm not going to go all out through the finish line." I believe that's when he said it.

Jonathan Lee: I know. Isn't that hilarious? That he was just that much better that he could basically just take a, start his victory lap about 10 meters early.

Albert Chu: Yeah. Yeah. He had arms wide open, he's like," Oh, I've won." The other question I have is because this is in big business and we see it now more so than ever is in major league sports, analytics is becoming more and more of a part of the business, right. They're using, I mean they used to use just videographers to track, every motion and movement of a person to measure player efficiency ratings and all these variables that I don't even want to think about. Is visual tracking... I don't know. How would you describe your discipline? Would you call it visual tracking? How do you guys describe it?

Jonathan Lee: Yeah. I think a visual tracking is good and just athlete tracking is inaudible what we say.

Albert Chu: Is athlete tracking going to get to the point where it gets into team sports. So for example, one of the things that, let's use football. Football teams regularly do, is they document what plays you run by down and situation. So they have on their play cards like," Hey, Jonathan is," let's say the opposing coach," he's going to run a 33 power. It's third and three, he likes 33 power on third and three, he's a 28% chance of calling this play." And you line up in formation, I could instantly, if I had this technology available to me, like you said, maybe I don't need 20 seconds. Maybe they can relay it to me right back. It's like," Hey, these are the odds of play by formation and personnel." It just spits it up... Is team sports going to get to that level? I feel like football it could potentially, because there's actually an opportunity to look at something and then make a signal in like," Hey, I want to counter that." Whereas soccer or basketball is more fluid. Hockey is more fluid of a game, it's harder to call plays in, but football for sure. But I know that all the sports are using some type of tracking and analytics to measure player efficiencies. Is that where this technology is going? Because I have no idea how they're doing it today. I think they're doing it today by simply rewatching plays over and over again and having people document this. So it would go to the super beneficial if I had the game on a DVD, load it to 3DAT and just be like," Yo, spit this data back out at me."

Jonathan Lee: Oh yeah, absolutely. There's a number of ways that this benefits team sports. So what you just described, so attracting players on the field, looking at that from a strategy standpoint, using AI to analyze what a coach's or team's preference is could be. That's a super interesting use case. I think a big focus right now is player safety and health. And this is actually one that we're starting to explore as well. Obviously with the NFL, the biggest concerns are concussions and across all sports soft tissue injuries are the things that can really derail a team's season, and that's true in every, every sport. So are there things that we can do to help to either baseline or monitor athletes for soft tissue injuries, either in a practice scenario or in games and help with rehab as well? That's actually a really exciting area for me, because to me that's really what 3DAT was built for. Now, we talked earlier about just bringing data and insights to athletes and and to consumers, and I think this is where we're at with 3DAT, which is we built a great tool. I'm not satisfied with just creating a great tool. It's got to be useful for athletes and we know intuitively, and you can work with the coaches and work with the trainers to understand," What is it that you're looking for? Because we can augment what you're, what you're doing with this technology." And so they're looking for things like, for example, asymmetries, right? So if you run, is one stride longer than the other, or if I look at you over time and look at you run over time, does your stride length change? And so that can be predictive of-

Albert Chu: Yeah, mine gets shorter, because I get tired very fast. I'll tell you that much.

Jonathan Lee: Yeah. You definitely got to feel yourself when you're first going and all that pep in your step. But as things change either by fatigue or by an upcoming potential for an injury, right, those are the things that we'd want to be able to flag and say," Oh, hold on. Because let's maybe change up the training Richmond today to make sure that we don't exacerbate or accelerate an injury. Let's see what we can prevent that or help to rehab it before it might be any kind of prehab it, even before it starts."

Albert Chu: Yeah. I mean, this technology is super fascinating, because I can see, like you said, all these medical, practical applications, especially if there ever gets to a point where people can just upload information as well. I mean, I know that that's going to require many companies to be involved in this. So my son is 12 and he plays ice hockey and hockey's obviously a contact sport and traumatic brain injuries are, of course, a bigger concern than when I was a kid growing up in the'90s. I got hit playing football and I was seeing stars. Nobody asked me any questions, just get back out there and play. But I also think that I've been through it, not my son personally, but some of his teammates... Concussion protocol testing is very, I mean, it's like an opinion. They asked you questions like," Do you know what day it is?" Or whatever questions they ask. And sometimes I joke to my wife, when's it like, how do they know if the kid's just dumb? You would fail the test if you were stupid too, but having the ability to upload video footage, whatever. I'm not saying we have the answer yet, but if there ever was an answer like," Hey, at this angle, at this speed, at this, the probabilities for concussion go up by whatever percentage." I mean, it could greatly help diagnose something that right now is, I mean, right now diagnosing a concussion's like, it's almost self- administered. It's crazy. And they just ask you a bunch of questions like," Oh, I guess you're fine."

Jonathan Lee: Yeah. It's just to see if you're coherent, if you will. And yeah, absolutely. I think that one of the things that Ashton likes to talk about, because he obviously comes from a professional sports background is the cool thing about 3DAT is that you're putting numbers and you're putting actual data to how you feel. And it's just not something that athletes have had in the past. And then, so that's not just from a performance side, but also from a, potentially from an injury side as well. So it's exactly what you just described, which is, I think that there's something that we'll hopefully help to enable. It's not something that we've looked into ourselves, but can you put numbers, something more quantitative to a concussion protocol?

Albert Chu: Yeah.

Jonathan Lee: So it's not just about how are you doing? How are you feeling?

Albert Chu: Yeah. Do you feel dizzy? Right? I mean, I'm pretty sure a lot of players, because I've seen, because you've probably seen an NFL game where the guy who clearly looks like he's not there, but I feel like they know how to pass the test. It's a test. It's a test of questions. They can pass it.

Jonathan Lee: Right.

Albert Chu: So can you play? Yes.

Jonathan Lee: Exactly, exactly.

Albert Chu: Yeah.

Jonathan Lee: How can we get to that point where we can have that be something that's available to players and coaches? Well, there's got to be a course of business behind it. And I think there is a business behind player health and safety. And I think that that's, again, one of the things that we want to do as Intel, as a sports performance team, which is how do we make 3DAT available to multiple developers, teams, leagues, customers, right? Because we can't do it all as Intel. But what we have is something really cool that other companies or other partners, they may not want to spend the time or have the ability to develop out these skeletal tracking technologies, but we have it. And you know, and we'd love to be able to work with a whole host of partners to be able to put this into their products.

Albert Chu: No, Jonathan, I appreciate you joining us today and sharing some of the stuff you're doing at Intel. And I agree with you. This is the kind of stuff that I get super pumped about hearing how advancements in technology, while it's not its intended purpose, it's clearly going to have an impact, I think, in other fields. Medical fields. It's going to change the way potentially we help evaluate each other. I want to thank you for joining us, but before you go, Jonathan, it's time for the lightning round. The lightning round is brought to you by the Salesforce platform, the number one cloud platform for digital transformation of every experience. Jonathan, this is where we ask you questions outside of the world of work so our audience can get to know you better. You ready?

Jonathan Lee: Let's do it.

Albert Chu: All right. So you're an avid sports fan. Did you play sports growing up?

Jonathan Lee: I did.

Albert Chu: What did you play?

Jonathan Lee: Ice hockey.

Albert Chu: Oh, yeah. What position did you play?

Jonathan Lee: Goalie.

Albert Chu: Okay, so you smelled extra bad.

Jonathan Lee: Oh yeah. Yeah. My bag, my pads and yeah, it was the worst.

Albert Chu: Where did you grow up?

Jonathan Lee: So originally from Chicago, but I moved out to California to the Bay area. So then I started playing mixed up ice and roller hockey.

Albert Chu: How old were you when you first moved to the Bay area?

Jonathan Lee: Seven.

Albert Chu: Oh, okay. so you spent most of your young adult, young childhood young, I don't know, teenage years all in California.

Jonathan Lee: Yeah. That's correct.

Albert Chu: Cool. We looked at you up on LinkedIn and we see that you went to UCLA and Oxford, which campus was more beautiful?

Jonathan Lee: That's a great question actually. I'll have to go to Oxford because I mean, it's a thousand years old, but UCLA is also a super nice campus. And the weather is better. In England, if it reaches, in Fahrenheit, like 50 degrees Fahrenheit, you see everyone wearing like shorts. And in LA of course, I mean, it's a nice almost all year round.

Albert Chu: So the first thing I noticed about going to London when I went to London, was that there's like no road signs. There's no road signs anywhere. And navigating was super hard. How long did it take you before you can navigate the outer, not campus. Campus is one thing. The outer areas where you knew where you were going.

Jonathan Lee: I have a pretty bad sense of direction regardless. So yeah, exact way to describe, I couldn't even cross the street comfortably. I'd have to look left, right, left again. I was like," Okay, which way are the cars supposed to be coming from?" So that'll give you an idea of my comfort level.

Albert Chu: How about today? Are you still currently a sports fan?

Jonathan Lee: Absolutely.

Albert Chu: What's your favorite sport to watch?

Jonathan Lee: I still love hockey, but I think recently with the put the Warriors doing so well it's been basketball, but I will watch just about anything though, from baseball, football, hockey, basketball. When there were no live sports, I watched like the cornhole championships on TV.

Albert Chu: Spikeball.

Jonathan Lee: Spikeball.

Albert Chu: I tried watching spikeball, man, it's so terrible. There's literally a stoppage of play every like three seconds. I don't know. It's like who watches this, and let alone play it? I tried playing with my kids too. I didn't like it very much. Last question. This one is a little bit related to work, but if you were to give advice to someone who wants to get in, like you, someone who wants to help people in the medical field, but then finds out the hard way," Hey, you're not very good. I can't stand the stomach injuries, wound, blood," whatever. What's the best advice you would give someone that wants to help in the medical field, but they don't have the stomach for medicine.

Jonathan Lee: I would say just try it. I think the first thing that I would say, especially for those who might be thinking of a career switch, or maybe it's that they've done, they're early in their career, but they still they've done a lot of work on one field and then they want to switch to another, is all that stuff that you've done is going to be valuable still. I think there's this misconception in health and medicine, and as well as technology to in general, which is you need to be able to, if you want to be an AI engineer, then you need to start with AI and do more AI and then, and never do anything else. But in reality, especially when it comes to creating new technologies, innovative technologies, having that variety of background is super useful. In fact, it's really part of what helped us to create 3DAT and I think for my own career, I've always kind of borrowed from different parts of my background and experience to help create and steer the products that we develop later, even when some of the connections were non- obvious. And so I would say, think about things that you've done in the past, but just be assured that all that stuff is good and diversity of thought and background, is really the key to innovation.

Albert Chu: There you go. Jonathan, thanks for joining us today on IT Visionary. It was a pleasure having you. Thanks for sharing your story. And yeah, we look forward to seeing some of your technology at work this upcoming Olympics. By the way, they're still calling it the 2020 Tokyo Olympics, even though it's clearly 2021, but yay. I guess they've already done the logo, so they're not going back. They're not changing it.

Jonathan Lee: Yeah, absolutely. All right. Thanks Albert.

Michael Rivo: That was Albert Chu host of IT Visionaries, a podcast sponsored by Salesforce that's packed with stories and trends from leaders on the front lines of technological innovation. If you like this episode, be sure to subscribe wherever you get your podcasts. I'm Michael Rivo from Salesforce studios. Thanks for listening today.