Here’s my interview with Suzanne Borders, CEO and Co-Founder of BadVR, that was conducted on Wednesday, October 10, 2018 at Magic Leap’s LEAPCON in Los Angeles, CA. This is part 1 of my conversation with Borders, see part 2 here. See more context in the rough transcript below.
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Rough Transcript
[00:00:05.458] Kent Bye: The Voices of VR Podcast. Hello, my name is Kent Bye, and welcome to the Voices of VR Podcast. It's a podcast that looks at the structures and forms of immersive storytelling and the future of spatial computing. You can support the podcast at patreon.com slash voicesofvr. So continuing my series of looking at AWE past and present, I'm going to be diving into a two-part series with Suzanne Borders, who's the CEO and co-founder of BadVR, which does data visualization and immersive analytics within the context of VR. So I first had a chance to meet and talk with Suzanne Borders at Magic Leap LeapCon back in Los Angeles in 2018, where I was just there covering all the different things that were happening at Magic Leap LeapCon. And both in 2018 and 2019, I ended up going to like 20 events over the course of the year. So it was like an average of like once every three weeks, I was going to all these different things that were happening. It was a really busy time within the XR industry. And so there's a lot of interviews that I've still haven't fully dove into all the different coverage. But Now that augmented reality is starting to come up more onto the discussions, then I'll be digging into both of the conversations that I had at Magically Be Calm, but also Microsoft Build. There's a bunch of unpublished interviews that I've done over the years. And also I'll be diving into a series of conversations that I've had with Jason Marsh of Flow Immersive. also doing data visualizations. And so both of these, I've had like a number of different unpublished interviews around data visualization. So we'll be doing a little bit of a mini series of looking at their different approaches to this topic of data visualization within the context of XR. So becoming all that and more on today's episode of the Voices of VR podcast. So this interview with Suzanne happened on Wednesday, October 10th, 2018 at Magic Leaps LeapCon in Los Angeles, California. So with that, let's go ahead and dive right in.
[00:01:49.989] Suzanne Borders: So I'm Suzanne Borders, I'm the CEO and co-founder of BadVR. We're building an immersive data visualization platform, so for both VR and for AR. Originally we focused on VR because we felt that the hardware was a little bit more plug and play, it was something that customers were a little bit more interested in doing actual deployments on. Magic Leap came out, really excited about that, really excited about the potential, diving in and doing a few demos with the Magic Leap now too. but really just focus on making really large complex data sets multi-dimensional and immersive.
[00:02:21.932] Kent Bye: Okay, so I was at a math conference, and I saw this guy, Dr. Gunnarsson, talking about topological data analysis, which was to do a little bit of association of data points, but to give a geometry to it, like a topology to that data, but that was allowing AI to help learn about it, but it was also helping them to see these different connections. So I'm just curious of what type of ways that you're adding spatialization to data to get deeper insight into it.
[00:02:50.825] Suzanne Borders: Right, so there's some data sets have a geospatial tie-in inherently. When you're looking at data sets like city planning, looking at real estate data sets, when you're looking at even say something like IoT sensor data where it actually has a place in space, that sort of geospatial data lends itself to immersive visualization because it's very easy to visualize, but there's a whole other set of data that's non-geospatial, that you have the ability to put into a geospatial sense and use different dimensions of it to communicate different things. An example of this would be like taking a data set of cryptocurrency transactions and building it into the topography like a city, like something you'd see from an airplane looking down. But then the placement of the user within this experience can indicate something. And if you're close to certain transactions, those would be more recent. The ones that are further away from you would be further in the past, just to give an example. So you can use these geometric shapes and the topography and the distance and all of these different dimensions to communicate a lot of different points on each individual data point, therefore making the data sets themselves much larger that you can visualize and much more complex and detailed.
[00:03:58.223] Kent Bye: Yeah, I saw one of these demos back in the early days, the Silicon Valley Virtual Reality Conference. It was either the first or second one. I think it was actually the second gathering and looking at stock market data. So they were able to plot the market capitalization value of all the different companies. So you get this skyscraper feeling where you're able to get a sense of the Apples and the Googles and Facebooks, you know, these companies with all the big... market capitalizations would be like the equivalent of being in a city but seeing these huge skyscrapers that were there. And I felt like that that was OK, but it was limited in its utility because you kind of already know what the big players are. So what is the value of being able to see the giant skyscrapers of the big companies that we already know are there? And so I guess my question is, what is it that you find is different or that you're able to start to see to kind of prevent this effect of already knowing what the top things already are?
[00:04:52.014] Suzanne Borders: Right. So I would sort of counter back to your point and like you do know who's big versus who's small, but like the actual like how much bigger, right? Like if you can think in your head of a million, what does a million really mean? Like how big is that really? And like how much larger is a billion from a million, you know, like that actual difference in scale is something you can really viscerally pick up when you're seeing it presented in a three dimensional format. But I think the real value of it beyond that is the ability to store and recall information when it's presented in a three dimensional format is so, so much increased. So when you're going through and you're looking at a dashboard or you're looking at, I don't know, a spreadsheet, you go and you think two days later, well, what was that data? It's really hard to recall it because it's not presented in a memorable format and it's not presented in a way that your brain is inherently structured to store data. So when you structure and you store data in your brain, you do it in three dimensions. You come up with memory palaces. So that data, when presented in that way, makes it easier for you to ingest, makes it faster, and makes the recall much higher. So I think those are valuable things as well.
[00:05:57.910] Kent Bye: Oh, that's interesting. Yeah, I was just talking about memory palaces throughout the course of this day, and so it's kind of a hot topic. Yeah, so the interviewer just stopped. So what does a memory palace of data visualization look like, or what kind of embodied experiences have you personally had where you have this architecture of the geometry of meaning that is associated with the spatialization of data?
[00:06:22.754] Suzanne Borders: Right. This is a very interesting one, actually. So I'm going to sort of preface this by saying that I, back in the day, was a really big Nine Inch Nails fan, and I would go to concerts. And I would go to a lot of concerts in amphitheaters. So if you take a data set, like, for instance, marketing data, and you want to segment that audience, right, and you want to see, like, maybe a thousand, or not a thousand, sorry, like a million or a million and a half different people that you've reached with your campaign, and you want to do filtering, and you want to see that, and you want to dive into it, the way that you filter think of that or store that data somewhat like at least from our experience and our research like a stadium because that's the closest real world experience where you have a really large number of items presented in an organized format that you can see all of them at once right because like if we store something and we organize it we put it in a drawer here it goes away but in a stadium everybody can see everybody else and if you're standing in the middle you can see all of them each individual point but in a way that's organized makes sense So to answer your question, I think that's a really good example of like a memory palace or a visualization of a data set that people inherently, they make in their heads when they're thinking of these things because it maps to a real world experience. It maps to something that is functional as well because you can segment each by a different section and a different row and each different position of each data point within the stadium means something. So it's categorized and it's organized and accessible in your head.
[00:07:48.214] Kent Bye: Okay, interesting. Yeah, the data visualization experience that I saw, they didn't organize the towers in a way that you could see everything all at once. And so it was just kind of like this chaotic, like there wasn't a deeper order to it. So it sounds like what you're saying is that you could think more like an architect, like if you're making this as a shape that you would want to have it have good sight lines. And so you kind of are able to have this visualization of the data and because you went to a lot of Nine Inch Nails concerts and went to a lot of concert venues, you start to associate the shape of that data from going to a concert and listening to Trent Reznor sing about having a head in your hole. A hole in your head.
[00:08:27.756] Suzanne Borders: A lot of different things. But yeah, brains store data in ways that make it easy to discover patterns, because that is the thing that our brain does very, very well, and that's the main function that we do in our day-to-day lives, is we look for patterns, we look for recognizable shapes so that i found that any sort of way that we store data tends to lend itself to easy organization and easy scanning to find correlations and patterns so we don't tend to store things in a disorganized format i guess is what i'm getting at i see so you said that you're drawing upon some different research like what is this neuroscience research or who's researching about memory palaces and how we cognitively understand data Lots of people are. The research that I'm referring to is my own personal research, like user going into my business, like when I first started this, staying close to the user, asking them, like, what are your problems that you're experiencing? And a lot of them would come to me and say, well, I have this really large data set, and this is how I see it in my head when I'm working with it, but I can't look at it that way. I can only look at it in scatterplots. Like, I see it in this really complicated three-dimensional format, and I want you to build something like that. I want to see it presented in that manner. And those sorts of customers are absolutely amazing because they know exactly what they want and it makes total sense. Others are not quite so articulate, so we can go through and ask specific questions of that customer, sort of figure out how it is that they're storing this data in their brain and how can we best visualize it for them. But there are other people, I actually met somebody here today who is doing his PhD thesis about data and how we store it in our memory and how we access it. So that was a really interesting conversation that I just had before I came over here.
[00:10:05.558] Kent Bye: There we go, it's a dual memory palace there, wow. Okay, so it's in the air that we need to create more of these memory palaces. And so, for you, how has working with this type of spatialization of data, how has it changed your memory?
[00:10:19.025] Suzanne Borders: That's an excellent question. So prior to this, I was a UX and product designer and I would make workflows. I was like, what I did, my primary thing, tons and tons of flow charts, mental workflows. And one of the biggest issues that I had is that I could see these things in my head. Like I could see the entire product. I could see the site map in my head. I could see the flows, but I had a really difficult time sort of communicating that to other people because my brain just automatically makes these amazing things. To me, I just always took that as a given. Like people had that capability to just look at a data set and organize it spatially in their heads, like super, super easy. And I think that I'm coming to realize that people do that, but it's not always quite so intuitive and natural for them to explain it to others. And it's a really big pain point of like getting that out. Like everybody does it, but they can't really articulate it. Maybe it takes them a little longer to do it. But I think we have these like hidden structures of like these hidden data worlds inside of our heads and just finding a bridge to build that into an actual item and get these images out of people's heads and into a real world setting and into virtual reality just sort of helps me understand the way other people's memories work, which helps me, of course, then understand my own. the value of sort of structuring these things and getting more data into my own head. Yeah, so I guess it's allowed me to increase my memory, and it's allowed me to store and structure data in a more recognizable, easily repeatable format. Long-term result makes it easier to sort of communicate to others, I guess.
[00:11:54.970] Kent Bye: Oh, fascinating. Wow, that sounds like a good thing all around. It reminds me of the early days of computing when they had a killer app of the spreadsheet. And now we have Excel spreadsheets. There's a lot of people whose entire job is to just look at an Excel spreadsheet all day. And so how is what you're doing with this specialization of those numbers in the spreadsheet, is that an easy translation? Or when I talk to some people within the information visualization realm, Some of them have been really skeptical for doing 3D spatialization because there's different occlusion problems. And the feedback I get is that the geospatial data is really interesting. Being able to express your agency and to be able to change different things and see how that changes dynamically in real time. Maybe having an algorithmic input to be able to see how complex data can change, and so there's this agency element of being able to interact and play with the data a little bit more, but that just by looking at singular spreadsheet data, it may not naturally be set up to be architected into a stadium because it has a time-based element where you can't rearrange things. It actually is, those lengths have a lot to do with where they're located in position, which tells you where it's located in time. And so I'm just curious what this translation has been to be able to translate Excel data into these specialized experiences.
[00:13:11.868] Suzanne Borders: It's funny that you say that on two points. First, sometimes when I describe this product, I say we want to be the Excel spreadsheet of the 22nd century. So our goal is to eliminate them. We don't think that or I don't think that they should be in concert. I think that our product can overtake that and replace the need to even see Excel spreadsheets, but right now, the second point, when we're inputting data, we actually take the format of the data we receive is an Excel spreadsheet, usually a CSV file, and we spatialize it in a very, it's not very easy, but when you have something like time, our experiences are completely interactive, so you can control that. So when it's something like a live data stream, then that obviously lends itself to real-time data, we're showing it as it happens, but if you have a historical format of the CSV file, you want to go backwards in time, you want to go forwards, this specific transaction happened at this time, we can allow you to filter backwards or go forward. And we can also say at this point in time, stop it and then layer other things on top of it. But one of the primary value adds of our visualization or our product is that you can do all of this, interact with these data points, like millions at a time, real time filtering. So you're not sort of stuck with just a static model of like, hey, this is what it is. And you can only look at it this view or that view. We allow you to have that full interactivity. So...
[00:14:30.956] Kent Bye: You're just swimming in data all the time, it sounds like.
[00:14:33.677] Suzanne Borders: Constantly moving the data around, constantly manipulating it, and doing it in a really, we have a patent pending on it, so we're like, I don't want to get into the whole details. We have a specialized data controller that allows you to do this in a really intuitive and non-messy, non-difficult sort of way. So that's really exciting, too.
[00:14:51.903] Kent Bye: Nice. And so maybe you could talk a bit about some of the clients that you have or some of the problems that you're able to use your product, how it's actually being used out there in the field.
[00:15:00.236] Suzanne Borders: Yeah, so we recently signed our first customer, which is a super big deal. We've only been operational since April of this year, so that big milestone. I can speak to some of the other use cases too that we have coming up in our pipeline. Our first customer is using it for insurance data. So their product is actually a machine learning model that helps identify insurance applicants that might have not been accepted in a traditional model. So you can set a different, like a credit threshold, and at this credit threshold, would somebody be approved for a loan? identifying people that maybe don't have a traditional credit history but are still safe to lend to. So they came to us and basically said, A, I have a really, really complex machine learning model and I have a bunch of data and I see it this way in my head and I would like to have that presented in VR in this three-dimensional format, number one. Number two, we have a really hard time communicating to people who buy our product what our product is. What is a machine learning model? On their end, they get a list of people that say, oh, well, you should really lend to them. But we have a hard time convincing them to pay money for that if they don't really understand all of the work that goes into that. And so we think we have this thesis that if we can give them a visual for what is a machine learning model and why are they paying for it and the value of it, If they can see it visually, they'll pay more for it and they'll get more perceived value out of it. So that was one of their problems. The other problem is that, so specifically in insurance, there is a lot of fair lending practices that need to be followed. And to make sure that everybody's included, to make sure that there's no bias, that there's no racism, that there's no sexism. And the machine learning, the models themselves and the algorithms, they don't know and they can be taught to some extent, but they don't really understand sometimes you're making really bad discriminatory decisions, and so they need to be monitored by a human, and they need to have explainability so that if there is a question about compliance, you can go back through, visualize that, and prove that the decision was made for this reason and not that discriminatory, illegal reason. So, I mean, going back to it, it's really wanting to show customers a product, like an abstract product, like AI or ML, And then you also have the wanting to monitor these algorithms internally, wanting to have a view of the entire data set, the entire, you know, machine learning model so that they can do real time monitoring of its behavior. So these were some of the problems that we were solving for them and their initial use case and coming to us. And it's expanded. There's been a lot more issues that we've sort of taken on a lot larger data sets that we've tackled internally for them, but they found a lot of value in it. So that's been amazing.
[00:17:40.336] Kent Bye: It's fascinating. I know that unconscious bias is a huge issue across all of the world today, but especially in AI and technology where you are trying to make sure that the data sets that you have and being curated to be able to train the AI models aren't even including that. But this sounds like that these are different decisions that a human is making and just to have a bit of a safety check to make sure that there's a paper trail of the data that they can point to and have some empirical reasons rather than something that may be an unconscious bias.
[00:18:09.508] Suzanne Borders: Yes, correct. And it's something like from a regulatory standpoint, they have to have to utilize this product in that space. They need to be able to if the federal lending person from the government says, well, why did you approve this person or not? They need to be able to submit this form, explain all of the decisions. And normally a human would make those decisions. But now it's a machine learning model. So they need to have a record of it. And speaking to our product, they need to be able to view that as those decisions are being made to say, I'm okay with that decision. I'm going to sign off on that. So when that paperwork, if it is ever submitted, I'm responsible for that decision. So I'm okay with it. So it was kind of like some of their machine learning models are going off in these like tangents. And it's like, oh, well, we have to shut them down because if there is some sort of paperwork needed, we can't sign off on those decisions. So really our tool is allowing them to monitor that and making sure, okay, that's a decision I'm okay with. And if it starts going off on a course, shut it down before it starts like really getting too crazy with it. But yeah, monitoring and then compliance reports, the value of that.
[00:19:07.010] Kent Bye: Great, yeah, so we're here at LeapCon and I'm just curious to hear how augmented reality starts to play into this if there's like this collaborative social dimension of being able to do collaborative sense making with data that's on a tabletop or what type of vision you have for what the future of data visualization and augmented reality and what the difference is between what you could do in AR that you couldn't do in VR.
[00:19:29.403] Suzanne Borders: Right, so a lot of people ask, oh, is augmented reality going to replace VR? And I think that they're two fundamentally different technologies. And speaking to their use with data, different data sets lend themselves more to one or the other, right? And if you have really, really large, complex data sets that you want to sort of see this like really big thing, a lot of the times VR is useful for that because it focuses your attention and takes you completely out of this world. And it allows you to see things on a really, really large scale because You can use all of these different dimensions. You can replace this environment. I found that with augmented reality, it's more about contextualizing data and making the decisions and the actions that you're making in your everyday life much more impactful and valuable because of data. And displaying that data contextually, displaying it with the correct sort of analysis, giving you the reports when you need to have the reports. So it's... Data that's like that and then also data that has a geospatial tie-in going back to what we were talking about before like IOT sensor data or neighborhood data or weather data. Data that has a geospatial tie-in is often well represented in AR because in VR you sort of have to recreate the natural environment and with AR you don't. You're just able to overlay the data onto whatever it is that you're looking at. But again, if you want to see that on a really large scale, it's better suited for VR. AR is more if you want that immediate context, augmenting your everyday decisions and just improving upon them and improving upon your existing workflows using data, visualizing data around certain behaviors. So, yeah.
[00:21:04.432] Kent Bye: I see, yeah, I can imagine that there is a geospatial element that you could be in that environment and see that layers of data just kind of painting the world with these different dimensions of data that's telling a deeper story of what's happening over time.
[00:21:16.026] Suzanne Borders: Yes, and a deeper story with the goal of making you more effective, with saving you time, you know, making your world a richer, deeper place. And it's not always, I mean, productivity obviously in like an enterprise and a workplace setting is really useful, but also just, you know, enhancing your life on a day-to-day basis of making it so, you know, you have more time with your family or when you are with your family and you need certain data points, you know, things to improve upon your life is the goal, I think, in the long-term vision on my end with data visualization and AR, so. yeah great and so for you what are some of the either open problems that you're trying to solve or open questions that you're trying to answer oh boy i mean i think on my end some of the problems that i'm trying to solve it like going back to what i said like how do you see a million or a billion of anything like individual data points like you can do it on a reductive sense of like grouping things together and viewing it from a high level and But how do you actually do that and do it in a way that makes sense to a user and gives value add to them? So if you can just see a million of something, but again, it's not categorized or you can't interact with it, is that really a value add? I want to present all of this stuff in a way that is valuable and then easy for people to understand and instantaneous to get some sort of deeper meaning. I'm trying to think like more than how to actually do it. I want to know the stories around why that's like really important and necessary in people's lives. How can I improve their lives? But it goes back to staying close to the user, just really engaging with them, learning their stories, learning their pain points. And I feel like we've done a really good job of that, but looking forward to continuing. And then also just very interested in the way that neuro... neuroscience sort of fits into this and like recognizing people's brain patterns and maybe they could people store data relatively in the same way in terms of like they want to find patterns and what have you but certain people might have different formats of storing it and I'd love to sort of use neurotechnology to find those different brain patterns and maybe at some long point in the future use them to build these visualizations that match with their specific brain brainwaves, you sort of personalize the visualization to make it even more valuable and customized to you. Use the way that you naturally store data to replicate that in the way that I'm showing you data to make it easier just for you to understand.
[00:23:40.553] Kent Bye: Yeah, with my conversation with Carlos, I was saying that each one of us have our own unique memory palace, and so we need to have a decentralized memory palace model, because I don't think that there's going to be a universal memory palace that is consistent and complete, because that kind of violates the Gödel's incompleteness theorem, which says that anything that is going to be consistent is going to be incomplete. So because it is complete, each one of us has our own sort of memory palace. So then it becomes a question of what are the universal dimensions of architecture that we can start to say that we can have an interface between these two. But are there different foundational architectures of these memory palaces that allow us to make sense of the world in different ways? And being able to have ways that, like you're saying, translate the data so that it fits into someone's preexisting way of thinking about things.
[00:24:25.103] Suzanne Borders: Yeah, I think that really customizing data and presenting it in formats, depending upon people's personality styles, depending upon the way that they think, depending upon their personal history, or their age maybe, or their location, or where they grew up, or their diet, and making that really, really hyper-customized is important, and I think the long-term vision. it removes the friction when it comes to interacting and gaining value from data. But I do think that right now, at these beginning stages, finding those patterns and those similarities is more important as you try to build this community, sort of like mental palace of how we think about data. and identifying commonalities and patterns, which is something that I've also been working on in terms of like visualizing. You know, we have, like I said, the geospatial and then the non-geospatial data. And then within the non-geospatial data, I've sort of noticed these patterns emerging of like, you know, audience segmentation can be useful for marketing, can be useful for machine learning, can be useful for AI. I want to say the closest analogy is like a pie chart, a line graph, a scatter plot. the equivalent of that in an immersive space, right? And not to take those same patterns and put them into a third dimension, but to identify these sort of like standard visualizations, immersive visualizations that work across multiple different data sets. Because I don't want to have to, the long-term vision for my business is not to build unique visualizations for each data set, it's to build a platform. So long-term I want to have like maybe a library of 12 common data visualizations that work for multiple different data sets. And so when you upload your data, we're matching you with a visualization that works with your specific type of data. And so we're trying to identify these commonalities and identify these common visualizations in immersive space that map to what was previously thought of as like pie charts and line graphs or whatever. But standardizing that, I think, is the first step. Standardizing that visualization patterns. Yeah.
[00:26:23.928] Kent Bye: Awesome, great. And finally, what do you think is the ultimate potential of virtual or augmented reality and what it might be able to enable?
[00:26:33.581] Suzanne Borders: Well, we're in Los Angeles, so I have to say it's like Iron Man stuff, you know, stepping. Matrix is plugging into the Matrix and having this instantaneous. I could list off a ton of different movie references, but I guess the best one is the Matrix. Just being able to plug in and have that instantaneous understanding and that vision of, you know, that one scene where he gets it in his head and he like suddenly knows Kung Fu. I mean, they sort of did this a little bit in Lawnmower Man, too, which is like my favorite movie of all time, I just have to say right now. where you use VR to have this instant, easy learning process. That's the total vision for me. And I think that data visualization is the first step to that. And my whole goal, beyond just finding this stuff fascinating, is that I want to remove the friction around understanding data and knowing my world. I want to immediately understand things. I don't want to have to spend time learning about them, digging up data, trying to find patterns. I want to just understand and I want it to work with my biology and I want it to work with my specific brain patterns. And I think there's just a big pain point around computer and human and meshing those two together. And I think the closer that we can get to making those two work in similar formats and having computers format their data or their experiences in ways that are easier for humans to use and humans to maybe understand a little bit more about the structure of computers, we're going to get to that point where the friction disappears and you do get that plug into the matrix, I understand everything, I'm going to rule the world now, you know? I can speak 17 different languages. That's, in my opinion, the long-term vision of data in VR and AR.
[00:28:13.069] Kent Bye: I was just talking to Carlos, and I had a similar version. And one thing he said is that whenever you're learning languages, you have to actually participate in actually doing it. So I do think that there is going to be a practice component there of that I'm not going to be able to just upload things in my brain, that part of the experience is actually the practice. So I think that, yeah, I love that vision as well. I want to have it and I want to build things, but just in the previous conversation, there was a bit of this idea that you could passively consume knowledge. I think there's an interactive participatory process by which you're taking information in, but you're able to categorize it. But I will refer back to Beat Saber, just in the sense that I've been playing that and you play Beat Saber, more and more, and you start to get higher and higher on the expert and expert plus levels, that your brain starts to do things at an unconscious level, that you can start to make sense and see patterns and take information in. And so I feel like that there could be something that taking this concept from David Eagleman, which is the sensory substitution or sensory addition, which is like as long as there's some sort of visual synchrony for what the data architecture that's coming into your brain, if you have a multimodal input for haptics and sound and the sonification and the visualization and you have a deeper meaning for what it means that you could synthesize all that data and that it's a bit of this struggle of doing the expert plus levels of beat saber and failing a lot until you finally get it but there's slow progress of doing it each and every day and i think that's the key is that there's a certain amount of like unconscious learning that our body is analogically as a gpu that can sort of parallel process information and send it into our brain And our brain is really great at finding patterns, but it needs time to really fix those patterns down. Right.
[00:29:49.293] Suzanne Borders: I mean, my response to that is I think it needs time because the information is presented in a way that is not in sync with how your brain... Your brain is looking for a way to structure it and store it. So imagine if it was presented in a way that is how you would structure and store it, maybe you wouldn't need as much learning. And I think the process of, like I said, the process of learning is just really finding a structure. How do I store? How do I access? How do I recall this data? And I think that's where it really comes down to figuring out how do people do that and then presenting the data in a way which makes it easy. I still believe that we will need to practice, and I think there's a lot, don't get me wrong, I think there's a lot of value in practicing and failing and deploying data. So I don't think that we'll ever get rid of that either, but I think when it comes to ingesting the raw data, I think that we can make that much easier and I think that we can remove a lot of the friction. And once the data is in you, you're going to have to practice sort of like expelling it. So I guess the way that an analogy for the language be, you can know internally the library, you can think in that language in your head, but actually having conversations with all the different human variables and permutations that happen in a face-to-face conversation something you're still going to have to practice. So I think it's really just the pipeline of getting the data in, getting the data processed, that can be eased. But the human to human pipeline of data is never going to get any easier. You're always going to have to have those face to face, you know, awkward conversations, learn how to do that. So, yeah.
[00:31:20.666] Kent Bye: So finding those neural architectures that are the frameworks that allow us to ingest massive amounts of data super fast, but still having the capacity to be able to interact and play with it or to work with it and practice it so that we can internalize it, but that you're optimizing the overall process.
[00:31:36.784] Suzanne Borders: Absolutely, that's the goal. Optimizing the front end, the data load in, and the data storage. The recall and the application, and the application to real world, that's what you're always going to have to work on. You're always going to have this practice. I don't think that that's anything you can just plug in and learn, unfortunately. Maybe I'm wrong, but we'll see.
[00:31:57.932] Kent Bye: Is there anything else that's left unsaid that you'd like to say to the AR or VR community?
[00:32:02.847] Suzanne Borders: No, I just wanted to thank Magic Leap for being such, I mean, they've been very, very wonderful in reaching out to sort of smaller teams like my own and really being supportive. And, you know, just the community in general here in Los Angeles. You know, I started on this VR journey. I met my co-founder, actually. He was the only person I knew who had a DK1 dev kit. And I really wanted to try the Oculus, and I didn't have the computer, and I didn't know where to buy it, and I was kind of broke at the time. And so I was like, okay, well, he's the only person I'm going to try it. And it... When I was a kid, I was a big hardcore Trekkie, and I actually went to some Star Trek conventions. I actually had a blueprint of the Starship Enterprise I got for Christmas when I was like eight and a half years old, or eight. So yeah, so it was like, I got this moment of like, oh my god, the holodeck is actually, it's like possible. Like this actually will happen in my lifetime. And I was like, oh my gosh, I have to do something with this. And it sort of was... I wanted to figure out the right idea for the platform and I wanted to really have a value add. I didn't want the VR or AR, eventually it became AR, I didn't want that component to be a gimmick. I wanted it to be a fundamental value add to the workflow for whatever idea that I was going to do. So I took the time to really try to find something where I felt like vr ar could add value and when i came upon the idea of data visualization i was like okay i'm ready but then the hardware wasn't quite there and i wasn't quite ready in my personal life and so okay i'm gonna wait so when i took the leap this spring and actually started doing this i just have been so overwhelmed with like how kind and how helpful the vr and ar community of people have been here in los angeles as like a female founder as a first-time founder as somebody sort of new to the scene. I was interested in it, but I'm kind of antisocial. I like social anxiety, so I don't go out as much. And so now as a founder, I'm sort of like, okay, I got to do this. I got to be part of the scene. And I had so much anxiety that people are going to be clicky. They're not going to like me or they're going to like make fun of me. And nobody has. They've been so kind and so wonderful and so helpful. And, you know, So I just wanted to say thank you to the community for being so accepting and so open. And I hope that I can give back and contribute back something absolutely wonderful and let you all see your data in the best possible format.
[00:34:13.466] Kent Bye: And upload information into our consciousness.
[00:34:16.548] Suzanne Borders: One plug-in at a time. Or one headset at a time, I guess.
[00:34:21.972] Kent Bye: Awesome. Great. Well, thank you so much for joining me today on the podcast. So thank you.
[00:34:25.255] Suzanne Borders: Thank you for having me. I appreciate it.
[00:34:28.237] Kent Bye: Thanks again for listening to this episode of the Voices of VR podcast. And if you enjoy the podcast, then please do spread the word, tell your friends, and consider becoming a member of the Patreon. This is a supported podcast, and so I do rely upon donations from people like yourself in order to continue to bring you this coverage. So you can become a member and donate today at patreon.com slash voicesofvr. Thanks for listening.
