(light electronic music) >> Announcer: Live from Stanford University, in Paolo Alto, California, it's theCUBE. Covering Women in Data Science Conference, 2018. Brought to you by Stanford. >> Welcome back to theCUBE, I'm Lisa Martin. At Stanford University, we're here for the third annual Women in Data Science Conference. #WiDS2018, check it out, be part of the conversation, WiDS is in it's third year, but it's aiming to reach about a hundred thousand people this week alone. There's 177 regional WiDS events in 53 countries. This event here, the main event at Stanford, features key notes, technical vision talks, a career panel, and we're excited to be joined next by Dr. Nathalie Henry Riche. I did that in French. >> Yes. (laughs) Who is a researcher at Microsoft, and Natalie, first of all, welcome to theCUBE. >> Thank you, I'm really thrilled to be here. >> Yeah, you gave a technical vision talk on data visualization, and data driven's story telling. Share with our audience, some of the key messages, that the WiDS audience heard from you earlier today. >> Well, I guess, I gave two main messages. The first one is, that a visualization has two superpowers. >> Lisa: Superpowers? >> Superpowers. >> Tell me girl. The first one is enable you to kind of think about your data in a new way. So, just kind of form hypothesis, and answer questions you didn't even know, you had by your data. So, that's the first one. The second super power, is it's really useful to communicate information, and communicate with a large audience. Visualization helps you, kind of convey your point with data, to back it up. So, that's kind of the short one minute. >> I love that, super super hero, super power. So, WiDS is, as I mentioned at the intro, in its third year, and reaching, it's grown dramatically in such a short period of time. This is your first WiDS, and your first WiDS you are a speaker. What was is that attracted you to WiDS, and you went, yes I want to give some of my time to this, and come down from Seattle? >> Well, so I'm French originally, and my studies I did at engineering school, and it was one of three out of 300 men, right? >> Wow. >> So, I was requested a lot for women in computer science, and engineering. So, I actually really like it. Just meeting all of those people, talking about, you know, trying to bring more women in. Part of the job I'm doing is very creative, so, we're trying to come up with new ideas for visualization. I think having, you know, a wide range of people adds to the mix, and we get so many more exciting ideas. So, I really want to try to have more diverse group of people I can work with, and connect to, and so that's why that attracted me to here. >> Excellent, couple of things that you said I've heard a number of times today. The first one is, what Daniela went and shared, who's also a speaker, that often times, some of the few women in tech, and you mentioned being one of three in 300? Are asked to do a lot of other things. Did you find that, that, okay you're one of the few females, you're articulate, you like speaking, we want you to do all these things. >> Yes, and I say no a lot. (laughs) >> 'Cause I have kids, too. >> That's a skill, too. But yeah, it happens a lot. I think as we go further, it's going to be less and less happening. It's better in the end. So, it's kind of a service, I see it as a service to, you know, my field, and my company. But, at the same time, we'll also get a lot of benefits from it. But that said, I try to cut it down to a manageable level, so two hours flight from Seattle works great. >> Right, right, right. Another thing is that, that you mentioned the creativity. I've heard that a number of times, today from our guest Margot Gerritsen, was on as well. Tell me about your thoughts about being in this data science role, the need for creativity. How does, how it, why is that you might consider it, like a softer skill versus the technical skills. But, how important is that creativity in your job, for example? >> So, my job is really like researcher. Trying to have new ideas, and innovate for Microsoft in particular. So, I'm not really a data scientist, but I build the tools for a data scientist. So, knowing that, creativity is important because you need to kind of think out of the box. What is the next generation of tools that they will need? In turn, they need to think out of the box, kind of get more insight out of the data they're collecting. So, creativity is just like, pervasive to this whole data science thing. Problem solving as well, so you need a lot the left brain, and a lot of the right brain. Kind of both of them together. I think that having different cultures, and different genders, even different age ranges just, you know, makes you think out of the box. That's just what's happening. Discussing with people, I was discussing with someone in cosmology, and I was like, whoa. That brought up a lot of different ideas in me, so, to me, that's really critical part of what I'm doing every day. >> I like that, that kind of aligns to what one of our guests said earlier, and that is the thought diversity. Wow, I've never >> Yes. thought of thought diversity. But, you bring up a good point about it's not just about having women in the field, it's also having diversity, in terms of generations. One of the things that's, I think, pretty unique about WiDS, is it's not just about reaching young women in their first semester at University, for example. Maria Clavijo said that's the ideal time to really inspire. But, it's also reinvigorating women who've been in academia, or industry in stem subjects for a long time. So, you have, we have multiple generations, and to your point, that diversity is important, it's not just about gender, ethnicity. It's also about the diverse perspectives that come from being >> Exactly. from different generations. >> So, it's funny, 'cause I was giving this talk earlier, and it was, one part of it was about time line. When I was researching, you know how people draw time? Well there's, depending some culture, it goes from left to right, but some other culture it's front to back, back to front, right to left. So, we need to be aware of all of that, and it's so much easier to just have the people to converse with right in your office, or next door, to be aware of those. So, that's very important, especially to big companies, like Microsoft, 'cause of, you know, a lot of customers world wide. So, it's very important to just be immersed in that. >> Definitely. So, you have been published, you've got published research, and over 60 articles in leading venues, and human-computer interaction, and information visualization. But, something we chatted about off camera, was very intriguing about visualization and children. Tell me a little bit more about that. >> So, I happen to have two kids, you know, seven and four. I'm passionate about what I'm doing, and I just couldn't keep it out of their hands, right? So, I was just starting, you know, seeing what does my daughter learn at school, like, what does she learn in kindergarten? In fact, in kindergarten, I remember one day, she brought back candies, and I'm like did you get candies from school? She's like no, because we were doing a bar chart. I was like, what? (laughs) So, I was very intrigued in, you know, what do we teach, what do your kids learn? It was fascinating to see that, you know, from an early age, they learn how to do those visualizations. But, they don't really learn how you can lie with them, or you know, to kind of think critically about that. That, you know, maybe you can start your bar chart at two, and you know, you would have less candy, I guess. But, you could, kind of convey the wrong messages. So, I became passionate about this, and decided we need to just improve our teaching about how we can represent data, and how we can also misrepresent it. In the hope that for the next generation to come, they'll be able to look at a chart, and think critically about it. Whether or not it tells the right story with the right data. Kind of beyond, just picture's worth a thousand words, then I'm not going to think about it. >> Yeah. >> This is kind of my personal effort that I try to move myself forward. (chuckles) >> Well, it's so important about having that passion, and I think that's one of things that seems to be inherent about WiDS. Even, you know, yesterday seeing on the Twitter stream, WiDS New Zealand starting in five minutes, and it's been really focused on being so, kind of inclusive. Just sort of naturally, and one of the things that I learned in some of my prep for the show, is the bias that is still there, in data interpretation. You kind of talked about that, and I never really thought about it in that way. But, if a particular group of people is looking at a data set, and thinking it says this, and no other opinions, perspectives, thoughts are able to be incorporated to go, well, maybe it says this. >> Yeah. >> Then we're limiting ourselves in terms of one, the potential that the data has to, you know, help a business, create a new business model. But also, we're limiting our perspectives on making a massive social impact with data. >> Yeah, what I find very interesting is visualization often people think about it at the end of the spectrum. Like, I've collected my data, I analyze it, and now I need to pretty picture to kind of explain what I found. But, the most powerful use of visualization, I think, comes early on. Where you actually just collected your data, and you look at it before you run any statistical test. I did that not long ago with French air traffic data in the Hollands, I put them in, and I saw the little airplanes moving around. Then, what we saw, is one air planes doing loops like this. I was like, what is this going on, right? It was just a drone, doing like tests, right? But, somehow it got looped in into that data set. So, by looking at your data early on, you can detect what's wrong with the data. So then, when you actually run your statistical test, and your analysis, you better reflect what was that data in the first place, you know, what could go wrong there? So, I think inserting visualization early on is also critical to understand what we can really know, and do, and ask, about the data in the first place. >> So, it's kind of like, watching the story unfold, rather than going, we've done all this analysis here's the picture, the story is this. The story is, your sort of, turning it sort of page by page, it sounds like, and watching it, and interpreting it, as it's unfolding. >> Rethinking what you collected in the first place. Is that the right data you collected to answer the question you wanted to ask? Is it a good match or not? Then, rethink that, you know, collect new data, or the missing one, and then go on with your analysis. So, I think to me, it's really a thinking tool. >> It also sounds like another, we talked about the technical skills that had, obviously that a computer scientist, data scientist needs to have. But, there's other skills. Empathy, communication, collaboration. Sounds like also, there needs to be an ideal kind of skill set, it has to include open mindedness. >> Yes. >> Tell me a little bit about some of your experiences there, and not being married to, the data must say this. So, if it doesn't, I'm not going to look anywhere else. Where is open mindedness, in terms of being a critical skill set that needs to come to the field? >> Yeah, I mean we, that's that is totally a re-critical point. Think already, when you're collecting the data, especially as a scientist, when I run experiment, I kind of know what I want to find. Sometimes, you don't find it. You need to kind of embrace it. But, it's hard to have because sometimes, it's like those unconscious bias you have. Like, you're not really necessarily controlling them, and just the way you collected the data in the first place, maybe just, you know, skewed your result. So, it's very important to kind of think ahead of time of all of those bias you could have, and think about all of what could go wrong. Often, the scientific process is actually that trying to think about all of the stuff that could go wrong, and then check whether or not they're wrong. We're trying to infuse that, a little bit over Microsoft as well, kind of, you know, the data that we collect, can we analyze them, can we have teams of people who really think is that the right data? Are we collecting like, world-wide for example? Are we just collecting from the US? So, there's a lot of those, kind of, ethical, and bias, kind of training, and effort to try and remove that. The maximum from our work, and I think that it's across the entire world. I think, with all of this data collection everywhere, we kind of have to do that, very consciously. >> I think two things kind of speak to me that out of what you just said, that we've heard a number of times today. One, that failure, and I don't mean to say that failure is not a bad thing. That's how you, >> That's how you learn, Exactly, >> and grow. Exactly, in many ways it's not a bad F-word, it's this is how everybody that's successful got to wherever they are. But, it's also about embracing, as you said, the word embracing, embracing the fact that you might be bring bias into this, and you have to be okay with maybe this is the wrong data set. If you consider that a failure, consider it, to your point, a growth opportunity. That is one of the themes that we've heard today, and you've, kind of, elaborated on that. The second one is, be okay getting uncomfortable, get out of that comfort zone. Consciously uncomfortable, because when you're able to do that, the possibilities are limitless. >> Yes, and that's what I try to do everyday, 'cause I try to push all of the software that we're doing, and Microsoft is so big, you know, and all of those software are like so there. (laughs) So trying to come up with new ideas, like so many are failures, you know. Oh they won't make money, or they don't actually work when you, you know, for this population. So, most of my work is failure. (laughs) But hey, one success when you know why, and I'm happy about it. >> Exactly, but it's just charting that course to getting to the ah, this is the pot of gold at the end of the rainbow. Well Nathalie, thank you so much for taking some time to talk with us on theCUBE, and sharing your stories. Congratulations on being a speaker, your first WiDS, and we look forward to seeing you back next year. >> Thank you very much. >> We want to thank you for watching theCUBE. I'm Lisa Martin, live from WiDS 2018 at Stanford University. Stick around, I'll be back with my next guest after a short break. (light electronic music)

(upbeat music) >> Hey welcome back everybody. Jeff Frick here with theCUBE. I wish I could give you my best John Miller impersonation but I'm just not that good. But we are at Oracle Park, home of the San Francisco Giants. We haven't really done a show here since 2014, so we're excited to be back. Pretty unique event, it's called Sports Tech Tokyo World Demo Day. About 25 companies representing about 100 different companies really demonstrating a bunch of cool technology that's used for sports as well as beyond sports, so we're excited to have one of the companies here who's demoing their software today, or their solution I should say. It's Albert Ng, he's the founder and CEO of Misapplied Sciences. Albert, great to see you. >> Great to see you, thank you for having me. >> So Misapplied Sciences. Now I want to hear about the debates on that name. So how did that come about? >> Yeah, so I used to work part time for Microsoft, at Microsoft Research, and one of the groups I worked for was called the Applied Sciences group. And so it was a little bit of a spin on that and it conveys the way that we come up with innovations at our company. We're a little bit more whimsical as a company that we take technologies that weren't intended for the ways that we apply them and so we misapply those technologies to create new innovations. >> Okay, so you're here today, you're showing a demo. So what is it? What is your technology all about? And what is the application in sports, and then we'll talk about beyond sports. >> Yeah, so Misapplied Sciences, we came up with a new display technology. Think like LED video wall, digital signage, that sort of display. But what's unique about our displays, is you can have a crowd of people, all looking at the same display at the same time, yet every single person sees something completely different. You don't need to have any special glasses or anything like that. You look at your displays with your naked eyes, except everyone gets their own personalized experience. >> Interesting. So how is that achieved? Obviously, we've all been on airplanes and we know privacy filters that people put on laptops so we know there's definitely some changes based on angle. Is it based on the angles that you're watching it? How do you accomplish that and is it completely different, or I just see a little bit of difference here, there, and in other places? >> Sure, so at the risk of sounding a little too technical, it's in the pixel technology that we developed itself. So each of our pixels can control the color of light that it sends in many different directions. So one time a single pixel can emit green light towards you, whereas red light towards the person sitting right next to you, so you perceive green, whereas the person right next to you perceives red at the same time. We can do that at a massive scale. So our pixels can control the color of light that they send between tens of thousands, up to a million different angles. So using our software, our processors on our back end, we can control what each of our pixels looks like from up to a million different angles. >> So how does it have an edge between a million points of a compass? That's got to be, obviously it's your secret sauce, but what's going on in layman's terms? >> Yeah, so it's a very sophisticated technology. It's a full stack technology, as we call it. So it's everything from new optics to new high performance computing. We had to develop our own custom processor to drive this. Computer vision, software user interfaces, everything. And so this is an innovation we can up with after four and a half years in stealth mode. So we started the company in late 2014, and we were all the way completely in stealth mode until middle of last year. So about four years just hardcore doing the development work, because the technology's very sophisticated. And I know when I say this, it does sound a little impossible, a little bit like science fiction, so we knew that. So now we have our first product coming on the market, our first public installation later next year and it's going to be really exciting. >> Great. So, obviously you're not going to have a million different feeds, 'cuz you have to have a different feed I would imagine, for each different view, 'cuz you designate this is the view from point A. This is the view from point B. Use feed A, use feed B. I assume you use something like that 'cuz obviously the controller's a big piece of the display. >> Exactly, so a lot of the technology underneath the hood is to reduce the calculations, or the rendering required from a normal computer, so you can actually drive our big displays that can control hundreds of different views using a normal PC, just using our platform. >> So what's the application. You know obviously it's cute and it's fun and I told you it's a dog, no it's a cat as you said, but what are some of the applications that you see in sports? What are you going to do in your first demo that you're putting out? >> Yeah, so what the technology enables is finally having personalized experiences when in a public environment, like a stadium, like an airport, like a shopping mall. So let me give an airport example. So imagine you go up to the giant flight board and instead of a list of a hundred flights, you see only your own flight information in big letters so you can see it from 50 feet away. You can have arrows that light your path towards your particular gate. The displays could let you know exactly how many minutes you have to board, and suggest places for you to eat and shop that are convenient for you. So the environment can be tailored just for you and you're not looking down at a smart phone, you're not wearing any special glasses to see everything that you want to see. So that ability to personalize a venue stretch, is to every single public venue, even in the stadium here, imagine the stadium knowing whether you're a home team fan or away team fan or your fantasy players. You can see it all on the jumbotron or any of the displays that are in the interstitial areas. We can have the entire stadium come alive just for you and personalize it. >> Except you're not going to have 10,000 different feeds, so is there going to be some subset of infinite that people are driving in terms of the content side? >> Mhmm. >> So on your first one, you're first installation, what's that installation going to be all about? >> The first installation is going to be at an airport, I can't see right now publicly where it's going to be or when it's going to be or what partner. But the idea is to be able to have a giant flight board that you only see your own flight information, navigating you to your particular gate. You know when you're at an airport, or any other public venue like a stadium, a lot of times you feel like cow in a herd, right? And it's not tailored for you in any way. You don't have as good of an experience. So we can personalize that for you. >> All right, Misapplied Sciences. Oh I'll come down and take a look at the booth a little bit later. And thanks for taking a few minutes. Good luck on the adventure. I look forward to watching it unfold. >> Appreciate it, thank you so much. >> All right, he's Albert I'm Jeff. You're watching theCUBE. We're at Oracle Park, on the shores of McCovey Cove. Thanks for watching, we'll see ya next time. >> Thank you. (upbeat music)

>> Narrator: Live from Austin, Texas, It's theCUBE, covering KubeCon and CloudNativeCon 2017. Brought to you by Red Hat, the Linux Foundation, and theCUBE's ecosystem partners. >> Welcome back and we're live here in Austin, Texas. theCUBE's exclusive coverage of CloudNativeCon and KubeCon, which stands for Kubernetes Conference, the not Cube, C-U-B-E, that's us. I'm John Furrier here with Matt Broberg, co-host in here for Stu Miniman, podcaster himself And we also have a special podcaster here on theCUBE, Edaena Salinas, who's the host of The Women in Tech Show @techwomenshow on Twitter, also a software engineer at Microsoft. Welcome to theCUBE, thanks for joining us. >> Thank you for having me. >> This is kind of like a podcast, we're like live though, we're streaming. >> Oh, okay. >> Love your sweater, that's a binary tree holiday tree. >> Binary Christmas tree. >> Binary Christmas tree. >> So perfect. >> I'm going to do a quick sort quickly, no I'm only kidding. So question for you, you've got a great program, you've got a desk over there, you're doing some interviews here, great to see you here doing The Women in Tech. We've done a lot of women in tech interviews on theCUBE, love to showcase women programming, women developers, women in stem, great that you do it so congratulations. So tell us what's the vibe like, are you people excited to do podcasting, is it all women, do you interview men, so tell us a little bit about the show. >> That's a good question. The motivation of the show is to have technical women talk about what they're working on, the products they're building or business strategy, instead of what does it feel like to be a woman in tech, or the only woman in the meeting room. Those conversations are valid, but I think we've heard a lot of those, and the community can benefit if they're just listening to what they're working on. >> It's great to get the education out there. So I have a question for you, I'd love to ask this. But I never really had a, talk about software engineering on theCUBE, what's the style difference in coding, do that's talked about, are women, do they code differently? Is it, probably neater, cleaner, is there biases in coding in that come into, because. >> I'm not aware of (laughs) difference like that, but, you could find that out if you run a script on the GitHub projects but, I don't think it affects. >> People don't, they don't talk about that, do they? >> They don't talk about that, and I certainly haven't experienced anything like that, and I learn from my coworkers and they learn from me. >> Now what are you working on at Microsoft? >> I am at Microsoft Research earlier this year, so what I work on is adding AI features to our existing products, like Outlook and Dynamics, so yeah. >> And I want to switch gears and talk about the podcast a little bit. So, I'm curious what was your inspiration to start it, and had you done podcasts before or did it just feel right, like this is the time to do something? >> I hadn't done a podcast before, but I had listened to a lot of shows. And the initial motivation of this is, at Microsoft where I work, they have this Meet Our Leader series, where they bring men and women in a leadership position. The audience is mostly women, and I was tuning in there by Skype, and there's 200 people listening to them plus people in the room, and they're asking questions about what's our business strategy or technical questions, so I'm like, women want to know about these things, and then in addition to that I noticed some women, technical women, they list on their website, I love giving talks, just not the diversity talk or the lady panel, I've given it several times, I just want to talk about cloud computing or the things that I work on. And then I looked if someone was doing this already, a show like this. I didn't find it, so I started it, and it helped that I listened to other shows. >> I mean I find when I talk to a lot of my women friends that are technical, sometimes CTOs and higher, and even down in programming, they don't want to, they just want to talk about what they're working on. They don't want to be the, that woman in tech on the panel, I've had a friend said to me privately over the weekend at a party, I don't, am sick and tired of being called and them saying, I need a woman on a panel. >> Yeah. >> I mean, it's kind of like a backlash, but they also feel obligated to do it. >> Yeah. There's kind of a new culture developing. Talk about that, and what that kind of conversation's like in your world. >> Well what I've heard, for example Sheryl Sandberg I think has said, there, we will reach a point someday where we won't be called a female CEO or a woman engineer, it would just be engineer. So, that's our goal, to just lose that label at someday, right now, the show has the label because I'm raising awareness of having them talk about technical topics. As more people hear about them, it's just going to be natural and normal like, sure I learned from Nicole about Kubernetes, and then men are also listening to the show, which I think benefits a lot the community. >> I have two daughters, one's in high school, one's in college, one's at Cal, and they're techies, they're science, they like science, not coding yet. Their mom doesn't want them to be like me and code, but, so they're, but they're-- >> Just give them the choice >> I said hey, do you do Cube interviews, it's also an option. But in their culture, when I ask them about this, they're like, we don't think about it. So there's a, at their level, they're all in school together and it's interesting, I think a time is coming now where the awareness is putting the old guard pressures away, there's still some bad behavior, no doubt about it, I see it everyday and it's being called out, thank god, but now it's just like, you're a person in tech. >> Exactly. >> So I think respect is the number one, respect for the individual is something that we always preach, independent of who the person is, male, female, whatever. >> Yeah, exactly, and we will reach that point soon, I hope so, where we lose the label. >> So you're 77 episodes in, I'm also a listener, I learned a ton from it, you have brilliant people on every week. I really admire you for that because I know how hard it is to produce a podcast. What are some of the things you didn't know before starting a podcast that like, oh wow, that takes more energy than it looked like at the time. >> That's a good point, yes. The very first few interviews that I did, I didn't take into account how the guest would respond. So I prepared the questions in advance, and then I would think, this is going to be a two-minute answer, but the person just ended up saying yes, no, or sure, that's a big problem, and I was counting on it to be more, so I needed to prepare in that aspect and what helps is just, if they've already given talks, just look them up on YouTube or find all their interviews they've done, just to get a sense of how they talk. There's also people that tend to give super long answers, and you need to prepare for that, how you're going to handle it. >> I noticed you had someone from Bitnami came by recently, was that Erica? >> Erica Brescia came on the show a few months ago, the COO of Bitnami, and in that episode we focused a lot on entrepreneurship, she came out of YC, so sort of building Bitnami to where it is, and today I interviewed the engineering manager of Bitnami, and she talked about Kube apps and all this security aspects. >> What are some of the innovations you're seeing in your interviews? Can you highlight some examples recently that jump out at you, that are, lot of innovation coming from these ladies, what are some of things that they've done? Shine the light on some of the awesome highlights from your guests. >> One of my favorite ones is Rachel Thomas, she works at Fast.ai, what she works on is bias in machine learning. Machine learning is about learning from your data, but I've heard, this woman at a conference bring it up, like, if I'm a minority, I'm a minority in the data. So you need to take that into account. So there's a lot of people working in the space. That was a really cool project I think. >> Data driven analysis. >> Yeah, but sort of, considering that bias that can be in that data, and make sure your data is better. So for example, it's a known fact that there's a lot of men in the technology field, so if you're going to get job recommendations, if a person like me, Mexican, I studied computer science but if I'm a minority in the dataset, maybe I'm not going to get the recommendation. I'm not saying that how it works, but that could potentially be an issue. >> It's a statistical fact. >> Yeah, but if you don't take that into account in your system, maybe women are not getting job recommendations, of openings. >> That's a good point. >> So, it brings up-- >> That's a really powerful observation, right, and I was curious, as a software engineer, software engineering is your craft and podcasting is your hobby, how has podcasting influenced your software engineering skills? Because ultimately that's the path you're going down career-wise. >> Well a big part of software engineering is about talking to your team and going to meetings, talking about solutions. Podcasting has help me a lot, improve my soft skills. For a period of time I was editing my own shows. One thing that I noticed is when I was talking to my guests, I'm listening to my recording, when I would say an idea, I would tend to lower my voice. So I noticed that, and then I said to myself, I'm probably doing this in the meetings at work, and then, I work-- >> What an amazing insight, right, like now you're seeing how you're presenting yourself in front of other people in technical ways and then you get to bring that into your work. >> Yeah, whenever I would say an idea I would just be like, what happens if we do this instead? That was like I have to-- >> That's a great example of self-awareness, right, I mean, everyone should do that, listen to their, look at their actions. >> Yes, so it helps with the soft skills. And it also helps if you're working in a certain area of software engineering, and you want to find out more about it, you can decide to do more shows on that and just share that with the community that women are working on this. >> It's great to see you have some Cube alumni like Erica on, we interviewed her on theCUBE at Google Next a few years ago. Share some coordinates, when does the show go out, when do you record it, does it ship on a regular cadence, share a little information. >> The show is released weekly. I publish Monday evenings, but I share it on social media on Tuesday mornings, so if you're subscribed, you would get it Monday evenings. >> Good for the week, running, on the bike, in the car. >> It's 30 minutes. >> Any video podcasting coming? >> I don't have any video, no. >> Lot more editing required, trust me on that one. Cool. What's the most exciting thing that you're working on right now? You have the podcast, which is a super cool hobby, great to get those voices out there, so congratulations. But at work, what are you working on? >> Yes, well like I mentioned earlier, I work on a team it's a team under Microsoft Research, a lot of it, we don't know what people working on there, but, my team works closely with product teams. So we're adding AI features to Outlook and Dynamics CRM. Just to increase the productivity aspect, in this sense. >> So you're bringing applied R and D to the product groups, mostly AI? >> Yes, yeah. >> What's the coolest thing in AI that you like? >> Oh wow, well I really like recommendation systems and things like that. >> All right, well thanks for coming on theCUBE, really appreciate it, The Tech Women podcast here, they got a booth over there. Doing great interviews, here's at theCUBE we're doing our share. Two days, the second day of live wall to wall coverage. Be right back with more live coverage, in Austin Texas. You here the music, this is the big D, Texas here in Austin Texas. More live coverage, that's Dallas, we're in Austin. Be right back with more live coverage after this short break. (futuristic music)