>> Reporter: Live from Stanford University, it's theCUBE, covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE media. >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare, and we're live at Stanford University covering the fifth annual WiDs, Women in Data Science conference. Joining us today is Emily Glassberg Sands, the Head of Data Science at Coursera, Emily, welcome to theCUBE. >> Thanks, so great to be on. >> So, tell us a little bit more about what you do at Coursera. >> Yeah, absolutely, so Coursera is the world's largest platform for higher education. We partner with about 160 universities and 20 industry partners and we provide top learning content from data science to child nutrition to about 50 million learners around the world. I lead the end to end data team so spanning data engineering, data science and machine learning. >> Wow, and we just had Daphne Koller on earlier this morning who is the co-founder of Coursera and she's also the one who hired you. >> Yeah. >> So tell us more about that relationship. >> Well, I love Daphne, I think the world of her, as I will talk about shortly, she actually didn't hire me from the start. The first answer I got one from Coursera was a no, that the company wasn't quite ready for someone who wasn't a full blown coder. But I eventually talked to her into bringing me on board, and she's been an inspiration ever since. I think one of my first memories of Daphne was when she was painting the vision of what's possible with online education, and she said, "think about the first movie." The first movie was literally just filming a play on stage. You'll appreciate this, given your background in film, and then fast forward to today and think about what's possible in movies that could never be possible on the brick-and-mortar stage. And the analog she was creating was the first MOOC, the first Massive Open Online Course was very simply filming a professor in a classroom. But she was thinking forward to today and tomorrow and five years from now, and what's possible in terms of how data and technology can transform, how educators teach and how learners learn. >> That's very cool. So, how has Coursera changed from when she started it to now? >> So, it's evolved a lot. So, I've been at Coursera about six years, when I joined the company, it had less than 50 people. Today we're 10 times that size, we have 500. I think there have been obviously dramatic growth in the platform over all the three main changes to our business model. The first is we've moved from partnering exclusively with universities to recognizing that actually, a lot of the most important education for folks in the labor market is being taught within companies. So, Google is super incentivized to train people in Google Cloud, Amazon and AWS. Folks need to learn Tableau and a whole host of other software's. So, we've expanded to including education that's provided not just by top institutions like Stanford, but also by top institutions that are companies like Amazon and Google. The second big change is we've recognized that while for many learners and individual course or a MOOC is sufficient, some learners need access to full degree, a diploma bearing credential. So we've moved to the degree space we now have 14 degrees live on the platform masters in computer science and data science but also in business, accounting, and so on. And the third major changes, I think just sort of as the world has evolved to recognize that folks need to be learning throughout their lives. There's also general consensus that it's not just on the individuals to learn, but also on their companies to train them and governments as well, and so we launched Coursera enterprise, which is about providing learning content through employers and through governments so we can reach a wider swath of individuals who might not be able to afford it themselves. >> And how are you able to use data science to track individual, user preferences and user behavior? >> Yeah, that's a great question so you can imagine right? 50 million learners, they're from almost every country in the world from a range of different backgrounds have a bunch of different goals, And so I think what you're getting out is that so much of creating the right learning experience for each person is about personalizing that experience. And we personalized throughout the learner journey so in discovery up-front, when you first joined the platform, we ask you, what's your career goal? What role are you in today? And then we help you find the right content to close the gap. As you're moving through courses we predict whether or not you need some additional support. Whether it's a fully automated intervention like a behavioral nudge, emphasizing growth mindset, or a pedagogical nudge like recommending the right review material and provide it to you, and then we also do the same to accelerate support staff on campus. So, we identify for each individual what type of human touch might they need, and we serve up to support staff recommendations for who they should reach out to, whether it's a counselor reaching out to degree student who hasn't logged in for a while, or a TA reaching out to a degree student who's struggling with an assignment. So, data really powers all of that, understanding someone's goals, their backgrounds, the content that's going to close the gap, as well as understanding where they need additional support and what type of help we can provide. >> And how are you able to track this data, are you using AV testing? >> Yeah, great question, so the, we call it a venting level data, which basically tracks what every learner is doing as they're moving through the platform. And then we use AV testing to understand the influence of kind of our big feature. So, say we roll out a new search ranking algorithm or a new learning experience we would AV-Test that, yes to understand how learners in the new variant compared to learners in the old variant. But for many of our machine learn systems, we're actually doing more of a multi-armed bandit approach where on the margin, we're changing a little bit the experience people have to understand what effect that has on their downstream behavior, separate from this mass hold-in or hold-out AV-Test. >> And so today, you're giving a talk about Coursera's latest data products so give us a little insight about that. >> So, I'm covering three data products that we've launched over the last couple of years. The first two are oriented around really helping learners be successful in the learning experience. So the first is predicting when learners are going to need additional nudges and intervening in fully automated ways to get them back on track. The second is about identifying learners who need human support and serving up really easily interpretable insights to support staff so they can reach out to the right learner with the right help. And then the third is a little bit different. It's about once learners are out in the labor market, how can they credibly signal what they know, so that they can be rewarded for that learning on the job. And this is a product called skill scoring, where we're actually measuring what skills each learner has up to what level so I can for example, compare that to the skills required in my target career or show it to my employer so I can be rewarded for what I know. >> That can be really helpful when people are creating resumes, by ranking how much of a skill that they have. >> Absolutely. So, it's really interesting when you talk about resumes, so many of what, so much of what's shown on resumes are traditional credentials, things like What school did you go to? what did you major in? what jobs have you had? And as you and I both know, there's unequal access to the school you go to or the early jobs you get. And so, part of the motivation behind skill scoring is to create more equitable or fair or accessible signals for the labor market. So, we're really excited about that direction. >> And do you think companies are taking that into consideration when they're hiring people who say have like a five out of five skills in computer science, but they didn't go to Stanford? >> Yeah. >> Think they're taking that >> Absolutely, I think companies are hungry to find more diverse talent and the biggest challenge is, when you look at people from diverse backgrounds, it's hard to know who has what skills. And so skill scoring provides a really valuable input, we're actually seeing it in use already by many of our enterprise customers who are using it to identify who have their internal employees is well positioned for new opportunities or new roles. For example, I may have a bunch of backend engineers, if I know who's good in math and machine learning and statistics, I can actually tap those folks to transition over to machine learning roles. And so it's used both as an external signal and external labor market, as well as an internal signal within companies. >> And just our last question here, what advice would you give to young women who are either out of college or just starting college who are interested in data science? Who maybe, don't haven't majored in a typical data science major? What advice would you give to them? >> So, I love that you asked you haven't made it, majored in a typical data science major. I'm actually an economist by training. And I think that's probably the reason why I was at first rejected from Coursera because an economist is a very strange background to go into data science. I think my primary advice to those young women would be to really not get too lost in the data science, in the math, in the algorithms and instead to remember that those are a means to an end, and the end is impact. So, think about the problems in the world that you care about. For me, it's education. For others, it's health care, or personal finance or a range of other issues. And remember that data science provides this vast set of tools that you can use to solve the problems you care about most. >> That's great, thank you so much for being on theCUBE. >> Thank you. I'm Sonia Tagare, thank you so much for watching theCUBE and stay tuned for more. (upbeat music)

>> Hello everyone. Welcome to theCUBE's coverage of International Women's Day. I'm John Furrier, host of theCUBE been profiling the leaders in the technology world, women in technology from developers to the boardroom, everything in between. We have two great guests promoting in from Malaysia. Nancy Wang is the general manager, also CUBE alumni from AWS Data Protection, and founder and board chair of Advancing Women in Tech, awit.org. And of course Kate Watts who's the executive director of Advancing Women in Tech.org. So it's awit.org. Nancy, Kate, thanks for coming all the way across remotely from Malaysia. >> Of course, we're coming to you as fast as our internet bandwidth will allow us. And you know, I'm just thrilled today that you get to see a whole nother aspect of my life, right? Because typically we talk about AWS, and here we're talking about a topic near and dear to my heart. >> Well, Nancy, I love the fact that you're spending a lot of time taking the empowerment to go out and help the industries and helping with the advancement of women in tech. Kate, the executive director it's a 501C3, it's nonprofit, dedicating to accelerating the careers of women in groups in tech. Can you talk about the organization? >> Yes, I can. So Advancing Women in Tech was founded in 2017 in order to fix some of the pathway problems that we're seeing on the rise to leadership in the industry. And so we specifically focus on supporting mid-level women in technical roles, get into higher positions. We do that in a few different ways through mentorship programs through building technical skills and by connecting people to a supportive community. So you have your peer network and then a vertical sort of relationships to help you navigate the next steps in your career. So to date we've served about 40,000 individuals globally and we're just looking to expand our reach and impact and be able to better support women in the industry. >> Nancy, talk about the creation, the origination story. How'd this all come together? Obviously the momentum, everyone in the industry's been focused on this for a long time. Where did AWIT come from? Advancing Women in Technology, that's the acronym. Advancing Women in Technology.org, where'd it come from? What's the origination story? >> Yeah, so AWIT really originated from this desire that I had, to Kate's point around, well if you look around right and you know, don't take my word for it, right? Look at stats, look at news reports, or just frankly go on your LinkedIn and see how many women in underrepresented groups are in senior technical leadership roles right out in the companies whose names we all know. And so that was my case back in 2016. And so when I first got the idea and back then I was actually at Google, just another large tech company in the valley, right? It was about how do we get more role models, how we get more, for example, women into leadership roles so they can bring up the next generation, right? And so this is actually part of a longer speech that I'm about to give on Wednesday and part of the US State Department speaker program. In fact, that's why Kate and I are here in Malaysia right now is working with over 200 women entrepreneurs from all over in Southeast Asia, including Malaysia Philippines, Vietnam, Borneo, you know, so many countries where having more women entrepreneurs can help raise the GDP right, and that fits within our overall mission of getting more women into top leadership roles in tech. >> You know, I was talking about Teresa Carlson she came on the program as well for this year this next season we're going to do. And she mentioned the decision between the US progress and international. And she's saying as much as it's still bad numbers, it's worse than outside the United States and needs to get better. Can you comment on the global aspect? You brought that up. I think it's super important to highlight that it's just not one area, it's a global evolution. >> Absolutely, so let me start, and I'd love to actually have Kate talk about our current programs and all of the international groups that we're working with. So as Teresa aptly mentioned there is so much work to be done not just outside the US and North Americas where typically tech nonprofits will focus, but rather if you think about the one to end model, right? For example when I was doing the product market fit workshop for the US State Department I had women dialing in from rice fields, right? So let me just pause there for a moment. They were holding their cell phones up near towers near trees just so that they can get a few minutes of time with me to do a workshop and how to accelerate their business. So if you don't call that the desire to propel oneself or accelerate oneself, not sure what is, right. And so it's really that passion that drove me to spend the next week and a half here working with local entrepreneurs working with policy makers so we can take advantage and really leverage that passion that people have, right? To accelerate more business globally. And so that's why, you know Kate will be leading our contingent with the United Nations Women Group, right? That is focused on women's economic empowerment because that's super important, right? One aspect can be sure, getting more directors, you know vice presidents into companies like Google and Amazon. But another is also how do you encourage more women around the world to start businesses, right? To reach economic and freedom independence, right? To overcome some of the maybe social barriers to becoming a leader in their own country. >> Yes, and if I think about our own programs and our model of being very intentional about supporting the learning development and skills of women and members of underrepresented groups we focused very much on providing global access to a number of our programs. For instance, our product management certification on Coursera or engineering management our upcoming women founders accelerator. We provide both access that you can get from anywhere. And then also very intentional programming that connects people into the networks to be able to further their networks and what they've learned through the skills online, so. >> Yeah, and something Kate just told me recently is these courses that Kate's mentioning, right? She was instrumental in working with the American Council on Education and so that our learners can actually get up to six college credits for taking these courses on product management engineering management, on cloud product management. And most recently we had our first organic one of our very first organic testimonials was from a woman's tech bootcamp in Nigeria, right? So if you think about the worldwide impact of these upskilling courses where frankly in the US we might take for granted right around the world as I mentioned, there are women dialing in from rice patties from other, you know, for example, outside the, you know corporate buildings in order to access this content. >> Can you think about the idea of, oh sorry, go ahead. >> Go ahead, no, go ahead Kate. >> I was going to say, if you can't see it, you can't become it. And so we are very intentional about ensuring that we have we're spotlighting the expertise of women and we are broadcasting that everywhere so that anybody coming up can gain the skills and the networks to be able to succeed in this industry. >> We'll make sure we get those links so we can promote them. Obviously we feel the same way getting the word out. I think a couple things I'd like to ask you guys cause I think you hit a great point. One is the economic advantage the numbers prove that diverse teams perform better number one, that's clear. So good point there. But I want to get your thoughts on the entrepreneurial equation. You mentioned founders and startups and there's also different makeups in different countries. It's not like the big corporations sometimes it's smaller business in certain areas the different cultures have different business sizes and business types. How do you guys see that factoring in outside the United States, say the big tech companies? Okay, yeah. The easy lower the access to get in education than stay with them, in other countries is it the same or is it more diverse in terms of business? >> So what really actually got us started with the US State Department was around our work with women founders. And I love for Kate to actually share her experience working with AWS startups in that capacity. But frankly, you know, we looked at the content and the mentor programs that were providing women who wanted to be executives, you know, quickly realize a lot of those same skills such as finding customers, right? Scaling your product and building channels can also apply to women founders, not just executives. And so early supporters of our efforts from firms such as Moderna up in Seattle, Emergence Ventures, Decibel Ventures in, you know, the Bay Area and a few others that we're working with right now. Right, they believed in the mission and really helped us scale out what is now our existing platform and offerings for women founders. >> Those are great firms by the way. And they also are very founder friendly and also understand the global workforce. I mean, that's a whole nother dimension. Okay, what's your reaction to all that? >> Yes, we have been very intentional about taking the product expertise and the learnings of women and in our network, we first worked with AWS startups to support the development of the curriculum for the recent accelerator for women founders that was held last spring. And so we're able to support 25 founders and also brought in the expertise of about 20 or 30 women from Advancing Women in Tech to be able to be the lead instructors and mentors for that. And so we have really realized that with this network and this individual sort of focus on product expertise building strong teams, we can take that information and bring it to folks everywhere. And so there is very much the intentionality of allowing founders allowing individuals to take the lessons and bring it to their individual circumstances and the cultures in which they are operating. But the product sense is a skill that we can support the development of and we're proud to do so. >> That's awesome. Nancy, I want to ask you some never really talk about data storage and AWS cloud greatness and goodness, here's different and you also work full-time at AWS and you're the founder or the chairman of this great organization. How do you balance both and do you get, they're getting behind you on this, Amazon is getting behind you on this. >> Well, as I say it's always easier to negotiate on the way in. But jokes aside, I have to say the leadership has been tremendously supportive. If you think about, for example, my leaders Wayne Duso who's also been on the show multiple times, Bill Vaas who's also been on the show multiple times, you know they're both founders and also operators entrepreneurs at heart. So they understand that it is important, right? For all of us, it's really incumbent on all of us who are in positions to do so, to create a pathway for more people to be in leadership roles for more people to be successful entrepreneurs. So, no, I mean if you just looked at LinkedIn they're always uploading my vote so they reach to more audiences. And frankly they're rooting for us back home in the US while we're in Malaysia this week. >> That's awesome. And I think that's a good culture to have that empowerment and I think that's very healthy. What's next for you guys? What's on the agenda? Take us through the activities. I know that you got a ton of things happening. You got your event out there, which is why you're out there. There's a bunch of other activities. I think you guys call it the Advancing Women in Tech week. >> Yes, this week we are having a week of programming that you can check out at Advancing Women in Tech.org. That is spotlighting the expertise of a number of women in our space. So it is three days of programming Tuesday, Wednesday and Thursday if you are in the US so the seventh through the ninth, but available globally. We are also going to be in New York next week for the event at the UN and are looking to continue to support our mentorship programs and also our work supporting women founders throughout the year. >> All right. I have to ask you guys if you don't mind get a little market data so you can share with us here at theCUBE. What are you hearing this year that's different in the conversation space around the topics, the interests? Obviously I've seen massive amounts of global acceleration around conversations, more video, things like this more stories are scaling, a lot more LinkedIn activity. It just seems like it's a lot different this year. Can you guys share any kind of current trends you're seeing relative to the conversations and topics being discussed across the the community? >> Well, I think from a needle moving perspective, right? I think due to the efforts of wonderful organizations including the Q for spotlighting all of these awesome women, right? Trailblazing women and the nonprofits the government entities that we work with there's definitely more emphasis on creating access and creating pathways. So that's probably one thing that you're seeing is more women, more investors posting about their activities. Number two, from a global trend perspective, right? The rise of women in security. I noticed that on your agenda today, you had Lena Smart who's a good friend of mine chief information security officer at MongoDB, right? She and I are actually quite involved in helping founders especially early stage founders in the security space. And so globally from a pure technical perspective, right? There's right more increasing regulations around data privacy, data sovereignty, right? For example, India's in a few weeks about to get their first data protection regulation there locally. So all of that is giving rise to yet another wave of opportunity and we want women founders uniquely positioned to take advantage of that opportunity. >> I love it. Kate, reaction to that? I mean founders, more pathways it sounds like a neural network, it sounds like AI enabled. >> Yes, and speaking of AI, with the rise of that we are also hearing from many community members the importance of continuing to build their skills upskill learn to be able to keep up with the latest trends. There's a lot of people wondering what does this mean for my own career? And so they're turning to organizations like Advancing Women in Tech to find communities to both learn the latest information, but also build their networks so that they are able to move forward regardless of what the industry does. >> I love the work you guys are doing. It's so impressive. I think the economic angle is new it's more amplified this year. It's always kind of been there and continues to be. What do you guys hope for by next year this time what do you hope to see different from a needle moving perspective, to use your word Nancy, for next year? What's the visual output in your mind? >> I want to see real effort made towards 50-50 representation in all tech leadership roles. And I'd like to see that happen by 2050. >> Kate, anything on your end? >> I love that. I'm going to go a little bit more touchy-feely. I want everybody in our space to understand that the skills that they build and that the networks they have carry with them regardless of wherever they go. And so to be able to really lean in and learn and continue to develop the career that you want to have. So whether that be at a large organization or within your own business, that you've got the potential to move forward on that within you. >> Nancy, Kate, thank you so much for your contribution. I'll give you the final word. Put a plug in for the organization. What are you guys looking for? Any kind of PSA you want to share with the folks watching? >> Absolutely, so if you're in a position to be a mentor, join as a mentor, right? Help elevate and accelerate the next generation of women leaders. If you're an investor help us invest in more women started companies, right? Women founded startups and lastly, if you are women looking to accelerate your career, come join our community. We have resources, we have mentors and who we have investors who are willing to come in on the ground floor and help you accelerate your business. >> Great work. Thank you so much for participating in our International Women's Day 23 program and we'd look to keep this going quarterly. We'll see you next year, next time. Thanks for coming on. Appreciate it. >> Thanks so much John. >> Thank you. >> Okay, women leaders here. >> Nancy: Thanks for having us >> All over the world, coming together for a great celebration but really highlighting the accomplishments, the pathways the investment, the mentoring, everything in between. It's theCUBE. Bring as much as we can. I'm John Furrier, your host. Thanks for watching.

(upbeat music) >> Hey, everyone. Welcome to theCUBE's coverage of the International Women's Showcase for 2022. I'm your host, Lisa Martin. I'm pleased to welcome Nancy Wong, the general manager of Data Protection and Governance at AWS to the program. Nancy, it's great to have you. >> Thanks so much for having me Lisa, and you know, I really hope that this is hopefully the last year that we'll be celebrating International Women's Day all virtually. >> I agree. I agree. Well, we're going in that right direction globally. So let's cross our fingers. Talk to me a little bit about your role at AWS and what you do there. >> Sure. So as a GM of AWS Data Protection and Governance, a lot of, we tackle quite a few problems that our biggest customers face, right? When they think about, "How do I manage my data?" Right. Especially in this digital world. And speaking of the pandemic, how much data has been generated by consumers, by devices, by systems, by servers? How do you protect all of that data? Right. Especially we hear about cyber crime, cyber attacks. Right. Data breaches. It's really important to make sure that all of our customers have a coherent strategy around not just management, right, but also protection and really how you govern your data. Right. And there's just so many awesome conversations that my team and I have had lately with CSOs or chief technology officers on this topic, as it evolves. >> Data protection is so critical. It's one of my favorite topics to talk about, cybersecurity as well. Talk to me about what it means though if we keep this at a bit of a different level to be an operator within the the big ecosystem that is AWS. >> Yeah. And that's actually one of the the favorite aspects of my role. Right. Which is, you know, I get to innovate every day on behalf of my customers. For example, I love having one-on-one dialogues. I love having architecture conversations where we brainstorm. Right. And so those type of conversations help inform how we deliver and develop products. And so in an operator role, right, for the the women in the audience today, is it really gives you that perspective into not just how, what type of products do you want to build that delight your customers but also from an engineering. Right. And a bottom line perspective of, well how do you make this happen? Right. How do you fund this? And how do you plan out your development milestones? >> What are, tell me a little bit about your background and then what makes women in technology such an important initiative for you to stand behind? >> Absolutely. So I'm so proud today to see that the number of women or the percentage of women enrolled at engineering curriculums just continue to rise. Right. And especially as someone who went through an engineering degree in her undergraduate studies, that was not always the case. Right. So oftentimes, you know, I would look around the classroom and be the only woman on the lab bench or only woman in a CS classroom. And so when you have roles in tech, specifically, that require an undergraduate degree in computer science or a degree in engineering, that helps to, or that only serves to really reduce the population of eligible candidates. Right. Who then, if you look at that pool of eligible candidates who then you can invest and accelerate through the career ladder to become leaders in tech, well that's where you may end up with a representation issue. Right. And that's why we have, for example, so few women leaders in tech that we can look up to as role models. And that's really the problem or the gap that I'm very passionate about solving. And also, Lisa, I'm really excited to tell you a little bit more about advancing women in tech, which is a 501c3 nonprofit organization that I started to tackle this exact problem. >> Talk to me about that, cause it's one of the things that you bring up is, you know, we always say when we're having conversations like this, we can't be what we can't see. We need to be able to see those female leaders. To your point, there aren't a ton in comparison to the male leaders. So talk to me about advancing women in technology, why you founded this, and what you guys are accomplishing. >> Absolutely. So it's been such a personal journey as well. Just starting this organization called Advancing Women In Tech because I started it in 2017. Right. So when I really was, you know, just starting out as a product manager, I was at another big tech company at the time. And what I really realized, right, is looking around you know, I had so many, for example, bosses, managers, peer leaders, who were really invested in growing me as a product manager and growing my tech and career. And this is right after I'd made the transition from the federal government into big tech. What that said though, looking around, there weren't that many women tech leaders that I could look up to, or get coffee, or just have a mentoring conversation. And quickly I realized, well, it's not so much that women can't do it. Right. It's the fact that we're not advancing enough women into leadership roles. And so really we have to look at why that is. Right. And we, you know, from a personal perspective, one contribution towards that angle is upskilling. Right. So if you think about what skills one needs as one climbs a career ladder, whether that's your first people management role, or your first manager manager's role, or obviously for bigger leaders when they start managing thousands, tens of thousands of individuals, well all of that requires different skills. And so learning those skills about how to manage people, how to motivate your teams effectively, super, super important. And of course on the other side, and one that I'm, you know, near to dear to me is that of mentorship and executive sponsorship because you can have all the skills in the world, right. And especially with digital learning and AWIT is very involved with Coursera and AWS in producing and making those resources readily available and accessible. Well, if you don't have those opportunities, if you don't have mentors and sponsors who are well to push you or give you a step ladder to those roles, well you're still not going to get there. Right. And so, that's why actually, if you look at the AWIT mission, it's really those two pillars working very closely together to help advance women into leadership roles. >> The idea of mentorship and sponsorship is so critical. And I think a lot of people don't understand the difference between a mentor and a sponsor. How do you define that difference and how do you bring them into the organization so that they can be mentors and sponsors? >> Yeah, absolutely. And there's, you know, these two terms are often used today so interchangeably that I do get a lot of questions around, well, what is the difference? Right. And how does, let's say a mentor become a sponsor? So, maybe just taking a few steps back, right. When you have let's say questions around compensation or, "Hey I have some job offers, which ones do I consider?" And you ask someone a question or advice, well that person's likely your mentor. Right? And typically a mentor is someone who you can ask those questions on a repeated basis. Who's very accessible to you. Well, a sponsor takes that a few steps forward in the sense that they are sponsoring you into a role or into a project or initiative that you on your own may not be able to achieve. And by doing so, I think what really differentiates a sponsor from a mentor is that the sponsor will actually put their own reputation on the line. Right. They're using their own political capital in order to make sure that you get into that role, you get into that room. Right. And that's why it's so key, for example, especially if you have that relationship already with a person who's your mentor, you're able to ask questions or advice from, to convert them into a sponsor so that you can accelerate your career. >> Great definition, description, and great recommendations for converting mentors to sponsors. You know, I only learned the difference about a mentor and a sponsor a few years ago at another women in tech event that I was hosting. And I thought, "It's brilliant. It makes perfect sense." We need more people to understand the difference, the synergies, and how to promote mentors to sponsors. Talk to me now about advancing women in tech plus the power of AWS. How are they helping this nonprofit to really accelerate? >> Sure. So from an organization perspective, right, there's many women, for example, across the the tech companies who are part of Advancing Women In Tech, obviously Amazon of course as an employee has a very large community within who's part of AWIT. But we also have members across the tech industry from startups to VC firms to of course, Google, Microsoft, and Netflix. You name it. With that said, you know, what AWS has done with AWIT is actually very special in the sense that if you go to the Coursera platform, coursera.org/awit you can see our two Coursera specializations. Four courses each that go through the real world product management fundamentals. Or the business side, the technical skills, and even interviewing for mid-career product management roles. And the second specialization, which I'm super excited to share today, is actually geared towards getting folks ramped up and prepared to successfully pass the Cloud Practitioner's Exam, which is one of the industry recognized standards about understanding the AWS Cloud and being functional in the AWS Cloud. This summer, of course, and I'm sharing kind of a sneak peek announcement that I'll be making tomorrow with the University of Pennsylvania, is that we're kicking off a program for the masters of CIS program, or the Computer Information Systems Master students, to actually go through this Coursera specialization, which is produced by AWIT, sponsored by AWS, and AWS Training and Certifications has so generously donated exam vouchers for these students so that they can then go on and be certified in the AWS Cloud. So that's one just really cool collaboration that we are doing between AWS and AWIT to get more qualified folks in the door in tech jobs, and hopefully at jobs in AWS. >> That's a great collaboration. What are some of the goals in terms of metrics, the number of women that you want to get into the program and complete the program? What are some of those on your radar? >> Absolutely. So one of the reasons, of course, that the Master's of CIS Program, the University of Pennsylvania caught my eye, not withstanding, I graduated from there, but also that just the statistics of women enrolled. Right. So what's really notable about this program is it's entirely online, which as a university creating a Master's degree fully online, well, it takes a ton of resources from the university, from the faculty. And what's really special about these students is that they're already full-time adult professionals, which means that they're working a full-time job, they might be taking care of family obligations, and they're still finding time to advance themselves, to acquire a Master's degree in CS. And best of all, 42% of these students are women. Right. And so that's a number that is multiples of what we're finding in engineering curriculums today. And so my theory is, well if you go to a student population that is over 40%, 42 to be exact percent women, and enable these women to be certified in AWS Cloud, to have direct interview prep and mentorship from AWS software development leaders, well, that greatly increases their chances of getting a full-time role, right, at AWS. Right. At which then we can help them advance their careers to further and further roles in software development. >> So is this curriculum also open to women who aren't currently in tech to be able to open the door for them to get into tech and STEM fields? >> Absolutely. And so in my bad and remiss in mentioning, which is students of this Master's in CS Program are actually students not from tech already. So they're not in a tech field. And they did not have a degree in CS or even engineering as part of their undergraduate studies. So it's truly folks who are outside of tech, that are 42% women, that we're getting into the tech industry with this collaboration between AWS, AWIT, and the University of Pennsylvania. >> That's outstanding to get them in from completely different fields into tech. >> Absolutely. >> How do you help women have the confidence to say, "I want to try this." Cause if we think about every company today is a tech company. It's a data company. It has to be to be competitive. You know, the pandemic taught us that everything we're able to do online and digitally, for example, but how do you help women get the confidence to say, "Okay, I'm going to go from a completely different field into tech." >> Absolutely. So if we, you know, define tech of course as big tech or, you know, now the main companies, right, I myself made that transition, which is why it is a topic near and dear to me because I can personally speak to my journey because I didn't start my career out in tech. Right. Yes. I studied engineering. But with that said, my first full-time job out of college was with the federal government because I wanted to go and build healthdata.gov, right, which gave folks a lot of access to the healthcare data, roles, right, that existed within the U.S. government and the CMS, NIH, you know, CDC, so on and so forth. But that was quite a big change from then taking a product management job at Google. Right. And so how did I make that change? Well, a lot of it came from, you know, the mentors that I had. Right. What I call my personal board of directors who gave me that confidence. And sure, I mean even today, I still have imposter syndrome where, you know, I think, "Am I good enough." Right. "Should I be leading this organization," right, "of data protection and governance." But I think what it boils down to is, you know, inner confidence. Right. And goes back to those two pillars of having the right skills and also the right mentors and sponsors who are willing to help sponsor you into those opportunities and help sponsor you to success. >> Absolutely. Great advice and recommendations. Thanks for sharing your background, Nancy, it's outstanding to see where you started to where you are now and also to what you're enabling for so many other females to get into tech with the AWIT program combined with AWS and UPenn. Exciting stuff. Can't wait to talk to you next year to see where you guys go from here. >> Absolutely Lisa. And what I'm really looking forward to sharing with you next year is the personal testimonials of other women who have gone through the AWIT, the AWS, the UPenn Program and have gotten their tech jobs and also promotions. >> That sounds like a great thing to look forward to. I'm looking forward to that. Nancy, thanks so much for your time and the insight that you shared. >> Thanks so much for having me, Lisa. >> My pleasure. For Nancy Wong, I'm Lisa Martin. You're watching theCUBE's coverage of the International Women's Showcase 2022. (upbeat music)

(upbeat music) >> Narrator: From around the globe, it's theCUBE with digital coverage of AWS Public Sector Online brought to you by Amazon Web Services. >> Welcome back to theCUBE's coverage of AWS Public Sector Summit Online. I'm John Furrier, your host of theCUBE. I wish we could be there in person, but we're doing remote because of the COVID and the pandemic. We've got a great guest, Paul Grist. Worldwide Public Sector, Head of Education International for AWS. Paul, thank you for coming on remotely. >> Great to be here, John. >> There's a lot of disruption in the education space this year with universities and schools still uncertain about what the future will look like. What are some of the biggest trends you're seeing? >> John, what we've seen is the rapid adoption of technology and the growth of flexible online learning, learning that can take place anytime, anywhere. What we've seen is universities, national education systems, and schools rapidly migrating systems and content to the cloud, spinning up new applications. And we've seen companies that provide technology and content and platforms, the ed techs and publishers of the world, increasing their capacity, increasing their capability to deliver new applications to education. >> What is some of this research that you're finding out there? >> Yeah. You know, a time of much change and things happening very, very fast. We responded fast to the changes, John. Got a load of customer conversations together, looking at speeches by educationalists who were responding to the changes at some of the online events that spun up very quickly at places like the University of Buckingham, ASU, JSV, Inside Higher Education, places like Blackboard World. And really just talked to those leaders about their responses to the change, what kinds of things they were doing, and brought that together into the research. It's underpinned by some in-depth research and insights from education reports and articles too. >> Thanks Paul, really appreciate it. Having that research is critical. I know you guys do a lot of work on that. I know you got some news, take a quick plug for the new research that's coming out. You guys just put out today, just take a minute to quickly explain what it's about and how to find it. >> We're publishing today some new research that shows the seven key emerging trends in this new world of education. Check it out on the AWS website. Two key trends, flexible learning and the new world of employability. >> So you guys got a lot of data. It's great with Amazon, got a lot of customers. Good to see you guys getting that research. The question I have for you Paul is, what amount of the research shows really the COVID situation? Because there's before COVID, there's kind of during, and then there's going to be a post-COVID mode. Was that prior research in place with COVID or after COVID? Can you share kind of the update on the relevance of your research? >> Yeah, I think the sector has changed. The sector has gone through the fastest change it's ever gone through. And undoubtedly most of the issues, most of the challenges and opportunities in the sector, predate the pandemic. But what we've seen is COVID accelerate many of the challenges and the opportunities, but also bring new opportunities. >> Yeah, one of the things we've seen with education is the disruption, and the forcing function with COVID. There's a problem, we all know what it is. It's important, there's consequences for those. And you can quantify the disruption with real business value and certainly student impact. There's been downsides with remote education. More teacher-parent involvement and students having to deal with isolation, less social interaction. How do you guys see that? Or what is Amazon doing to solve these problems? Can you talk about that? >> Yeah. I think you know, education is very much a people business. And what we've been trying to do is partner with organizations to ensure that the people are kept at the center of the business. So working with organizations such as LS, sorry, Los Angeles United School District in the US to spin up a call center to allow students to contact their tutors. And parents to interact with tutors, to get questions answered. >> So one of the challenges these academic institutions are facing is speed, it's pace of change. What's going on with competition? How are they competing? How are universities and colleges staying relevant? Obviously there's a financial crisis involved. There's also the actual delivery aspect of it. More and more mergers. You're starting to see ecosystem changes. Can you talk about what's going on in the educational ecosystem? >> Yeah I mean, educational institutions are being forced to rethink their business models. It's an international marketplace in higher education. It's been a growing marketplace for many, many years. That suddenly stopped overnight, so every university has had to rethink about where their revenues are coming from, where the students are coming from. There's been some surprises too. I mean in the UK, actually international enrollments are up post-COVID because one of the strange side effects of COVID is without being able to travel, there's actually a cost saving for students. And so we've seen universities in the UK benefit from students who want to study, perhaps travel and the cost of study was too high previously. Now being able to study remotely. It's an unexpected and unintended consequence. But it kind of shows how there are opportunities for all organizations during this time. >> Many countries had to cancel exams altogether this year, which has been a big, huge problem. I mean people are outraged and people want to learn. It's been, you know, the silver lining in all this is that you have the internet (laughs). You have the cloud. I want to get your thoughts. How are universities and schools dealing with this challenge? Because you have a multi-sided marketplace. You've got the institutions, you've got the students, you got the educators, they all have to be successful. How are universities dealing with this challenge? >> Yeah I think, you know, teaching and learning has been online for 20, 30 years. And I think a lot of organizations have adopted online teaching and learning. But I think assessment is the one big area of education that remains to be made available at scale at low cost. So most assessment is still a pen-and-paper-based. There's big trust and identity issues. And what we're seeing through this COVID change is organizations really getting to grip with both of those issues. So, having the confidence to put assessment online, to make it available at scale, and then also having the confidence to tackle trust and identity questions. So who is taking the exam, where are they sitting? Can we be sure that it's actually that person taking that exam? So you know, the rise of things like proctoring technologies giving organizations the opportunity to assess remotely. >> How has this crisis affected research at academic institutions? Because certainly we know that if you need a lab or something, certainly we're seeing students need to be physically in person. But with remote and all those changes going on with the scale and the pace of change, how has research at academic institutions been impacted? >> Yeah I mean, research has always been a really collaborative activity, but we've seen that collaboration increase. It's had to increase. Researchers have had to go remote. Many of them work in labs. They haven't been able to do that. They've needed to spin up applications and new technologies in the cloud to continue working. But what we're seeing is governments taking an increased interest in the research being applicable, making sure that it leads to innovation which is meaningful. Getting much more involved and insisting that the research is made available now. And of course there's no place that that's clearer than in health research and trying to find a cure for COVID. And then secondly, we're seeing that research is becoming much more collaborative not just across institutions but also countries. So one of the great projects we're involved in at the moment is with the University of Adelaide who are collaborating with researchers from the Breeding and Acclimatization Institute in Poland on a project to study the increase in crop yield of wheat. >> One of the things that's coming out of this, whether it's research or students is open online courses, virtual capabilities. But a concept called stackable learning. Can you explain what that is? >> Yeah. We're in a global marketplace in education and there's increased competition between universities and education providers to make new types of certificates and online badges available. We know that employers are looking for ever more agile methods of scaling and upskilling. And stackable learning is a concept that's been around for a couple of decades now, where learning is broken down into smaller chunks, put together in a more personalized way from a number of different providers. Spun up very, very quickly to respond to need and then delivered to students. We're seeing some of the big providers like edX and Coursera who, again have been around for over a decade become really prominent in the provision of some of these stackable credentials. Their systems run on the cloud. They're easy to access, in many, many cases they're free. We're seeing an increasing number of employers and education institutions adopt and embed these types of delivery systems into their curriculum. >> Totally a fan of stackable learning, it's called the Lego model, whatever I call it. But also online brings the nonlinear progressions. The role of data is super important. So I'm very bullish on education being disrupted by cloud providers and new apps. So you know, I wanted to call that out because I think it's super important. Let me get to a really important piece that it has to be addressed, and I want to get your thoughts on. Cyber security. Okay, cyber attacks and privacy of students are two areas that are super important for institutions to address. What's your reaction to that? >> Yeah, I mean the use of more technology becomes, you know again, a target for cyber attack and unfortunately it's an increasing phenomenon. Simply put, every organization needs to put security first. Needs to operate as a security-first organization. They need to adopt technologies, people and processes that can protect their investments. And work with data management vendors, cloud vendors who've got the compliances and the common privacy and security frameworks such as GDPR in place to make sure that they provide secure services. AWS's security offerings include auditing, login and identity management, data encryption capabilities that offer more transparency and control, to allow institutions protect student data. >> Super important, thanks for sharing. Finally, what's the steps institutions can take to close the digital divide because now some people are taking gap years. Research is changing. People might not even have PCs or internet connections. There's still, this exposes the haves and have nots. What steps can institutions take to do their part? >> Yeah, digital learning is here to stay, John. We've learned that many learners do not have access to technology necessary for online learning. Whether those are devices or a reliable internet connection. But again, you know governments, states, educational authorities have all turned their attention to these issues over the last few months. And we're seeing organizations partner with technology providers that can provide internet connections. Partners in AWS, such as Kajeet who've installed hotspot devices on buses to deploy in areas with no connectivity. You know whether that's a place like Denver, Colorado or whether it's a place, you know, in Nigeria in Africa, remote connection remains a problem everywhere. And we're seeing everybody addressing that issue now. >> Paul, great to have you on theCUBE and sharing your insights on what's going on in international education. Final question for you. In your own words, why is this year at the AWS Public Sector Summit Online important? What's the most important story that people should walk away in this educational industry? >> The most important story, John, is it's a time of incredible change but also incredible opportunity. And we're seeing organizations who have wanted to change, who've wanted to deliver more to their students, who want to deliver a greater experience, who want to access more students and have much greater reach. Now with the appetite to do that. re:Invent is a great opportunity to work with AWS, to understand what's going on with our partners, with our customers. And look at some of the common solutions for the challenges that they're looking to solve. >> Paul Grist, thank you for coming on theCUBE. Really appreciate it. Worldwide Head of Education for International AWS. Thank you for sharing. >> Thanks John, great to be here. >> Okay, this is theCUBE's coverage of AWS Public Sector Online Summit. Remote, virtual, this is theCUBE virtual. I'm John Furrier, your host. Thanks for watching. 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live from Stanford University it's the hue covering Stanford women in data science 2020 brought to you by Silicon angle media hi and welcome to the cube I'm your host Sonia - Garrett and we're live at Stanford University covering wigs women in data science conference the fifth annual one and joining us today is Daphne Koller who is the co-founder who sari is the CEO and founder of in seat row that Daphne welcome to the cube nice to be here Sonia thank you for having me so tell us a little bit about in seat row how you how it you got it founded and more about your role so I've been working in the intersection of machine learning and biology and health for quite a while and it was always a bit of a an interesting journey in that the data sets were quite small and limited we're now in a different world where there's tools that are allowing us to create massive biological data sets that I think can help us solve really significant societal problems and one of those problems that I think is really important is drug discovery development where despite many important advancements the costs just keep going up and up and up and the question is can we use machine learning to solve that problem better and you talk about this more in your keynote so give us a few highlights of what you talked about so in the last you can think of drug discovery and development in the last 50 to 70 years as being a bit of a glass half-full glass half-empty the glass half-full is the fact that there's diseases that used to be a death sentence or of the sentence still a life long of pain and suffering that are now addressed by some of the modern-day medicines and I think that's absolutely amazing the other side of it is that the cost of developing new drugs has been growing exponentially in what's come to be known as Arun was law being the inverse of Moore's Law which is the one we're all familiar with because the number of drugs approved per billion u.s. dollars just keeps going down exponentially so the question is can we change that curve and you talked in your keynote about the interdisciplinary cold to tell us more about that I think in order to address some of the critical problems that were facing one needs to really build a culture of people who work together at from different disciplines each bringing their own insights and their own ideas into the mix so and in seat row we actually have a company that's half-life scientists many of whom are producing data for the purpose of driving machine learning models and the other half are machine learning people and data scientists who are working on those but it's not a handoff where one group produces the data and the other one consumes and interpreted but really they start from the very beginning to understand what are the problems that one could solve together how do you design the experiment how do you build the model and how do you derive insights from that that can help us make better medicines for people and I also wanted to ask you you co-founded Coursera so tell us a little bit more about that platform so I founded Coursera as a result of work that I'd been doing at Stanford working on how technology can make education better and more accessible this was a project that I did here a number of my colleagues as well and at some point in the fall of 2011 there was an experiment let's take some of the content that we've been we've been developing within it's within Stanford and put it out there for people to just benefit from and we didn't know what would happen would it be a few thousand people but within a matter of weeks with minimal advertising other than one New York Times article that went viral we had a hundred thousand people in each of those courses and that was a moment in time where you know we looked at this and said can we just go back to writing more papers or is there an incredible opportunity to transform access to education to people all over the world and so I ended up taking a what was supposed to be a teary leave of absence from Stanford to go and co-found Coursera and I thought I'd go back after two years but the but at the end of that two-year period the there was just so much more to be done and so much more impact that we could bring to people all over the world people of both genders people of the different social economic status every single country around the world we I just felt like this was something that I couldn't not do and how did you why did you decide to go from an educational platform to then going into machine learning and biomedicine so I've been doing Coursera for about five years in 2016 and the company was on a great trajectory but it's primarily a Content company and around me machine learning was transforming the world and I wanted to come back and be part of that and when I looked around I saw machine learning being applied to ecommerce and the natural language and to self-driving cars but there really wasn't a lot of impact being made on the life science area and I wanted to be part of making that happen partly because I felt like coming back to our earlier comment that in order to really have that impact you need to have someone who speaks both languages and while there's a new generation of researchers who are bilingual in biology and in machine learning there's still a small group and there very few of those in kind of my age cohort and I thought that I would be able to have a real impact by building and company in the space so it sounds like your background is pretty varied what advice would you give to women who are just starting college now who may be interested in a similar field would you tell them they have to major in math or or do you think that maybe like there are some other majors that may be influential as well I think there's a lot of ways to get into data science math is one of them but there's also statistics or physics and I would say that especially for the field that I'm currently in which is at the intersection of machine learning data science on the one hand and biology and health on the other one can get there from biology or medicine as well but what I think is important is not to shy away from the more mathematically oriented courses in whatever major you're in because that found the is a really strong one there's a lot of people out there who are basically lightweight consumers of data science and they don't really understand how the methods that they're deploying how they work and that limits them in their ability to advance the field and come up with new methods that are better suited perhaps to the problems that they're tackling so I think it's totally fine and in fact there's a lot of value to coming into data science from fields other than a third computer science but I think taking courses in those fields even while you're majoring in whatever field you're interested in is going to make you a much better person who lives at that intersection and how do you think having a technology background has helped you in in founding your companies and has helped you become a successful CEO in companies that are very strongly Rd focused like like in C tro and others having a technical co-founder is absolutely essential because it's fine to have an understanding of whatever the user needs and so on and come from the business side of it and a lot of companies have a business co-founder but not understanding what the technology can actually do is highly limiting because you end up hallucinating oh if we could only do this and yet that would be great but you can't and people end up oftentimes making ridiculous promises about what technology will or will not do because they just don't understand where the land mines sit and and where you're gonna hit real obstacles and in the path so I think it's really important to have a strong technical foundation in these companies and that being said where do you see an teacher in the future and and how do you see it solving say Nash that you talked about in your keynote so we hope that in seat row we'll be a fully integrated drug discovery and development company that is based on a slightly different foundation than a traditional pharma company where they grew up in the old approach of that is very much bespoke scientific analysis of the biology of different diseases and then going after targets or our ways of dealing with the disease that are driven by human intuition where I think we have the opportunity to go today is to build a very data-driven approach that collects massive amounts of data and then let analysis of those data really reveal new hypotheses that might not be the ones that the cord with people's preconceptions of what matters and what doesn't and so hopefully we'll be able to over time create enough data and apply machine learning to address key bottlenecks in the drug discovery development process so we can bring better drugs to people and we can do it faster and hopefully at much lower cost that's great and you also mentioned in your keynote that you think that 2020s is like a digital biology era so tell us more about that so I think if you look if you take a historical perspective on science and think back you realize that there's periods in history where one discipline has made a tremendous amount of progress in a relatively short amount of time because of a new technology or a new way of looking at things in the 1870s that discipline was chemistry was the understanding of the periodic table and that you actually couldn't turn lead into gold in the 1900s that was physics with understanding the connection between matter and energy and between space and time in the 1950s that was computing where silicon chips were suddenly able to perform calculations that up until that point only people have been able to do and then in 1990s there was an interesting bifurcation one was the era of data which is related to computing but also involves elements statistics and optimization of neuroscience and the other one was quantitative biology in which biology moved from a descriptive science of techsan amaizing phenomena to really probing and measuring biology in a very detailed and a high-throughput way using techniques like microarrays that measure the activity of 20,000 genes at once Oh the human genome sequencing of the human genome and many others but these two feels kind of evolved in parallel and what I think is coming now 30 years later is the convergence of those two fields into one field that I like to think of as digital biology where we are able using the tools that have and continue to be developed measure biology in entirely new levels of detail of fidelity of scale we can use the techniques of machine learning and data science to interpret what we're seeing and then use some of the technologies that are also emerging to engineer biology to do things that it otherwise wouldn't do and that will have implications in biomaterials in energy in the environment in agriculture and I think also in human health and it's an incredibly exciting space to be in right now because just so much is happening and the opportunities to make a difference and make the world a better place are just so large that sounds awesome Daphne thank you for your insight and thank you for being on cute thank you I'm so neat agario thanks for watching stay tuned for more great

(upbeat music) >> From our studios in the heart of Silicon Valley, Palo Alto, California, this is a CUBE Conversation. >> Welcome everyone to this special CUBE Conversation Around the CUBE segment, Unpacking AI, number two, sponsored by Juniper Networks. We've got a great lineup here to go around the CUBE and unpack AI. We have Ken Jennings, all-time Jeopardy champion with us. Celebrity, great story there, we'll dig into that. John Hinson, director of AI at Evotek and Charna Parkey, who's the applied scientist at Textio. Thanks for joining us here for Around the CUBE Unpacking AI, appreciate it. First question I want to get to, Ken, you're notable for being beaten by a machine on Jeopardy. Everyone knows that story, but it really brings out the question of AI and the role AI is playing in society around obsolescence. We've been hearing gloom and doom around AI replacing people's jobs, and it's not really that way. What's your take on AI and replacing people's jobs? >> You know, I'm not an economist, so I can't speak to how easy it's going to be to retrain and re-skill tens of millions of people once these clerical and food prep and driving and whatever jobs go away, but I can definitely speak to the personal feeling of being in that situation, kind of watching the machine take your job on the assembly line and realizing that the thing you thought made you special no longer exists. If IBM throws enough money at it, your skill essentially is now obsolete. And it was kind of a disconcerting feeling. I think that what people need is to feel like they matter, and that went away for me very quickly when I realized that a black rectangle can now beat me at a game show. >> Okay John, what's your take on AI replacing jobs? What's your view on this? >> I think, look, we're all going to have to adapt. There's a lot of changes coming. There's changes coming socially, economically, politically. I think it's a disservice to us all to get to too indulgent around the idea that these things are going to change. We have to absorb these things, we have to be really smart about how we approach them. We have to be very open-minded about how these things are going to actually change us all. But ultimately, I think it's going to be positive at the end of the day. It's definitely going to be a little rough for a couple of years as we make all these adjustments, but I think what AI brings to the table is heads above kind of where we are today. >> Charna, your take around this, because the role of humans versus machines are pretty significant, they help each other. But is AI going to dominate over humans? >> Yeah, absolutely. I think there's a thing that we see over and over again in every bubble and collapse where, you know, in the automotive industry we certainly saw a bunch of jobs were lost, but a bunch of jobs were gained. And so we're just now actually getting into the phase where people are realizing that AI isn't just replacement, it has to be augmentation, right? We can't simply use images to replace recognition of people, we can't just use black box to give our FICO credit scores, it has to be inspectable. So there's a new field coming up now called explainable AI that actually is where we're moving towards and it's actually going to help society and create jobs. >> All right so let's stay on that next point for the next round, explainable AI. This points to a golden age. There's a debate around are we in a bubble or a golden age. A lot of people are negative right now on tech. You can see all the tech backlash. Amazon, the big tech companies like Apple and Facebook, there's a huge backlash around this so-called tech for society. Is this an indicator of a golden age coming? >> I think so, absolutely. We can take two examples of this. One would be where, you remember when Amazon built a hiring algorithm based upon their own resume data and they found that it was discriminating against women because they had only had men apply for it. Now with Textio we're building augmented writing across the audience and not from a single company and so companies like Johnson and Johnson are increasing the pipeline by more than nine percent which converts to 90,000 more women applying for their jobs. And so part of the difference there is one is explainable, one isn't, and one is using the right data set representing the audience that is consuming it and not a single company's hiring. So I think we're absolutely headed into more of a golden age, and I think these are some of the signs that people are starting to use it in the right way. >> John, what's your take? Obviously golden age doesn't look that to us right now. You see Facebook approving lies as ads, Twitter banning political ads. AI was supposed to solve all these problems. Is there light at the end of this dark tunnel we're on? >> Yeah, golden age for sure. I'm definitely a big believer in that. I think there's a new era amongst us on how we handle data in general. I think the most important thing we have here though is education around what this stuff is, how it works, how it's affecting our lives individually and at the corporate level. This is a new era of informing and augmenting literally everything we do. I see nothing but positives coming out of this. We have to be obviously very careful with our approaching all the biases that already exist today that are only going to be magnified with these types of algorithms at mass scale. But ultimately if we can get over that hurdle, which I believe collectively we all need to do together, I think we'd live in much better, less wasteful world just by approaching the data that's already at hand. >> Ken, what's your take on this? It's like a daily double question. Is it going to be a golden age? >> Laughs >> It's going to come sooner or later. We have to have catastrophe before, we have to have reality hit us in the face before we realize that tech is good, and shaping it? It's pretty ugly right now in some of the situations out there, especially in the political scene with the election in the US. You're seeing some negative things happening. What's your take on this? >> I'm much more skeptical than John and Charna. I feel like that kind of just blinkered, it's going to be great, is something you have to actually be in the tech industry and hearing all day to actually believe. I remember seeing kind of lay-person's exposure to Watson when Watson was on Jeopardy and hearing the questions reporters would ask and seeing the memes that would appear, and everyone's immediate reaction just to something as innocuous as a AI algorithm playing on a game show was to ask, is this Skynet from Terminator 2? Is this the computer from The Matrix? Is this HAL pushing us out of the airlock? Everybody immediately first goes to the tech is going to kill us. That's like everybody's first reaction, and it's weird. I don't know, you might say it's just because Hollywood has trained us to expect that plot development, but I almost think it's the other way around. Like that's a story we tell because we're deeply worried about our own meaning and obsolescence when we see how little these skills might be valued in 10, 20, 30 years. >> I can't tell you how much, by the way, Star Trek, Star Wars and Terminators probably affected the nomenclature of the technology. Everyone references Skynet. Oh my God, we're going to be taken over and killed by aliens and machines. This is a real fear. I thinks it's an initial reaction. You felt that Ken, so I've got to ask you, where do you think the crossover point is for people to internalize the benefits of say, AI for instance? Because people will say hey, look back at life before the iPhone, look at life before these tools were out there. Some will say society's gotten better, but yet there's this surveillance culture, things... And on and on. So what do you guys think the crossover point is for the reaction to change from oh my God, it's Skynet, gloom and doom to this actually could be good? >> It's incredibly tricky because as we've seen, the perception of AI both in and out of the industry changes as AI advances. As soon as machine learning can actually do a task, there's a tendency to say there's this no true Scotsman problem where we say well, that clearly can't be AI because I see how the trick worked. And yeah, humans lose at chess now. So when these small advances happen, the reaction is often oh, that's not really AI. And by the same token, it's not a game-changer when your email client can start to auto-complete your emails. That's a minor convenience to you. But you don't think oh, maybe Skynet is good. I really do think it's going to have to be, maybe the inflection point is when it starts to become so disruptive that actually public policy has to change. So we get serious about >> And public policy has started changing. >> whatever their reactions are. >> Charna, your thoughts. >> The public policy has started changing though. We just saw, I think it was in September, where California banned the use of AI in the body cameras, both real-time and after the fact. So I think that's part of the pivot point that we're actually seeing is that public policy is changing.` The state of Washington currently has a task force for AI who's making a set of recommendations for policy starting in December. But I think part of what we're missing is that we don't have enough digital natives in office to even attempt to, to your point Ken, predict what we're even going to be able to do with it, right? There is this fear because of misunderstanding, but we also don't have a respect of our political climate right now by a lot of our digital natives, and they need to be there to be making this policy. >> John, weigh in on this because you're director of AI, you're seeing positive, you have to deal with the uncertainty as well, the growth of machine learning. And just this week Google announced more TensorFlow for everybody. You're seeing Open Source. So there's a tech push, almost a democratization, going on with AI. So I think this crossover point might be sooner in front of us than people think. What's your thoughts? >> Yeah it's here right now. All these things can be essentially put into an environment. You can see these into products, or making business decisions or political decisions. These are all available right now. They're available today and its within 10 to 15 lines of code. It's all about the data sets, so you have to be really good stewards of the data that you're using to train your models. But I think the most important thing, back to the Skynet and all this science-fiction side, we have to collectively start telling the right stories. We need better stories than just this robots are going to take us over and destroy all of our jobs. I think more interesting stories really revolve around, what about public defenders who can have this informant augmentation algorithm that's going to help them get their job done? What about tailor-made medicine that's going to tell me exactly what the conditions are based off of a particular treatment plan instead of guessing? What about tailored education that's going to look at all of my strengths and weaknesses and present a plan for me? These are things that AI can do. Charna's exactly right, where if we don't get this into the right political atmosphere that's helping balance the capitalist side with the social side, we're going to be in trouble. So that's got to be embedded in every layer of enterprise as well as society in general. It's here, it's now, and it's real. >> Ken, before we move on to the ethics question, I want to get your thoughts on this because we have an Alexa at home. We had an Alexa at home; my wife made me get rid of it. We had an Apple device, what they're called... the Home pods, that's gone. I bought a Portal from Facebook because I always buy the earliest stuff, that's gone. We don't want listening devices in our house because in order to get that AI, you have to give up listening, and this has been an issue. What do you have to give to get? This has been a big question. What's your thoughts on all this? >> I was at an Amazon event where they were trumpeting how no technology had ever caught on faster than these personal digital assistants, and yet every time I'm in a use case, a household that's trying to use them, something goes terribly wrong. My friend had to rename his because the neighbor kids kept telling Alexa to do awful things. He renamed it computer, and now every time we use the word computer, the wall tells us something we don't want to know. >> (laughs) >> This is just anecdata, but maybe it speaks to something deeper, the fact that we don't necessarily like the feeling of being surveilled. IBM was always trying to push Watson as the star Trek computer that helpfully tells you exactly what you need to know in the right moment, but that's got downsides too. I feel like we're going to, if nothing else, we may start to value individual learning and knowledge less when we feel like a voice from the ceiling can deliver unto us the fact that we need. I think decision-making might suffer in that kind of a world. >> All right, this brings up ethics because I bring up the Amazon and the voice stuff because this is the new interface people want to have with machines. I didn't mention phones, Androids and Apple, they need to listen in order to make decisions. This brings up the ethics question around who sets the laws, what society should do about this, because we want the benefits of AI. John, you point out some of them. You got to give to get. Where are we on ethics? What's the opinion, what's the current view on this? John, we'll start with you on your ethics view on what needs to change now to move the ball faster. >> Data is gold. Data is gold at an exponential rate when you're talking about AI. There should be no situation where these companies get to collect data at no cost or no benefit to the end consumer. So ultimately we should have the option to opt out of any of these products and any of this type of surveillance wherever we can. Public safety is a little bit different situation, but on the commercial side, there is a lot of more expensive and even more difficult ways to train these models with a data set that isn't just basically grabbing everything our of your personal lives. I think that should be an option for consumers and that's one of those ethical check-marks. Again, ethics in general, the way that data's trained, the way that data's handled, the way models actually work, it has to be a primary reason for and approach of how you actually go about developing and delivering AI. That said, we cannot get over-indulgent in the fact that we can't do it because we're so fearful of the ethical outcomes. We have to find some middle ground and we have to find it quickly and collectively. >> Charna, what's your take on this? Ethics is super important to set the agenda for society to take advantage of all this. >> Yeah. I think we've got three ethical components here. We certainly have, as John mentioned, the data sets. However, it's also what behavior we're trying to change. So I believe the industry could benefit from a lot more behavioral science, so that we can understand whether or not the algorithms that we're building are changing behaviors that we actually want to change, right? And if we aren't, that's unethical. There is an entire field of ethics that needs to start getting put into our companies. We need an ethics board internally. A few companies are doing this already actually. I know a lot of the military companies do. I used to be in the defense industry, and so they've got a board of ethics before you can do things. The challenge is also though that as we're democratizing the algorithms themselves, people don't understand that you can't just get a set of data that represents the population. So this is true of image processing, where if we only used 100 images of a black woman, and we used 1,000 images of a white man because that was the distribution in our population, and then the algorithm could not detect the difference between skin tones for people of color, then we end up with situations where we end up in a police state where you put in an image of one black woman and it looks like ten of them and you can't distinguish between them. And yet, the confidence rate for the humans are actually higher, because they now have a machine backing their decision. And so they stop questioning, to your point, Ken, about what is the decision I'm making, they're like I'm so confident, this data told me so. And so there's a little bit of you need some expert in the loop and you also can't just have experts, because then you end up with Cambridge Analytica and all of the political things that happened there, not just in the US, but across 200 different elections and 30 different countries. And we are upset because it happened in the US, but this has been happening for years. So its just this ethical challenge of behavior change. It's not even AI and we do it all the time. Its why the cigarette industry is regulated (laughs). >> So Ken, what's your take on this? Obviously because society needs to have ethics. Who runs that? Companies? The law-makers? Someone's got to be responsible. >> I'm honestly a little pessimistic the general public will even demand this the way we're maybe hoping that they will. When I think about an example like Facebook, people just being able to, being willing to give away insane amounts of data through social media companies for the smallest of benefits: keeping in touch with people from high school they don't like. I mean, it really shows how little we value not being a product in this kind of situation. But I would like to see this kind of ethical decisions being made at the company-level. I feel like Google kind of surreptitiously moved away from it's little don't be evil mantra with the subtext that eh, maybe we'll be a little evil now. It just reminds me of Manhattan Project era thinking, where you could've gone to any of these nuclear scientists and said you're working on a real interesting puzzle here, it might advance the field, but like 200,000 civilians might die this summer. And I feel like they would've just looked at you and thought that's not really my bailiwick. I'm just trying to solve the fission problem. I would like to see these 10 companies actually having that kind of thinking internally. Not being so busy thinking if they can do something that they don't wonder if they should. >> That's a great point. This brings up the point of who is responsible. Almost as if who is less evil than the other person? Google, they don't do evil, but they're less evil than Amazon and Facebook and others. Who is responsible? The companies or the law-makers? Because if you look up some of the hearings in Washington, D.C., some of the law-makers we see up there, they don't know how the internet works, and it's pretty obvious that this is a problem. >> Yeah, well that's why Jack Dorsey of Twitter posted yesterday that he banned not just political ads, but also issue ads. This isn't something that they're making him do, but he understands that when you're using AI to target people, that it's not okay. At some point, while Mark is sitting on (laughs) this committee and giving his testimony, he's essentially asking to be regulated because he can't regulate himself. He's like well, everyone's doing it, so I'm going to do it too. That's not an okay excuse. We see this in the labor market though actually, where there's existing laws that prevent discrimination. It's actually the company's responsibility to make sure that the products that they purchase from any vendor isn't introducing discrimination into that process. So its not even the vendor that's held responsible, it's the company and their use of it. We saw in the NYPD actually that one of those image recognition systems came up and someone said well, he looked like, I forget the name of what the actor was, but some actor's name is what the perpetrator looked like and so they used an image of the actor to try and find the person who actually assaulted someone else. And that's, it's also the user problem that I'm super concerned about. >> So John, what's your take on this? Because these are companies are in business to make money, for profit, they're not the government. And who's the role, what should the government do? AI has to move forward. >> Yeah, we're all responsible. The companies are responsible. The companies that we work with, I have yet to interact with customers, or with our customers here, that have some insidious goal, that they're trying to outsmart their customers. They're not. Everyone's looking to do the best and deliver the most relevant products in the marketplace. The government, they absolutely... The political structure we have, it has to be really intelligent and it's got to get up-skilled in this space and it needs to do it quickly, both at the economy level, as well as for our defense. But the individuals, all of us as individuals, we are already subjected to this type of artificial intelligence in our everyday lives. Look at streaming, streaming media. Right now every single one of us goes out through a streaming source, and we're getting recommendations on what we should watch next. And we're already adapting to these things, I am. I'm like stop showing me all the stuff you know I want to watch, that's not interesting to me. I want to find something I don't know I want to watch, right? So we all have to get educated, we're all responsible for these things. And again, I see a much more positive side of this. I'm not trying to get into the fear-mongering side of all the things that could go wrong, I want to focus on the good stories, the positive stories. If I'm in a courtroom and I lose a court case because I couldn't afford the best attorney and I have the bias of a judge, I would certainly like artificial intelligence to make a determination that allows me to drive an appeal, as one example. Things like that are really creative in the world that we need to do. Tampering down this wild speculation we have on the markets. I mean, we are all victims of really bad data decisions right now, almost the worst data decisions. For me, I see this as a way to actually improve all those things. Fraud fees will be reduced. That helps everybody, right? Less speculation and these wild swings, these are all helpful things. >> Well Ken, John and Charna, thank- (audio feedback) >> Go ahead, finish. Get that word in. >> Sorry. I think that point you were making though John, is we are still a capitalist society, but we're no longer a shareholder capitalist society, we are a stakeholder capitalist society and the stakeholder is the society itself. It is us, it what we want to see. And so yes, I still want money. Obviously there are things that I want to buy, but I also care about well-being. I think it's that little shift that we're seeing that is actually you and I holding our own teams accountable for what they do. >> Yeah, culture first is a whole new shift going on in these companies that's a for-profit, mission-based. Ken, John, Charna, thanks for coming on Around the CUBE, Unpacking AI. Let's go around the CUBE Ken, John and Charna in that order, and just real quickly, unpacking AI, what's your final word? >> (laughs) I really... I'm interested in John's take that there's a democratization coming provided these tools will be available to everyone. I would certainly love to believe that. It seems like in the past, we've seen no, that access to these kind of powerful, paradigm-changing tools tend to be concentrated among a very small group of people and the benefits accrue to a very small group of people. But I hope that doesn't happen here. You know, I'm optimistic as well. I like the utopian side where we all have this amazing access to information and so many new problems can get solved with amazing amounts of data that we never could've touched before. Though you know, I think about that. I try to let that help me sleep at night, and not the fact that, you know... every public figure I see on TV is kind of out of touch about technology and only one candidate suggests the universal basic income, and it's kind of a crackpot idea. Those are the kind of things that keep me up at night. >> All right, John, final word. >> I think it's beautiful, AI's beautiful. We're on the cusp of a whole new world, it's nothing but positivity I see. We have to be careful. We're all nervous about it. None of us know how to approach these things, but as human beings, we've been here before. We're here all the time. And I believe that we can all collectively get a better lives for ourselves, for the environment, for everything that's out there. It's here, it's now, it's definitely real. I encourage everyone to hurry up on their own education. Every company, every layer of government to start really embracing these things and start paying attention. It's catching us all a little bit by surprise, but once you see it in production, you see it real, you'll be impressed. >> Okay, Charna, final word. >> I think one thing I want to leave people with is what we incentivize is what we end up optimizing for. This is the same for human behavior. You're training a new employee, you put incentives on the way that they sell, and that's, they game the system. AI's specifically find the optimum route, that is their job. So if we don't understand more complex cost functions, more complex representative ways of training, we're going to end up in a space, before we know it, that we can't get out of. And especially if we're using uninspectable AI. We really need to move towards augmentation. There are some companies that are implementing this now that you may not even know. Zillow, for example, is using AI to give you a cost for your home just by the photos and the words that you describe it, but they're also purchasing houses without a human in the loop in certain markets, based upon an inspection later by a human. And so there are these big bets that we're making within these massive corporations, but if you're going to do it as an individual, take a Coursera class on AI and take a Coursera class on ethics so that you can understand what the pitfalls are going to be, because that cost function is incredibly important. >> Okay, that's a wrap. Looks like we have a winner here. Charna, you got 18, John 16. Ken came in with 12, beaten again! (both laugh) Okay, Ken, seriously, great to have you guys on, a pleasure to meet everyone. Thanks for sharing on Around the CUBE Unpacking AI, panel number two. Thank you. >> Thanks a lot. >> Thank you. >> Thanks. I've been defeated by artificial intelligence again! (all laugh) (upbeat music)

>> Live from Las Vegas, it's theCUBE Covering Informatica World 2019 Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019 here in Las Vegas. I'm your host, Rebecca Knight, along with my co-host, John Furrier. We are joined by Ariel Kelman. He is the VP, Worldwide Marketing at AWS. Thank you so much for coming on theCUBE. >> Thanks so much for having me on today. >> So let's start out just at ten thousand feet and talk a little bit about what you're seeing as the major cloud and AI trends and what your customers are telling you. >> Yeah, so I mean, clearly, machine learning and AI is really the forefront of a lot of discussions in enterprise IT and there's massive interest but it's still really early. And one of the things that we're seeing companies really focused on now is just getting all their data ready to do the machine learning training. And as opposed to also, in addition I mean, training up all their people to be able to use these new skills. But we're seeing tons of interest, it's still very early, but you know one of the reasons here at Informatica World is that getting all the data imported and ready is, you know, it's almost doubled or tripled in importance as it was when people were just trying to do analytics. Now they're doing machine learning as well. You know, we're seeing huge interest in that. >> I want to get into some of the cloud trends with your business, but first, what's the relationship with Informatica, and you know we see them certainly at re:Invent. Why are you here? Was there an announcement? What's the big story? >> I mean, we've been working together for a long time and it's very complementary products and number varies. I think the relationship really started deepening when we released Redshift in 2013, and having so many customers that wanted to get data into the cloud to do data we're housing, we're already using Informatica in, to help get the data loaded and cleansed and so really they're one of the great partners that's fueling moving data into the cloud and helping our customers be more successful with Redshift. >> Yeah, one of the things I really admire about you guys is that you're very customer centric. We've been following Amazon as you know since their, actually second reinvent, Cube's been there every time, and just watching the growth, you know, Cloud certainly has been a power source for innovation, SAS companies that are born in the cloud have exponentially scaled faster than most enterprises because they use data. And so data's been a heart of all the successful SAS businesses, that's why start ups gravitated to the Cloud right away. But now that you guys got enterprise adoption, you guys have been customer centric and as you listen to customers, what are you guys hearing from that? Because the data on premises, you've got more compliance, you've got more regulation, you've got-- news today-- more privacy and now you've got regions, countries with different laws. So the complexity around even just regulatory, nevermind tech complexity, how are you guys helping customers when they say, you know what, I want to get to the cloud, love Amazon, love the cloud, but I've got my, I've got to clean up my on param house. >> Yeah, I would say like a lot, if you look at a lot of the professional services work that we do, a lot of it is around getting the company prepared and organized with all their data before they move to the cloud: segmenting it, understanding the different security regulatory requirements, coming up with a plan of what they need, what data they're going to maybe abstract up, before they load it, and there's a lot of work there. And, you know, we've been focused on trying to help customers.. >> And is there a part in you're helping migrate to the cloud, is that.. >> Yeah, there's technology pieces, companies like Informatica helping to extract and transform and load the data and on data governance policies. But then also, for a lot of our systems integrator partners, Cognizant, Accenture, Deloitte-- they're very involved in these projects. There's a lot of work that goes on; a lot of people don't talk about just before you can even start doing the machine learning, and a lot of that's getting your data ready. >> So how, what are some of the best practices that have emerged in working with companies that, as you said, there's a lot of pre-work that needs to be done and they need to be very thoughtful about about sort of getting their data sorted. >> Well I think the number one thing that I see and I recommend is to actually first take a step back from the data and to focus on what are the business requirements of, what questions are you trying to answer, let's say with machine learning, or with data science advanced analytics, and then back out the data from that. What we see a lot of, you know companies sometimes will have it be a data science driven project. Okay, here's all the data that we have, let's put it in one place, when you may not be spending time proportionate to the value of the data. And so that's one of the key things that we see, and to come up-- just come up with a strong plan around what answers you're, what business questions you're trying to answer. >> On the growth of Amazon, you guys certainly have had great record numbers, growth, even in the double digit kind of growth you're seeing on top of your baseline has been phenomenal. Clearly number one on the cloud. Enterprise has been a big focus. I noticed that on the NHL, your logo's on the ice during the playoffs; you've got the Statcast. You guys are creating a lot of aware-- I see a lot of billboards everywhere, a lot of TV ads. Is that part of the strategy is to get you guys more brand awareness? What's the.. >> We're trying, you know, it's part of our overall brand awareness strategy. What we're trying to do is to help, we're trying to communicate to the world how our customers are being successful using our technology, specifically machine learning and AI. It's one of these things where so many companies want to do it but they say, well, what am I supposed to use it for? And so, you know, one of, if you dumb down what marketing is at AWS, it's inspiring people about what they can run in the cloud with AWS, what use cases they should consider us for, and then we spend a lot of energy giving them the technical education and enablement so they can be successful using our products. At the end of the day, we make money when our customers are successful using our products. >> One of the hot products was SageMaker, we see in that group, AI's gone mainstream. That's a great tail wind for you guys because it kind of encapsulates or kind of doesn't have to get all nerdy about cloud, you know, infrastructure and SAS. AI kind of speaks to many people. It's one of the hottest curriculums and topics in the world. >> Yeah, and with SageMaker, we're trying to address a problem that we see in most of our customers where the everyday developer is not, does not have expertise in machine learning. They want to learn it, so we think that anything we can do to make it easier for every developer to ramp up on machine learning the better. So that's why we came up with SageMaker as a platform to really make all three stages of machine learning easier: getting your data prepared for training, training in optimized models, and then running inference to make the predictions and incorporate that into people's applications. >> One of the themes that's really emerging in this conversation is the need to make sure developers are ready and that your people are skilled up and know what they need to know. How are, how is AWS thinking about the skills gap, and what are you doing to remedy it? >> Yeah, a couple things. I mean, we're really, like a lot of things we do, we'll say what are all the ways we can attack the problem and let's try and help. So, we have free training that we've been creating online. We've been partnering with large online training firms like Udacity and Coursera. We have an ML solutions lab that help companies prototype, we have a pretty significant professional services team, and then we're working with all of out systems integrators partners to build up their machine learning practices. It's a new area for a lot of them and we've been pushing them to add more people so they can help their customers. >> Talk about the conferences, you have re:Invent, the CORE conference, we've been theCUBE there. We've just also covered London, Amazon's Web Services summit, and 22,000 registered, 14,000 showed up. Got huge global reach now. How do you keep up with this? I mean it's a... >> Well we're trying to help our customers keep up with all the technology. I mean, really, we have about, maybe 25 or so of these summits around the world-- usually around two days, several thousand people, free conferences. And what we're trying to do is >> They're free? >> The summits are free and it's like, we introduce so much new technology, new services, deeper functionality within our exiting services, and our customers are very hungry to learn the latest best practices and how they can use these, and so we're trying to be in all the major areas to come in and provide deep educational content to help our customers be more successful. >> And re:Invent's coming around the corner. Any themes there early on, numbers wise? Last year you had, again, record numbers. I mean at some point, is Vegas too small >> Yeah, we had over 50,000 people. We're going to have even more, and we've been expanding to more and more locations around Las Vegas and you know we're going to keep growing. There's a lot of demand. I mean, we want to be able to provide the re:Invent experience for as many people as want to attend. >> What's the biggest skill set, you know the folks graduating this month, my daughter's graduating from Cal Berkeley, and a lot of others are graduating >> Congratulations >> high school. Everyone wants to either jump into some sort of data related field, doesn't have to be computer science, those numbers are up. What's your view of skill sets that are needed right now that weren't in curriculum, or what pieces of curriculum should people be learning to be successful if machine learning continues to grow from helping videos surface to collecting customer data. Machine learning's going to be feeding the AI applications and SAS businesses. >> Yeah, I mean look, you just forget about machine learning, you go to a higher level. There's not enough good developers. I mean, we're in a world now where any enterprise that is going to be successful is going to have their own software developers. They're going to be writing their own software. That's not how the world was 15 years ago. But if you're a large corporation and you're outsourcing your technology, you're going to get disrupted by someone else who does believe in custom software and developers. So the demand for really good software engineers, I mean we deal with all the time, we're hiring. It is always going to outstrip supply. And so, for young people, I would encourage them to start coding and to not be over reliant on the university curriculums, which don't always keep pace with, you know, with the latest trends. >> And you guys got a ton of material online too, you can always go to your site. Okay, on the next question around, as someone figures out, okay, enterprise versus pure SAS, you guys have proven with the Cloud that start ups can grow very fast and then the list goes on: AirBnB, Pinterest, Zoom Communications, disrupting existing big, mature markets by having access to the data. So how do you talk about customers when you say, hey, you know, I want to be like a SAS company, like a consumer company, leverage data, but I've got a lot of stuff on premise. So how do I not make that data constrained? How do you guys feel about that conversation because that seems to be the top conversation here, is you know, it's not to say be consumer, it's consumer-like. Leveraging data, cause if data's not into AI, there's no, AI doesn't work, right? So >> Right >> It can't be constrained by anything. >> Well, you know, you talk to all these companies and at first they don't even know what they don't know in terms of what is that data? And where is it? And what are the pieces that are important? And so, you know, we encourage people to do a good amount of strategy work before they even start to move bits up to the cloud. And of course, then we have a lot of ways we can help them, from our Snowball machines that they can plug in, all the way to our Snowmobile, which is the semi truck that you can drive up to your data center and offload very large amounts of data and drive it over to our data centers. >> One of the things that is trending-- we had Ali from Data Bricks talk about, he absolutely believes a lot of the same philosophies you guys do-- data in the cloud. And one of his arguments was is that there's a lot of data sets in these marketplaces now where you can really leverage other people's data, and we see that on cybersecurity where people are starting to share data, and Cloud is a better model for that than trying to ship drives around, and there's a time for Snowball, I get that, and Snowmobile, the big trucks for large ingestion into the cloud, but the enterprise, this is a new phenomenon. No one really shared a lot in the old days. This is a new dynamic. Talk about that, is it-- >> I mean, sharing, selling, monetizing data. If there's something that is important, there will be a market for it. And I think we're seeing that just the hunger, everything from enterprises to startups, that want more data, whether it's for machine learning to train their models, or it's just to run analytics and compare against their data sets. So I think the commercial opportunity is pretty large. >> I think you're right on that. I think that's a great insight. I mean, no one ever thought about data as a service from our data set standpoint, 'cause data sets feed machine learning. All right, so let's do, give the plug on what's going on with AWS. What's new, what's on your plate, what's notable. I mean I love the NHL, I couldn't resist that plug for you being a hockey fan. But what's new in your world? >> Um, you know, we're, we're in early planning stages on our re:Invent conference, our engineers are hard at work on a lot of new technology that we're going to have ready between now and our re:Invent show. You know, also we're, my team's been doing a lot of work with the sports organizations. We've had some interesting machine learning work with major league baseball. They rolled out this year a new machine learning model to do stolen base predictions. So, you can see on some of the broadcasts, as a runner goes past first base, we'll have a ticker that will show what the probability is that they'll be successful stealing second base if they choose to run. Trying to make a little more entertaining all those scenes we've seen in the past of the pitcher throwing the ball back to first, trying to use AI machine leaning to give a little bit more insight into what's going on. >> And that's the Statcast. Part of that's the Statcast >> That's Statcast, yeah >> And you got anything new coming around that besides that new.. >> Yeah, I think that yeah, major league baseball is hard at work on some new models that I think will be announced fairly soon. >> All right, to wrap up Informatica real quick, an announcement here, news coming I hear. How are you guys working with Informatica in the field? Is there any, can you share more about relationship >> Yeah I mean I think we're going to have an announcement a little bit later today, I mean it's around the subject we've been talking about: making it easier for customers to, you know, be successful moving their data to the Cloud so that they can start to benefit from the agility, the speed and the cost savings of data analytics and machine learning in the Cloud. >> And so when you're working with customers, I mean, because this is the thing about Amazon. It is a famously innovative, cutting edge company, and when you talk about the hunger that you describe, that these customers, isn't it just that they want to be around Amazon and kind of rub shoulders with this really creative, thinking four steps ahead kind of company. I mean how do you let your innovation rub off on these customers? >> I mean there's a couple ways We do, one of the things we've done recently is these innovation workshops. We have this thing we talk about a lot this working backwards process where we force the engineers to write a press release before we'll green light the product because we feel like if you can't clearly articulate the customer benefit, then we probably shouldn't start investing, right? And so we, that's one of the processes that we use to help us innovate better, more effectively and so we've been walk-- we walk customers through this. We have them come, you know there's an international company that I was, part of one of the efforts we did in Palo Alto last year where we had a bunch of their leadership team out for two days of workshops where we worked a bunch of ideas through, through our process. And so we do some of that but the other area is we try and capture area where we think that we've innovated in some interesting way into a service that then customers can use. Like Amazon Connect I think is a good example of it. This is our contact center call routing technology and you know, one of the things Amazon's consumer business is known for is having great customer support, customer service, and they spent a lot of time and energy making sure that calls get routed intelligently to the right people, that you don't sit on hold forever, and so we figure we're probably not the only company that could benefit from that. Kind of like with AWS, when we figure out how to run infrastructure securely and high performance and availability, and so we turn that into a service and it's become a very successful service for us. A lot of companies have similar contact center problems. >> As a customer, I can attest to being on hold a lot. Ariel, thank you so much for coming on theCUBE. It's been great talking to you. >> I appreciate it. Thank you. >> Thanks for coming out, appreciate it. >> I'm Rebecca Knight, for John Furrier. You are watching theCUBE. Stay tuned. (upbeat music)

>> Announcer: Live, from Miami Beach, Florida, it's theCUBE, covering UiPath Forward Americas. Brought to you by UiPath. >> Welcome back to Miami everybody, you're watching theCUBE, the leader in live tech coverage. We go out to events, we extract the signal from the noise. The signal here is all about automation, robotic process automation, software robots, we're seeing the ascendancy of that market space. I'm Dave Vellante with Stu Miniman. This is UiPath's Forward conference, big user conference, UiPath Forward Americas, about 1500 people here, Stu. They have conferences all over the world, I think I heard 14,000 people in the last year have attended such shows. They're intimate, there are a lot of partners here, they're loud, they're a lot of good energy. Ana Cinca is here, she's the Vice President of Enabling Technologies, and she's joined by old friend Tom Clancy, who's the Senior Vice President of UiPath Learning, both folks from UiPath, welcome. Thanks for coming to theCUBE. >> Thank you for having us. >> So Ana, let's start with you. VP of Enabling Technologies. What does that mean, what's that role? >> Well, my role in the organization is to generate a set of non-core products and programs that are creating an ecosystem that is actually contributing actively into accelerating the adoption of the core platform. And that would be through learning, through generating new products like the UiPath Go!, the Marketplace, or constantly engaging the community of users and so on. >> Okay, so you started the training program, correct? >> Ana: Yeah. >> How did that get started? What was your kind of mission, how'd you do it? >> Well it started from a very simple need. Back then, about two years ago, we were, a bunch of my team members were a bunch of RPA developers, who were losing their time only delivering training, so, two years ago about 500 trainings, five days per week, per year. That were a lot of training, so we said, we need to automate this, we need to do something about it. And the only thing that could come into our mind was to, we got inspired by the Udemy, by Coursera, by all the right courses out there, like platforms out there, which were very democratic in sharing the knowledge. So we said, how about we actually create a set of online courses that are really, really good, RPA focused, UiPath focused, courses, and put it out there? That's how it all started, we just wanted to get rid of these repetitive trainings, ultimately. >> Alright, so you had to do it for yourselves and then. >> Ana: Absolutely, yeah. >> So Stu, we heard today from Daniel, he kind of did the moon shot. He said we are going to train a million people in three years, right? >> Well, Tom, it seems like you've got a challenge in front of you to really scale this business. We've talked with you for years, back in your EMC days, your not just storage but new architectures, this convergent approach to the silos, and then cloud architects, really training kind of next generation of the work force in IT, give us a little bit, what's the same, what's different between what you did back at EMC and what you're doing now here with RPA? >> So the biggest difference between EMC and UiPath is EMC had a technology that a lot of people thought was kind of commodity, right? So, the excitement wasn't there when you started going outside of your partners and customers, right? This technology, there is passion about this throughout the entire globe. This is the next big wave, and so, if you're going to scale a program like this, you have to have a bunch of different factors on your side. What Ana just talked about is the academy, you have to bring value somehow, and that starts with having the right courses. If you don't have the courses built up, then you're starting from zero, right, from scratch. But, the other thing that's even more important, is the passion from the CEO. You know, when I first met with Daniel, it was actually sort of an interview, he was, he talked about, you know, employee training, partner training, customer training, but his passion and forty-five minutes of the hour was talking about educating the planet, right? And so he started with universities, which that was kind of a no brainer. And then he went to Youth in Action, under-represented groups, and so forth. The other factor that's really important is having the right team, so, at UiPath, the team is the company, everybody wants to do this. If you're the leader in India, Japan, China, the US, they're all coming to us saying "We need this program." Not just universities but all the way down to the youths. And then, you need a good academic alliance team. So the team that we're building is going to leverage academy, but we are bringing in some of those EMC academic alliance people, we're bringing in a person from Salesforce.com that was running a big piece of it, starts today. We're bringing in a VMware person, a Cisco person, so we're getting all the best. Those are the best programs in the industry. >> Tom, there's one underlying thing, that I saw, a similarity, is back when you talked about convergence or cloud, there was an underlying fear of "Oh my gosh, I'm not going to have the skills, I'm going to be out of a job." Automation's always been that thing "Oh wait, if I automate it, what's that mean for me?" How do you address that? >> Well, first of all, there's a report all that says by 2030, 1.5 billion jobs will be impacted. It doesn't say negative, it just says impacted. So, everybody is going to have to understand that this is coming, and how does it impact me? We're going to put together, as part of this, we'll have an upscaling rescaling, so everybody, it doesn't matter who you are, will be able to leverage the academy, and we'll be tweaking the academy courses, so if it's upscaling rescaling, they will take the courses in a different way, in a different format, than the university students, than the Youth in Action, so we'll target those different audiences, and the other, one other thing is marketing is hugely important, because you can't rely on the training group to get the word out. So, Bobby Patrick and his team, are working hand-in-hand with us to drive the awareness across the globe. >> So Ana, when we first heard about RPA and UiPath, we read the Forrester report, and said "Okay, there's a few leaders out there, let's "play with it, let's go download the software "and see how hard it is to do." Turned out, we could only get our hands on UiPath software, it was very easy to get our hands on the software, it was very open. Some of the other guys were like, "Why do you want to use it?" Forget it. But then we built some automations, and it was kind of, you know, it took a little, there was a little bit of a learning curve, but it was not a developer who did it, so it was relatively low code, or even no code. So, when you started this program and as you scale it, who are you targeting? Is it the hardcore developer, is it the, you know, RPA developer, is it the citizen developer, both? And how do you adjust the training correspondingly? >> Yeah, so, first of all, the way we set up the trainings, were, we wanted to make sure that, exactly like we did with the core platform, that was the first RPA software that had a trial version that was available for everyone, right? We had to do the same thing in learning and we're an academy, so what we said were we're launching courses which are free of charge, online, for everyone to use. But, moreover than that, what we wanted to do, is to, have courses that take someone from a very basic foundation level, of basic programming, and actually guide him or her through a learning curve that will get them to an expert level. So, the way we built the courses, are in such a matter that it is very easy to be followed by anyone, actually. And now, that's the reason why, now we're having not only courses for the RPA developers, the techie guys, or solution architects, or infrastructure engineers, but, moreover than that, we're tackling into the space of non-technical people who are equally very important in the RPA journey. Like business analysts, the RPA project managers, and so on. So we're trying to cover all the personas that are critical in an RPA COE set up. >> So it's interesting, Tom, hearing you say you're recruiting people from Cisco, Vmware, some EMC folks, a lot of the traditional, some would say legacy, enterprise companies, who are constantly in the process of reskilling, so I would think that these folks would be very receptive to that. Now you think about Vmware admin, Cisco certified engineers, Microsoft certifications, they sort of led to full employment for at least some period of time. Do you think RPA skills are going to be similar, in that they are going to be in such demand, if young people start to get trained in RPA they're going to essentially have full employment for life, or do you think it's more fleeting that that? You're thoughts? >> So I've been here for three months now, so I guess that makes me a veteran at UiPath, but robotics is going to be in everybody's job. So one of the things that it took me a while to kind of grasp when I was talking to Daniel the first time, the first meeting I mentioned, is he said that there will be at least one robot on every desktop moving forward. This is going to be, you know, when you had the flip phone before, well actually, when people went from the big cell phones and people were saying everybody's going to have a cell phone, you know, everybody looked like "That's kind of crazy," but then, next thing you know, you have a computer on your phone, and everybody has at least one phone. This is going to be the same way with robots. It's going to be ubiquitous across the entire industry. So, people will grow up understanding what robots are. That's why we're going after the youth, so they understand robots right from the get go. And then, it will integrated into everybody's job across the globe, so it's not fleeting at all, it's actually the complete opposite. >> How do you guys measure success? Obviously, you got to get to a million in three years, that's a lot of training. How else do you measure success? What kind of parameters do you set? Tests you take, how do you measure it? >> Want to take that one up for scaling? >> So, one of the things we did, well Ana, one of the things that Ana did before I got here, was they built certification. Certification is going to continue to get more and more important for us. You know, so, think Microsoft, Cisco, certification, and so forth, and so, we believe we will have the industry standard certification program, period. But one of the things we did, was we built our own certification platform, high stakes certification. So what that does is, we do not have to charge, or charge much, any of the people going through our courses and certification. So, today, because we had to go through a third party, we're charging 850 dollars per test. This quarter, through the end of the year, it's going to be zero, just to bring more people in. And then, going forward, it would be significantly lower than 150. What we want to do, and what we will do, is democratize learning and certification for robots. >> I think this is huge, go on you want to add something? >> Yeah, I really want to add one more thing, because what we're doing together, is actually, through the way we're approaching community, and through the spaces that we have already built so far like the academy, the forum, we're bringing now the UiPath Go! in October, the end of October, the project space, all holistically wrapped up in a new version of the community. What we're trying to get out there is an RPA developer getting trained on the academy, being certified, but then practicing within the UiPath universe. Ultimately, where we want to get to, is to measure success also through the number of community users, of end-users, who are not only certified, but we will be able to see what is their activity status, like reputation, and recognition, within the community itself. And, hence, ultimately, reaching up to a stage, where we will be able to pinpoint to a true UiPath expert elite of people throughout the world. >> I love that it's a community driven measurement. >> Everything goes into building up a holistic and global community. >> Very open-- >> If I could just say one thing on community if you just look at the education and the different audiences, you know, let's say, you know, people that do robotics and they get certified, all the way down to youth, we will have a community, where all these different organizations are talking to each other, and to professionals. So, you might have a ten year old in Bangladesh, that is on the community asking questions, and you might have an engineer in Romania at UiPath answering those questions because they're part of the community. Or, it could be a customer or partner, you know, in Philadelphia, but they're all part of the community, we're bringing all these people together. So, things like STEM, Women in Coding, one person came up to me last night, he was so excited, he said "I represent a lot of the black community when "it comes to education and I really want to get my teams "across the country involved in this." >> Phenomenal, now, the no cost training is available roughly when? >> Yeah, right now. >> It's today? >> Well no cost training has been available-- >> Since the beginning. >> That was a decision that Ana made 18 months ago. If somebody, if a customer wants to have a seminar, or something like that, we have third-party training companies that will go in, and they'll charge, but if you go online to the academy, 100 percent free. And the certification for the next quarter is going to be 100 percent free. >> That's unbelievable, because, you know, I got three kids in college and one of them is he's doing Python, he's doing R, he's doing Tableau and he's texting me, "Hey, these Tableau courses "are really expensive, can you pay for it?" And I'm like well, what's the ROI? And I'm sayin' learn about RPA, because it's going to change the world, you know, visualizations important and all that stuff's important, but that's, I think, a huge investment that you guys are making, and then also, helps me understand how you guys plan on staying ahead. So congratulations on getting this started, Tom, you basically came out of retirement, you know, quasi-retirement so it had to be pretty alluring. Extremely successful career at EMC, so great to have you back in the game. >> Thanks, it's great to be here. >> Thanks so much, you guys, for coming on theCUBE. >> Okay, thank you. >> Right there, everybody, you're watching theCUBE, live, from the Fontainebleau in Miami. We'll be right back, right after this short break, you're watching UiPathForward Americas, we'll be right back.