>>Hi, everyone. Good afternoon. Thank you for joining this session. I feel honored to be invited to speak here today. And I also appreciate entity research Summit members for organ organizing and giving this great opportunity. Please let me give a quick introduction. First, I'm a Takashi from Marvin American population, and I'm leading technology scouting and global ation with digital health companies such as Business Alliance and Strategically Investment in North America. And since we started to focus on this space in 2016 our team is growing. And in order to bring more new technologies and services to Japan market Thesis year, we founded the new service theories for digital health business, especially, uh, in medical diagnosis space in Japan. And today I would like to talk how health care has been transformed for my micro perspective, and I hope you enjoy reasoning it. So what's happened since the US identify the first case in the middle of January, As everyone knows, unfortunately, is the damaged by this pandemic was unequal amongst the people in us. It had more determined tal impact on those who are socially and economically vulnerable because of the long, long lasting structural program off the U. S. Society and the Light Charity about daily case rating elevator country shows. Even in the community, the infection rate off the low income were 4.5 times higher than, uh, those of the high income and due to czar straight off the Corvette, about 14 million people are unemployed. The unique point off the U. S. Is that more than 60% of insurance is tied with employment, so losing a job can mean losing access to health care. And the point point here is that the Corvette did not create healthcare disparity but, uh nearly highlighted the underlying program and necessity off affordable care for all. And when the country had a need to increase the testing capacity and geographic out, treat the pharmacies and retails joined forces with existing stakeholders more than 90% off the U. S Corporation live within five miles off a community pharmacy such as CVS and Walgreen, so they can technically provide the test to everyone in all the community. And they also have a huge workforce memory pharmacist who are eligible to perform the testing scale, and this very made their potential in community based health care. Stand out and about your health has provided on alternative way for people to access to health care. At affordable applies under the unusual setting where social distancing, which required required mhm and people have a fear of infection. So they are afraid to take a public transportacion and visit >>the doctor the same thing supplied to doctor and the chart. Here is a number of total visit cranes by service type after stay at home order was issued across the U. S. By Ali April patient physical visits to doctor's offices or clinics declined by ALAN 70%. On the other hand, that share, or telehealth, accounted for 25% of the total total. Doctor's visit in April, while many states studied to re opening face to face visit is gradually recovering. And overall Tele Health Service did not offset the crime. Physician Physical doctor's visit and telehealth John never fully replace in person care. However, Telehealth has established a new way to provide affordable care, especially to vulnerable people, and I don't explain each player's today. But as an example, the chart shows the significant growth of the tell a dog who is one of the largest badger care and tell his provider, I believe there are three factors off paradox. Success under the pandemic. First, obviously tell Doc could reach >>the job between those patients and doctors. Majority of the patients who needed to see doctors who are those who have underlying health conditions and are high risk for Kelowna, Bilis and Secondary. They showed their business model is highly scalable. In the first quarter of this year, they moved quickly to expand their physical physicians network to increase their capacity and catch up growing demand. To some extent, they also contributed to create flexible job for the doctors who suffered from Lydia's appointment and surgery. They utilized. There are legalism to maximize the efficiency for doctors and doing so, uh, they have university maintained high quality care at affordable applies Yeah, and at the same time, the government recognize the body of about your care and de regulated traditional rules to sum up she m s temporary automated to pay a wide range of tell Her services, including hospital visit and HHS temporarily waived hip hop minorities for telehealth cases and they're changed allowed provider to use communication tools such as facetime and the messenger. During their appointment on August start, the government issued a new executive order to expand tell his services beyond the pandemic. So the government is also moving to support about your health care. So it was a quick review of the health care challenges and somewhat advancement in the pandemic. But as you understand, since those challenges are not caused by the pandemic, problems will stay remain and events off this year will continuously catalyze the transformation. So how was his cherished reshaped and where will we go? The topic from here can be also applied to Japan market. Okay, I believe democratization and decentralization healthcare more important than ever. So what does A. The traditional healthcare was defined in a framework over patient and a doctor. But in the new normal, the range of beneficiaries will be expanded from patient to all citizens, including the country uninsured people. Thanks to the technology evolution, as you can download health management off for free on iTunes stores while the range of the digital health services unable everyone to participate in new health system system. And in this slide, I put three essential element to fully realize democratization and decentralization off health care, health, literacy, data sharing and security, privacy and safety in addition, taken. In addition, technology is put at the bottom as a foundation off three point first. Health stimulus is obviously important because if people don't understand how the system works, what options are available to them or what are the pros and cons of each options? They can not navigate themselves and utilize the service. It can even cause a different disparity. Issue and secondary data must be technically flee to transfer. While it keeps interoperability ease. More options are becoming available to patient. But if data cannot be shared among stakeholders, including patient hospitals in strollers and budget your providers, patient data will be fragmented and people cannot yet continue to care which they benefited under current centralized care system. And this is most challenging part. But the last one is that the security aspect more players will involving decentralized health care outside of conventional healthcare system. So obviously, both the number of healthcare channels and our frequency of data sharing will increase more. It's create ah, higher data about no beauty, and so, under the new health care framework, we needed to ensure patient privacy and safety and also re examine a Scott write lines for sharing patient data and off course. Corbett Wasa Stone Catalyst off this you saved. But what folly. Our drivers in Macro and Micro Perspective from Mark Lowe. The challenges in healthcare system have been widely recognized for decades, and now he's a big pain. The pandemic reminded us all the key values. Misha, our current pain point as I left the church shores. Those are increasing the population, health sustainability for doctors and other social system and value based care for better and more affordable care. And all the elements are co dependent on each other. The light chart explained that providing preventive care and Alan Dimension is the best way threes to meet the key values here. Similarly, the direction of community based care and about your care is in line with thes three values, and they are acting to maximize the number of beneficiaries form. A micro uh, initiative by nonconventional players is a big driver, and both CBS and Walmart are being actively engaged in healthcare healthcare businesses for many years. And CBS has the largest walking clinic called MinuteClinic, Ottawa 1100 locations, and Walmart also has 20 primary clinics. I didn't talk to them. But the most interesting things off their recent innovation, I believe, is that they are adjusted and expanded their focus, from primary care to community health Center to out less to every every customer's needs. And CBS Front to provide affordable preventive health and chronic health monitoring services at 1500 CBS Health have, which they are now setting up and along a similar line would Mark is deploying Walmart Health Center, where, utilizing tech driven solutions, they provide affordable one stop service for core healthcare. They got less, uh, insurance status. For example, more than 40% of the people in U. S visit will not every big, so liberating the huge customer base and physical locations. Both companies being reading decentralization off health care and consumer device company such as Apple and Fitbit also have helped in transform forming healthcare in two ways. First, they are growing the boundaries between traditional healthcare and consumer product after their long development airport available, getting healthcare device and secondary. They acted as the best healthcare educators to consumers and increase people's healthcare awareness because they're taking an important role in the enhancement, health, literacy and healthcare democratization. And based on the story so far, I'd like to touch to business concept which can be applied to both Japan and the US and one expected change. It will be the emergence of data integration plot home while the telehealth. While the healthcare data data volume has increased 15 times for the last seven years and will continuously increase, we have a chance to improve the health care by harnessing the data. So meaning the new system, which unify the each patient data from multiple data sources and create 360 degrees longitudinal view each individual and then it sensitized the unified data to gain additional insights seen from structure data and unable to provide personal lives care. Finally, it's aggregate each individual data and reanalyzed to provide inside for population health. This is one specific model I envision. And, uh, health care will be provided slew online or offline and at the hospital or detail store. In order to amplify the impact of health care. The law off the mediator between health care between hospital and citizen will become more important. They can be a pharmacy toe health stand out about your care providers. They provide wide range of fundamental care and medication instruction and management. They also help individuals to manage their health care data. I will not explain the details today, but Japan has similar challenges in health care, such as increasing healthcare expenditure and lack of doctors and care givers. For example, they people in Japan have physical physician visit more than 20 times a year on average, while those in the U. S. On >>the do full times it sounds a joke, but people say because the artery are healthy, say visit hospitals to see friends. So we need to utilize thes mediators to reduce cost while they maintained social place for citizens in Japan, the government has promoted, uh, usual family, pharmacist and primary doctors and views the community based medical system as a policy. There was division of dispensing fees in Japan this year to ship the core load or pharmacist to the new role as a health management service providers. And so >>I believe we will see the change in those spaces not only in the U. S, but also in Japan, and we went through so unprecedented times. But I believe it's been resulting accelerating our healthcare transformation and creating a new business innovation. And this brings me to the end of my presentation. Thank you for your attention and hope you could find something somehow useful for your business. And if you have any questions >>or comments, please for you feel free to contact me.

>>Hi, everyone. Good afternoon. Thank you for joining this session. I feel honored to be invited to speak here today. And I also appreciate entity research Summit members for organ organizing and giving this great opportunity. Please let me give a quick introduction. First, I'm a Takashi from Marvin American population, and I'm leading technology scouting and global ation with digital health companies such as Business Alliance and Strategically Investment in North America. And since we started to focus on this space in 2016 our team is growing. And in order to bring more new technologies and services to Japan market Thesis year, we founded the new service theories for digital health business, especially, uh, in medical diagnosis space in Japan. And today I would like to talk how health care has been transformed for my micro perspective, and I hope you enjoy reasoning it. So what's happened since the US identify the first case in the middle of January, As everyone knows, unfortunately, is the damaged by this pandemic was unequal amongst the people in us. It had more determined tal impact on those who are socially and economically vulnerable because of the long, long lasting structural program off the U. S. Society and the Light Charity about daily case rating elevator country shows. Even in the community, the infection rate off the low income were 4.5 times higher than, uh, those of the high income and due to czar straight off the Corvette, about 14 million people are unemployed. The unique point off the U. S. Is that more than 60% of insurance is tied with employment, so losing a job can mean losing access to health care. And the point point here is that the Corvette did not create healthcare disparity but, uh nearly highlighted the underlying program and necessity off affordable care for all. And when the country had a need to increase the testing capacity and geographic out, treat the pharmacies and retails joined forces with existing stakeholders more than 90% off the U. S Corporation live within five miles off a community pharmacy such as CVS and Walgreen, so they can technically provide the test to everyone in all the community. And they also have a huge workforce memory pharmacist who are eligible to perform the testing scale, and this very made their potential in community based health care. Stand out and about your health has provided on alternative way for people to access to health care. At affordable applies under the unusual setting where social distancing, which required required mhm and people have a fear of infection. So they are afraid to take a public transportacion and visit >>the doctor the same thing supplied to doctor and the chart. Here is a number of total visit cranes by service type after stay at home order was issued across the U. S. By Ali April patient physical visits to doctor's offices or clinics declined by ALAN 70%. On the other hand, that share, or telehealth, accounted for 25% of the total total. Doctor's >>visit in April, while many states studied to re opening face to face visit is gradually recovering. And overall Tele Health Service did not offset the crime. Physician Physical doctor's visit and telehealth John never fully replace in person care. However, Telehealth has established a new way to provide affordable care, especially to vulnerable people, and I don't explain each player's today. But as an example, the chart shows the significant growth of >>the tell a dog who is one of the largest badger care and tell his provider, I believe there are three factors off paradox. Success under the pandemic. First, obviously tell Doc could reach >>the job between those patients and doctors. Majority of the patients who needed to see doctors who are those who have underlying health conditions and are high risk for Kelowna, Bilis and Secondary. They showed their business model is highly scalable. In the first quarter of this year, they moved quickly to expand their physical physicians network to increase their capacity and catch up growing demand. To some extent, they also contributed to create flexible job for the doctors who suffered from Lydia's appointment and surgery. They utilized. There are legalism to maximize the efficiency for doctors and doing so, uh, they have university maintained high quality care at affordable applies Yeah, and at the same time, the government recognize the body of about your care and de regulated traditional rules to sum up she m s temporary automated to pay a wide range of tell Her services, including hospital visit and HHS temporarily waived hip hop minorities for telehealth cases and they're changed allowed provider to use communication tools such as facetime and the messenger. During their appointment on August start, the government issued a new executive order to expand tell his services beyond the pandemic. So the government is also moving to support about your health care. So it was a quick review of the health care challenges and somewhat advancement in the pandemic. But as you understand, since those challenges are not caused by the pandemic, problems will stay remain and events off this year will continuously catalyze the transformation. So how was his cherished reshaped and where will we go? The topic from here can be also applied to Japan market. Okay, I believe democratization and decentralization healthcare more important than ever. So what does A. The traditional healthcare was defined in a framework over patient and a doctor. But in the new normal, the range of beneficiaries will be expanded from patient to all citizens, including the country uninsured people. Thanks to the technology evolution, as you can download health management off for free on iTunes stores while the range of the digital health services unable everyone to participate in new health system system. And in this slide, I put three essential element to fully realize democratization and decentralization off health care, health, literacy, data sharing and security, privacy and safety in addition, taken. In addition, technology is put at the bottom as a foundation off three point first. Health stimulus is obviously important because if people don't understand how the system works, what options are available to them or what are the pros and cons of each options? They can not navigate themselves and utilize the service. It can even cause a different disparity. Issue and secondary data must be technically flee to transfer. While it keeps interoperability ease. More options are becoming available to patient. But if data cannot be shared among stakeholders, including patient hospitals in strollers and budget your providers, patient data will be fragmented and people cannot yet continue to care which they benefited under current centralized care system. And this is most challenging part. But the last one is that the security aspect more players will involving decentralized health care outside of conventional healthcare system. So obviously, both the number of healthcare channels and our frequency of data sharing will increase more. It's create ah, higher data about no beauty, and so, under the new health care framework, we needed to ensure patient privacy and safety and also re examine a Scott write lines for sharing patient data and off course. Corbett Wasa Stone Catalyst off this you saved. But what folly. Our drivers in Macro and Micro Perspective from Mark Lowe. The challenges in healthcare system have been widely recognized for decades, and now he's a big pain. The pandemic reminded us all the key values. Misha, our current pain point as I left the church shores. Those are increasing the population, health sustainability for doctors and other social system and value based care for better and more affordable care. And all the elements are co dependent on each other. The light chart explained that providing preventive care and Alan Dimension is the best way threes to meet the key values here. Similarly, the direction of community based care and about your care is in line with thes three values, and they are acting to maximize the number of beneficiaries form. A micro uh, initiative by nonconventional players is a big driver, and both CBS and Walmart are being actively engaged in healthcare healthcare businesses for many years. And CBS has the largest walking clinic called MinuteClinic, Ottawa 1100 locations, and Walmart also has 20 primary clinics. I didn't talk to them. But the most interesting things off their recent innovation, I believe, is that they are adjusted and expanded their focus, from primary care to community health Center to out less to every every customer's needs. And CBS Front to provide affordable preventive health and chronic health monitoring services at 1500 CBS Health have, which they are now setting up and along a similar line would Mark is deploying Walmart Health Center, where, utilizing tech driven solutions, they provide affordable one stop service for core healthcare. They got less, uh, insurance status. For example, more than 40% of the people in U. S visit will not every big, so liberating the huge customer base and physical locations. Both companies being reading decentralization off health care and consumer device company such as Apple and Fitbit also have helped in transform forming healthcare in two ways. First, they are growing the boundaries between traditional healthcare and consumer product after their long development airport available, getting healthcare device and secondary. They acted as the best healthcare educators to consumers and increase people's healthcare awareness because they're taking an important role in the enhancement, health, literacy and healthcare democratization. And based on the story so far, I'd like to touch to business concept which can be applied to both Japan and the US and one expected change. It will be the emergence of data integration plot home while the telehealth. While the healthcare data data volume has increased 15 times for the last seven years and will continuously increase, we have a chance to improve the health care by harnessing the data. So meaning the new system, which unify the each patient data from multiple data sources and create 360 degrees longitudinal view each individual and then it sensitized the unified data to gain additional insights seen from structure data and unable to provide personal lives care. Finally, it's aggregate each individual data and reanalyzed to provide inside for population health. This is one specific model I envision. And, uh, health care will be provided slew online or offline and at the hospital or detail store. In order to amplify the impact of health care. The law off the mediator between health care between hospital and citizen will become more important. They can be a pharmacy toe health stand out about your care providers. They provide wide range of fundamental care and medication instruction and management. They also help individuals to manage their health care data. I will not explain the details today, but Japan has similar challenges in health care, such as increasing healthcare expenditure and lack of doctors and care givers. For example, they people in Japan have physical physician visit more than 20 times a year on average, while those in the U. S. On the do full times it sounds a joke, but people say because the artery are healthy, say visit hospitals to see friends. So we need to utilize thes mediators to reduce cost while they maintained social place for citizens in Japan, the government has promoted, uh, usual family, pharmacist and primary doctors and views the community based medical system as a policy. There was division of dispensing fees in Japan this year to ship the core load or pharmacist to the new role as a health management service providers. And so I believe we will see the change in those spaces not only in the U. S, but also in Japan, and we went through so unprecedented times. But I believe it's been resulting accelerating our healthcare transformation and creating a new business innovation. And this brings me to the end of my presentation. Thank you for your attention and hope you could find something somehow useful for your business. And if you have any questions >>or comments, please for you feel free to contact me. Thank you.

>>live from Stanford University. It's the queue covering Stanford women in Data Science 2020. Brought to you by Silicon Angle Media >>and welcome to the Cube. I'm your host Sonia category, and we're live at Stanford University, covering the fifth annual Woods Women in Data Science conference. Joining us today is Tilapia Williams, who's the associate professor of mathematics at Harvey Mudd College and host of Nova Wonders at PBS to leave a welcome to the Cappy to be here. Thanks for having me. So you have a lot of rules. So let's first tell us about being an associate professor at Harvey Mudd. >>Yeah, I've been at Harvey Mudd now for 11 years, so it's been really a lot of fun in the math department, but I'm a statistician by training, so I teach a lot of courses and statistics and data science and things like that. >>Very cool. And you're also a host of API s show called Novo Wonders. >>Yeah, that came about a couple of years ago. Folks at PBS reached out they had seen my Ted talk, and they said, Hey, it looks like you could be fund host of this science documentary shows So, Nova Wonders, is a six episode Siri's. It kind of takes viewers on a journey of what the cutting edge questions and science are. Um, so I got to host the show with a couple other co host and really think about like, you know, what are what are the animals saying? And so we've got some really fun episodes to do. What's the universe made of? Was one of them what's living inside of us. That was definitely a gross win. Todo figure out all the different micro organisms that live inside our body. So, yeah, it's been funded in hopes that show as well. >>And you talk about data science and AI and all that stuff on >>Yeah. Oh, yeah, yeah, one of the episodes. Can we build a Brain was dealt with a lot of data, big data and artificial intelligence, and you know, how good can we get? How good can computers get and really sort of compared to what we see in the movies? We're a long way away from that, but it seems like you know we're getting better every year, building technology that is truly intelligent, >>and you gave a talk today about mining for your own personal data. So give us some highlights from your talk. Yeah, >>so that talks sort of stemmed out of the Ted talk that I gave on owning your body's data. And it's really challenging people to think about how they can use data that they collect about their bodies to help make better health decisions on DSO ways that you can use, like your temperature data or your heart rate. Dina. Or what is data say over time? What does it say about your body's health and really challenging the audience to get excited about looking at that data? We have so many devices that collect data automatically for us, and often we don't pause on enough to actually look at that historical data. And so that was what the talk was about today, like, here's what you can find when you actually sit down and look at that data. >>What's the most important data you think people should be collecting about themselves? >>Well, definitely not. Your weight is. I don't >>want to know what that >>is. Um, it depends, you know, I think for women who are in the fertile years of life taking your daily waking temperature can tell you when your body's fertile. When you're ovulating, it can. So that information could give women during that time period really critical information. But in general, I think it's just a matter of being aware of of how your body is changing. So for some people, maybe it's your blood pressure or your blood sugar. You have high blood pressure or high blood sugar. Those things become really critical to keep an eye on. And, um, and I really encourage people whatever data they take, too, the active in the understanding of an interpretation of the data. It's not like if you take this data, you'll be healthy radio. You live to 100. It's really a matter of challenging people to own the data that they have and get excited about understanding the data that they are taking. So >>absolutely put putting people in charge of their >>own bodies. That's >>right. >>And actually speaking about that in your Ted talk, you mentioned how you were. Your doctor told you to have a C section and you looked at the data and he said, No, I'm gonna have this baby naturally. So tell us more about that. >>Yes, you should always listen to your medical pressures. But in this case, I will say that it was It was definitely more of a dialogue. And so I wasn't just sort of trying to lean on the fact that, like, I have a PhD in statistics and I know data, he was really kind of objectively with the on call doctor at the time, looking at the data >>and talking about it. >>And this doctor was this is his first time seeing me. And so I think it would have been different had my personal midwife or my doctor been telling me that. But this person would have only looked at this one chart and was it was making a decision without thinking about my historical data. And so I tried to bring that to the conversation and say, like, let me tell you more about you know, my body and this is pregnancy number three like, here's how my body works. And I think this person in particular just wasn't really hearing any of that. It was like, Here's my advice. We just need to do this. I'm like, >>Oh, >>you know, and so is gently as possible. I tried to really share that data. Um, and then it got to the point where it was sort of like either you're gonna do what I say or you're gonna have to sign a waiver. And we were like, Well, to sign the waiver that cost quite a buzz in the hospital that day. But we came back and had a very successful labor and delivery. And so, yeah, >>I think >>that at the time, >>But, >>you know, with that caveat that you should listen to what, your doctors >>Yeah. I mean, there's really interesting, like, what's the boundary between, Like what the numbers tell you and what professional >>tells me Because I don't have an MD. Right. And so, you know, I'm cautious not to overstep that, but I felt like in that case, the doctor wasn't really even considering the data that I was bringing. Um, I was we were actually induced with our first son, but again, that was more of a conversation, more of a dialogue. Here's what's happening here is what we're concerned about and the data to really back it up. And so I felt like in that case, like Yeah, I'm happy to go with your suggestion, but I could number three. It was just like, No, this isn't really >>great. Um, so you also wrote a book called Power In Numbers. The Rebel Women of Mathematics. So what inspired you to write this book? And what do you hope readers take away from it? >>A couple different things. I remember when I saw the movie hidden figures. And, um, I spent three summers at NASA working at JPL, the Jet Propulsion Laboratory. And so I had this very fun connection toe, you know, having worked at NASA. And, um, when this movie came out and I'm sitting there watching it and I'm, like ball in just crying, like I didn't know that there were black women who worked at NASA like, before me, you know, um and so it felt it felt it was just so transformative for me to see these stories just sort of unfold. And I thought, like, Well, why didn't I learn about these women growing up? Like imagine, Had I known about Katherine Johnsons of the world? Maybe that would have really inspired Not just me, but, you know, thinking of all the women of color who aren't in mathematics or who don't see themselves working at at NASA. And so for me, the book was really a way to leave that legacy to the generation that's coming up and say, like, there have been women who've done mathematics, um, and statistics and data science for years, and they're women who are doing it now. So a lot of the about 1/3 of the book are women who were still here and, like, active in the field and doing great things. And so I really wanted to highlight sort of where we've been, where we've been, but also where we're going and the amazing women that are doing work in it. And it's very visual. So some things like, Oh my gosh, >>women in math >>It is really like a very picturesque book of showing this beautiful images of the women and their mathematics and their work. And yes, I'm really proud of it. >>That's awesome. And even though there is like greater diversity now in the tech industry, there's still very few African American women, especially who are part of this industry. So what advice would you give to those women who who feel like they don't belong. >>Yeah, well, a they really do belong. Um, and I think it's also incumbent of people in the industry to sort of recognize ways that they could be advocate for women, and especially for women of color, because often it takes someone who's already at the table to invite other people to the table. And I can't just walk up like move over, get out the way I'm here now. But really being thoughtful about who's not representative, how do we get those voices here? And so I think the onus is often mawr on. People who occupy those spaces are ready to think about how they can be more intentional in bringing diversity in other spaces >>and going back to your talk a little bit. Um uh, how how should people use their data? >>Yeah, so I mean, I think, um, the ways that we've used our data, um, have been to change our lifestyle practices. And so, for example, when I first got a Fitbit, um, it wasn't really that I was like, Oh, I have a goal. It was just like I want something to keep track of my steps And then I look at him and I feel like, Oh, gosh, I didn't even do anything today. And so I think having sort of even that baseline data gave me a place to say, Okay, let me see if I hit 10 stuff, you know, 10,000 >>steps in a day or >>and so, in some ways, having the data allows you to set goals. Some people come in knowing, like, I've got this goal. I want to hit it. But for me, it was just sort of like, um and so I think that's also how I've started to use additional data. So when I take my heart rate data or my pulse, I'm really trying to see if I can get lower than how it was before. So the push is really like, how is my exercise and my diet changing so that I can bring my resting heart rate down? And so having the data gives me a gold up, restore it, and it also gives me that historical information to see like, Oh, this is how far I've come. Like I can't stop there, you know, >>that's a great social impact. >>That's right. Yeah, absolutely. >>and, um, Do you think that so in terms of, like, a security and privacy point of view, like if you're recording all your personal data on these devices, how do you navigate that? >>Yeah, that's a tough one. I mean, because you are giving up that data privacy. Um, I usually make sure that the data that I'm allowing access to this sort of data that I wouldn't care if it got published on the cover of you know, the New York Times. Maybe I wouldn't want everyone to see what my weight is, but, um, and so in some ways, while it is my personal data, there's something that's a bit abstract from it. Like it could be anyone's data as opposed to, say, my DNA. Like I'm not going to do a DNA test. You know, I don't want my data to be mapped it out there for the world. Um, but I think that that's increasingly become a concern because people are giving access to of their information to different companies. It's not clear how companies would use that information, so if they're using my data to build a product will make a product better. You know we don't see any world from that way. We don't have the benefit of it, but they have access to our data. And so I think in terms of data, privacy and data ethics, there's a huge conversation to have around that. We're only kind >>of at the beginning of understanding what that is. Yeah, >>well, thank you so much for being on the Cube. Really having you here. Thank you. Thanks. So I'm Sonia to Gary. Thanks so much for watching the cube and stay tuned for more. Yeah, yeah, yeah.

>> Narrator: Live from San Francisco It's theCUBE. Covering RSA Conference 2020, San Francisco. Brought to you by Silicon Angled Media >> Hi everyone, welcome to theCUBE's coverage here at RSA Conference 2020. I'm John Furrier, host of theCUBE We're on the floor getting all the data, sharing it with you here, Cube coverage. Got the best new generation shift happening as cloud computing goes to the whole other level. Multi-cloud, hybrid cloud changing the game. You're seeing the companies transition from an on-premises to cloud architecture. This is forcing all the companies to change. So a new generation of security is here and we've got a great guest, so a hot start-up. Masha Sedova, co-founder of Elevate Security. Welcome to theCUBE, thanks for joining us. >> Thank you so much for having me, John. >> So the next generation in what will be a multi-generational security paradigm, is kind of happening right now with the beginning of, we're seeing the transition, Palo Alto Networks announced earnings yesterday down 13% after hours because of the shift to the cloud. Now I think they're going to do well, they're well positioned, but it highlights this next generation security. You guys are a hot start-up, Elevate Security. What is the sea change? What is going on with security? What is this next generation paradigm about? >> Yeah, so it's interesting that you talk about this as next generation. In some ways, I see this as a two-prong move between, yes, we're moving more into the cloud but we're also going back to our roots. We're figuring out how to do asset management right, we're figuring out how to do patching right, and for the first time, we're figuring how to do the human element right. And that's what where we come in. >> You know, the disruption of these new shifts, it also kind of hits like this, the old expression, 'same wine, new bottle', all this, but it's a data problem. Security has always been a data problem, and we've seen some learnings around data. Visualization, wrangling, there's a lot of best practices around there. You guys are trying to change the security paradigm by incorporating a data-centric view with changing the behavior of the humans and the machines and kind of making it easier to manage. Could you share what you guys are doing? What's the vision for Elevate? >> Yeah, so we believe and we've seen, from our experience being practitioners, you can't change what you can't measure. If you don't have visibility, you don't know where you're going. And that's probably been one of the biggest pain-point in the security awareness space traditionally. We just roll out training and hope it works. And it doesn't, which is why human error is a huge source of our breaches. But we keep rolling out the same one-size fits all approach without wanting to measure or, being able to. So, we've decided to turn the problem on its head and we use existing data sets that most organizations who have a baseline level of maturity already have in place. Your end point protections, your DLP solutions, your proxies, your email security gateways and using that to understand what your employees are doing on the network to see if user generated incidents are getting better over time or getting worse. And using that as the instrumentation and the level of visibility into understanding how you should be orchestrating your program in this space. >> You know, that's a great point. I was just having a conversation last night at one of the cocktail parties here around RSA and we were debating on, we talk about the kind of breaches, you mentioned breaches, well there's the pure breach where I'm going to attack and penetrate the well fortified network. But then there's just human error, an S3 bucket laying open or some configuration problem. I guess it's not really a breach, it's kind of an open door so the kind of notion of a breach is multifold. How do you see that, because again, human error, insider threats or human error, these are enabling the hackers. >> Yeah >> This is not new. >> Yeah. >> How bad is the problem? >> It depends on what report you read. The biggest number I've seen so far is something like 95% of breaches have human error. But I honestly, I couldn't tell you what the 5% that don't include it because if you go far enough back, it's because a patch wasn't applied and there is a human being involved there because there is vulnerability in code, that's probably a secure coding practice when you're a development organization. Maybe it's a process that wasn't followed or even created in the first place. There's a human being at the core of every one of these breaches and, it needs to be addressed as holistically as our technologies and our processes right now in the space. >> The evolution of human intelligence augmented by machines will certainly help. >> That's it, yeah. >> I mean, I've got to ask you, obviously you're well-funded. Costanova Ventures well known in the enterprise space, Greg Sands and the team there, really strong, but you guys entered the market, why? I mean you guys, you and your founder both at Salesforce.com. Salesforce gurus doing a lot of work there. Obviously you've seen the large scale, first wave of the cloud. >> Yeah >> Why do the start-up? What was the problem statement you guys were going after? >> So, my co-founder and I both came from the world of being practitioners and we saw how limited the space was and actually changing human behavior, I was given some animated PowerPoints, said use this to keep the Russians out of your network, which is a practical joke unless your job is on the line, so I took a huge step back and I said, there are other fields that have figured this out. Behavioral science being one of them, they use positive reinforcement, gamification, marketing and advertisements have figured out how to engage the human element, just look around the RSA floor, and there's so many learnings of how we make decisions as human beings that can be applied into changing people's behaviors in security. So that's what we did. >> And what was the behavior you're trying to change? >> Yeah, so the top one's always that our attackers are getting into organizations, so, reducing phishing click-throughs an obvious one, increasing reporting rates, reducing malware infection rates, improving sensitive data handling, all of which have ties back to, as I was mentioning earlier, security data sources. So, we get to map those and use that data to then drive behavior change that's rooted in concepts like social proof, how are you doing compared to your peers? We make dinner decisions on that and Amazon buying decisions on that, why not influence security like that? >> So building some intelligence into the system, is there a particular market you're targeting? I mean, here people like to talk in segments, is there a certain market that you guys are targeting? >> Yeah, so the amazing thing about this is, and probably no surprise, the human element is a ubiquitous problem. We are in over a dozen different industries and we've seen this approach work across all of those industries because human beings make the same mistakes, no matter what kind of company they're in. We really work well with larger enterprises. We work well with larger enterprises because they tend to have the data sets that really provides insights into human behavior. >> And what's the business model you guys envision happening with your service product? >> We sell to enterprises and security, the CISO and the package as a whole, gives them the tools to have the voice internally in their organization We sell to Fortune 1000 companies, >> So it's a SAAS service? >> Yeah, SAAS service, yeah. >> And so what's the technology secret sauce? (laughing) >> Um, that's a great question but really, our expertise is understanding what information people need at what time and under what circumstances, that best changes their behavior. So we really are content diagnostic, we are much more about the engine that understands what content needs to be presented to whom and why. So that everyone is getting only the information they need, they understand why they need it and they don't need anything extra-superfluous to their... >> Okay, so I was saying on theCUBE, my last event was at, CIO's can have good days and bad days. They have good days, CISOs really have good days, many will say bad days, >> Masha: Yeah, it's a hard job. >> So how do I know I need the Elevate Solution? What problem do I have, what's in it for me? What do I get out of it? When do I know when to engage with you guys? >> I take a look at how many user generated incidents your (mumbles) responding to, and I would imagine it is a large majority of them. We've seen, while we were working at Salesforce and across our current customers, close to a 40% reduction rate in user generated incidents, which clearly correlates to time spent on much more useful things than cleaning up mistakes. It's also one of the biggest ROI's you can get for the cheapest investment. By investing a little bit in your organization now, the impact you have in your culture and investing in the future decision, the future mistakes that never get made, are actually untold, the benefit of that is untold. >> So you're really kind of coming in as a holistic, kind of a security data plane if you will, aggregating the data points, making a visualization in human component. >> You've got it. >> Now, what's the human touchpoint? Is it a dashboard? Is it notifications? Personalization? How is the benefit rendered for the customer? >> So we give security teams and CSOs a dashboard that maps their organization's strengths and weaknesses. But for every employee, we give personalized, tailored feedback. Right now it shows up in an email that they get on an ongoing basis. We also have one that we tailor for executives, so the executive gets one for their department and we create an executive leaderboard that compares their performance to fellow peers and I'll tell you, execs love to win, so we've seen immense change from that move alone. >> Well, impressive pedigree on your entrepreneurial background, I see Salesforce has really kind of, I consider real first generation cloud before cloud actually happened, and there's a lot of learn, it was always an Apple case, now it's AWS, but it's it's own cloud as we all know, what are the learnings that you saw from Salesforce that you said hey, I'm going to connect those dots to the new opportunity? What's the real key there? >> So, I had two major aha's that I've been sharing with my work since. One, it's not what people know, but it's what they do that matters, and if you can sit with a moment and think about that, you realize it's not more training, because people might actually know the information, but they just choose not to do it. How many people smoke, and they still know it kills them? They think that it doesn't apply to them, same thing with security. I know what I need to do, I'm just not incentivized to do it, so there's a huge motivation factor that needs to be addressed. That's one thing that I don't see a lot of other players on the market doing and one thing we just really wanted to do as well. >> So it sounds like you guys are providing a vision around using sheet learning and AI and data synthesis wrangling and all that good stuff, to be an assistant, a personal assistant to security folks, because it sounds like you're trying to make their life easier, make better decisions. Sounds like you guys are trying to distract away all these signals, >> You're right. >> See what to pay attention to. >> And make it more relevant, yeah. Well think about what Fitbit did for your own personal fitness. It curates a personal relationship based on a whole bunch of data. How you're doing, goals you've set, and all of a sudden, a couple of miles walk leads to an immense lifestyle change. Same thing with security, yeah. >> That's interesting, I love the Fitbit analogy because if you think about the digital ecosystem of an enterprise, it used to be siloed, IT driven, now with digital, everything's connected so technically, you're instrumenting a lot of things for everything. >> Yeah. >> So the question's not so much instrumentation, it's what's happening when and contextually why. >> That's it, why, that's exactly it. Yeah, you totally got it. >> Okay. I got it. >> Yeah, I can see the light bulb. >> Okay, aha, ding ding. All right, so back to the customer pain point. You mentioned some data points around KPI's that they might or things that they might want to call you so it's incidents, what kind of incidents? When do I know I need to get you involved? Will you repeat those again? >> There's two places where it's a great time to involve. Now, because of the human element is, or think about this as an investment. If you do non-investor security culture, one way or another, you have security culture. It's either hurting you or it's helping you and by hurting you, people are choosing to forego investing security processes or secure cultures and you are just increasing your security debt. By stepping in to address that now, you are actually paying it forward. The second best time, is after you realize you should have done that. Post-breaches or post incidents, is a really great time to come in and look at your culture because people are willing to suspend their beliefs of what good behavior looks like, what's acceptable and when you look at an organization and their culture, it is most valuable after a time of crisis, public or otherwise, and that is a really great time to consider it. >> I think that human error is a huge thing, whether it's as trivial as leaving an S3 bucket open or whatever, I think it's going to get more acute with service meshes and cloud-native microservices. It's going to get much more dynamic and sometimes services can be stood up and torn down without any human knowledge, so there's a lot of blind spots potentially. This brings up the question of how does the collaboration piece, because one of the things about the security industry is, it's a community. Sharing data's important, having access to data, how do you think about that as the founder of a start-up that has a 20 mile steer to the future around data access, data diversity, blind spots, how do you look at that and how do you advise your clients to think about that? >> I've always been really pro data sharing. I think it's one of the things that has held us back as an industry, we're very siloed in this space, especially as it relates to human behavior. I have no idea, as a regular CISO of a company, if I am doing enough to protect my employees, is my phishing click (mumbles), are my malware download rates above normal, below or should I invest more, am I doing enough? How do I do compared to my peers and without sharing industry stats, we have no idea if we're investing enough or quite honestly, not enough in this space. And the second thing is, what are approaches that are most effective? So let's say I have a malware infection problem, which approach, is it this training? Is it a communication? Is it positive reinforcement, is it punishment? What is the most effective to leverage this type of output? What's the input output relation? And we're real excited to have shared data with Horizon Data Breach Report for the first time this year, to start giving back to the communities, specifically to help answer some of these questions. >> Well, I think you're onto something with this behavioral science intersection with human behavior and executive around security practices. I think it's going to be an awesome, thanks for sharing the insights, Miss Masha on theCUBE here. A quick plug for your company, (mumbles) you're funded, Series A funding, take us through the stats, you're hiring what kind of positions, give a plug to the company. >> So, Elevate Security, we're three years old. We have raised ten million to date. We're based in both Berkeley and Montreal and we're hiring sales reps on the west coast, a security product manager and any engineering talent really focused on building an awesome data warehouse infrastructure. So, please check out our website, www.elevatesecurity.com/careers for jobs. >> Two hot engineering markets, Berkeley I see poaching out of Cal, and also Montreal, >> Montreal, McGill and Monterey. >> You got that whole top belt of computer science up in Canada. >> Yeah. >> Well, congratulations. Thanks for coming on theCUBE, sharing your story. >> Thank you. >> Security kind of giving the next generation all kinds of new opportunities to make security better. Some CUBE coverage here in San Francisco, at the Moscone Center. I'm John Furrier, we'll be right back after this break. (upbeat music)

>>Live from Orlando, Florida. It's the cube covering Microsoft ignite brought to you by Cohesity. >>Welcome back everyone. We are wrapping up three at days of wall to wall coverage of Microsoft ignite. It is a game day atmosphere on the show floor at the orange County civic center. Thank you so much to Cohesity for hosting the cube for this fantastic three days. I'm your host, Rebecca Knight along with my co host Stu Miniman. Still this is awesome. We talk about the buzz on the floor and the energy on the show and definitely guy here Cohesity always bright and activity >>in the booth and it's been a lot of fun hanging out here for the week with you Rebecca and our hosts and all, all of the guests. Yes, absolutely. So this is day three. We are starting our series of interviews, but I want to hear because you are so in this community you have a lot of connections, a lot of buddies, a lot of colleagues, former colleagues, current colleagues. What has impressed you about the show and what is missing? Let's start with the positives and it's interesting because this is only my second year coming. One of those, you know, my background networking, I've interacted with Microsoft for most of my career. I would not say I am deep in the community, but I know enough of the MVPs, have friends here and really have learned a lot in these two years. So first of all, the breadth of this show is just so impressive. >>One of the things that you and I've been talking about the last two years, years, what is the show? It started out as a windows admin show. Lot of discussion about office migration to windows 10 was the big thing last year. We haven't heard as much about this this year. Yesterday was a big developer day. Of course Azure sits at the center of everything. Lots of big announcements here. Felt like a kind of on par with what we hear at AWS. It shows with just so many announcements across the board. But really when you talk about the applications of business productivity, people come to this show. When I talk to people in the booth, I'm looking for solutions and how do I put those together? It's not some of the tech shows where you just, you're constantly down in the speeds and feeds and what they're doing and some of the competitive dynamics. >>I have a problem, my business needs something in, this is what I'm looking to solve. And Microsoft has a broad and diverse ecosystem and the word we kept coming back to the word of the week I think is of course trust. >> Absolutely. I couldn't agree more with what you've just said. That is what we hear. And the other thing about Microsoft is that at a time when big tech is really under a lot of fire, there's a lot of suspicion policymakers, regulators are bearing down on a lot of the tech CEOs. Microsoft really stands above. And when you think about antitrust, there's major presidential candidates talking about breaking up big Chuck, big tech. Microsoft is really riding above that fray. There's sort of a feeling of deja VU for Microsoft, I'm sure. But that they're really been there, done that. They're not. Yeah, I mean it was Satya Nadella to, you know, really put a pointed attack. >>He did not say it, but we all know it's Google. You know the company that was do no evil at the start. Now everybody's concerned because Google's model is primarily selling ads and while Google will say what they're doing in the enterprise, they just acquired Fitbit and said, you're not going to get ads on your Fitbit. We're not going to leverage that way, but there's not that trust built up. And then the number one competitor out there is AWS. And if you talk about the ecosystem, the concern that every AWS show is, Oh my gosh, what announcements are Amazon going to make and are they going to steal my lunch money if you were or put me out of business for the years worth of work on doing. Microsoft doesn't feel that way. They, you know, if you talk about the ecosystem I was talking, they made announcements that do compete against number the products, RPA, or was announced as part of the power platform out there. >>There's a number of RPA companies here. I talked to them there. Microsoft's a strong partner. We've been doing breakouts, we're talking with them. Yes, they are just like SAP getting into this market, but it's a Microsoft shop and it's not, you know, it is new. It's not the best of breed. They're on it. They are not concerned that they can still live in this environment. And I'd say both AWS and Azure very much about choice and ecosystem and building them out. >> So you're talking about the marketplace here. So in terms of the marketplace, what is Microsoft doing to drive business and is it effective? Well actually I'm glad you, so specifically we talk about the marketplace. So there's the ecosystem and then there's actually the marketplace. So AWS has what we really consider, it's the enterprise app store. If I want to go buy software, you know there's Salesforce and all of their connectors and everyone that uses Salesforce knows that. >>But AWS really has driven a robust ecosystem just like on amazon.com most of the products that are sold are from third parties. The AWS marketplace is mostly how I can procure and buy software. And they drive a lot of it. So a lot of the AWS adoption is through the marketplace and the ecosystem makes lots of dollars. Reminds me, we used to talk about VMware for years is for every dollar of VMware you bought you would buy, you know, 10 $20 worth of third party ecosystem. But we were talking about things like storage and like for AWS it's on procuring software and underneath on leveraging the AWS services. While Microsoft Azure has a marketplace, it is not as mature. They don't really push as many people through it. So while I've talked to a number of the partners that are, yes we're part of the marketplace, but people buy lots of different ways as opposed to AWS is trying to get everybody from a customer and an ecosystem through it. >>And part of that is to simplify the environment, how I purchase it. But it's that balance of trust and you know, ease of use out there. So when I look forward, what do I like to see from Azure is how will they mature there. I was actually something John furrier had had us digging into here and the marketplace at Azure definitely is, I would say years behind where AWS is, is there, but you know, Azure great growth, doing really well, a strong trusted ecosystem. Just some areas for improvement that I would look for going forward. >>But maybe that's part of their, their approach and their strategy is we'll work with you, we, we collaborate, we can do this together. Whereas AWS there is that, that feeling sometimes when you're at reinvent, as you said, roll out the beer, CURT's early please. My business is over. So, so, so comparing the two show, the three, the various cloud shows, and this is not just a cloud show, of course we're going to get into that more. But when you think about re-invent and you think about VM world, how does the, the feel and the energy here differ? >>Yeah. So the thing that always strikes me when I go to an AWS show, and I have been to many of them from the regional shows through the big one and reinvent, which is more than twice the size of this 26,000 person show. The customers there are always trying new things. They are open and looking for the environment that they can do new things. Here what we're talking about here feels like it's like a tweener. We had a lot of conversations about building bridges to where customers are while AWS is starting to talk hybrid more and meet you in your data center and doing outpost Microsoft, they have their windows install base, they have their own three 65 pieces. So there's a broad spectrum of from the latest and greatest autonomous systems. You want to talk about it. Microsoft has that through, you know, I'm a, you know, 20 year CIS admin and I, you know, I'm going to hold on to, you know, my servers, you know, as long as I can, they're there for you. >>So Microsoft does bam, that gamut and VMware is more, once again making that transition as we go to the cloud. So Microsoft right in the middle of that transition, we talked a bunch about digital transformation with the customers on here. So it really, it has all a lot for a lot of different people. You know is one of the things I've heard is they really ramped up some of the developer activity at this show. They just bought get hub, get hub, has their own show, get hub universe next week, which will stay very focused on that environment. But Microsoft also has a conference build and there's been some rumblings that maybe build an ignite get wrapped together. We saw that with IBM. IBM had lots of different shows and they put all the wood behind think and made that a massive show. There's pros and cons of that, seeing lots of companies that have taken a big show and put it into a 40 show around the globe. >>Now someone like Amazon has reinvented, but then they have of second tier and third tier regional shows to push that out. So lots of different ways to, to get to customers. Um, and it is interesting, you know, we spent a lot of time talking about Azure Ark. I'll be at the cube con cloud-native clown show in just two weeks and San Diego and expect that to be talked. And really it is in preview mode. So when I look at it at the end of the day is, you know, you've got red hat open shift, you have Google, you have what AWS is doing with outpost and welcome to the party. Microsoft, they have got a strong hybrid solution already because they played at both ends. But really as your arc is unifying and pulling those together so that it's not just my data center and Azure, but even AWS, they're saying, we'll see how this all plays out. >>Microsoft definitely has a strong data focus and a strong application focus. And so it be interesting to see where that adoption happens. I've been saying for a couple of weeks. Really Kubernetes just get baked in everywhere and you know, customers aren't going to have to think about it in a most Microsoft definitely strong partner focus. Just to reinforce something I've said a couple times this week, they still have a partnership with red hat. They still have a partnership with VMware. The Azure arc is not the only way to get the Kubernetes story in play into your Microsoft environment. And Microsoft's done well with that. We all know from the early days of Microsoft living on tops of lots of hardware. Now Microsoft software will live a lot of places. Yes, their cloud is large growing one of the top two choices out there. But they truly embrace that it will be multi-cloud and be able to live in lots of environments. >>So I want to talk about something that's more in my wheel, hasn't met his productivity. So we have heard a little bit about teams. I mean there was a lot of announcements. It's not exactly where we focused a lot here on the cube this week, but there were some really interesting announcements about the ways in which Microsoft is thinking about human productivity, both at individual productivity and team collaboration, the way teams interact and communicate. There are a lot of interesting new uh, characteristics and elements to what they're doing in terms of Cortana re read me my emails. Uh, I'm going to send this email but I'm actually gonna wait, it's good. It's going to be a scheduled send. It's going to send when the, when the, the person I'm sending it to is, is actually at his or her desk. Um, and so those are just some interesting things to me that really speak volumes about how Microsoft views the future of work and views the, the future of our, of our lives. And, and, and understanding how much technology has encroached in our lives because they're saying, read me my emails while I take my dog for a walk while I am actually doing, while I'm on a run first thing in the morning. I, you know, make me more productive but also give me my time back. And so I think those are some really, really interesting ways in which Microsoft, as I said, understands the technology has taken over and they're trying to give you a bit of your time back. >>It's interesting cause you know when I look back, Microsoft has a bit of a checkered history when it comes to some of those environments. We all know the office suite teams is now part of O three 65 and I hear very strong. The people that use it really do like it. But those of us look back and we said, Oh I used to like using Skype and then Microsoft got ahold of it and Oh my gosh, what a horrendous mess. Skype was for a long time when it taught to a collaborative environment. Google really jumped Microsoft with the G suite and many smaller companies were like, Oh, it's relatively easy to use and I can collaborate there. Well teams really has gone through and understand that and we talk about a collaborative environment, you know, Microsoft teams, best of breeds. I attended an enterprise connect earlier this year and I couldn't hear enough about how much that was going on. >>And you know, strong ecosystem of companies that Microsoft worked with. So it's very strong, but it's kind of, if you're a Microsoft shop, you're doing it. But they did lose many companies too free or less expensive or lighter weight options out there. And then everything from Slack ate into it. But you know, Microsoft has a good product. Absolutely. It just, some of it is the perception and some of it is the pricing. You know, they do a good job of making sure that when you get get to college, you, you want to use some of these environments. Oh yeah, the pricing is graded free. But then when you get in the real world, hopefully you'll like it. So Microsoft does a little bit about now something we focused a lot on but did hear really good things about it. And it does get lost a little bit in some of the general discussion about all the other pieces, you know, autonomous systems, AI and the leaders. This stuff of Azure take a little bit of precedent over the, some of the things that are a little bit more on just as you said, business productivity or even on the consumer side of the house for Microsoft. >>So we are, we're, we're wrapping up here but I want to hear just final thoughts, final predictions for 2020 and you've really gotten, you've, we've, we've covered a lot of ground here, this wording, but I'm interested to hear what you think is on tap for Microsoft in 2020 I'll bring >>back to something we kicked off with the jet ideal coming in here really has that that whole process of winning that bid was a fortune function from Microsoft to rapidly mature some of their environment. You talk about security and trust, you know the government is not going to give that environment if it to Microsoft, if they could not trust them. Back when AWS won a CIA deal, it was like, Oh wait, if the security is good enough for the CIA, it's probably good enough for me to consider it. So the government agencies, which historically is not who you think about when you talk about innovation in driving change today. Public sector is really interesting. Even when we were talking to some of the people about, Hey, how can we haven't heard as much about Azure stack over the years? Well, it's been a lot of service providers and government agencies that have been deploying this and therefore we'll do it. So Microsoft still has a lot of work to do contracts. They still have to get some more security clearances. They need to make sure their performance and reliability is up to snuff on because they just can't have outages. If I, if this becomes a greater and greater piece of my overall how I run my business, I can't say, oops, wait, you know the Internet's down. This is now 2019 going into 2020 and in 2020 we'll all have perfect. >>Oh, of course. Oh yes indeed. Sue, I'm looking forward to another great day of coverage with you, and thank you again to Cohesity for hosting us in this really cool booth. Uh, so please stay tuned for more of the cubes live coverage of Microsoft ignite coming up in just a little bit.

>> Live from San Francisco, it's theCUBE! Covering the RSA Conference 2019. Brought to you by ForeScout. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We're at RSA at Moscone at downtown San Francisco. We're in the ForeScout booth, our first time in the ForeScout booth, we're really excited to be here and we're talking about cyber security, I don't know what the official number is this year, probably 45 thousand professionals walkin' around, talkin' about security. And we've got our next guest on, he is Russell Jones, partner on cyber risk services for Deloitte. Russell, great to meet you! >> Same to meet you as well. >> So, I asked him before we turned on, what's getting you excited these days and he said, everything! So, this is a crazy busy space. What have you been working on lately, what's kind of your take away from the first couple days at the show? >> Yeah, it is a crazy, busy space and if you look at the cyber landscape, everything's moving at the speed of the internet, so it's this cat and mouse game in terms of attackers trying to find new ways to get into systems that is driving the industry. When you talk about health care though, the issue is these systems, like medical devices, often times are connected to people. >> Right. >> And so, the implications of a hack against, let's say, a MRI machine or a fusion pump, could be devastating to an actual person connected to it. And that's really what's driving a lot of innovation in terms of some of the technologies you see, like ForeScout, and also, a lot of what's going on from a regulatory perspective, and also the hospitals and the health care system themselves. >> Right. >> Trying to solve that problem, managing cyber risk as it relates to clinical technology. >> And a lot of that stuff wasn't connected before, right? There weren't IP addresses on every MRI machine or all these pump machines or, you know, you have a pacemaker, all these things. How are they looking at kind of the risk reward from a connected device that gives you all kinds of benefits-- >> Yeah. >> but it does open up this attack surface that previously had maybe an air gap there? >> That's a great point, bottom line is the life saving, life extending attributes of these medical technologies and medical devices far outweighs the risk of cyber, however, we got to be smart about managing that risk. So, we're going to see more connectivity, not less. Train's left the station, in terms of what's coming and in the future of the healthcare, connecting more of, not only the medical devices, but the information in them and being able to share that and then bring it together and aggregate it in ways that, you know, with analytics on top of it allows doctors and researchers in the clinical community to connect dots in ways that solve cancer, solve some different maladies that have plagued us forever. >> Right. >> So I think, on the one hand, it's great, this connectivity is extending healthcare out to people in rural locations and it's also bringing together a lot of different data from everything from your Fitbit to your pacemaker to apps that you have on your phone in a way that's going to benefit us. >> Right, right, so, one of the things about healthcare is they're way out in front of, kind of, not healthcare in terms of regulations. >> Yeah. >> You know, and HIPAA's been around for a long time, GDPR just went into place in Europe last year, so when you look at it from a regulatory environment, which people have to consider, there's not only the complexity of the machines, there's not only the complexity of the security, but you also have regulatory environment. >> Yeah. >> How is the cyber security in healthcare, with their very unique regulations, kind of impacting the way people should think about the problem, the way they should implement solutions? >> That's a good question, I think we've thought about, in the cyber community, forever. We talk about confidentiality, integrity, availability, right, the triangle. When you think about healthcare and clinical technology and medical devices, you need to flip that triangle upside down and the focus is integrity and availability, those things together equal patient safety. So, in other words, as we're connecting more of these devices to each other, to electronic health record systems, to the cloud, the integrity of the information in there, which is being used by doctors and other folks to make decisions about treatment, about surgical procedures, about medicines, it's crucial that that information and the integrity of it is maintained. And then the availability of the device is critical, right? If you're going in to get an MRI and it's down because it's been hacked, there's usually not a spare MRI and so there's a profound impact for patients that are scheduled back to back to back to back to go get that procedure, that MRI that's going to be used by a doctor to do some surgery or some other kind of a treatment plan >> Right. >> So integrity and availability are huge in the cyber world. And, if you look at the regulations, depending on which one we're talking and which part of the world, right? You mentioned HIPAA, we've got security and privacy, you've got GDPR, you've got the FDA that have guidance around what they want the manufacturers to do, building security into the devices. >> Right. >> They all have an impact on cyber and how it's going to be addressed, how we're going to manage cyber risk in the healthcare world. >> Right. >> In that environment. >> And then there's this whole new thing, I went to the Wall Street Journal Health Conference a couple weeks back, I don't know if you were there, but there was two people up where you now you can take your genetic footprint, right? >> Yeah. >> You can take your 23andMe results and after you figure out where your family's from, you can actually sell it back into a research market-- >> Yeah. >> so that doctors and clinicians and people doing trials on new drugs can now take your data in kind of a marketplace, back into a whole nother application so it's kind of outside of the core healthcare system, if you will. >> That's right. >> But I mean, it's basically, it's me, right? (laughs) In the form of my DNA footprint. >> Yup. >> It's crazy, crazy amounts of strange data that now is potentially exposed to a hack. >> That's right, and so the implications there, obviously, privacy, right? That's a huge issue, I think, that we're going to have to address and that's why you see GDPR and that's why you see the California Consumer Privacy Act. >> Right. >> There's a recognition that, again, the train's left the station, there's a lot of good things that come out of sharing data and sharing information, there's a lot benefits that can come out of it for the consumers, patients. There's a dark side as well and that has to be managed. That's why we have the privacy regulations that we have, we're probably going to see more, probably going to see more things like the California Consumer Privacy Act. >> Right. >> More states and eventually-- >> Right. >> probably a federal act for the US. >> Do you think that the healthcare industry is better equipped to deal with GDPR and the California Healthcare Act because of things like HIPAA and they kind of come from that world? Or is this just a whole new level of regulation that they now have to account for? >> I think it's probably a mixed bag. On the one hand, healthcare has been dealing with privacy for a long time, even before HIPAA, right. And then HIPAA has very specific requirements around how you have to manage that information and consent and notifying the patient of their rights. On your other hand, you look at some of the new things, like GDPR, it goes way beyond HIPAA, and I think-- >> It goes way beyond HIPAA? >> Goes way behind HIPAA, like for example, this whole notion of the right to be forgotten. >> Right. >> Right, that's a requirement on the GDPR. That means, me as a patient, if I tell my doctor, I want you to get rid of all my medical records, everything in your system everywhere about me, I want it gone. Not that it makes sense-- >> Right, right. >> but, at least in Europe, if they ask to do that, you have to be able to comply. From a technology perspective and a medical device perspective, some of these devices are very complex, ecosystem of devices, components that make up the product. >> Right >> That's a very difficult thing to do. There's no one delete button-- >> Right. >> that you hit that can delete you from all different instances, downstream from where you came into the healthcare system. >> Right. >> And so, when you think about it from a cyber perspective, it gets to be very challenging. >> The other thing, right, is health care's always under tremendous kind of price pressure from the insurers and the consumers and a bad medical event can wipe-- >> Yeah. >> people out, right? >> Yeah. >> Especially when they're later in life and they're not properly insured, when they're making kind of an ROI analysis on cyber investments versus all the other things they can spend their money on, and they can't spend it all on security, that's not possible, how are they factoring in kind of the cyber investment, it's kind of this new layer of investment that they have to make because all these things are invested versus just investing in better beds and better machines and better people? >> That's the million dollar question. (laughs) I would say, some hospitals and health systems are doing it better than others, so maybe a little bit more further along and mature about thinking about the total cost of ownership and also, the patient factor, right? What has to be balanced, obviously, is not just the costs, but at the end of the day, what's best for the patient. And you hear this term, patient centricity, a lot today. And there's a recognition from all the players in the echo system, it's all about the patient. >> I'm so glad you say that 'cause I think a lot of people probably think that the patient sometimes gets lost in this whole thing, but you're saying no. >> There is an acknowledgement over the last few years and it's called patient centricity, it's an acknowledgement that the way we're going into the future of healthcare and the kinds of medical devices and technology and cloud solutions that are becoming part of the healthcare fabric, they're all being built and geared towards the patient being the center of the equation, not the doctor, not the hospital, it's the patient. >> Right, right, right, that's good to hear. >> And so, to answer your original question, we're in early days and really trying to balance the patient and patient centricity versus we've got vulnerabilities in our environment that could impact the patient and we've only got limited people and costs. >> Right, right. >> Making decisions that kind of balance all of those things. >> Right, alright Russell, last question, we're sitting here in the ForeScout booth. >> Yes. >> Obviously you have a relationship with them, talk about kind of what their solution adds to some of the stuff that you're workin' on. >> So, ForeScout, one of the reasons that we're working closely with ForeScout, their solution, really, they've taken an approach that's holistic around these issues that we're talking about, right, managing cyber risk, complex environment, a lot of different devices that are connected to each other and to the cloud and to the internet. They have built a solution that focuses on ability to have visibility into those devices that are on your network, some of which you may not even know exists, and then being able to kind of build an asset inventory around that visibility that allows you to do things like detect, based on policy, activity that suggests that you might be hacked or there might be some internal processes or players that are doing things that are going to put patients at risk or have you in non-compliance with GDPR, HIPAA and the rest. >> Right. >> And then their solution goes beyond ability to kind of visibility and detect, but to actually do something actionable, right? Security controls and orchestration with other technologies, like Simp Solutions and SOAR Solutions. Being able to orchestrate, hey, I know that I detected some activity on this infusion pump that suggests that we may being hacked, let me send an alert out, but then let me also, maybe, quarantine that part of the network. So, it's the ability to orchestrate between different security technologies that exist in a hospital environment, that's what we like about ForeScout. >> I'm just curious, when they run their first kind of crawl, if you will-- >> Yeah. >> are people surprised at the results of what's on there, that they had no clue? >> I mean, yes and no. >> Yes and no, okay. >> I think, most of the big hospitals that we work with, they know that, what they don't know, and especially when-- >> They know what they don't know. >> you're talkin' about a health system that maybe has a 100 thousand connected medical devices across the health system, they know what they don't know. They're looking for solutions to help them better manage and understand the things that they don't know, that they don't know. >> Right. >> Versus what they do know about. >> Right. >> And I think that's what we bring to the table in terms of kind of cyber risk services Deloitte brings, and then that's what ForeScout brings with their solution to be able to kind of help solve those problems. >> Well Russell, thanks for taking a few minutes out of your day to share those stories, super-- >> Thank you. >> super important work, you know, it's one thing to steal a few bucks out of the bank account, like you said. >> Yeah. >> It's another thing to start taking down machines at the hospital, not a good thing. >> Not a good thing. >> Alright >> Thank you. >> He's Russell, I'm Jeff, you're watchin' theCUBE, we're at RSA in Moscone in the ForeScout booth, thanks for watching, we'll see you next time. (techno music)

>> Announcer: Live from San Francisco, it's theCUBE, covering Red Hat Summit 2018. Brought to you by Red Hat. >> Welcome back, here, when we here live, it's theCube, here in San Francisco live for Red Hat, Summit 2018. I'm John Furrier, the host of theCUBE. Our next three guests is the Delisa Alexander, Executive Vice President, Chief People Officer at Red Hat. Welcome to theCUBE. >> Thanks. >> Zui Dighe, who's the... Did I get that right? Zz-- >> Zui, yeah, mm-hmm. >> Zui? OK, winner of the Open Source Academic Award from Duke University, Go Blue Devils (chuckles). >> Zui: Yes. >> And we have Dana Lewis, winner of Open AP with OpenAPS, which stands for? >> The Open Source Artificial Pancreas System. >> Artificial open-source Pancreas System, great stuff. So congratulations, you guys are all award winners. Before we get into some of the questions, love your applications, talk about the program. What is this about? What's the awards program here at Red Hat Summit, and why are these guys here? >> So as Red Hat, we believe, as an open-source leader, we have a responsibility to promote women in technology and particularly women in open-source. And so, one of the things we thought we could do is to create an award that really spotlights the contributions women are making in open-source to inspire future generations to consider being open-source developers or contributors. >> Congrat, Delisa, love that you're doing that. It's fantastic. We'll start with the young student gun here. What's your degree, first of all? What are you studying? >> I'm studying biomedical engineering and computer science. >> John: Tough major, huh? >> Yep, very tough. (Delisa laughing) Not easy, but I'm-- >> This is an easy-- >> First question is, how do you in a block chain impact? It's funny, Jim always asked that question on day one. No, in all serious, tell about what your application is. This is super important. >> Yeah, yeah. So I'm basically working on researching and creating a tracking system for vaccines that enter into developing countries. So through that, you're able to understand how exactly do vaccines travel through these countries as well as where does the system break. And if you can pinpoint that, you can actually solve the problem. >> And how did you get the idea? How did this all come together? >> I was in a research course at Duke, which has collaboration with the university in Uganda, and we actually got to travel to Uganda and interview various stakeholders, pharmaceutical companies, health system, and understand how does the-- We wanted to be in vaccines, but we didn't know what exactly to do. And so after interviewing, I kind of came up with the idea of why don't we actually put a tracker on these devices that gives off the GPS location and the temperature so we can actually understand the entire system. >> It's going to get that ground truth, too, and again, the local areas. >> Yeah. >> The big walk away, what, about vaccines. This is important to track it from the origination to destination and making sure it all kind of matches up. >> Making sure, first of all, you don't have any data on exactly where they're going because this box is just carried by hand. And the pharmaceutical companies, once they ship the vaccines into Uganda, after that, they don't provide any data on what's going on. So that data is also important, and it's also, you want to know when does the system break because often in last end, when the vaccines are actually administered, they've already gone out of their cold chain cycle, and so they don't work anymore. >> That's a great story. How 'about your story? This is a good one. This is a real practical one for people with diabetes. Talk about, first of all, show the product 'cause it's always good to a little live prop there. So turn, yeah, there it is. So what is that? >> So this is an open-source hardware board. It's actually got an Intel Edison on the back side. But what this does is, it talks to my insulin pump and my continuous glucose monitor, brings the data together, runs it through an algorithm, and sends commands back to the insulin pump to tell it what to do. So this is what we call a close-loop system where we have the computer doing the math instead of the human with diabetes doing the math several times throughout the day. >> And does it do auto-injections as well? So it kind of feeds the glucose levels as well? So it's data-- >> Right. So the insulin pump is automatically dosing the insulin, and we also have a continuous feed of the blood sugar every five minutes as well. >> And that's what you mean by close-loop. >> Exactly. >> For people have these monitors, they have remotes, statistics. Does it talk to a device as well? The mobile device, how does that work? >> Yeah, so I can glance down at my watch and see how I'm doing, on my phone. My loved ones, wherever they are, can see how things are going. So if they need to intervene, they're able to do that remotely. So it really provides peace of mind as well as a lot better outcomes for those of us living with Type I diabetes. >> And what was the motivation here, to get involved deeply in this project? >> It was really selfish, I wanted to sleep, and I couldn't hear my CGM alarm, my glucose alarm. And so my project actually started of, just get the data off to make a louder alarm. And then we built an algorithm that allowed us to look into the future and do proactive alarms. And then we worked with other people to actually communicate with the insulin pump, and that's how we progressed to closing the loop. And because I've been helped so much by other people in open-source, it was a no-brainer to also make our work open-source. >> And so you open-source everything. What other progress can you share? I mean, you have predictive analytics that tell you that, "OK, I'm going to go for a hike soon, "so therefore, I'm going to do this," and all kinds of cool data gathering. Does that play into it? Is it a lifestyle and-- >> Absolutely. >> So it's like a FitBit meets close-loop. (Women laugh) >> It's more like taking standard medical devices and boosting their capacities with the help of computing technologies. It's not fancy machine learning. It's the same math a person with diabetes would do, but the benefit is, it's automated to go every five minutes, and it doesn't fall asleep, it doesn't get lazy, it doesn't round up or vary down. It's going to be giving really precise increments so that when your situation changes, you skip a meal that you though you were going to eat, you're going to go hiking, for whatever reason, if you're going up or down more than expected, it can react instantaneously and much better than a human can. >> I'm so glad you're doing that, too. How does someone get involved with this project? Obviously, it's open-source software, but you have devices. Is it in market? Is there? >> So this is an open-source project because we are not a company, so we cannot distribute medical devices. That's frowned upon by the FDA. And so this is an open-source DIY project for people who want to get involved either to help with the project or build one themselves. They can go to OpenAPS.org. We've written a plain language reference design to help anybody, whether you're a person with diabetes, a loved one, a healthcare provider, a researcher or developer understand how the system works, and then that leads you to the documentation of how to build one as well as to the code where anybody can get involved and help out. >> So that's the loophole, (Dana laughs) to say it plainly, get around that whole being a company. You build your own. >> Yes. >> So that's the way, that's here. OK, great, so congratulations. So where's this all going? This is fantastic, this story. How many other people are involved in the program that you have? Share more about how people can get involved, too. >> This is our fourth year of having the program, and we're really just thrilled with the quality of the nominations. We had over 100 nominations. Our judges then narrowed the field down to 10, and then the community selected the winners. We don't really see an end to this. We just see the community adding and growing organically. So one thing we did this time is, we introduced our winners to our CO.LAB students, and so now they're creating a network. And that network density is just increasing and improving and, I think, getting stronger. >> It's really amazing. And one thing I've always loved about open-source, and you guys see the benefit of it, obviously, with winning and succeeding, is that democratization and community are coming together at a whole nother level. And I think what's interesting about the projects that you guys have is, you got good things happening with tech. So it's tech for good. But since Obama put the Jobs Act in, means fund these projects now as entrepreneurial ventures and be mission-driven OFFLEM. You don't have to do it as a non-profit. So we're seeing a huge growth in entrepreneurial activity around tech for good on projects that would never would funded before. So you're seeing a whole nother generation of great tools and technologies saying, "Hey, let's solve a problem." >> Yeah, and I think that's one of thing I love about us both being in healthcare is, it really shows that there's amazing applications. We can take this technology and apply it in healthcare and do it in different ways, and it doesn't have to be a company right away. It doesn't have to be either a for profit or non for profit. There's a lot of ways open-source is bringing people together to solve the very problems we need to be solving. >> Do you feel good that you built something great like that, and think now you got people using the software? What's the feeling like? >> Oh, it's just incredibly rewarding. I mean, myself, I just have the peace of mind to be able to go to sleep at night. That is a priceless feeling, but then when I hear other people using it, they build the project for different reasons. Some, they want to be able to remotely monitor their loved ones. Others are doing it for their children so that they have better health outcomes. But there's just these amazing stories outpouring from the community. And to me, that's the beauty of open sources. You can really apply it however you need to apply it to your lifestyle. >> Where can someone get involved in your project? Is there like a GitHub repository? >> Yep. >> Is there a site? >> Everything's on GitHub for us, but I would go to OpenAPS.org first. It links to the documentation and the code where people can connect. >> OpenAPS.org. >> That's right. >> OK, great. How 'about your project? How do people get involved with what you're doing? >> Ours is on GitHub right now, so you can get involved through there. But I guess we're kind of right now developing in the backend stages. Soon we'll be at that stage where you can contribute more. And right now, we've just been using other open-source libraries and kind of contributed in that way. But actually, we talked earlier about how do you get involved in open-source, and especially being a student, I kind of fell into coding because of open-source in a sense >> Working on your project? where, yeah, yeah, yeah. So coming into college, I wanted to apply the engineering concepts I was learning in the classroom, and I got involved in a lot of entrepreneurship on campus, and through that, I was asked to make a front-end interface, and I didn't really know how to go about doing that. So then I found an open-source library stumbling around that was doing a similar thing. And that's how I kind of taught myself, and then from there, I branched out and learned more and more. And I think for any budding student, budding entrepreneur, open-source is a great way to take your ideas further. And my interest is in healthcare, so that's where I went, but anyone could have an idea, "Oh, I want to start this business in this way." And they might not think that open-source is a way to go about doing that, but it is a great way to learn more. >> It's a good way to change a lot of things, not just career or projects. >> Yeah. >> There's a nonlinear progression of learning happening. You can come in, you're stumbling around, quote, learning. >> Yeah, yeah. >> It's not like chapter one course, online course. Go to chapter two. >> Right, that is true. >> There's a YouTube, there's stuff on GitHub, open-source. There's people involved. This points to a whole new generational shift. >> It is. >> Of learning, connecting, you're tapping into it. >> It's so exciting because she's the role model we're talking about. We want girls to see that you can become a coder later. You don't have to necessarily start-- >> She's 14, she'd coding in unity. >> Yeah! >> I tell a soliloquy, great. (Delisa laughing) Do some smart contracts and get the bobchain action. (Delisa laughing) Bobchain's the future, you're the Bitcoin in intheoreum. Some cool stuff. >> Yeah. Congratulations, thanks for doing this. >> Thank you very much. >> Very inspirational, and thanks for sharing the story on theCUBE, and keep in touch, thanks for coming, appreciate it. >> Thank you. >> Thanks for having us. >> Great women in tech, great leaders doing some great stuff. Award winners, celebrities here on theCUBE. I'm John Furrier. Be back with more live coverage after this short break. (electronic musical flourish)

>> Narrator: Live from Las Vegas, it's theCUBE. Covering Dell Technologies World, 2018. Brought to you by Dell EMC and it's Ecosystem partners. >> Welcome back here on theCUBE. We continue our live coverage here from Dell Technologies World 2018. We are live, we are in Las Vegas. I'd say it was kind of warm when we first got here, but it's chilled off a little bit so I hope the weather is a little bit better wherever you are. But it's red hot inside here as far as what's happening on the show floor. Along with Stu Miniman, I'm John Walls. It is now our pleasure to welcome to our set, Susan Sharpe who's a senior consultant product manager at Dell. Susan, good to see you. >> Thank you very much. >> John: And Brian Henderson, director of Storage Portfolio Product Marketing at Dell EMC. Brian, good to have you both. >> Thank you >> Cube, rookies right? This is your-- >> I'm a rookie. >> First-timer >> Your debut, right? >> Yes >> First-timer >> Glad to break you in that way. It's good to have you here. Let's talk about the show. Just first off. Because we are starting to wind down, just a little bit. But you know strong attendance. I've been out on the solutions expo floor, that's really cool. A lot of great stuff going on out there. So the two of you what's your take on what you've seen here over the last three days? >> Yeah, I think there's a lot of transformation, right? It's all about transformation I think we're seeing that across the industry overall. Everything is changing everything is connected. It's all about apps these days. It's all about digitizing your business. Anywhere you can add technology to really add that element of your technology and digital modernization to your business. It's really starting to take shape, I think. A couple of years back, we were talking it but it wasn't really happening. And now we're seeing this huge trend towards everybody is actually starting to do it. >> John: Making it real, right? >> We're making it real. >> Alright Susan what do you think? >> So, I think he summarized it really well, I would just add to that automation and intelligence. Looking for systems to provide the insights and intelligence about the environment and simplify people's work. >> Brian and Susan, since it is your first time on the program tell us a little bit about what you've worked on. I've got some history with you, you're both what we call legacy EMC now, I guess. Like myself, I never worked for Dell EMC but I did work for a company that used to be called EMC. >> Absolutely, yeah we go way back Stu and I. Right now we're seeing a lot of sensible decisions being made. I'd say if you go way back, There was just a lot of things happening, there was a lot of a lot of smart moves being made these days. Michael Dell obviously made a huge investment in picking up EMC and for a lot of us, it's super exciting to see kind of it come together and there's been a lot of changes a lot of investments in the technologies of the future. Things like Cloud IQ which we're going to talk about. But it's been really fun. >> Great and Susan, what projects are you working on these days? >> So, Cloud IQ is my primary focus. As we talk more about the product I'll give some examples. But, we started with Cloud IQ very focused on one particular storage platform and now what we're looking at doing is expanding that across multiple platforms. So I get to be singularly focused on the Cloud IQ, but looking at it spanning across multiple platforms. >> I attended an event that Dell held towards the end of last year, they called it IQT and it was IOT with intelligence put together and some of us the analysts it was like okay, I see what you're doing but IOT everybody knows. Cloud IQ I think there's intelligence built into it. One of the themes, I've actually been looking this week, we've talked about intelligent storage, and intelligent management for a couple of decades in our industry but maybe explain a little bit more about the product and why is this actually intelligent now. No offense to the things we've tried in the past (Susan laughs) >> Susan: Sure >> But definitely, to your point Brian, it feels a little more real some of these things we're talking about. >> Yeah, absolutely so if you see what's going on with the industry today, everybody's connecting things and you know we've been collecting a lot of data in a very secure way from our customers for years. Just until recently we started to kind of talk about that and market that capability. It's really exciting what we can do with it. We make sure and we honor each customer their privacy rights, of course. But you're able to do a lot of in-depth analysis, collection We're able to look for anomalies in the system. So, the analogy I like to use is like a Fitbit for storage. It's not just storage, so we're kind of starting at storage which is the exciting part we're starting with unity we're now directed availability on the SC series which was formerly Compellent and then we're going to expand that to VMAX we're going to expand that to Xtremio so we're going to go cross portfolio with that and, can we talk about virtualization? >> Sure >> So we're going to expand into the vmware layer as well. So we're going to really start with a discrete use case we've got what, over 3,500 arrays already connected today. We're adding about 100 per week So, it's really exciting to see the data that we're able to get. We give it back to our customers and partners actually, so a lot of our key partners they want to be able to act as that intermediary for their customer and give them guidance on what to do. So, we've opened that up. >> Let me get into the Fitbit analogy. So what is the health that we're looking at there because we could all relate to that, right? We're looking at my pulse and blood pressure, all those things so what's the pulse and the blood pressure inside stories that you're looking at? >> A perfect lead in, so you talk about the typical metrics from a Fitbit in terms of the human body. The metrics that we're looking at in terms of the health and the categories that we're looking at are the typical things that you would care about in terms of your storage environment. So the things like data protection, are you maintaining your data protection windows and recovery point objectives and ensuring that your data is being protected the way that you expect. Things like capacity and ensuring that you are not at an imminent risk of running out of capacity. Nobody likes that phone call at two in the morning so being able to be proactive about indicating when storage administrators need to start taking action to be able to prevent that call at two in the morning. So some of those areas are where we're looking at our health score. >> Susan, I think back years ago EMC was one of the leaders in doing some of this. It was the phone home capability and we understood what was there. Customers always say, "Oh! The tech showed up with some part "that was ready to fail before we even knew." How is this different? What's this update? How did this change really how businesses are working when it comes to everything? >> I'm glad you lead with that because I think it's really important as a side note to emphasize that that is the foundation and has been the foundation for proactive health for many years. Now what we're doing is we're adding on additional areas of focus like the example that I gave for the data protection. That wouldn't result in the phone home necessarily and it doesn't need to result in the storage engineer showing up or the drive showing up at the door. Instead we can proactively alert our storage administrators to the fact, again, that their data is not being protected with the service level that they expect, and then provide that clear remediation about what they need to do to bring those into that compliance. So instead of break/fix type things, it's more about how they can better optimize their environment to be able to meet the goals that they have. >> When you're talking about support these days I mean that games changing right? >> Absolutely >> And so, as you develop new capabilities and new evaluation tools I mean your service in general, the support your giving, you've got to come out with almost like a new paradigm is it not? How is that changing in your world now? >> So, we see that I mean Susan talked about what we've done in the past how we're changing it and now it's, I go back to analogies right? So you used to go to the doctor when you got sick now it's all about wellness so you're encouraged to go a little more often to get a checkup, so we're doing the same types of things. We give health scores on a range of zero to 100 and we're able to drill in to those specific parameters that Susan talked about to be able to show people how to kind of set up a best practices environment. So we're really starting to get a lot more proactive about how people can understand the health of their system. We now have an app so people can actually check it out remotely. You could be on a beach somewhere on your vacation and you don't have to worry about your system because you can quickly scan it, and check in on the status of your system. So that's what I think people want, they want more access to things so they're able to proactively understand it instead of react and it's all crazy. >> Let me ask you about the number let's just pretend 85, I got 85 whatever, is that telling me that I'm doing something wrong? Or that something has gone wrong within the system? I mean, what is that telling me exactly about what irregularity has occurred? Is it because of something by commission, or is it omission and I've got a systemic problem? >> Well that's a great question. It could be any of those things, right? So, one of the main things that we're looking at, I gave the example, for instance, of a storage pool that is already oversubscribed because we have great efficiencies on our storage systems. But if that pool is oversubscribed and is starting to reach using our predictive analytics we can identify when that pool is starting to reach full capacity starting within a quarter. And so, by being able to look at that it may be that a storage administrator provisioned more storage in a given pool than was intended. But it may just be that the storage ended up being consumed faster than what was expected by everyone involved. So, it's not necessarily that someone did something wrong per se, but it's that it's now time to pay attention take action be proactive and alleviate the risk. >> I got you. >> Brian, walk us through just some of the basics of this product itself. >> Brian: Sure >> Is it something stand alone? Is it part of a maintenance package? >> Brian: Yeah yeah yeah >> Available today? How many customers are using this? >> Sure, so the product became available in kind of an early release capacity when we announced Unity two years back. Since then it's grown over 3,500 array. We're probably up around 4,000 arrays now. And we keep adding about 100 per week. The product is built with our own pivotal cloud foundry so it can be kind of ported across multiple different clouds it lives in the cloud and so you can access it anywhere, and what you're able to do is quickly get the health score. So it's plugged into your system, the back-end is also plugged into our big data lake so we're understanding what's happening across multiple systems, but we give specific guidance to each system. It's going to be really really valuable when we span it across the entire portfolio. Because then you'll get this dashboard kind of health score across the entire environment and you're basically looking at the dashboard of systems and you'll see kind of the red, yellow, green type markings of what to do next. Like Susan said, you're not going to find out everything just from that number, you'll drill in and what they've done is they've programmed in remediation tips for each one. So you're able to start really kind of high level and then drill into each component after that. >> Does that come with unities? Is it a SaaS offering? >> Comes free with that. It's SaaS offering that comes with that. >> Great, so maybe Susan walk us through this expansion that we've talked a little bit about. Once it's on the next platform everybody that has the platform gets it? >> Everybody has access to that, so Cloud IQ, one thing I want to add and I will get to that in just a moment is the benefit this is probably obvious already but the benefit of the fact that it is hosted in the cloud means that customers don't have anything to deploy and just like your smart phone, you get all of the latest upgrades with no effort at all. And we have a little "What's new in Cloud IQ" feature that you can always be up to date. So, the process is this it's very simple once the customer sets up the storage system and then the secure pipe, so secure remote services for heritage EMC products and support assist for the SC series bringing that data into the data lake then at that point the customer simply logs onto CloudIQ.DellEMC.com supply us their support credentials and they will see the systems that are being managed by Cloud IQ. And if I may just add another thing, we were talking about the proactive health score that is based on rules and best practices from the subject matter experts for each platform and those scans, those health checks start running within the first hour of the systems being in Cloud IQ so you're automatically, customer's are automatically getting the benefit of Cloud IQ. Excuse me. >> So, is it self-fix then? I mean, if I see red do I have the tools to get to yellow get to green? Or do I-- (stammers) What do I do? Do I call you? Or am I equipped enough that I can plug the leak myself? >> Absolutely, I'd say most of the issues are best practices recommendations. So you'll be able to get in there and see alright, uh something happened. Let's go back to the health analogy. If your resting heart rate is 75 and then one day it's all of a sudden 125, there's probably an issue, right? So that's uh that's a bad health score. >> Right, that's a red. (Susan laughs) >> That's a red flag what you need to do is probably get a little more exercise or maybe there's something stressing you out. That's kind of a similar analogy of what's happening. So, there's something in the system we have an anomaly prediction system that's part of this and so if you're normal IOPS pattern is a certain thing and then one day it's really really low or really high compared to the average we're also going to red flag that and we're going to tell you you ought to just look at what's happening in your environment. Most of the issues we're going to say, "Okay, you're running out of space. "There's a configuration issue. "Your network may not be hooked up just right "Go check it out and by the way, "based on your signature pattern "we're going to actually recommend what to do next." So we're collecting all these problem signatures and that's able to kind of get to a resolution very quickly. >> Yeah, Susan I know one of the things that people attending this show from the Dell EMC slide love the most get to talk to a lot of customers. So what kind of asks they're giving you, what kind of feedback they're giving you, what's on their wish list and you know, general feedback on Cloud IQ. >> The general feedback is more, faster. (laughs) We talked about the platforms that we're going to be adding in. There's a lot of enthusiasm about that. Those are based on asks from last year, so we are addressing those asks. And now that they see the momentum, they're wanting us to continue that momentum and continue to expand work Cloud IQ will be applied. I would say, hands down, that's the biggest request. And I love that request! I would love to see Cloud IQ expand as much as possible. >> Well here's to wishing 100s across the board for everybody's score card. Nothing but green, right? That's all we want. (Susan laughs) >> Brian: Absolutely >> Thanks for joining us. >> Thank you >> John: We appreciate the time and the insight. >> Thank you very much >> John: Fitbit for your IT operations All right back with more you are watching theCUBE here we are live at Dell Technologies World 2018 and we're in Las Vegas. (upbeat music)

>> Narrator: Live from Stanford University in Palo Alto, California, it's theCube, covering Women in Data Science conference 2018. Brought to you by Stanford. >> Welcome back to theCube. I'm Lisa Martin and we're live at Stanford University, the third annual Women in Data Science conference, WiDS. This is a great one day technical event with keynote speakers, with technical vision tracks, career panel and some very inspiring leaders. It's also expected to reach over 100,000 people today, which is incredible. So we're very fortunate to be joined by our next guest, Ruth Marinshaw, the CTO for Research Computing at Stanford University. Welcome to theCube, Ruth. >> Thank you. It's an honor to be here. >> It's great to have you here. You've been in this role as CTO for Research Computing at Stanford for nearly six years. >> That's correct. I came here after about 25 years at the University of North Carolina Chapel Hill. >> So tell us a little bit about what you do in terms of the services that you support to the Institute for Computational Mathematics and Engineering. >> So our team and we're about 17 now supports systems, file systems storage, databases, software across the university to support computational and data intensive science. So ICME, being really the home of computational science education at Stanford from a degree perspective, is a close partner with us. We help them with training opportunities. We try to do some collaborative planning, event promotion, sharing of ideas. We have joint office hours where we can provide system support. Margot's graduate students and data scientists can provide algorithmic support to some thousands of users across the campus, about 500 faculty. >> Wow. So this is the third year for WiDS, your third year here. >> Ruth: It is. >> When you spoke with Margot Gerritsen, who's going to be joining us later today, about the idea for WiDS, what were some of your thoughts about that? Did you expect it to make as big of >> Ruth: No. >> an impact? >> No, no people have been talking about this data tsunami and the rise of big data, literally for 10 years, but actually it arrived. This is the world we live in, data everywhere, that data deluge that had been foreseen or promised or feared was really there. And so when Margot had the idea to start WiDS, I actually thought what a nice campus event. There are women all over Stanford, across this disciplines who are engaged in data science and more who should. Stanford, if anything, is known for its interdisciplinary research and data science is one of those fields that really crosses the schools and the disciplines. So I thought, what a great way to bring women together at Stanford. I clearly did not expect that it would turn into this global phenomenon. >> That is exactly. I love that word, it is a phenomenon. It's a movement. They're expecting, there's, I said over a 100,000 participants today, at more than 150 regional events. I think that number will go up. >> Ruth: Yes. >> During the day. And more than 50 countries. >> Ruth: Yes. >> But it shows, even in three years, not only is there a need for this, there's a demand for it. That last year, I think it was upwards of 75,000 people. To make that massive of a jump in one year and global impact, is huge. But it also speaks to some of the things that Margot and her team have said. It may have been comfortable as one of or the only woman at a boardroom table, but maybe there are others that aren't comfortable and how do we help them >> Ruth: Exactly. >> and inspire them and inspire the next generation. >> Exactly. I think it's a really very powerful statement and demonstration of the importance of community and building technical teams in making, as you said, people comfortable and feeling like they're not alone. We see what 100,000 women maybe joining in internationally over this week for these events. That's such a small fraction compared to what the need probably is to what the hunger probably is. And as Margot said, we're a room full of women here today, but we're still such a minority in the industry, in the field. >> Yes. So you mentioned, you've been here at Stanford for over five years, but you were at Chapel Hill before. >> Ruth: Yes. >> Tell me a little bit about your career path in the STEM field. What was your inspiration all those years ago to study this? >> My background is actually computational social sciences. >> Lisa: Oh interesting. >> And so from an undergraduate and graduate perspective and this was the dawn of western civilization, long ago, not quite that long (Lisa laughs) but long ago and even then, I was drawn to programming and data analysis and data sort of discovery. I as a graduate student and then for a career worked at a demographic research center at UNC Chapel Hill, where firsthand you did data science, you did original data collection and data analysis, data manipulation, interpretation. And then parlayed that into more of a technical role, learning more programming languages, computer hardware, software systems and the like. And went on to find that this was really my love, was technology. And it's so exciting to be here at Stanford from that perspective because this is the birthplace of many technologies and again, referencing the interdisciplinary nature of work here, we have some of the best data scientists in the world. We have some of the best statisticians and algorithm developers and social scientists, humanists, who together can really make a difference in solving, using big data, data science, to solve some of the pressing problems. >> The social impact that data science and computer science alone can make with ideally a diverse set of eyes and perspectives looking at it, is infinite. >> Absolutely. And that's one reason I'm super excited today, this third WiDS for one of the keynote speakers, Latanya from Harvard. She's going to be talking, she's from government and sort of political science, but she's going to be talking about data science from the policy perspective and also the privacy perspective. >> Lisa: Oh yes. >> I think that this data science provides such great opportunity, not just to have the traditional STEM fields participating but really to leverage the ethicists and the humanists and the social sciences so we have that diversity of opinions shaping decision making. >> Exactly. And as much as big data and those technologies open up a lot of opportunities for new business models for corporations, I think so does it also in parallel open up new opportunities for career paths and for women in the field all over the world to make a big, big difference. >> Exactly. I think that's another value add for WiDS over it's three years is to expose young women to the range of career paths in which data science can have an impact. It's not just about coding, although that's an important part. As we heard this morning, investment banking, go figure. Right now SAP is talking about the impact on precision medicine and precision healthcare. Last year, we had the National Security Agency here, talking about use of data. We've had geographers. So I think it helps broaden the perspective about where you can take your skills in data science. And also expose you to the full range of skills that's needed to make a good data science team. >> Right. The hard skills, right, the data and statistical analyses, the computational skills, but also the softer skills. >> Ruth: Exactly. >> How do you see that in your career as those two sides, the hard skills, the soft skills coming together to formulate the things that you're doing today? >> Well we have to have a diverse team, so I think the soft skills come into play not just from having women on your team but a diversity of opinions. In all that we do in managing our systems and making decisions about what to do, we do look at data. They may not be data at scale that we see in healthcare or mobile devices or you know, our mobile health, our Fitbit data. But we try to base our decisions on an analysis of data. And purely running an algorithm or applying a formula to something will give you one perspective, but it's only part of the answer. So working as a team to evaluate other alternative methods. There never is just one right way to model something, right. And I think that, having the diversity across the team and pulling in external decision makers as well to help us evaluate the data. We look at the hard science and then we ask about, is this the right thing to do, is this really what the data are telling us. >> So with WiDS being aimed at inspiring and educating data scientists worldwide, we kind of talked a little bit already about inspiring the younger generation who are maybe as Maria Callaway said that the ideal time to inspire young females is first semester of college. But there's also sort of a flip side to that and I think that's reinvigorating. >> Yes. >> That the women who've been in the STEM field or in technology for awhile. What are some of the things that you have found invigorating in your own career about WiDS and the collaboration with other females in the industry? >> I think hearing inspirational speakers like Maria, last here and this year, Diane Greene from Google last year, talk about just the point you made that there's always opportunity, there's always time to learn new things, to start a new career. We don't have to be first year freshmen in college in order to start a career. We're all lifelong learners and to hear women present and to see and meet with people at the breakout sessions and the lunch, whose careers have been shaped by and some cases remade by the opportunity to learn new things and apply those skills in new areas. It's just exciting. Today for this conference, I brought along four or five of my colleagues from IT at Stanford, who are not data scientists. They would not call themselves data scientists, but there are data elements to all of their careers. And watching them in there this morning as they see what people are doing and hear about the possibilities, it's just exciting. It's exciting and it's empowering as well. Again back to that idea of community, you're not in it alone. >> Lisa: Right. >> And to be connected to all of these women across a generation is really, it's just invigorating. >> I love that. It's empowering, it is invigorating. Did you have mentors when you were in your undergraduate >> Ruth: I did. >> days? Were they males, females, both? >> I'd say in undergraduate and graduate school, actually they were more males from an academic perspective. But as a graduate student, I worked in a programming unit and my mentors there were all females and one in particular became then my boss. And she was a lifelong mentor to me. And I found that really important. She believed in women. She believed that programming was not a male field. She did not believe that technology was the domain only of men. And she really was supportive throughout. And I think it's important for young women as well as mid-career women to continue to have mentors to help bounce ideas off of and to help encourage inquiries. >> Definitely, definitely. I'm always surprised every now and then when I'm interviewing females in tech, they'll say I didn't have a mentor. >> Lisa: Oh. >> So I had to become one. But I think you know we think maybe think of mentors in an earlier stage of our careers, but at a later stage we talked about that reinvigoration. Are you finding WiDS as a source of maybe not only for you to have the opportunity to mentor more women but also are you finding more mentors of different generations >> Oh sure. >> as being part of WiDS? >> Absolutely, think of Karen Mathis, not just Margot but Karen, getting to know her. And we go for sort of walks around the campus and bounce ideas of each other. I think it is a community for yes, for all of us. It's not just for the young women and we want to remain engaged in this. The fact that it's global now, I think a new challenge is how do we leverage this international community now. So our opportunities for mentorship and partnership aren't limited to our local WiDS. They're an important group. But how do we connect across those different communities? >> Lisa: Exactly. >> They're international now. >> Exactly. I think I was on Twitter last night and there was the WiDS New Zealand about to go live. >> Yeah, yeah. >> And I just thought, wow it's this great community. But you make a good point that it's reached such scale so quickly. Now it's about how can we learn from women in different industries in other parts of the world. How can they learn from us? To really grow this foundation of collaboration and to a word you said earlier, community. >> It really is amazing though that in three years WiDS has become what it has because if you think about other organizations, special interest groups and the like, often they really are, they're not parochial. But they tend to be local and if they're national, they're not at this scale. >> Right. >> And so again back to it's the right time, it's the right set of organizers. I mean Margot, anything that she touches, she puts it herself completely into it and it's almost always successful. The right people, the right time. And finding ways to harness and encourage enthusiasm in really productive ways. I think it's just been fabulous. >> I agree. Last question for you. Looking back at your career, what advice would you have given young Ruth? >> Oh gosh. That's a really great question. I think to try to connect as much as you can outside your comfort zone. Back to that idea of mentorship. You think when you're an undergraduate, you explore curricula, you take crazy classes, Chinese or, not that that's crazy, but you know if you're a math major and you go take art or something. To really explore not just your academic breadth but also career opportunities and career understanding earlier on that really, oh I want to be a doctor, actually what do you know about being a doctor. I don't want to be a statistician, well why not? So I think to encourage more curiosity outside the classroom in terms of thinking about what is the world about and how can you make a difference. >> I love that, getting out of the comfort zone. One of my mentors says get comfortably uncomfortable and I love that. >> Ruth: That's great, yeah. >> I love that. Well Ruth, thank you so much for joining us on theCube today. It's our pleasure to have you here and we hope you have a great time at the event. We look forward to talking with you next time. >> We'll see you next year. >> Lisa: Excellent. >> Thank you. Buh-bye. >> I'm Lisa Martin. You're watching theCube live from Stanford University at the third annual Women in Data Science conference. #WiDS2018, join the conversation. After this short break, I'll be right back with my next guest. Stick around. (techno music)

Live from Berlin Germany, it's the queue Covering NetApp insight 2017 brought to you by Neda Welcome back to the cubes live coverage of net app insight here in Berlin Germany I'm your host Rebecca Knight along with my co-host Peter Burris. We are joined by Mark Bregman. He is the CTO of net app Thanks so much for coming on the cube Thanks for taking the time so you have been recently looking into your crystal ball to predict the future and you have some some fun sometimes counterintuitive Predictions about what we're going to be seeing in the next Year and decade to come right so so your first pitch in you said data will become Self-aware right what do you mean by that? Well the title is kind of provocative really the idea is that? Data is going to carry with it much more of its metadata Metadata becomes almost more important than the data in many cases and we can anticipate Sort of architectures in which the data drives the processing whereas today? We always have data is sort of a pile of data over here And then we have a process that we execute against the data that's our been our tradition in the computing world for a long long time as data becomes more self-aware the data as it passes through Will determine what processes get executed on it? So let me give you a simple analogy from a different field from the past in The communications world we used to have circuit switched systems There was some central authority that understood the whole network If you and I wanted to communicate it would figure out the circuit set up the circuit And then we would communicate and that's sort of similar to traditional Processing of data the process knows everything it wants to do it knows where to find the data. It does that it puts It somewhere else But in the communications world we move to packets which data, so now the packet the data Carries with it the information about what should happen to it And I no longer have to know everything about the network nobody has to know everything about the network I pass it to the nearest neighbor who says well I don't know where it's ultimately going, but I know it's going generally in that direction and eventually it gets there now Why is that better? It's very robust it's much more scalable and Particularly in a world where the rules might be changing. I don't have to necessarily redo the program I can change the the markup if you will the tagging of the data You can think of different examples imagine the data That's sitting in a autonomous vehicle and there's an accident now There are many people who want access to that data the insurance company the authorities the manufacturer the data has contained within it the Knowledge of who can do what would that data? So I don't have to now have a separate program that can determine Can I use that data or not the data says sorry you're not allowed to see this. This is private data You can't see this part of it Maybe the identify our data for the obviously the insurance company needs to know who the car owner is But maybe they don't need to know something else like where I came from The authorities might need both well he came from a bar So you can imagine that as an example if you the implications, yes marker are important for example if I Wanted to develop an application. That would be enhanced by having access to data I had to do programming to get to that data because some other application control that data and that data was defined contextually by that application right and so everything was handled by the application by moving the metadata into the data now I can bring that data to my Application more easily less overhead and that's crucial because the value of data accretes It grows as you can combine it in new and interesting ways so by putting the metadata end of the data I can envision a world where it becomes much faster much more Fasil to combine data and new and Exactly it. Also is easier to move the Processing through the data to the data because the processing is no longer a monolithic program It's some large set of micro services and the data organizes which ones to execute So I think we'll see I mean this is not a near-term prediction This is not one for next year because it requires rethinking How we think about data and processing, but I think we'll see it with the emergence of micro services compositional programming Metadata together with the data will see more functional programs little programs well That's your quick rush before we go on to the next one. It's almost like in the early night or the late 1970s It was networks of devices ARPANET the became the Internet and then the web was networks of pages And then we moved into networks of application services Do you foresee a day where it's going to be literally networks of data? Yes, and in fact That's a great example because if you think about what happened in the evolution of the web through what we called web 2.0 That the pages were static data They came alive in the web 2.0, and there was a much less of a distinction between the data and the program In the web layer right so that's what we're saying we see that emerging even further Next prediction was about virtual machines becoming rideshare machines well this is somewhat complementary to the first one they all kind of fit together and Here the idea is you know if we go back in the earlier days of IT it wasn't that long ago that if you needed? Something you ordered the server, and you installed it you owned it and then we got to the model of the public cloud, which is like a rental and by the same analogy if in the past if I wanted a vehicle I had to buy it and Then the rental car agencies came up, and I said well, you know when I go to Berlin I'm not gonna buy a car for three days I'll rent a car, but I can choose which car I want do I want the BMW, or do I want you know of Volkswagen That's very similar to the way the cloud works today. I pick what instances I want and They they meet my needs And if I make the right choice great and by the way I pay for it while I have it not for the work It's getting done so if I forget to return that instance. I'm still getting charged But the rideshare is kind of like uber and we're starting to see that with things like serverless computing In the model that I say I want to get this work done The infrastructure decides what shows up in the same way that when I call uber I don't get to pick what car shows up they send me the one that's most convenient for them and me and I get charged for the work going from point A to point B. Not for the amount of time There's some differentiation if there is so cool Ah, they come to that and and so that's more like a rideshare But as you point out even in the rideshare world. I have some choices. I can't choose if I want a large SUV I might get a BMW SUV or I might get a Mercedes SUV I can't choose that I can't choose it the silver or black But I get a higher class and what we're seeing with the cloud Or these kind of instances virtual solutions is they are also becoming more specialized I might it might be that for a particular workload I want some instance that has have GPUs in them or some neural chip or something else In much the same way that The rental model would say go choose the exact one you want The rideshare model would say I need to get this work done and the infrastructure might decide this is best serviced by five instances with GPU or Because of availability and cost maybe it's 25 instances of standard processors because you don't care about how long it takes so It's this compromise and it's really very analogous to the rideshare model now coming back to the earlier discussion as The units of work gets smaller and smaller and smaller and become really micro services Now I can imagine the data driving that decision hailing the cab hailing the rideshare and driving What needs to be done? So that's why I see them in somewhat complementary and so what's the upshot though? For the employee and for the company I think there are two things one is you got to make the right decision? You know if I were to use uber to commute to Sunnyvale every day It'd break the bank, and it would be kind of stupid so for that particular task I own my vehicle But if I'm gonna go to Tahoe for the weekend, and I meet an SUV I'm not gonna buy one neither am I going to take an uber I'm in a rent one because that's the right vehicle on the other hand when I'm going from you know where I live to the marina within San Francisco, that's a 15 minute drive I On demand I take an uber and I don't really care now if I have 10 friends I might pick a big one or a small one But again that the distinction is there so I think for companies They need to understand the implications and a lot of times as with many people they make the wrong initial choice And then they have then they learn from it so You know there are people who take uber everywhere And I talked and I said I had a friend who was commuting to HP every day by uber from the city from San Francisco That just didn't make sense he kind of knew that but The next one is data will grow faster than the ability to transport it, but that's ok it doesn't sound ok it Doesn't sound ok and for a long time. We've worried about that. We've done compression, and we've done all kinds of things We've built bigger pipes And we've but we were fundamentally transporting data between data centers or more recently between the data center and the cloud big chunks of data What this really talks about is with the emergence of quality IOT in a broad sense? Telematics IOT digital health many different cases there's going to be more and more and more data both generated and ultimately stored at the edge and That will not be able to be shipped all of that will not be able to be shipped back to the core And it's okay not to do that because there's also Processing at the edge so in an autonomous vehicle where you may be generating 20 megabytes per hour or more You're not gonna ship that all back You're gonna store it you're gonna do some local processing you're gonna send the summary of it the appropriate summary back But you're also gonna keep it there for a while because maybe there's an accident and now I do need all that data I didn't ship it back from every vehicle But that one I care about and now I'm gonna bring it back or I'm gonna do some different processing than I originally Thought I would do so again the ability to Manage this is going to be important, but it's managed in a different way. It means we need to figure out ways to do overall Data lifecycle management all the way from the edge where historically that was a silo we didn't care about it Probably all the way through the archive or through the cloud where we're doing machine learning rules generation and so on but it also suggests that we're going to need to do a better job of Discriminating or demarcating different characteristic yen classes of data, and so that data at the edge Real-world data that has real-world implications right now is different from data that summarizes business events which is different from data that Summarized as things models that might be integrated something somewhere else And we have to do a better job of really understanding the relationships between data It's use its asset characteristics etcetera, would you agree with that absolutely and maybe you see the method in my madness now? Which is that data will have? Associated with it the metadata that describes that so that I don't misuse it you know think about The video data off of a vehicle I might want to have a sample of that every I don't know 30 seconds, but now if there's really a problem and it may be not an accident Maybe it's a performance problem. You skidded I'd like to go back and see why was there a Physical issue with the vehicle that I need to think about as an engineering problem was it Your driving ability was it a cat jumped in front of the car so But I need to be able to as you pointed out in a systematic way distinguish what data I'm looking at and where it belongs and where it came from The final prediction it concerns the evolution from Big Data to huge data so that is Really driven by the Increasing need we have to do machine learning AI Very large amounts of data being analyzed in near real time to meet new needs for business And there's again a little like many of these things There's a little bit of a feedback loop so that drives us to new architectures for example being able to do in memory analytics But in-memory analytics with all that important data. I want to have persistence technologies are coming along like Storage class memories that are allowing us to build persistent storage persistent memory We'll have to re our Kotak the applications, but at the same time that persistent memory data I don't want to lose it so it has to be thought of also as a part of the storage system Historically we've had systems the compute system, and there's a pipe and there's a storage system And they're separate they're kind of coming together, and so you're seeing the storage Impinge on the system the compute system our announcement of Plexus store acquisition is how we're getting there But at the same time you see what might have been thought of is the memory of the computer System really be an extended part of the storage system with all the things related to copy management backup and and And so on so that's really what that's talking about and you know it's being driven by another factor I think which is a higher level factor. We started in the first 50 years of the IT industry was all about automating processes That ran the business they didn't change the business. They made it more efficient accounting systems etc since probably 2000 there's been a little bit of a shift Because of the web and mobile to say oh I can use this to change the relationship with my customer Customer in density I can use mobile and and I can change the banking business Maybe you don't ever come to the bank for cash anymore even to an ATM because they've changed that The wave that's starting now which is driving This is the realization in many organizations, and I truly believe eventually in all organizations that They can have new data-driven businesses That are transforming their fundamental view of their business so an example I would use is imagine a shoe maker a shoe manufacturer well for 50 years. They made better shoes They had better distribution, and they could do better inventory management and get better cost and all of that with IT in the last Seven or ten years, they've started to be able to build a relationship with their client. Maybe they put some Sensors in the shoe, and they're doing you know Fitbit like stuff mostly for them That was about a better client relationship, so they could sell better shoes cuz I wrench eiated now The next step is what happens if they wake up and say wait a minute We could take all this data and sell it to the insurance companies or healthcare companies or the city planners Because we now know where everyone's walking all the time That's a completely different business But that requires new kind of lytx that we can't almost not imagine in the current storage model so it drives these new architectures And there is one more prediction, okay? Which is that and it comes back again? It kind of closed the whole cycle as we see these Intelligence coming to the data and new processing forms and so on we also need a way to change data management to give us really Understanding of data through its whole lifecycle one of the one example would be how can I ensure? That I understand the chain of custody of data the example of an automobile there's an accent well How do I know that data was an alter or? how can I know whose touch this data along the way because I might have an audit trail and So we see the emergence of a new Distributed and mutable management framework if when I say those two words together you probably think Blockchain which is the right thing to think but it's not the blockchain. We know today there may be something It's something like that But it will be a distributed and immutable ledger that will give us new ways to access and understand our data Once you open up the once you open up Trying to get the metaphor once you decide to put the metadata next to the data Then you're going to decide to put a lot more control information in that metadata Exactly, so this is just an extension said it kind of closes the loop exactly Mark well, thanks so much for coming on the show and for talking about the future with us It was really fun to have you on the show we should come back in a year and see if maybe you're right exactly exactly Thank you. I'm Rebecca night. We will have more from NetApp insight. Just after this

>> Announcer: Live from New York City It's theCUBE. Covering CyberConnect 2017. Brought to you by Centrify and the Institute for Critical Infrastructure Technology. >> Hey welcome back everyone, this is theCUBE's coverage of CyberConnect 2017. Live here in New York City at the Grand Hyatt downtown on 42nd street. I'm John Furrier, my co-host Dave Vellante. This is Centrify's inaugural event that they're presenting and they're underwriting. It's the industry event between industry and government and really around the crisis of our generation which is cyber security and it's impact to the transformation to global society and our coverage here. Our next guest is Shira Rubinoff who is the President of SecureMySocial, which is really cutting edge human aspect of social engineering meets security. Primetech partners, Cybersecurity, IoT and an influencer but also doing some great work advising start-ups great participant in the community and certainly great to have you back on theCUBE. Thanks for joining us. >> Shira: Thank you, pleasure. >> So, you're in the front row. I saw you and Dave, I couldn't get a seat I was in the back of the bus here at the General Keith Alexanders keynote, among other great keynotes here. Really an inaugural event and inaugural events are great because it's the sign of the trends but also you know if they do a second even, it worked. Right, so you never know there's never going to be another event so an inaugural event means something. It means that the world has to the realization that the world is changed, the realities are here and that the old way isn't good enough. >> Shira: Yup. >> And you're in the middle of it. What's your thoughts? What's your reaction to the program? >> Well you know it's interesting, it also even goes back to the old technology days when you buy by brand. No ones going to fault you for buying the brand names. Everyone just went along with buying the trend, buying the brand. And as technology advanced itself as well we started seeing doing it the old way is just not going anywhere today. Especially with the millennials entering the workforce, how things are done, how people approach technology and security is very different. The human factors of information security is taking a front row today, in terms of security, in terms of the weakest link of the chain. Whether it being phishing, finding the entree into an organization through the human ... the weak link of the human, or in terms of tricking people for doing other things while they're downloading malware or even circumventing different technologies that are layered upon each other because there's just too many layers of security on each other and not making it easy for somebody to use the technology and keeping it strong. >> This year you bring up a good point about the human aspect of it. There's an old joke in IT where there's a fork with a cork in it and someone says why is that there? So they don't stick the fork in their eye. And that's a joke on the old system admin joke around human error, around updating. That's been around for a while, but now there's a whole other social engineering going on around the business of cyber attacks. Whether it's mafias or organized hacker units that do it for business, for profit to state governments where the social engineering around the human vulnerabilities are key. This isn't your area, it's your wheelhouse. What is the key thing that's happening? What should people be aware of? What's your analysis? >> Well I think people have to be careful of oversharing. I think there's many different entrees into finding, again when we talk about the human factors whether being government, whether being a technology company, whether being a seasuite, whether it being through social media. It's being trusted the wrong people, trusting the wrong sources, and just being open and not being over careful in checking your sources and making sure you're actually linking up whether it being on the LinkedIn. Also, I was talking to someone earlier that people were accepting LinkedIn invitations from non-trusted sources. And they seemed to look okay but again, a social engineering piece that comes in that allowed others in to actually see context and find a breech within an organization. Sometimes, somewhat like a government it can always be across all communities. >> So that's a very nuance point, lets take LinkedIn for example, mind if I picked on LinkedIn but Facebook I'm an oversharer so I'm probably being hacked 10 ways from Sunday but you can have whatever you want. But lets take LinkedIn as an example. A practitioner could say I work on the servers for Chase Bank and I handle the Apache whatever project. That's metadata that can be used against that person. He's putting it out there, he or she, for a job potentially to showcase their skills. Yet, the bad actors can use that and figure out what communities they're ... >> Exactly. >> And github their participants so it's a gesture signal point, that you ... Am I right, am I getting it right? >> Correct. Correct. And that's what some of the companies actually put allowances around what people are allowed to share on LinkedIn, however there's the double-edged sword because they're telling their employees do not overshare and say specifically what you're doing. The employee themselves are saying, hey I want to be open to recruiters to come find me because who knows what my next gig is. So they're going to over share what they're doing to show all the experience that they have so they're open to other job opportunities. >> This is a really interesting conflict, and again I'm torn because religiously I'm a big believer in the democratization of media and society but what you're talking about really is a counter against the democratization because that's based on sharing, which that's where open sources from and so this is going to be some sort of shift. >> Correct. Correct. Well, that also plays into the whole millennial shift. Of how it's approached through the workforce. Millennial generation share everything, everything is open. My whole life is opening itself up on social media. I want you to know what I'm having for breakfast because you might want to have it too. By the way, this is what I'm working on at work because you might find it interesting. Whether it being their boss or saying don't do this they're saying don't tell me what to do and I'm going to work from home half the time. It's millennial shift and we have to shift with it. It's going that route. >> So to what degree can we take bad human behavior out of the equation? Toiling, technology, maybe it's process education. >> Well I think it has to be many factors. You know, there has to be the education around it. There also has to be implementing the right technology. To warn users if they're doing things the wrong way. For example, my company SecureMySocial, we are a technology assisted self-monitoring company for allow for employers to give employees to self monitor across social media based on compliance organization real time warnings. So it would warn the employee if they the employee themselves would be doing something wrong. So implementing technologies of that sort whether being whatever the organization may be open to. So you have the education piece, you have the partnerships with the right technology companies, and you also have allowing the employees to have the right types of security around what they're doing themselves. Without being so involved in what they're doing because then they're going to have a big push back. So there's a very fine line you have to walk here. >> And the psychology is interesting you mention the millennials too, because that's their norm. >> Shira: Correct. And they want to be part of a tribe, right? >> Shira: Yes. >> So that the belonging aspect of social is becoming a norm. But now we have to have practices. So what do you, what's your vision of this? Because that probably won't stop, that's a behavior that will constantly be there. Is that going to come in a form of product? Solutions? A better identity? I mean ... >> Well it's going to come everywhere, if you look across all generations from the boomers, gen x, millennials. Things shift with the generations as it comes down the path. So certainly through technology is going to shift to, easy to use, no extra steps to download. As Centrify has, they want a one point to contact. They don't want to overlay technologies on technologies which is what I speak about a lot. My background is heavily in psychology and the human aspect. So make things as strong as they can be without cumbersome to the employee. You want them to use it, not break it, not go around it and not just throw it out the window. >> Gee, you're a great guest and music to our ears because as Dave knows, I've been on this rant for a long time. User experience is really about user expectations. And as expectations shift, that's kind of where the puck will be or whether you're skating through the puck or skating with the puck, as some people are. The question comes down to this young generation because General talked about this new cyber warfare but there's West Point, there's no Navy SEAL, and that's going to come from a gamer culture potentially or the younger generation, so I got to ask ya. Do you think that we're going to have a counter culture? Because in every revolution, take the 60's. We're the 50's parents now, right? We're the 50's generation, or are we? So I've been kind of speculating that I think we're on the cusp of a counter culture revolution. The summer of love of digital is coming. Or maybe not, what do you think? >> You know, I think it's very interesting the way it's shifting across generations. I think that the generation, our generation before us are trying to take this millennial generation and put them in a box and saying follow my rules or else you're out and the millennial generations like make me. So it's not going to happen that way. They're going to actually drive the force of how technology is going to be created and how the business world is actually going to react and act towards them and how things are going to flow after them. And just wait for the following generation, things are going to be a lot looser. >> So you think there's going to be some massive change being shifted from their expectations. >> Shira: Correct. Correct. Yes. >> Well, I feel like millennials are in for a great awakening because now they don't have a ton to lose. >> Shira: Yes. >> As they get older and accrue more wealth. >> John: Well millennials are generally lazy, right? (laughter) >> You've got to be careful when you say that. >> As my son would say, they're smart or they're lazy. >> They're the make me generation. >> Exactly >> Alright, fine. Be careful what you wish for. But is there a gamification involved. The psychology of getting humans to behave the way that you need them to behave in order to have good security practices. >> Yes, no I think that's a great question. I think that based on what the millennials are doing now and how the shift is happening through the gen x and millennials kind of intertwining the businesses and the way technology is created and moved forward. I think that it's going to somehow have to combine forces. I think there's going to have to be a little give and take. And I think as time progresses and things mature that it's going to be understood and it's going to be adapted by them and adopted by them, as well. >> So, talk a little bit more about your company. MySocial ... >> Shira: SecureMySocial, yes. >> What does it do? How does it help solve some of these issues? >> So SecureMySocial is just technology assisted self monitoring tool for employers to give employees to self monitor across social media, based on compliance and regulations of the organization. With real time warnings and auto-delete capabilities. Basically, the organization would buy it. Based on where a person would fall in the organization there will be specific rules set to apply to them. Whether it being group rule sets for C level people, marketing and the like, you don't want false positives. And they the people themselves would get a real time warning to their known device. But I will back track a little bit because most organizations, if not all today have certain criteria. What you can and can't do across social media. But the most of the problems, if not 98 or more percent of data loss or reputation happen outside of the office. It happens on lunch breaks, vacations, weekends. We can't monitor peoples personal accounts. So we're making the users themselves, they would get the real time warnings. There's nothing to download, nothing to install. They don't give over any personal information, yet they're protected and we're able to keep it across the whole thing. >> So it's an insurance policy for the employee saying, look here's a little notification because you know that if you say that drunk tweet, let's get real right or do something that's at a concert ... >> The CFO of Twitter mistakenly tweeted out the earnings of Twitter instead of doing a direct tweet. Things happen, mistakes happen. It's the human factors of it all. >> Dave: And your technology could have stopped that? >> We could have stopped it, we could have actually auto deleted it before it even went out. >> It's almost, I don't know if it's happening on the west coast, but around where I live there's all these ... There's speed signs going up. Tells you how fast you're going. >> It's like that angel on your shoulder saying, do you really want to do this? >> It might be 25 and you see it and you go, you're going too fast and it's flashing and you slow down, and it actually works. >> We use ways in California that's more ... >> It lets you know where the cops are. (John laughing) >> There's no cops! There's no cops around. >> I know that's the same, it's just more effective. You get there faster, you don't ... >> If you don't mind I'd like to ... >> It's this subliminal message, says hey whoa yo slow down. >> Like that angel on your shoulder tapping you on the shoulder letting you know. >> Like you said, it's the good angel. >> Now I just wanted to mention also a new venture actually launching at the end of the month. It's called Prime Tech Partners. We're an incubator here in New York City. Near the flat iron district. We're going to be launching the end of November. Focusing on augmented reality, cyber security, information security and e-commerce. Opening up to start-ups. And please check it out, Prime Tech Partners. >> Shira you did some great work, I got to ask you the question because start-ups are the canary in the coal mine. >> Shira: Yup. >> They'll tell you kind of what's happening, give you a barometer. What is going on in the start-up areas around security because there's now a range, diverse range opportunities from lock chain all the way to enterprise. >> Sheri: Sure. >> So, and everything in between. What's the chirping happening in the mines of the start-ups as they create new ventures. >> Well it's interesting because when you talk about what's out there we talk about almost like an umbrella. Sometimes people would put cyber security over the whole umbrella and then fit artificial intelligence, augmented reality, virtual reality, blockchain. Everything kind of falls under there. So, you know it's actually moving along with the system. There's a lot of artificial intelligences making a big play. IoT world, there's quite a bit of technology coming out there. All finding the whole problems and if you look at everything there's a lot of the human aspects of information security that they have to take into account when developing and when pushing it out because at the end of the day, it's all social engineering. It's the human factor, whatever you're creating. >> And we're seeing the same thing on theCUBE entries. We go to hundreds of shows a year. The trend is every part of the stack is impacted by this. >> Shira: Exactly. >> At the infrastructure low level, from multi factor authentication all the way up to Docker and Cooper and Eddies at the dev ops level, the app level. To wearables ... >> Well, wearables certainly. Right? Gaining some ones information. >> John: Geo information. >> Right. Well, here was an interesting ... I went into, I have a law firm that contacted me. They wanted me to some consulting for them. They implement this most beautiful, high-tech, gorgeous office. So I was in there talking to some of the partners and they were plugging in their new smart TV's and their smart fridges. Everything into their network. You don't have breech their network to get their information, we'll breech Sony! You breech into Sony, whatever whoever the manufacturer of the TV, the fridge, whatever it is. They're thinking IoT, well they can gain access into that law firm, gain information and just take all that information and utilize that. So there's so much thought to be put around even the IoT world, artificial intelligence. The human factor takes a step back. >> If it's a network device it can be hacked. >> Exactly. Yes. >> So is part of your mission just to make people aware of humans role in bad security practices? Is that a big part of this? >> Shira: Yes. >> This sort of shining a light on it. >> Yes, I think there's almost like a stop and pause. When you're creating a technology, whatever it is, and people are looking, Oh I'm going to make this stronger. I'm going to make this better, I'm going to make this faster. Oh here let me put another control over it, and here's another control, and by the way they have to go around this and do five things, we're going to have the best thing out there. They're not going to use it, they're going to break it and circumvent it. Stop, there's a person there. How are we going to make the person use this to the best capacity? How's it going to be strong without giving them all those extra layers? Anything you're doing, there's a person there. You got to stop and think and figure out how to utilize the best way. >> Shira, give us some predictions for next year, the end of the year, so predictions are coming. We had our meeting this week, or last week on our predictions, so we're going to put you in the hot seat. Your predictions for next year. Hot trends you expect to see. What are you expecting? What's your prediction for next year? Well, I think IoT is going to take a big forefront. Especially with the smarter cities, the smarter homes. As you're talking about the wearables. Artificial intelligence is going to kind of play into that as well, but I think the people are very excited about becoming let's quote unquote smart, no extra steps, right? When you have the no extra steps, remember you're opening yourself up for something, do it smart. But IoT is really expanding itself into every infrastructure whether it being utilizing, engineering. Whether it being cities itself, whether it being homes. And the wearables are also ... If you look at what's going on with Fitbit, then you have the next Apple and then there's something else every other day that you could put on yourself and you could get any information that you want. >> So people are connecting the IoT to the industrial side of their analog to digital. >> Exactly. Yes. Yes. And I think that's going to become a forefront in the next year. >> Right. What do you think of the event here, so far? >> I think the event is terrific. We've had some amazing speakers here and I think they're all highlighting the fact that we have to share expertise and really come together to bypass the problems that are out there and work as a unit, and certainly Centrify is doing a great job here. I'm very happy to be here. >> Great. Well, good luck with everything next year. Thanks for coming on theCUBE, we really appreciate it. >> Shira: Thank you. Happy to be here. That was commentary, great analysis. An opinion here on theCUBE, here at Centrify's event that they're underwriting for the industry as an industry event called CyberConnect presented by Centrify. I'm John Furrier with Dave Vellante, stay tuned for more live coverage here in New York City after this short break. (electronic music)

>> Narrator: Live from Orlando, Florida, it's theCUBE covering Grace Hopper Celebration of Women in Computing brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of the Grace Hopper conference here in Orlando, Florida. I'm your host, Rebecca Knight, along with my cohost, Jeff Frick. We are joined by Yasmine Mustafa. She is the founder of ROAR. Thanks so much for joining us. >> Thank you. >> So ROAR is a self-defense wearable technology for women. Tell our viewers a little bit more about the technology and also really where you got the idea. >> Sure, I got the idea about four years ago. I decided to do something a little bit crazy. I got rid of all my possessions. I got rid of my apartment. I put a backpack on, and I booked a solo trip to South America for six months, and I did it for two reasons. The first reason is refugee, and when I came here, even though I was brought here when I was 15 applying for colleges, I actually found out I was undocumented, so I spent about 10 years working under the table trying to become legalized, and it was a very long, hard battle. It was very difficult to go to school and get a real job, and once I became a US citizen which happened five years ago, I was also able to sell my first company. I had a software company before ROAR. And after those two events, I said, "You know what, I'm 30 years old. "I deserve a break. "I've had a long journey. "I'm going to go celebrate." >> Jeff: Start another long journey. (laughs) >> Yeah, exactly. (laughing) I wanted to travel for so long and I couldn't 'cause when you're undocumented, it's really-- >> Hard to get back into the country. >> And you don't have the right credentials and even after I got my Green Card, I could. You can travel after getting your Green Card but I was so worried that I wouldn't be able to come back 'cause I've heard stories that I intentionally didn't, and so I booked this six-month trip as a way to reward myself and as a way to kind of make up for everything that had happened beforehand, and it was amazing trip. It was really life-changing. When I talk about it, I talk about my life in relation to before the trip and after the trip because it was so transformational, and I went to Spanish school for three weeks, did full Spanish immersions, stayed with a Spanish family in Ecuador, and then I went to Colombia and Argentina, Chile, Bolivia, Peru. I spent a month in each country but as incredible as it was, it was also incredibly eye-opening because everywhere that I went and visited, I just kept hearing story after story of a time a woman had been attacked or abused or harassed, and it really opened my eyes to the violence women face every day, and a week after I came back to Philadelphia, it was in a downtown, when my neighbor went out to her car. It's a horrible story. She was grabbed from behind. she was dragged into an alley. She was severely assaulted, brutally assaulted. When I saw the news story the next day, that was when the light bulb moment hit, and I called up my cofounder, my formal adviser of my last company and told him about it, Anthony Gold, and we ended together to start ROAR for Good, and the concept initially was completely different. We thought the problem was that existing self-defense tools, pepper sprays, tasers was that you have to pull them out of your pocket or your purse for them to be useful, and it's not like you could just be like, "Excuse me. "One second," (laughing) and dig it out, so we thought let's make it wearable so that it's readily accessible. This is when Fitbit was huge, and the initial idea was actually called the macelet, mace in a bracelet, and (laughs) exactly, and as clever as that name was, we found out through market research that it was actually a terrible idea, that the number one fear that women had of self-defense tools is, "I'm afraid I'm going to be overpowered, and my own self-defense device used as a weapon against me," and another one, "What if I use it against myself accidentally?" And when we did more research, we found that existing self-defense tools are actually made by men for other men, and when the market opportunity for women came about, they shrunk it, they shrinked it and pinked it, and they didn't really account for women's needs, so we went back to the drawing board, and we said, "All right, we need to make something "that's stylish but discreet, "something that can call for help, "something that can ward off an attack, "and something that cannot be used "against the person wearing it", and that's how we came up with Athena. >> So do you have one that you can show are yours, what it looks like? >> I do, I do, yes. >> This is what it looks like. >> How it works, okay. >> So it has a magnetic band. Initially it was actually a bracelet, and when we were doing self-defense classes with prototypes, we actually found out the worst place to wear a safety device is on your wrist, and can you guess why? >> Somebody grabs your wrist, grabs your arm, right? >> Exactly, or now you only have the opposite hand to activate it, so we said, "No, we need to make something "that's more readily accessible "where both hands can be free," so we designed it with this magnetic strip so that you can clip it on any which way you want. The most popular options we've seen are purse, pocket bra strap, or lapel, and the way it works is if you feel nervous, if you want someone to watch over you, you triple press the button, and it sends your coordinates to your family and friends showing exactly where you are, and if there is danger, if you really need help right away, you press and hold it for three seconds, and it will also sound an alarm, and in about seven rings, you'll also be able to call emergency number, the local PSAP, 911 center in your neighborhood. >> Wow. >> It's such a great concept. As are so many great inventions are, it's really assembling a bunch of components that already exist, your cellphone, an app on your phone, your network of your contacts, the GPS in your phone, and assembling it in a slightly different way for a very specific application. >> Everything that's commonplace, it's in the device. There's nothing proprietary about it. It's just the way that we put it together. Again, we took existing technology and put it together in a way and tested it to make sure that it's something that can work, and we worked with police officers and self-defense instructors to put it together, which is really eye-opening as well. >> And the other part, if you can explore, it's a different way to interact with 911 so if it is an emergency, you're not picking up the phone, you're not talking but according to your website, it's faster, in a lot of ways, it's more efficient. There's a lot of benefits to a not phone call connection with what traditionally has been the way you ask for help, and how did getting that through, is that a regulatory thing? How did that whole process work? >> That's a great question. It's something that we probably spent about a year working on, and we actually have a partner that does it for us, so this partner, what's really cool about them is that they have a relationship with all 500 PSAPs, so a PSAP is just your local 911 center in your area, and our service is going to be able to to leverage their partnership to be able to connect with all of them. The way their system works is they can actually better track you through their service than your normal cellphone can, which is also really cool, and if you're my emergency contacts and I press this button 'cause I can't call 911 and you're in Orlando, I'm in Philadelphia, it will actually route you to the PSAP in my neighborhood versus your local PSAP so then it saves the time in terms of calling the Orlando PSAP and then having them call the Philadelphia PSAP and then finding me, so we're really, really excited about this opportunity. >> So apart from the technology, I want to talk to you a little bit about funding. Funding is one of the greatest barriers that really, all technologists but in particular, women founders face. Can you describe a little bit about how you went about finding sources of money? You already sold a company by then so you'd already been successful. >> Yes. >> But what about people without the track record? What would you say? >> Sure. I'd love to touch on the social mission aspect at some point too if you don't mind. For funding, I'm very lucky in the sense that my cofounder, he's also founded several companies in the past and fundraising is his thing, so he's been the one to lead it but what we did initially, so we spent about 18 months in product development, and we did a lot of testing, I mean really awkward, we put ourselves in really awkward situations where we went to parks and coffee shops, and showed people this and said, "Why would you not use this? "Tell me why you don't like this," and then we went back to the drawing board and did it again and again, and then we got to the point where people said, "Yeah, I want this. "I want this for my mom. "I want this for my child. "I want this for my college student." But there is a world of difference between, say, yeah, I want it versus buying it, so what we did initially is we actually launched a crowdfunding campaign. We launched an Indiegogo campaign, and for us, it was really a way to test if we really had, we were onto something. We initially had the goal of $40,000. The results really blew us away. We hit that $40,000 goal within the second day, got to 100 by the 10th day, 100,000, and then we ended the campaign with a little bit over 300,000 funding, and that really allowed us to do our seat stage round, and we were lucky from the sense we have a really interesting story. There is a billionaire couple in the UK that found out about us through the campaign after it took off. We had sales in every state in the country, 50 countries worldwide. Ashton Kutcher tweeted about it. It was amazing. It went viral for a little bit, which was incredible, but they learned about it, and then reached out to Indiegogo and said, "We want to meet this team, the company behind this team," and we connected with them, and they immediately put $2 million into the company. We went and met with them in Chicago after they came over, and within three days, we had the money in our bank account, so we got a little bit lucky but having that crowdfunding campaign, the success as validation really helped us to be able to raise that additional funding, and then we went to Ben Franklin Technology Partners, and they put in $250,000, our local economic resource center that does matching, and that's how we raised our initial seed to growth. >> And you mentioned the social mission piece so I want you to tell our viewers a little bit more. >> Yeah, so I, for a long tIme, lived in fear, so being undocumented, not really knowing what could happen, and I'm actually giving a talk tomorrow about my whole journey, and learning about women living in fear in another different way while traveling throughout South America. I didn't want to build a company that just built products and sold them to women that just put the onus on women 'cause it's too common for us to say were you drinking when something happens or don't do, don't wear this, don't go here, and we wanted to change that narrative, hence, the ROAR for Good aspect, and what we found after talking with psychologists and researchers is that violence against women stems from gender discrimination and inequality, and that there is one trait, if taught to young kids when they're most impressionable, can actually reduce violence against women, and that's empathy, and that empathy has actually decreased 40% over the last 20 years, and there is a controversy on whether or not it's something that's learned or innate but wherever you fall in that category, there is no denying that it is falling regardless, so we invest, we have what we call a ROAR Back program, which is we invest a portion of proceeds of every sale to nonprofits that specifically focus on teaching respect and healthy relationships to young kids when it matters most. >> Yasmine, thank you so much for joining us. >> Thank you. >> It's a really exciting technology. Thank you. >> Hopefully we'll see you at Philly. We got to have a Philly show. >> Come to Philly, please. >> So you got Josh as a buddy so-- >> Yes. >> Come on, Josh. We got to have us some Philly. (laughing) >> I'm Rebecca Knight with Jeff Frick. We will have more from Grace Hopper just after this. (light music)

>> Narrator: Live from Las Vegas, it's the Cube, covering VMWorld 2017. Brought to you by VMWare and its ecosystem partners. (bright music) >> Hi, I'm Stu Miniman with the Cube, here with my guest host, Justin Warren. Happy to have a returning Cube alum, but in a different role then we had. It's been a few years. Tal Klein, who is the author of The Punch Escrow. >> Au-tor, please. No, I'm just kidding. (laughing) Tal, thanks so much for joining us. It's great for you to be able to find time to hang out with the tech geeks rather than all the Hollywood people that you've been with recently. (laughing) >> You guys are more interesting. (laughing) >> Well thank you for saying that. So last time we interviewed you, you were working for a sizable tech company. You were talking about things like, you know, virtualization, everything like that. Your Twitter handle's VirtualTal. So how does a guy like that become not only an author but an author that's been optioned for a movie, which those of us that, you know, are geeks and everything are looking at, as a matter of fact, Pac Elsiger this morning said, "we are seeing science fiction become science fact." >> That's right. >> Stu: So tell us a little of the journey. >> Yeah, cool, I hope you read the book. (laughing) I don't know, the journey is really about marketing, right? Cause a lot of times when we talk about virtual, like, in fact last time I was on the Cube, we were talking about the idea that desktops could be virtual. Cause back then it was still this, you know, almost hypothetical notion, like could desktops be virtual, and so today, you know, so much of our life is virtual. So much of the things that we do are not actually direct. I was watching this great video by Apple's new augmented reality product, where you sit in the restaurant and you look at it with your iPad, and it's your plate, and you can just shift the menu items, and you see the menu items on your plate in the context of the restaurant and your seat and the person you're sitting across from. So I think the future is now. >> Yeah, it reminds of, you know, the movie Wall-E, the animated one. We're all going to be sitting in chairs with our devices or Ready Player One, you know, very popular sci-fi book that's being done by Speilberg, I believe. >> Yes, yeah, very exciting. >> Tell us a little bit about your book, you know, we talked, when I was younger and used to read a lot of sci-fi, it was like, what stuff had they done 50 years ago that now's reality, and what stuff had they predicted, like, you know, we're going to go away from currency and go digital currency, and it's like we're almost there. But we still don't have flying cars. >> Yeah, we're, I mean, the main problem with flying cars is that we need pilots. And I think actually we're very close to flying cars, cause once we have self-driving vehicles and we no longer need to worry about it being a person behind the joystick, then we're in really good shape. That's really the issue, you know, the problem with flying cars is that we are so incompetent at driving and or flying. That's not our core competency, so let's just put things that do understand how to make those things happen and eliminate us from the equation. >> Everything is a people problem. >> Yeah, so when I wrote the book, Punch Escrow, Punch Escrow, (laughing) when I wrote the book, I really thought about all the things that I read growing up in science fiction, you know, things like teleportation, things like nanotechnology, things like digital currency, you know, how do we make those, how do we present those in a viable way that doesn't seem too science fictiony. Like one of the things I really get when people read the book is it feels really near-future, even though it's set like 100 plus years in the future, all the concepts in it feel very pragmatic or within reach, you know? >> Yeah, absolutely. It's interesting, we look at, you know, what things happen in a couple of years and what things take a long time. So artificial intelligence, machine learning, it's not like these are new concepts, you know? I read a great book by, you know, it was Isaacson, The Innovators. You go back to like Aida Lovelace, and the idea of what a machine or computer would be able to do. So 100 years from now, what's real, what's not real? We still all have jobs or something? >> We have jobs but different. Remember, I don't know if you're a historian, but back in the industrial age, there was a whole bunch of people screaming doom and gloom. In fact, if we go way back to the age of the Luddites, who just hated machines of any kind. I think that in general, we don't like, you know, we're scared of change. So I do think a lot of the jobs that exist today are going to be done by machines or code. That doesn't mean the jobs are going away. It means jobs are changing. A lot of the jobs that people have today didn't exist in the industrial age. So I think that we have to accept that we are going to be pragmatic enough to accept the fact that humans will continue to evolve as the infrastructure powering our world evolves, you know? We talk about living in the age of the quantified self, right? There's a whole bunch that we don't understand how to do yet. For example, I can think of a whole industry that tethers my FitBit to my nutrition. You know, like there's so much opportunity that for us to say, oh that's going to be the end of jobs, or the end of innovation or the end of capitalism, is insane. I think this just ushers in a whole new age of opportunity. And that's me, I'm just an optimist that way, you know. >> So the Luddites did famously try to destroy the machines. But the thing is, the Luddites weren't wrong. They did lose their jobs. So what about the people whose jobs are replaced, as you say net new, there's a net new number of jobs. But specific individuals, like people who manufacture cars for example, lose their jobs because a robot can do that job safer and better and faster than a human can do it. So what do we do with those humans? Because how do we get people to have new jobs and retrain themselves? >> I address some of these notions in the book. For example, one of the weird things that we're suffering from is the lack of welders in society today, cause welding has become this weird thing that we don't think we need people for, so people don't really get trained up in it because, you know, machines do a lot of welding but there's actually specialty welding that machines can't do. So I think the people who are really good at the things that they do will continue to have careers. I think their careers will become more niche. Therefore they'll be able to create, to demand a higher wage for it because almost like a carpenter, you know, a specialist carpenter will be able to earn a much higher wage today by having fewer customers who want really custom carpentry versus things that can be carved up by a machine. So I think what we end up seeing is that it's not that those jobs go away. It's they become more specialized. People still want Rolls Royces. People still want McLarens. Those are not done by machines. Those are hand-made, you know? >> That's an interesting point, so the value of something being hand-made becomes, instead of it being a worse product, it's actually- >> Tal: That's a big concept in the book. >> Oh okay, right. >> A big concept in the book is that we place a lot of value on the uniqueness of an object. And that parlays in multiple ways. So one of the examples that I use in the book is the value of a Big Mac actually coming from McDonald's. Like, you can make a Big Mac. We know the recipe for a Big Mac. But there is a weird sort of nacent value to getting a Big Mac from McDonald's. It's something in our brain that clicks that tethers it to an originality. Diamonds, another really good example. Or you know, we know there's synthetic diamonds. We still want the ones that get mined in the cave. Why? We don't know. Right, they're just special. >> Because De Beers still has really good marketing. (laughing) >> So I think there's- >> That's interesting, so the concept of uniqueness, which again comes to scarcity and so on. As an author, someone who is no doubt, signed a lot of his book, that means that that book is unique because it's signed by the author, unlike something which is mass produced and there is hopefully thousands and thousands of copies that you sell. >> Going into this, I actually thought about that a lot. And that's why I've created like multiple editions of the book. So like the first 500 people who pre-ordered it, they get like a special edition of the book that's like stamped and all this kind of stuff. I even used different pens. (laughs) I appreciate that because I'm also a collector. I collect music, I collect books. And you know, so I see those aspects in myself. So I know what I value about them, you know? >> And the crossover between music and books is interesting. So as someone who has a musical background, I know that there's a lot of musicians who'll come out with special editions, and you know, because this is an age where we can download it. You can download the book. Do you think there is something, is there something that is intrinsic to having a physical object in a virtual world? >> I think to our generation, yes. I'm not so sure about millennials, when they grow up. But there are, for example, I'm going to see U2 next week, I'm very lucky to see that. But part of the U2 buying experience, to get access to the presale, you need to be part of their fan club. To be a part of their fan club, you need to get, you get like a whole bunch of limited edition posters, limited edition vinyl, and all this kind of stuff. So there's an experience. It's no longer just about going to see U2 at a concert. There's like the entire package of you being a special U2 fan. And they surround it with uniqueness. It's not necessarily limited, but there's an enhanced experience that can't just be, it's not just about you having a ticket to a single concert. >> Justin: Yeah, okay. >> I'm curious, the genre, if you'd call it, is hard science fiction. >> Yes. >> The challenge with that is, you know, what is an extension of what we're doing, and what is fiction? And people probably poke at that. Have you had any interesting experience, things like that? I mean, I've listened to a lot of stuff like Andy Weir, like let the community give feedback before he created the final The Martian. (laughing) But so yeah, what's it like, cause we can, the geeks can be really harsh. >> Yes, I've learned from my Reddit experience that, so what's really funny about it is the first draft of this novel was hard as nails. It was crazy. And my publisher read it, and it would have made all the hard science fiction guys super happy. My publisher read it, he was like, you've written a really great hard science fiction book, and all five people who read it are going to love it. (laughing) You know, but like, I came here with my buddy Danny. He couldn't even get through the first three pages of it. He's like, he wanted to read it. So part of working through the editorial process is saying, look, I care a lot about the science because one of my deep goals is to write a STEM-oriented book that gets people excited about technology and present the future as not a dystopian place. And so I wanted the science to be there and have a sort of gravity to the narrative. But yeah, it's tough. I worked with a physicist, a biologist, a geneticist, an anthropologist, and a lawyer. (laughs) Just to try to figure out, how do we carve out, you know, what does the future look like, what does the evolution of each individual sciences, we talked about the mosquitoes, right? You know, we're already doing a lot of crazy stuff with mosquitoes. We're modifying them so that the males mate with females that carry the Zika virus, you know, give birth to offspring that never reach maturity. I mean, this is just crazy, it's science fiction. And now that they're working on modifying female mosquitoes into vaccine carriers instead of disease carriers. I mean, this is science fiction, right? Like who believes this stuff? It's crazy. >> Christopher is amazing. >> Yeah, I've loved, there's been a bunch of movies recently that have kind of helped to educate on STEM some, you know, Martian got a lot of people excited, you know, Hidden Figures, the one that I could being my kids that are teenagers now into it and they get excited, oh, science is great. So the movie, how much will you be involved? You know, what can you share about that experience, too, so far? >> It's been, it's very surreal. That's the word is use to describe it, the honest, god's honest truth, I mean. I've been very lucky in that my representation in Hollywood is this rock-solid guy called Howie Sanders. And he's this bigger-than-life Hollywood agent guy. He's hooked me up, we've made a lot of business decisions that we're focused less on the money and more on the team, which is nice to be, like when you're in your 40s and you're more financially settled, you're not in the kind of situation where you might be in your 20s and just going to sign the first deal that people give you. So we really focused on hooking up with like the director, James Bovin is, you know, he's the guy who co-created Flight of the Concords. He did the Muppets movie, you know, Alice Through the Looking Glass. Really professional guy but also really understands the tone of the book, which is like humorous, you know, kind of sarcastic. It's not just about the technology. It's also about the characters. Same thing with the production team. The two producers, Mandeville Productions, I was just talking to Todd Lieberman, and we're talking about just what is augmented reality, like how does it look like on the screen? So I'm not- >> It's not going to look like Blade Runner is what I'm hearing. >> (laughs) I don't know. It's going to look real. I imagine, I don't know, they're going to make whatever movie they're going to make, but their perspective, one of the things we talked about is keeping the movie very grounded. Like you know, one of the big questions they ask first going into it is before we even had any sort of movie discussions is like is this more of like a Looper, Gattica, or District Nine, or is it more like The Fifth Element, you know, I mean, is it like, do you want it to be this sort of grounded movie that feels authentic and real and near future or do you want this to be like completely alien and weird and out of it. And the story is more grounded. So I think a lot, hopefully what we display on the screen will not feel that far away from reality. >> Okay, yeah. >> You do marketing in your day job. >> I do. >> I'm curious as you look at this, kind of the balance of educating, reaching a broad audience, you have passion for STEM, what's your thoughts around that? Is it, I worry there's so much general, like television or things like that, when I see the science stuff, it like makes me groan. Because you know, it's like I don't understand that. >> I am the worst, because I got a security background too, so that's the one I get scrambled on. The war, I mean, like. >> Wait, thank goodness I updated my firewall settings because I saved the world from terrorists. >> Hang on, we're breaking through the first firewall. Now we're through the second firewall. (laughing) Now we're going through the third firewall, like 15 firewalls. And let me upload the virus, like all that stuff. It's difficult for me. I think that, you know, hopefully, there's also a group in Hollywood called the Hollywood Science and Entertainment Exchange. And they're a group of scientists who work with film makers on, you know, reigning things in. And film makers don't usually take all their advice, i.e. Interstellar, (laughing) but you know, I think (laughing) in many cases there's some really good ideas that come to play into it that hopefully bring up, like I think Jarvis for example, in Iron Man or the Avengers is a really cool implementation of what the future of AI systems might be like. And I know they used the Hollywood Science Exchange to figure out how is that going to work? And I think the marketing aspect is, you know, the reason I came up with the idea for this book is because my CEO of a company I used to work for, he had this whole conversation about teleportation, like teleportation was impossible. And he's like, it's not because the science, yes, the science is a problem right now, but we'll get over it. The main issue is that nobody would ever step foot into a device that vaporizes them and then printed them out somewhere else. And I said, well that's great, cause that's a marketing problem. (laughing) >> Yeah, you're dead every time you do it. But it's the same you, I can't tell the difference. >> Well, you say you're dead, I'm saying you're just moving. (laughing) >> Artificial intelligence, you know, kind of a big gap between the hype to where we need to go. What's your thoughts on that space in general? >> I think that we have, it's a great question because I feel like that's a term that gets thrown around a lot, and I think as a result it's becoming watered down. So you've this sort of artificial intelligence that comes with like, you know, Google building an app that can beat the world's best Go player, which is a really, really difficult puzzle. The problem is, that app can do one thing, and that's play Go. You put in it a chess game, and it's like I don't know what's going on. >> It's a very specialized kind of intelligence, yeah. >> Now with Open AI, you know, they just had some pretty interesting implementations where they actually played video games with a real live competition and won. Again, you know, but without the smack talk, which really I think would add a lot. Now you got to get an AI to smack talk. So I think the problem is we haven't figured out a really good way of creating a general purpose AI. And there's a lot of parallels to the evolution of computing in general because if you look at how computers were before we had general purpose operating systems like Unix, every computer was built to do a very, very specific function, and that's kind of what AI is right now. So we're still waiting to have a sort of general purpose AI that can do a lot of specialized activities. >> Even most robots are still very single-purpose today. >> That's the fundamental problem. But you're seeing the Cambridge guys are working on sort of the bipedal robot that can do lots of things. And Siri's getting better, Cortana's getting better, Watson's getting better, but we're not there. We still need to find a really good way of integrating deep knowledge with general purpose conversational AI. Cause that's really what you need to like, Stu, what do you need? Here, let me give it to you, you know? >> Do you draw a distinction between AI that's able to simply sort of react as a fairly complex machine or something that can create new things and add something? >> That's in the book as well. So the fundamental thing that I don't think we get around even in the future is giving computers the ability to actually come up with new ideas. There's actually a career, the main job of the protagonist in the book, his job is a salter. And his job is to salt AI algorithms to introduce entropy so they can come up with new ideas. >> Okay, interesting. >> So based off the sort of chaos theory. >> Like chaos monkey, right? >> Yeah. And that's really what you're trying to do is like, okay, react to things that are happening because you can't just come up with them on their own. There's a whole, I don't want to bore you, but there's a whole bunch of stuff in the book about how that works. >> It's like hand-carving ideas that are then mass produced by machines. >> Yeah, I don't know if you guys are going to have Simon Crosby on here, he's kind of like an expert on that. He was the Dean of Kings College, which is where Turing came from. So he really knows a lot about that. He's got a lot of strong ideas about it. But I learned a lot from him in that regard. There's a lot of like, the snarky spirit of Simon Crosby lives on in my book somewhere. But he's just funny cause he's, coming from that field, he immediately sees a lot of BS right off the bat, whenever anybody's presenting. He's got like the ability to just cut through it. Because he understands what it would actually take to make that happen, you know? So I tried to preserve some of that in the book. >> That is refreshing in the tech industry. >> So Tal, I need to let you, you know, wrap this up. Give us a plug for the book, tell us, when are we going to be able to see this on the big screen? >> I don't know about the big screen, but the Punch Escrow is now available. You can get it on Amazon, Barnes and Noble, anywhere books are sold. It's been optioned by Lionsgate. The director attached to it is James Bovin, production team is Mandeville Productions. I'm very excited about it. Go check it out. It's a pretty quick read, reads like a technothriller. It's not too hard. And it's fun for the whole family. I think one of the coolest things about it is that the feedback I've been getting has been that it really is appealing to everybody. I've got mother-in-laws reading it, you know, it's pretty cool. Initially I sold it, my initial audience is like us, but it's kind of cool, like, Stu will finish the book, he'll give it to, you know, wife, daughter, anything, and they're really digging it. So it's kind of fun. >> Justin: Thanks a lot. >> Tal Klein, really appreciate you coming. Congratulations on the book, we look forward to the movie. Maybe, you know, we'll get the Cube involved down the road. (laughing) >> And we're giving away 75 copies of it here at Lakeside booth, if you guys want to come. >> Tal Klein, author of The Punch Escrow, also CMO of Lakeside, who is here in the thing. But yeah, (laughing) a lot of stuff. Justin and I will be back with more coverage here from VMWorld 2017. You're watching the Cube. (bright music)

(clicks) >> Hey welcome back everybody. Jeff Frick here with theCUBE. We're in downtown San Francisco with Zuora Subscribe. About 2,000 people all focused on the subscription economy. And we're looking at some really cool products. We've had GE on, we're going to have Caterpillar on, but this is something new. You know, kind of these medical devices. Fitbit on steroids. I don't know how you describe it. Dr Sumon Pal. He is the cofounder and Chief Scientific Officer for Thync. Welcome. >> Thank you, thank you for having me. >> Absolutely. So give us a little bit of background on Thync, and then we'll jump into the device. >> Absolutely. So we're the first subscription service for wellness and better mental health. >> Okay. >> And the way it works is that there's hardware which is a neuromodulator, and they interface with your skin which is some pads and basically you put this on the back of your neck. There's software, there are programs that come along in the app and what these are are algorithms that have been developed to stimulate certain nerves in the right way. Those nerves in turn connect with your brain stem and that is the center for stress, for sleep cycles, for mood in general. And over the last five years we've developed a way to safely stimulate those nerves, such that you can sleep better, your mood is improved and you can de-stress. >> Okay, so let's back, back way up. You covered like a, you went the whole enchilada there. So you basically did some research. You guys figured out that nerve can stimulation can give better wellness. >> Right. >> And is that just during sleeping hours, during waking hours, all the above, kind of? >> Yeah, so it's both. I mean a session lasts about 10 to 15 minutes. >> Okay. >> In that time, what's happening is that it's dampening the stress response in your body. >> Jeff: Okay >> So if you do this on a daily basis or you do this in the evenings when you come home from work, you are kind of detaching from that stress that's built up during the day. >> Jeff: Without drinking a glass of wine or a bottle of beer. >> Absolutely. Without really any toxicity, without any side effects, without any addiction. Without any of the issues that come along with pills and substances. >> All those other things. Okay so then you put this thing on. >> That's right. >> So you put it on like right after you get home from work, or? >> Sure. >> Or when you go to sleep? Does it make a difference? >> Or if you just had a bad meeting. You had a rough morning. If there's kind of an acute occasion where you're anxious or highly stressed then you can use it then, too. >> Okay, so it's kind of yoga in a box. If I would be so presumptuous. >> Without any effort, right. >> No damage to the knees. >> Right, right. >> All right, super. So, a little bit in terms of the history of the company, so you said this is version two that you just came out with >> Right. Yeah, we've been developing the product, the technology in general for about five years. We've done three published studies. We've tested thousands of subjects. The first product has over two million minutes of use without any adverse side effects or, you know, we know that it's a really safe and powerful method to help people. >> Okay, and what does it retail for? >> So the hardware costs 149. >> Okay. >> And then there's the subscription. And the subscription is because there's a consumable involved, which are these pads. Which are actually a proprietary formulation so that this is absolutely painless, absolutely comfortable. And we have algorithms, so you're actually streaming these programs and those programs are highly complex, changing over time and constantly being updated. So for the software, service, and for the pads you pay either $29.99 a month or you pay $19.99 a month depending on a longer commitment. >> Okay. And when you decided to go with the subscription pricing, versus just selling it and if I need more pads, I order a 12-pack of pads or whatever. What were some of the things you thought about and then what are some of the outcomes that you have found? Both kind of expected and unexpected in having a subscription relationship with your customers. >> Yeah, it's a great question. So, one of the things that's really important about, so stress leads to a huge number of health issues. Everything from cardiovascular issues to being linked with diabetes, to being linked with premature aging. And so it's important to chronically reduce your stress levels. And you want to have all the components around when you need it. It's not one of those things where you've had a terrible day, you're extremely anxious. You know, you want everything to be there. You don't want to go and then order some pads online, order what you need online. >> Right, right. >> So that's one aspect, and the second is that you want access to the programs that are being updated all of the time. And what we find is that when people are on a subscription service, that kind of constant use which is so critical for your health, mental health, general well-being, is maintained in a better way than if you're kind of having to reorder these things or buy them. So really it's about supporting and promoting this kind of continuous regular use and routines. >> And I would presume that then you also get the benefit too 'cause you're getting all those data. >> Absolutely. >> Points that are feeding your algorithms. >> Absolutely. >> So you can make changes to the application, changes to the algorithm. >> That's right and also we have a library, about a thousand programs. And it's also about, we can, for any customer switch out the programs that they have if it's not working for whatever reason. So to kind of rescue people, it's also important to get that data of, what is happening month to month. >> So is a program the sequence of, of, I don't want to say charges but stimulations or whatever. >> Yeah, that's right. >> That set a different pattern, a different frequency and that creates like a program. >> That's right. >> And you experiment to find out what works best for you? >> That's right, it's a lot like music. It's a stimulation pattern that's built in blocks and those blocks change over time. And that is one of the things that we figured out how to do, that no one really had done before. >> Alright, well, pretty exciting stuff. >> Thank you. >> I look forward to watching you guys grow and see how things continue to progress. >> Absolutely, thank you very much. >> Alright, thanks for stopping by theCUBE. Alright, he's Dr. Sumon Pal, I'm Jeff Rick. You're watching theCUBE from Zuora Subscribe. Thanks for watching. (clicks)

>> Welcome to the Intel AI Lounge. Today, we're very excited to share with you the Precision Medicine panel discussion. I'll be moderating the session. My name is Kay Erin. I'm the general manager of Health and Life Sciences at Intel. And I'm excited to share with you these three panelists that we have here. First is John Madison. He is a chief information medical officer and he is part of Kaiser Permanente. We're very excited to have you here. Thank you, John. >> Thank you. >> We also have Naveen Rao. He is the VP and general manager for the Artificial Intelligence Solutions at Intel. He's also the former CEO of Nervana, which was acquired by Intel. And we also have Bob Rogers, who's the chief data scientist at our AI solutions group. So, why don't we get started with our questions. I'm going to ask each of the panelists to talk, introduce themselves, as well as talk about how they got started with AI. So why don't we start with John? >> Sure, so can you hear me okay in the back? Can you hear? Okay, cool. So, I am a recovering evolutionary biologist and a recovering physician and a recovering geek. And I implemented the health record system for the first and largest region of Kaiser Permanente. And it's pretty obvious that most of the useful data in a health record, in lies in free text. So I started up a natural language processing team to be able to mine free text about a dozen years ago. So we can do things with that that you can't otherwise get out of health information. I'll give you an example. I read an article online from the New England Journal of Medicine about four years ago that said over half of all people who have had their spleen taken out were not properly vaccinated for a common form of pneumonia, and when your spleen's missing, you must have that vaccine or you die a very sudden death with sepsis. In fact, our medical director in Northern California's father died of that exact same scenario. So, when I read the article, I went to my structured data analytics team and to my natural language processing team and said please show me everybody who has had their spleen taken out and hasn't been appropriately vaccinated and we ran through about 20 million records in about three hours with the NLP team, and it took about three weeks with a structured data analytics team. That sounds counterintuitive but it actually happened that way. And it's not a competition for time only. It's a competition for quality and sensitivity and specificity. So we were able to indentify all of our members who had their spleen taken out, who should've had a pneumococcal vaccine. We vaccinated them and there are a number of people alive today who otherwise would've died absent that capability. So people don't really commonly associate natural language processing with machine learning, but in fact, natural language processing relies heavily and is the first really, highly successful example of machine learning. So we've done dozens of similar projects, mining free text data in millions of records very efficiently, very effectively. But it really helped advance the quality of care and reduce the cost of care. It's a natural step forward to go into the world of personalized medicine with the arrival of a 100-dollar genome, which is actually what it costs today to do a full genome sequence. Microbiomics, that is the ecosystem of bacteria that are in every organ of the body actually. And we know now that there is a profound influence of what's in our gut and how we metabolize drugs, what diseases we get. You can tell in a five year old, whether or not they were born by a vaginal delivery or a C-section delivery by virtue of the bacteria in the gut five years later. So if you look at the complexity of the data that exists in the genome, in the microbiome, in the health record with free text and you look at all the other sources of data like this streaming data from my wearable monitor that I'm part of a research study on Precision Medicine out of Stanford, there is a vast amount of disparate data, not to mention all the imaging, that really can collectively produce much more useful information to advance our understanding of science, and to advance our understanding of every individual. And then we can do the mash up of a much broader range of science in health care with a much deeper sense of data from an individual and to do that with structured questions and structured data is very yesterday. The only way we're going to be able to disambiguate those data and be able to operate on those data in concert and generate real useful answers from the broad array of data types and the massive quantity of data, is to let loose machine learning on all of those data substrates. So my team is moving down that pathway and we're very excited about the future prospects for doing that. >> Yeah, great. I think that's actually some of the things I'm very excited about in the future with some of the technologies we're developing. My background, I started actually being fascinated with computation in biological forms when I was nine. Reading and watching sci-fi, I was kind of a big dork which I pretty much still am. I haven't really changed a whole lot. Just basically seeing that machines really aren't all that different from biological entities, right? We are biological machines and kind of understanding how a computer works and how we engineer those things and trying to pull together concepts that learn from biology into that has always been a fascination of mine. As an undergrad, I was in the EE, CS world. Even then, I did some research projects around that. I worked in the industry for about 10 years designing chips, microprocessors, various kinds of ASICs, and then actually went back to school, quit my job, got a Ph.D. in neuroscience, computational neuroscience, to specifically understand what's the state of the art. What do we really understand about the brain? And are there concepts that we can take and bring back? Inspiration's always been we want to... We watch birds fly around. We want to figure out how to make something that flies. We extract those principles, and then build a plane. Don't necessarily want to build a bird. And so Nervana's really was the combination of all those experiences, bringing it together. Trying to push computation in a new a direction. Now, as part of Intel, we can really add a lot of fuel to that fire. I'm super excited to be part of Intel in that the technologies that we were developing can really proliferate and be applied to health care, can be applied to Internet, can be applied to every facet of our lives. And some of the examples that John mentioned are extremely exciting right now and these are things we can do today. And the generality of these solutions are just really going to hit every part of health care. I mean from a personal viewpoint, my whole family are MDs. I'm sort of the black sheep of the family. I don't have an MD. And it's always been kind of funny to me that knowledge is concentrated in a few individuals. Like you have a rare tumor or something like that, you need the guy who knows how to read this MRI. Why? Why is it like that? Can't we encapsulate that knowledge into a computer or into an algorithm, and democratize it. And the reason we couldn't do it is we just didn't know how. And now we're really getting to a point where we know how to do that. And so I want that capability to go to everybody. It'll bring the cost of healthcare down. It'll make all of us healthier. That affects everything about our society. So that's really what's exciting about it to me. >> That's great. So, as you heard, I'm Bob Rogers. I'm chief data scientist for analytics and artificial intelligence solutions at Intel. My mission is to put powerful analytics in the hands of every decision maker and when I think about Precision Medicine, decision makers are not just doctors and surgeons and nurses, but they're also case managers and care coordinators and probably most of all, patients. So the mission is really to put powerful analytics and AI capabilities in the hands of everyone in health care. It's a very complex world and we need tools to help us navigate it. So my background, I started with a Ph.D. in physics and I was computer modeling stuff, falling into super massive black holes. And there's a lot of applications for that in the real world. No, I'm kidding. (laughter) >> John: There will be, I'm sure. Yeah, one of these days. Soon as we have time travel. Okay so, I actually, about 1991, I was working on my post doctoral research, and I heard about neural networks, these things that could compute the way the brain computes. And so, I started doing some research on that. I wrote some papers and actually, it was an interesting story. The problem that we solved that got me really excited about neural networks, which have become deep learning, my office mate would come in. He was this young guy who was about to go off to grad school. He'd come in every morning. "I hate my project." Finally, after two weeks, what's your project? What's the problem? It turns out he had to circle these little fuzzy spots on these images from a telescope. So they were looking for the interesting things in a sky survey, and he had to circle them and write down their coordinates all summer. Anyone want to volunteer to do that? No? Yeah, he was very unhappy. So we took the first two weeks of data that he created doing his work by hand, and we trained an artificial neural network to do his summer project and finished it in about eight hours of computing. (crowd laughs) And so he was like yeah, this is amazing. I'm so happy. And we wrote a paper. I was the first author of course, because I was the senior guy at age 24. And he was second author. His first paper ever. He was very, very excited. So we have to fast forward about 20 years. His name popped up on the Internet. And so it caught my attention. He had just won the Nobel Prize in physics. (laughter) So that's where artificial intelligence will get you. (laughter) So thanks Naveen. Fast forwarding, I also developed some time series forecasting capabilities that allowed me to create a hedge fund that I ran for 12 years. After that, I got into health care, which really is the center of my passion. Applying health care to figuring out how to get all the data from all those siloed sources, put it into the cloud in a secure way, and analyze it so you can actually understand those cases that John was just talking about. How do you know that that person had had a splenectomy and that they needed to get that pneumovax? You need to be able to search all the data, so we used AI, natural language processing, machine learning, to do that and then two years ago, I was lucky enough to join Intel and, in the intervening time, people like Naveen actually thawed the AI winter and we're really in a spring of amazing opportunities with AI, not just in health care but everywhere, but of course, the health care applications are incredibly life saving and empowering so, excited to be here on this stage with you guys. >> I just want to cue off of your comment about the role of physics in AI and health care. So the field of microbiomics that I referred to earlier, bacteria in our gut. There's more bacteria in our gut than there are cells in our body. There's 100 times more DNA in that bacteria than there is in the human genome. And we're now discovering a couple hundred species of bacteria a year that have never been identified under a microscope just by their DNA. So it turns out the person who really catapulted the study and the science of microbiomics forward was an astrophysicist who did his Ph.D. in Steven Hawking's lab on the collision of black holes and then subsequently, put the other team in a virtual reality, and he developed the first super computing center and so how did he get an interest in microbiomics? He has the capacity to do high performance computing and the kind of advanced analytics that are required to look at a 100 times the volume of 3.2 billion base pairs of the human genome that are represented in the bacteria in our gut, and that has unleashed the whole science of microbiomics, which is going to really turn a lot of our assumptions of health and health care upside down. >> That's great, I mean, that's really transformational. So a lot of data. So I just wanted to let the audience know that we want to make this an interactive session, so I'll be asking for questions in a little bit, but I will start off with one question so that you can think about it. So I wanted to ask you, it looks like you've been thinking a lot about AI over the years. And I wanted to understand, even though AI's just really starting in health care, what are some of the new trends or the changes that you've seen in the last few years that'll impact how AI's being used going forward? >> So I'll start off. There was a paper published by a guy by the name of Tegmark at Harvard last summer that, for the first time, explained why neural networks are efficient beyond any mathematical model we predict. And the title of the paper's fun. It's called Deep Learning Versus Cheap Learning. So there were two sort of punchlines of the paper. One is is that the reason that mathematics doesn't explain the efficiency of neural networks is because there's a higher order of mathematics called physics. And the physics of the underlying data structures determined how efficient you could mine those data using machine learning tools. Much more so than any mathematical modeling. And so the second thing that was a reel from that paper is that the substrate of the data that you're operating on and the natural physics of those data have inherent levels of complexity that determine whether or not a 12th layer of neural net will get you where you want to go really fast, because when you do the modeling, for those math geeks in the audience, a factorial. So if there's 12 layers, there's 12 factorial permutations of different ways you could sequence the learning through those data. When you have 140 layers of a neural net, it's a much, much, much bigger number of permutations and so you end up being hardware-bound. And so, what Max Tegmark basically said is you can determine whether to do deep learning or cheap learning based upon the underlying physics of the data substrates you're operating on and have a good insight into how to optimize your hardware and software approach to that problem. >> So another way to put that is that neural networks represent the world in the way the world is sort of built. >> Exactly. >> It's kind of hierarchical. It's funny because, sort of in retrospect, like oh yeah, that kind of makes sense. But when you're thinking about it mathematically, we're like well, anything... The way a neural can represent any mathematical function, therfore, it's fully general. And that's the way we used to look at it, right? So now we're saying, well actually decomposing the world into different types of features that are layered upon each other is actually a much more efficient, compact representation of the world, right? I think this is actually, precisely the point of kind of what you're getting at. What's really exciting now is that what we were doing before was sort of building these bespoke solutions for different kinds of data. NLP, natural language processing. There's a whole field, 25 plus years of people devoted to figuring out features, figuring out what structures make sense in this particular context. Those didn't carry over at all to computer vision. Didn't carry over at all to time series analysis. Now, with neural networks, we've seen it at Nervana, and now part of Intel, solving customers' problems. We apply a very similar set of techniques across all these different types of data domains and solve them. All data in the real world seems to be hierarchical. You can decompose it into this hierarchy. And it works really well. Our brains are actually general structures. As a neuroscientist, you can look at different parts of your brain and there are differences. Something that takes in visual information, versus auditory information is slightly different but they're much more similar than they are different. So there is something invariant, something very common between all of these different modalities and we're starting to learn that. And this is extremely exciting to me trying to understand the biological machine that is a computer, right? We're figurig it out, right? >> One of the really fun things that Ray Chrisfall likes to talk about is, and it falls in the genre of biomimmicry, and how we actually replicate biologic evolution in our technical solutions so if you look at, and we're beginning to understand more and more how real neural nets work in our cerebral cortex. And it's sort of a pyramid structure so that the first pass of a broad base of analytics, it gets constrained to the next pass, gets constrained to the next pass, which is how information is processed in the brain. So we're discovering increasingly that what we've been evolving towards, in term of architectures of neural nets, is approximating the architecture of the human cortex and the more we understand the human cortex, the more insight we get to how to optimize neural nets, so when you think about it, with millions of years of evolution of how the cortex is structured, it shouldn't be a surprise that the optimization protocols, if you will, in our genetic code are profoundly efficient in how they operate. So there's a real role for looking at biologic evolutionary solutions, vis a vis technical solutions, and there's a friend of mine who worked with who worked with George Church at Harvard and actually published a book on biomimmicry and they wrote the book completely in DNA so if all of you have your home DNA decoder, you can actually read the book on your DNA reader, just kidding. >> There's actually a start up I just saw in the-- >> Read-Write DNA, yeah. >> Actually it's a... He writes something. What was it? (response from crowd member) Yeah, they're basically encoding information in DNA as a storage medium. (laughter) The company, right? >> Yeah, that same friend of mine who coauthored that biomimmicry book in DNA also did the estimate of the density of information storage. So a cubic centimeter of DNA can store an hexabyte of data. I mean that's mind blowing. >> Naveen: Highly done soon. >> Yeah that's amazing. Also you hit upon a really important point there, that one of the things that's changed is... Well, there are two major things that have changed in my perception from let's say five to 10 years ago, when we were using machine learning. You could use data to train models and make predictions to understand complex phenomena. But they had limited utility and the challenge was that if I'm trying to build on these things, I had to do a lot of work up front. It was called feature engineering. I had to do a lot of work to figure out what are the key attributes of that data? What are the 10 or 20 or 100 pieces of information that I should pull out of the data to feed to the model, and then the model can turn it into a predictive machine. And so, what's really exciting about the new generation of machine learning technology, and particularly deep learning, is that it can actually learn from example data those features without you having to do any preprogramming. That's why Naveen is saying you can take the same sort of overall approach and apply it to a bunch of different problems. Because you're not having to fine tune those features. So at the end of the day, the two things that have changed to really enable this evolution is access to more data, and I'd be curious to hear from you where you're seeing data come from, what are the strategies around that. So access to data, and I'm talking millions of examples. So 10,000 examples most times isn't going to cut it. But millions of examples will do it. And then, the other piece is the computing capability to actually take millions of examples and optimize this algorithm in a single lifetime. I mean, back in '91, when I started, we literally would have thousands of examples and it would take overnight to run the thing. So now in the world of millions, and you're putting together all of these combinations, the computing has changed a lot. I know you've made some revolutionary advances in that. But I'm curious about the data. Where are you seeing interesting sources of data for analytics? >> So I do some work in the genomics space and there are more viable permutations of the human genome than there are people who have ever walked the face of the earth. And the polygenic determination of a phenotypic expression translation, what are genome does to us in our physical experience in health and disease is determined by many, many genes and the interaction of many, many genes and how they are up and down regulated. And the complexity of disambiguating which 27 genes are affecting your diabetes and how are they up and down regulated by different interventions is going to be different than his. It's going to be different than his. And we already know that there's four or five distinct genetic subtypes of type II diabetes. So physicians still think there's one disease called type II diabetes. There's actually at least four or five genetic variants that have been identified. And so, when you start thinking about disambiguating, particularly when we don't know what 95 percent of DNA does still, what actually is the underlining cause, it will require this massive capability of developing these feature vectors, sometimes intuiting it, if you will, from the data itself. And other times, taking what's known knowledge to develop some of those feature vectors, and be able to really understand the interaction of the genome and the microbiome and the phenotypic data. So the complexity is high and because the variation complexity is high, you do need these massive members. Now I'm going to make a very personal pitch here. So forgive me, but if any of you have any role in policy at all, let me tell you what's happening right now. The Genomic Information Nondiscrimination Act, so called GINA, written by a friend of mine, passed a number of years ago, says that no one can be discriminated against for health insurance based upon their genomic information. That's cool. That should allow all of you to feel comfortable donating your DNA to science right? Wrong. You are 100% unprotected from discrimination for life insurance, long term care and disability. And it's being practiced legally today and there's legislation in the House, in mark up right now to completely undermine the existing GINA legislation and say that whenever there's another applicable statute like HIPAA, that the GINA is irrelevant, that none of the fines and penalties are applicable at all. So we need a ton of data to be able to operate on. We will not be getting a ton of data to operate on until we have the kind of protection we need to tell people, you can trust us. You can give us your data, you will not be subject to discrimination. And that is not the case today. And it's being further undermined. So I want to make a plea to any of you that have any policy influence to go after that because we need this data to help the understanding of human health and disease and we're not going to get it when people look behind the curtain and see that discrimination is occurring today based upon genetic information. >> Well, I don't like the idea of being discriminated against based on my DNA. Especially given how little we actually know. There's so much complexity in how these things unfold in our own bodies, that I think anything that's being done is probably childishly immature and oversimplifying. So it's pretty rough. >> I guess the translation here is that we're all unique. It's not just a Disney movie. (laughter) We really are. And I think one of the strengths that I'm seeing, kind of going back to the original point, of these new techniques is it's going across different data types. It will actually allow us to learn more about the uniqueness of the individual. It's not going to be just from one data source. They were collecting data from many different modalities. We're collecting behavioral data from wearables. We're collecting things from scans, from blood tests, from genome, from many different sources. The ability to integrate those into a unified picture, that's the important thing that we're getting toward now. That's what I think is going to be super exciting here. Think about it, right. I can tell you to visual a coin, right? You can visualize a coin. Not only do you visualize it. You also know what it feels like. You know how heavy it is. You have a mental model of that from many different perspectives. And if I take away one of those senses, you can still identify the coin, right? If I tell you to put your hand in your pocket, and pick out a coin, you probably can do that with 100% reliability. And that's because we have this generalized capability to build a model of something in the world. And that's what we need to do for individuals is actually take all these different data sources and come up with a model for an individual and you can actually then say what drug works best on this. What treatment works best on this? It's going to get better with time. It's not going to be perfect, because this is what a doctor does, right? A doctor who's very experienced, you're a practicing physician right? Back me up here. That's what you're doing. You basically have some categories. You're taking information from the patient when you talk with them, and you're building a mental model. And you apply what you know can work on that patient, right? >> I don't have clinic hours anymore, but I do take care of many friends and family. (laughter) >> You used to, you used to. >> I practiced for many years before I became a full-time geek. >> I thought you were a recovering geek. >> I am. (laughter) I do more policy now. >> He's off the wagon. >> I just want to take a moment and see if there's anyone from the audience who would like to ask, oh. Go ahead. >> We've got a mic here, hang on one second. >> I have tons and tons of questions. (crosstalk) Yes, so first of all, the microbiome and the genome are really complex. You already hit about that. Yet most of the studies we do are small scale and we have difficulty repeating them from study to study. How are we going to reconcile all that and what are some of the technical hurdles to get to the vision that you want? >> So primarily, it's been the cost of sequencing. Up until a year ago, it's $1000, true cost. Now it's $100, true cost. And so that barrier is going to enable fairly pervasive testing. It's not a real competitive market becaue there's one sequencer that is way ahead of everybody else. So the price is not $100 yet. The cost is below $100. So as soon as there's competition to drive the cost down, and hopefully, as soon as we all have the protection we need against discrimination, as I mentioned earlier, then we will have large enough sample sizes. And so, it is our expectation that we will be able to pool data from local sources. I chair the e-health work group at the Global Alliance for Genomics and Health which is working on this very issue. And rather than pooling all the data into a single, common repository, the strategy, and we're developing our five-year plan in a month in London, but the goal is to have a federation of essentially credentialed data enclaves. That's a formal method. HHS already does that so you can get credentialed to search all the data that Medicare has on people that's been deidentified according to HIPPA. So we want to provide the same kind of service with appropriate consent, at an international scale. And there's a lot of nations that are talking very much about data nationality so that you can't export data. So this approach of a federated model to get at data from all the countries is important. The other thing is a block-chain technology is going to be very profoundly useful in this context. So David Haussler of UC Santa Cruz is right now working on a protocol using an open block-chain, public ledger, where you can put out. So for any typical cancer, you may have a half dozen, what are called sematic variance. Cancer is a genetic disease so what has mutated to cause it to behave like a cancer? And if we look at those biologically active sematic variants, publish them on a block chain that's public, so there's not enough data there to reidentify the patient. But if I'm a physician treating a woman with breast cancer, rather than say what's the protocol for treating a 50-year-old woman with this cell type of cancer, I can say show me all the people in the world who have had this cancer at the age of 50, wit these exact six sematic variants. Find the 200 people worldwide with that. Ask them for consent through a secondary mechanism to donate everything about their medical record, pool that information of the core of 200 that exactly resembles the one sitting in front of me, and find out, of the 200 ways they were treated, what got the best results. And so, that's the kind of future where a distributed, federated architecture will allow us to query and obtain a very, very relevant cohort, so we can basically be treating patients like mine, sitting right in front of me. Same thing applies for establishing research cohorts. There's some very exciting stuff at the convergence of big data analytics, machine learning, and block chaining. >> And this is an area that I'm really excited about and I think we're excited about generally at Intel. They actually have something called the Collaborative Cancer Cloud, which is this kind of federated model. We have three different academic research centers. Each of them has a very sizable and valuable collection of genomic data with phenotypic annotations. So you know, pancreatic cancer, colon cancer, et cetera, and we've actually built a secure computing architecture that can allow a person who's given the right permissions by those organizations to ask a specific question of specific data without ever sharing the data. So the idea is my data's really important to me. It's valuable. I want us to be able to do a study that gets the number from the 20 pancreatic cancer patients in my cohort, up to the 80 that we have in the whole group. But I can't do that if I'm going to just spill my data all over the world. And there are HIPAA and compliance reasons for that. There are business reasons for that. So what we've built at Intel is this platform that allows you to do different kinds of queries on this genetic data. And reach out to these different sources without sharing it. And then, the work that I'm really involved in right now and that I'm extremely excited about... This also touches on something that both of you said is it's not sufficient to just get the genome sequences. You also have to have the phenotypic data. You have to know what cancer they've had. You have to know that they've been treated with this drug and they've survived for three months or that they had this side effect. That clinical data also needs to be put together. It's owned by other organizations, right? Other hospitals. So the broader generalization of the Collaborative Cancer Cloud is something we call the data exchange. And it's a misnomer in a sense that we're not actually exchanging data. We're doing analytics on aggregated data sets without sharing it. But it really opens up a world where we can have huge populations and big enough amounts of data to actually train these models and draw the thread in. Of course, that really then hits home for the techniques that Nervana is bringing to the table, and of course-- >> Stanford's one of your academic medical centers? >> Not for that Collaborative Cancer Cloud. >> The reason I mentioned Standford is because the reason I'm wearing this FitBit is because I'm a research subject at Mike Snyder's, the chair of genetics at Stanford, IPOP, intrapersonal omics profile. So I was fully sequenced five years ago and I get four full microbiomes. My gut, my mouth, my nose, my ears. Every three months and I've done that for four years now. And about a pint of blood. And so, to your question of the density of data, so a lot of the problem with applying these techniques to health care data is that it's basically a sparse matrix and there's a lot of discontinuities in what you can find and operate on. So what Mike is doing with the IPOP study is much the same as you described. Creating a highly dense longitudinal set of data that will help us mitigate the sparse matrix problem. (low volume response from audience member) Pardon me. >> What's that? (low volume response) (laughter) >> Right, okay. >> John: Lost the school sample. That's got to be a new one I've heard now. >> Okay, well, thank you so much. That was a great question. So I'm going to repeat this and ask if there's another question. You want to go ahead? >> Hi, thanks. So I'm a journalist and I report a lot on these neural networks, a system that's beter at reading mammograms than your human radiologists. Or a system that's better at predicting which patients in the ICU will get sepsis. These sort of fascinating academic studies that I don't really see being translated very quickly into actual hospitals or clinical practice. Seems like a lot of the problems are regulatory, or liability, or human factors, but how do you get past that and really make this stuff practical? >> I think there's a few things that we can do there and I think the proof points of the technology are really important to start with in this specific space. In other places, sometimes, you can start with other things. But here, there's a real confidence problem when it comes to health care, and for good reason. We have doctors trained for many, many years. School and then residencies and other kinds of training. Because we are really, really conservative with health care. So we need to make sure that technology's well beyond just the paper, right? These papers are proof points. They get people interested. They even fuel entire grant cycles sometimes. And that's what we need to happen. It's just an inherent problem, its' going to take a while. To get those things to a point where it's like well, I really do trust what this is saying. And I really think it's okay to now start integrating that into our standard of care. I think that's where you're seeing it. It's frustrating for all of us, believe me. I mean, like I said, I think personally one of the biggest things, I want to have an impact. Like when I go to my grave, is that we used machine learning to improve health care. We really do feel that way. But it's just not something we can do very quickly and as a business person, I don't actually look at those use cases right away because I know the cycle is just going to be longer. >> So to your point, the FDA, for about four years now, has understood that the process that has been given to them by their board of directors, otherwise known as Congress, is broken. And so they've been very actively seeking new models of regulation and what's really forcing their hand is regulation of devices and software because, in many cases, there are black box aspects of that and there's a black box aspect to machine learning. Historically, Intel and others are making inroads into providing some sort of traceability and transparency into what happens in that black box rather than say, overall we get better results but once in a while we kill somebody. Right? So there is progress being made on that front. And there's a concept that I like to use. Everyone knows Ray Kurzweil's book The Singularity Is Near? Well, I like to think that diadarity is near. And the diadarity is where you have human transparency into what goes on in the black box and so maybe Bob, you want to speak a little bit about... You mentioned that, in a prior discussion, that there's some work going on at Intel there. >> Yeah, absolutely. So we're working with a number of groups to really build tools that allow us... In fact Naveen probably can talk in even more detail than I can, but there are tools that allow us to actually interrogate machine learning and deep learning systems to understand, not only how they respond to a wide variety of situations but also where are there biases? I mean, one of the things that's shocking is that if you look at the clinical studies that our drug safety rules are based on, 50 year old white guys are the peak of that distribution, which I don't see any problem with that, but some of you out there might not like that if you're taking a drug. So yeah, we want to understand what are the biases in the data, right? And so, there's some new technologies. There's actually some very interesting data-generative technologies. And this is something I'm also curious what Naveen has to say about, that you can generate from small sets of observed data, much broader sets of varied data that help probe and fill in your training for some of these systems that are very data dependent. So that takes us to a place where we're going to start to see deep learning systems generating data to train other deep learning systems. And they start to sort of go back and forth and you start to have some very nice ways to, at least, expose the weakness of these underlying technologies. >> And that feeds back to your question about regulatory oversight of this. And there's the fascinating, but little known origin of why very few women are in clinical studies. Thalidomide causes birth defects. So rather than say pregnant women can't be enrolled in drug trials, they said any woman who is at risk of getting pregnant cannot be enrolled. So there was actually a scientific meritorious argument back in the day when they really didn't know what was going to happen post-thalidomide. So it turns out that the adverse, unintended consequence of that decision was we don't have data on women and we know in certain drugs, like Xanax, that the metabolism is so much slower, that the typical dosing of Xanax is women should be less than half of that for men. And a lot of women have had very serious adverse effects by virtue of the fact that they weren't studied. So the point I want to illustrate with that is that regulatory cycles... So people have known for a long time that was like a bad way of doing regulations. It should be changed. It's only recently getting changed in any meaningful way. So regulatory cycles and legislative cycles are incredibly slow. The rate of exponential growth in technology is exponential. And so there's impedance mismatch between the cycle time for regulation cycle time for innovation. And what we need to do... I'm working with the FDA. I've done four workshops with them on this very issue. Is that they recognize that they need to completely revitalize their process. They're very interested in doing it. They're not resisting it. People think, oh, they're bad, the FDA, they're resisting. Trust me, there's nobody on the planet who wants to revise these review processes more than the FDA itself. And so they're looking at models and what I recommended is global cloud sourcing and the FDA could shift from a regulatory role to one of doing two things, assuring the people who do their reviews are competent, and assuring that their conflicts of interest are managed, because if you don't have a conflict of interest in this very interconnected space, you probably don't know enough to be a reviewer. So there has to be a way to manage the conflict of interest and I think those are some of the keypoints that the FDA is wrestling with because there's type one and type two errors. If you underregulate, you end up with another thalidomide and people born without fingers. If you overregulate, you prevent life saving drugs from coming to market. So striking that balance across all these different technologies is extraordinarily difficult. If it were easy, the FDA would've done it four years ago. It's very complicated. >> Jumping on that question, so all three of you are in some ways entrepreneurs, right? Within your organization or started companies. And I think it would be good to talk a little bit about the business opportunity here, where there's a huge ecosystem in health care, different segments, biotech, pharma, insurance payers, etc. Where do you see is the ripe opportunity or industry, ready to really take this on and to make AI the competitive advantage. >> Well, the last question also included why aren't you using the result of the sepsis detection? We do. There were six or seven published ways of doing it. We did our own data, looked at it, we found a way that was superior to all the published methods and we apply that today, so we are actually using that technology to change clinical outcomes. As far as where the opportunities are... So it's interesting. Because if you look at what's going to be here in three years, we're not going to be using those big data analytics models for sepsis that we are deploying today, because we're just going to be getting a tiny aliquot of blood, looking for the DNA or RNA of any potential infection and we won't have to infer that there's a bacterial infection from all these other ancillary, secondary phenomenon. We'll see if the DNA's in the blood. So things are changing so fast that the opportunities that people need to look for are what are generalizable and sustainable kind of wins that are going to lead to a revenue cycle that are justified, a venture capital world investing. So there's a lot of interesting opportunities in the space. But I think some of the biggest opportunities relate to what Bob has talked about in bringing many different disparate data sources together and really looking for things that are not comprehensible in the human brain or in traditional analytic models. >> I think we also got to look a little bit beyond direct care. We're talking about policy and how we set up standards, these kinds of things. That's one area. That's going to drive innovation forward. I completely agree with that. Direct care is one piece. How do we scale out many of the knowledge kinds of things that are embedded into one person's head and get them out to the world, democratize that. Then there's also development. The underlying technology's of medicine, right? Pharmaceuticals. The traditional way that pharmaceuticals is developed is actually kind of funny, right? A lot of it was started just by chance. Penicillin, a very famous story right? It's not that different today unfortunately, right? It's conceptually very similar. Now we've got more science behind it. We talk about domains and interactions, these kinds of things but fundamentally, the problem is what we in computer science called NP hard, it's too difficult to model. You can't solve it analytically. And this is true for all these kinds of natural sorts of problems by the way. And so there's a whole field around this, molecular dynamics and modeling these sorts of things, that are actually being driven forward by these AI techniques. Because it turns out, our brain doesn't do magic. It actually doesn't solve these problems. It approximates them very well. And experience allows you to approximate them better and better. Actually, it goes a little bit to what you were saying before. It's like simulations and forming your own networks and training off each other. There are these emerging dynamics. You can simulate steps of physics. And you come up with a system that's much too complicated to ever solve. Three pool balls on a table is one such system. It seems pretty simple. You know how to model that, but it actual turns out you can't predict where a balls going to be once you inject some energy into that table. So something that simple is already too complex. So neural network techniques actually allow us to start making those tractable. These NP hard problems. And things like molecular dynamics and actually understanding how different medications and genetics will interact with each other is something we're seeing today. And so I think there's a huge opportunity there. We've actually worked with customers in this space. And I'm seeing it. Like Rosch is acquiring a few different companies in space. They really want to drive it forward, using big data to drive drug development. It's kind of counterintuitive. I never would've thought it had I not seen it myself. >> And there's a big related challenge. Because in personalized medicine, there's smaller and smaller cohorts of people who will benefit from a drug that still takes two billion dollars on average to develop. That is unsustainable. So there's an economic imperative of overcoming the cost and the cycle time for drug development. >> I want to take a go at this question a little bit differently, thinking about not so much where are the industry segments that can benefit from AI, but what are the kinds of applications that I think are most impactful. So if this is what a skilled surgeon needs to know at a particular time to care properly for a patient, this is where most, this area here, is where most surgeons are. They are close to the maximum knowledge and ability to assimilate as they can be. So it's possible to build complex AI that can pick up on that one little thing and move them up to here. But it's not a gigantic accelerator, amplifier of their capability. But think about other actors in health care. I mentioned a couple of them earlier. Who do you think the least trained actor in health care is? >> John: Patients. >> Yes, the patients. The patients are really very poorly trained, including me. I'm abysmal at figuring out who to call and where to go. >> Naveen: You know as much the doctor right? (laughing) >> Yeah, that's right. >> My doctor friends always hate that. Know your diagnosis, right? >> Yeah, Dr. Google knows. So the opportunities that I see that are really, really exciting are when you take an AI agent, like sometimes I like to call it contextually intelligent agent, or a CIA, and apply it to a problem where a patient has a complex future ahead of them that they need help navigating. And you use the AI to help them work through. Post operative. You've got PT. You've got drugs. You've got to be looking for side effects. An agent can actually help you navigate. It's like your own personal GPS for health care. So it's giving you the inforamation that you need about you for your care. That's my definition of Precision Medicine. And it can include genomics, of course. But it's much bigger. It's that broader picture and I think that a sort of agent way of thinking about things and filling in the gaps where there's less training and more opportunity, is very exciting. >> Great start up idea right there by the way. >> Oh yes, right. We'll meet you all out back for the next start up. >> I had a conversation with the head of the American Association of Medical Specialties just a couple of days ago. And what she was saying, and I'm aware of this phenomenon, but all of the medical specialists are saying, you're killing us with these stupid board recertification trivia tests that you're giving us. So if you're a cardiologist, you have to remember something that happens in one in 10 million people, right? And they're saying that irrelevant anymore, because we've got advanced decision support coming. We have these kinds of analytics coming. Precisely what you're saying. So it's human augmentation of decision support that is coming at blazing speed towards health care. So in that context, it's much more important that you have a basic foundation, you know how to think, you know how to learn, and you know where to look. So we're going to be human-augmented learning systems much more so than in the past. And so the whole recertification process is being revised right now. (inaudible audience member speaking) Speak up, yeah. (person speaking) >> What makes it fathomable is that you can-- (audience member interjects inaudibly) >> Sure. She was saying that our brain is really complex and large and even our brains don't know how our brains work, so... are there ways to-- >> What hope do we have kind of thing? (laughter) >> It's a metaphysical question. >> It circles all the way down, exactly. It's a great quote. I mean basically, you can decompose every system. Every complicated system can be decomposed into simpler, emergent properties. You lose something perhaps with each of those, but you get enough to actually understand most of the behavior. And that's really how we understand the world. And that's what we've learned in the last few years what neural network techniques can allow us to do. And that's why our brain can understand our brain. (laughing) >> Yeah, I'd recommend reading Chris Farley's last book because he addresses that issue in there very elegantly. >> Yeah we're seeing some really interesting technologies emerging right now where neural network systems are actually connecting other neural network systems in networks. You can see some very compelling behavior because one of the things I like to distinguish AI versus traditional analytics is we used to have question-answering systems. I used to query a database and create a report to find out how many widgets I sold. Then I started using regression or machine learning to classify complex situations from this is one of these and that's one of those. And then as we've moved more recently, we've got these AI-like capabilities like being able to recognize that there's a kitty in the photograph. But if you think about it, if I were to show you a photograph that happened to have a cat in it, and I said, what's the answer, you'd look at me like, what are you talking about? I have to know the question. So where we're cresting with these connected sets of neural systems, and with AI in general, is that the systems are starting to be able to, from the context, understand what the question is. Why would I be asking about this picture? I'm a marketing guy, and I'm curious about what Legos are in the thing or what kind of cat it is. So it's being able to ask a question, and then take these question-answering systems, and actually apply them so that's this ability to understand context and ask questions that we're starting to see emerge from these more complex hierarchical neural systems. >> There's a person dying to ask a question. >> Sorry. You have hit on several different topics that all coalesce together. You mentioned personalized models. You mentioned AI agents that could help you as you're going through a transitionary period. You mentioned data sources, especially across long time periods. Who today has access to enough data to make meaningful progress on that, not just when you're dealing with an issue, but day-to-day improvement of your life and your health? >> Go ahead, great question. >> That was a great question. And I don't think we have a good answer to it. (laughter) I'm sure John does. Well, I think every large healthcare organization and various healthcare consortiums are working very hard to achieve that goal. The problem remains in creating semantic interoperatability. So I spent a lot of my career working on semantic interoperatability. And the problem is that if you don't have well-defined, or self-defined data, and if you don't have well-defined and documented metadata, and you start operating on it, it's real easy to reach false conclusions and I can give you a classic example. It's well known, with hundreds of studies looking at when you give an antibiotic before surgery and how effective it is in preventing a post-op infection. Simple question, right? So most of the literature done prosectively was done in institutions where they had small sample sizes. So if you pool that, you get a little bit more noise, but you get a more confirming answer. What was done at a very large, not my own, but a very large institution... I won't name them for obvious reasons, but they pooled lots of data from lots of different hospitals, where the data definitions and the metadata were different. Two examples. When did they indicate the antibiotic was given? Was it when it was ordered, dispensed from the pharmacy, delivered to the floor, brought to the bedside, put in the IV, or the IV starts flowing? Different hospitals used a different metric of when it started. When did surgery occur? When they were wheeled into the OR, when they were prepped and drapped, when the first incision occurred? All different. And they concluded quite dramatically that it didn't matter when you gave the pre-op antibiotic and whether or not you get a post-op infection. And everybody who was intimate with the prior studies just completely ignored and discounted that study. It was wrong. And it was wrong because of the lack of commonality and the normalization of data definitions and metadata definitions. So because of that, this problem is much more challenging than you would think. If it were so easy as to put all these data together and operate on it, normalize and operate on it, we would've done that a long time ago. It's... Semantic interoperatability remains a big problem and we have a lot of heavy lifting ahead of us. I'm working with the Global Alliance, for example, of Genomics and Health. There's like 30 different major ontologies for how you represent genetic information. And different institutions are using different ones in different ways in different versions over different periods of time. That's a mess. >> Our all those issues applicable when you're talking about a personalized data set versus a population? >> Well, so N of 1 studies and single-subject research is an emerging field of statistics. So there's some really interesting new models like step wedge analytics for doing that on small sample sizes, recruiting people asynchronously. There's single-subject research statistics. You compare yourself with yourself at a different point in time, in a different context. So there are emerging statistics to do that and as long as you use the same sensor, you won't have a problem. But people are changing their remote sensors and you're getting different data. It's measured in different ways with different sensors at different normalization and different calibration. So yes. It even persists in the N of 1 environment. >> Yeah, you have to get started with a large N that you can apply to the N of 1. I'm actually going to attack your question from a different perspective. So who has the data? The millions of examples to train a deep learning system from scratch. It's a very limited set right now. Technology such as the Collaborative Cancer Cloud and The Data Exchange are definitely impacting that and creating larger and larger sets of critical mass. And again, not withstanding the very challenging semantic interoperability questions. But there's another opportunity Kay asked about what's changed recently. One of the things that's changed in deep learning is that we now have modules that have been trained on massive data sets that are actually very smart as certain kinds of problems. So, for instance, you can go online and find deep learning systems that actually can recognize, better than humans, whether there's a cat, dog, motorcycle, house, in a photograph. >> From Intel, open source. >> Yes, from Intel, open source. So here's what happens next. Because most of that deep learning system is very expressive. That combinatorial mixture of features that Naveen was talking about, when you have all these layers, there's a lot of features there. They're actually very general to images, not just finding cats, dogs, trees. So what happens is you can do something called transfer learning, where you take a small or modest data set and actually reoptimize it for your specific problem very, very quickly. And so we're starting to see a place where you can... On one end of the spectrum, we're getting access to the computing capabilities and the data to build these incredibly expressive deep learning systems. And over here on the right, we're able to start using those deep learning systems to solve custom versions of problems. Just last weekend or two weekends ago, in 20 minutes, I was able to take one of those general systems and create one that could recognize all different kinds of flowers. Very subtle distinctions, that I would never be able to know on my own. But I happen to be able to get the data set and literally, it took 20 minutes and I have this vision system that I could now use for a specific problem. I think that's incredibly profound and I think we're going to see this spectrum of wherever you are in your ability to get data and to define problems and to put hardware in place to see really neat customizations and a proliferation of applications of this kind of technology. >> So one other trend I think, I'm very hopeful about it... So this is a hard problem clearly, right? I mean, getting data together, formatting it from many different sources, it's one of these things that's probably never going to happen perfectly. But one trend I think that is extremely hopeful to me is the fact that the cost of gathering data has precipitously dropped. Building that thing is almost free these days. I can write software and put it on 100 million cell phones in an instance. You couldn't do that five years ago even right? And so, the amount of information we can gain from a cell phone today has gone up. We have more sensors. We're bringing online more sensors. People have Apple Watches and they're sending blood data back to the phone, so once we can actually start gathering more data and do it cheaper and cheaper, it actually doesn't matter where the data is. I can write my own app. I can gather that data and I can start driving the correct inferences or useful inferences back to you. So that is a positive trend I think here and personally, I think that's how we're going to solve it, is by gathering from that many different sources cheaply. >> Hi, my name is Pete. I've very much enjoyed the conversation so far but I was hoping perhaps to bring a little bit more focus into Precision Medicine and ask two questions. Number one, how have you applied the AI technologies as you're emerging so rapidly to your natural language processing? I'm particularly interested in, if you look at things like Amazon Echo or Siri, or the other voice recognition systems that are based on AI, they've just become incredibly accurate and I'm interested in specifics about how I might use technology like that in medicine. So where would I find a medical nomenclature and perhaps some reference to a back end that works that way? And the second thing is, what specifically is Intel doing, or making available? You mentioned some open source stuff on cats and dogs and stuff but I'm the doc, so I'm looking at the medical side of that. What are you guys providing that would allow us who are kind of geeks on the software side, as well as being docs, to experiment a little bit more thoroughly with AI technology? Google has a free AI toolkit. Several other people have come out with free AI toolkits in order to accelerate that. There's special hardware now with graphics, and different processors, hitting amazing speeds. And so I was wondering, where do I go in Intel to find some of those tools and perhaps learn a bit about the fantastic work that you guys are already doing at Kaiser? >> Let me take that first part and then we'll be able to talk about the MD part. So in terms of technology, this is what's extremely exciting now about what Intel is focusing on. We're providing those pieces. So you can actually assemble and build the application. How you build that application specific for MDs and the use cases is up to you or the one who's filling out the application. But we're going to power that technology for multiple perspectives. So Intel is already the main force behind The Data Center, right? Cloud computing, all this is already Intel. We're making that extremely amenable to AI and setting the standard for AI in the future, so we can do that from a number of different mechanisms. For somebody who wants to develop an application quickly, we have hosted solutions. Intel Nervana is kind of the brand for these kinds of things. Hosted solutions will get you going very quickly. Once you get to a certain level of scale, where costs start making more sense, things can be bought on premise. We're supplying that. We're also supplying software that makes that transition essentially free. Then taking those solutions that you develop in the cloud, or develop in The Data Center, and actually deploying them on device. You want to write something on your smartphone or PC or whatever. We're actually providing those hooks as well, so we want to make it very easy for developers to take these pieces and actually build solutions out of them quickly so you probably don't even care what hardware it's running on. You're like here's my data set, this is what I want to do. Train it, make it work. Go fast. Make my developers efficient. That's all you care about, right? And that's what we're doing. We're taking it from that point at how do we best do that? We're going to provide those technologies. In the next couple of years, there's going to be a lot of new stuff coming from Intel. >> Do you want to talk about AI Academy as well? >> Yeah, that's a great segway there. In addition to this, we have an entire set of tutorials and other online resources and things we're going to be bringing into the academic world for people to get going quickly. So that's not just enabling them on our tools, but also just general concepts. What is a neural network? How does it work? How does it train? All of these things are available now and we've made a nice, digestible class format that you can actually go and play with. >> Let me give a couple of quick answers in addition to the great answers already. So you're asking why can't we use medical terminology and do what Alexa does? Well, no, you may not be aware of this, but Andrew Ian, who was the AI guy at Google, who was recruited by Google, they have a medical chat bot in China today. I don't speak Chinese. I haven't been able to use it yet. There are two similar initiatives in this country that I know of. There's probably a dozen more in stealth mode. But Lumiata and Health Cap are doing chat bots for health care today, using medical terminology. You have the compound problem of semantic normalization within language, compounded by a cross language. I've done a lot of work with an international organization called Snowmed, which translates medical terminology. So you're aware of that. We can talk offline if you want, because I'm pretty deep into the semantic space. >> Go google Intel Nervana and you'll see all the websites there. It's intel.com/ai or nervanasys.com. >> Okay, great. Well this has been fantastic. I want to, first of all, thank all the people here for coming and asking great questions. I also want to thank our fantastic panelists today. (applause) >> Thanks, everyone. >> Thank you. >> And lastly, I just want to share one bit of information. We will have more discussions on AI next Tuesday at 9:30 AM. Diane Bryant, who is our general manager of Data Centers Group will be here to do a keynote. So I hope you all get to join that. Thanks for coming. (applause) (light electronic music)