talking about the future of artificial intelligence. can we trust what the computers are telling us? >> leah vonn bitter, helping grow the world one happy pregnancy at the time with her startup. our reporters t" i'm scott mcgrew. i was hosting entrepreneur of the year awards and saw that tom siebel was up for an award.

i said something to the effect of, tom, if you're out there in the crowd, if you don't win tonight, keep at it and you'll be successful some day. i got a polite laugh from the audience which is what you're hoping for. the joke is that tom siebel has already been wildly successful, the company that invented customer relationship management sold to oracle for nearly $6 billion, he developed c3, video here of its headquarters not far from oracle. tomoi us once again. joined by connie. you told cnbc you expect to double your workforce this year an double it again next year. is that still accurate? >> we'll go from 300 employees currently to 600 in a year, 1,200 in two years. >> are you able to find those

kind of employees? in machine learning, there are not that many people doing it. >> that's true. it's a highly competitive market. c3 seems to be perceived of as a highly desirable place to work. in the last year we had 100 open positions posted for which we had 26,000 applications. >> you're going to be okay? >> where are you mostly hiring people from? from social media companies? >> a lot of these people are phds coming out of stanford, burkely, from google, facebook, from virtually everywhere. >> it's interesting because we hear so much on ai on the consumer side and so much on the enterprise side. is it the same qualifications, the same skill set? >> they're very similar skill sets. i think most of the people that

we are hiring have hard -- backgrounds in hard science, industrial engineering, physics, electrical engineering, human buy olgs combined with data science. it's a little more industrial and commercially focused than what we see on the consumer side with an amazon or google. >> i'm curious going forward, is that who you're going to be hiring, a lot of issues around ai. we talk about the skill set, the kinds of talent we want building the system. what other kinds of positions are you looking to hire? >> we're building the company, offices in paris, rome, london,. we're hiring people in marketing, marketing communications, finance, administration. >> i was thinking more on the ai

side? >> ethicists. >> we held a conference with 400 scholars talking about it. we think about this a lot. i think there are some -- we're going to see enormous social and economic benefits from the application of ai to processes like health care, for example, persistent health. the social and economic implications of that are remarkable. >> can any of you give me an example of -- we or examining the ethics, worried about the ethics. give me a for instance. >> disease prediction. for example, if we aggregate the health care records for the population of the united states and then build deep learning models ohineseea with high leve precision. so that's good,y is going

to be diagnosed with this disease in the next three years, we can intervene, clinical intervention to avoid the diagnosis. the social and economic implications of that are dramatic if you, say, take diabetes or breast cancer. what's the downside? this insurance company or this government agency knows what disease we're going to come down with in the next five years. do you want to know that? i'm not sure i do. how are those data going to be used. think about the implications for privacy. think about who cares about pre-existing conditions if we know what you're going to get two years from now. so the ethical implications associated with privacy are dramatic. i think they are largely unaddressed certainly by the leadership in overnment. europe. i think the phenomena that we're seeing with these facebook phenomena are candidly

unconscionable. this is an opportunity where the government needs to do its job. >> we also saw last year when google duplex was announced which is an assistant that will make reservations for you or call and make an appointment at your hair dresser. there was a big debate in terms of what were they thinking -- >> sounded like a real person. >> very humanly sounding. what are the implications with that? how do we interact with ai should we be aware of it? yes to all those things. that's my point, are you hiring ethicists, economist,lre, that we're seeing, what's the right processes that companies should take upon themselves before there are even bigger proble step. who do you hire should there be

a chief ethicists. what are the processes you can implement to make sure you're developing software, still competitive, still agile in all of that and moving quick lily. >> with all due respect, i think it's more fundamental. we have lots of ethicists at facebook and at google. we have full staffs of them. the industry is not regulating itself. government has to do its role. i think i have been most outspoken. publishing a book in march on the s -- there are very serious privacy implications. k t internet of things. >> the internet of things, where we'll have 50 billion shoring the value chain of

heal this increases the surface area that's basically available for attack by people who either want to steal our wallet or people who want to shut down our critical infrastructure. both bad actors i would say of various forms. we're entirely exposed. these intfrastructures are entirely vulable and penetratable and nobody is doing anything about it. >> tom, another thing you've been giving thought to is the bias that can be in ai, because it is a machine after all, programmed by people. we see this with algorithms in googless or amazon's facial recognition. what do you do to keep bias out of -- how y start with a blank slate on a machine, teach it how to learn top of it? >> for many applications, for example, if we wanted to predict device failure in an aircraft.

>> just as an example, not that you do that or anything. >> which is something we do. there's no bias there. there's simply thousands of signals and we can build machinery and models that will predict the device is going to fail before it fails with very high levels of precision. let's look at the criminal justice system. we have a bias problem. if we were to, for example, take the data of true positives, people that went to prison in the last ten years, and build a machine learning model that would predict -- as we roll forward, i can tell you what the answer is. it's a black male with an incom age. so we have all this. there we have a bias problem in the data and the machine learning problem kind of breaks down. we need to figure out how to deal with that. >> are you staying away from these sorts of applications

because they'reth bias. are you guarding against that bias? >> we are. there i think we're dealing with simply a biology and a physics problem. i don't think there is a bias. >> assuming your sample size and your sample is wide enough. >> one that we're doing is 40% of the population of the united states. 125 million people. a reasonably large sample set. we are not involved in any applications today where the bias and the data are problematic. we will be involved in them in the future and we need to think about that carefully. >> only about a minute left. i'm going to steal the last question. we touched on this to some degree. who polices that? do we trust tom siebel to police that i have some software a black box and it d he bias and it's perfectly safe and you can trust the answers that come out of it? do we trust the government to regulate it? who do we trust, the things that are going to affect our health

and our everyday life and the airplanes we fly in? >> i think we have a new generation of people that tend to be inclined to trust the technologies. that's very ressues they're rai are very troubling. i think we have a situation where the federal government needs to do its job. i'm not a big government guy. i don't have a lot of use for government. this is one where, if the government doesn't do its role and get involved and regulate this industry and protect consumers, people will be hurt. i think this is an issue where it needs to be talked about, needs to be talked about on shows like this today. thank you for making it a topic and it needs to get on the floor of congress and somebody needs to address it. >> will you come back in march after you published your book? >> if i'm invited, i'll be delighted. >> you are invited, sir. off the top i mentioned tom

had been in the running for entrepreneur of the year. he won. >> it's my great honor to accept this award on behalf of the exceptionally talented group of men and women with whom i have the opportunity to work every day. up next on press here, a discussions of birds, beads and bracelets when "press:here" continues.

welcome back to "press:here." i'm going to make a quick aside and say the conversation we're about to have will be about making babies. i don't expect we'll get more explicit than perhaps using the word intercourse. i am well aware this television ai when children are around and i thought there might be some parents of young children who would appreciate the heads-up. we'll talk about a device called ava. beyond that, the products called fem tag. it tracks a woman's cycle to know when conditions optimal.

leah is from switzerland, went to school there, then in canada, internship in germany before going back to school in france, then china. finishing her masters degree in indiana. she went to india where she started a chocolate factory. she splits her time between zurich and san francisco. thank you for being with us. i have so many questions, but i've got to start with an indian chocolate factory? why did you start a chocolate factory? >> i'm swiss. you have to live up to the cliche a little bit. >> second part, followup question, india? >> i went to bi s 18 and though great place. i started a chocolate factory in india. >> you're the most interesting woman. >> good ice breaker. >> all jokes aside. i think when one is young one

worries a great deal about getting pregnant. then when one wants to get pregnant, you find out it's a little more difficult tan you feared. it is not that split second you feared it was when you were younger. talk about your market. who is using this and how much trouble are families having getting pregnant? >> so basically ava is a women's health ai company. we aim to basically bring ai into difficult fields of women's health. from trying to get pregnant, pregnancy, menopause. the first product we have is for women to detect their fertile days. we also have customers who want to understand their cycles better. surprisingly it's exactly what you mentioned. i think a lot of couples go into this. they're really excited and looking forward to the time in life when they want to start a

family. they realize it might nmonth, t month. they usually quickly get to the conclusion that timing your cycle is vital to get pregnant. that's the moment they turn to a product like ours. you have six days you can actually get pregnant. 70% of pregnancies happen within a three-day window. it's important to time that accurately. >> what data goes into the system? >> everything we do is based on physiological parameters we collect in the night. our first product is the bracelet that you wear only at night. tracks heart rate, temperature and a few others. around 3 million data points per night. we window starts and when a lot. it's really interesting. a lot of women always think basically, the eng is the most interesting part of the cycle. it really isn't.

the most interesting happens around your fertile window around in the middle of your cycle, depending on when you cycle is. we've published a couple things. what we've published on is an increase of heart rate that occurs at the fertile window. women are aware of the increase in temperature at the end of the fertile window. we see changes in so many physiological parameters. >> there's so many questions about this. one i'd like to know is your accuracy level? >> peopleeen menstruating for a while. >> half of people. >> talk about that. how long have people been using it, what data do you have and what is the success rate? >> with the algorithm, we're at 89% to detect 5.3 fertile days. we're pretty accurate. the change is people have been menstruating for a while. that is very true. the change is we now have

technology that can capture more physiological data than ever before. before you actually have to rely on one or two data points. it might have been i menstruated then, more or less plus 14. or a little later in the history of human beings we started using temperature. but it's kind of a flawed parameter to use. the change we've seen with ava and technology in general, with wearables we can predict so much more physiological data. it allows us a much higher accuracy than before with just one data point. >> why do you think there's finally more attention and investment going into this whole stem tech area. it seems like it's -- sorry -- fefrtal for innovation. i interviewed another female entrepreneur a while back starting new tampon company. no innovation in that space.

a venture capitalist who said this seems like a niche product. half of our population menstruates. >> one firm last year got $10 billion more than all female entrepreneurs got. >> combined. >> combined, over the course of 2018. you're obviously in a position to speak to that. >> we were born out of the vision and mission to bring women's health to the we're very aware of the gap. a lot of areas within women's health are still highly stigmatized. you mentioned the places i lived. you have a stigma, not enough money around research and development in those areas. you have the whole women's health space that's fairly often been used as a political weapon. all of that leads to women not having adequate solutions in different stages of their

productive life. trying to get pregnant, menopause. that's why everyone at ava gets up monday morning and we're working on this. we believe there is a gap that we need to fill. to your question, why is there more investment, i think there's more investment, more came in the last three years, but we're still at the very beginning. >> i have to take a quick commercial break. when i come in, i want to talk about your experiences. you've been all over the world, about how women are excelling or not in different places. we'll be back in just a minute.

. welcome back to "press:here." we're talking with leah van bidder of ava. >> i love there are more companies in your sce privy. this is really valuable private information. what are you doing to make sure you're securing people's information. there's been some concerns and controversies over leaked information from similar companies. >> it's actually a really good question, especially in our space. it's something that concerns me a lot because i feel like, especially in this cycle tracking space, you've seen apps popping up. there's thousands of menstrual cycle tracking apps. i'm sure every woman has three different ones on their phone. why it's concerning is most of

those are free. they're trying to monetize in whatever way possible. when you're pushed into that corner as a company, one easy way out is to say, hey, it's worth a lot of money. we made it a point that we have unit economics that can cover our costs. ava doesn't sell data, do lead generation. with our price you pay for the product and the research. a quarter the money goes basically back into research on women's health. >> the data that you're collecting, is it protecta? >> it's not protected under hipaa. because it doesn't apply to us. >> why doesn't it apply to you? >> hipaa is about information you share with your health care professional. we don't do that. you don't share it with the doctor over our platform. we have a doctor's report. if you want to, you can print it out at home and bring to your

doctor. that's between you and your health care pro fesh. that's not something we share. >> at some point, if you wanted any partnerships with medical providers -- >> hipaa is definitely something we look at and we comply with it mostly, but it's not something where we have to. >> leah, you're swiss, you went to school canada, germany, france. help me out here. as we're talking about women in business, what opportunity does it best? include san francisco in there. compare san francisco to, say, switzerland. >> it's a really good question. your question really refers to what country does it bests in terms of an environment that fosters women. >> brings women to the table. >> we need to do better in all of those regions and we have to do better by far. we mentioned venture capital and funding. that's a big one. it's also about role models, having an environment that really fosters that.

i personally believe -- i moved with this company from switzerland to san francisco. i think one of the most important things around that move is i finally had a peer network that i didn't have before. just by the size of san francisco, that really helps you to find other female founders that might be your age which is something that would have been much more difficult for me to find in switzerland just because the eco system is so muc smaller. that makes san francisco a better place because you have rolebyhe size of it, it makes i better. i think both countries and both markets need to improve. >> leah van bidder, we thank you. i presume there's future products come og out. >> digital contraception, monitoring pregnancy and menopause. >> by the way, this conversation was totally safe for children. >> it ended up being. the worst word that ever came out was intercourse and i said

it. we'll be back in just a minute.

that's our show for this week. earlier i mentioned the northern california entrepreneur of the year awarz. past winners include jeff weiner and rita hoffman from linkedin. jessica haren from stella. all of whom have been on this show before. the ceremony is run by ey. it's a lot of fun and it's very co nominations are open now. i'll add a link to my twitter so you can nominate your boss or nominate yourself. who's to know? my twitter isank u for making u your sunday morning.

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