PayPal co-founder Max Levchin about his new start-up Glow, which helps couples with mobile technology to become pregnant.
Actually, you know Levchin only as the founder of companies such as PayPal or slideshow. Now the technology expert has brought along with his partner Mike Huang an iPhone app on the market that will help couples to show children. The Glow-called software collects data to as many as possible – be it the personal mood, sex position during the last generation or test the temperature of the morning womanWith all this information, you should then calculate the optimal conditions. can, under which there is a successful pregnancy. For this purpose the data of numerous users and medical models are combined. In an interview with Technology Review, the Levchin explains how this will work exactly
Technology Review. Mr. Levchin, why did you specifically created an app that will help couples to get pregnant
Max Levchin: We came to the problem of infertility, because it is an area that is not covered by traditional health insurance in the United States. The health system works here is not optimal, the cost is too high. So it’s not just about helping people to bring babies into the world, even if it is a noble goal – we want to change the health care system
So we still have a glow from within. program called “First Glow”, which is a kind of health mutual funds. Who pays per month for ten months up to $ 50 and is not pregnant, receives a proportionate sum of the funds for these 10 months – contributed by all, regardless of whether they were pregnant or not. The money then goes to a fertility doctor who can help those affected. So you could say that we organize a kind of crowdfunding for babies. The long-term goal is to extend this approach to many other areas in healthcare
TR:. Background glow but also puts a lot of technology. How can algorithms from the field of machine learning to help bring about a pregnancy
Levchin: We are trying to speak mathematically predict a function that is relatively well known . There is a sufficiently large width of medical literature, which we can use to set up some basic assumptions. But we also know that the majority of these studies reflects the experience of a virtual average couple – for special cases they are of course not adapted well enough
So we start with this basic data and then supplement it with the real time information of the user. . We have already collected information from our nearly 250 beta testers to identify some of the key signals. Now we consider how we have to adjust it. Then we can individualize the prediction model. And with each new user we can improve the learning process
TR. Glow We might just be in time
Levchin: It would be pretty huge if we could one day say, ovulation will take place at this exact time. If we are not logically. But our growing collection of data and the increasing availability of passive sensors that are likely to advance quickly. There are a whole range of people who work on these cheap devices that can be worn throughout the day, such as a thermometer for measuring the basal body temperature. With such signals is likely what we can achieve, are quite impressive
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