Machine Learning, Big Data, and Your Family History

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How can artificial intelligence, machine learning, and deep learning benefit your family? These technologies are moving into every field, industry, and hobby, including what some say is the United State's second most popular hobby, family history. Today, it's so much easier to trace your roots back to find out more about your progenitors. Tyler Folkman, senior manager at Ancestry, the leading family history company, describes to us how he and his team use convolutional neural networks, LSTMs, conditional random fields, and the like to more easily piece together the puzzle of your family tree. Ginette: Today we peek into an area rich in data that has lots of interesting AI and machine learning problems. Curtis: The second most popular hobby in the United States, some claim, is family history research. And whether that’s true or not, it's has had a lot of growth recently. Personal DNA testing products have exploded in popular over the past three years, but beyond this popular product, lots of people go a step further and start tracing their roots back to piece together the puzzle of their family tree. Today we’re going to dive into the data side of this hobby with the leading family history research company. Ginette: I’m Ginette. Curtis: And I’m Curtis. Ginette: And you are listening to Data Crunch. Curtis: A podcast about how data and prediction shape our world. Ginette: A Vault Analytics production. Ginette: This episode of Data Crunch is supported by Lightpost Analytics, a company helping bridge the last mile of AI: making data and algorithms understandable and actionable for a non-technical person, like the CEO of your company. Lightpost Analytics is offering a training academy to teach you Tableau, an industry-leading data visualization software. According to Indeed.com, the average salary for a Tableau Developer is above $50 per hour. If done well, making data understandable can create breakthroughs in your company and lead to recognition and promotions in your job. Go to lightpostanalytics.com/datacrunch to learn more and get some freebies. Tyler: My name's Tyler Folkman. Curtis: Who is a Senior manager of data science at Ancestry. Tyler: As I look across Ancestry and family history, we almost have, like, every kind of machine learning problem you might want, I mean, probably not every kind, but we have genetically based machine learning problems on the DNA science side. We have search optimization because people need to search our databases. We have recommendation problems because we want to hint the best resources out to people or provide them. For example, if we have a hundred things we think might be relevant to a person, what order do we showed them? So we use recommendation algorithms for that. We have a lot of computer vision problems because people upload pictures and a lot of our documents, if they're not like digitized yet, meaning that they’ve extracted the text, they might just be raw photos, or even just the things that our pictures uploaded, we want to understand what's in them, so is this a picture of a graveyard is it a family portrait? Is it an old photo? And so tons of computers vision stuff, natural language processing. On the business side, we have marketing problems just like any other business, like how do you optimize marketing spend? How do you optimize customer experience, customer flow? And so it's really a cool place because you really can get exposed to almost any type of problem you might be interested in. Curtis: So back in the 80s, before you could go easily find information on the Internet, genealogists had to spend a ton of time trekking around to libraries to try to find information on their ancestors. Ancestry saw a business opportunity and started selling floppy disks, and eventually CDs, full of genealogical resources for genealogists to easily access in their home. Tyler: And then they grew up through the Internet age and moved out ...

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