Data Crunch

Machine learning is becoming a bigger part of chemistry as of the last two or three years. Industries need to have people trained in both fields, and it's taken time for them to make their way into this sector. Olexandr Isayev is at the forefront of that wave, and he talks to us about what he's done while melding deep learning and chemistry together and his vision of where he sees this field going with this new tech. Olexandr Isayev: Historically, chemistry was empirical science. It's been driven by experiment. So, you find the observation, you formulate a hypothesis, you make a prediction, and do a test, so it's the standard scientific method. Now, those new machine learning methods allow us to do a data-driven discovery. Ginette: I’m Ginette. Curtis: And I’m Curtis. Ginette: And you are listening to Data Crunch. Curtis: A podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Ginette: A Vault Analytics production. Ginette: Tableau is the leading software in data analysis, preparation, and interactive analytics, and we’re huge fans of it because we’ve seen how it facilitates quickly finding business value from data—helping you do this faster than anything else can. If you and your team have recently purchased Tableau licenses, you’re off to a great start, but in our experience working with companies over the years, many deployments of Tableau fail to realize their potential value because of a lack of training and understanding of how to use it well—which is a shame because it can truly transform your business when used well. We’ve helped dozens of companies learn how to use Tableau and get real results for their businesses because we focus not only on the technical skills, but also on how to be a good analyst and solve real-world problems your business cares about. We come onsite to your business, get to know your employees and your business problems, and train you on the skills you need to make Tableau a success for your needs. We’ll even customize the training to your own data so your employees learn how to work with the specific data and problems that are relevant to them, building out analysis and dashboards that can immediately be used after the training to drive business value. We train on Tableau at basic, intermediate, and advanced levels. We’d love to hear from you and help you transform your business with Tableau with an onsite training—send us an email at hello@vaultanalytics.com or visit our site at vaultanalytics.com, and we’ll be in touch! Curtis: Chemistry—while it can conjure up images of begoggled scientists in white coats donning blue rubber gloves in sterile laboratories—it touches more of our lives than we probably give it credit for. Think of lithium batteries that power our electronics, plastics that exist almost ubiquitously around us, dyes that color much of your world, jet engines, among so many other things. And as a personal note, I happen to find the science fascinating. I majored in chemistry in college. Ginette: Today we speak with a man who is at the forefront of connecting AI with chemical sciences. His work has been published in some well known journals, and he’s headed up some impressive projects. Olexandr Isayev: My name is Olexandr Isayev, or Olis for short. I grew up in Ukraine then I move to US. Did my PhD in computational chemistry, and I also have a minor in computer science. So I’ve worked in a lot of different topics in chemical sciences, and in particular, we use a computer simulation, like high performance computing simulation, and how we can address the challenges of chemical sciences, and recently, I started the faculty position at UNC, so I’m an assistant professor at the University of North Carolina ...

Python versus R. It's a heated debate. We won't solve this raging controversy today, but we will peek into the history of Python, particularly in the open source community surrounding it, and see how it came to be what it is today—a well used and flexible programming language. Travis Oliphant: Wes McKinney did a great job in creating Pandas . . . not just creating it but organized a community around it, which are two independent steps and both necessary, by the way. A lot of people get confused by open source. They sometimes think you just kind of going to get people together and open source emerges from the foam, but what ends up happening, I’ve seen this now at least eight, nine different times, both with projects I’ve had a chance and privilege to interact with, but also other people's projects. It really takes a core set of motivated people, usually not more than three. Ginette: I’m Ginette. Curtis: And I’m Curtis. Ginette: And you are listening to Data Crunch. Curtis: A podcast about how applied data science, machine learning, and artificial intelligence are changing the 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. Here at Data Crunch, we love playing with artificial intelligence, machine learning, and deep learning, so we started a fun new side project. We just launched a new podcast that tests the boundaries of what can be done with Google’s cutting-edge deep learning speech generation algorithms. We use surprisingly human-like voices to host the podcast that reads all the unusual Wikipedia articles you haven’t had a chance to read yet, like chicken hypnosis, the history of an amusing German conspiracy theory, strange trends in Russian politics, and much more to come. It’s worth listening to to hear what this tech sounds like and you’ll learn unique and bizarre trivia that you can share at your next dinner party. Search for a podcast called “Griswold the AI Reads Unusual Wikipedia Articles,” now found on all your favorite popular podcast platforms. Curtis: There has been a heated, ongoing debate about which programming language is better when working with machine learning and data analytics: Python or R, and while we won’t be wresting that particular question, we will overview a bit of history for both and then dive into significant history behind one of these languages, Python, with a major contributor to the language, a man who significantly influenced the way that data scientists use Python today. Ginette: As a very short historical background, Python came to the scene in 1991 when Guido Van Rossem developed it. His language has developed a reputation as easy to use because it’s syntax is simple, it’s versatile, and it has a shallow learning curve. It’s also a general purpose language that is used beyond data analysis and great for implementing algorithms for production use. As for R, it followed shortly after Python. In 1995, Ross Ihaka and Robert Gentleman created it as an easier way to do data analysis, statistics, and graphic models, and it was mainly used in academia and research until more recently.

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.

When Bryan Mazlish's son was diagnosed with Type I diabetes, there were unexpected challenges. Managing diabetes on a day-to-day basis was tough, so he hacked into his son's insulin pump and continuous glucose monitor to create the world's first ambulatory real-world artificial pancreas. Now his mission is to make it available to everyone. Bryan Mazlish: A nice demo that we showed at Google IO earlier this summer, where we showed our use case for one of their forthcoming APIs. We’re really at the vanguard of digital health medical device enterprise software, and it's incredibly exciting but also challenging place to be. We're enthusiastic about the prospects for what we can do for a whole lot of people. 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. This episode of Data Crunch is brought to you 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. Curtis: Today we get to speak with a man who, after studying computer science at Harvard, went to start a stock-trading algorithm company on Wall Street until his life experienced a twist. Now he’s the president and co-founder of one of the leading digital health medical device enterprise software companies, which employs machine learning to customize and automate medicine intake, all because of an unexpected challenge that showed up in his life. Bryan: My name is Bryan Mazlish. I’m one of the founders of Bigfoot biomedical. My background is in quantitative finance. I spent 20 years on Wall Street, first at a large investment bank and then about a decade running a fully automated trading business where we built algorithms to buy and sell stocks completely automated fashion, and it was about 6 or 7 years ago that my path took a change . . . Ginette: Bryan’s son was diagnosed with Type 1 diabetes, which Bryan says wasn’t entirely unexpected because his wife has the same disease. But what was unexpected was the intensity of managing the disease on a day-to-day basis. He was surprised with how antiquated the insulin management technology was. There wasn’t technology that could anticipate his son’s insulin needs and automatically give him the insulin he needed. Bryan: You have a need to take insulin to just simply to live. This is something that needs to be delivered on a constant basis, 24 hours a day. You can take this in one of two ways: you can use an insulin pump that delivers this in a continuous basis, and you can also take a once-a-day injection, and the benefit of the pump is that you can vary that at different points in the day. When you take an injection, it lasts for up to 24 hours, and it doesn't have the same flexibility, but it does have the benefit of not having to wear a device to deliver the insulin. And that's just the baseline, on top of that you need to take insulin to offset meals, primarily carbohydrates and high glucose levels. So when you're going to sit down to eat breakfast, lunch, or dinner, or even a snack, you need to estimate the amount of carbohydrate and glucose impact of the meal that you're about to...

In a world where so many things are Internet connected, how is machine learning playing a role? Bruce Sinclair speaks with us about the intersection of IoT, AI/ML, and the digital twin. Bruce: Where AI, and in particular machine learning, and then in particular neural networks, and then in particular deep learning neural networks, where they apply is mostly in this model making, so with IoT, there are two types of models for the digital twin: we have the analytical model that's created through more analytical techniques, and then we have the cognitive models that are being created through a machine learning and artificial intelligence techniques. I kind of like to separate the two, but the the impact in both cases are profound. 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. Today, if you haven’t guessed already, we’re talking about the intersection of data, artificial intelligence, and the internet of things, or IoT. So we’re talking to an expert well versed in this topic. A little bit about his background: Among many other things he’s done, like found and head companies, he’s authored a book on the Internet of Things, created a certification program for people who want to become certified IoT professionals, and he explains all things IoT on his podcast called “The Internet of Things Business Show.” Today, we’ll learn about AI in the IoT world and more specifically digital twins—a concept named by Gartner two years in a row now as one of the top ten strategic technology trends for both 2017 and 2018. Let’s dive into this topic with our guest. Bruce Sinclair: My name is Bruce Sinclair. I am the president of IoT Inc. We consult for brands, manufacturers, and vendors and help them with their IoT strategies, both on the business side and on the product side, and we produce content, so part of the content is the podcast, and we do trainings, so we're training executives on how to introduce IoT within their business, and how to—most importantly—be profitable with IoT, and the reason I started IoT Inc. was that I saw pretty quickly that there was a lot of hype around the Internet of Things, and this hype was all around the shiny new things, in particular the technology, but as most technologies, they run out of steam if they can't make any money. And so I was very deliberate in focusing on the business aspect of IoT to try to help executives and managers to understand how to apply this technology. Curtis: One of the most important concepts in IoT is the digital twin, which is a virtual reflection of a physical object. One major use of the digital twin is taking the virtual reflection of an object and virtually change it before actually changing things in the physical object in the real world. Today a digital twin is generated from data coming from sensors embedded in a physical object. Bruce: So the Internet of Things, for everyone that’s listening, is really just the Internet being put into physical objects. The Internet being networking, things being the device. That's really, at least when you look at it from a business perspective, that’s not where the action’s at, and not coincidentally, where the action’s at is in data analytics, data science, and a subset of that being AI, and the purpose of putting the Internet in the physical objects at the highest level is to capture data. So we capture data in our sensors, which is more of our internal data sources, and we capture data on the Internet, and that is using business systems, that is using microservices, and coincidentally or interestingly, it's also other products, and this leads us to the most important technology for the Internet of things and this is the digital twin,

The most important thing is to have an AI-enable infrastructure. It sounds very boring, but that was the learning that I got from the bank as well. It’s actually very easy for us to build the model, but what took a long time was to have the AI infrastructure that enables us to do so. Per: The most important thing is to have an AI-enable infrastructure. It sounds very boring, but that was the learning that I got from the bank as well. It’s actually very easy for us to build the model, but what took a long time was to have the AI infrastructure that enables us to do so. 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: Before we get into this episode, let’s bring you behind the scenes at Data Crunch. We’re going to show you what we’ve learned about your tastes so far. According to the podcast analytics, which are still rudimentary and can only tell us so much, you really liked our last episode with DataOps. You also enjoyed the "No PhD Necessary" episode, the "How Artificial Intelligence Might Change Your World" episode. Almost all of you have loved the history of data science series. In fact, the third one in the series is our most popular episode in terms of how much of the show you listen to. But in terms of sheer listening numbers, the Hilary Mason episode, titled "The Complex World of Data Scientists and Black-Box Algorithms," tops our charts, with the Ran Levi episode, titled "Deep Learning—A Powerful Tool with a Name that Means Nothing," coming in second place. What this seems to tell us is you like interesting data history, you like interesting projections into the future, and you like learning practical ways you can be successful with data projects. But since the podcast analytics are still rudimentary, we want to hear if our conclusions are correct. So if you want to steer our future seasons, let us know what you want to hear more about by filling out a short survey. Just go to datacrunchpodcast.com/survey, and we would love to hear from you! Today we talk to the cofounder and CEO of a Danish company that employs machine learning to gather insights on what content on your website leads people to take action. If you’re looking into building a company using artificial intelligence or machine learning, this episode will be of particular interest to you because he talks about the impetus for his idea, some tools he used to build his product, some challenges, how he hired his team, when he uses or discards algorithms, and how he packages his product. And you can even try a free version of his product, which he mentions at the end of the show. Per Damgaard Husted: My name is Per Damgaard Husted. I'm the founder and CEO of Canecto. Canecto is a new way of doing web analytics based on machine learning, and the reason we do machine learning is because we want to understand the intention of the users so that we can predict how they are interacting on the website. We focus a lot on how content influences people to make decisions on a website, so it sort of compliments the user journey that you have and the UX and the SEO, but we focus on the content. Curtis: So how did Per come up with this idea of extracting insights from users’ interaction with content? Per: The background was that actually I needed this tool. I was a manager in one of the big Danish banks, and I was in charge of the online banking elements, and I got a lot of traffic, or we got a lot of traffic statistics about what's going on, but I didn't really know anything about that users’ intent. I wanted to make our website better. I wanted to understand what motivates them. I wanted to understand what content we produced...

If you’re building a data product, these questions are likely occupying your mind: how do you get your customers to trust your data? How do you know your product’s something your customers will want? How do you produce those products more quickly without compromising accuracy? Today we talk with someone who has a lot of experience answering these questions. Ginette: If you’re building a data product, these questions are likely occupying your mind: how do you get your customers to trust your data? How do you know your product’s something your customers will want? How do you produce those products more quickly without compromising accuracy? Today we talk with someone who has a lot of experience answering these questions. 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. Curtis: If you’re a company aiming to research emerging technologies, like AI, ML, IoT, or edge computing, and you find your company lacking expertise, we know where you can the expertise to pad your research team: this team is a group of ex-fortune 500, b2b tech product managers with in-depth market analysis, product planning, and development expertise in bringing successful products, software, and services to the market, and they have significant in-depth technology skills on their team. They drive emerging tech research, product strategy, and tech marketing that resonates with customers, and they’re good at it. If a service like this would be helpful to you for a proposal you’re writing or a for a product that you’re creating, reach out to us at hello@vaultanalytics.com, and we’ll be in touch. Ginette: Now let’s jump into today’s episode. We’re talking with someone who’s worked with data teams for many years and has learned a thing or two. This is Chris Bergh. Chris: I’m Chris Bergh. I'm head chef of a company called Data Kitchen in Cambridge, Massachusetts, and we're a company that helps teams of people who do AI or machine learning or data engineering or data visualization deliver insight faster with higher-quality, and so how did I, how did I get to this point to found a company to focus on what we called dataops? Well, I guess I'm a working class kid from Wisconsin. I went to, in the late 80s actually, I went to Columbia to study AI back when AI was just a corner of the world that people, no one knew what it was, and you didn't walk through an airport and run into it, and then I worked on some AI systems at NASA and MIT to automate air traffic control, and then I sort of got into software development and managing software teams. Curtis: To fill out this picture a little more, Chris has two patents under his belt and has had two companies acquired, one by Microsoft, while he was building the company in the C-suite. So he’s no stranger to the difficult experiences that come with companies’ growing pains.   Chris: About 10 years ago I got into data and analytics, and the company I worked for was about a 60 person company. We did everything that you could do in analytics, and we did data visualization. We had data scientists. We had data engineers. We even decided to build our own complete software platform that did everything in analytics, and I was the chief operating officer, and I worked with a guy who was from Harvard Medical School, really knew, it was a healthcare analytics company, really knew health care and really could talk to customers and figure out what they wanted, but then he'd come back to me and say, “Chris, here I've got this idea. Customer has this pain. Could you get some people together and figure out how to solve it, so I would go off and pull the data scientist and maybe data engine...

We are joined by the host of podcast Commercial Drones FM, Ian Smith, who gives us a fascinating understanding of how drones are being used today and in the future. From petri-dish wielding drones that follow whales, to miniature drones working in warehouses, to thermal sensing drones in the mining industry—drones are starting to be used extensively and will continue to grow in the future. We go over the technology, the use cases, the regulations, and the future. Intro: There’s never been a good way, ever, to get snot from a whale to see how healthy they are or do other types of experiments. It can hover right above the whale as it’s surfacing, and it will just have a little petri dish that when the whale blows it’s blowhole, all the snot just goes on it. Then they bring it back to the boat, and then they analyze it later. Curtis: One big area that uses AI and will continue to increase use of it is drone technology. One of the big things that machine learning enables drones to do is be aware of its surroundings. Computer vision classifiers help the drones identify objects that it is seeing and take appropriate action, such as avoiding obstacles, performing maintenance recon, and charting autonomous flight paths. Ginette: Let’s talk to someone steeped in all things drones who can give us insights into drones and how AI currently plays a role and will continue to play a role as drones evolve. This is Ian Smith. Ian: I got into drones in 2013, but before that I had actually built and flown model aircraft, like RCE aircraft with little tiny gas engines, and the balsa wood, and the glue that you have to wait overnight for it to set, and yeah it was a lot of work, and I wound up flying helicopters for my career, so I’m a commercial helicopter pilot. I was a flight instructor, and I heard in 2013 that RC aircraft that model aircraft had come so far that there was people that were using them. They were calling them drones, and they were taking pictures with them and selling them to people, but it was illegal in the United States because there was no regulation from the FAA at the time. So of course I decided to get into this as much as I could, since I wasn’t flying at the time, and ever since then in 2013 it’s been my career, and I worked for a company in France called Delair, and today I work for a company in San Francisco where I’m based now called DroneDeploy, and I host a podcast about drones called Commercial Drones FM as a side project. Curtis: So if you’re looking for more on drones after this episode, go check out Ian’s podcast. He covers all things drone and will keep you up on the latest. Let’s take a broad look at some of the use cases for drones. Ian: Some of the use cases, some of the industries that are using drones really are . . . agriculture was one that everyone latched on to. The construction industry of course. Inspecting assets, so whether that’s oil and gas or utilities or something else entirely, like wind turbines, or something like that. There’s general land surveyors that use drones for mapping activities, and of course there’s the film and photography. Everybody’s by now has seen a Youtube video of a drone or a drone shot in a movie or TV show. . . . Then there’s the mining industry who use them to calculate volumetrics of stockpiles, and search and rescue for finding people and putting crazy sensors on these drones that can sense thermal signatures. The way they’re being used, it’s really up to your imagination. Pretty much anything outside that can get a GPS signal these days. They're going to go towards more indoors things and closed, confined spaces too, so we're seeing just amazing use cases. People have these incredible imaginations, and the more you ask somebody what would a drone do for you? You just get these awesome responses, and it’s really cool to hear what people come up...

Travel’s an interesting industry because it’s inherently global which makes it inherently complex, and it’s so behind other industries when it comes to innovative and advanced technology being applied. A great example of that is when you buy a ticket on an Expedia or Priceline, etc., it’s likely that 75% of the time that a fax is sent to the hotel to tell them that you’ll be staying there that night. 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: Data Crunch is brought to you by data.world, the productive, secure platform for modern data teamwork. Organizations like The Associated Press, Rare, Encast, and Square Panda use data.world to replace outdated barriers with deep connections among data, people, and impact. This makes data easier to find, helps people work together better, and puts data and insights in the hands of those who need it. To learn more, visit data.world and request a demo. Curtis: Envision in your mind’s eye our globe and all the airplane flights in the sky at any given time. Now, zoom into a busy city on that globe and notice all the cars being rented by business professionals and the hotels that they’re checking into. Even in just one city, the amount of transactions is dizzying. The travel industry has a lot going on, and yet, sometimes it’s surprisingly antiquated. Devon: I'm Devon Tivona. I'm a founder at Pana. My background is actually technical. I went to school for engineering, spent the first five years of my career as a engineer, then a product lead, and most recently as a founder of this company. Ginette: The founders of Pana were intrigued with the possibilities of what they could do in the professional travel space, and as they talked with travelers, they saw an opportunity. Devon: We were talking particularly to frequent traveler[s]. And we kept hearing over and over again two primary pain points. One was felt like “with all the new found technology in the travel space, I still have to be my own travel agent. And it was great 10 years ago when I could just email someone, and they would take care of all of the logistics for me, but now all the technology has made it so I have to do all that work.” And then the second pain point that we started hearing was “then once I buy my plane ticket or my hotel ticket, if I need to make a change or something goes wrong and I want to get ahold of a real human being, that's like pulling teeth from these companies, particularly if I bought my ticket online.” So we kind of had this vision for could we build the 21st century version of the travel agent, but do so, you know, in a scalable Internet business sort of way. We didn’t want to build a boutique travel agency. We wanted to build something big. Travel’s an interesting industry because it’s inherently global which makes it inherently complex, and it’s so behind other industries when it comes to innovative and advanced technology being applied, particularly because it’s so big, not because it doesn’t have awesome people working in the space. A great example of that is when you buy a ticket on an Expedia or Priceline, etc., it’s likely that 75% of the time that a fax is sent to the hotel that you’ll be staying there that night. And for me when I heard that I was like, “okay, this is a really interesting industry because I can always be building stuff here as a technologist.” Curtis: Pana focused on the corporate travel space in particular because it felt it had more user pain points than other travel workflows. Devon: I think that there's, a there's a lot of a lot of varied user pain that are experienced throughout a travel journey,

Before the airplane was invented, some people were concerned that everything that could be invented had been invented. Obviously, that was not the case then, and it's certainly not the case now. So as you create novel inventions, how do you protect them? What's the process? And what tools can help you and your team navigate the world of patents? Janal Kalis: It was like a black hole. Almost nothing got out of there alive. So it became slightly more possible to try and steer your application away by using magic words . . . it didn’t always work but sometimes it did. 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. Here at Data Crunch, we research how data, artificial intelligence, and machine learning are changing things. We see new applications every single day as we research, and we realize we can’t possibly keep you well enough informed with just our podcast. So to help keep you, our listeners, informed, we’ve started collecting and categorizing all of the artificial applications we see in our daily research. It’s on a website we just launched. Go explore the future at datacrunchpodcast.com/ai, and if you want to keep up with the artificial intelligence beat, we send a weekly newsletter highlighting the top three to four applications we find each week that you can sign up for on the website. It’s an easy read, we really enjoy writing it, and we hope you’ll enjoy reading. And, now let’s get back to today’s episode. Curtis: Today we dive into a world filled with strategy, intrigue, and artful negotiation, a world located in the wild west of innovation. Ginette: In this world, you fight for your right to own something you can’t touch: your ideas. You and your team ride out into this wild west to mark your territory, drawing a border with words. Sometimes during this land grab, people get a lot of what they want, but generally they don’t, so you have to negotiate with the people in charge, called examiners, to decide what you can own, but what if you’re assigned someone who isn’t fair? Or what if you want to avoid someone who isn’t fair? Is there anything you can do? Maybe, but first you need to understand how the system works. Let’s dive into the world of patents and hear from Trent Ostler, a patent practitioner at Illumina. Trent: The kind of back and forth that goes on oftentimes is trying to get broad coverage for a particular invention, and chances are, the examiner, at least initially, will reject those claims. Curtis: Claims define the boundaries of the invention you’re seeking to protect. It’s like buying a plot of land. There are boundaries that come with the property. These claims define how far your ownership of the invention extends. Claims can be used to tell the examiner why he or she should allow, or approve, your exclusive rights to your idea, giving you ownership over that idea, or in other words, grant you a patent. Trent: The examiner will say that they are broad. The claims don't deserve patent protection. And he could say that they would have been obvious. He could say that it's been done before—it's not novel, and so what this means for anyone trying to get a patent is that it's very complex. There are thousands of pages of rules and cases that come out that further refine what it is that's too broad or what it is that makes something obvious, and oftentimes there is a balancing act of coming close to the line to get the protection that you deserve but not going overboard. Ginette: So there’s a back-and-forth volley between the inventor’s lawyers and the examiner. The examiner says, “hey, you don’t deserve these claims,” and he or she gives you a sound reason or argu...

Transparency International started when a rebellious World Bank employee quit to dedicated himself to exposing corruption. Now the organization claims the media's attention for about one week a year when it publishes its annual Corruption Perceptions Index, an index that ranks countries in order of perceived corruption. Find out how the organization sources the data, what an important bias is in that data, and how that data ultimately impacts the world. Alejandro Salas: I studied political science and I got very interested in all the topics related to good governance, to ethics in the public sector, etc., and I started working in the Mexican public sector, and—oh, the things I could see there. I was a very junior person working in the civil service, and I got all sorts of offers of presents and things in order to gain access to certain information, access to my boss—so very early on in my professional career, I started to see corruption from very close to me, and I think that's something that marked my interest in this topic. 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 Here at Data Crunch, we research how data, artificial intelligence, and machine learning are changing things, and we’re noticing an explosion of real-world applications of artificial intelligence and machine learning that are changing how people work and live today. We see new applications every single day as we research, and we realize we can’t possibly keep you well enough informed with just our podcast. At the same time, we think it’s really important that people understand the impact machine learning is having on our world, because it’s changing and is going to change nearly every industry. So to help keep our listeners informed, we’ve started collecting and categorizing all of the artificial applications we see in our daily research and adding them on generally a daily basis to a collection available on a website we just launched. Go explore the future at datacrunchpodcast.com/ai, and if you want to keep up with the artificial intelligence beat, we send out a weekly newsletter highlighting the top 3–4 applications we find each week that you can sign up for on the website. It’s an easy read, we really enjoy writing it, and we hope you’ll enjoy reading. And now let’s get back to today’s podcast. Curtis: We’ve spent a lot of time on our episodes talking to interesting people about what creative things they’ve done with data, like detecting eye cancer in children, identifying how to save the honey bees, and catching pirates on the high seas, but today we’re going to talk about a simple measurement. A creative and clever way to measure something that is incredibly hard to measure. And powerful results come from a measurement that puts some numbers behind a murky issue so people can start to have important conversations about it. And we’re going to look at an example that’s all over the news right now. Ginette: This dataset that’s all over the news right now has an interesting history. While it draws criticism from some sources, it draws high praise from others. But before we get too ahead of ourselves, let’s officially meet Alejandro, the man at the beginning of this episode. Alejandro: My name is Alejandro Salas. I am the regional director for the Americas at Transparency International. I come from Mexico. I started 14 years ago, and I was hired to work mainly in the Central America region, which is also a region where there's a lot of corruption that affects mainly public security, access to health services, access to education. In general the basic public services are broadly affected by corruption.

Few things are as controversial in these perilous times as Donald Trump's Twitter account, often laced with derogatory language, hateful invective, and fifth-grade name-calling. But not all of Trump's tweets sound like they came straight out of a dystopian dictator's mouth. Some of them are actually nice. Probably because he didn't write them. Join us on a discerning journey as two data scientists tackle Donald Trump's Twitter account and, through quantitative methods, reveal to us which hands are behind the tweets. Episode Transcript For the full episode, listen by selecting the Play button above or by selecting this link, or you can also listen to the podcast through Apple Podcasts, Google Play, Stitcher, and Overcast. Dave Robinson: So the original Trump analysis is certainly the most popular blog post I’ve ever written. It got more than half a million hits in the first week and it still gets visits . . . and the post still gets a number of visits each week. I was able to write it up for the Washington Post and was interviewed by NPR. 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.” Curtis: Here at Data Crunch, as we research how data and machine learning are changing things, we’re noticing an explosion of real-world applications of artificial intelligence that are changing how people work and live today. We see new applications every single day as we research, and we realize we can’t possibly keep you well enough informed with just our podcast. At the same time, we think it’s really important that people understand the impact machine learning is having on our world, because it’s changing and is going to change nearly every industry. So to help keep our listeners informed, we’ve started collecting and categorizing all of the artificial intelligence applications we see in our daily research. These are all available on a website we just launched, which Data Elixir recently recognized as a recommended website for their readers to check out. The website includes, for example, a drone taxi that will one day autonomously fly you to work, a prosthetic arm that uses AI to aid a disabled pianist to play again, and a pocket-sized ultrasound that uses AI to detect cancer. Go explore the future at datacrunchpodcast.com/ai, and if you want to keep up with the artificial intelligence beat, we send out a weekly newsletter highlighting the top 3-4 applications we find each week that you can sign up for on the website. It’s an easy read, we really enjoy writing it, and we hope you’ll enjoy reading. And now let’s get back to today’s podcast. Ginette: Today, we’re chatting with someone who made waves over a year ago with a study he conducted and he recently did a follow up study that we’ll hear about. Here’s Dave Robinson. Dave: I'm a data scientist at Stack Overflow, we’re a programming question-and-answer website, and I help analyze data and build machine learning features to help get developers answers to their questions and help them move their career forward, and I came from originally an academic background where I was doing research in computational biology, and after my PhD I was really interested in what other kinds of data I could apply a combination of statistics and data analysis and comput...

The ubiquity of and demand for data has increased the need for better data tools, and as the tools get better and better, they ease the entry into data work. In turn, as more people enjoy the ease of use, data literacy becomes the norm. 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.” “We have a gift for you this holiday season. We’re giving you, our listeners, a website . . . it’s a website of all the AI applications we come across or hear about in our daily research. We post bite-size snippets about the interesting applications we are finding that we can’t feature on the podcast so that you can stay informed and see how AI is changing the world right now. There are so many interesting ways that AI is being used to change the way people are doing things. For example, did you know that there is an AI application for translating chicken chatter? Or using drones to detect and prevent shark attacks on coastal waters? To experience your holiday gift, go to datacrunchpodcast.com/ai.” Curtis: “If you’ve listened to our History of Data Science series, you know about the amazing advances in technology behind the leaps we’ve seen in data science over the past several years, and how AI and machine learning are changing the way people work and live. “But there is another trend that’s also been happening that isn’t talked about as much, and it’s playing an increasingly important role in the story of how data science is changing the world. “To introduce the topic, we talked with someone who is part of this trend, Nick Goodhartz.” Nick Goodhartz: “So I went to school at Baylor University, and I studied finance and entrepreneurship and a minor in music. I ended up taking a job with a start-up as a data analyst essentially. So it was an ad technology company that was a broker between websites and advertisers, and so I analyzed all the transactions between those and tried to find out what we are missing. “We were building out these reports in Excel, but there was a breaking point when we had this report that we all worked off of, but it got too big to even email to each other. It was this massive monolith of an Excel report, and we figured there's got to be a better way, and someone else on our team had heard of Tableau, and so we got a trial of it. In 14 days we—actually less than 14 days—we were able to get our data into Tableau, take a look at some things we were curious about, and pinpointed a possible customer who had popped their head out and then disappeared. We approached them and signed a half million dollar deal, and that paid for Tableau a hundred times over, so it was one of those moments where you really realize, ‘man, there’s something to this.’ “That's what got me into Tableau and what changed my mind about data analysis because at school analyzing finance it was nothing but Excel and mindless tables of stock capitalization and all this stuff and what made it fascinating was finding a way to look at it and answer questions on the fly, and then it actually changed the way I look at things around me. I find myself now watching a television show and thinking ‘well this episode wasn't as interesting. I wonder what the trends of the ratings look like.’ It really has changed the way I think about data because of how easy it's been to access it.” Ginette: “Nick is a member of a growing portion of people who didn’t think they’d end up doing analytics. He didn’t have the specific training for it, he doesn’t have a computer science or statistics degree, and he doesn’t spend nights and weekends writing code. And yet, he was able to produce extremely useful insights from his company’s data s...

  The ubiquity of and demand for data has increased the need for better data tools, and as the tools get better and better, they ease the entry into data work. In turn, as more people enjoy the ease of use, data literacy becomes the norm.   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.” “We have a gift for you this holiday season. We’re giving you, our listeners, a website . . . it’s a website of all the AI applications we come across or hear about in our daily research. We post bite-size snippets about the interesting applications we are finding that we can’t feature on the podcast so that you can stay informed and see how AI is changing the world right now. There are so many interesting ways that AI is being used to change the way people are doing things. For example, did you know that there is an AI application for translating chicken chatter? Or using drones to detect and prevent shark attacks on coastal waters? To experience your holiday gift, go to datacrunchpodcast.com/ai.” Curtis: “If you’ve listened to our History of Data Science series, you know about the amazing advances in technology behind the leaps we’ve seen in data science over the past several years, and how AI and machine learning are changing the way people work and live. “But there is another trend that’s also been happening that isn’t talked about as much, and it’s playing an increasingly important role in the story of how data science is changing the world. “To introduce the topic, we talked with someone who is part of this trend, Nick Goodhartz.” Nick Goodhartz: “So I went to school at Baylor University, and I studied finance and entrepreneurship and a minor in music. I ended up taking a job with a start-up as a data analyst essentially. So it was an ad technology company that was a broker between websites and advertisers, and so I analyzed all the transactions between those and tried to find out what we are missing. “We were building out these reports in Excel, but there was a breaking point when we had this report that we all worked off of, but it got too big to even email to each other. It was this massive monolith of an Excel report, and we figured there's got to be a better way, and someone else on our team had heard of Tableau, and so we got a trial of it. In 14 days we—actually less than 14 days—we were able to get our data into Tableau, take a look at some things we were curious about, and pinpointed a possible customer who had popped their head out and then disappeared. We approached them and signed a half million dollar deal, and that paid for Tableau a hundred times over, so it was one of those moments where you really realize, ‘man, there’s something to this.’ “That's what got me into Tableau and what changed my mind about data analysis because at school analyzing finance it was nothing but Excel and mindless tables of stock capitalization and all this stuff and what made it fascinating was finding a way to look at it and answer questions on the fly, and then it actually changed the way I look at things around me. I find myself now watching a television show and thinking ‘well this episode wasn't as interesting. I wonder what the trends of the ratings look like.’ It really has changed the way I think about data because of how easy it's been to access it.” Ginette: “Nick is a member of a growing portion of people who didn’t think they’d end up doing analytics. He didn’t have the specific training for it, he doesn’t have a computer science or statistics degree, and he doesn’t spend nights and weekends writing code. And yet, he was able to produce extremely useful insights from his ...

The growth of the Internet of Things, or IoT, is often compared with the industrial revolution. A completely new phase of existence. But what does it take to be part of this revolution by building an IoT product? It's complex, and Daniel Elizalde gives us a peek into what the successful process looks like. For the full episode, listen by selecting the Play button above or by selecting this link, or you can also listen to the podcast through Apple Podcasts, Google Play, Stitcher, and Overcast. Donate 15 Seconds If you liked this episode, please consider giving us a review on iTunes! It helps other people find the show and lets us know how we’re doing. Partial Transcript (for the full episode, select play above or go here) Ginette: “So, today, we’re defining an IoT product, or an Internet of Things product, as “a product that has a combination of hardware and software. It acquires signals from the real world, sends that information to the cloud through the Internet, and it provides some value to your customers. ”Okay, so before we introduce you to our guest, consider this: The IoT Market is infernally hot. In 2016, we had 6.4 billion connected ‘things’ in use worldwide, and Gartner research firm projects that number will nearly double to 11.2 billion in 2018, and then nearly doubling again to 20.4 billion IoT products in 2020. For context, this last number is about 2 and a half times the number of people on earth. “Let’s look at an example of IoT at work. Let’s say you’re an oyster farmer, and you need to keep your oysters under a certain temperature because harmful bacteria might grow if you don’t—which would result in people getting very sick after eating your product. If that happened, the FDA could shut your operation down. “This is where IoT products can help you. You can track water temperature with sensors. Those sensors can send that data to the cloud, where you can access it. The system will even send you an alert if the temperature ranges outside your chosen temperature criteria. You can use cameras that show when the oysters are harvested and how long the oysters are out of cold water before they’re put on ice. By using these sensors and cameras to record harvest date, time, location, and temperature at all stages of harvest, you have recorded evidence that you’ve properly handled the harvest. “So, for the purposes of today’s episode, let’s now switch to the other perspective—to the perspective of someone who wants to make and sell an IoT product. Imagine you and two of your friends recently launched an IoT startup—you’re able to secure funding to build your IoT product, and you’ve hired some team members to help you get your beta version off the ground. But you’re new to building products like this, and the rest of your team is also pretty new to it as well. So you decide to talk with someone who is an expert in the IoT space who can give you and your team pointers—and you’re lucky enough to find this man.” Daniel: “My name is Daniel Elizalde. I am the founder of Tech Product Management. My company focuses on providing training for companies building IoT products, specifically I focus on training product managers. I've been doing IoT really for over 18 years, before it was called IoT,