Data Crunch

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 mil

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 dat

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 u

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

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 surroun

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 lea

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 podc

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 inter

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

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

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 customer

We’ve seen photos of disasters depicting fearful and fleeing victims, ravaged properties, and despondent survivors. In this episode, we explore two ways data can help survivors heal and how data also tells their stories. 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) Aaron Titus: “I almost disbelieved my own numbers, even though I chose the most conservative ones. It's just outrageous. I'm like, ‘Really? A 233x ROI?’ That's insane.” 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

Hilary Mason is a huge name in the data science space, and she has an extensive understanding of what's happening in this space. Today, she answers these questions for us: What are the backgrounds of your typical data scientists? What are key differences between software engineering and data science that most companies get wrong? How should you measure the effectiveness of your work or your team's work as a data scientist for the best results? What is a good approach for creating a successful data product? How can we peak behind the curtain of black-box deep learning algorithms? Below is a partial transcript. For the full interview, listen to the podcast episode 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. Curtis: Today we hear from one of the biggest thinkers in the data science space, someone who DJ Patil endorses on LinkedIn for data science skills. She worked at bit.ly, the url

Tesla isn’t the only car brand in the world producing or aiming to produce self-driving cars. Every single car brand is working on developing self-driving cars. But what does this mean for our future? We talk about this and other interesting deep learning projects and history with Ran Levi, science and technology observer and podcaster, who explains in thought-provoking ways what we have to look forward to. Below is a partial transcript. For the full interview, listen to the podcast episode 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. Ran Levi: “I actually had the pleasure of being invited to Google's Mountain View headquarters, and they took me for a drive in one of their autonomous vehicles, and it was, to tell you about that drive because it was boring—boring in a good way. Nothing happened! We were just driving around. The car was driving itself all around Mountain View. And it worked. “T

When Julia Silge's personal interests meet her professional proficiencies, she discovers new meaning in Jane Austen's literature, and she gauges the cultural influence of locations in pop songs. Even more impressive than these finds, though, is that she and her collaborator, Dave Robinson, have developed some new, efficient ways to mine text data. Check out the book they've written called Tidy Text Mining with R. Below is a partial transcript. For the full interview, listen to the podcast episode 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. Transcript Julia Silge: “One that I worked on that was really fun was about song lyrics. The last 50 years or so of pop songs, we have all these lyrics, so all this text data, and I wanted to ask the question, what places are mentioned more or less often in these pop songs.” Ginette: “I’m Ginette.” Curtis: “And I’m Curtis.” Ginette: “And you are listeni

According to the CDC, people have been writing descriptions of malaria—or a disease strikingly similar to it—for over 4,000 years. How is data helping Zambian officials eradicate these parasites? Tableau Foundation's Neal Myrick opens the story to us. Below is a partial transcript. For the full interview, listen to the podcast episode 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. Neal: “When somebody walks from their village to their clinic because they're sick, health officials can see that person now as the canary in a coal mine.” 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: “This episode is brought to you by data.world, the social network for data people. Discover and share cool data, connect with interesting people, an

What does the creation of new artificial intelligence products look like today, and what do experts in this field foresee realistically happening in the near future? One thing's for sure, the way we work and function in life will change as a result of growth in this field. Listen and find out more. Below is a partial transcript. For the full interview, listen to the podcast episode 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. Transcript Irmak Sirer: “It’s kind of like a Where’s Waldo of finding an expert in this entire giant ocean 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.” Curtis: “Brought to you by data.world, the social network for data people. Discover and share cool data, connect with interesting people, and

What would the world look like without honeybees? In theory, if there were no honeybees, it could drastically change our lives. Bjorn Lagerman, though, never wants to know the actual answer to that question. but the honeybees current worst foe, Varroa Destructor, is killing off honeybee hives at intense rates. Bjorn's in the middle of a machine learning project to save the bees from the vampirish Varroa. Below is a partial transcript. For the full interview, listen to the podcast episode by selecting the Play button above or by selecting this link or you can also listen to the podcast through iTunes, Google Play, Stitcher, and Overcast. Bjorn Lagerman: “My name is Bjorn Lagerman. I live in the middle of Sweden. When I look back in my younger days, I remember, I sat in school, looked outside the window and decided I wanted to be outside. You know, I was raised in a stone desert in the middle of Stockholm in the old town; that's a medieval town. And inside the blocks, there were sort of an oasis of water and

What if you found out your infant had eye cancer? That news would rock anyone’s world. But what if you had a tool that helped you catch it early enough that your baby didn’t have to lose his or her eye and didn’t have to go through chemo? You’d probably do almost anything to get it. Bryan Shaw has dedicated his time to helping parents detect this cancer sooner so their children don't have to go through what his son went through—and he’s doing it for free. With computer scientists from Baylor University, he's harnessed the power of a machine learning algorithm to detect cancer that no human eye can detect. Below is a partial transcript. For the full interview, listen to the podcast episode by selecting the Play button above or by selecting this link or you can also listen to the podcast through iTunes, Google Play, Stitcher, and Overcast.  Bryan Shaw: “The very first person who ever contacted me because our app helped them was a gentleman in Washington State, and his little girl had myelin retinal nerve fiber

If someone came up to you and randomly asked you, "How many slaves work for you?" maybe you'd think, "Slavery ended a long time ago, Bro." Or maybe you would take the question seriously. With 20 million to 46 million people enslaved in the world, it is a serious question, and while we don't see it daily, some of these enslaved people make things for us. Even if we're judicious about what we buy, we would be surprised just how much global slavery goes into producing the goods we do buy. But how can we quantify it? How can we solve this? Justin Dillon, who has worked with the U.S. State Department and hundreds of businesses, thinks he has the answer. Transcript: Ginette: “Our world today is an extremely vast, complicated, and interconnected web of 7.5 billion people. We’re directly connected to some, and it’s really easy to see those connections on Facebook, Instagram, Twitter, LinkedIn. But there’s a whole other group of people we are much more subtly connected to—people who are basically (who are essentiall