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    Natural Sciences

    Data Crunch

    If you want to learn how data science, artificial intelligence, machine learning, and deep learning are being used to change our world for the better, you’ve subscribed to the right podcast. We talk to entrepreneurs and experts about their experiences employing new technology—their approach, their successes, their failures, and the outcomes of their work. We make these difficult concepts accessible to a wide audience.

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    Latest Episodes:
    Preventing a Honeybee Fallout May 13, 2017
    Show notes

    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 fountains and green in this stone desert, but the streets were very old streets. And then the contrast was that in the summertime, I spent that in the countryside, and that was total freedom—you kow, lakes, rivers, forests, and my parents let us do what we wished during all the days, just come home for dinner. So when I was 22, I thought bees might be a reason to spend more time in nature. So I went to the nearest beekeeper, . . . and he sold me my first colony, and from there on, I was really hooked.”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, and work together to solve problems faster at data.world. A complex dataset with a ton of files can quickly become scary and unwieldy, but you need not fear! Now you can use file labels and descriptions to manage and organize your many files on data.world. With file labels and descriptions, you can quickly see what type of file it is, view a short description, and also filter down by file type. Wanna see an example of how data.world users are using file labels and descriptions to keep their dataset organized? Search ‘data4democracy/drug-spending’ on data.world.”Ginette: “Imagine for a minute what the world would look like without bees. The image is potentially pretty bleak: we’d have much less guacamole, fruit smoothies, chocolate everything, various vegetables, pumpkin pie, peach cobbler, almond butter, cashews, watermelons, coconuts, lemon, limes, and many more food products. Let’s not forget the obvious—we wouldn’t have honey, which man can’t replicate well. “But fruits, vegetables, and chocolate aren’t the only food stuffs that would be affected. Bees support other animal life. They pollinate alfalfa, which helps feed dairy cows and boost their milk production, and on a more limited basis, alfalfa helps feed beef cows, sheep, and goats. Statistics vary, but bee pollination affects somewhere between one to two thirds of food on American’s plates. Beyond food, bees help grow cotton, so without bees, we’d have to rely more on synthetics for our cloth.“Honeybees in particular are incredibly hard workers. They pollinate 85 percent of all flowering plants. They collect from just one flower specie at a time, and in turn, the pollen they carry fertilizes the flower’s egg cells. One industrious honeybee worker can pollinate up to 5,000 flowers a day. One honeybee hive worth of workers can visit up to 500 million flowers a year. “With a reduced bee population, it gets harder to produce food. Let’s take an example. California grows 85 percent of the world’s almonds, and it takes at least 1.7 million hives to pollinate them,


    When a Picture Is Worth a Life Apr 29, 2017
    Show notes

    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 layer, which is an abnormal myelination of the retina, and it can cause blindness, but it presents with white eye. And his little girl was five years old, and he kept seeing white-eye pics. He heard our story. He downloaded our app. Our app detected the white-eye pics. That emboldened him enough to grill the child's doctor. You know, 'My camera's telling me this. Look, this app. I heard this story . . .’ The doctor takes a close look. The girl had been 75 percent blind in one of her eyes for years, and nobody had ever caught it.”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: “Data Crunch is again brought to you by data.world, the social network for data people. Discover and share cool data, connect with interesting people, and work together to solve problems faster at data.world. Did you know that you can add files via URL to your data sets on data.world? Data.world APIs allow you to pull live survey data into your data set, enable automatic file updates, and more. Get the full details on data.world APIs at docs.data.world, or search ‘Austin Cycling Survey’ on data.world to see live survey sync in action in Rafael Pereira's data set!”Ginette: “One quick reminder that our data competition is currently up on data.world. Be sure to post your submissions by May 5.“Okay, now back to the story. If you know someone who’s about to have a child, has a child five or under, or plans to have children, you need to send them this episode, and you’re about to find out why from this man, Bryan Shaw.”Bryan: “When Noah was three-months-old, we started noticing that a lot of his pictures had white pupillary reflections, what doctors call leukocoria, white core, white pupil, and that can be a symptom of a lot of different eye diseases.”Ginette: “You probably put this together, but Noah is Bryan’s son. And to add in Noah’s mom’s perspective here, when she started noticing this strange white reflection in Noah’s eyes, like most moms today, she aggressively searched the Internet for answers. Like Bryan said, leukocoria could indicate a disease, or it could indicate nothing, but the Shaws decided they needed to tell their pediatrician about what they’d found.”Bryan: “Noah passed all his red reflex tests, until we told his pediatrician that we noticed leukocoria, and he had a very good pediatrician—Pearl Riney, Cambridge, Massachusetts. And then she really looked really, really closely. And on that test, she noticed a white pupillary reflection and immediately sent us that afternoon to an opthamologist.”Ginette: “At this point, Bryan’s wife, Elizabeth, was freaking out because she’d done all the research about leukocoria, or white eye, and she knew what white eye might mean for their four-month-old son.”Bryan: “In Noah's case,


    How Many Slaves Work for You? Apr 14, 2017
    Show notes

    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 essentially working for us) invisible to us, 20 to 46 million of them.“Our guest today deals with this invisible web every day.”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: “Today’s episode is brought to you by data.world, the social network for data people. Discover and share cool data, connect with interesting people, and work together to solve problems faster at data.world. Quickly locating data, understanding it, and combining it with other sources can be difficult. The data.world Python library allows you to bring data.world datasets straight into your workflow. Easily work with data and metadata in your Python scripts and Jupyter notebooks. Ready to dive in? Learn how to use data.world’s Python library at meta.data.world. Curtis: “Before we get going, one other note about data.world—starting today until May 5th, we are hosting a data competition on their site, and we’d love your participation. Donald Trump’s tweets have been the source of a lot of media attention recently—many high profile news outlets have asserted his tweets show signs of authoritarianism, some say he’s using his twitter account to shape the new cycle, and some have even built algorithms to make stock market decisions based on his tweets. Whatever your stance is on the subject, we’ve uploaded a dataset of every single one of his Tweets up to data.world, and we want to see what you can make of the data. This is a create competition by nature—submissions can be of any format, but the point is we want to see what you can learn, assert, or create with this data set. It’s easy to participate—just go to data.world/datacrunch, and you’ll find the dataset and all of the details. Submit by May 5, and we’re going to take all the submissions that tell the most compelling stories, we want to feature them on a future podcast episode.”Ginette: “Now back to the story. A few months ago, I ran across a website. It sucked me in. It asked me a provocative question, which we’ll get to in just a second, but first, we’ll introduce you to the man who’ll situate the story for you—the main person behind the website.” Justin: “My name’s Justin Dillon. I’m the founder and CEO of Made in a Free World. We started off years ago. I would say probably the genesis for us was me getting a call from the State Department in about 2010. I’d already been doing some projects, a few websites and, films that I was producing, around human trafficking and modern-day slavery.”Curtis: “Justin directed a documentary he released in 2008 called ‘Call + Response,’ which ranked as one of the top documentaries in 2011.”Justin: “And the State Department called and said, we would like to do a project with you, we like the way that you use data and tell stories,


    Predicting the Unpredictable Apr 01, 2017
    Show notes

    We now know black swans exist, but Europeans once believed that spying one of their kind would be like stumbling across a unicorn in the woods—impossible. Then, Willem de Vlamingh spotted black swans in Australia, and this black bird, which once represented the impossible to Europeans, shifted to represent the unpredictable. One company now dons the name "Black Swan." Find out how it aims to predict what we currently consider to be unpredictable.TranscriptGinette: “Submerse yourself in early 1600s London culture for a minute. Shakespeare’s alive and in his late career. The first permanent English settlement in the Americas just happened. Oxygen hasn’t been discovered yet. But a lesser known cultural idiosyncrasy has to do with a large white bird, the swan. In Europe, the only swans anyone had seen or heard about were white, so of course, in their minds, a swan couldn’t be any other color. From this concept, a popular saying develops, originally stemming from a poem. You use it when you want to make a point that something either doesn't exist or couldn’t happen. You’d say something like this: ‘you’re not going to find out because it’s about as likely as seeing a black swan,’ meaning that, that thing or event was impossible.“But then a discovery blows everyone’s minds. Dutch explorer Willem de Vlamingh is sent on a highly important rescue mission. A lost ship with 325 people on it probably ran aground near Australia, and they needed him to go rescue these people and the goods on board. While Willem and the three ships under his command go and search Australia for this lost ship, they find lots of fish; unique trees; quokka, a cat-sized kangaroo-like creature; and . . . black swans. This last discovery inevitably permanently shifts the meaning of this saying. After this, people start using it more to say when something’s highly unlikely or an unpredictable moment. “Now this concept of an unpredictable moment is why Steve King named his company Black Swan, because they predict the seemingly unpredictable.”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.”Steve King: “I am Steve King; I’m the CEO of Black Swan. Black Swan is 250 people who focus on trying to predict consumer behavior using data science, artificial intelligence, and big data. We have lots of large clients. We mostly work with big companies that have big problems to solve. Our work sort of splits across the US and the UK. Black Swan is absolutely full of stories. A lot of the work we really do is finding a hard problem that no one’s really solved before and then using data science to crack it, but there always quite interesting stories because, you know, they’re stories of a little bit of adventure, luck, and skill.’”Ginette: “The UK’s Sunday Times has consistently placed Black Swan on its lists: in 2014, it was on the ‘Ones to Watch’ list in its Tech Track. In 2015, it was ranked number one on the Start-Up Track. And in 2016, it was ranked number one in the Export Track 100, because it had the fastest growing international sales for the UK’s small to medium enterprises. “So what’s the secret sauce to the rapid growth and success of Black Swan, a company that solves problems for large companies in many different industries? It turns out, they aim to be better than anyone else at accessing and crunching a specific datasource.”Steve: “The reason we’re quite broad is it actually sits on one simple idea, and the simple idea really is that the Internet is really the world’s biggest data source, and we call, we call the Internet the world’s biggest focus group. So pretty much every opinion of a consumer or the open data that governments are laying out is all there for you to consume, but the, the trick is can you consume it in a way to help you find patterns so you can ...


    The Golden Age of Data Science Mar 18, 2017
    Show notes

    How did one boy's stuffed yellow elephant permanently intertwine itself in history? What is a data scientist? Why is right now the golden age for data science? We take a crack at all three of these questions—the second two, with the help of Gregory Piatetsky-Shapiro and Ryan Henning.TranscriptGinette: “Over the past few years, we’ve seen these news flashes:“An article in Harvard Business Review in 2014, titled: Data Scientist: the Sexiest Job of the 21st Century“Mashable’s article in 2015: So You Wanna Be a Data Scientist? A Guide to 2015’s Hottest Profession“Business Insider, 2016: Data Science was the #1 Profession as Rated by Glassdoor“A data science industry observer, KDnuggets, 2017: Data Scientist: Best Job in America, Again, which cites the most recent Glassdoor report outlining the very top jobs in America:“It turns out, four of the five top US jobs deal with data. In descending order, we find data scientist, devops engineer, data engineer, and analytics manager.”Curtis: “With four out of five of these top jobs orbiting data, clearly something’s going on here.” 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: “Today is a culmination of everything we’ve talked about in our series on the history of data science. This is where all the contributions of Florence Nightingale, William Playfair, Ronald Fisher, Ada Lovelace, and many others come together in one place. We’ll add a couple more people to this list to answer these two questions: ‘What is a data scientist? And why is right now the golden age of data science?’” Curtis: “According to IBM, ‘everyday, we create 2.5 quintillion bytes of data.’ But what does a quintillion actually look like?“Well, if you take one quintillion pennies, you could actually place them face up end to end can and blanket the entire surface of the earth 1.5 times over. Or think about one quintillion ants. That would be like taking all of the ants that exist today on planet earth according to some estimates, and then you have to take that number and multiply it by 100. So, that ant pile in your front yard becomes 100 ant piles in your front yard. Basically ants take over the earth. And we make 2.5 quintillion bytes every single day!“The next question is, how much information does that actually represent? It’s 250,000 times the amount of information that all the printed material in the Library of Congress contains. And we make that every single day.” Ginette: “In 2013, SINTEF published this stat, quote: ‘90% of the world’s data has been created in the preceding two years.’ According to one Ph.D. technologist, this has been true for the last 30 years because every two years, we produce 10 times as much data.”Curtis: “This exponential growth is insane. Just as an example of this type of growth rate, if you take a hypothetical scenario, and you take the world’s population, and say it starts growing as rapidly as data is growing now, it would look like this: Currently, the world’s population, 7 billion people, could fit in the size of Texas if they were living as densely as they do in New York City. Now, in two year’s time with this growth rate, you’d actually have to cover the entire United States and half of Canada with people living in New York City-like density. And if you extrapolate that out ten years keeping the growth rate the same, you’d have to cover the entire planet, including all of the oceans, with New York Cities, and then you’d have to do that with 100–150 additional earths to fit all of those people. That’s the kind of growth rate we’re talking about.”Ginette: “With data collection on the rise, one report goes so far as to say that only the data literate will have the chops to be executives in the future, quote:


    The Curated History of Data Science, Part 3 Mar 01, 2017
    Show notes

    From a small building in Pennsylvania to widespread usage across the world, we track the compelling story of one of the greatest technological innovations in history, setting the stage for the age of data science.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: “Today our story starts at a business building.” Curtis: “The building is in Philadelphia, Pennsylvania, on Broad and Spring Garden Streets to be precise. Envision the late 1940s.” Ginette: “You see a man absorbed in thought entering the building, and you decide to follow him in.”Curtis: “When you walk through his office, you find some bright engineering minds working on a fairly new startup in town: the Eckert-Mauchly Computer Corporation, or EMCC. It turns out, this is the very first large-scale computer business in the United States.”Ginette: “While this business environment on the surface is vibrant and innovative, behind the scenes, it’s a pressure cooker full of confusion.” Curtis: “The owners, John Mauchly, who you followed into the office, and his business partner, J. Presper Eckert, are talking about something strange that’s been happening: most of their clients had been from the government, and now they’re quietly pulling away from doing business with EMCC without any explanation, which is both alarming and confusing to the business owners. It’d be one thing if the government gave a reason each time it pulled out of a contract, but without one, they have no idea what’s wrong or how to try and fix the situation. It’s like going through several breakups where the only explanation offered is, ‘it’s not you; it’s me.’ “So what’s actually going on here?”Ginette: “The answer is woven into John’s backstory, a backstory that also includes the story of the ENIAC, the very first fully electric general purpose computer.“In John’s earlier career, he was involved with scientific clubs and academia. He started as an engineer and eventually became a professor at the prestigious Moore School of Engineering at UPENN. At one point, he got lucky. He asked essentially this question to the right military person on campus: what if I could build a machine that would significantly reduce your trajectory calculation time for projectiles?”Curtis: “So the military ends up formally accepting his proposal, and John and Presper team up for three years on this top-secret military project to build the ENIAC. “At the time, the ENIAC is really impressive in both size and ability. It weighs about the same as nine adult elephants, which is 27 tons, and it has about 17,500 vacuum tubes, each about the size of your average household light bulb. It has 5,000,000 hand-melted joints. And it’s the size of a small house—about 1,800 square feet. And in today’s dollars, it costs about $7 million.“It’s the very first of its kind. It’s both completely electric and a general purpose machine, meaning you can use it to calculate almost anything as long as you give it the right parameters. The bottom line is that it’s a lot faster than anything before it. It’s 2,400 times faster than human computers, and 1,000 times faster than any other type of machine computer at the time. For example, it took the calculation of a 60-second projectile down from 20 hours to just 30 seconds. To understand the magnitude of this, it's like moving from an average snail’s pace to the average speed of a car on a highway.”Ginette: “Here’s another way to look at this: if you drive your car (the ENIAC) across the country from L.A. to New York City at about 70 miles per hour without stopping, it would take you a little over a day and a half to drive there. In contrast, it’d take a snail (the human computer) without stopping about 11 years.” Curtis: “So it turns out the ENIAC isn’t ready in time f...


    The Curated History of Data Science, Part 2 Feb 16, 2017
    Show notes

    She isn’t your typical English girl from the early 1800s. She’s a girl who, because of her fortunate and unfortunate family circumstances, ends up perfectly situated to become part of something that will revolutionize the world.Ginette: “For many reasons, she isn’t your typical English girl from the early 1800s. She’s a girl who at one point examines birds to discover their body-to-wing ratio so she can invent a flying machine and write a book about it. These are goals that show mathematical skill, creativity, and initiative. She’s also a girl who, because of her fortunate and unfortunate family circumstances, ends up perfectly situated to become part of something that will revolutionize the world.”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: “In our last episode on the history of data science, we talked about the origins of charts and data visualization, which are an important to data science, but in today’s story, we’re going to start a new thread that’s absolutely essential to the fabric of this history. We’re going to talk about some brilliant inventors that gave rise to an idea that would change the course of history—arguably one of the most powerful ideas that has shaped our modern world. It’s a story of triumph and innovation, but also of tragedy, because even though the ideas they moved forward had a dramatic effect on all of us in the long run, in the short term, many of these people saw their dreams fall apart before their eyes. So today and in our next episode, we pay homage to some key people who started the wave that gave us technology that makes our modern lives possible. And we’re gonna to do that first by getting back to the story of the girl we mentioned in the intro.”Ginette: “Interestingly enough, this episode ties into our last episode in an unexpected way. The little girl we introduced to you earlier is born about the same time as Florence Nightingale. She’s about five years older. “We have to understand a little bit about her parents, Annabella and George, to have a better insight into her, so here’s a peek into their lives: They’re both highly intelligent, capable, and well-educated, and they’re from high society. George is more verbal and artistic, and Annabella is more logical and mathematical. “From the start, the pair is not a good match. Annabella sees George’s flaws, but she also sees George’s potential. Beyond that, Annabella is probably attracted to his very handsome (as a lot of people describe him), bad-boy, wild-and-wooly type. One good example of his rebellious nature and disdain for authority is how he exploits a loophole in college to flout what he considers is an absolutely outrageous school rule: since the university won’t let him bring his cherished pet dog with him, he defiantly keeps in his Cambridge University apartments a tame pet bear. Essentially, as loopholes work, the rule doesn’t explicitly say no pet bears, so the university in his mind can’t immediately do anything about it—this may be partly why he only lasts there a term. Anyway, these are the types of things Annabella thinks she can change about George. “On George’s side of things, he notices Annabella’s sharp intellect. She’s incredibly smart. From early childhood, her parents recognize her natural brilliance and essentially give her what most women can’t get in those days—the equivalent of a Cambridge University education. Something else George likes about Annabella is that she’s down to earth. So eventually, he proposes to her, and probably against her better judgement, she says ‘yes’, and they get married, but within a year, things get messy.“She notices George’s strange behavior. He’s dark, he’s angry, he’s brooding. And over time, he starts doing other odd things and even lashes out at her.


    Eyes on the Pirates, Part 2 Jan 31, 2017
    Show notes

    Pirates in folk stories and popular movies conjure up strong imagery: eye patches, Jolly Rogers, parrots, swashbuckling, scruffy voices that say “Aye, Matey.” But what do the lives of successful pirates look like today? And what's being done to stop them from plundering and smuggling our ocean's precious resources? World Wildlife Fund's project Detect IT: Fish takes aim at these pirates and other illegal actors with this cutting-edge project that reduces a time-consuming tracking process from days to minutes.Ginette Methot-Seare: “After nearly 15 years of lucrative, illegal activity, he was caught and convicted. The judge in this key case stated that his business activities were an ‘astonishing display of the arrogance of wealth and power.’ He destroyed evidence, and while under investigation, even hired a private I to follow an agent around. After serving prison time, the main perpetrator and his accomplices were ordered to pay 22.5 million dollars in restitution to South Africa for the damage they had done.”Curtis Seare: “Who was this man? Arnold Bengis, a modern-day pirate.”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: “Believe it or not, these episodes take hours and hours of hard work to produce, and the success of this show depends in large part on the listener reviews and ratings we get. If you like what we do, the best way to support us is to go to iTunes, Google Play, or your favorite medium for getting the episodes, and leave us a review. “If you’re willing to do that, a big thank you in advance, and a big thank you to those who already done it.”“At the end of our last episode, we promised you the story of one of the biggest pirate busts in history, and we will deliver, but before we go on, if you’re new to Data Crunch, you may want to start with the last episode, which will give you more background and context.“By some accounts, this is what happened: Arnold Bengis became incredibly wealthy after growing a business in South Africa. He had a house in Bridgehampton, New York, worth several million dollars, an apartment in the Upper West Side of Manhattan on the 41 floor, and a house in Four Beaches, an exclusive neighborhood in Cape Town, South Africa. “His 6,000-plus square foot Bridgehampton house, a large Spanish-tile stucco villa, overlooked the beautiful Mecox Bay to one side and the Atlantic ocean on the other. His six bedroom, seven full bathroom single-family home had what you’d expect to find at a palatial place: a well-manicured golf green; a luxurious pool; large, well-decorated rooms with chandeliers, and expensive furniture. When the house last sold, it went for 10 and a half million dollars. One of the agents of the National Oceanic and Atmospheric Administration, or NOAA, who investigated Bengis’s case even said he was in partial awe of the lifestyle Bengis was living, which was supported by illegal fishing business.“Bengis held his money, both personal and business, in a highly complex network of trusts and asset havens. The money was scattered abroad in many different places, like Switzerland, Gibraltar, Jersey Islands, and Britain. While authorities didn’t know everything about his money, what they did know was that he had vast assets. For example, in just one year, he deposited $13 million into one of his accounts. His lawyer said that one of his several trusts was worth more than $25 million, according to the book Hooked: Pirates, Poaching, and the Perfect Fish. “I know what you’re probably thinking: ‘How did this man make so much money from illegal fishing?’ We told you in our last episode that IUU fishing rakes in between $10 billion and $23.5 billion dollars a year, and that’s a conservative estimate. The larger picture is this: When you consider that the entire world’s trade...


    Eyes on the Pirates, Part 1 Jan 12, 2017
    Show notes

    The history books teach that slavery ended, but it still exists; it’s just morphed its form—different commodity, different location, but same abuses. The commodity is seafood. The location, Southeast Asia. The abuses, forced servitude with all its ugly associations. Some people make a substantial living off illegal, unregulated, and unreported (IUU) fishing, which fuels a dark underground. How is big data angling to stop it? Find out in our next two episodes.Transcript:Michele Kuruc: “People who were seeking better lives and, and coming to look for work were kidnapped by unscrupulous dealers, who forced them into lives we can’t even imagine.”Ginette Methot: “I’m Ginette.”Curtis Seare: “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: “Welcome back to Data Crunch! We took a bit of a break over the holidays, and we hope you were able to too. “So upward and onward to 2017. What are we up to this year? We’ll be finishing our data science history miniseries for you, and we’ll be meeting some really cool people from KDnuggets, Galvanize Austin, and Datascope in Chicago. But before we do those episodes, we have to pivot because with major recent developments, this particular episode deserves to come out now.“The lives we can’t even imagine look like this according to the Associated Press. One Burmese man left his village when he was 18 years old. He followed a recruiter who promised him a construction job. When he arrived in Thailand, his captors held him with little food or water for a month. He was then forced onto a fishing boat. He was told that he was sold and would never be rescued. In that fishing environment, sometimes he worked 24-hours a day. He and his fellow fishers were whipped with stingray tails and shocked with electric devices. They were told during their time fishing that they would never be let go, not even when they died, and men in his similar situation were sometimes sold from ship captain to ship captain. “If they tried to escape the work, they were locked in cages on remote islands. In the 22 years he was away from home, he asked to go home twice. The first time he asked, the company official chucked a helmet at his head, which left a bloody gash that he had to hold closed. The second time he begged to go home, he was chained to the boat deck for three days in the blistering sun and when the night came, it was rainy, and he could do little to protect himself from it. During that three-day period, he had no food. He amazingly fashioned a lock pick and unlocked his shackles. He knew if he was caught, he’d be killed, so he dove into the water in the cover of night and swam ashore, hiding for his life.“You might ask why he didn’t go to local officials. The answer is he couldn’t because they might sell him back to the ship captains. So after eight years in the jungle hiding from the fishing companies, he finally got to go home because of the AP’s reporting. This is modern-day slavery. Every year, thousands of people are tricked or sold into this type of slavery in order to catch fish for lucrative markets.“If you’ve ever read Solomon Northup’s gripping autobiography, Twelve Years a Slave, the similarity is eery. They are both free men who are initially unknowingly abducted. They’re shackled, beaten into servitude, and forced to work in harsh conditions for many, many years. Both are desperate to go home to their families, and both experience miraculous escapes from tyrannical systems. But unfortunately, not everyone escapes.“This is a huge problem, and it’s frequently linked to illegal, unregulated, and unreported fishing, well known as IUU fishing. Unfortunately, IUU fishing is linked to some of the ugliest transnational crimes: modern-day slavery, human trafficking, drug trafficking,


    The Curated History of Data Science, Part 1 Dec 09, 2016
    Show notes

    Who were the people pushing the limits of their time and circumstances to bring us what we know today as data science? We examine what motivated them to do their important work and how they laid the foundations for our modern world where algorithms and analytics affect everything from communications to transportation to health care—to basically every aspect of our lives.This is their story.Transcript:Ginette: “She was obsessed with her failure—she thought she hadn’t done enough. And it didn’t matter that the public saw her as a heroine. So she ended up writing an 830-page report where she employed some power graphics, and this paired with her other efforts ended up changing the entire system.”Ginette and Curtis: “I’m Ginette, and I’m Curtis, and you are listening to Data Crunch, a podcast about how data and prediction shape our world. A Vault Analytics production.”Ginette: “In our last three episodes, we have just thrown you into the middle of data and prediction and the explosion of data science. And some of you have had some questions, like, How did data science become a thing?“In the next three episodes, we’re doing a miniseries where we’re going to address some of these questions, and I think you’ll find it very interesting. Our story starts with an impressive woman. “It’s 1854. It’s the Crimean War, and a woman shows up at a hospital to help. She finds horrifying conditions. To paint an accurate picture for you, here’s a little bit of what she found: the sewage and ventilation systems were broken; the floor was an inch thick with waste—probably human and rodent; the water was contaminated because, come to find out, the hospital was built over a sewer; rats were hiding under beds and scurrying past, as were bugs; and the soldiers’ clothing was swarming with lice and fleas; and on top of that, there were no towels, no basins, no soap, and there were only 14 baths for 2,000 soldiers. Keep in mind this was 20 years before Pasteur and Koch spread Germ Theory. “So she and the 37 nurses that she brought with her set to work, and they did their best to clean up the hospital and help the soldiers. Eventually, because of her, the government sent a sanitary commission. They flushed the sewers; they improved the ventilation. And this helped the situation dramatically. In the end, she reduced the death rate by two thirds.“But Florence Nightingale went home feeling like she had failed, which you’ll remember we mentioned right at the beginning of the podcast. She felt a lot of soldiers had died needlessly. This drove her to write her famous 830-page report. And she ended up working with lead statistician William Farr, who actually helped invent medical statistics. He would say to her, ‘We don’t want impressions, we want facts.’ And working under that type of context, she gathered vast amounts of complex army data and analyzed it to find something rather shocking: 16,000 of 18,000 deaths in hospitals were not due to battle wounds but to preventable diseases spread by poor sanitation.”“So these statistics completely changed her understanding. She thought the deaths were due to inadequate food and lack of supplies, but after the sanitary commission came in, she noticed that the mortality rate dropped significantly. So as Florence prepared her report, she was afraid that people’s eyes would glaze over the numbers and that they wouldn’t grasp the significance of what she was trying to say. So she came up with a clever way to present her data: she ended up using graphics, in particular what she’s known form the rose chart, to convey her message.”Curtis: “Nowadays, charts are everywhere, but back in her day, the idea of creating a picture that was defined by certain data points was not very common, and so the fact that Nightingale thought to do this was very innovative and clever, and it was important because it was able to communicate what she needed to communicate. “Her mentor,


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