Welcome to another exciting episode of Data Crunch! In this episode, we dive deep into the world of data analytics and manufacturing with our special guests, Alex Reid and Jay Minutrie. Both Alex and Jay bring unique entrepreneurial and data-focused experiences to the table, making them the perfect duo for an engaging conversation about the future of manufacturing.We start with the origin story of their innovative technology, which began as an idea in Alex's father's laboratory at Tulane University. Alex, a young entrepreneur, teamed up with Jay, a seasoned industry veteran, to bring this patented technology to the market. Their complementary backgrounds served as the foundation for their successful partnership, leading to significant advancements in the manufacturing industry.As we continue the conversation, Alex and Jay discuss the challenges and opportunities they faced while introducing their hard tech into the industrial space, and how they managed to successfully navigate these obstacles with the help of a strategic acquisition by Yokogawa. The pair also share insights on how they plan to leverage their new partnership to drive even more innovation and growth in their industry.In this episode, we also explore the concept of on-demand designer materials at commodity scale. Alex shares his vision of a future where bespoke materials can be produced for specific customers at an affordable cost, leading to greater efficiency, reduced waste, and the ability to create customized products tailored to individual needs. This groundbreaking concept has the potential to revolutionize the manufacturing industry, and we dive into what it could mean for industry players and end-users alike.Alongside this fascinating discussion, we also touch on the importance of mentorship for young entrepreneurs and the value of having experienced individuals to guide and support you through the challenges of starting a business. Alex and Jay's unique partnership serves as an inspiring example of how collaboration and shared knowledge can lead to incredible success.Furthermore, our guests delve into the impact of their technology on the biopharma space, highlighting the potential for advancements in life-saving drugs and personalized medicine. With opportunities for growth in both the polymer and biopharma sectors, Alex and Jay's enthusiasm for the future is infectious, and their determination to make a difference is truly inspiring.So, join us as we explore the world of data science in manufacturing, the potential for on-demand designer materials, and the inspiring journey of two entrepreneurs who are pushing the boundaries of innovation. Don't miss this enlightening episode of Data Crunch, filled with valuable insights, engaging conversations, and a glimpse into the future of manufacturing.

Welcome to another exciting episode of Data Crunch! In this episode, we dive deep into the world of data analytics and manufacturing with our special guests, Alex Reid and Jay Minutrie. Both Alex and Jay bring unique entrepreneurial and data-focused experiences to the table, making them the perfect duo for an engaging conversation about the future of manufacturing. We start with the origin story of their innovative technology, which began as an idea in Alex's father's laboratory at Tulane University. Alex, a young entrepreneur, teamed up with Jay, a seasoned industry veteran, to bring this patented technology to the market. Their complementary backgrounds served as the foundation for their successful partnership, leading to significant advancements in the manufacturing industry. As we continue the conversation, Alex and Jay discuss the challenges and opportunities they faced while introducing their hard tech into the industrial space, and how they managed to successfully navigate these obstacles with the help of a strategic acquisition by Yokogawa. The pair also share insights on how they plan to leverage their new partnership to drive even more innovation and growth in their industry. In this episode, we also explore the concept of on-demand designer materials at commodity scale. Alex shares his vision of a future where bespoke materials can be produced for specific customers at an affordable cost, leading to greater efficiency, reduced waste, and the ability to create customized products tailored to individual needs. This groundbreaking concept has the potential to revolutionize the manufacturing industry, and we dive into what it could mean for industry players and end-users alike. Alongside this fascinating discussion, we also touch on the importance of mentorship for young entrepreneurs and the value of having experienced individuals to guide and support you through the challenges of starting a business. Alex and Jay's unique partnership serves as an inspiring example of how collaboration and shared knowledge can lead to incredible success. Furthermore, our guests delve into the impact of their technology on the biopharma space, highlighting the potential for advancements in life-saving drugs and personalized medicine. With opportunities for growth in both the polymer and biopharma sectors, Alex and Jay's enthusiasm for the future is infectious, and their determination to make a difference is truly inspiring. So, join us as we explore the world of data science in manufacturing, the potential for on-demand designer materials, and the inspiring journey of two entrepreneurs who are pushing the boundaries of innovation. Don't miss this enlightening episode of Data Crunch, filled with valuable insights, engaging conversations, and a glimpse into the future of manufacturing.

Data Science in Manufacturing: On-Demand Designer Materials at Commodity Scale

Working with structured and semistructured data can be hard, but it's currently much easier than working with unstructured data, like images, video, audio, and text. We talk with Davit Buniatyan, CEO of Activeloop, who chats with us about how he works to make unstructured data for machine learning easier and faster to work with. 

Working with structured and semistructured data can be hard, but it's currently much easier than working with unstructured data, like images, video, audio, and text. We talk with Davit Buniatyan, CEO of Activeloop, who chats with us about how he works to make unstructured data for machine learning easier and faster to work with.

Creating a Database for AI with Activeloop

What's it like to build an AI product in an industry that still uses outdated project management technology and has no clear conceptual model? After receiving his PhD at Stanford, René Morkos speaks to this very situation. He describes his journey building an AI product in the construction industry.

What's it like to build an AI product in an industry that still uses outdated project management technology and has no clear conceptual model? After receiving his PhD at Stanford, René Morkos speaks to this very situation. He describes his journey building an AI product in the construction industry.

Streamlining Construction with AI

After graduating from the University of Pennsylvania with a master’s degree in artificial intelligence/robotics, Edward Cui  was one of the first Uber self-driving car engineers. He’s had a lot of experience working with unstructured data and shares how we can increase the efficiency of modeling unstructured data by an order of magnitude.

After graduating from the University of Pennsylvania with a master’s degree in artificial intelligence/robotics, Edward Cui  was one of the first Uber self-driving car engineers. He’s had a lot of experience working with unstructured data and shares how we can increase the efficiency of modeling unstructured data by an order of magnitude.

The Future of Unstructured Data with Graviti

What is the secret culprit behind overworked teachers and administrators in much of the educational system? We're joined by one of Data Crunch's finest, James Thomas, who tells from both a technological and personal standpoint the real difficulties faced by our teachers and students, and how the right approach to data can solve many of their problems.

What is the secret culprit behind overworked teachers and administrators in much of the educational system? We're joined by one of Data Crunch's finest, James Thomas, who tells from both a technological and personal standpoint the real difficulties faced by our teachers and students, and how the right approach to data can solve many of their problems.

Data Strategy in the Education Sector

If you haven't heard, GPT-3 is a machine learning model that can write text. Text that looks like a human wrote it—almost out of thin air. The real opportunity is in the ease of use. GPT-3 doesn't replace your writers. It augments them, making writing faster and more accessible without compromising your results. The last thing you want to do is compromise the quality of your content because copy is an exponential business multiplier. The more you can apply it, the more business you'll get. Despite advances in technology, one thing that hasn't changed is that people still talk about products and services—and what they say matters. 

If you haven't heard, GPT-3 is a machine learning model that can write text. Text that looks like a human wrote it—almost out of thin air.  The real opportunity is in the ease of use. GPT-3 doesn't replace your writers. It augments them, making writing faster and more accessible without compromising your results. The last thing you want to do is compromise the quality of your content because copy is an exponential business multiplier. The more you can apply it, the more business you'll get. Despite advances in technology, one thing that hasn't changed is that people still talk about products and services—and what they say matters.

CEOs: Here Is What You Should Know about GPT-3

Higher education institutions house lots of important data that bad actors can access and sell on the dark web, like students' social security numbers, financial aid information, and national security research. Protecting this information should be a high priority for institutions, but it's not always easy to apply best practices and enforce compliance measures. We chat with Brandon Sherman about these issues.

Higher education institutions house lots of important data that bad actors can access and sell on the dark web, like students' social security numbers, financial aid information, and national security research. Protecting this information should be a high priority for institutions, but it's not always easy to apply best practices and enforce compliance measures. We chat with Brandon Sherman about these issues.

Cyber Security in Higher Ed

As the VP of Data Science and Analytics for GE Aviation, Dinakar Deshmukh talks about how he, and the large team he is over, solve big problems internally and externally by splicing the power of data science with deep domain knowledge. 

As the VP of Data Science and Analytics for GE Aviation, Dinakar Deshmukh talks about how he, and the large team he is over, solve big problems internally and externally by splicing the power of data science with deep domain knowledge.

GE Aviation's Dinakar Deshmukh Discusses Data

Telmo Silva created ClicData, an end-to-end SAAS BI platform, which as he describes, is the little guy coming up in the BI platform world. He talks about how his company was started, where it’s been, and where it’s going with cutting-edge R&amp;D. He also offers additional thoughts on the role of data in the business world today.

Telmo Silva created ClicData, an end-to-end SAAS BI platform, which as he describes, is the little guy coming up in the BI platform world. He talks about how his company was started, where it’s been, and where it’s going with cutting-edge R&amp;D. He also offers additional thoughts on the role of data in the business world today.

Telmo Silva Talks ClicData

Keeping quality customers is the aim of nearly every healthy business. Cactus Raazi challenges the typical methods of doing this and suggests alternative data-focused pricing strategies in order for businesses to survive in the future. 

Keeping quality customers is the aim of nearly every healthy business. Cactus Raazi challenges the typical methods of doing this and suggests alternative data-focused pricing strategies in order for businesses to survive in the future.

Pricing with Cactus Raazi

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.

Thought provoking, great interviews, excellent background research, informative and entertaining!

Great Podcast!

I love this podcast! I can’t wait to hear more!

Love it

Data Crunch

What does it take to become a data scientist? We speak with three people who have become data scientists in the last three years and find out what it takes, in their opinions, to land a data science job and to be prepared for a career in the field.Curtis: We’ve talked a lot in our recent episodes about all the interesting things you can do with data science, and we’ve only talked a little bit recently about what it actually takes to get into the field, which is a topic that a lot of you have reached out to us and asked us to cover in a more thorough way. So today, we’re taking a broader approach on this topic by talking to three data scientists who have become data scientists in the last three years. You’re going to be able to hear all the details of each of their three journeys, how they got started, how they landed their jobs, and what their best advice is for getting into the field, and this will give you a broad view about how to get into data science from three people who have actually done it.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: Here at Data Crunch we’ve been hard at work developing a technology that allows executives and business leaders to gain insight from their data instantly—simply by talking to the air. We hook up your data to an Alexa device with custom skills built in to understand the questions you have about your business - and give you answers. Figure out sales forecasts, marketing performance, operational compliance, progress on KPIs, and more by just talking to Alexa. We are officially launching the product this week and have room for three initial customers—if you're interested, head over to datacrunchcorp.com/alexa or datacrunchpodcast.com/alexa (both work), and book some time to chat with us. We’ll assess if your company is a good fit, and if so, we look forward to working with you!Tyler Folkman: My name’s Tyler Folkman. I've gotten into data science in kind of a strange route to be honest. I did my undergrad in economics, actually originally thinking to get into computer science, but for some reason, I had this thought that computer science was going to get outsourced; I don't know if that was a thing, but I think people back in the early 2000s were talking about computer science getting outsourced, so I thought about business, which ended up begin economics, which I really liked, and then ended up doing economic consulting, which is, basically in usually large litigation cases, lawyers hire economists to value damages, so for example, when Samsung and Apple were suing each other, I worked on the Samsung side to help value how much they might sue Apple for, for patent infringement, and a lot of that involves statistical analyses, data analytics, econometrics as economists would call it. And I got really interested in just this idea of data being a really powerful tool for making decisions and coming to conclusions, and so I started hearing about machine learning on the Internet, kind of dabbling with Python, which at the time, I was a Windows user, and it was a huge pain to get Python installed, but I kind of got it up and running, played around with things like SciKit learn, read some blogs, and really got into machine learning and found that it was really housed more in the computer science department at that time, and just kind of decided to apply to some computer science departments and was lucky to get in at University of Texas at Austin and do some studies there, join a machine learning lab and got to do some work at Amazon. Really got a really good set of experiences to kind of help me learn how to be both a programmer and a machine learning person, a little bit of statistics, and jumped straight from there over here to Ancestry and was luc...

What does it take to become a data scientist? We speak with three people who have become data scientists in the last three years and find out what it takes, in their opinions, to land a data science job and to be prepared for a career in the field.Curtis: We’ve talked a lot in our recent episodes about all the interesting things you can do with data science, and we’ve only talked a little bit recently about what it actually takes to get into the field, which is a topic that a lot of you have reached out to us and asked us to cover in a more thorough way. So today, we’re taking a broader approach on this topic by talking to three data scientists who have become data scientists in the last three years. You’re going to be able to hear all the details of each of their three journeys, how they got started, how they landed their jobs, and what their best advice is for getting into the field, and this will give you a broad view about how to get into data science from three people who have actually done it.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: Here at Data Crunch we’ve been hard at work developing a technology that allows executives and business leaders to gain insight from their data instantly—simply by talking to the air. We hook up your data to an Alexa device with custom skills built in to understand the questions you have about your business - and give you answers. Figure out sales forecasts, marketing performance, operational compliance, progress on KPIs, and more by just talking to Alexa. We are officially launching the product this week and have room for three initial customers—if you're interested, head over to <a href="http://datacrunchcorp.com/alexa" rel="nofollow" target="_blank">datacrunchcorp.com/alexa</a> or <a href="http://datacrunchpodcast.com/alexa" rel="nofollow" target="_blank">datacrunchpodcast.com/alexa</a> (both work), and book some time to chat with us. We’ll assess if your company is a good fit, and if so, we look forward to working with you!Tyler Folkman: My name’s Tyler Folkman. I've gotten into data science in kind of a strange route to be honest. I did my undergrad in economics, actually originally thinking to get into computer science, but for some reason, I had this thought that computer science was going to get outsourced; I don't know if that was a thing, but I think people back in the early 2000s were talking about computer science getting outsourced, so I thought about business, which ended up begin economics, which I really liked, and then ended up doing economic consulting, which is, basically in usually large litigation cases, lawyers hire economists to value damages, so for example, when Samsung and Apple were suing each other, I worked on the Samsung side to help value how much they might sue Apple for, for patent infringement, and a lot of that involves statistical analyses, data analytics, econometrics as economists would call it. And I got really interested in just this idea of data being a really powerful tool for making decisions and coming to conclusions, and so I started hearing about machine learning on the Internet, kind of dabbling with Python, which at the time, I was a Windows user, and it was a huge pain to get Python installed, but I kind of got it up and running, played around with things like SciKit learn, read some blogs, and really got into machine learning and found that it was really housed more in the computer science department at that time, and just kind of decided to apply to some computer science departments and was lucky to get in at University of Texas at Austin and do some studies there, join a machine learning lab and got to do some work at Amazon. Really got a really good set of experiences to kind of help me learn how to be both a programmer and a machine learning person, a little bit of statistics, and jumped straight from there over here to Ancestry and was luc...

Getting into Data Science

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 Golden Age of Data Science

How do you whittle the murky business of creating a data-driven culture down to a proven process? Today we talk to a guest who has done this time and time again, helping companies transform their operations. He points out the small nuances and details about the process, like questions to ask to start on the right foot, critical feedback loops to put in place along the way, and how to overcome some of the most common problems that make people give up.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: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.Now, let's jump into our interview with Ryan Deeds, VP of technology and data management at Assurex Global.Ginette Methot:            How do you whittle the murky business of creating a data driven culture down to proven process? Today we talk to a guest who has done this time and time again helping companies transform their operations. He points out the small nuances and details about the process, like questions to ask to start on the right foot, critical feedback loops to put in place along the way and how to overcome some of the most common problems that make people give up. I'm Ginette and I'm Curtis and you are listening to data crunch, a podcast about how applied data science, machine learning and artificial intelligence are changing the world, a vault analytics production. Let's jump into our interview with Ryan deeds that VP of technology and data management at Assurex global.Ryan Deeds:                  Uh, I think it's an interesting time in the whole a data experience because I think so many people failed. You know, in the last like decade that this next couple of years everybody's now trying to look at root cause. And so culture actually is becoming important now, you know? And so that's kind of a cool thing.Curtis Seare:                 What do you mean by that? In terms of a lot of people have failed.Ryan Deeds:                  I think when you look at bi projects from 2003 to 2013, they were just, companies went through litany of failures and trying to get data to a place that what made sense was easily accessible, had had a good quality. Um, but they didn't address that. They just put the visualizations on top of kind of crappy data and they did that over and over and over again. Um, and then finally it seems like, you know, in the last year or two years, we start really having a conversation about what has to happen inside an organization to make data usable. I mean, it's just like water, right? You can't just take water from a stream and start drinking it. You got to process it and clean it and make it and make it valuable and make it worthy of consumption. And that's exactly the thing we got to do with data.Curtis Seare:                 Sure. Maybe we can dive into that as well, because you've had this experience taking a lot of companies through those steps, right? So what do you see as the major roadblocks? How do you start this process of helping people get their hands around? How do I get value from my data?Ryan Deeds:                  So it's interesting. I kind of have, uh, you know, I've done this a lot and so I have, uh, organizations that come to me and they say, hey, you know, we want to, we were ready to start leveraging data. Um, and the, the typical thing is there's just a lack of expectation of the time it takes. Um, and so I threw together like a timeline to try to help, uh, educate individuals on that, you know, and kind of like the steps that it would take to get to usable data, um, in, and the first is really a recognition that today we don't, you know, the organization that we're in is not effectively using data, um, as a, as a strategic advantage.

The Road to a Data-Driven Culture in Your Organization

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. It’s specifically aimed at statistics, and it has extensive libraries and a solid community.As a controversial side note, according to Gregory Piatetsky Shapiro’s KDNuggets poll, late last year,

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 <a href="http://Indeed.com" rel="nofollow" target="_blank">Indeed.com</a>, 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 <a href="http://lightpostanalytics.com/datacrunch" rel="nofollow" target="_blank">lightpostanalytics.com/datacrunch</a> 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. It’s specifically aimed at statistics, and it has extensive libraries and a solid <a href="http://community.As" rel="nofollow" target="_blank">community.As</a> a controversial side note, according to Gregory Piatetsky Shapiro’s KDNuggets poll, late last year,

Python and the Open Source Community

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 ...

Predicting the Unpredictable

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 engineer and maybe someone who knew Tableau and maybe a software engineer in a room, and we’d talked it through.And I’d, I’d, you know,

Why DataOps Matter

Donald Trump

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 TranscriptFor 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 computer programming too.Curtis: Dave studied stats at Harvard and then went on to get his PhD in Quantitative and Computational Biology from Princeton. He did a study on Donald Trump’s tweets in 2016 you may have heard about and posted it to his blog, Variance Explained.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.SourcesPicture SourcePhoto by Kayla Velasquez on UnsplashMusic

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 TranscriptFor 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 <a href="http://datacrunchpodcast.com/ai" rel="nofollow" target="_blank">datacrunchpodcast.com/ai</a>, 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 computer programming too.Curtis: Dave studied stats at Harvard and then went on to get his PhD in Quantitative and Computational Biology from Princeton. He did a study on Donald Trump’s tweets in 2016 you may have heard about and posted it to his blog, Variance Explained.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.SourcesPicture SourcePhoto by Kayla Velasquez on UnsplashMusic

Data Science Reveals When Donald Trump Isn't Donald Trump

Joe Kleinhenz talks about his journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader.Joe Kleinhenz: The critical skills you bring to the table is the ability to break down complex ideas into ones that translate for nontechnical folks.Ginette Methot: I'm Ginette.Curtis Seare: And I'm Curtis.Ginette: And you are listening to Data Crunch—a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Data Crunch is produced by the Data Crunch Corporation and analytics training and consulting company. One of the biggest challenges companies have in getting value from their data is finding the right talent. Good talent is scarce and building a top-tier team is hard if not impossible for some companies. If you are having this challenge try out our analytics as a service offering: we bring a fully equipped data science team to bear on your projects, on demand and with no long-term contract constraints. If you want to start seeing success for your data science efforts quickly and economically, head over to datacrunchcorp.com for more details.Today we'll be hearing about Joe Kleinhenz's journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader. There's lots of unpack in this episode, so let's get to it.Curtis: If we could just start out just by talking about what got you interested in data in the first place, where your journey started, and we can go from there.Joe: I actually first started thinking about using math to predict future outcomes when I was a teenager. I read a book by Asimov called Foundation and whole premise of the book series was I'm using mathematics to predict the future. It's all science fiction stuff in it that point, but that's kind of what certainly got me first interested in it.Curtis: So it was a, it was a work of fiction that got you interested.Joe: Yeah, that captured my imagination. I didn't even at that point even know, it was a, you know, data science was a thing, and as I got my path into the technology, within IT, I was doing business consulting for awhile and got into data warehousing, and this was in the late nineties. From there, ended up in part of GE financial that was doing a lot of direct marketing, and they had a group called database marketing, which was essentially the precursors for data scientists. They had predictive modelers, statisticians essentially in there that were, by today's standards, relatively simplistic tools like linear regression to build, you know, models predicting who would respond to direct-drip marketing offers. I used to joke with people that I ran a team of bad people that decided to call you at dinner with an offer. You can just have the here. Um,Curtis: And you made those people very effective at, at being bad, I assume.Joe: Yes. Yes. At that point there was very few restrictions on what you could do. We were even using credit data for some of the, the algorithms cause we were with credit card companies. Credit data at the time, there wasn't the regulatory restrictions there is now, it's incredibly predictive. When you combine that with recency frequency data on purchasing behavior, you'd really kind of tune in on, you know, what someone would be interested in.

Joe Kleinhenz talks about his journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader.Joe Kleinhenz: The critical skills you bring to the table is the ability to break down complex ideas into ones that translate for nontechnical folks.Ginette Methot: I'm Ginette.Curtis Seare: And I'm Curtis.Ginette: And you are listening to Data Crunch—a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Data Crunch is produced by the Data Crunch Corporation and analytics training and consulting company. One of the biggest challenges companies have in getting value from their data is finding the right talent. Good talent is scarce and building a top-tier team is hard if not impossible for some companies. If you are having this challenge try out our analytics as a service offering: we bring a fully equipped data science team to bear on your projects, on demand and with no long-term contract constraints. If you want to start seeing success for your data science efforts quickly and economically, head over to <a href="http://datacrunchcorp.com" rel="nofollow" target="_blank">datacrunchcorp.com</a> for more details.Today we'll be hearing about Joe Kleinhenz's journey from starting out in data all the way to becoming a leader in one of the largest insurance organizations in the United States. We'll learn about the importance of staying on top of technology, how to win hearts and minds of nontechnical folks, centralized versus decentralized team, pros and cons, how to hold effective conversations with stakeholders and how to go from individual contributor to leader. There's lots of unpack in this episode, so let's get to it.Curtis: If we could just start out just by talking about what got you interested in data in the first place, where your journey started, and we can go from there.Joe: I actually first started thinking about using math to predict future outcomes when I was a teenager. I read a book by Asimov called Foundation and whole premise of the book series was I'm using mathematics to predict the future. It's all science fiction stuff in it that point, but that's kind of what certainly got me first interested in it.Curtis: So it was a, it was a work of fiction that got you interested.Joe: Yeah, that captured my imagination. I didn't even at that point even know, it was a, you know, data science was a thing, and as I got my path into the technology, within IT, I was doing business consulting for awhile and got into data warehousing, and this was in the late nineties. From there, ended up in part of GE financial that was doing a lot of direct marketing, and they had a group called database marketing, which was essentially the precursors for data scientists. They had predictive modelers, statisticians essentially in there that were, by today's standards, relatively simplistic tools like linear regression to build, you know, models predicting who would respond to direct-drip marketing offers. I used to joke with people that I ran a team of bad people that decided to call you at dinner with an offer. You can just have the here. Um,Curtis: And you made those people very effective at, at being bad, I assume.Joe: Yes. Yes. At that point there was very few restrictions on what you could do. We were even using credit data for some of the, the algorithms cause we were with credit card companies. Credit data at the time, there wasn't the regulatory restrictions there is now, it's incredibly predictive. When you combine that with recency frequency data on purchasing behavior, you'd really kind of tune in on, you know, what someone would be interested in.

How to Win Hearts and Minds as a Data Leader

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 shortener, and is a data scientist in residence at venture capital firm Accel Partners, a firm that helped fund some companies you may know, like Facebook, Slack, Etsy, Venmo, Vox Media, Lynda.com, Cloudera, Trifacta—and you get the picture.Ginette: The partner of this VC firm said that Accel wouldn’t have brought on just any data scientist. This position was specifically created because this particular data scientist might be able to join their team. Curtis: But beyond her position as data in residence with Accel, she founded a company that’s doing very interesting research, and today, she shares with us some of her experiences and perspective on where AI is headed.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.Hilary: I'm Hilary Mason, and I'm the founder and CEO of Fast Forward Labs (Please note that Hilary is now the VP of Research at Cloudera). In addition to that, I'm a data science in residence for Accel Partners. And I've been working in what we now call data science, or even now call AI, for about twenty years at this point. Started my career in academic machine learning and decided startups were more fun and have been doing that for about 10,   12 years depending on how you count now, and it's a lot of fun!Ginette: Something I’d like to note here is there’s been a very recent change: Hilary’s company, Fast Forward Labs, and Cloudera recently joined forces, and Hilary’s new position is Vice President of Research at Cloudera. Now, one thing that Hilary talks to is where the data scientists she works with come from, which is a great example of the different paths people take to get into this field.Hilary I am a computer scientist, and I have studied computer science. It's funny because now at Fast Forward, our team only has only two computer scientists on it, and one of them is our general counsel, and one is me, and I'm running the business, so most of the people doing data science here come from very different backgrounds. We have a bunch of physicists, mathematicians, a   neuroscientist, a person who does brilliant machine learning design who was an English major, and so data science is one of those fields where one of the things I really love about it is that people come to it from so many different backgrounds, but mine happens to be computer science.The people on our team at Fast Forward   typically have a PhD in a quantitative field, such as physics, neuroscience, electrical engineering, and then have, through that, learned sufficient programming skill. One of the jokes I make about my team is that we're essentially a halfway house for wayward academics in the sense that we can absorb people and teach them to be good software engineers, help them understand the difference between theoretical machine learning an...

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 <a href="http://bit.ly" rel="nofollow" target="_blank">bit.ly</a>, the url shortener, and is a data scientist in residence at venture capital firm Accel Partners, a firm that helped fund some companies you may know, like Facebook, Slack, Etsy, Venmo, Vox Media, <a href="http://Lynda.com" rel="nofollow" target="_blank">Lynda.com</a>, Cloudera, Trifacta—and you get the picture.Ginette: The partner of this VC firm said that Accel wouldn’t have brought on just any data scientist. This position was specifically created because this particular data scientist might be able to join their team. Curtis: But beyond her position as data in residence with Accel, she founded a company that’s doing very interesting research, and today, she shares with us some of her experiences and perspective on where AI is headed.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.Hilary: I'm Hilary Mason, and I'm the founder and CEO of Fast Forward Labs (Please note that Hilary is now the VP of Research at Cloudera). In addition to that, I'm a data science in residence for Accel Partners. And I've been working in what we now call data science, or even now call AI, for about twenty years at this point. Started my career in academic machine learning and decided startups were more fun and have been doing that for about 10,   12 years depending on how you count now, and it's a lot of fun!Ginette: Something I’d like to note here is there’s been a very recent change: Hilary’s company, Fast Forward Labs, and Cloudera recently joined forces, and Hilary’s new position is Vice President of Research at Cloudera. Now, one thing that Hilary talks to is where the data scientists she works with come from, which is a great example of the different paths people take to get into this field.Hilary I am a computer scientist, and I have studied computer science. It's funny because now at Fast Forward, our team only has only two computer scientists on it, and one of them is our general counsel, and one is me, and I'm running the business, so most of the people doing data science here come from very different backgrounds. We have a bunch of physicists, mathematicians, a   neuroscientist, a person who does brilliant machine learning design who was an English major, and so data science is one of those fields where one of the things I really love about it is that people come to it from so many different backgrounds, but mine happens to be computer science.The people on our team at Fast Forward   typically have a PhD in a quantitative field, such as physics, neuroscience, electrical engineering, and then have, through that, learned sufficient programming skill. One of the jokes I make about my team is that we're essentially a halfway house for wayward academics in the sense that we can absorb people and teach them to be good software engineers, help them understand the difference between theoretical machine learning an...

The Complex World of Data Scientists and Black-Box Algorithms

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, and the digital twin is the virtualization of the physical into the digital, so this is where it kind of allows us to take the ...

Digital Twins, the Internet of Things, and Machine Learning

How do you build a comprehensive view of a topic on social media? Jordan Breslauer would say you let a machine learning tool scan the social sphere and add information as conversations evolve, with help from humans in the loop.Ginette Methot: I’m Ginette,Curtis Seare: 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: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.Ginette: Many of you want to gain a deeper understanding of data science and machine learning, and Brilliant.org is a great place to dig deeper into these topics. Their classes help you understand algorithms, machine learning concepts, computer science basics, probability, computer memory, and many other important concepts in data science and machine learning. The nice thing about Brilliant.org is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code-writing, and interactive challenges, which makes them entertaining, challenging, and educational.Sign up for free and start learning by going to Brilliant.org slash Data Crunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription.Let’s get into our conversation with Jordan Breslauer, senior director of data analytics and customer success at social standards. Jordan: My name is Jordan Breslauer. I'm the senior director of data analytics and customer success at social standards. I've always been a data geek as it pertains to sports. I think of Moneyball when I was younger, I always wanted to be kind of a the next Billy Bean and I, when I started working for sports franchises right after high school and early college days, I just realized that, that type of work culture is wasn't for me, but I was so, so into trying to answer questions with data that had no previously clear answer, you know? I loved answering subjective questions like, or what makes the best player or how do, how do I know who the best player is? And I thought what was always fun was to try and bring some sort of structured subjectivity to those sorts of questions through using data. And that's really what got me passionate about data in the first place. But then I just started to apply it to a number of different business questions that I always thought were quite interesting, which have a great deal of subjectivity. And that led me to Nielsen originally where my main question that I was answering on a day-to-day basis, what was, what makes a great ad? Uh, what I found though is that advertising at least, especially as it pertains to TV, is really where brands were moving away from and a lot of the real consumer analytics that people were looking for were trying to underpin people in their natural environment, particularly on social media. And I hadn't seen any company that had done it well. Uh, and I happened to meet social standards during my time at Nielsen and was truly just blown away with this ability to essentially take a large input of conversations that people were happening or happening, I should say, and bring some sort of structure to them to actually be able to analyze them and understand what people were talking about as it pertained to different types of topics. And so I think that's really what brought me here was the fascination with this huge amount of data behind the ways that people were talking about on social. And the fact that it had some structure to it, which actually allowed for real analytics to be put behind it. Curtis: It's a hard thing to do though. Right? You know, to answer this question of how do we extract real value or real insight from social media and you'd mentioned historically or up to this point, companies that that are trying to do that missed the mark.

How do you build a comprehensive view of a topic on social media? Jordan Breslauer would say you let a machine learning tool scan the social sphere and add information as conversations evolve, with help from humans in the loop.Ginette Methot: I’m Ginette,Curtis Seare: 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: Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.Ginette: Many of you want to gain a deeper understanding of data science and machine learning, and <a href="http://Brilliant.org" rel="nofollow" target="_blank">Brilliant.org</a> is a great place to dig deeper into these topics. Their classes help you understand algorithms, machine learning concepts, computer science basics, probability, computer memory, and many other important concepts in data science and machine learning. The nice thing about <a href="http://Brilliant.org" rel="nofollow" target="_blank">Brilliant.org</a> is that you can learn in bite-sized pieces at your own pace. Their courses have storytelling, code-writing, and interactive challenges, which makes them entertaining, challenging, and educational.Sign up for free and start learning by going to <a href="http://Brilliant.org" rel="nofollow" target="_blank">Brilliant.org</a> slash Data Crunch, and also the first 200 people that go to that link will get 20% off the annual premium subscription.Let’s get into our conversation with Jordan Breslauer, senior director of data analytics and customer success at social standards. Jordan: My name is Jordan Breslauer. I'm the senior director of data analytics and customer success at social standards. I've always been a data geek as it pertains to sports. I think of Moneyball when I was younger, I always wanted to be kind of a the next Billy Bean and I, when I started working for sports franchises right after high school and early college days, I just realized that, that type of work culture is wasn't for me, but I was so, so into trying to answer questions with data that had no previously clear answer, you know? I loved answering subjective questions like, or what makes the best player or how do, how do I know who the best player is? And I thought what was always fun was to try and bring some sort of structured subjectivity to those sorts of questions through using data. And that's really what got me passionate about data in the first place. But then I just started to apply it to a number of different business questions that I always thought were quite interesting, which have a great deal of subjectivity. And that led me to Nielsen originally where my main question that I was answering on a day-to-day basis, what was, what makes a great ad? Uh, what I found though is that advertising at least, especially as it pertains to TV, is really where brands were moving away from and a lot of the real consumer analytics that people were looking for were trying to underpin people in their natural environment, particularly on social media. And I hadn't seen any company that had done it well. Uh, and I happened to meet social standards during my time at Nielsen and was truly just blown away with this ability to essentially take a large input of conversations that people were happening or happening, I should say, and bring some sort of structure to them to actually be able to analyze them and understand what people were talking about as it pertained to different types of topics. And so I think that's really what brought me here was the fascination with this huge amount of data behind the ways that people were talking about on social. And the fact that it had some structure to it, which actually allowed for real analytics to be put behind it. Curtis: It's a hard thing to do though. Right? You know, to answer this question of how do we extract real value or real insight from social media and you'd mentioned historically or up to this point, companies that that are trying to do that missed the mark.

Social Media and Machine Learning

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.“The first time I entered such a car, I didn't know what to expect. I mean, I didn't know how reliable are those kinds of cars. So I had the idea that maybe I should sit somewhere where I can maybe jump and grab the wheel if necessary. You know, I was a bit dumb. They don't need me, really. And probably if I touch the steering wheel, I would probably make some mistake and ruin the car. It drives better without me.”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: “We have a great live show planned that we hope to give at SXSW 2018. It's a really awesome show about the power of niche artificial intelligence, and we’re going to share details from our research into what amazing things AI is doing right now on the fringe and in mainstream AI projects. We're really excited to share it, so if you’re going to SXSW, or you just want to be good hearted and help us out, please vote on our dual panel by going to panelpicker.sxsw.com, signing in, and liking our topic, which you can find by searching for ‘The Power of Niche AI: From Cucumbers to Cancer.’“Today we get to talk to Ran Levi, who’s been researching and reporting on science and technology for the past 10 years. He’s a hugely successful science and tech podcaster in Israel, producing a Hebrew-language show called Making History, and he’s also producing two English podcasts right now for an international audience, so since he’s steeped in the subject, he has a lot of very interesting insights for us.”Ran: “I'm actually an electronics engineer by trade. I was an engineer for 15 years. I was both a hardware and software developer for several companies in Israel. And during my day job as an engineer, I wrote some books about the history of science and technology, which was always a big hobby of mine. And actually, I started a podcast about this very subject about 10 years ago, and it became quite a hit in Israel I’m happy to say. So about four years ago, I quit my day job, and I actually started my own podcasting company, and now we are podcasting both in Israel and in the U.S. for international audience and actually launched my brand new podcast last week. It's called Malicious Life about the history of malware and cybersecurity, which is a fun topic. Actually, the day I launched the podcast, there was a big ransom attack in Europe mostly. So it was . . . I didn't plan it. You've got no proof against me.”Ginette: “This is a topic well worth learning more about because cyber attacks can affect anything from your access to electricity to your bank account, so check out his new podcast on the website Malicious.life. But today, we’re talking about a different topic—deep learning. This is something Ran knows quite a bit about, technically and historically.”

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.“The first time I entered such a car, I didn't know what to expect. I mean, I didn't know how reliable are those kinds of cars. So I had the idea that maybe I should sit somewhere where I can maybe jump and grab the wheel if necessary. You know, I was a bit dumb. They don't need me, really. And probably if I touch the steering wheel, I would probably make some mistake and ruin the car. It drives better without me.”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: “We have a great live show planned that we hope to give at SXSW 2018. It's a really awesome show about the power of niche artificial intelligence, and we’re going to share details from our research into what amazing things AI is doing right now on the fringe and in mainstream AI projects. We're really excited to share it, so if you’re going to SXSW, or you just want to be good hearted and help us out, please vote on our dual panel by going to <a href="http://panelpicker.sxsw.com" rel="nofollow" target="_blank">panelpicker.sxsw.com</a>, signing in, and liking our topic, which you can find by searching for ‘The Power of Niche AI: From Cucumbers to Cancer.’“Today we get to talk to Ran Levi, who’s been researching and reporting on science and technology for the past 10 years. He’s a hugely successful science and tech podcaster in Israel, producing a Hebrew-language show called Making History, and he’s also producing two English podcasts right now for an international audience, so since he’s steeped in the subject, he has a lot of very interesting insights for us.”Ran: “I'm actually an electronics engineer by trade. I was an engineer for 15 years. I was both a hardware and software developer for several companies in Israel. And during my day job as an engineer, I wrote some books about the history of science and technology, which was always a big hobby of mine. And actually, I started a podcast about this very subject about 10 years ago, and it became quite a hit in Israel I’m happy to say. So about four years ago, I quit my day job, and I actually started my own podcasting company, and now we are podcasting both in Israel and in the U.S. for international audience and actually launched my brand new podcast last week. It's called Malicious Life about the history of malware and cybersecurity, which is a fun topic. Actually, the day I launched the podcast, there was a big ransom attack in Europe mostly. So it was . . . I didn't plan it. You've got no proof against me.”Ginette: “This is a topic well worth learning more about because cyber attacks can affect anything from your access to electricity to your bank account, so check out his new podcast on the website Malicious.life. But today, we’re talking about a different topic—deep learning. This is something Ran knows quite a bit about, technically and historically.”

Deep Learning—A Powerful Tool, with a Name that Means Nothing

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.TranscriptIrmak 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 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: “Today we’re taking a closer look at something that is starting to seep into our daily lives. In one of its forms, it’s something Stephen Hawking, Bill Gates, and Elon Musk are concerned will eventually be a threat to mankind. In another form, though, you’re probably already using it, and it’s becoming a major game changer, kind of like the early days of the desktop computer. We’re talking about artificial intelligence. You use AI when you talk to Siri or your in-home assistant, Alexa or Echo, and some people are using it in the form of a self-driving car. “So daily applications of artificial intelligence are on the rise, becoming much more of a staple in our society, but AI’s definition shifts according to the source. Popular movies depict AI as having a consciousness, emotions, and exhibiting human-like characteristics. Usually it’s involved in some sort of world-domination plot to kill all the humans. Although most experts agree that artificial intelligence will never actually think and feel like a human, the existential threat still exists. This kind of apocalyptic AI is known as ‘general AI.’ But that’s a topic for another episode. Today, we’re focusing on the kind of AI that currently exists, otherwise known as narrow AI.”Curtis: “A narrow AI is called narrow because it’s usually focused on one specific task, where as a general AI would be able to be good pretty much any task thrown its way. The Google search bar is probably the most ubiquitous example of a narrow AI that most people use on a daily basis. The process usually goes like this: you give it an input like ‘How to own a llama as a pet.’ It does its processing. It gives you an output in the form of the 10 most relevant web pages to answer your questions (along, of course, with some paid advertisers who are trying to sell you a pet llama).“The simplicity of the interaction belies the complexity of the cognitive work that’s going on behind the scenes. Imagine if you had to do the same cognitive task without the help of Google. What would that actually entail? You would have to individually look at and read every single website, and there are over 1 billion, and peruse them to see if they have anything to do about llamas, not to mention then find the individual pages on those websites that actually answer your questions. “This is a really big task!

How Artificial Intelligence Might Change Your World

Our guest today holds a PhD in organizational psychology and has been working on data products in the health and wellness space for over a decade. We cover a lot of ground in this interview: how to create data products that work, how to avoid the unexpected consequences of poorly designed data interventions, and the importance of ethnographic thinking in data science.We'll also talk about reducing friction in data collection, the coaching data product model, and surprising things we can learn when people's routine's are broken. From today's episode, you'll come away with a better understanding of how to build contextually relevant data products that make a difference in people's lives.

Building Data Products that Work in the Health and Wellness Industry

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. That was my point of entry to this organization.Curtis: Something important to note here is Transparency International’s origins. It’s a surprising story because Transparency Internationa...

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 <a href="http://datacrunchpodcast.com/ai" rel="nofollow" target="_blank">datacrunchpodcast.com/ai</a>, 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. That was my point of entry to this organization.Curtis: Something important to note here is Transparency International’s origins. It’s a surprising story because Transparency Internationa...

Exposing World Corruption with a Unique Dataset

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. We produced a lot of content in the bank, and we had no tools that could explain how the users’ interaction with the content drove them to take specific a...

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 <a href="http://datacrunchpodcast.com/survey" rel="nofollow" target="_blank">datacrunchpodcast.com/survey</a>, 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. We produced a lot of content in the bank, and we had no tools that could explain how the users’ interaction with the content drove them to take specific a...

Building a Machine Learning Company that Decodes Web Analytics, with Per Damgaard

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 with. They’re even using them for wildlife monitoring,

Drones and AI

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,

How Many Slaves Work for You?

Today we speak with Professor Ram Bala, an expert in supply chain management analytics, particularly last-mile delivery. He has very interesting insights into how today’s supply chain is evolving. He talks about various methods and algorithms he uses, the specific challenges inherent in doing last mile logistics and deliver, how pricing factors in, and how everyone is trying to catch up to Amazon.Ram Bala: Then there is this great opportunity to actually use the data effectively. But that is a long way to go in terms of coming up with the right algorithms, both on predictions, as well as the optimization to actually get this done in a meaningful way. And if you look at the landscape today in terms of industry, I would say very few companies that actually there yet. Right? I mean, Amazon obviously is a clear example of the leaders in the space, but everyone's trying to get there as well.Ginette Methot: I'm GinetteCurtis Seare: And I'm CurtisGinette: And you are listening to Data CrunchCurtis: A podcast about how applied data science, machine learning and artificial intelligence are changing the world.Intro: Today we speak with Professor Ram Bala, an expert in supply chain management analytics, particularly last-mile delivery. He has very interesting insights into how today’s supply chain is evolving.Ram Bala: My name is Ram Bala. I'm a professor at Santa Clara University as well as a data science leader at CH Robinson, which is the largest logistics marketplace in North America. I've been working with topics in supply chain, belated data science even before it was called data science for the past 15 years. I got my Ph.D. in operations research and a supply chain from UCLA and a, I've been working on these problems both for companies as well as within the academic context that I've been working on research problems. And more recently I think there's been a lot of excitement in this space. And then that's where my involvement with both startups and as well as larger companies has gone up and I, I came into the CH Robinson fold as a consequence of an acquisition. So I was part of a startup that was working on last mile logistics and how to, how to improve that.Curtis Seare: Got It. That's awesome. And the space that you're in is really interesting. Could you give the audience just to contextualize the problem set that you're focused on?Ram: So I think one of the major things that has changed in logistics is the growth of e-commerce and also personal mobility. I mean if you think about Uber Logistics as a larger concept that covers both moving people as well as products and what's really happened is the, the availability of real time data has had a significant consequences on how we are able to predict as well as optimize how we move things and that's then also raised the bar in terms of customer expectations. We expect to get a get a ride to go somewhere within and within five minutes, we expect to get a product within a day and those expectations have been set by specific companies say Uber in the case of personal mobility. In the case of products, it's Amazon and having set the stage, everyone's now trying to be competitive with them, which means that in the product space, certainly all e-commerce companies as well as companies that were in brick and mortar are trying to achieve that same end goal, which is how do I get products to consumers quickly at the same time and not spend too much money? Right? That's the core problem. Now doing that as hard, it's become easier simply because we have real time access to real time data in terms of location as well as you know where products are at an even point. But it is a hard problem to solve.Curtis: Some of the intricacy and you know, routing and pricing and kind of interplay there. Can we dive into a little bit of those details?Ram: Absolutely. So I think uh, routing problems have been around ever since transportation's been around,

Last-Mile Logistics Analytics—for Everyone Who Isn't Amazon

The way you organize your data science team will greatly affect your business’s outcome. This episode discusses different structures for a data science team, as well as top down versus bottom up approaches, how to get data science solutions into production organically, and how to be part of the business while remaining in contact with other data scientists on the team.Mark Lowe: Having lived through small scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity.Ginette Methot: I’m Ginette, and I’m Curtis, and you are listening to Data Crunch, a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.Building effective data science processes is tough. Mode, the data science platform, has compiled three tips to make it a bit easier: don’t over plan, there’s no one process that fits everyone, and waste time. That’s right. Waste time. Read more at mode.com/dsp M O D E.com/D S P.Today we’re going to talk about effective ways you can organize your data science team, and we’ll hear lots of great insights from our guest. Let’s get to it.Mark: My name is Mark Lowe. I’m currently the senior principal data scientist here at Valassis.Curtis Seare: Describe just a little bit about what Valassis does.Mark: So we work with pretty much every major manufacturer retailer in the U.S. Our work kind of runs the gamut in terms of solving problems for them in terms of how do I influence customers. And so we manage a lot of print products that go reach every household, every week and of course a lot of digital products. So everything from display advertising, campaign, search campaign, social. Pretty much any distribution mechanism that can influence customers, we try to use those channels.Curtis: And in working on these problems we talked a little bit about earlier what the approaches for data science. Some people try to bin it in a software development kind of a role, an agile role, and how that usually doesn’t work for data science cause it’s more of an experimental type of a thing. Can you comment on its similarities and differences and how you should be approaching data sites?Mark: I think that’s a great question. Honestly, if you, if you asked me 10 years ago if this was an interesting question, I would have found it very boring. But having, having lived through small-scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity, and there’s no one size that fits all. Honestly, you kind of have to cater the organization of the data science team to where the company is. For example, the two common models that are deployed and, and we’ve, we’ve lived in both of them is kinda thinking about data science as an internal consulting group. So I have a a pool of data scientists. Stakeholders throughout the company come to me and ask, they say, “I have this problem. I think it needs data science” and then the data science lead or team.Yes, we do need a data scientist working on that. Here’s a person with that specialty. So kind of farming out individuals on the team to solve particular problems. So it’s a fairly centralized organization and that, you know, there’s a lot of benefits to that. One, you’ve got strong sense of community as a team. Oftentimes you’re very tightly organized together. You function as a data science unit. You can try to make sure that you’re putting the right skillset for the right problem. As you know, as you’ve talked to that, there’s, there is no one definition of data science, there’s no one skillset. So oftentimes the data science team has a mixture of skills across the team,

The way you organize your data science team will greatly affect your business’s outcome. This episode discusses different structures for a data science team, as well as top down versus bottom up approaches, how to get data science solutions into production organically, and how to be part of the business while remaining in contact with other data scientists on the team.Mark Lowe: Having lived through small scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity.Ginette Methot: I’m Ginette, and I’m Curtis, and you are listening to Data Crunch, a podcast about how applied data science, machine learning, and artificial intelligence are changing the world. Data Crunch is produced by the Data Crunch Corporation, an analytics training and consulting company.Building effective data science processes is tough. Mode, the data science platform, has compiled three tips to make it a bit easier: don’t over plan, there’s no one process that fits everyone, and waste time. That’s right. Waste time. Read more at <a href="http://mode.com/dsp" rel="nofollow" target="_blank">mode.com/dsp</a> M O D <a href="http://E.com/D" rel="nofollow" target="_blank">E.com/D</a> S P.Today we’re going to talk about effective ways you can organize your data science team, and we’ll hear lots of great insights from our guest. Let’s get to it.Mark: My name is Mark Lowe. I’m currently the senior principal data scientist here at Valassis.Curtis Seare: Describe just a little bit about what Valassis does.Mark: So we work with pretty much every major manufacturer retailer in the U.S. Our work kind of runs the gamut in terms of solving problems for them in terms of how do I influence customers. And so we manage a lot of print products that go reach every household, every week and of course a lot of digital products. So everything from display advertising, campaign, search campaign, social. Pretty much any distribution mechanism that can influence customers, we try to use those channels.Curtis: And in working on these problems we talked a little bit about earlier what the approaches for data science. Some people try to bin it in a software development kind of a role, an agile role, and how that usually doesn’t work for data science cause it’s more of an experimental type of a thing. Can you comment on its similarities and differences and how you should be approaching data sites?Mark: I think that’s a great question. Honestly, if you, if you asked me 10 years ago if this was an interesting question, I would have found it very boring. But having, having lived through small-scale, two people working, to large scale, thousands of people in your organization, the way that you organize the data science team has dramatic effect on its productivity, and there’s no one size that fits all. Honestly, you kind of have to cater the organization of the data science team to where the company is. For example, the two common models that are deployed and, and we’ve, we’ve lived in both of them is kinda thinking about data science as an internal consulting group. So I have a a pool of data scientists. Stakeholders throughout the company come to me and ask, they say, “I have this problem. I think it needs data science” and then the data science lead or team.Yes, we do need a data scientist working on that. Here’s a person with that specialty. So kind of farming out individuals on the team to solve particular problems. So it’s a fairly centralized organization and that, you know, there’s a lot of benefits to that. One, you’ve got strong sense of community as a team. Oftentimes you’re very tightly organized together. You function as a data science unit. You can try to make sure that you’re putting the right skillset for the right problem. As you know, as you’ve talked to that, there’s, there is no one definition of data science, there’s no one skillset. So oftentimes the data science team has a mixture of skills across the team,

Structuring Your Data Science Dream Team

The best podcast episodes on non-current politics. Covering topics such as universal basic income, democracy, poverty & justice and more. For anyone interested in learning more about politics that affect that world, not just now, but in the longer term. Add episodes you're interested into your queue below!

Best Non-Current Politics Podcasts

The best philosophy podcast episodes. Asking questions about how to live a better life, what it means to be and do good, to the idea of God. This topic includes episodes that bring about important questions that everyone should be asking themselves. Add episodes you're interested into your queue below!