Podcast

Data Skeptic

Data Skeptic is a data science podcast exploring machine learning, statistics, artificial intelligence, and other data topics through short tutorials and interviews with domain experts.

Episodes

  • Recurrent Relational Networks

    Feb 15 2019

    One of the most challenging NLP tasks is natural language understanding and reasoning. How can we construct algorithms that are able to achieve human level understanding of text and be able to answer general questions about it? This is truly an open problem, and one with the bAbI dataset has been constructed to facilitate. bAbI presents a variety of different language understanding and reasoning tasks and exists as benchmark for comparing approaches. In this episode, Kyle talks to Rasmus Berg Pa...more

  • Text World and Word Embedding Lower Bounds

    Feb 08 2019

    In the first half of this episode, Kyle speaks with Marc-Alexandre Côté and Wendy Tay about Text World.  Text World is an engine that simulates text adventure games.  Developers are encouraged to try out their reinforcement learning skills building agents that can programmatically interact with the generated text adventure games.   In the second half of this episode, Kyle interviews Kevin Patel about his paper Towards Lower Bounds on Number of Dimensions for Word Embeddings.  In this research, t...more

  • word2vec

    Feb 01 2019

    Word2vec is an unsupervised machine learning model which is able to capture semantic information from the text it is trained on. The model is based on neural networks. Several large organizations like Google and Facebook have trained word embeddings (the result of word2vec) on large corpora and shared them for others to use. The key algorithmic ideas involved in word2vec is the continuous bag of words model (CBOW). In this episode, Kyle uses excerpts from the 1983 cinematic masterpiece War Games...more

  • Authorship Attribution

    Jan 25 2019

    In a recent paper, Leveraging Discourse Information Effectively for Authorship Attribution, authors Su Wang, Elisa Ferracane, and Raymond J. Mooney describe a deep learning methodology for predict which of a collection of authors was the author of a given document.

  • Very Large Corpora and Zipf's Law

    Jan 18 2019

    The earliest efforts to apply machine learning to natural language tended to convert every token (every word, more or less) into a unique feature. While techniques like stemming may have cut the number of unique tokens down, researchers always had to face a problem that was highly dimensional. Naive Bayes algorithm was celebrated in NLP applications because of its ability to efficiently process highly dimensional data. Of course, other algorithms were applied to natural language tasks as well. W...more

  • Semantic search at Github

    Jan 11 2019

    Github is many things besides source control. It's a social network, even though not everyone realizes it. It's a vast repository of code. It's a ticketing and project management system. And of course, it has search as well. In this episode, Kyle interviews Hamel Husain about his research into semantic code search.

  • Let's Talk About Natural Language Processing

    Jan 04 2019

    This episode reboots our podcast with the theme of Natural Language Processing for the next few months. We begin with introductions of Yoshi and Linh Da and then get into a broad discussion about natural language processing: what it is, what some of the classic problems are, and just a bit on approaches. Finishing out the show is an interview with Lucy Park about her work on the KoNLPy library for Korean NLP in Python. If you want to share your NLP project, please join our Slack channel.  We're ...more

  • Data Science Hiring Processes

    Dec 28 2018

    Kyle shares a few thoughts on mistakes observed by job applicants and also shares a few procedural insights listeners at early stages in their careers might find value in.

  • Holiday Reading - Epicac

    Dec 25 2018

    Epicac by Kurt Vonnegut.

  • Drug Discovery with Machine Learning

    Dec 21 2018

    In today's episode, Kyle chats with Alexander Zhebrak, CTO of Insilico Medicine, Inc. Insilico self describes as artificial intelligence for drug discovery, biomarker development, and aging research. The conversation in this episode explores the ways in which machine learning, in particular, deep learning, is contributing to the advancement of drug discovery. This happens not just through research but also through software development. Insilico works on data pipelines and tools like MOSES, a ben...more

  • Sign Language Recognition

    Dec 14 2018

    At the NeurIPS 2018 conference, Stradigi AI premiered a training game which helps players learn American Sign Language. This episode brings the first of many interviews conducted at NeurIPS 2018. In this episode, Kyle interviews Chief Data Scientist Carolina Bessega about the deep learning architecture used in this project. The Stradigi AI team was exhibiting a project called the American Sign Language (ASL) Alphabet Game at the recent NeurIPS 2018 conference. They also published a detailed blog...more

  • Data Ethics

    Dec 07 2018

     This week, Kyle interviews Scott Nestler on the topic of Data Ethics. Today, no ubiquitous, formal ethical protocol exists for data science, although some have been proposed. One example is the INFORMS Ethics Guidelines. Guidelines like this are rather informal compared to other professions, like the Hippocratic Oath. Yet not every profession requires such a formal commitment. In this episode, Scott shares his perspective on a variety of ethical questions specific to data and analytics.

  • Escaping the Rabbit Hole

    Nov 30 2018

    Kyle interviews Mick West, author of Escaping the Rabbit Hole: How to Debunk Conspiracy Theories Using Facts, Logic, and Respect about the nature of conspiracy theories, the people that believe them, and how to help people escape the belief in false information. Mick is also the creator of metabunk.org. The discussion explores conspiracies like chemtrails, 9/11 conspiracy theories, JFK assassination theories, and the flat Earth theory. We live in a complex world in which no person can have a suf...more

  • Theorem Provers

    Nov 23 2018

    Fake news attempts to lead readers/listeners/viewers to conclusions that are not descriptions of reality.  They do this most often by presenting false premises, but sometimes by presenting flawed logic. An argument is only sound and valid if the conclusions are drawn directly from all the state premises, and if there exists a path of logical reasoning leading from those premises to the conclusion. While creating a theorem does feel to most mathematicians as a creative act of discovery, some theo...more

  • Automated Fact Checking

    Nov 16 2018

    Fake news can be responded to with fact-checking. However, it's easier to create fake news than the fact check it. Full Fact is the UK's independent fact-checking organization. In this episode, Kyle interviews Mevan Babakar, head of automated fact-checking at Full Fact. Our discussion talks about the process and challenges in doing fact-checking. Full Fact has been exploring ways in which machine learning can assist in automating parts of the fact-checking process. Progress in areas like this al...more

  • Single Source of Truth

    Nov 09 2018

    In mathematics, truth is universal.  In data, truth lies in the where clause of the query. As large organizations have grown to rely on their data more significantly for decision making, a common problem is not being able to agree on what the data is. As the volume and velocity of data grow, challenges emerge in answering questions with precision.  A simple question like "what was the revenue yesterday" could become mired in details.  Did your query account for transactions that haven't been fin...more

  • Detecting Fast Radio Bursts with Deep Learning

    Nov 02 2018

    Fast radio bursts are an astrophysical phenomenon first observed in 2007. While many observations have been made, science has yet to explain the mechanism for these events. This has led some to ask: could it be a form of extra-terrestrial communication? Probably not. Kyle asks Gerry Zhang who works at the Berkeley SETI Research Center about this possibility and more importantly, about his applications of deep learning to detect fast radio bursts. Radio astronomy captures observations from space ...more

  • Being Bayesian

    Oct 26 2018

    This episode explores the root concept of what it is to be Bayesian: describing knowledge of a system probabilistically, having an appropriate prior probability, know how to weigh new evidence, and following Bayes's rule to compute the revised distribution. We present this concept in a few different contexts but primarily focus on how our bird Yoshi sends signals about her food preferences. Like many animals, Yoshi is a complex creature whose preferences cannot easily be summarized by a straight...more

  • Modeling Fake News

    Oct 19 2018

    This is our interview with Dorje Brody about his recent paper with David Meier, How to model fake news. This paper uses the tools of communication theory and a sub-topic called filtering theory to describe the mathematical basis for an information channel which can contain fake news.   Thanks to our sponsor Gartner.

  • The Louvain Method for Community Detection

    Oct 12 2018

    Without getting into definitions, we have an intuitive sense of what a "community" is. The Louvain Method for Community Detection is one of the best known mathematical techniques designed to detect communities. This method requires typical graph data in which people are nodes and edges are their connections. It's easy to imagine this data in the context of Facebook or LinkedIn but the technique applies just as well to any other dataset like cellular phone calling records or pen-pals. The Louvain...more

  • Cultural Cognition of Scientific Consensus

    Oct 05 2018

    In this episode, our guest is Dan Kahan about his research into how people consume and interpret science news. In an era of fake news, motivated reasoning, and alternative facts, important questions need to be asked about how people understand new information. Dan is a member of the Cultural Cognition Project at Yale University, a group of scholars interested in studying how cultural values shape public risk perceptions and related policy beliefs. In a paper titled Cultural cognition of scientif...more

  • False Discovery Rates

    Sep 28 2018

    A false discovery rate (FDR) is a methodology that can be useful when struggling with the problem of multiple comparisons. In any experiment, if the experimenter checks more than one dependent variable, then they are making multiple comparisons. Naturally, if you make enough comparisons, you will eventually find some correlation. Classically, people applied the Bonferroni Correction. In essence, this procedure dictates that you should lower your p-value (raise your standard of evidence) by a spe...more

  • Deep Fakes

    Sep 21 2018

    Digital videos can be described as sequences of still images and associated audio. Audio is easy to fake. What about video? A video can easily be broken down into a sequence of still images replayed rapidly in sequence. In this context, videos are simply very high dimensional sequences of observations, ripe for input into a machine learning algorithm. The availability of commodity hardware, clever algorithms, and well-designed software to implement those algorithms at scale make it possible to d...more

  • Fake News Midterm

    Sep 14 2018

    In this episode, Kyle reviews what we've learned so far in our series on Fake News and talks briefly about where we're going next.

  • Quality Score

    Sep 07 2018

    Two weeks ago we discussed click through rates or CTRs and their usefulness and limits as a metric. Today, we discuss a related metric known as quality score. While that phrase has probably been used to mean dozens of different things in different contexts, our discussion focuses around the idea of quality score encountered in Search Engine Marketing (SEM). SEM is the practice of purchasing keyword targeted ads shown to customers using a search engine. Most SEM is managed via an auction mechanis...more

  • The Knowledge Illusion

    Aug 31 2018

    Kyle interviews Steven Sloman, Professor in the school of Cognitive, Linguistic, and Psychological Sciences at Brown University. Steven is co-author of The Knowledge Illusion: Why We Never Think Alone and Causal Models: How People Think about the World and Its Alternatives. Steven shares his perspective and research into how people process information and what this teaches us about the existence of and belief in fake news.

  • Click Through Rates

    Aug 24 2018

    A Click Through Rate (CTR) is the proportion of clicks to impressions of some item of content shared online. This terminology is most commonly used in digital advertising but applies just as well to content websites might choose to feature on their homepage or in search results. A CTR is intuitively appealing as a metric for optimization. After all, if users are disinterested in some content, under normal circumstances, it's reasonable to assume they would ignore the content, rather than clickin...more

  • Algorithmic Detection of Fake News

    Aug 17 2018

    The scale and frequency with which information can be distributed on social media makes the problem of fake news a rapidly metastasizing issue. To do any content filtering or labeling demands an algorithmic solution. In today's episode, Kyle interviews Kai Shu and Mike Tamir about their independent work exploring the use of machine learning to detect fake news. Kai Shu and his co-authors published Fake News Detection on Social Media: A Data Mining Perspective, a research paper which both surveys...more

  • Ant Intelligence

    Aug 10 2018

    If you prepared a list of creatures regarded as highly intelligent, it's unlikely ants would make the cut. This is expected, as on an individual level, ants do not generally display behavior that most humans would regard as intelligence. In fact, it might even be true that most species of ants are unable to learn. Despite this, ant colonies have evolved excellent survival mechanisms through the careful orchestration of ants.

  • Human Detection of Fake News

    Aug 03 2018

    With publications such as "Prior exposure increases perceived accuracy of fake news", "Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning", and "The science of fake news", Gordon Pennycook is asking and answering analytical questions about the nature of human intuition and fake news. Gordon appeared on Data Skeptic in 2016 to discuss people's ability to recognize pseudo-profound bullshit.  This episode explores his work in ...more

  • Spam Filtering with Naive Bayes

    Jul 27 2018

    Today's spam filters are advanced data driven tools. They rely on a variety of techniques to effectively and often seamlessly filter out junk email from good email. Whitelists, blacklists, traffic analysis, network analysis, and a variety of other tools are probably employed by most major players in this area. Naturally content analysis can be an especially powerful tool for detecting spam. Given the binary nature of the problem ( or ) its clear that this is a great problem to use machine learni...more

  • The Spread of Fake News

    Jul 20 2018

    How does fake news get spread online? Its not just a matter of manipulating search algorithms. The social platforms for sharing play a major role in the distribution of fake news. But how significant of an impact can there be? How significantly can bots influence the spread of fake news? In this episode, Kyle interviews Filippo Menczer, Professor of Computer Science and Informatics. Fil is part of the Observatory on Social Media ([OSoMe][https://osome.iuni.iu.edu/tools/]). OSoMe are the creators...more

  • Fake News

    Jul 13 2018

    This episode kicks off our new theme of "Fake News" with guests Robert Sheaffer and Brad Schwartz. Fake news is a new label for an old idea. For our purposes, we will define fake news information created to deliberately mislead while masquerading as a legitimate, journalistic source of truth. It's become a modern topic of discussion as our cultures evolve to the fledgling mechanisms of communication introduced by online platforms. What was the earliest incident of fake news? That's a question fo...more

  • Dev Ops for Data Science

    Jul 11 2018

    We revisit the 2018 Microsoft Build in this episode, focusing on the latest ideas in DevOps. Kyle interviews Cloud Developer Advocates Damien Brady, Paige Bailey, and Donovan Brown to talk about DevOps and data science and databases. For a data scientist, what does it even mean to “build”? Packaging and deployment are things that a data scientist doesn't normally have to consider in their day-to-day work. The process of making an AI app is usually divided into two streams of work: data scientist...more

  • First Order Logic

    Jul 06 2018

    Logic is a fundamental of mathematical systems. It's roots are the values true and false and it's power is in what it's rules allow you to prove. Prepositional logic provides it's user variables. This episode gets into First Order Logic, an extension to prepositional logic.

  • Blind Spots in Reinforcement Learning

    Jun 29 2018

    An intelligent agent trained in a simulated environment may be prone to making mistakes in the real world due to discrepancies between the training and real-world conditions. The areas where an agent makes mistakes are hard to find, known as "blind spots," and can stem from various reasons. In this week’s episode, Kyle is joined by Ramya Ramakrishnan, a PhD candidate at MIT, to discuss the idea “blind spots” in reinforcement learning and approaches to discover them.

  • Defending Against Adversarial Attacks

    Jun 22 2018

    In this week’s episode, our host Kyle interviews Gokula Krishnan from ETH Zurich, about his recent contributions to defenses against adversarial attacks. The discussion centers around his latest paper, titled “Defending Against Adversarial Attacks by Leveraging an Entire GAN,” and his proposed algorithm, aptly named ‘Cowboy.’

  • Transfer Learning

    Jun 15 2018

    On a long car ride, Linhda and Kyle record a short episode. This discussion is about transfer learning, a technique using in machine learning to leverage training from one domain to have a head start learning in another domain. Transfer learning has some obvious appealing features. Take the example of an image recognition problem. There are now many widely available models that do general image recognition. Detecting that an image contains a "sofa" is an impressive feat. However, for a furniture...more

  • Medical Imaging Training Techniques

    Jun 08 2018

    Medical imaging is a highly effective tool used by clinicians to diagnose a wide array of diseases and injuries. However, it often requires exceptionally trained specialists such as radiologists to interpret accurately. In this episode of Data Skeptic, our host Kyle Polich is joined by Gabriel Maicas, a PhD candidate at the University of Adelaide, to discuss machine learning systems that can be used by radiologists to improve their accuracy and speed of diagnosis.

  • Kalman Filters

    Jun 01 2018

    Thanks to our sponsor Galvanize A Kalman Filter is a technique for taking a sequence of observations about an object or variable and determining the most likely current state of that object. In this episode, we discuss it in the context of tracking our lilac crowned amazon parrot Yoshi. Kalman filters have many applications but the one of particular interest under our current theme of artificial intelligence is to efficiently update one's beliefs in light of new information. The Kalman filter is...more

  • AI in Industry

    May 25 2018

    There's so much to discuss on the AI side, it's hard to know where to begin. Luckily,  Steve Guggenheimer, Microsoft’s corporate vice president of AI Business, and Carlos Pessoa, a software engineering manager for the company’s Cloud AI Platform, talked to Kyle about announcements related to AI in industry.

  • AI in Games

    May 18 2018

    Today's interview is with the authors of the textbook Artificial Intelligence and Games.

  • Game Theory

    May 11 2018

    Thanks to our sponsor The Great Courses. This week's episode is a short primer on game theory. For tickets to the free Data Skeptic meetup in Chicago on Tuesday, May 15 at the Mendoza College of Business (224 South Michigan Avenue, Suite 350), click here,

  • The Experimental Design of Paranormal Claims

    May 04 2018

    In this episode of Data Skeptic, Kyle chats with Jerry Schwarz from the Independent Investigations Group (IIG)'s SF Bay Area chapter about testing claims of the paranormal. The IIG is a volunteer-based organization dedicated to investigating paranormal or extraordinary claim from a scientific viewpoint. The group, headquartered at the Center for Inquiry-Los Angeles in Hollywood, offers a $100,000 prize to anyone who can show, under proper observing conditions, evidence of any paranormal, superna...more

  • Winograd Schema Challenge

    Apr 27 2018

    Our guest this week, Hector Levesque, joins us to discuss an alternative way to measure a machine’s intelligence, called Winograd Schemas Challenge. The challenge was proposed as a possible alternative to the Turing test during the 2011 AAAI Spring Symposium. The challenge involves a small reading comprehension test about common sense knowledge.

  • The Imitation Game

    Apr 20 2018

    This week on Data Skeptic, we begin with a skit to introduce the topic of this show: The Imitation Game. We open with a scene in the distant future. The year is 2027, and a company called Shamony is announcing their new product, Ada, the most advanced artificial intelligence agent. To prove its superiority, the lead scientist announces that it will use the Turing Test that Alan Turing proposed in 1950. During this we introduce Turing’s “objections” outlined in his famous paper, “Computing Machin...more

  • Eugene Goostman

    Apr 13 2018

    In this episode, Kyle shares his perspective on the chatbot Eugene Goostman which (some claim) "passed" the Turing Test. As a second topic Kyle also does an intro of the Winograd Schema Challenge.

  • The Theory of Formal Languages

    Apr 06 2018

    In this episode, Kyle and Linhda discuss the theory of formal languages. Any language can (theoretically) be a formal language. The requirement is that the language can be rigorously described as a set of strings which are considered part of the language. Those strings are any combination of alphabet characters in the given language. Read more  

  • The Loebner Prize

    Mar 30 2018

    The Loebner Prize is a competition in the spirit of the Turing Test.  Participants are welcome to submit conversational agent software to be judged by a panel of humans.  This episode includes interviews with Charlie Maloney, a judge in the Loebner Prize, and Bruce Wilcox, a winner of the Loebner Prize.

  • Chatbots

    Mar 23 2018

    In this episode, Kyle chats with Vince from iv.ai and Heather Shapiro who works on the Microsoft Bot Framework. We solicit their advice on building a good chatbot both creatively and technically. Our sponsor today is Warby Parker.

  • The Master Algorithm

    Mar 16 2018

    In this week’s episode, Kyle Polich interviews Pedro Domingos about his book, The Master Algorithm: How the quest for the ultimate learning machine will remake our world. In the book, Domingos describes what machine learning is doing for humanity, how it works and what it could do in the future. He also hints at the possibility of an ultimate learning algorithm, in which the machine uses it will be able to derive all knowledge — past, present, and future.

  • The No Free Lunch Theorems

    Mar 09 2018

    What's the best machine learning algorithm to use? I hear that XGBoost wins most of the Kaggle competitions that aren't won with deep learning. Should I just use XGBoost all the time? That might work out most of the time in practice, but a proof exists which tells us that there cannot be one true algorithm to rule them.

  • ML at Sloan Kettering Cancer Center

    Mar 02 2018

    For a long time, physicians have recognized that the tools they have aren't powerful enough to treat complex diseases, like cancer. In addition to data science and models, clinicians also needed actual products — tools that physicians and researchers can draw upon to answer questions they regularly confront, such as “what clinical trials are available for this patient that I'm seeing right now?” In this episode, our host Kyle interviews guests Alex Grigorenko and Iker Huerga from Memorial Sloan ...more

  • Optimal Decision Making with POMDPs

    Feb 23 2018

    In a previous episode, we discussed Markov Decision Processes or MDPs, a framework for decision making and planning. This episode explores the generalization Partially Observable MDPs (POMDPs) which are an incredibly general framework that describes most every agent based system.

  • AI Decision-Making

    Feb 16 2018

    Making a decision is a complex task. Today's guest Dongho Kim discusses how he and his team at Prowler has been building a platform that will be accessible by way of APIs and a set of pre-made scripts for autonomous decision making based on probabilistic modeling, reinforcement learning, and game theory. The aim is so that an AI system could make decisions just as good as humans can.

  • [MINI] Reinforcement Learning

    Feb 09 2018

    In many real world situations, a person/agent doesn't necessarily know their own objectives or the mechanics of the world they're interacting with. However, if the agent receives rewards which are correlated with the both their actions and the state of the world, then reinforcement learning can be used to discover behaviors that maximize the reward earned.

  • Evolutionary Computation

    Feb 02 2018

    In this week’s episode, Kyle is joined by Risto Miikkulainen, a professor of computer science and neuroscience at the University of Texas at Austin. They talk about evolutionary computation, its applications in deep learning, and how it’s inspired by biology. They also discuss some of the things Sentient Technologies is working on in stock and finances, retail, e-commerce and web design, as well as the technology behind it-- evolutionary algorithms.

  • [MINI] Markov Decision Processes

    Jan 26 2018

    Formally, an MDP is defined as the tuple containing states, actions, the transition function, and the reward function. This podcast examines each of these and presents them in the context of simple examples.  Despite MDPs suffering from the curse of dimensionality, they're a useful formalism and a basic concept we will expand on in future episodes.

  • Neuroscience Frontiers

    Jan 19 2018

    Last week on Data Skeptic, we visited the Laboratory of Neuroimaging, or LONI, at USC and learned about their data-driven platform that enables scientists from all over the world to share, transform, store, manage and analyze their data to understand neurological diseases better. We talked about how neuroscientists measure the brain using data from MRI scans, and how that data is processed and analyzed to understand the brain. This week, we'll continue the second half of our two-part episode on ...more

  • Neuroimaging and Big Data

    Jan 12 2018

    Last year, Kyle had a chance to visit the Laboratory of Neuroimaging, or LONI, at USC, and learn about how some researchers are using data science to study the function of the brain. We’re going to be covering some of their work in two episodes on Data Skeptic. In this first part of our two-part episode, we'll talk about the data collection and brain imaging and the LONI pipeline. We'll then continue our coverage in the second episode, where we'll talk more about how researchers can gain insight...more

  • The Agent Model of Artificial Intelligence

    Jan 05 2018

    In artificial intelligence, the term 'agent' is used to mean an autonomous, thinking agent with the ability to interact with their environment. An agent could be a person or a piece of software. In either case, we can describe aspects of the agent in a standard framework.

  • Artificial Intelligence, a Podcast Approach

    Dec 29 2017

    This episode kicks off the next theme on Data Skeptic: artificial intelligence.  Kyle discusses what's to come for the show in 2018, why this topic is relevant, and how we intend to cover it.

  • Holiday reading 2017

    Dec 22 2017

    We break format from our regular programming today and bring you an excerpt from Max Tegmark's book "Life 3.0".  The first chapter is a short story titled "The Tale of the Omega Team".  Audio excerpted courtesy of Penguin Random House Audio from LIFE 3.0 by Max Tegmark, narrated by Rob Shapiro.  You can find "Life 3.0" at your favorite bookstore and the audio edition via penguinrandomhouseaudio.com. Kyle will be giving a talk at the Monterey County SkeptiCamp 2018.

  • Complexity and Cryptography

    Dec 15 2017

    This week, our host Kyle Polich is joined by guest Tim Henderson from Google to talk about the computational complexity foundations of modern cryptography and the complexity issues that underlie the field. A key question that arises during the discussion is whether we should trust the security of modern cryptography.

  • Mercedes Benz Machine Learning Research

    Dec 14 2017

    This episode features an interview with Rigel Smiroldo recorded at NIPS 2017 in Long Beach California.  We discuss data privacy, machine learning use cases, model deployment, and end-to-end machine learning.

  • [MINI] Parallel Algorithms

    Dec 08 2017

    When computers became commodity hardware and storage became incredibly cheap, we entered the era of so-call "big" data. Most definitions of big data will include something about not being able to process all the data on a single machine. Distributed computing is required for such large datasets. Getting an algorithm to run on data spread out over a variety of different machines introduced new challenges for designing large-scale systems. First, there are concerns about the best strategy for spre...more

  • Quantum Computing

    Dec 01 2017

    In this week's episode, Scott Aaronson, a professor at the University of Texas at Austin, explains what a quantum computer is, various possible applications, the types of problems they are good at solving and much more. Kyle and Scott have a lively discussion about the capabilities and limits of quantum computers and computational complexity.

  • Azure Databricks

    Nov 28 2017

    I sat down with Ali Ghodsi, CEO and found of Databricks, and John Chirapurath, GM for Data Platform Marketing at Microsoft related to the recent announcement of Azure Databricks. When I heard about the announcement, my first thoughts were two-fold.  First, the possibility of optimized integrations with existing Azure services.  This would be a big benefit to heavy Azure users who also want to use Spark.  Second, the benefits of active directory to control Databricks access for large enterprise. ...more

  • [MINI] Exponential Time Algorithms

    Nov 24 2017

    In this episode we discuss the complexity class of EXP-Time which contains algorithms which require $O(2^{p(n)})$ time to run.  In other words, the worst case runtime is exponential in some polynomial of the input size.  Problems in this class are even more difficult than problems in NP since you can't even verify a solution in polynomial time. We mostly discuss Generalized Chess as an intuitive example of a problem in EXP-Time.  Another well-known problem is determining if a given algorithm wil...more

  • P vs NP

    Nov 17 2017

    In this week's episode, host Kyle Polich interviews author Lance Fortnow about whether P will ever be equal to NP and solve all of life’s problems. Fortnow begins the discussion with the example question: Are there 100 people on Facebook who are all friends with each other? Even if you were an employee of Facebook and had access to all its data, answering this question naively would require checking more possibilities than any computer, now or in the future, could possibly do. The P/NP question ...more

  • [MINI] Sudoku \in NP

    Nov 10 2017

    Algorithms with similar runtimes are said to be in the same complexity class. That runtime is measured in the how many steps an algorithm takes relative to the input size. The class P contains all algorithms which run in polynomial time (basically, a nested for loop iterating over the input).  NP are algorithms which seem to require brute force.  Brute force search cannot be done in polynomial time, so it seems that problems in NP are more difficult than problems in P.  I say it "seems" this way...more

  • The Computational Complexity of Machine Learning

    Nov 03 2017

    In this episode, Professor Michael Kearns from the University of Pennsylvania joins host Kyle Polich to talk about the computational complexity of machine learning, complexity in game theory, and algorithmic fairness. Michael's doctoral thesis gave an early broad overview of computational learning theory, in which he emphasizes the mathematical study of efficient learning algorithms by machines or computational systems. When we look at machine learning algorithms they are almost like meta-algori...more

  • [MINI] Turing Machines

    Oct 27 2017

    TMs are a model of computation at the heart of algorithmic analysis.  A Turing Machine has two components.  An infinitely long piece of tape (memory) with re-writable squares and a read/write head which is programmed to change it's state as it processes the input.  This exceptionally simple mechanical computer can compute anything that is intuitively computable, thus says the Church-Turing Thesis. Attempts to make a "better" Turing Machine by adding things like additional tapes can make the prog...more

  • The Complexity of Learning Neural Networks

    Oct 20 2017

    Over the past several years, we have seen many success stories in machine learning brought about by deep learning techniques. While the practical success of deep learning has been phenomenal, the formal guarantees have been lacking. Our current theoretical understanding of the many techniques that are central to the current ongoing big-data revolution is far from being sufficient for rigorous analysis, at best. In this episode of Data Skeptic, our host Kyle Polich welcomes guest John Wilmes, a m...more

  • [MINI] Big Oh Analysis

    Oct 13 2017

    How long an algorithm takes to run depends on many factors including implementation details and hardware.  However, the formal analysis of algorithms focuses on how they will perform in the worst case as the input size grows.  We refer to an algorithm's runtime as it's "O" which is a function of its input size "n".  For example, O(n) represents a linear algorithm - one that takes roughly twice as long to run if you double the input size.  In this episode, we discuss a few everyday examples of al...more

  • Data science tools and other announcements from Ignite

    Oct 06 2017

    In this episode, Microsoft's Corporate Vice President for Cloud Artificial Intelligence, Joseph Sirosh, joins host Kyle Polich to share some of the Microsoft's latest and most exciting innovations in AI development platforms. Last month, Microsoft launched a set of three powerful new capabilities in Azure Machine Learning for advanced developers to exploit big data, GPUs, data wrangling and container-based model deployment. Extended show notes found here. Thanks to our sponsor Springboard.  Che...more

  • Generative AI for Content Creation

    Sep 29 2017

    Last year, the film development and production company End Cue produced a short film, called Sunspring, that was entirely written by an artificial intelligence using neural networks. More specifically, it was authored by a recurrent neural network (RNN) called long short-term memory (LSTM). According to End Cue’s Chief Technical Officer, Deb Ray, the company has come a long way in improving the generative AI aspect of the bot. In this episode, Deb Ray joins host Kyle Polich to discuss how genera...more

  • [MINI] One Shot Learning

    Sep 22 2017

    One Shot Learning is the class of machine learning procedures that focuses learning something from a small number of examples.  This is in contrast to "traditional" machine learning which typically requires a very large training set to build a reasonable model. In this episode, Kyle presents a coded message to Linhda who is able to recognize that many of these new symbols created are likely to be the same symbol, despite having extremely few examples of each.  Why can the human brain recognize a...more

  • Recommender Systems Live from FARCON 2017

    Sep 15 2017

    Recommender systems play an important role in providing personalized content to online users. Yet, typical data mining techniques are not well suited for the unique challenges that recommender systems face. In this episode, host Kyle Polich joins Dr. Joseph Konstan from the University of Minnesota at a live recording at FARCON 2017 in Minneapolis to discuss recommender systems and how machine learning can create better user experiences. 

  • [MINI] Long Short Term Memory

    Sep 08 2017

    Thanks to our sponsor brilliant.org/dataskeptics A Long Short Term Memory (LSTM) is a neural unit, often used in Recurrent Neural Network (RNN) which attempts to provide the network the capacity to store information for longer periods of time. An LSTM unit remembers values for either long or short time periods. The key to this ability is that it uses no activation function within its recurrent components. Thus, the stored value is not iteratively modified and the gradient does not tend to vanish...more

  • Zillow Zestimate

    Sep 01 2017

    Zillow is a leading real estate information and home-related marketplace. We interviewed Andrew Martin, a data science Research Manager at Zillow, to learn more about how Zillow uses data science and big data to make real estate predictions.

  • Cardiologist Level Arrhythmia Detection with CNNs

    Aug 25 2017

    Our guest Pranav Rajpurkar and his coauthored recently published Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks, a paper in which they demonstrate the use of Convolutional Neural Networks which outperform board certified cardiologists in detecting a wide range of heart arrhythmias from ECG data.

  • [MINI] Recurrent Neural Networks

    Aug 18 2017

    RNNs are a class of deep learning models designed to capture sequential behavior.  An RNN trains a set of weights which depend not just on new input but also on the previous state of the neural network.  This directed cycle allows the training phase to find solutions which rely on the state at a previous time, thus giving the network a form of memory.  RNNs have been used effectively in language analysis, translation, speech recognition, and many other tasks.

  • Project Common Voice

    Aug 11 2017

    Thanks to our sponsor Springboard. In this week's episode, guest Andre Natal from Mozilla joins our host, Kyle Polich, to discuss a couple exciting new developments in open source speech recognition systems, which include Project Common Voice. In June 2017, Mozilla launched a new open source project, Common Voice, a novel complementary project to the TensorFlow-based DeepSpeech implementation. DeepSpeech is a deep learning-based voice recognition system that was designed by Baidu, which they des...more

  • [MINI] Bayesian Belief Networks

    Aug 04 2017

    A Bayesian Belief Network is an acyclic directed graph composed of nodes that represent random variables and edges that imply a conditional dependence between them. It's an intuitive way of encoding your statistical knowledge about a system and is efficient to propagate belief updates throughout the network when new information is added.

  • pix2code

    Jul 28 2017

    In this episode, Tony Beltramelli of UIzard Technologies joins our host, Kyle Polich, to talk about the ideas behind his latest app that can transform graphic design into functioning code, as well as his previous work on spying with wearables.

  • [MINI] Conditional Independence

    Jul 21 2017

    In statistics, two random variables might depend on one another (for example, interest rates and new home purchases). We call this conditional dependence. An important related concept exists called conditional independence. This phrase describes situations in which two variables are independent of one another given some other variable. For example, the probability that a vendor will pay their bill on time could depend on many factors such as the company's market cap. Thus, a statistical analysis...more

  • Estimating Sheep Pain with Facial Recognition

    Jul 14 2017

    Animals can't tell us when they're experiencing pain, so we have to rely on other cues to help treat their discomfort. But it is often difficult to tell how much an animal is suffering. The sheep, for instance, is the most inscrutable of animals. However, scientists have figured out a way to understand sheep facial expressions using artificial intelligence. On this week's episode, Dr. Marwa Mahmoud from the University of Cambridge joins us to discuss her recent study, "Estimating Sheep Pain Leve...more

  • CosmosDB

    Jul 07 2017

    This episode collects interviews from my recent trip to Microsoft Build where I had the opportunity to speak with Dharma Shukla and Syam Nair about the recently announced CosmosDB. CosmosDB is a globally consistent, distributed datastore that supports all the popular persistent storage formats (relational, key/value pair, document database, and graph) under a single streamlined API. The system provides tunable consistency, allowing the user to make choices about how consistency trade-offs are ma...more

  • [MINI] The Vanishing Gradient

    Jun 30 2017

    This episode discusses the vanishing gradient - a problem that arises when training deep neural networks in which nearly all the gradients are very close to zero by the time back-propagation has reached the first hidden layer. This makes learning virtually impossible without some clever trick or improved methodology to help earlier layers begin to learn.

  • Doctor AI

    Jun 23 2017

    hen faced with medical issues, would you want to be seen by a human or a machine? In this episode, guest Edward Choi, co-author of the study titled Doctor AI: Predicting Clinical Events via Recurrent Neural Network shares his thoughts. Edward presents his team’s efforts in developing a temporal model that can learn from human doctors based on their collective knowledge, i.e. the large amount of Electronic Health Record (EHR) data.

  • [MINI] Activation Functions

    Jun 16 2017

    In a neural network, the output value of a neuron is almost always transformed in some way using a function. A trivial choice would be a linear transformation which can only scale the data. However, other transformations, like a step function allow for non-linear properties to be introduced. Activation functions can also help to standardize your data between layers. Some functions such as the sigmoid have the effect of "focusing" the area of interest on data. Extreme values are placed close toge...more

  • MS Build 2017

    Jun 09 2017

    This episode recaps the Microsoft Build Conference.  Kyle recently attended and shares some thoughts on cloud, databases, cognitive services, and artificial intelligence.  The episode includes interviews with Rohan Kumar and David Carmona.  

  • [MINI] Max-pooling

    Jun 02 2017

    Max-pooling is a procedure in a neural network which has several benefits. It performs dimensionality reduction by taking a collection of neurons and reducing them to a single value for future layers to receive as input. It can also prevent overfitting, since it takes a large set of inputs and admits only one value, making it harder to memorize the input. In this episode, we discuss the intuitive interpretation of max-pooling and why it's more common than mean-pooling or (theoretically) quartile...more

  • Unsupervised Depth Perception

    May 26 2017

    This episode is an interview with Tinghui Zhou.  In the recent paper "Unsupervised Learning of Depth and Ego-motion from Video", Tinghui and collaborators propose a deep learning architecture which is able to learn depth and pose information from unlabeled videos.  We discuss details of this project and its applications.

  • [MINI] Convolutional Neural Networks

    May 19 2017

    CNNs are characterized by their use of a group of neurons typically referred to as a filter or kernel.  In image recognition, this kernel is repeated over the entire image.  In this way, CNNs may achieve the property of translational invariance - once trained to recognize certain things, changing the position of that thing in an image should not disrupt the CNN's ability to recognize it.  In this episode, we discuss a few high-level details of this important architecture.

  • Multi-Agent Diverse Generative Adversarial Networks

    May 12 2017

    Despite the success of GANs in imaging, one of its major drawbacks is the problem of 'mode collapse,' where the generator learns to produce samples with extremely low variety. To address this issue, today's guests Arnab Ghosh and Viveka Kulharia proposed two different extensions. The first involves tweaking the generator's objective function with a diversity enforcing term that would assess similarities between the different samples generated by different generators. The second comprises modifyi...more

  • [MINI] Generative Adversarial Networks

    May 05 2017

    GANs are an unsupervised learning method involving two neural networks iteratively competing. The discriminator is a typical learning system. It attempts to develop the ability to recognize members of a certain class, such as all photos which have birds in them. The generator attempts to create false examples which the discriminator incorrectly classifies. In successive training rounds, the networks examine each and play a mini-max game of trying to harm the performance of the other. In addition...more

  • Opinion Polls for Presidential Elections

    Apr 28 2017

    Recently, we've seen opinion polls come under some skepticism.  But is that skepticism truly justified?  The recent Brexit referendum and US 2016 Presidential Election are examples where some claims the polls "got it wrong".  This episode explores this idea.

  • OpenHouse

    Apr 21 2017

    No reliable, complete database cataloging home sales data at a transaction level is available for the average person to access. To a data scientist interesting in studying this data, our hands are complete tied. Opportunities like testing sociological theories, exploring economic impacts, study market forces, or simply research the value of an investment when buying a home are all blocked by the lack of easy access to this dataset. OpenHouse seeks to correct that by centralizing and standardizin...more

  • [MINI] GPU CPU

    Apr 14 2017

    There's more than one type of computer processor. The central processing unit (CPU) is typically what one means when they say "processor". GPUs were introduced to be highly optimized for doing floating point computations in parallel. These types of operations were very useful for high end video games, but as it turns out, those same processors are extremely useful for machine learning. In this mini-episode we discuss why.

  • [MINI] Backpropagation

    Apr 07 2017

    Backpropagation is a common algorithm for training a neural network.  It works by computing the gradient of each weight with respect to the overall error, and using stochastic gradient descent to iteratively fine tune the weights of the network.  In this episode, we compare this concept to finding a location on a map, marble maze games, and golf.

  • Data Science at Patreon

    Mar 31 2017

    In this week's episode of Data Skeptic, host Kyle Polich talks with guest Maura Church, Patreon's data science manager. Patreon is a fast-growing crowdfunding platform that allows artists and creators of all kinds build their own subscription content service. The platform allows fans to become patrons of their favorite artists- an idea similar the Renaissance times, when musicians would rely on benefactors to become their patrons so they could make more art. At Patreon, Maura's data science team...more

  • [MINI] Feed Forward Neural Networks

    Mar 24 2017

    Feed Forward Neural Networks In a feed forward neural network, neurons cannot form a cycle. In this episode, we explore how such a network would be able to represent three common logical operators: OR, AND, and XOR. The XOR operation is the interesting case. Below are the truth tables that describe each of these functions. AND Truth Table Input 1 Input 2 Output 0 0 0 0 1 0 1 0 0 1 1 1 OR Truth Table Input 1 Input 2 Output 0 0 0 0 1 1 1 0 1 1 1 1 XOR Truth Table ...more

  • Reinventing Sponsored Search Auctions

    Mar 17 2017

    In this Data Skeptic episode, Kyle is joined by guest Ruggiero Cavallo to discuss his latest efforts to mitigate the problems presented in this new world of online advertising. Working with his collaborators, Ruggiero reconsiders the search ad allocation and pricing problems from the ground up and redesigns a search ad selling system. He discusses a mechanism that optimizes an entire page of ads globally based on efficiency-maximizing search allocation and a novel technical approach to computing...more

  • [MINI] The Perceptron

    Mar 10 2017

    Today's episode overviews the perceptron algorithm. This rather simple approach is characterized by a few particular features. It updates its weights after seeing every example, rather than as a batch. It uses a step function as an activation function. It's only appropriate for linearly separable data, and it will converge to a solution if the data meets these criteria. Being a fairly simple algorithm, it can run very efficiently. Although we don't discuss it in this episode, multi-layer percept...more

  • The Data Refuge Project

    Mar 03 2017

    DataRefuge is a public collaborative, grassroots effort around the United States in which scientists, researchers, computer scientists, librarians and other volunteers are working to download, save, and re-upload government data. The DataRefuge Project, which is led by the UPenn Program in Environmental Humanities and the Penn Libraries group at University of Pennsylvania, aims to foster resilience in an era of anthropogenic global climate change and raise awareness of how social and political e...more

  • [MINI] Automated Feature Engineering

    Feb 24 2017

    If a CEO wants to know the state of their business, they ask their highest ranking executives. These executives, in turn, should know the state of the business through reports from their subordinates. This structure is roughly analogous to a process observed in deep learning, where each layer of the business reports up different types of observations, KPIs, and reports to be interpreted by the next layer of the business. In deep learning, this process can be thought of as automated feature engin...more

  • Big Data Tools and Trends

    Feb 17 2017

    In this episode, I speak with Raghu Ramakrishnan, CTO for Data at Microsoft.  We discuss services, tools, and developments in the big data sphere as well as the underlying needs that drove these innovations.

  • [MINI] Primer on Deep Learning

    Feb 10 2017

    In this episode, we talk about a high-level description of deep learning.  Kyle presents a simple game (pictured below), which is more of a puzzle really, to try and give  Linh Da the basic concept.     Thanks to our sponsor for this week, the Data Science Association. Please check out their upcoming Dallas conference at dallasdatascience.eventbrite.com

  • Data Provenance and Reproducibility with Pachyderm

    Feb 03 2017

    Versioning isn't just for source code. Being able to track changes to data is critical for answering questions about data provenance, quality, and reproducibility. Daniel Whitenack joins me this week to talk about these concepts and share his work on Pachyderm. Pachyderm is an open source containerized data lake. During the show, Daniel mentioned the Gopher Data Science github repo as a great resource for any data scientists interested in the Go language. Although we didn't mention it, Daniel al...more

  • [MINI] Logistic Regression on Audio Data

    Jan 27 2017

    Logistic Regression is a popular classification algorithm. In this episode, we discuss how it can be used to determine if an audio clip represents one of two given speakers. It assumes an output variable (isLinhda) is a linear combination of available features, which are spectral bands in the discussion on this episode.   Keep an eye on the dataskeptic.com blog this week as we post more details about this project.   Thanks to our sponsor this week, the Data Science Association.  Please check out...more

  • Studying Competition and Gender Through Chess

    Jan 20 2017

    Prior work has shown that people's response to competition is in part predicted by their gender. Understanding why and when this occurs is important in areas such as labor market outcomes. A well structured study is challenging due to numerous confounding factors. Peter Backus and his colleagues have identified competitive chess as an ideal arena to study the topic. Find out why and what conclusions they reached. Our discussion centers around Gender, Competition and Performance: Evidence from Re...more

  • [MINI] Dropout

    Jan 13 2017

    Deep learning can be prone to overfit a given problem. This is especially frustrating given how much time and computational resources are often required to converge. One technique for fighting overfitting is to use dropout. Dropout is the method of randomly selecting some neurons in one's network to set to zero during iterations of learning. The core idea is that each particular input in a given layer is not always available and therefore not a signal that can be relied on too heavily.  

  • The Police Data and the Data Driven Justice Initiatives

    Jan 06 2017

    In this episode I speak with Clarence Wardell and Kelly Jin about their mutual service as part of the White House's Police Data Initiative and Data Driven Justice Initiative respectively. The Police Data Initiative was organized to use open data to increase transparency and community trust as well as to help police agencies use data for internal accountability. The PDI emerged from recommendations made by the Task Force on 21st Century Policing. The Data Driven Justice Initiative was organized ...more

  • The Library Problem

    Dec 30 2016

    We close out 2016 with a discussion of a basic interview question which might get asked when applying for a data science job. Specifically, how a library might build a model to predict if a book will be returned late or not.

  • 2016 Holiday Special

    Dec 23 2016

    Today's episode is a reading of Isaac Asimov's Franchise.  As mentioned on the show, this is just a work of fiction to be enjoyed and not in any way some obfuscated political statement.  Enjoy, and happy holidays!

  • [MINI] Entropy

    Dec 16 2016

    Classically, entropy is a measure of disorder in a system. From a statistical perspective, it is more useful to say it's a measure of the unpredictability of the system. In this episode we discuss how information reduces the entropy in deciding whether or not Yoshi the parrot will like a new chew toy. A few other everyday examples help us examine why entropy is a nice metric for constructing a decision tree.

  • MS Connect Conference

    Dec 09 2016

    Cloud services are now ubiquitous in data science and more broadly in technology as well. This week, I speak to Mark Souza, Tobias Ternström, and Corey Sanders about various aspects of data at scale. We discuss the embedding of R into SQLServer, SQLServer on linux, open source, and a few other cloud topics.

  • Causal Impact

    Dec 02 2016

    Today's episode is all about Causal Impact, a technique for estimating the impact of a particular event on a time series. We talk to William Martin about his research into the impact releases have on app and we also chat with Karen Blakemore about a project she helped us build to explore the impact of a Saturday Night Live appearance on a musician's career. Martin's work culminated in a paper Causal Impact for App Store Analysis. A shorter summary version can be found here. His company helping...more

  • [MINI] The Bootstrap

    Nov 25 2016

    The Bootstrap is a method of resampling a dataset to possibly refine it's accuracy and produce useful metrics on the result. The bootstrap is a useful statistical technique and is leveraged in Bagging (bootstrap aggregation) algorithms such as Random Forest. We discuss this technique related to polling and surveys.

  • [MINI] Gini Coefficients

    Nov 18 2016

    The Gini Coefficient (as it relates to decision trees) is one approach to determining the optimal decision to introduce which splits your dataset as part of a decision tree. To pick the right feature to split on, it considers the frequency of the values of that feature and how well the values correlate with specific outcomes that you are trying to predict.

  • Unstructured Data for Finance

    Nov 11 2016

    Financial analysis techniques for studying numeric, well structured data are very mature. While using unstructured data in finance is not necessarily a new idea, the area is still very greenfield. On this episode,Delia Rusu shares her thoughts on the potential of unstructured data and discusses her work analyzing Wikipedia to help inform financial decisions. Delia's talk at PyData Berlin can be watched on Youtube (Estimating stock price correlations using Wikipedia). The slides can be found here...more

  • [MINI] AdaBoost

    Nov 04 2016

    AdaBoost is a canonical example of the class of AnyBoost algorithms that create ensembles of weak learners. We discuss how a complex problem like predicting restaurant failure (which is surely caused by different problems in different situations) might benefit from this technique.

  • Stealing Models from the Cloud

    Oct 28 2016

    Platform as a service is a growing trend in data science where services like fraud analysis and face detection can be provided via APIs. Such services turn the actual model into a black box to the consumer. But can the model be reverse engineered? Florian Tramèr shares his work in this episode showing that it can. The paper Stealing Machine Learning Models via Prediction APIs is definitely worth your time to read if you enjoy this episode. Related source code can be found in https://github.com/f...more

  • [MINI] Calculating Feature Importance

    Oct 21 2016

    For machine learning models created with the random forest algorithm, there is no obvious diagnostic to inform you which features are more important in the output of the model. Some straightforward but useful techniques exist revolving around removing a feature and measuring the decrease in accuracy or Gini values in the leaves. We broadly discuss these techniques in this episode.

  • NYC Bike Share Rebalancing

    Oct 14 2016

    As cities provide bike sharing services, they must also plan for how to redistribute bicycles as they inevitably build up at more popular destination stations. In this episode, Hui Xiong talks about the solution he and his colleagues developed to rebalance bike sharing systems.

  • [MINI] Random Forest

    Oct 07 2016

    Random forest is a popular ensemble learning algorithm which leverages bagging both for sampling and feature selection. In this episode we make an analogy to the process of running a bookstore.

  • Election Predictions

    Sep 30 2016

    Jo Hardin joins us this week to discuss the ASA's Election Prediction Contest. This is a competition aimed at forecasting the results of the upcoming US presidential election competition. More details are available in Jo's blog post found here. You can find some useful R code for getting started automatically gathering data from 538 via Jo's github and official contest details are available here. During the interview we also mention Daily Kos and 538.

  • [MINI] F1 Score

    Sep 23 2016

    The F1 score is a model diagnostic that combines precision and recall to provide a singular evaluation for model comparison.  In this episode we discuss how it applies to selecting an interior designer.

  • Urban Congestion

    Sep 16 2016

    Urban congestion effects every person living in a city of any reasonable size. Lewis Lehe joins us in this episode to share his work on downtown congestion pricing. We explore topics of how different pricing mechanisms effect congestion as well as how data visualization can inform choices. You can find examples of Lewis's work at setosa.io. His paper which we discussed during the interview isDistance-dependent congestion pricing for downtown zones. On this episode, we discuss State of California...more

  • [MINI] Heteroskedasticity

    Sep 09 2016

    Heteroskedasticity is a term used to describe a relationship between two variables which has unequal variance over the range.  For example, the variance in the length of a cat's tail almost certainly changes (grows) with age.  On the other hand, the average amount of chewing gum a person consume probably has a consistent variance over a wide range of human heights. We also discuss some issues with the visualization shown in the tweet embedded below.

  • Music21

    Sep 02 2016

    Our guest today is Michael Cuthbert, an associate professor of music at MIT and principal investigator of the Music21 project, which we focus our discussion on today. Music21 is a python library making analysis of music accessible and fun. It supports integration with popular formats such as MIDI, MusicXML, Lilypond, and others. It's also well integrated with The Elvis Project, enabling users to import large volumes of music for easy analysis. Music21 is a great platform for musicologists and ma...more

  • [MINI] Paxos

    Aug 26 2016

    Paxos is a protocol for arriving a consensus in a distributed computing system which accounts for unreliability of the nodes.  We discuss how this might be used in the real world in the event of a massive disaster.

  • Trusting Machine Learning Models with LIME

    Aug 19 2016

    Machine learning models are often criticized for being black boxes. If a human cannot determine why the model arrives at the decision it made, there's good cause for skepticism. Classic inspection approaches to model interpretability are only useful for simple models, which are likely to only cover simple problems. The LIME project seeks to help us trust machine learning models. At a high level, it takes advantage of local fidelity. For a given example, a separate model trained on neighbors of t...more

  • [MINI] ANOVA

    Aug 12 2016

    Analysis of variance is a method used to evaluate differences between the two or more groups.  It works by breaking down the total variance of the system into the between group variance and within group variance.  We discuss this method in the context of wait times getting coffee at Starbucks.

  • Machine Learning on Images with Noisy Human-centric Labels

    Aug 05 2016

    When humans describe images, they have a reporting bias, in that the report only what they consider important. Thus, in addition to considering whether something is present in an image, one should consider whether it is also relevant to the image before labeling it. Ishan Misra joins us this week to discuss his recent paper Seeing through the Human Reporting Bias: Visual Classifiers from Noisy Human-Centric Labels which explores a novel architecture for learning to distinguish presence and relev...more

  • [MINI] Survival Analysis

    Jul 29 2016

    Survival analysis techniques are useful for studying the longevity of groups of elements or individuals, taking into account time considerations and right censorship. This episode explores how survival analysis can describe marriages, in particular, using the non-parametric Cox proportional hazard model. This episode discusses some good summaries of survey data on marriage and divorce which can be found here. The python lifelines library is a good place to get started for people that want to d...more

  • Predictive Models on Random Data

    Jul 22 2016

    This week is an insightful discussion with Claudia Perlich about some situations in machine learning where models can be built, perhaps by well-intentioned practitioners, to appear to be highly predictive despite being trained on random data. Our discussion covers some novel observations about ROC and AUC, as well as an informative discussion of leakage. Much of our discussion is inspired by two excellent papers Claudia authored: Leakage in Data Mining: Formulation, Detection, and Avoidance and...more

  • [MINI] Receiver Operating Characteristic (ROC) Curve

    Jul 15 2016

    An ROC curve is a plot that compares the trade off of true positives and false positives of a binary classifier under different thresholds. The area under the curve (AUC) is useful in determining how discriminating a model is. Together, ROC and AUC are very useful diagnostics for understanding the power of one's model and how to tune it.

  • Multiple Comparisons and Conversion Optimization

    Jul 08 2016

    I'm joined by Chris Stucchio this week to discuss how deliberate or uninformed statistical practitioners can derive spurious and arbitrary results via multiple comparisons. We discuss p-hacking and a variety of other important lessons and tips for proper analysis. You can enjoy Chris's writing on his blog at chrisstucchio.com and you may also like his recent talk Multiple Comparisons: Make Your Boss Happy with False Positives, Guarenteed.

  • [MINI] Leakage

    Jul 01 2016

    If you'd like to make a good prediction, your best bet is to invent a time machine, visit the future, observe the value, and return to the past. For those without access to time travel technology, we need to avoid including information about the future in our training data when building machine learning models. Similarly, if any other feature whose value would not actually be available in practice at the time you'd want to use the model to make a prediction, is a feature that can introduce leaka...more

  • Predictive Policing

    Jun 24 2016

    Kristian Lum (@KLdivergence) joins me this week to discuss her work at @hrdag on predictive policing. We also discuss Multiple Systems Estimation, a technique for inferring statistical information about a population from separate sources of observation. If you enjoy this discussion, check out the panel Tyranny of the Algorithm? Predictive Analytics & Human Rights which was mentioned in the episode.

  • [MINI] The CAP Theorem

    Jun 17 2016

    Distributed computing cannot guarantee consistency, accuracy, and partition tolerance. Most system architects need to think carefully about how they should appropriately balance the needs of their application across these competing objectives. Linh Da and Kyle discuss the CAP Theorem using the analogy of a phone tree for alerting people about a school snow day.

  • Detecting Terrorists with Facial Recognition?

    Jun 10 2016

    A startup is claiming that they can detect terrorists purely through facial recognition. In this solo episode, Kyle explores the plausibility of these claims.

  • [MINI] Goodhart's Law

    Jun 03 2016

    Goodhart's law states that "When a measure becomes a target, it ceases to be a good measure". In this mini-episode we discuss how this affects SEO, call centers, and Scrum.

  • Data Science at eHarmony

    May 27 2016

    I'm joined this week by Jon Morra, director of data science at eHarmony to discuss a variety of ways in which machine learning and data science are being applied to help connect people for successful long term relationships. Interesting open source projects mentioned in the interview include Face-parts, a web service for detecting faces and extracting a robust set of fiducial markers (features) from the image, and Aloha, a Scala based machine learning library. You can learn more about these and ...more

  • [MINI] Stationarity and Differencing

    May 20 2016

    Mystery shoppers and fruit cultivation help us discuss stationarity - a property of some time serieses that are invariant to time in several ways. Differencing is one approach that can often convert a non-stationary process into a stationary one. If you have a stationary process, you get the benefits of many known statistical properties that can enable you to do a significant amount of inferencing and prediction.

  • Feather

    May 13 2016

    I'm joined by Wes McKinney (@wesmckinn) and Hadley Wickham (@hadleywickham) on this episode to discuss their joint project Feather. Feather is a file format for storing data frames along with some metadata, to help with interoperability between languages. At the time of recording, libraries are available for R and Python, making it easy for data scientists working in these languages to quickly and effectively share datasets and collaborate.

  • [MINI] Bargaining

    May 06 2016

    Bargaining is the process of two (or more) parties attempting to agree on the price for a transaction.  Game theoretic approaches attempt to find two strategies from which neither party is motivated to deviate.  These strategies are said to be in equilibrium with one another.  The equilibriums available in bargaining depend on the the transaction mechanism and the information of the parties.  Discounting (how long parties are willing to wait) has a significant effect in this process.  This episo...more

  • deepjazz

    Apr 29 2016

    Deepjazz is a project from Ji-Sung Kim, a computer science student at Princeton University. It is built using Theano, Keras, music21, and Evan Chow's project jazzml. Deepjazz is a computational music project that creates original jazz compositions using recurrent neural networks trained on Pat Metheny's "And Then I Knew". You can hear some of deepjazz's original compositions on soundcloud.

  • [MINI] Auto-correlative functions and correlograms

    Apr 22 2016

    When working with time series data, there are a number of important diagnostics one should consider to help understand more about the data. The auto-correlative function, plotted as a correlogram, helps explain how a given observations relates to recent preceding observations. A very random process (like lottery numbers) would show very low values, while temperature (our topic in this episode) does correlate highly with recent days.   See the show notes with details about Chapel Hill, NC weat...more

  • Early Identification of Violent Criminal Gang Members

    Apr 15 2016

    This week I spoke with Elham Shaabani and Paulo Shakarian (@PauloShakASU) about their recent paper Early Identification of Violent Criminal Gang Members (also available onarXiv). In this paper, they use social network analysis techniques and machine learning to provide early detection of known criminal offenders who are in a high risk group for committing violent crimes in the future. Their techniques outperform existing techniques used by the police. Elham and Paulo are part of the Cyber-Socio ...more

  • [MINI] Fractional Factorial Design

    Apr 08 2016

    A dinner party at Data Skeptic HQ helps teach the uses of fractional factorial design for studying 2-way interactions.

  • Machine Learning Done Wrong

    Apr 01 2016

    Cheng-tao Chu (@chengtao_chu) joins us this week to discuss his perspective on common mistakes and pitfalls that are made when doing machine learning. This episode is filled with sage advice for beginners and intermediate users of machine learning, and possibly some good reminders for experts as well. Our discussion parallels his recent blog postMachine Learning Done Wrong. Cheng-tao Chu is an entrepreneur who has worked at many well known silicon valley companies. His paper Map-Reduce for Mach...more

  • Potholes

    Mar 25 2016

    Co-host Linh Da was in a biking accident after hitting a pothole. She sustained an injury that required stitches. This is the story of our quest to file a 311 complaint and track it through the City of Los Angeles's open data portal. My guests this episode are Chelsea Ursaner (LA City Open Data Team), Ben Berkowitz (CEO and founder of SeeClickFix), and Russ Klettke (Editor of pothole.info)

  • [MINI] The Elbow Method

    Mar 18 2016

    Certain data mining algorithms (including k-means clustering and k-nearest neighbors) require a user defined parameter k. A user of these algorithms is required to select this value, which raises the questions: what is the "best" value of k that one should select to solve their problem? This mini-episode explores the appropriate value of k to use when trying to estimate the cost of a house in Los Angeles based on the closests sales in it's area.

  • Too Good to be True

    Mar 11 2016

    Today on Data Skeptic, Lachlan Gunn joins us to discuss his recent paper Too Good to be True. This paper highlights a somewhat paradoxical / counterintuitive fact about how unanimity is unexpected in cases where perfect measurements cannot be taken. With large enough data, some amount of error is expected. The "Too Good to be True" paper highlights three interesting examples which we discuss in the podcast. You can also watch a lecture from Lachlan on this topic via youtube here.

  • [MINI] R-squared

    Mar 04 2016

    How well does your model explain your data? R-squared is a useful statistic for answering this question. In this episode we explore how it applies to the problem of valuing a house. Aspects like the number of bedrooms go a long way in explaining why different houses have different prices. There's some amount of variance that can be explained by a model, and some amount that cannot be directly measured. R-squared is the ratio of the explained variance to the total variance. It's not a measure of ...more

  • Models of Mental Simulation

    Feb 26 2016

    Jessica Hamrick joins us this week to discuss her work studying mental simulation. Her research combines machine learning approaches iwth behavioral method from cognitive science to help explain how people reason and predict outcomes. Her recent paper Think again? The amount of mental simulation tracks uncertainty in the outcome is the focus of our conversation in this episode. Lastly, Kyle invited Samuel Hansen from the Relative Prime podcast to mention the Relatively Prime Season 3 kickstarte...more

  • [MINI] Multiple Regression

    Feb 19 2016

    This episode is a discussion of multiple regression: the use of observations that are a vector of values to predict a response variable. For this episode, we consider how features of a home such as the number of bedrooms, number of bathrooms, and square footage can predict the sale price. Unlike a typical episode of Data Skeptic, these show notes are not just supporting material, but are actually featured in the episode. The site Redfin gratiously allows users to download a CSV of ...more

  • Scientific Studies of People's Relationship to Music

    Feb 12 2016

    Samuel Mehr joins us this week to share his perspective on why people are musical, where music comes from, and why it works the way it does. We discuss a number of empirical studies related to music and musical cognition, and dispense a few myths about music along the way. Some of Sam's work discussed in this episode include Music in the Home: New Evidence for an Intergenerational Link,Two randomized trials provide no consistent evidence for nonmusical cognitive benefits of brief preschool musi...more

  • [MINI] k-d trees

    Feb 05 2016

    This episode reviews the concept of k-d trees: an efficient data structure for holding multidimensional objects. Kyle gives Linhda a dictionary and asks her to look up words as a way of introducing the concept of binary search. We actually spend most of the episode talking about binary search before getting into k-d trees, but this is a necessary prerequisite.

  • Auditing Algorithms

    Jan 29 2016

    Algorithms are pervasive in our society and make thousands of automated decisions on our behalf every day. The possibility of digital discrimination is a very real threat, and it is very plausible for discrimination to occur accidentally (i.e. outside the intent of the system designers and programmers). Christian Sandvig joins us in this episode to talk about his work and the concept of auditing algorithms. Christian Sandvig (@niftyc) has a PhD in communications from Stanford and is currently a...more

  • [MINI] The Bonferroni Correction

    Jan 22 2016

    Today's episode begins by asking how many left handed employees we should expect to be at a company before anyone should claim left handedness discrimination. If not lefties, let's consider eye color, hair color, favorite ska band, most recent grocery store used, and any number of characteristics could be studied to look for deviations from the norm in a company. When multiple comparisons are to be made simultaneous, one must account for this, and a common method for doing so is with the Bonfer...more

  • Detecting Pseudo-profound BS

    Jan 15 2016

    A recent paper in the journal of Judgment and Decision Making titled On the reception and detection of pseudo-profound bullshit explores empirical questions around a reader's ability to detect statements which may sound profound but are actually a collection of buzzwords that fail to contain adequate meaning or truth. These statements are definitively different from lies and nonesense, as we discuss in the episode. This paper proposes the Bullshit Receptivity scale (BSR) and empirically demonst...more

  • [MINI] Gradient Descent

    Jan 08 2016

    Today's mini episode discusses the widely known optimization algorithm gradient descent in the context of hiking in a foggy hillside.

  • Let's Kill the Word Cloud

    Jan 01 2016

    This episode is a discussion of data visualization and a proposed New Year's resolution for Data Skeptic listeners. Let's kill the word cloud.

  • 2015 Holiday Special

    Dec 25 2015

    Today's episode is a reading of Isaac Asimov's The Machine that Won the War. I can't think of a story that's more appropriate for Data Skeptic.

  • Wikipedia Revision Scoring as a Service

    Dec 18 2015

    In this interview with Aaron Halfaker of the Wikimedia Foundation, we discuss his research and career related to the study of Wikipedia. In his paper The Rise and Decline of an open Collaboration Community, he highlights a trend in the declining rate of active editors on Wikipedia which began in 2007. I asked Aaron about a variety of possible hypotheses for the phenomenon, in particular, how automated quality control tools that revert edits automatically could play a role. This lead Aaron and hi...more

  • [MINI] Term Frequency - Inverse Document Frequency

    Dec 11 2015

    Today's topic is term frequency inverse document frequency, which is a statistic for estimating the importance of words and phrases in a set of documents.

  • The Hunt for Vulcan

    Dec 04 2015

    Early astronomers could see several of the planets with the naked eye. The invention of the telescope allowed for further understanding of our solar system. The work of Isaac Newton allowed later scientists to accurately predict Neptune, which was later observationally confirmed exactly where predicted. It seemed only natural that a similar unknown body might explain anomalies in the orbit of Mercury, and thus began the search for the hypothesized planet Vulcan. Thomas Levenson's book "The Hunt...more

  • [MINI] The Accuracy Paradox

    Nov 27 2015

    Today's episode discusses the accuracy paradox. There are cases when one might prefer a less accurate model because it yields more predictive power or better captures the underlying causal factors describing the outcome variable you are interested in. This is especially relevant in machine learning when trying to predict rare events. We discuss how the accuracy paradox might apply if you were trying to predict the likelihood a person was a bird owner.

  • Neuroscience from a Data Scientist's Perspective

    Nov 20 2015

    ... or should this have been called data science from a neuroscientist's perspective? Either way, I'm sure you'll enjoy this discussion with Laurie Skelly. Laurie earned a PhD in Integrative Neuroscience from the Department of Psychology at the University of Chicago. In her life as a social neuroscientist, using fMRI to study the neural processes behind empathy and psychopathy, she learned the ropes of zooming in and out between the macroscopic and the microscopic -- how millions of data points ...more

  • [MINI] Bias Variance Tradeoff

    Nov 13 2015

    A discussion of the expected number of cars at a stoplight frames today's discussion of the bias variance tradeoff. The central ideal of this concept relates to model complexity. A very simple model will likely generalize well from training to testing data, but will have a very high variance since it's simplicity can prevent it from capturing the relationship between the covariates and the output. As a model grows more and more complex, it may capture more of the underlying data but the risk tha...more

  • Big Data Doesn't Exist

    Nov 06 2015

    The recent opinion piece Big Data Doesn't Exist on Tech Crunch by Slater Victoroff is an interesting discussion about the usefulness of data both big and small. Slater joins me this episode to discuss and expand on this discussion. Slater Victoroff is CEO of indico Data Solutions, a company whose services turn raw text and image data into human insight. He, and his co-founders, studied at Olin College of Engineering where indico was born. indico was then accepted into the "Techstars Accelarator...more

  • [MINI] Covariance and Correlation

    Oct 30 2015

    The degree to which two variables change together can be calculated in the form of their covariance. This value can be normalized to the correlation coefficient, which has the advantage of transforming it to a unitless measure strictly bounded between -1 and 1. This episode discusses how we arrive at these values and why they are important.

  • Bayesian A/B Testing

    Oct 23 2015

    Today's guest is Cameron Davidson-Pilon. Cameron has a masters degree in quantitative finance from the University of Waterloo. Think of it as statistics on stock markets. For the last two years he's been the team lead of data science at Shopify. He's the founder of dataoragami.net which produces screencasts teaching methods and techniques of applied data science. He's also the author of the just released in print book Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference...more

  • [MINI] The Central Limit Theorem

    Oct 16 2015

    The central limit theorem is an important statistical result which states that typically, the mean of a large enough set of independent trials is approximately normally distributed.  This episode explores how this might be used to determine if an amazon parrot like Yoshi produces or or less waste than an African Grey, under the assumption that the individual distributions are not normal.

  • Accessible Technology

    Oct 09 2015

    Today's guest is Chris Hofstader (@gonz_blinko), an accessibility researcher and advocate, as well as an activist for causes such as improving access to information for blind and vision impaired people. His background in computer programming enabled him to be the leader of JAWS, a Windows program that allowed people with a visual impairment to read their screen either through text-to-speech or a refreshable braille display. He's the Managing Member of 3 Mouse Technology. He's also a frequent blo...more

  • [MINI] Multi-armed Bandit Problems

    Oct 02 2015

    The multi-armed bandit problem is named with reference to slot machines (one armed bandits). Given the chance to play from a pool of slot machines, all with unknown payout frequencies, how can you maximize your reward? If you knew in advance which machine was best, you would play exclusively that machine. Any strategy less than this will, on average, earn less payout, and the difference can be called the "regret". You can try each slot machine to learn about it, which we refer to as exploration...more

  • Shakespeare, Abiogenesis, and Exoplanets

    Sep 25 2015

    Our episode this week begins with a correction. Back in episode 28 (Monkeys on Typewriters), Kyle made some bold claims about the probability that monkeys banging on typewriters might produce the entire works of Shakespeare by chance. The proof shown in the show notes turned out to be a bit dubious and Dave Spiegel joins us in this episode to set the record straight. In addition to that, out discussion explores a number of interesting topics in astronomy and astrophysics. This includes a paper ...more

  • [MINI] Sample Sizes

    Sep 18 2015

    There are several factors that are important to selecting an appropriate sample size and dealing with small samples. The most important questions are around representativeness - how well does your sample represent the total population and capture all it's variance? Linhda and Kyle talk through a few examples including elections, picking an Airbnb, produce selection, and home shopping as examples of cases in which the amount of observations one has are more or less important depending on how com...more

  • The Model Complexity Myth

    Sep 11 2015

    There's an old adage which says you cannot fit a model which has more parameters than you have data. While this is often the case, it's not a universal truth. Today's guest Jake VanderPlas explains this topic in detail and provides some excellent examples of when it holds and doesn't. Some excellent visuals articulating the points can be found on Jake's blog Pythonic Perambulations, specifically on his post The Model Complexity Myth. We also touch on Jake's work as an astronomer, his noteworthy...more

  • [MINI] Distance Measures

    Sep 04 2015

    There are many occasions in which one might want to know the distance or similarity between two things, for which the means of calculating that distance is not necessarily clear. The distance between two points in Euclidean space is generally straightforward, but what about the distance between the top of Mount Everest to the bottom of the ocean? What about the distance between two sentences? This mini-episode summarizes some of the considerations and a few of the means of calculating distance....more

  • ContentMine

    Aug 28 2015

    ContentMine is a project which provides the tools and workflow to convert scientific literature into machine readable and machine interpretable data in order to facilitate better and more effective access to the accumulated knowledge of human kind. The program's founder Peter Murray-Rust joins us this week to discuss ContentMine. Our discussion covers the project, the scientific publication process, copywrite, and several other interesting topics.

  • [MINI] Structured and Unstructured Data

    Aug 21 2015

    Today's mini-episode explains the distinction between structured and unstructured data, and debates which of these categories best describe recipes.

  • Measuring the Influence of Fashion Designers

    Aug 14 2015

    Yusan Lin shares her research on using data science to explore the fashion industry in this episode. She has applied techniques from data mining, natural language processing, and social network analysis to explore who are the innovators in the fashion world and how their influence effects other designers. If you found this episode interesting and would like to read more, Yusan's papers Text-Generated Fashion Influence Model: An Empirical Study on Style.com and The Hidden Influence Network in th...more

  • [MINI] PageRank

    Aug 07 2015

    PageRank is the algorithm most famous for being one of the original innovations that made Google stand out as a search engine. It was defined in the classic paper The Anatomy of a Large-Scale Hypertextual Web Search Engine by Sergey Brin and Larry Page. While this algorithm clearly impacted web searching, it has also been useful in a variety of other applications. This episode presents a high level description of this algorithm and how it might apply when trying to establish who writes the most ...more

  • Data Science at Work in LA County

    Jul 29 2015

    In this episode, Benjamin Uminsky enlightens us about some of the ways the Los Angeles County Registrar-Recorder/County Clerk leverages data science and analysis to help be more effective and efficient with the services and expectations they provide citizens. Our topics range from forecasting to predicting the likelihood that people will volunteer to be poll workers. Benjamin recently spoke at Big Data Day LA. Videos have not yet been posted, but you can see the slides from his talk Data Mining...more

  • [MINI] k-Nearest Neighbors

    Jul 24 2015

    This episode explores the k-nearest neighbors algorithm which is an unsupervised, non-parametric method that can be used for both classification and regression. The basica concept is that it leverages some distance function on your dataset to find the $k$ closests other observations of the dataset and averaging them to impute an unknown value or unlabelled datapoint.

  • Crypto

    Jul 17 2015

    How do people think rationally about small probability events? What is the optimal statistical process by which one can update their beliefs in light of new evidence? This episode of Data Skeptic explores questions like this as Kyle consults a cast of previous guests and experts to try and answer the question "What is the probability, however small, that Bigfoot is real?"

  • [MINI] MapReduce

    Jul 10 2015

    This mini-episode is a high level explanation of the basic idea behind MapReduce, which is a fundamental concept in big data. The origin of the idea comes from a Google paper titled MapReduce: Simplified Data Processing on Large Clusters. This episode makes an analogy to tabulating paper voting ballets as a means of helping to explain how and why MapReduce is an important concept.

  • Genetically Engineered Food and Trends in Herbicide Usage

    Jul 03 2015

    The Credible Hulk joins me in this episode to discuss a recent blog post he wrote about glyphosate and the data about how it's introduction changed the historical usage trends of other herbicides. Links to all the sources and references can be found in the blog post. In this discussion, we also mention the food babe and Last Thursdayism which may be worth some further reading. Kyle also mentioned the list of ingredients or chemical composition of a banana. Credible Hulk mentioned the Mommy PhD...more

  • [MINI] The Curse of Dimensionality

    Jun 26 2015

    More features are not always better! With an increasing number of features to consider, machine learning algorithms suffer from the curse of dimensionality, as they have a wider set and often sparser coverage of examples to consider. This episode explores a real life example of this as Kyle and Linhda discuss their thoughts on purchasing a home. The curse of dimensionality was defined by Richard Bellman, and applies in several slightly nuanced cases. This mini-episode discusses how it applies o...more

  • Video Game Analytics

    Jun 19 2015

    This episode discusses video game analytics with guest Anders Drachen. The way in which people get access to games and the opportunity for game designers to ask interesting questions with data has changed quite a bit in the last two decades. Anders shares his insights about the past, present, and future of game analytics. We explore not only some of the innovations and interesting ways of examining user experience in the gaming industry, but also touch on some of the exciting opportunities for i...more

  • [MINI] Anscombe's Quartet

    Jun 12 2015

    This mini-episode discusses Anscombe's Quartet, a series of four datasets which are clearly very different but share some similar statistical properties with one another. For example, each of the four plots has the same mean and variance on both axis, as well as the same correlation coefficient, and same linear regression.   The episode tries to add some context by imagining each of these datasets as data about a sports team, and why it can be important to look beyond basic summary statistics ...more

  • Proposing Annoyance Mining

    Jun 09 2015

    A recent episode of the Skeptics Guide to the Universe included a slight rant by Dr. Novella and the rouges about a shortcoming in operating systems.  This episode explores why such a (seemingly obvious) flaw might make sense from an engineering perspective, and how data science might be the solution. In this solo episode, Kyle proposes the concept of "annoyance mining" - the idea that with proper logging and enough feedback, data scientists could be provided the right dataset from which they c...more

  • Preserving History at Cyark

    Jun 05 2015

    Elizabeth Lee from CyArk joins us in this episode to share stories of the work done capturing important historical sites digitally. CyArk is a non-profit focused on using technology to preserve the world's important historic and cultural locations digitally. CyArk's founder Ben Kacyra, a pioneer in 3D capture technology, and his wife, founded CyArk after seeing the need to preserve important artifacts and locations digitally before they are lost to natural disasters, human destruction, or the pa...more

  • [MINI] A Critical Examination of a Study of Marriage by Political Affiliation

    May 29 2015

    Linhda and Kyle review a New York Times article titled How Your Hometown Affects Your Chances of Marriage. This article explores research about what correlates with the likelihood of being married by age 26 by county. Kyle and LinhDa discuss some of the fine points of this research and the process of identifying factors for consideration.

  • Detecting Cheating in Chess

    May 22 2015

    With the advent of algorithms capable of beating highly ranked chess players, the temptation to cheat has emmerged as a potential threat to the integrity of this ancient and complex game. Yet, there are aspects of computer play that are measurably different than human play. Dr. Kenneth Regan has developed a methodology for looking at a long series of modes and measuring the likelihood that the moves may have been selected by an algorithm. The full transcript of this episode is well annotated an...more

  • [MINI] z-scores

    May 15 2015

    This week's episode dicusses z-scores, also known as standard score. This score describes the distance (in standard deviations) that an observation is away from the mean of the population. A closely related top is the 68-95-99.7 rule which tells us that (approximately) 68% of a normally distributed population lies within one standard deviation of the mean, 95 within 2, and 99.7 within 3. Kyle and Linh Da discuss z-scores in the context of human height. If you'd like to calculate your own z-scor...more

  • Using Data to Help Those in Crisis

    May 08 2015

    This week Noelle Sio Saldana discusses her volunteer work at Crisis Text Line - a 24/7 service that connects anyone with crisis counselors. In the episode we discuss Noelle's career and how, as a participant in the Pivotal for Good program (a partnership with DataKind), she spent three months helping find insights in the messaging data collected by Crisis Text Line. These insights helped give visibility into a number of different aspects of Crisis Text Line's services. Listen to this episode to ...more

  • The Ghost in the MP3

    May 01 2015

    Have you ever wondered what is lost when you compress a song into an MP3? This week's guest Ryan Maguire did more than that. He worked on software to issolate the sounds that are lost when you convert a lossless digital audio recording into a compressed MP3 file. To complete his project, Ryan worked primarily in python using the pyo library as well as the Bregman Toolkit Ryan mentioned humans having a dynamic range of hearing from 20 hz to 20,000 hz, if you'd like to hear those tones, check th...more

  • Data Fest 2015

    Apr 28 2015

    This episode contains converage of the 2015 Data Fest hosted at UCLA.  Data Fest is an analysis competition that gives teams of students 48 hours to explore a new dataset and present novel findings.  This year, data from Edmunds.com was provided, and students competed in three categories: best recommendation, best use of external data, and best visualization.

  • [MINI] Cornbread and Overdispersion

    Apr 24 2015

    For our 50th episode we enduldge a bit by cooking Linhda's previously mentioned "healthy" cornbread.  This leads to a discussion of the statistical topic of overdispersion in which the variance of some distribution is larger than what one's underlying model will account for.

  • [MINI] Natural Language Processing

    Apr 17 2015

    This episode overviews some of the fundamental concepts of natural language processing including stemming, n-grams, part of speech tagging, and th bag of words approach.

  • Computer-based Personality Judgments

    Apr 10 2015

    Guest Youyou Wu discuses the work she and her collaborators did to measure the accuracy of computer based personality judgments. Using Facebook "like" data, they found that machine learning approaches could be used to estimate user's self assessment of the "big five" personality traits: openness, agreeableness, extraversion, conscientiousness, and neuroticism. Interestingly, the computer-based assessments outperformed some of the assessments of certain groups of human beings. Listen to the episo...more

  • [MINI] Markov Chain Monte Carlo

    Apr 03 2015

    This episode explores how going wine testing could teach us about using markov chain monte carlo (mcmc).

  • [MINI] Markov Chains

    Mar 20 2015

    This episode introduces the idea of a Markov Chain. A Markov Chain has a set of states describing a particular system, and a probability of moving from one state to another along every valid connected state. Markov Chains are memoryless, meaning they don't rely on a long history of previous observations. The current state of a system depends only on the previous state and the results of a random outcome. Markov Chains are a useful way method for describing non-deterministic systems. They are us...more

  • Oceanography and Data Science

    Mar 13 2015

    Nicole Goebel joins us this week to share her experiences in oceanography studying phytoplankton and other aspects of the ocean and how data plays a role in that science.   We also discuss Thinkful where Nicole and I are both mentors for the Introduction to Data Science course. Last but not least, check out Nicole's blog Data Science Girl and the videos Kyle mentioned on her Youtube channel featuring one on the diversity of phytoplankton and how that changes in time and space.

  • [MINI] Ordinary Least Squares Regression

    Mar 06 2015

    This episode explores Ordinary Least Squares or OLS - a method for finding a good fit which describes a given dataset.

  • NYC Speed Camera Analysis with Tim Schmeier

    Feb 27 2015

    New York State approved the use of automated speed cameras within a specific range of schools. Tim Schmeier did an analysis of publically available data related to these cameras as part of a project at the NYC Data Science Academy. Tim's work leverages several open data sets to ask the questions: are the speed cameras succeeding in their intended purpose of increasing public safety near schools? What he found using open data may surprise you. You can read Tim's write up titled Speed Cameras: Re...more

  • [MINI] k-means clustering

    Feb 20 2015

    The k-means clustering algorithm is an algorithm that computes a deterministic label for a given "k" number of clusters from an n-dimensional datset.  This mini-episode explores how Yoshi, our lilac crowned amazon's biological processes might be a useful way of measuring where she sits when there are no humans around.  Listen to find out how!

  • Shadow Profiles on Social Networks

    Feb 13 2015

    Emre Sarigol joins me this week to discuss his paper Online Privacy as a Collective Phenomenon. This paper studies data collected from social networks and how the sharing behaviors of individuals can unintentionally reveal private information about other people, including those that have not even joined the social network! For the specific test discussed, the researchers were able to accurately predict the sexual orientation of individuals, even when this information was withheld during the trai...more

  • [MINI] The Chi-Squared Test

    Feb 06 2015

    The χ2 (Chi-Squared) test is a methodology for hypothesis testing. When one has categorical data, in the form of frequency counts or observations (e.g. Vegetarian, Pescetarian, and Omnivore), split into two or more categories (e.g. Male, Female), a question may arrise such as "Are women more likely than men to be vegetarian?" or put more accurately, "Is any observed difference in the frequency with which women report being vegetarian differ in a statistically significant way from the frequency m...more

  • Mapping Reddit Topics with Randy Olson

    Jan 30 2015

    My quest this week is noteworthy a.i. researcher Randy Olson who joins me to share his work creating the Reddit World Map - a visualization that illuminates clusters in the reddit community based on user behavior. Randy's blog post on created the reddit world map is well complimented by a more detailed write up titled Navigating the massive world of reddit: using backbone networks to map user interests in social media. Last but not least, an interactive version of the results (which leverages G...more

  • [MINI] Partially Observable State Spaces

    Jan 23 2015

    When dealing with dynamic systems that are potentially undergoing constant change, its helpful to describe what "state" they are in.  In many applications the manner in which the state changes from one to another is not completely predictable, thus, there is uncertainty over how it transitions from state to state.  Further, in many applications, one cannot directly observe the true state, and thus we describe such situations as partially observable state spaces.  This episode explores what this ...more

  • Easily Fooling Deep Neural Networks

    Jan 16 2015

    My guest this week is Anh Nguyen, a PhD student at the University of Wyoming working in the Evolving AI lab. The episode discusses the paper Deep Neural Networks are Easily Fooled [pdf] by Anh Nguyen, Jason Yosinski, and Jeff Clune. It describes a process for creating images that a trained deep neural network will mis-classify. If you have a deep neural network that has been trained to recognize certain types of objects in images, these "fooling" images can be constructed in a way which the netw...more

  • [MINI] Data Provenance

    Jan 09 2015

    This episode introduces a high level discussion on the topic of Data Provenance, with more MINI episodes to follow to get into specific topics. Thanks to listener Sara L who wrote in to point out the Data Skeptic Podcast has focused alot about using data to be skeptical, but not necessarily being skeptical of data. Data Provenance is the concept of knowing the full origin of your dataset. Where did it come from? Who collected it? How as it collected? Does it combine independent sources or one s...more

  • Doubtful News, Geology, Investigating Paranormal Groups, and Thinking Scientifically with Sharon Hill

    Jan 03 2015

    I had the change to speak with well known Sharon Hill (@idoubtit) for the first episode of 2015. We discuss a number of interesting topics including the contributions Doubtful News makes to getting scientific and skeptical information ranked highly in search results, sink holes, why earthquakes are hard to predict, and data collection about paranormal groups via the internet.

  • [MINI] Belief in Santa

    Dec 26 2014

    In this quick holiday episode, we touch on how one would approach modeling the statistical distribution over the probability of belief in Santa Claus given age.

  • Economic Modeling and Prediction, Charitable Giving, and a Follow Up with Peter Backus

    Dec 19 2014

    Economist Peter Backus joins me in this episode to discuss a few interesting topics. You may recall Linhda and I previously discussed his paper "The Girlfriend Equation" on a recent mini-episode. We start by touching base on this fun paper and get a follow up on where Peter stands years after writing w.r.t. a successful romantic union. Additionally, we delve in to some fascinating economics topics. We touch on questions of the role models, for better or for worse, played a role in the ~2008 eco...more

  • [MINI] The Battle of the Sexes

    Dec 12 2014

    Love and Data is the continued theme in this mini-episode as we discuss the game theory example of The Battle of the Sexes. In this textbook example, a couple must strategize about how to spend their Friday night. One partner prefers football games while the other partner prefers to attend the opera. Yet, each person would rather be at their non-preferred location so long as they are still with their spouse. So where should they decide to go?

  • The Science of Online Data at Plenty of Fish with Thomas Levi

    Dec 05 2014

    Can algorithms help you find love? Many happy couples successfully brought together via online dating websites show us that data science can help you find love. I'm joined this week by Thomas Levi, Senior Data Scientist at Plenty of Fish, to discuss some of his work which helps people find one another as efficiently as possible. Matchmaking is a truly non-trivial problem, and one that's dynamically changing all the time as new users join and leave the "pool of fish". This episode explores the a...more

  • [MINI] The Girlfriend Equation

    Nov 28 2014

    Economist Peter Backus put forward "The Girlfriend Equation" while working on his PhD - a probabilistic model attempting to estimate the likelihood of him finding a girlfriend. In this mini episode we explore the soundness of his model and also share some stories about how Linhda and Kyle met.

  • The Secret and the Global Consciousness Project with Alex Boklin

    Nov 21 2014

    I'm joined this week by Alex Boklin to explore the topic of magical thinking especially in the context of Rhonda Byrne's "The Secret", and the similarities it bears to The Global Consciousness Project (GCP). The GCP puts forward the hypothesis that random number generators elicit statistically significant changes as a result of major world events.

  • [MINI] Monkeys on Typewriters

    Nov 14 2014

    What is randomness? How can we determine if some results are randomly generated or not? Why are random numbers important to us in our everyday life? These topics and more are discussed in this mini-episode on random numbers. Many readers will be vaguely familar with the idea of "X number of monkeys banging on Y number of typewriters for Z number of years" - the idea being that such a setup would produce random sequences of letters. The origin of this idea was the mathemetician Borel who was int...more

  • Mining the Social Web with Matthew Russell

    Nov 07 2014

    This week's episode explores the possibilities of extracting novel insights from the many great social web APIs available. Matthew Russell's Mining the Social Web is a fantastic exploration of the tools and methods, and we explore a few related topics. One helpful feature of the book is it's use of a Vagrant virtual machine. Using it, readers can easily reproduce the examples from the book, and there's a short video available that will walk you through setting up the Mining the Social Web virt...more

  • [MINI] Is the Internet Secure?

    Oct 31 2014

    This episode explores the basis of why we can trust encryption.  Suprisingly, a discussion of looking up a word in the dictionary (binary search) and efficiently going wine tasting (the travelling salesman problem) help introduce computational complexity as well as the P ?= NP question, which is paramount to the trustworthiness RSA encryption. With a high level foundation of computational theory, we talk about NP problems, and why prime factorization is a difficult problem, thus making it a gre...more

  • Practicing and Communicating Data Science with Jeff Stanton

    Oct 24 2014

    Jeff Stanton joins me in this episode to discuss his book An Introduction to Data Science, and some of the unique challenges and issues faced by someone doing applied data science. A challenge to any data scientist is making sure they have a good input data set and apply any necessary data munging steps before their analysis. We cover some good advise for how to approach such problems.

  • [MINI] The T-Test

    Oct 17 2014

    The t-test is this week's mini-episode topic. The t-test is a statistical testing procedure used to determine if the mean of two datasets differs by a statistically significant amount. We discuss how a wine manufacturer might apply a t-test to determine if the sweetness, acidity, or some other property of two separate grape vines might differ in a statistically meaningful way.

  • Data Myths with Karl Mamer

    Oct 10 2014

    This week I'm joined by Karl Mamer to discuss the data behind three well known urban legends. Did a large blackout in New York and surrounding areas result in a baby boom nine months later? Do subliminal messages affect our behavior? Is placing beer alongside diapers a recipe for generating more revenue than these products in separate locations? Listen as Karl and I explore these claims.

  • Contest Announcement

    Oct 08 2014

    The Data Skeptic Podcast is launching a contest- not one of chance, but one of skill. Listeners are encouraged to put their data science skills to good use, or if all else fails, guess! The contest works as follows. Below is some data about the cumulative number of downloads the podcast has achieved on a few given dates. Your job is to predict the date and time at which the podcast will recieve download number 27,182. Why this arbitrary number? It's as good as any other arbitrary number! Use w...more

  • [MINI] Selection Bias

    Oct 03 2014

    A discussion about conducting US presidential election polls helps frame a converation about selection bias.

  • [MINI] Confidence Intervals

    Sep 26 2014

    Commute times and BBQ invites help frame a discussion about the statistical concept of confidence intervals.

  • [MINI] Value of Information

    Sep 19 2014

    A discussion about getting ready in the morning, negotiating a used car purchase, and selecting the best AirBnB place to stay at help frame a conversation about the decision theoretic principal known as the Value of Information equation.

  • Game Science Dice with Louis Zocchi

    Sep 17 2014

    In this bonus episode, guest Louis Zocchi discusses his background in the gaming industry, specifically, how he became a manufacturer of dice designed to produce statistically uniform outcomes. During the show Louis mentioned a two part video listeners might enjoy: part 1 and part 2 can both be found on youtube. Kyle mentioned a robot capable of unnoticably cheating at Rock Paper Scissors / Ro Sham Bo. More details can be found here. Louis mentioned dice collector Kevin Cook whose website is Dic...more

  • Data Science at ZestFinance with Marick Sinay

    Sep 12 2014

    Marick Sinay from ZestFianance is our guest this weel.  This episode explores how data science techniques are applied in the financial world, specifically in assessing credit worthiness.  

  • [MINI] Decision Tree Learning

    Sep 05 2014

    Linhda and Kyle talk about Decision Tree Learning in this miniepisode.  Decision Tree Learning is the algorithmic process of trying to generate an optimal decision tree to properly classify or forecast some future unlabeled element based by following each step in the tree.

  • Jackson Pollock Authentication Analysis with Kate Jones-Smith

    Aug 29 2014

    Our guest this week is Hamilton physics professor Kate Jones-Smith who joins us to discuss the evidence for the claim that drip paintings of Jackson Pollock contain fractal patterns. This hypothesis originates in a paper by Taylor, Micolich, and Jonas titled Fractal analysis of Pollock's drip paintings which appeared in Nature. Kate and co-author Harsh Mathur wrote a paper titled Revisiting Pollock's Drip Paintings which also appeared in Nature. A full text PDF can be found here, but lacks the h...more

  • [MINI] Noise!!

    Aug 22 2014

    Our topic for this week is "noise" as in signal vs. noise.  This is not a signal processing discussions, but rather a brief introduction to how the work noise is used to describe how much information in a dataset is useless (as opposed to useful). Also, Kyle announces having recently had the pleasure of appearing as a guest on The Conspiracy Skeptic Podcast to discussion The Bible Code.  Please check out this other fine program for this and it's many other great episodes.

  • Guerilla Skepticism on Wikipedia with Susan Gerbic

    Aug 15 2014

    Our guest this week is Susan Gerbic. Susan is a skeptical activist involved in many activities, the one we focus on most in this episode is Guerrilla Skepticism on Wikipedia, an organization working to improve the content and citations of Wikipedia. During the episode, Kyle recommended Susan's talk a The Amazing Meeting 9 which can be found here. Some noteworthy topics mentioned during the podcast were Neil deGrasse Tyson's endorsement of the Penny for NASA project. As well as the Web of Trust a...more

  • [MINI] Ant Colony Optimization

    Aug 08 2014

    In this week's mini episode, Linhda and Kyle discuss Ant Colony Optimization - a numerical / stochastic optimization technique which models its search after the process ants employ in using random walks to find a goal (food) and then leaving a pheremone trail in their walk back to the nest.  We even find some way of relating the city of San Francisco and running a restaurant into the discussion.

  • Data in Healthcare IT with Shahid Shah

    Aug 01 2014

    Our guest this week is Shahid Shah. Shahid is CEO at Netspective, and writes three blogs: Health Care Guy, Shahid Shah, and HitSphere - the Healthcare IT Supersite. During the program, Kyle recommended a talk from the 2014 MIT Sloan CIO Symposium entitled Transforming "Digital Silos" to "Digital Care Enterprise" which was hosted by our guest Shahid Shah. In addition to his work in Healthcare IT, he also the chairperson for Open Source Electronic Health Record Alliance, an non-profit organiza...more

  • [MINI] Cross Validation

    Jul 25 2014

    This miniepisode discusses the technique called Cross Validation - a process by which one randomly divides up a dataset into numerous small partitions. Next, (typically) one is held out, and the rest are used to train some model. The hold out set can then be used to validate how good the model does at describing/predicting new data.

  • Streetlight Outage and Crime Rate Analysis with Zach Seeskin

    Jul 18 2014

    This episode features a discussion with statistics PhD student Zach Seeskin about a project he was involved in as part of the Eric and Wendy Schmidt Data Science for Social Good Summer Fellowship.  The project involved exploring the relationship (if any) between streetlight outages and crime in the City of Chicago.  We discuss how the data was accessed via the City of Chicago data portal, how the analysis was done, and what correlations were discovered in the data.  Won't you listen and hear wha...more

  • [MINI] Experimental Design

    Jul 11 2014

    This episode loosely explores the topic of Experimental Design including hypothesis testing, the importance of statistical tests, and an everyday and business example.

  • The Right (big data) Tool for the Job with Jay Shankar

    Jul 07 2014

    In this week's episode, we discuss applied solutions to big data problem with big data engineer Jay Shankar.  The episode explores approaches and design philosophy to solving real world big data business problems, and the exploration of the wide array of tools available.  

  • [MINI] Bayesian Updating

    Jun 27 2014

    In this minisode, we discuss Bayesian Updating - the process by which one can calculate the most likely hypothesis might be true given one's older / prior belief and all new evidence.

  • Personalized Medicine with Niki Athanasiadou

    Jun 20 2014

    In the second full length episode of the podcast, we discuss the current state of personalized medicine and the advancements in genetics that have made it possible.

  • [MINI] p-values

    Jun 13 2014

    In this mini, we discuss p-values and their use in hypothesis testing, in the context of an hypothetical experiment on plant flowering, and end with a reference to the Particle Fever documentary and how statistical significance played a role.

  • Advertising Attribution with Nathan Janos

    Jun 06 2014

    A conversation with Convertro's Nathan Janos about methodologies used to help advertisers understand the affect each of their marketing efforts (print, SEM, display, skywriting, etc.) contributes to their overall return.

  • [MINI] type i / type ii errors

    May 30 2014

    In this first mini-episode of the Data Skeptic Podcast, we define and discuss type i and type ii errors (a.k.a. false positives and false negatives).

  • Introduction

    May 23 2014

    The Data Skeptic Podcast features conversations with topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches. This first episode is a short discussion about what this podcast is all about.