Data Skeptic

Data Skeptic
Applying critical thinking to Data Science

Human Computer Interaction and Online Privacy
Jul 27 • 32 min
from the joins us to discuss his research into the interaction between privacy and human-computer interaction.
Authorship Attribution of Lennon McCartney Songs
Jul 20 • 33 min
joins us to discuss the paper .
GANs Can Be Interpretable
Jul 10 • 26 min
joins us to discuss the paper . During the interview, Kyle makes reference to this amazing video and it’s accompanying codebase found . Erik mentions the which is a rapid way to try these ideas out for yourself.
Sentiment Preserving Fake Reviews
Jul 6 • 28 min
joins us to discuss .
Interpretability Practitioners
Jun 26 • 32 min
joins us to discuss the paper .
Facial Recognition Auditing
Jun 19 • 47 min
joins us to discuss her recent publication .
Robust Fit to Nature
Jun 12 • 38 min
joins us this week to discuss the paper .
Black Boxes Are Not Required
Jun 5 • 32 min
Deep neural networks are undeniably effective. They rely on such a high number of parameters, that they are appropriately described as “black boxes”. While black boxes lack desirably properties like interpretability and explainability, in some…
Robustness to Unforeseen Adversarial Attacks
May 30 • 21 min
joins us to discuss the paper .
Estimating the Size of Language Acquisition
May 22 • 25 min
joins us to discuss the paper
Interpretable AI in Healthcare
May 15 • 35 min
joins us to discuss the recent paper .
Understanding Neural Networks
May 8 • 34 min
What does it mean to understand a neural network? That’s the question posted on . Kyle speaks with about this and several other big questions.
Self-Explaining AI
May 2 • 32 min
joins us to discuss self-explaining AI. What could be better than an interpretable model? How about a model wich explains itself in a conversational way, engaging in a back and forth with the user. We discuss the paper which presents…
Plastic Bag Bans
Apr 24 • 34 min
joins us to discuss her work studying the impact of plastic bag bans as published in from the Journal of Environmental Economics and Management. How does one measure the impact of these bans? Are they achieving their intended goals?…
Self Driving Cars and Pedestrians
Apr 18 • 30 min
We are joined by to discuss .
Computer Vision is Not Perfect
Apr 10 • 26 min
Computer Vision is not Perfect joins us help answer the question . Kyle talks to Julia about her hands-on work fooling neural networks. Julia runs which publishes works such as . You can find her on Twitter
Uncertainty Representations
Apr 4 • 39 min
Jessica Hullman joins us to share her expertise on data visualization and communication of data in the media. We discuss Jessica’s work on visualizing uncertainty, interviewing visualization designers on why they don’t visualize uncertainty, and…
AlphaGo, COVID-19 Contact Tracing and New Data Set
Mar 28 • 33 min
Announcing Journal Club I am pleased to announce Data Skeptic is launching a new spin-off show called “Journal Club” with similar themes but a very different format to the Data Skeptic everyone is used to. In Journal Club, we will have a regular panel…
Visualizing Uncertainty
Mar 20 • 32 min
Interpretability Tooling
Mar 13 • 42 min
joins us to talk about the methodologies and tools used to assist with model interpretability.
Shapley Values
Mar 6 • 20 min
Kyle and Linhda discuss how Shapley Values might be a good tool for determining what makes the cut for a home renovation.
Anchors as Explanations
Feb 28 • 37 min
We welcome back to discuss research he has done since our original discussion on . In particular, we ask the question and discuss how provide high precision model-agnostic explanations. Please take our .
Mathematical Models of Ecological Systems
Feb 21 • 36 min
Adversarial Explanations
Feb 14 • 36 min
Walt Woods joins us to discuss his paper with co-authors Jack Chen and Christof Teuscher.
Feb 7 • 38 min
joins us to discuss - a new kind of vision dataset. In contrast to ImageNet, ObjectNet seeks to provide images that are more representative of the types of images an autonomous machine is likely to encounter in the real world….
Visualization and Interpretability
Jan 31 • 35 min
joins us to discuss how data visualization can be used to help make machine learning more interpretable and explainable. Find out more about Enrico at . More from Enrico with co-host Moritz Stefaner on the podcast!
Interpretable One Shot Learning
Jan 26 • 30 min
We welcome back to Data Skeptic to discuss the paper .
Fooling Computer Vision
Jan 22 • 25 min
joins us to talk about a project in which specially designed printed images can fool a computer vision system, preventing it from identifying a person. Their attack targets the popular YOLO2 pre-trained image recognition model, and thus, is…
Algorithmic Fairness
Jan 13 • 42 min
This episode includes an interview with Aaron Roth author of .
Jan 7 • 32 min
Interpretability Machine learning has shown a rapid expansion into every sector and industry. With increasing reliance on models and increasing stakes for the decisions of models, questions of how models actually work are becoming increasingly…
NLP in 2019
Dec 31, 2019 • 38 min
A year in recap.
The Limits of NLP
Dec 23, 2019 • 29 min
We are joined by Colin Raffel to discuss the paper “Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer”.
Jumpstart Your ML Project
Dec 15, 2019 • 20 min
Seth Juarez joins us to discuss the toolbox of options available to a data scientist to jumpstart or extend their machine learning efforts.
Serverless NLP Model Training
Dec 9, 2019 • 29 min
Alex Reeves joins us to discuss some of the challenges around building a serverless, scalable, generic machine learning pipeline. The is a technical deep dive on architecting solutions and a discussion of some of the design choices made.
Team Data Science Process
Dec 3, 2019 • 41 min
Buck Woody joins Kyle to share experiences from the field and the application of the - a popular six-phase workflow for doing data science.
Ancient Text Restoration
Dec 1, 2019 • 41 min
Thea Sommerschield joins us this week to discuss the development of Pythia - a machine learning model trained to assist in the reconstruction of ancient language text.
ML Ops
Nov 27, 2019 • 36 min
Kyle met up with Damian Brady at MS Ignite 2019 to discuss machine learning operations.
Annotator Bias
Nov 23, 2019 • 25 min
The modern deep learning approaches to natural language processing are voracious in their demands for large corpora to train on. Folk wisdom estimates used to be around 100k documents were required for effective training. The availability…
NLP for Developers
Nov 19, 2019 • 29 min
While at MS Build 2019, Kyle sat down with Lance Olson from the Applied AI team about how tools like cognitive services and cognitive search enable non-data scientists to access relatively advanced NLP tools out of box, and how more advanced data…
Indigenous American Language Research
Nov 13, 2019 • 22 min
joins us to discuss natural language processing for low and under-resourced languages. We discuss current work in this area and the which aggregates research on NLP for native and indigenous languages of the American continent.
Talking to GPT-2
Oct 31, 2019 • 29 min
GPT-2 is yet another in a succession of models like ELMo and BERT which adopt a similar deep learning architecture and train an unsupervised model on a massive text corpus. As we have been covering recently, these approaches are showing tremendous…
Reproducing Deep Learning Models
Oct 22, 2019 • 22 min
attempted to reproduce an earthquake-predicting deep learning model. His results exposed some issues with the model. Kyle and Rajiv discuss the original paper and Rajiv’s analysis.
What BERT is Not
Oct 14, 2019 • 27 min
joins us to discuss her work in computational linguistics, specifically in exploring some of the ways in which the popular natural language processing approach BERT has limitations.
Oct 8, 2019 • 24 min
Omer Levy joins us to discuss “SpanBERT: Improving Pre-training by Representing and Predicting Spans”.
BERT is Shallow
Sep 23, 2019 • 20 min
Tim Niven joins us this week to discuss his work exploring the limits of what BERT can do on certain natural language tasks such as adversarial attacks, compositional learning, and systematic learning.
BERT is Magic
Sep 16, 2019 • 18 min
Kyle pontificates on how impressed he is with BERT.
Applied Data Science in Industry
Sep 6, 2019 • 21 min
Kyle sits down with to inquire about her experiences helping companies deploy data science solutions in a variety of different settings.
Building the howto100m Video Corpus
Aug 19, 2019 • 22 min
Video annotation is an expensive and time-consuming process. As a consequence, the available video datasets are useful but small. The availability of machine transcribed explainer videos offers a unique opportunity to rapidly develop a useful, if…
Jul 29, 2019 • 13 min
Kyle provides a non-technical overview of why Bidirectional Encoder Representations from Transformers (BERT) is a powerful tool for natural language processing projects.
Jul 22, 2019 • 20 min
Kyle interviews Prasanth Pulavarthi about the Onnx format for deep neural networks.
Catastrophic Forgetting
Jul 15, 2019 • 21 min
Kyle and Linhda discuss some high level theory of mind and overview the concept machine learning concept of catastrophic forgetting.
Transfer Learning
Jul 8, 2019 • 29 min
Sebastian Ruder is a research scientist at DeepMind. In this episode, he joins us to discuss the state of the art in transfer learning and his contributions to it.
Facebook Bargaining Bots Invented a Language
Jun 21, 2019 • 23 min
In 2017, Facebook published a paper called . In this research, the reinforcement learning agents developed a mechanism of communication (which could be called a language) that made them able to optimize their scores in the negotiation game. Many…
Under Resourced Languages
Jun 15, 2019 • 16 min
Priyanka Biswas joins us in this episode to discuss natural language processing for languages that do not have as many resources as those that are more commonly studied such as English. Successful NLP projects benefit from the availability of…
Named Entity Recognition
Jun 8, 2019 • 17 min
Kyle and Linh Da discuss the class of approaches called “Named Entity Recognition” or NER. NER algorithms take any string as input and return a list of “entities” - specific facts and agents in the text along with a classification of the type…
The Death of a Language
Jun 1, 2019 • 20 min
USC students from the student organization have created a variety of novel projects under the mission statement of “artificial intelligence for social good”. In this episode, Kyle interviews Zane and Leena about the .
Neural Turing Machines
May 25, 2019 • 25 min
Kyle and Linh Da discuss the concepts behind the neural Turing machine.
Data Infrastructure in the Cloud
May 18, 2019 • 30 min
Kyle chats with about hyperscale, data at the edge, and a variety of other trends in data engineering in the cloud.
NCAA Predictions on Spark
May 11, 2019 • 23 min
In this episode, Kyle interviews Laura Edell at MS Build 2019. The conversation covers a number of topics, notably her NCAA Final 4 prediction model.
The Transformer
May 3, 2019 • 15 min
Kyle and Linhda discuss attention and the transformer - an encoder/decoder architecture that extends the basic ideas of vector embeddings like word2vec into a more contextual use case.
Mapping Dialects with Twitter Data
Apr 26, 2019 • 25 min
When users on Twitter post with geographic tags, it creates the opportunity for a variety of interesting questions to be posed having to do with language, dialects, and location. In this episode, Kyle interviews Bruno Gonçalves about his…
Sentiment Analysis
Apr 19, 2019 • 27 min
This is an interview with Ellen Loeshelle, Director of Product Management at Clarabridge. We primarily discuss sentiment analysis.
Attention Primer
Apr 12, 2019 • 14 min
A gentle introduction to the very high-level idea of “attention” in machine learning, as it will play a major role in some upcoming episodes over the next few weeks.
Cross-lingual Short-text Matching
Apr 5, 2019 • 24 min
Modern messaging technology has facilitated a trend towards highly compact, short messages send by users who can presume a great amount of context held between the communicating parties. The rules of grammar may be discarded and often visible…
Mar 29, 2019 • 23 min
ELMo (Embeddings from Language Models) introduced the idea of deep contextualized word representations. It extends previous ideas like word2vec and GloVe. The ELMo model is a neural network able to map natural language into a vector space. This vector…
Mar 23, 2019 • 42 min
Bilingual evaluation understudy (or BLEU) is a metric for evaluating the quality of machine translation using human translation as examples of acceptable quality results. This metric has become a widely used standard in the research literature. But is…
Simultaneous Translation at Baidu
Mar 15, 2019 • 24 min
While at NeurIPS 2018, Kyle chatted with Liang Huang about his work with Baidu research on simultaneous translation, which was demoed at the conference.
Human vs Machine Transcription
Mar 8, 2019 • 32 min
Machine transcription (the process of translating audio recordings of language to text) has come a long way in recent years. But how do the errors made during machine transcription compare to the errors made by a human transcriber? Find out in this…
Mar 1, 2019 • 21 min
A sequence to sequence (or seq2seq) model is neural architecture used for translation (and other tasks) which consists of an encoder and a decoder. The encoder/decoder architecture has obvious promise for machine translation, and has been successfully…
Text Mining in R
Feb 22, 2019 • 20 min
Kyle interviews about her path into data science, her book , and some of the ways in which she’s used natural language processing in projects both personal and professional. Related Links
Recurrent Relational Networks
Feb 15, 2019 • 19 min
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…
Text World and Word Embedding Lower Bounds
Feb 8, 2019 • 39 min
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…
Feb 1, 2019 • 31 min
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…
Authorship Attribution
Jan 25, 2019 • 50 min
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…
Very Large Corpora and Zipf’s Law
Jan 18, 2019 • 24 min
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…
Semantic search at Github
Jan 11, 2019 • 34 min
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…
Let’s Talk About Natural Language Processing
Jan 4, 2019 • 36 min
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…
Data Science Hiring Processes
Dec 28, 2018 • 33 min
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 • 21 min
Epicac by Kurt Vonnegut.
Drug Discovery with Machine Learning
Dec 21, 2018 • 28 min
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…
Sign Language Recognition
Dec 14, 2018 • 19 min
At the NeurIPS 2018 conference, premiered a training game which helps players learn . This episode brings the first of many interviews conducted at NeurIPS 2018. In this episode, Kyle interviews Chief Data Scientist about…
Data Ethics
Dec 7, 2018 • 19 min
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 . Guidelines like this are rather informal…
Escaping the Rabbit Hole
Nov 30, 2018 • 33 min
Kyle interviews Mick West, author of 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 . The discussion explores…
Theorem Provers
Nov 23, 2018 • 18 min
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…
Automated Fact Checking
Nov 16, 2018 • 31 min
Fake news can be responded to with fact-checking. However, it’s easier to create fake news than the fact check it. is the UK’s independent fact-checking organization. In this episode, Kyle interviews , head of automated fact-checking at…
Single Source of Truth
Nov 9, 2018 • 29 min
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…
Detecting Fast Radio Bursts with Deep Learning
Nov 2, 2018 • 44 min
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…
Being Bayesian
Oct 26, 2018 • 24 min
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…
Modeling Fake News
Oct 19, 2018 • 33 min
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…
The Louvain Method for Community Detection
Oct 12, 2018 • 26 min
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…
Cultural Cognition of Scientific Consensus
Oct 5, 2018 • 31 min
In this episode, our guest is 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….
False Discovery Rates
Sep 28, 2018 • 25 min
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….
Deep Fakes
Sep 21, 2018 • 30 min
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…
Fake News Midterm
Sep 14, 2018 • 19 min
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 7, 2018 • 18 min
Two weeks ago we discussed 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,…
The Knowledge Illusion
Aug 31, 2018 • 40 min
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…
Click Through Rates
Aug 24, 2018 • 31 min
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…
Algorithmic Detection of Fake News
Aug 17, 2018 • 46 min
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…
Ant Intelligence
Aug 10, 2018 • 28 min
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…
Human Detection of Fake News
Aug 3, 2018 • 28 min
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”,…
Spam Filtering with Naive Bayes
Jul 27, 2018 • 19 min
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…
The Spread of Fake News
Jul 20, 2018 • 45 min
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…
Fake News
Jul 13, 2018 • 38 min
This episode kicks off our new theme of “Fake News” with guests and . 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,…
Dev Ops for Data Science
Jul 11, 2018 • 38 min
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…
First Order Logic
Jul 6, 2018 • 16 min
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…
Blind Spots in Reinforcement Learning
Jun 29, 2018 • 27 min
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…
Defending Against Adversarial Attacks
Jun 22, 2018 • 31 min
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…
Transfer Learning
Jun 15, 2018 • 18 min
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…
Medical Imaging Training Techniques
Jun 8, 2018 • 25 min
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…
Kalman Filters
Jun 1, 2018 • 21 min
Thanks to our sponsor 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…
AI in Industry
May 25, 2018 • 43 min
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…
AI in Games
May 18, 2018 • 25 min
Today’s interview is with the authors of the textbook Artificial Intelligence and Games.
Game Theory
May 11, 2018 • 24 min
Thanks to our sponsor . 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 ,
The Experimental Design of Paranormal Claims
May 4, 2018 • 27 min
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…
Winograd Schema Challenge
Apr 27, 2018 • 36 min
Our guest this week, Hector Levesque, joins us to discuss an alternative way to measure a machine’s intelligence, called . The challenge was proposed as a possible alternative to the during the 2011 AAAI Spring Symposium. The…
The Imitation Game
Apr 20, 2018 • 60 min
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…
Eugene Goostman
Apr 13, 2018 • 17 min
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 6, 2018 • 23 min
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…
The Loebner Prize
Mar 30, 2018 • 33 min
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…
Mar 23, 2018 • 27 min
In this episode, Kyle chats with Vince from and Heather Shapiro who works on the . We solicit their advice on building a good chatbot both creatively and technically. Our sponsor today is .
The Master Algorithm
Mar 16, 2018 • 46 min
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…
The No Free Lunch Theorems
Mar 9, 2018 • 27 min
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…
ML at Sloan Kettering Cancer Center
Mar 2, 2018 • 38 min
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…
Optimal Decision Making with POMDPs
Feb 23, 2018 • 18 min
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…
AI Decision-Making
Feb 16, 2018 • 42 min
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…
[MINI] Reinforcement Learning
Feb 9, 2018 • 23 min
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…
Evolutionary Computation
Feb 2, 2018 • 24 min
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…
[MINI] Markov Decision Processes
Jan 26, 2018 • 20 min
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 ,…
Neuroscience Frontiers
Jan 19, 2018 • 29 min
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…
Neuroimaging and Big Data
Jan 12, 2018 • 26 min
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…
The Agent Model of Artificial Intelligence
Jan 5, 2018 • 17 min
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…
Artificial Intelligence, a Podcast Approach
Dec 29, 2017 • 33 min
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 • 12 min
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…
Complexity and Cryptography
Dec 15, 2017 • 35 min
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…
Mercedes Benz Machine Learning Research
Dec 14, 2017 • 27 min
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 8, 2017 • 20 min
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…
Quantum Computing
Dec 1, 2017 • 47 min
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…
Azure Databricks
Nov 28, 2017 • 28 min
I sat down with , CEO and found of Databricks, and , 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…
[MINI] Exponential Time Algorithms
Nov 24, 2017 • 15 min
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…
P vs NP
Nov 17, 2017 • 38 min
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…
[MINI] Sudoku \in NP
Nov 10, 2017 • 18 min
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…
The Computational Complexity of Machine Learning
Nov 3, 2017 • 47 min
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…
[MINI] Turing Machines
Oct 27, 2017 • 13 min
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…
The Complexity of Learning Neural Networks
Oct 20, 2017 • 38 min
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…
[MINI] Big Oh Analysis
Oct 13, 2017 • 18 min
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…
Data science tools and other announcements from Ignite
Oct 6, 2017 • 31 min
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…
Generative AI for Content Creation
Sep 29, 2017 • 34 min
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…
[MINI] One Shot Learning
Sep 22, 2017 • 17 min
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…
Recommender Systems Live from FARCON 2017
Sep 15, 2017 • 46 min
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….
[MINI] Long Short Term Memory
Sep 8, 2017 • 15 min
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. A LSTM unit remembers values for either long or short time…
Zillow Zestimate
Sep 1, 2017 • 37 min
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 • 32 min
Our guest Pranav Rajpurkar and his coauthored recently published , 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 • 17 min
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…
Project Common Voice
Aug 11, 2017 • 31 min
Thanks to our sponsor . 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 . In June 2017, Mozilla…
[MINI] Bayesian Belief Networks
Aug 4, 2017 • 17 min
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…
Jul 28, 2017 • 26 min
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 • 14 min
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…
Estimating Sheep Pain with Facial Recognition
Jul 14, 2017 • 27 min
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….
Jul 7, 2017 • 33 min
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…
[MINI] The Vanishing Gradient
Jun 30, 2017 • 15 min
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…
Doctor AI
Jun 23, 2017 • 41 min
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 shares his thoughts. Edward presents his team’s efforts in developing a temporal model that can…
[MINI] Activation Functions
Jun 16, 2017 • 14 min
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…
MS Build 2017
Jun 9, 2017 • 27 min
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…
[MINI] Max-pooling
Jun 2, 2017 • 12 min
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…
Unsupervised Depth Perception
May 26, 2017 • 23 min
This episode is an interview with . In the recent paper “”, 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…
[MINI] Convolutional Neural Networks
May 19, 2017 • 14 min
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…
Multi-Agent Diverse Generative Adversarial Networks
May 12, 2017 • 29 min
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…
[MINI] Generative Adversarial Networks
May 5, 2017 • 9 min
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…
Opinion Polls for Presidential Elections
Apr 28, 2017 • 52 min
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…
Apr 21, 2017 • 26 min
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…
Apr 14, 2017 • 11 min
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…
[MINI] Backpropagation
Apr 7, 2017 • 15 min
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….
Data Science at Patreon
Mar 31, 2017 • 32 min
In this week’s episode of Data Skeptic, host Kyle Polich talks with guest Maura Church, Patreon’s data science manager. is a fast-growing crowdfunding platform that allows artists and creators of all kinds build their own subscription…
[MINI] Feed Forward Neural Networks
Mar 24, 2017 • 15 min
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…
Reinventing Sponsored Search Auctions
Mar 17, 2017 • 41 min
In this Data Skeptic episode, Kyle is joined by guest 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…
[MINI] The Perceptron
Mar 10, 2017 • 14 min
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…
The Data Refuge Project
Mar 3, 2017 • 24 min
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…
[MINI] Automated Feature Engineering
Feb 24, 2017 • 16 min
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…
Big Data Tools and Trends
Feb 17, 2017 • 30 min
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 • 14 min
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…
Data Provenance and Reproducibility with Pachyderm
Feb 3, 2017 • 40 min
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…
[MINI] Logistic Regression on Audio Data
Jan 27, 2017 • 20 min
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…
Studying Competition and Gender Through Chess
Jan 20, 2017 • 34 min
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…
[MINI] Dropout
Jan 13, 2017 • 15 min
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…
The Police Data and the Data Driven Justice Initiatives
Jan 6, 2017 • 49 min
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 was organized to use open data to increase transparency…
The Library Problem
Dec 30, 2016 • 35 min
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 • 39 min
Today’s episode is a reading of Isaac Asimov’s . 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 • 16 min
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…
MS Connect Conference
Dec 9, 2016 • 42 min
Cloud services are now ubiquitous in data science and more broadly in technology as well. This week, I speak to , , and about various aspects of data at scale. We discuss the embedding of R into SQLServer, SQLServer on linux, open source, and a few…
Causal Impact
Dec 2, 2016 • 34 min
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 about his research into the impact releases have on app and we also chat with about a project she helped us build to…
[MINI] The Bootstrap
Nov 25, 2016 • 10 min
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…
[MINI] Gini Coefficients
Nov 18, 2016 • 15 min
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…
Unstructured Data for Finance
Nov 11, 2016 • 33 min
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, shares her thoughts on the potential…
[MINI] AdaBoost
Nov 4, 2016 • 10 min
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)…
Stealing Models from the Cloud
Oct 28, 2016 • 37 min
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? …
[MINI] Calculating Feature Importance
Oct 21, 2016 • 13 min
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…
NYC Bike Share Rebalancing
Oct 14, 2016 • 29 min
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 .
[MINI] Random Forest
Oct 7, 2016 • 12 min
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 • 21 min
joins us this week to discuss the ‘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 . You can find some…
[MINI] F1 Score
Sep 23, 2016 • 9 min
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 • 35 min
Urban congestion effects every person living in a city of any reasonable size. 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…
[MINI] Heteroskedasticity
Sep 9, 2016 • 8 min
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…
Sep 2, 2016 • 34 min
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. is a python library making analysis of music accessible and fun. It supports…
[MINI] Paxos
Aug 26, 2016 • 14 min
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 • 35 min
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…
Aug 12, 2016 • 12 min
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…
Machine Learning on Images with Noisy Human-centric Labels
Aug 5, 2016 • 23 min
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…
[MINI] Survival Analysis
Jul 29, 2016 • 14 min
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,…
Predictive Models on Random Data
Jul 22, 2016 • 36 min
This week is an insightful discussion with 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…
[MINI] Receiver Operating Characteristic (ROC) Curve
Jul 15, 2016 • 11 min
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…
Multiple Comparisons and Conversion Optimization
Jul 8, 2016 • 30 min
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…
[MINI] Leakage
Jul 1, 2016 • 12 min
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…
Predictive Policing
Jun 24, 2016 • 36 min
Kristian Lum () joins me this week to discuss her work at on . We also discuss , a technique for inferring statistical information about a population from separate sources of observation. If you enjoy this discussion, check out the panel which…
[MINI] The CAP Theorem
Jun 17, 2016 • 10 min
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…
Detecting Terrorists with Facial Recognition?
Jun 10, 2016 • 33 min
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 3, 2016 • 10 min
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 • 42 min
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…
[MINI] Stationarity and Differencing
May 20, 2016 • 13 min
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….
May 13, 2016 • 23 min
I’m joined by Wes McKinney () and Hadley Wickham () on this episode to discuss their joint project . Feather is a file format for storing data frames along with some metadata, to help with interoperability between languages. At the time of recording,…
[MINI] Bargaining
May 6, 2016 • 15 min
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…
Apr 29, 2016 • 29 min
is a project from , 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…
[MINI] Auto-correlative functions and correlograms
Apr 22, 2016 • 14 min
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…
Early Identification of Violent Criminal Gang Members
Apr 15, 2016 • 27 min
This week I spoke with and () about their recent paper (also available on). 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…
[MINI] Fractional Factorial Design
Apr 8, 2016 • 11 min
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 1, 2016 • 25 min
Cheng-tao 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…
Mar 25, 2016 • 41 min
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…
[MINI] The Elbow Method
Mar 18, 2016 • 15 min
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…
Too Good to be True
Mar 11, 2016 • 35 min
Today on Data Skeptic, joins us to discuss his recent paper . 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…
[MINI] R-squared
Mar 4, 2016 • 13 min
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…
Models of Mental Simulation
Feb 26, 2016 • 39 min
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…
[MINI] Multiple Regression
Feb 19, 2016 • 18 min
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…
Scientific Studies of People’s Relationship to Music
Feb 12, 2016 • 42 min
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…
[MINI] k-d trees
Feb 5, 2016 • 14 min
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…
Auditing Algorithms
Jan 29, 2016 • 42 min
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…
[MINI] The Bonferroni Correction
Jan 22, 2016 • 14 min
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…
Detecting Pseudo-profound BS
Jan 15, 2016 • 37 min
A recent paper in the journal of Judgment and Decision Making titled 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…
[MINI] Gradient Descent
Jan 8, 2016 • 14 min
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 1, 2016 • 15 min
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 • 14 min
Today’s episode is a reading of Isaac Asimov’s . I can’t think of a story that’s more appropriate for Data Skeptic.
Wikipedia Revision Scoring as a Service
Dec 18, 2015 • 42 min
In this interview with of the Wikimedia Foundation, we discuss his research and career related to the study of Wikipedia. In his paper , he highlights a trend in the declining rate of active editors on Wikipedia which began in 2007. I asked Aaron…
[MINI] Term Frequency - Inverse Document Frequency
Dec 11, 2015 • 10 min
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 4, 2015 • 41 min
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…
[MINI] The Accuracy Paradox
Nov 27, 2015 • 17 min
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….
Neuroscience from a Data Scientist’s Perspective
Nov 20, 2015 • 40 min
… 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 earned a PhD in Integrative Neuroscience from the Department of Psychology at the University of…
[MINI] Bias Variance Tradeoff
Nov 13, 2015 • 13 min
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…
Big Data Doesn’t Exist
Nov 6, 2015 • 32 min
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…
[MINI] Covariance and Correlation
Oct 30, 2015 • 14 min
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…
Bayesian A/B Testing
Oct 23, 2015 • 30 min
Today’s guest is . 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 . He’s the founder of which…
[MINI] The Central Limit Theorem
Oct 16, 2015 • 13 min
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…
Accessible Technology
Oct 9, 2015 • 38 min
Today’s guest is Chris Hofstader , 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…
[MINI] Multi-armed Bandit Problems
Oct 2, 2015 • 12 min
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…
Shakespeare, Abiogenesis, and Exoplanets
Sep 25, 2015 • 58 min
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…
[MINI] Sample Sizes
Sep 18, 2015 • 13 min
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…
The Model Complexity Myth
Sep 11, 2015 • 30 min
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…
[MINI] Distance Measures
Sep 4, 2015 • 12 min
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…
Aug 28, 2015 • 53 min
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….
[MINI] Structured and Unstructured Data
Aug 21, 2015 • 13 min
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 • 24 min
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…
[MINI] PageRank
Aug 7, 2015 • 8 min
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 by Sergey Brin and Larry Page. While this algorithm clearly impacted web searching, it…
Data Science at Work in LA County
Jul 29, 2015 • 41 min
with Benjamin Uminsky
[MINI] k-Nearest Neighbors
Jul 24, 2015 • 8 min
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…
Jul 17, 2015 • 84 min
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…
[MINI] MapReduce
Jul 10, 2015 • 12 min
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 . This episode makes an analogy to tabulating paper voting…
Genetically Engineered Food and Trends in Herbicide Usage
Jul 3, 2015 • 34 min
The joins me in this episode to discuss a recent 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…
[MINI] The Curse of Dimensionality
Jun 26, 2015 • 10 min
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…
Video Game Analytics
Jun 19, 2015 • 31 min
with Anders Drachen
[MINI] Anscombe’s Quartet
Jun 12, 2015 • 9 min
This mini-episode discusses , 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…
Proposing Annoyance Mining
Jun 9, 2015 • 30 min
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…
Preserving History at Cyark
Jun 5, 2015 • 23 min
with Elizabeth Lee
[MINI] A Critical Examination of a Study of Marriage by Political Affiliation
May 29, 2015 • 10 min
Linhda and Kyle review a New York Times article titled . 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…
Detecting Cheating in Chess
May 22, 2015 • 44 min
with Dr. Kenneth Regan
[MINI] z-scores
May 15, 2015 • 10 min
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 which tells us that…
Using Data to Help Those in Crisis
May 8, 2015 • 34 min
A DataKind project with Crisis Text Line and Pivotal for Good
The Ghost in the MP3
May 1, 2015 • 35 min
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…
Data Fest 2015
Apr 28, 2015 • 27 min
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…
[MINI] Cornbread and Overdispersion
Apr 24, 2015 • 15 min
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…
[MINI] Natural Language Processing
Apr 17, 2015 • 13 min
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 9, 2015 • 31 min
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…
[MINI] Markov Chain Monte Carlo
Apr 3, 2015 • 15 min
This episode explores how going wine testing could teach us about using markov chain monte carlo (mcmc).
[MINI] Markov Chains
Mar 20, 2015 • 11 min
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…
Oceanography and Data Science
Mar 13, 2015 • 33 min
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 where Nicole and I are both mentors for the…
[MINI] Ordinary Least Squares Regression
Mar 6, 2015 • 18 min
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 • 16 min
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 . Tim’s work leverages several open data…
[MINI] k-means clustering
Feb 20, 2015 • 14 min
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…
Shadow Profiles on Social Networks
Feb 12, 2015 • 38 min
joins me this week to discuss his paper . 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…
[MINI] The Chi-Squared Test
Feb 6, 2015 • 17 min
The χ2 () 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…
Mapping Reddit Topics with Randy Olson
Jan 30, 2015 • 29 min
My quest this week is noteworthy a.i. researcher who joins me to share his work creating the - a visualization that illuminates clusters in the reddit community based on user behavior. Randy’s blog post on is well…
[MINI] Partially Observable State Spaces
Jan 23, 2015 • 12 min
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,…
Easily Fooling Deep Neural Networks
Jan 15, 2015 • 28 min
My guest this week is Anh Nguyen, a PhD student at the University of Wyoming working in the . The episode discusses the paper by Anh Nguyen, Jason Yosinski, and Jeff Clune. It describes a process for creating images that a trained deep…
[MINI] Data Provenance
Jan 8, 2015 • 10 min
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…
Doubtful News, Geology, Investigating Paranormal Groups, and Thinking Scientifically with Sharon Hill
Jan 2, 2015 • 31 min
I had the change to speak with well known () for the first episode of 2015. We discuss a number of interesting topics including the contributions makes to getting scientific and skeptical information ranked highly in search…
[MINI] Belief in Santa
Dec 26, 2014 • 9 min
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 • 23 min
Economist Peter Backus joins me in this episode to discuss a few interesting topics. You may recall Linhda and I previously discussed his paper “” on a recent mini-episode. We start by touching base on this fun paper and get a follow up on where Peter…
[MINI] The Battle of the Sexes
Dec 11, 2014 • 18 min
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…
The Science of Online Data at Plenty of Fish with Thomas Levi
Dec 5, 2014 • 58 min
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 , to discuss some of…
[MINI] The Girlfriend Equation
Nov 28, 2014 • 16 min
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…
The Secret and the Global Consciousness Project with Alex Boklin
Nov 21, 2014 • 41 min
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…
[MINI] Monkeys on Typewriters
Nov 13, 2014 • 3 min
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…
Mining the Social Web with Matthew Russell
Nov 7, 2014 • 50 min
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…
[MINI] Is the Internet Secure?
Oct 31, 2014 • 26 min
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…
Practicing and Communicating Data Science with Jeff Stanton
Oct 24, 2014 • 36 min
joins me in this episode to discuss his book , 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…
[MINI] The T-Test
Oct 16, 2014 • 17 min
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…
Data Myths with Karl Mamer
Oct 9, 2014 • 48 min
Black out baby boom | Subliminal messages | Beer and Diapers
Contest Announcement
Oct 8, 2014 • 12 min
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…
[MINI] Selection Bias
Oct 3, 2014 • 14 min
A discussion about conducting US presidential election polls helps frame a converation about selection bias.
[MINI] Confidence Intervals
Sep 26, 2014 • 11 min
Commute times and BBQ invites help frame a discussion about the statistical concept of confidence intervals.
[MINI] Value of Information
Sep 19, 2014 • 14 min
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 • 47 min
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…
Data Science at ZestFinance with Marick Sinay
Sep 12, 2014 • 31 min
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 5, 2014 • 13 min
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…
Jackson Pollock Authentication Analysis with Kate Jones-Smith
Aug 29, 2014 • 49 min
Our guest this week is 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 which appeared…
[MINI] Noise!!
Aug 22, 2014 • 16 min
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…
Guerilla Skepticism on Wikipedia with Susan Gerbic
Aug 15, 2014 • 69 min
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 , an organization working to improve the content and citations of Wikipedia. During the episode, Kyle…
[MINI] Ant Colony Optimization
Aug 8, 2014 • 15 min
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…
Data in Healthcare IT with Shahid Shah
Aug 1, 2014 • 57 min
Our guest this week is Shahid Shah. Shahid is CEO at , and writes three blogs: , , and . During the program, Kyle recommended a talk from the entitled which was hosted by our guest . In addition to his work in Healthcare IT, he also the…
[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…
Streetlight Outage and Crime Rate Analysis with Zach Seeskin
Jul 18, 2014 • 33 min
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…
[MINI] Experimental Design
Jul 11, 2014 • 15 min
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 7, 2014 • 49 min
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…
[MINI] Bayesian Updating
Jun 27, 2014 • 11 min
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 • 57 min
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 • 16 min
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 6, 2014 • 76 min
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 • 11 min
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).
May 23, 2014 • 3 min
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…