Data Skeptic

By Kyle Polich

Technology

What's Data Skeptic about?

The Data Skeptic Podcast features interviews and discussion of 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.

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LAST EPISODE

Adwords with Unknown Budgets

08.15.2022

Rajan Udwani, an Assistant Professor at the University of California Berkeley joins us to discuss his work on AdWords with unknown budgets. He discussed the previous approaches to ad allocation, as well as his maiden approach that introduced randomization for ...

Data Skeptic episodes:

12.04.2020 Arrow's Impossibility Theorem Above all, everyone wants voting to be fair. What does fair mean and how can we measure it? Kenneth Arrow posited a simple set of conditions that one would certainly desire in a voting system. For example, unanimity - if ...
11.27.2020 Face Mask Sentiment Analysis As the COVID-19 pandemic continues, the public (or at least those with Twitter accounts) are sharing their personal opinions about mask-wearing via Twitter. What does this data tell us about public opinion? How does it vary by demographic? What, if ...
11.20.2020 Counting Briberies in Elections Niclas Boehmer, second year PhD student at Berlin Institute of Technology, comes on today to discuss the computational complexity of bribery in elections through the paper “On the Robustness of Winners: Counting Briberies in Elections.” Links Mentioned: https://www.akt.tu-berlin.de/menue/team/boehmer_niclas/ Works Mentioned: ...
11.13.2020 Sybil Attacks on Federated Learning Clement Fung, a Societal Computing PhD student at Carnegie Mellon University, discusses his research in security of machine learning systems and a defense against targeted sybil-based poisoning called FoolsGold. Works Mentioned: The Limitations of Federated Learning in Sybil Settings Twitter: ...
11.06.2020 Differential Privacy at the US Census Simson Garfinkel, Senior Computer Scientist for Confidentiality and Data Access at the US Census Bureau, discusses his work modernizing the Census Bureau disclosure avoidance system from private to public disclosure avoidance techniques using differential privacy. Some of the discussion revolves ...
10.30.2020 Distributed Consensus Computer Science research fellow of Cambridge University, Heidi Howard discusses Paxos, Raft, and distributed consensus in distributed systems alongside with her work “Paxos vs. Raft: Have we reached consensus on distributed consensus?” She goes into detail about the leaders in ...
10.23.2020 ACID Compliance Linhda joins Kyle today to talk through A.C.I.D. Compliance (atomicity, consistency, isolation, and durability). The presence of these four components can ensure that a database’s transaction is completed in a timely manner. Kyle uses examples such as google sheets, bank ...
10.16.2020 National Popular Vote Interstate Compact Patrick Rosenstiel joins us to discuss the The National Popular Vote.
10.12.2020 Defending the p-value Yudi Pawitan joins us to discuss his paper Defending the P-value.
10.05.2020 Retraction Watch Ivan Oransky joins us to discuss his work documenting the scientific peer-review process at retractionwatch.com.
09.21.2020 Crowdsourced Expertise Derek Lim joins us to discuss the paper Expertise and Dynamics within Crowdsourced Musical Knowledge Curation: A Case Study of the Genius Platform.
09.14.2020 The Spread of Misinformation Online Neil Johnson joins us to discuss the paper The online competition between pro- and anti-vaccination views.
09.07.2020 Consensus Voting Mashbat Suzuki joins us to discuss the paper How Many Freemasons Are There? The Consensus Voting Mechanism in Metric Spaces. Check out Mashbat’s and many other great talks at the 13th Symposium on Algorithmic Game Theory (SAGT 2020)
08.31.2020 Voting Mechanisms Steven Heilman joins us to discuss his paper Designing Stable Elections. For a general interest article, see: https://theconversation.com/the-electoral-college-is-surprisingly-vulnerable-to-popular-vote-changes-141104 Steven Heilman receives funding from the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those ...
08.24.2020 False Consensus Sami Yousif joins us to discuss the paper The Illusion of Consensus: A Failure to Distinguish Between True and False Consensus. This work empirically explores how individuals evaluate consensus under different experimental conditions reviewing online news articles. More from Sami ...
08.18.2020 Fraud Detection in Real Time In this solo episode, Kyle overviews the field of fraud detection with eCommerce as a use case. He discusses some of the techniques and system architectures used by companies to fight fraud with a focus on why these things need ...
08.11.2020 Listener Survey Review In this episode, Kyle and Linhda review the results of our recent survey. Hear all about the demographic details and how we interpret these results.
07.27.2020 Human Computer Interaction and Online Privacy Moses Namara from the HATLab joins us to discuss his research into the interaction between privacy and human-computer interaction.
07.20.2020 Authorship Attribution of Lennon McCartney Songs Mark Glickman joins us to discuss the paper Data in the Life: Authorship Attribution in Lennon-McCartney Songs.
07.11.2020 GANs Can Be Interpretable Erik Härkönen joins us to discuss the paper GANSpace: Discovering Interpretable GAN Controls. During the interview, Kyle makes reference to this amazing interpretable GAN controls video and it’s accompanying codebase found here. Erik mentions the GANspace collab notebook which is ...
07.06.2020 Sentiment Preserving Fake Reviews David Ifeoluwa Adelani joins us to discuss Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection.
06.26.2020 Interpretability Practitioners Sungsoo Ray Hong joins us to discuss the paper Human Factors in Model Interpretability: Industry Practices, Challenges, and Needs.
06.19.2020 Facial Recognition Auditing Deb Raji joins us to discuss her recent publication Saving Face: Investigating the Ethical Concerns of Facial Recognition Auditing.
06.12.2020 Robust Fit to Nature Uri Hasson joins us this week to discuss the paper Robust-fit to Nature: An Evolutionary Perspective on Biological (and Artificial) Neural Networks.
06.05.2020 Black Boxes Are Not Required 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 cases, their accuracy makes them ...
05.30.2020 Robustness to Unforeseen Adversarial Attacks Daniel Kang joins us to discuss the paper Testing Robustness Against Unforeseen Adversaries.
05.22.2020 Estimating the Size of Language Acquisition Frank Mollica joins us to discuss the paper Humans store about 1.5 megabytes of information during language acquisition
05.15.2020 Interpretable AI in Healthcare Jayaraman Thiagarajan joins us to discuss the recent paper Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models.
05.08.2020 Understanding Neural Networks What does it mean to understand a neural network? That’s the question posted on this arXiv paper. Kyle speaks with Tim Lillicrap about this and several other big questions.
05.02.2020 Self-Explaining AI Dan Elton 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 ...
04.24.2020 Plastic Bag Bans Becca Taylor joins us to discuss her work studying the impact of plastic bag bans as published in Bag Leakage: The Effect of Disposable Carryout Bag Regulations on Unregulated Bags from the Journal of Environmental Economics and Management. How does ...
04.18.2020 Self Driving Cars and Pedestrians We are joined by Arash Kalatian to discuss Decoding pedestrian and automated vehicle interactions using immersive virtual reality and interpretable deep learning.
04.10.2020 Computer Vision is Not Perfect Computer Vision is not Perfect Julia Evans joins us help answer the question why do neural networks think a panda is a vulture. Kyle talks to Julia about her hands-on work fooling neural networks. Julia runs Wizard Zines which publishes ...
04.04.2020 Uncertainty Representations 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 modeling interactions with visualizations as ...
03.28.2020 AlphaGo, COVID-19 Contact Tracing and New Data Set 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 ...
03.20.2020 Visualizing Uncertainty
03.13.2020 Interpretability Tooling Pramit Choudhary joins us to talk about the methodologies and tools used to assist with model interpretability.
03.06.2020 Shapley Values Kyle and Linhda discuss how Shapley Values might be a good tool for determining what makes the cut for a home renovation.
02.28.2020 Anchors as Explanations We welcome back Marco Tulio Ribeiro to discuss research he has done since our original discussion on LIME. In particular, we ask the question Are Red Roses Red? and discuss how Anchors provide high precision model-agnostic explanations. Please take our ...
02.22.2020 Mathematical Models of Ecological Systems
02.14.2020 Adversarial Explanations Walt Woods joins us to discuss his paper Adversarial Explanations for Understanding Image Classification Decisions and Improved Neural Network Robustness with co-authors Jack Chen and Christof Teuscher.
02.07.2020 ObjectNet Andrei Barbu joins us to discuss ObjectNet - 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 ...
01.31.2020 Visualization and Interpretability Enrico Bertini 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 http://enrico.bertini.io/. More from Enrico with co-host Moritz Stefaner on the Data Stories podcast!
01.26.2020 Interpretable One Shot Learning We welcome Su Wang back to Data Skeptic to discuss the paper Distributional modeling on a diet: One-shot word learning from text only.
01.22.2020 Fooling Computer Vision Wiebe van Ranst 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, ...
01.14.2020 Algorithmic Fairness This episode includes an interview with Aaron Roth author of The Ethical Algorithm.
01.07.2020 Interpretability 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 important to ask. Welcome to ...
12.31.2019 NLP in 2019 A year in recap.
12.24.2019 The Limits of NLP We are joined by Colin Raffel to discuss the paper "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer".
12.15.2019 Jumpstart Your ML Project Seth Juarez joins us to discuss the toolbox of options available to a data scientist to jumpstart or extend their machine learning efforts.

Newer episodes

Older episodes

Data Skeptic

What's Data Skeptic about?

Download our free app to listen on your phone

Data Skeptic episodes:

Arrow's Impossibility Theorem

Face Mask Sentiment Analysis

Counting Briberies in Elections

Sybil Attacks on Federated Learning

Differential Privacy at the US Census

Distributed Consensus

ACID Compliance

National Popular Vote Interstate Compact

Defending the p-value

Retraction Watch

Crowdsourced Expertise

The Spread of Misinformation Online

Consensus Voting

Voting Mechanisms

False Consensus

Fraud Detection in Real Time

Listener Survey Review

Human Computer Interaction and Online Privacy

Authorship Attribution of Lennon McCartney Songs

GANs Can Be Interpretable

Sentiment Preserving Fake Reviews

Interpretability Practitioners

Facial Recognition Auditing

Robust Fit to Nature

Black Boxes Are Not Required

Robustness to Unforeseen Adversarial Attacks

Estimating the Size of Language Acquisition

Interpretable AI in Healthcare

Understanding Neural Networks

Self-Explaining AI

Plastic Bag Bans

Self Driving Cars and Pedestrians

Computer Vision is Not Perfect

Uncertainty Representations

AlphaGo, COVID-19 Contact Tracing and New Data Set

Visualizing Uncertainty

Interpretability Tooling

Shapley Values

Anchors as Explanations

Mathematical Models of Ecological Systems

Adversarial Explanations

ObjectNet

Visualization and Interpretability

Interpretable One Shot Learning

Fooling Computer Vision

Algorithmic Fairness

Interpretability

NLP in 2019

The Limits of NLP

Jumpstart Your ML Project