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Data Skeptic

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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.

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.

Location:

United States

Description:

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.

Language:

English


Episodes

Fairness Aware Outlier Detection

4/9/2021
Today on the show we have Shubhranshu Shekar, a Ph. D Student at Carnegie Mellon University, who joins us to talk about his work, FAIROD: Fairness-aware Outlier Detection.

Duration:00:39:31

Life May be Rare

4/5/2021
Today on the show Dr. Anders Sandburg, Senior Research Fellow at the Future of Humanity Institute at Oxford University, comes on to share his work “The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.” Works Mentioned: Paper: “The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.”by Andrew E Snyder-Beattie, Anders Sandberg, K Eric Drexler, Michael B Bonsall Twitter: @anderssandburg

Duration:00:43:11

Social Networks

3/29/2021
Mayank Kejriwal, Research Professor at the University of Southern California and Researcher at the Information Sciences Institute, joins us today to discuss his work and his new book Knowledge, Graphs, Fundamentals, Techniques and Applications by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley. Works Mentioned “Knowledge, Graphs, Fundamentals, Techniques and Applications”by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley

Duration:00:49:49

The QAnon Conspiracy

3/22/2021
QAnon is a conspiracy theory born in the underbelly of the internet. While easy to disprove, these cryptic ideas captured the minds of many people and (in part) paved the way to the 2021 storming of the US Capital. This is a contemporary conspiracy which came into existence and grew in a very digital way. This makes it possible for researchers to study this phenomenon in a way not accessible in previous conspiracy theories of similar popularity. This episode is not so much a debunking of...

Duration:00:43:53

Benchmarking Vision on Edge vs Cloud

3/15/2021
Karthick Shankar, Masters Student at Carnegie Mellon University, and Somali Chaterji, Assistant Professor at Purdue University, join us today to discuss the paper "JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads" Works Mentioned: https://ieeexplore.ieee.org/abstract/document/9284314 “JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.” by: Karthick Shankar,...

Duration:00:47:51

Goodhart's Law in Reinforcement Learning

3/5/2021
Hal Ashton, a PhD student from the University College of London, joins us today to discuss a recent work Causal Campbell-Goodhart’s law and Reinforcement Learning. "Only buy honey from a local producer." - Hal Ashton Works Mentioned: “Causal Campbell-Goodhart’s law and Reinforcement Learning”by Hal AshtonBook “The Book of Why”by Judea PearlPaper Thanks to our sponsor! When your business is ready to make that next hire, find the right person with LinkedIn Jobs. Just visit...

Duration:00:37:09

Video Anomaly Detection

3/1/2021
Yuqi Ouyang, in his second year of PhD study at the University of Warwick in England, joins us today to discuss his work “Video Anomaly Detection by Estimating Likelihood of Representations.”Works Mentioned: Video Anomaly Detection by Estimating Likelihood of Representations https://arxiv.org/abs/2012.01468 by: Yuqi Ouyang, Victor Sanchez

Duration:00:24:04

Fault Tolerant Distributed Gradient Descent

2/22/2021
Nirupam Gupta, a Computer Science Post Doctoral Researcher at EDFL University in Switzerland, joins us today to discuss his work “Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent.” Works Mentioned: https://arxiv.org/abs/2101.12316 Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent by Nirupam Gupta and Nitin H. Vaidya Conference Details: https://georgetown.zoom.us/meeting/register/tJ0sc-2grDwjEtfnLI0zPnN-GwkDvJdaOxXF

Duration:00:36:05

Decentralized Information Gathering

2/15/2021
Mikko Lauri, Post Doctoral researcher at the University of Hamburg, Germany, comes on the show today to discuss the work Information Gathering in Decentralized POMDPs by Policy Graph Improvements. Follow Mikko: @mikko_lauri Github https://laurimi.github.io/

Duration:00:32:55

Leaderless Consensus

2/5/2021
Balaji Arun, a PhD Student in the Systems of Software Research Group at Virginia Tech, joins us today to discuss his research of distributed systems through the paper “Taming the Contention in Consensus-based Distributed Systems.” Works Mentioned “Taming the Contention in Consensus-based Distributed Systems” by Balaji Arun, Sebastiano Peluso, Roberto Palmieri, Giuliano Losa, and Binoy Ravindran https://www.ssrg.ece.vt.edu/papers/tdsc20-author-version.pdf “Fast Paxos” by Leslie...

Duration:00:27:23

Automatic Summarization

1/29/2021
Maartje ter Hoeve, PhD Student at the University of Amsterdam, joins us today to discuss her research in automated summarization through the paper “What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.” Works Mentioned “What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.” by Maartje der Hoeve, Juilia Kiseleva, and Maarten de...

Duration:00:27:56

Gerrymandering

1/22/2021
Brian Brubach, Assistant Professor in the Computer Science Department at Wellesley College, joins us today to discuss his work “Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives". WORKS MENTIONED: Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives by Brian Brubach, Aravind Srinivasan, and Shawn Zhao

Duration:00:34:07

Even Cooperative Chess is Hard

1/15/2021
Aside from victory questions like “can black force a checkmate on white in 5 moves?” many novel questions can be asked about a game of chess. Some questions are trivial (e.g. “How many pieces does white have?") while more computationally challenging questions can contribute interesting results in computational complexity theory. In this episode, Josh Brunner, Master's student in Theoretical Computer Science at MIT, joins us to discuss his recent paper Complexity of Retrograde and Helpmate...

Duration:00:23:07

Consecutive Votes in Paxos

1/11/2021
Eil Goldweber, a graduate student at the University of Michigan, comes on today to share his work in applying formal verification to systems and a modification to the Paxos protocol discussed in the paper Significance on Consecutive Ballots in Paxos. Works Mentioned : Previous Episode on Paxos https://dataskeptic.com/blog/episodes/2020/distributed-consensus Paper: On the Significance on Consecutive Ballots in Paxos by: Eli Goldweber, Nuda Zhang, and Manos Kapritsos Thanks to our...

Duration:00:30:09

Visual Illusions Deceiving Neural Networks

1/1/2021
Today on the show we have Adrian Martin, a Post-doctoral researcher from the University of Pompeu Fabra in Barcelona, Spain. He comes on the show today to discuss his research from the paper “Convolutional Neural Networks can be Deceived by Visual Illusions.” Works Mentioned in Paper: “Convolutional Neural Networks can be Decieved by Visual Illusions.” by Alexander Gomez-Villa, Adrian Martin, Javier Vazquez-Corral, and Marcelo Bertalmio Examples: Snake...

Duration:00:33:41

Earthquake Detection with Crowd-sourced Data

12/25/2020
Have you ever wanted to hear what an earthquake sounds like? Today on the show we have Omkar Ranadive, Computer Science Masters student at NorthWestern University, who collaborates with Suzan van der Lee, an Earth and Planetary Sciences professor at Northwestern University, on the crowd-sourcing project Earthquake Detective. Email Links: Suzan: suzan@earth.northwestern.edu Omkar: omkar.ranadive@u.northwestern.edu Works Mentioned: Paper: Applying Machine Learning to Crowd-sourced Data...

Duration:00:29:26

Byzantine Fault Tolerant Consensus

12/22/2020
Byzantine fault tolerance (BFT) is a desirable property in a distributed computing environment. BFT means the system can survive the loss of nodes and nodes becoming unreliable. There are many different protocols for achieving BFT, though not all options can scale to large network sizes. Ted Yin joins us to explain BFT, survey the wide variety of protocols, and share details about HotStuff.

Duration:00:35:31

Alpha Fold

12/11/2020
Kyle shared some initial reactions to the announcement about Alpha Fold 2's celebrated performance in the CASP14 prediction. By many accounts, this exciting result means protein folding is now a solved problem. Thanks to our sponsors! Brilliant brilliant.org/dataskepticBetterhelpbetterhelp.com/dataskeptic

Duration:00:23:13

Arrow's Impossibility Theorem

12/4/2020
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 everyone picks candidate A, then A should win! Yet surprisingly, under a few basic assumptions, this theorem demonstrates that no voting system exists which can satisfy all the criteria. This episode is a discussion about the structure of the proof and some of its...

Duration:00:26:18

Face Mask Sentiment Analysis

11/27/2020
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 anything, can make people change their minds? Today we speak to, Neil Yeung and Jonathan Lai, Undergraduate students in the Department of Computer Science at the University of Rochester, and Professor of Computer Science, Jiebo-Luoto to discuss...

Duration:00:41:10