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.
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.”
“The Timing of Evolutionary Transitions Suggest Intelligent Life is Rare.”by Andrew E Snyder-Beattie, Anders Sandberg, K Eric Drexler, Michael B Bonsall
Saknas det avsnitt?
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.
“Knowledge, Graphs, Fundamentals, Techniques and Applications”by Mayank Kejriwal, Craig A. Knoblock, and Pedro Szekley
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 this debunked theory, but rather an exploration of the metadata and origins of this conspiracy.
This episode is also the first in our 2021 Pilot Season in which we are going to test out a few formats for Data Skeptic to see what our next season should be. This is the first installment. In a few weeks, we're going to ask everyone to vote for their favorite theme for our next season.
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"
“JANUS: Benchmarking Commercial and Open-Source Cloud and Edge Platforms for Object and Anomaly Detection Workloads.”
by: Karthick Shankar, Pengcheng Wang, Ran Xu, Ashraf Mahgoub, Somali ChaterjiSocial Media
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
“Causal Campbell-Goodhart’s law and Reinforcement Learning”by Hal AshtonBook
“The Book of Why”by Judea PearlPaper
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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
by: Yuqi Ouyang, Victor Sanchez
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.”
Byzantine Fault-Tolerance in Peer-to-Peer Distributed Gradient-Descent
by Nirupam Gupta and Nitin H. Vaidya
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.
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.”
“Taming the Contention in Consensus-based Distributed Systems”
by Balaji Arun, Sebastiano Peluso, Roberto Palmieri, Giuliano Losa, and Binoy Ravindran
by Leslie Lamport
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.”
“What Makes a Good Summary? Reconsidering the Focus of Automatic Summarization.”
by Maartje der Hoeve, Juilia Kiseleva, and Maarten de Rijke
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".
Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives
by Brian Brubach, Aravind Srinivasan, and Shawn Zhao
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 Chess Problems: Even Cooperative Chess is Hard.
Complexity of Retrograde and Helpmate Chess Problems: Even Cooperative Chess is Hard
by Josh Brunner, Erik D. Demaine, Dylan Hendrickson, and Juilian Wellman
1x1 Rush Hour With Fixed Blocks is PSPACE Complete
by Josh Brunner, Lily Chung, Erik D. Demaine, Dylan Hendrickson, Adam Hesterberg, Adam Suhl, Avi Zeff
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
On the Significance on Consecutive Ballots in Paxos by: Eli Goldweber, Nuda Zhang, and Manos Kapritsos
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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
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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.
Paper: Applying Machine Learning to Crowd-sourced Data from Earthquake Detective
by Omkar Ranadive, Suzan van der Lee, Vivan Tang, and Kevin Chao
Earthquake Detective: https://www.zooniverse.org/projects/vivitang/earthquake-detective
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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.
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.
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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 implications.
Works MentionedA Difficulty in the Concept of Social Welfare by Kenneth J. Arrow Three Brief Proofs of Arrows Impossibility Theorem by John Geanakoplos Thank you to our sponsors! Better Help is much more affordable than traditional offline counseling, and financial aid is available! Get started in less than 24 hours. Data Skeptic listeners get 10% off your first month when you visit: betterhelp.com/dataskeptic Let Springboard School of Data jumpstart your data career! With 100% online and remote schooling, supported by a vast network of professional mentors with a tuition-back guarantee, you can't go wrong. Up to twenty $500 scholarships will be awarded to Data Skeptic listeners. Check them out at springboard.com/dataskeptic and enroll using code: DATASK
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 their recent paper. Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic.
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