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.

Excitement brews as Alpha Fold 2 is hailed for solving the protein folding conundrum, showing its prowess at the CASP14 competition. The podcast dives into the relationship between protein structure and function, highlighting traditional prediction challenges. Alpha Fold's revolutionary impact on drug discovery is explored, along with its deep learning technology. Listeners learn about the complexities of protein interactions and the surprising connections between soap bubbles and advanced algorithms. This discussion promises to reshape future scientific research.

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 Mentioned A 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. Works Mentioned https://arxiv.org/abs/2011.00336 Emails: Neil Yeung nyeung@u.rochester.edu Jonathan Lia jlai11@u.rochester.edu Jiebo Luo jluo@cs.rochester.edu Thanks to our sponsors! Springboard School of Data offers a comprehensive career program encompassing data science, analytics, engineering, and Machine Learning. All courses are online and tailored to fit the lifestyle of working professionals. Up to 20 Data Skeptic listeners will receive $500 scholarships. Apply today at springboard.com/datasketpic Check out Brilliant's group theory course to learn about object-oriented design! Brilliant is great for learning something new or to get an easy-to-look-at review of something you already know. Check them out a Brilliant.org/dataskeptic to get 20% off of a year of Brilliant Premium!

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: “On the Robustness of Winners: Counting Briberies in Elections.” by Niclas Boehmer, Robert Bredereck, Piotr Faliszewski. Rolf Niedermier Thanks to our sponsors: Springboard School of Data: Springboard is a comprehensive end-to-end online data career program. Create a portfolio of projects to spring your career into action. Learn more about how you can be one of twenty $500 scholarship recipients at springboard.com/dataskeptic. This opportunity is exclusive to Data Skeptic listeners. (Enroll with code: DATASK) Nord VPN: Protect your home internet connection with unlimited bandwidth. Data Skeptic Listeners-- take advantage of their Black Friday offer: purchase a 2-year plan, get 4 additional months free. nordvpn.com/dataskeptic (Use coupon code DATASKEPTIC)

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: @clemfung Website: https://clementfung.github.io/ Thanks to our sponsors: Brilliant - Online learning platform. Check out Geometry Fundamentals! Visit Brilliant.org/dataskeptic for 20% off Brilliant Premium! BetterHelp - Convenient, professional, and affordable online counseling. Take 10% off your first month at betterhelp.com/dataskeptic

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 around the topics in the paper Randomness Concerns When Deploying Differential Privacy.   WORKS MENTIONED: “Calibrating Noise to Sensitivity in Private Data Analysis” by Cynthia Dwork, Frank McSherry, Kobbi Nissim, Adam Smith "Issues Encountered Deploying Differential Privacy" by Simson L Garfinkel, John M Abowd, and Sarah Powazek "Randomness Concerns When Deploying Differential Privacy" by Simson L. Garfinkel and Philip Leclerc  Check out: https://simson.net/page/Differential_privacy Thank you to our sponsor, BetterHelp. Professional and confidential in-app counseling for everyone. Save 10% on your first month of services with www.betterhelp.com/dataskeptic

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 Paxos and Raft and how The Raft Consensus Algorithm actually inspired her to pursue her PhD. Paxos vs Raft paper: https://arxiv.org/abs/2004.05074 Leslie Lamport paper “part-time Parliament” https://lamport.azurewebsites.net/pubs/lamport-paxos.pdf Leslie Lamport paper "Paxos Made Simple" https://lamport.azurewebsites.net/pubs/paxos-simple.pdf Twitter : @heidiann360 Thank you to our sponsor Monday.com! Their apps challenge is still accepting submissions! find more information at monday.com/dataskeptic

The podcast discusses A.C.I.D. Compliance and its importance in database transactions. They illustrate transactional consistency with examples like Google Sheets and bank transactions. The hosts also explore ACID compliance in board games and databases, and discuss durability and scalability in database design. Additionally, they touch on accuracy and precision in data, introduce the Monday Apps Challenge, and delve into the relationship between ACID compliance, consistency, and consensus in databases.

Patrick Rosenstiel joins us to discuss the The National Popular Vote.