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#60 Modeling Dialogues & Languages, with J.P. de Ruiter

#60 Modeling Dialogues & Languages, with J.P. de Ruiter

FromLearning Bayesian Statistics


#60 Modeling Dialogues & Languages, with J.P. de Ruiter

FromLearning Bayesian Statistics

ratings:
Length:
73 minutes
Released:
Apr 30, 2022
Format:
Podcast episode

Description

Why do we, humans, communicate? And how? And isn’t that a problem that to study communication we have to… communicate?
Did you ever ask yourself that? Because  J.P. de Ruiter did — and does everyday. But he’s got good reasons: JP is a cognitive scientist whose primary research focus is on the cognitive foundations of human communication. He aims to improve our understanding of how humans and artificial agents use language, gesture and other types of signals to effectively communicate with each other.
Currently he has one of the two Bridge Professorship at Tufts University, and has been appointed in both the Computer Science and Psychology departments.
In this episode, we’ll look at why Bayes is helpful in dialogue research, what the future of the field looks like to JP, and how he uses PyMC in his own teaching.
Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ (https://bababrinkman.com/) !
Thank you to my Patrons for making this episode possible!
Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, James Ahloy, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Adam C. Smith, Will Kurt, Andrew Moskowitz, Hector Munoz, Marco Gorelli, Simon Kessell, Bradley Rode, Patrick Kelley, Rick Anderson, Casper de Bruin, Philippe Labonde, Matthew McAnear, Michael Hankin, Cameron Smith, Luis Iberico, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Aaron Jones, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland and Aubrey Clayton.
Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;)

Links from the show:
JP’s page: https://sites.tufts.edu/hilab/people/ (https://sites.tufts.edu/hilab/people/)
Projecting the End of a Speaker's Turn – A Cognitive Cornerstone of Conversation: https://www.researchgate.net/publication/236787756_Projecting_the_End_of_a_Speaker's_Turn_A_Cognitive_Cornerstone_of_Conversation (https://www.researchgate.net/publication/236787756_Projecting_the_End_of_a_Speaker's_Turn_A_Cognitive_Cornerstone_of_Conversation)
Cognitive and social delays in the initiation of conversational repair: https://journals.uic.edu/ojs/index.php/dad/article/view/11388 (https://journals.uic.edu/ojs/index.php/dad/article/view/11388)
Using uh and um in spontaneous speaking: http://www.columbia.edu/~rmk7/HC/HC_Readings/Clark_Fox.pdf (http://www.columbia.edu/~rmk7/HC/HC_Readings/Clark_Fox.pdf)
Status of Frustrator as an Inhibitor of Horn-Honking Responses: https://www.tandfonline.com/doi/abs/10.1080/00224545.1968.9933615 (https://www.tandfonline.com/doi/abs/10.1080/00224545.1968.9933615)
A Simplest Systematics for the Organization of Turn-Taking for Conversation: https://www.jstor.org/stable/412243 (https://www.jstor.org/stable/412243)
Richard McElreath, Science Before Statistics – Intro to Causal Inference: https://www.youtube.com/watch?v=KNPYUVmY3NM (https://www.youtube.com/watch?v=KNPYUVmY3NM)
The Prosecutor's fallacy: https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy (https://en.wikipedia.org/wiki/Prosecutor%27s_fallacy)
The Monty Hall problem: https://en.wikipedia.org/wiki/Monty_Hall_problem (https://en.wikipedia.org/wiki/Monty_Hall_problem)
LBS #50, Ta(l)king Risks & Embracing Uncertainty, with David Spiegelhalter:...
Released:
Apr 30, 2022
Format:
Podcast episode

Titles in the series (100)

Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the people who make all that possible. So I created "Learning Bayesian Statistics", where you'll get to hear how Bayesian statistics are used to detect black matter in outer space, forecast elections or understand how diseases spread and can ultimately be stopped. But this show is not only about successes -- it's also about failures, because that's how we learn best. So you'll often hear the guests talking about what *didn't* work in their projects, why, and how they overcame these challenges. Because, in the end, we're all lifelong learners! My name is Alex Andorra by the way, and I live in Paris. By day, I'm a data scientist and modeler at the https://www.pymc-labs.io/ (PyMC Labs) consultancy. By night, I don't (yet) fight crime, but I'm an open-source enthusiast and core contributor to the python packages https://docs.pymc.io/ (PyMC) and https://arviz-devs.github.io/arviz/ (ArviZ). I also love https://www.pollsposition.com/ (election forecasting) and, most importantly, Nutella. But I don't like talking about it – I prefer eating it. So, whether you want to learn Bayesian statistics or hear about the latest libraries, books and applications, this podcast is for you -- just subscribe! You can also support the show and https://www.patreon.com/learnbayesstats (unlock exclusive Bayesian swag on Patreon)! This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy