When the Camera Turns on Police
Play • 18 min

Moves have been made to restrict the use of facial recognition across the globe. In part one of this series on face ID, Jennifer Strong and the team at MIT Technology Review explore the unexpected ways the technology is being used, including how the technology is being turned on police.  


We meet: 

Christopher Howell, data scientist and protester. 


Credits: 

This episode was reported and produced by Jennifer Strong, Tate Ryan-Mosley and Emma Cillekens, and Karen Hao. We’re edited by Michael Reilly and Gideon Lichfield.

storytelling with data podcast
storytelling with data podcast
storytelling with data author, speaker and dataviz guru Cole Nussbaumer Kna
storytelling with data: #39 Better Data Visualizations with Jon Schwabish
Cole talks with Jon Schwabish about his latest book, Better Data Visualizations. Tune in to hear about Jon’s goal to make people aware of a wider array of graphs, which less common graphs he wishes people would use more, his favorite Sankey diagram, and how Luxembourg highlighted an important lesson about maps. Jon also addresses viewer questions on fact-checking, communicating qualitative data, and his work on racial equity in data visualization, including things we should all be thinking about when we make graphs. Pre-order: Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks Other books by Jon: Better Presentations, Elevate the Debate Follow Jon: @jschwabish | www.policyviz.com | Data@Urban Other books mentioned: Storyteller’s Secret, Resonate, Slide:ology, Presentation Zen, How Charts Lie, Avoiding Data Pitfalls People mentioned: Kim Rees, Ann Emery, RJ Andrews, Moritz Stefaner, Nadieh Bremer, Pedro Cruz Jon's 2014 article “An Economist’s Guide to Visualizing Data” Jon’s projects: The Graphic Continuum, One Chart at a Time video series Exploratory vs. explanatory: Form and Function: Let Your Audience’s Needs Drive Your Data Visualization Choices Sankey diagram from Reddit: How 52 Ninth-Graders Spell Camouflage Interactive Sankey from The Pudding: The Gyllenhaal Experiment Medium article: "Word Clouds: We Can’t Make Them Go Away, So Let’s Improve Them" by Marti Hearst Research resource: Our World In Data Medium article: "Applying Racial Equity Awareness in Data Visualization" by Jon Schwabish and Alice Feng
59 min
The Future of Everything presented by Stanford Engineering
The Future of Everything presented by Stanford Engineering
Stanford Radio
Karen Liu: How robots perceive the physical world
Stanford’s Karen Liu is a computer scientist who works in robotics. She hopes that someday machines might take on caregiving roles, like helping medical patients get dressed and undressed each day. That quest has provided her a special insight into just what a monumental challenge such seemingly simple tasks are. After all, she points out, it takes a human child several years to learn to dress themselves — imagine what it takes to teach a robot to help a person who is frail or physically compromised? Liu is among a growing coterie of scientists who are promoting “physics-based simulations” that are speeding up the learning process for robots. That is, rather than building actual robots and refining them as they go, she’s using computer simulations to improve how robots sense the physical world around them and to make intelligent decisions under changes and perturbations in the real world, like those involved in tasks like getting dressed for the day. To do that, a robot must understand the physical characteristics of human flesh and bone as well as the movements and underlying human intention to be able to comprehend when a garment is or is not going on as expected. The stakes are high. The downside consequence could be physical harm to the patient, as Liu tells _Stanford Engineering’s The Future of Everything_ podcast hosted by bioengineer Russ Altman. Listen and subscribe here.
28 min
DeepMind: The podcast
DeepMind: The podcast
DeepMind: The podcast
8: Demis Hassabis: The interview
In this special extended episode, Hannah Fry meets Demis Hassabis, the CEO and co-founder of DeepMind. She digs into his former life as a chess player, games designer and neuroscientist and explores how his love of chess helped him to get start-up funding, what drives him and his vision, and why AI keeps him up at night. If you have a question or feedback on the series, message us on Twitter (@DeepMindAI (https://twitter.com/deepmindai?lang=en) using the hashtag #DMpodcast) or emailing us at podcast@deepmind.com (mailto:podcast@deepmind.com) . Further reading: Wired: Inside DeepMind's epic mission to solve science's trickiest problem (https://www.wired.co.uk/article/deepmind-protein-folding) Quanta magazine: How Artificial Intelligence Is Changing Science (https://www.quantamagazine.org/how-artificial-intelligence-is-changing-science-20190311/) Demis Hassabis: A systems neuroscience approach to building AGI. Talk at the 2010 Singularity Summit (https://www.youtube.com/watch?v=Qgd3OK5DZWI) Demis Hassabis: The power of self-learning systems. Talk at MIT 2019 (https://cbmm.mit.edu/video/power-self-learning-systems) Demis Hassabis: Talk on Creativity and AI (https://www.youtube.com/watch?v=d-bvsJWmqlc) Financial Times: The mind in the machine: Demis Hassabis on artificial intelligence (2017) (https://www.ft.com/content/048f418c-2487-11e7-a34a-538b4cb30025) The Times: Interview with Demis Hassabis (https://www.thetimes.co.uk/article/demis-hassabis-interview-the-brains-behind-deepmind-on-the-future-of-artificial-intelligence-mzk0zhsp8) The Economist Babbage podcast: DeepMind Games (https://play.acast.com/s/theeconomistbabbage/99af5224-b955-4a3c-930c-91a68bfe6c88?autoplay=true) Interview with Demis Hassabis (https://storage.googleapis.com/deepmind-media/podcast/Game%20Changer%20-%20Demis%20Hassabis%20Interview.pdf) from the book Game Changer (https://www.newinchess.com/game-changer) , which also features an introduction from Demis Interviewees: Deepmind CEO and co-founder, Demis Hassabis Credits: Presenter: Hannah Fry Editor: David Prest Senior Producer: Louisa Field Producers: Amy Racs, Dan Hardoon Binaural Sound: Lucinda Mason-Brown Music composition: Eleni Shaw (with help from Sander Dieleman and WaveNet) Commissioned by DeepMind
37 min
Learning Bayesian Statistics
Learning Bayesian Statistics
Alexandre ANDORRA
#31 Bayesian Cognitive Modeling & Decision-Making, with Michael Lee
I don’t know if you noticed, but I have a fondness for any topic related to decision-making under uncertainty — when it’s studied scientifically of course. Understanding how and why people make decisions when they don’t have all the facts is fascinating to me. That’s why I like electoral forecasting and I love cognitive sciences. So, for the first episode of 2021, I have a special treat: I had the great pleasure of hosting Michael Lee on the podcast! Yes, the Michael Lee who co-authored the book Bayesian Cognitive Modeling with Eric-Jan Wagenmakers in 2013 — by the way, the book was ported to PyMC3, I put the link in the show notes ;) This book was inspired from Michael’s work as a professor of cognitive sciences at University of California, Irvine. He works a lot on representation, memory, learning, and decision making, with a special focus on individual differences and collective cognition. Using naturally occurring behavioral data, he builds probabilistic generative models to try and answer hard real-world questions: how does memory impairment work (that’s modeled with multinomial processing trees)? How complex are simple decisions, and how do people change strategies? Echoing episode 18 with Daniel Lakens, Michael and I also talked about the reproducibility crisis: how are cognitive sciences doing, which progress was made, and what is still to do? Living now in California, Michael is originally from Australia, where he did his Bachelors of Psychology and Mathematics, and his PhD in psychology. But Michael is also found of the city of Amsterdam, which he sees as “the perfect antidote to southern California with old buildings, public transport, great bread and beer, and crappy weather”. 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, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Vincent Arel-Bundock, 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 and Nathaniel Burbank. Visit https://www.patreon.com/learnbayesstats (https://www.patreon.com/learnbayesstats) to unlock exclusive Bayesian swag ;) Links from the show: Michael's website: https://faculty.sites.uci.edu/mdlee/ (https://faculty.sites.uci.edu/mdlee/) Michael on GitHub: https://twitter.com/mdlBayes (https://twitter.com/mdlBayes) Bayesian Cognitive Modeling book: https://faculty.sites.uci.edu/mdlee/bgm/ (https://faculty.sites.uci.edu/mdlee/bgm/) Bayesian Cognitive Modeling in PyMC3: https://github.com/pymc-devs/resources/tree/master/BCM (https://github.com/pymc-devs/resources/tree/master/BCM) An application of multinomial processing tree models and Bayesian methods to understanding memory impairment: https://drive.google.com/file/d/1NHml_YUsnpbUaqFhu0h8EiLeJCx6q403/view (https://drive.google.com/file/d/1NHml_YUsnpbUaqFhu0h8EiLeJCx6q403/view) Understanding the Complexity of Simple Decisions -- Modeling Multiple Behaviors and Switching Strategies: https://webfiles.uci.edu/mdlee/LeeGluckWalsh2018.pdf (https://webfiles.uci.edu/mdlee/LeeGluckWalsh2018.pdf) Robust Modeling in Cognitive Science: https://link.springer.com/article/10.1007/s42113-019-00029-y (https://link.springer.com/article/10.1007/s42113-019-00029-y) This podcast uses the following third-party services for analysis: Podcorn - https://podcorn.com/privacy Support this podcast
1 hr 9 min
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