Podcasts about bayesian

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia, Michael Cao, Yiğit Aşık and Suyog Chandramouli.Takeaways:Epidemiology requires a blend of mathematical and statistical understanding.Models are essential for informing public health decisions during epidemics.The COVID-19 pandemic highlighted the importance of rapid modeling.Misconceptions about data can lead to misunderstandings in public health.Effective communication is crucial for conveying complex epidemiological concepts.Epidemic thinking can be applied to various fields, including marketing and finance.Public health policies should be informed by robust modeling and data analysis.Automation can help streamline data analysis in epidemic response.Understanding the limitations of models...

Agents of Innovation: AI-Powered Product Ideation with Synthetic Consumer Testing // MLOps Podcast #306 with Luca Fiaschi, Partner of PyMC Labs.Join the Community: https://go.mlops.community/YTJoinIn Get the newsletter: https://go.mlops.community/YTNewsletter // AbstractTraditional product development cycles require extensive consumer research and market testing, resulting in lengthy development timelines and significant resource investment. We've transformed this process by building a distributed multi-agent system that enables parallel quantitative evaluation of hundreds of product concepts. Our system combines three key components: an Agentic innovation lab generating high-quality product concepts, synthetic consumer panels using fine-tuned foundational models validated against historical data, and an evaluation framework that correlates with real-world testing outcomes. We can talk about how this architecture enables rapid concept discovery and digital experimentation, delivering insights into product success probability before development begins. Through case studies and technical deep-dives, you'll learn how we built an AI powered innovation lab that compresses months of product development and testing into minutes - without sacrificing the accuracy of insights. // BioWith over 15 years of leadership experience in AI, data science, and analytics, Luca has driven transformative growth in technology-first businesses. As Chief Data & AI Officer at Mistplay, he led the company's revenue growth through AI-powered personalization and data-driven pricing. Prior to that, he held executive roles at global industry leaders such as HelloFresh ($8B), Stitch Fix ($1.2B) and Rocket Internet ($1B). Luca's core competencies include machine learning, artificial intelligence, data mining, data engineering, and computer vision, which he has applied to various domains such as marketing, logistics, personalization, product, experimentation and pricing.He is currently a partner at PyMC Labs, a leading data science consultancy, providing insights and guidance on applications of Bayesian and Causal Inference techniques and Generative AI to fortune 500 companies. Luca holds a PhD in AI and Computer Vision from Heidelberg University and has more than 450 citations on his research work.// Related LinksWebsite: https://www.pymc-labs.com/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Luca on LinkedIn: /lfiaschi

As regulatory expectations evolve under the FDA's Project Optimus oncology dosing initiative, biostatistics is emerging as a central pillar in designing and executing trials that move beyond the traditional maximum tolerated dose (MTD) approach.In this fourth episode of our Project Optimus series, host Dr. Wael Harb is joined by biostatistics expert X.Q Xue, PhD, Vice President and Global Head, Biostatistics at Syneos Health to explore how statistical science is transforming dose optimization in oncology drug development. Dr. Xue discusses the limitations of legacy 3+3 dose-escalation designs and introduces innovative alternatives, including Bayesian modeling, adaptive trial strategies and randomized parallel dose-response studies, which support more precise dose selection and can ultimately improve patient outcomes and trial efficiency.Together, Drs. Harb and Xue examine how smaller biotech companies can overcome barriers to implementation, the role of simulation and AI in trial planning and how a biostatistics-driven approach may increase the likelihood of late-phase success, reduce post-marketing adjustments and support faster regulatory approvals.The views expressed in this podcast belong solely to the speakers and do not represent those of their organization. If you want access to more future-focused, actionable insights to help biopharmaceutical companies better execute and succeed in a constantly evolving environment, visit the Syneos Health Insights Hub. The perspectives you'll find there are driven by dynamic research and crafted by subject matter experts focused on real answers to help guide decision-making and investment. You can find it all at insightshub.health. Like what you're hearing? Be sure to rate and review us! We want to hear from you! If there's a topic you'd like us to cover on a future episode, contact us at podcast@syneoshealth.com.

Today's clip is from episode 129 of the podcast, with AI expert and researcher Vincent Fortuin.This conversation delves into the intricacies of Bayesian deep learning, contrasting it with traditional deep learning and exploring its applications and challenges.Get the full discussion at https://learnbayesstats.com/episode/129-bayesian-deep-learning-ai-for-science-vincent-fortuinIntro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)TranscriptThis is an automatic transcript and may therefore contain errors. Please get in touch if you're willing to correct them.

Prof. Kevin Ellis and Dr. Zenna Tavares talk about making AI smarter, like humans. They want AI to learn from just a little bit of information by actively trying things out, not just by looking at tons of data.They discuss two main ways AI can "think": one way is like following specific rules or steps (like a computer program), and the other is more intuitive, like guessing based on patterns (like modern AI often does). They found combining both methods works well for solving complex puzzles like ARC.A key idea is "compositionality" - building big ideas from small ones, like LEGOs. This is powerful but can also be overwhelming. Another important idea is "abstraction" - understanding things simply, without getting lost in details, and knowing there are different levels of understanding.Ultimately, they believe the best AI will need to explore, experiment, and build models of the world, much like humans do when learning something new.SPONSOR MESSAGES:***Tufa AI Labs is a brand new research lab in Zurich started by Benjamin Crouzier focussed on o-series style reasoning and AGI. They are hiring a Chief Engineer and ML engineers. Events in Zurich. Goto https://tufalabs.ai/***TRANSCRIPT:https://www.dropbox.com/scl/fi/3ngggvhb3tnemw879er5y/BASIS.pdf?rlkey=lr2zbj3317mex1q5l0c2rsk0h&dl=0 Zenna Tavares:http://www.zenna.org/Kevin Ellis:https://www.cs.cornell.edu/~ellisk/TOC:1. Compositionality and Learning Foundations [00:00:00] 1.1 Compositional Search and Learning Challenges [00:03:55] 1.2 Bayesian Learning and World Models [00:12:05] 1.3 Programming Languages and Compositionality Trade-offs [00:15:35] 1.4 Inductive vs Transductive Approaches in AI Systems2. Neural-Symbolic Program Synthesis [00:27:20] 2.1 Integration of LLMs with Traditional Programming and Meta-Programming [00:30:43] 2.2 Wake-Sleep Learning and DreamCoder Architecture [00:38:26] 2.3 Program Synthesis from Interactions and Hidden State Inference [00:41:36] 2.4 Abstraction Mechanisms and Resource Rationality [00:48:38] 2.5 Inductive Biases and Causal Abstraction in AI Systems3. Abstract Reasoning Systems [00:52:10] 3.1 Abstract Concepts and Grid-Based Transformations in ARC [00:56:08] 3.2 Induction vs Transduction Approaches in Abstract Reasoning [00:59:12] 3.3 ARC Limitations and Interactive Learning Extensions [01:06:30] 3.4 Wake-Sleep Program Learning and Hybrid Approaches [01:11:37] 3.5 Project MARA and Future Research DirectionsREFS:[00:00:25] DreamCoder, Kevin Ellis et al.https://arxiv.org/abs/2006.08381[00:01:10] Mind Your Step, Ryan Liu et al.https://arxiv.org/abs/2410.21333[00:06:05] Bayesian inference, Griffiths, T. L., Kemp, C., & Tenenbaum, J. B.https://psycnet.apa.org/record/2008-06911-003[00:13:00] Induction and Transduction, Wen-Ding Li, Zenna Tavares, Yewen Pu, Kevin Ellishttps://arxiv.org/abs/2411.02272[00:23:15] Neurosymbolic AI, Garcez, Artur d'Avila et al.https://arxiv.org/abs/2012.05876[00:33:50] Induction and Transduction (II), Wen-Ding Li, Kevin Ellis et al.https://arxiv.org/abs/2411.02272[00:38:35] ARC, François Chollethttps://arxiv.org/abs/1911.01547[00:39:20] Causal Reactive Programs, Ria Das, Joshua B. Tenenbaum, Armando Solar-Lezama, Zenna Tavareshttp://www.zenna.org/publications/autumn2022.pdf[00:42:50] MuZero, Julian Schrittwieser et al.http://arxiv.org/pdf/1911.08265[00:43:20] VisualPredicator, Yichao Lianghttps://arxiv.org/abs/2410.23156[00:48:55] Bayesian models of cognition, Joshua B. Tenenbaumhttps://mitpress.mit.edu/9780262049412/bayesian-models-of-cognition/[00:49:30] The Bitter Lesson, Rich Suttonhttp://www.incompleteideas.net/IncIdeas/BitterLesson.html[01:06:35] Program induction, Kevin Ellis, Wen-Ding Lihttps://arxiv.org/pdf/2411.02272[01:06:50] DreamCoder (II), Kevin Ellis et al.https://arxiv.org/abs/2006.08381[01:11:55] Project MARA, Zenna Tavares, Kevin Ellishttps://www.basis.ai/blog/mara/

Race Oncology CEO and MD Daniel Tillett talked with Proactive about the opening of patient enrollment at the first Australian clinical site for the RC220 Phase I solid tumour trial. Tillett confirmed that the trial is now underway, with additional sites expected to follow soon. The focus of this early-stage study is to determine a safe and effective combination dose of RC220 with the chemotherapy drug doxorubicin. According to Tillett, “our drug has the advantage… not only does it improve the anti-cancer treatment, but it protects the heart, at least in animals and cells to date.” He noted the choice of Southside Cancer Centre as the initial site, citing the faster startup process compared to public hospitals. Other sites, including Gosford and Wyong, have also received ethics approval and are expected to join the trial following regulatory clearance. Tillett discussed the use of a Bayesian trial design, which allows quicker progression and minimises patient exposure to suboptimal doses. While this approach can introduce uncertainty in timelines, it is expected to be more beneficial overall for patients and the company. Early preclinical data has shown that RC220 can reduce doxorubicin-related toxicity while enhancing cancer treatment outcomes, a combination Tillett described as unique among current therapies. Beyond the trial, Race Oncology is conducting separate preclinical studies to identify the most effective drug combinations using RC220. Tillett noted that more updates on this work are expected in the coming months. Watch the full video to hear more from Daniel Tillett and stay informed on Race Oncology's developments. ➡️ For more interviews like this, visit Proactive's YouTube channel. Don't forget to like the video, subscribe to the channel, and hit the notification bell for future updates. #RaceOncology #RC220 #CancerResearch #ClinicalTrials #OncologyUpdates #Doxorubicin #CardioProtection #BayesianDesign #CancerTreatment #BiotechNews #ProactiveInvestors

Kevin Werbach speaks with Eric Bradlow, Vice Dean of AI & Analytics at Wharton. Bradlow highlights the transformative impacts of AI from his perspective as an applied statistician and quantitative marketing expert. He describes the distinctive approach of Wharton's analytics program, and its recent evolution with the rise of AI. The conversation highlights the significance of legal and ethical responsibility within the AI field, and the genesis of the new Wharton Accountable AI Lab. Werbach and Bradlow then examine the role of academic institutions in shaping the future of AI, and how institutions like Wharton can lead the way in promoting accountability, learning and responsible AI deployment. Eric Bradlow is the Vice Dean of AI & Analytics at Wharton, Chair of the Marketing Department, and also a professor of Economics, Education, Statistics, and Data Science. His research interests include Bayesian modeling, statistical computing, and developing new methodology for unique data structures with application to business problems. In addition to publishing in a variety of top journals, he has won numerous teaching awards at Wharton, including the MBA Core Curriculum teaching award, the Miller-Sherrerd MBA Core Teaching Award and the Excellence in Teaching Award.  Episode Transcript Wharton AI & Analytics Initiative Eric Bradlow - Knowledge at Wharton   Want to learn more? ​​Engage live with Professor Werbach and other Wharton faculty experts in Wharton's new Strategies for Accountable AI online executive education program. It's perfect for managers, entrepreneurs, and advisors looking to harness AI's power while addressing its risks.  

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:The hype around AI in science often fails to deliver practical results.Bayesian deep learning combines the strengths of deep learning and Bayesian statistics.Fine-tuning LLMs with Bayesian methods improves prediction calibration.There is no single dominant library for Bayesian deep learning yet.Real-world applications of Bayesian deep learning exist in various fields.Prior knowledge is crucial for the effectiveness of Bayesian deep learning.Data efficiency in AI can be enhanced by incorporating prior knowledge.Generative AI and Bayesian deep learning can inform each other.The complexity of a problem influences the choice between Bayesian and traditional deep learning.Meta-learning enhances the efficiency of Bayesian models.PAC-Bayesian theory merges Bayesian and frequentist ideas.Laplace inference offers a cost-effective approximation.Subspace inference can optimize parameter efficiency.Bayesian deep learning is crucial for reliable predictions.Effective communication of uncertainty is essential.Realistic benchmarks are needed for Bayesian methodsCollaboration and communication in the AI community are vital.Chapters:00:00 Introduction to Bayesian Deep Learning04:24 Vincent Fortuin's Journey to Bayesian Deep Learning11:52 Understanding Bayesian Deep Learning16:29 Current Landscape of Bayesian Libraries21:11 Real-World Applications of Bayesian Deep Learning23:33 When to Use Bayesian Deep Learning28:22 Data Efficiency in AI and Generative Modeling30:18 Integrating Bayesian Knowledge into Generative Models31:44 The Role of Meta-Learning in Bayesian Deep Learning34:06 Understanding Pack Bayesian Theory37:55 Algorithms for Bayesian Deep Learning Models

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Matt emphasizes the importance of Bayesian statistics in scenarios with limited data.Communicating insights to coaches is a crucial skill for data analysts.Building a data team requires understanding the needs of the coaching staff.Player recruitment is a significant focus in football analytics.The integration of data science in sports is still evolving.Effective data modeling must consider the practical application in games.Collaboration between data analysts and coaches enhances decision-making.Having a robust data infrastructure is essential for efficient analysis.The landscape of sports analytics is becoming increasingly competitive. Player recruitment involves analyzing various data models.Biases in traditional football statistics can skew player evaluations.Statistical techniques should leverage the structure of football data.Tracking data opens new avenues for understanding player movements.The role of data analysis in football will continue to grow.Aspiring analysts should focus on curiosity and practical experience.Chapters:00:00 Introduction to Football Analytics and Matt's Journey04:54 The Role of Bayesian Methods in Football10:20 Challenges in Communicating Data Insights17:03 Building Relationships with Coaches22:09 The Structure of the Data Team at Como26:18 Focus on Player Recruitment and Transfer Strategies28:48 January Transfer Window Insights30:54 Biases in Football Data Analysis34:11 Comparative Analysis of Men's and Women's Football36:55 Statistical Techniques in Football Analysis42:48 The Impact of Tracking Data on Football Analysis45:49 The Future of Data-Driven Football Strategies47:27 Advice for Aspiring Football Analysts

Today's revolutionary idea is something a bit different: David talks to statistician David Spiegelhalter about how an eighteenth-century theory of probability emerged from relative obscurity in the twentieth century to reconfigure our understanding of the relationship between past, present and future. What was Thomas Bayes's original idea about doing probability in reverse: from effect to cause? What happened when this way of thinking passed through the vortex of the French Revolution? How has it come to lie behind recent innovations in political polling, AI, self-driving cars, medical research and so much more? Why does it remain controversial to this day? The latest edition of our free fortnightly newsletter is available: to get it in your inbox sign up now https://www.ppfideas.com/newsletter  Next time: 1848: The Liberal Revolution w/Chris Clark Past Present Future is part of the Airwave Podcast Network Learn more about your ad choices. Visit megaphone.fm/adchoices

Are We Looking at the End of Atheism? Dr. Christopher Sernaque sits down with Otangelo Grassio, an advocate for Intelligent Design, to break down the biggest questions in science and faith! In the final episode of Current Topics in Science Season 6, we dive deep into his latest book, Unraveling the Theistic Worldview, tackling Bayesian probability, the limits of naturalism, and why the demand for definitive proof of God might be a trap! Don't miss this eye-opening discussion that could change the way you see science forever! Watch now and decide for yourself!

Today's episode is about a very different revolution from any we've discussed so far: David talks to historian Hank Gonzalez about the Haitian Revolution, which for the first time in history saw a slave revolt result in an independent free state. How did the Haitian Revolution intersect with the American and French Revolutions that preceded it? Why were European powers unable to reverse it despite massive military intervention? What is its legacy for the state of Haiti today? Tickets are still available for PPF Live at the Bath Curious Minds Festival: join us on Saturday 29th March to hear David in conversation with Robert Saunders about the legacy of Winston Churchill: The Politician with Nine Lives https://bit.ly/42GPp3X Out tomorrow the latest edition of our free fortnightly newsletter: to get it in your inbox sign up now https://www.ppfideas.com/newsletters Next time: The Bayesian revolution w/David Spiegelhalter Past Present Future is part of the Airwave Podcast Network Learn more about your ad choices. Visit megaphone.fm/adchoices

This episode covers: Cardiology This Week: A concise summary of recent studies AI and the future of the Electrocardiogram The heart in rheumatic disorders and autoimmune diseases Statistics Made Easy: Bayesian analysis Host: Susanna Price Guests: Carlos Aguiar, Paul Friedman, Maya Buch  Want to watch that episode? Go to: https://esc365.escardio.org/event/1801 Disclaimer: ESC TV Today is supported by Bristol Myers Squibb. This scientific content and opinions expressed in the programme have not been influenced in any way by its sponsor. This programme is intended for health care professionals only and is to be used for educational purposes. The European Society of Cardiology (ESC) does not aim to promote medicinal products nor devices. Any views or opinions expressed are the presenters' own and do not reflect the views of the ESC. Declarations of interests: Stephan Achenbach, Antonio Greco, Nicolle Kraenkel and Susanna Price have declared to have no potential conflicts of interest to report. Carlos Aguiar has declared to have potential conflicts of interest to report: personal fees for consultancy and/or speaker fees from Abbott, AbbVie, Alnylam, Amgen, AstraZeneca, Bayer, BiAL, Boehringer-Ingelheim, Daiichi-Sankyo, Ferrer, Gilead, GSK, Lilly, Novartis, Novo Nordisk, Pfizer, Sanofi, Servier, Takeda, Tecnimede. Maya Buch has declared to have potential conflicts of interest to report: grant/research support paid to University of Manchester from Gilead and Galapagos; consultant and/or speaker with funds paid to University of Manchester for AbbVie, Boehringer Ingelheim, CESAS Medical, Eli Lilly, Galapagos, Gilead Sciences, Medistream and Pfizer Inc; member of the Speakers' Bureau for AbbVie with funds paid to University of Manchester. Davide Capodanno has declared to have potential conflicts of interest to report: Bristol Myers Squibb, Daiichi Sankyo, Sanofi Aventis, Novo Nordisk, Terumo. Paul Friedman has declared to have potential conflicts of interest to report: co-inventor of AI ECG algorithms. Steffen Petersen has declared to have potential conflicts of interest to report: consultancy for Circle Cardiovascular Imaging Inc. Calgary, Alberta, Canada. Emma Svennberg has declared to have potential conflicts of interest to report: Abbott, Astra Zeneca, Bayer, Bristol-Myers, Squibb-Pfizer, Johnson & Johnson.

Did Jeffrey Epstein die by murder or suicide? In this episode, I argue that we should use Bayesian statistics to frame the debate. Indeed, we should use this approach to frame most "conspiracy theories". Most such theories are derided as compelling storytellers weaving half truths to fit their narrative.Bayes offers a more analytical approach.1. Make an educated guess about the probability of an event occurring. The likelihood of Epstein dying by suicide.2. Identify authenticated clues that support that hypothesis.3. Assess the odds of each clue happening independently, i.e. jail cameras not working, hyoid bone being broken, both guards falling asleep.4. Then calculate odds of those happening together.5. Perform calculation and then update original probability estimate based upon the probability those clues happening.Using Bayes with a "little" help from Grok, I identify the odds of murder versus suicide.I also identify ways that you should attack this analysis and not just my use of Grok.This approach should be used more frequently as we try to resolve debates surrounding "conspiracies". I don't even really like that word. We're really trying to assess whether Event x was caused by y or z.

When billionaire British entrepreneur Mike Lynch drowned during the sinking of the superyacht Bayesian in August, it sent shockwaves around the world.Having just successfully fought off the US Justice Department on fourteen counts of fraud and conspiracy, he was celebrating his newfound freedom when he was tragically killed during a freak storm.After months of work by our senior reporter, Henry Bodkin, the Daily T investigates what might have caused a boat that was previously described as unsinkable to vanish beneath the waves.Clips in this episode from:BBC NewsnightBBC NewsUniversity of Cambridge Judge Business SchoolBBC Radio 4Sky NewsAPPlanning Editor: Venetia RaineyExecutive Producer: Louisa WellsSound Design: Elliot LampittSocial Media Producer: Niamh WalshStudio Operator: Meghan Searle Hosted on Acast. See acast.com/privacy for more information.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire, Mike Loncaric, David McCormick, Ronald Legere, Sergio Dolia and Michael Cao.Takeaways:Sharks play a crucial role in maintaining healthy ocean ecosystems.Bayesian statistics are particularly useful in data-poor environments like ecology.Teaching Bayesian statistics requires a shift in mindset from traditional statistical methods.The shark meat trade is significant and often overlooked.Ray meat trade is as large as shark meat trade, with specific markets dominating.Understanding the ecological roles of species is essential for effective conservation.Causal language is important in ecological research and should be encouraged.Evidence-driven decision-making is crucial in balancing human and ecological needs.Expert opinions are...

Plus AI Robots Helps SeniorsLike this? Get AIDAILY, delivered to your inbox, every weekday. Subscribe to our newsletter at https://aidaily.usTeleperformance Introduces AI to Neutralize Indian Call Center AccentsTeleperformance, the world's largest call center operator, has implemented AI technology developed by Sanas to "neutralize" the accents of Indian customer service agents in real-time. This initiative aims to enhance clarity and improve customer interactions. While the company asserts that this will foster better connections between customers and agents, critics express concerns about potential impacts on cultural identity and authenticity in customer service.AI Robots May Hold Key to Nursing Japan's Ageing PopulationJapan faces a critical shortage of aged-care workers due to its rapidly ageing population and declining birth rate. Researchers in Tokyo have developed AIREC, an AI-driven humanoid robot capable of assisting with tasks like patient movement and household chores. While promising, these robots require significant advancements in precision and safety before widespread adoption, anticipated around 2030.Tencent's Hunyuan Turbo S AI Model Outpaces DeepSeek R1Tencent has unveiled its latest AI model, Hunyuan Turbo S, which delivers responses in under a second, surpassing the speed of DeepSeek's R1 model. This development intensifies the AI competition among Chinese tech giants, with Tencent emphasizing Turbo S's cost-efficiency and advanced capabilities in knowledge, mathematics, and reasoning. citeturn0news3Majority of Small Businesses Now Embracing Artificial IntelligenceA 2024 survey by Goldman Sachs 10,000 Small Businesses reveals that 69% of small businesses have integrated AI into their operations, a significant increase from 56% in 2023. Business owners report that AI adoption has led to time and cost savings, with applications ranging from coding assistance and content creation to application screening and inventory management. A Breakthrough in AI-Designed Lightweight, Strong MaterialsResearchers have utilized AI to develop nanostructured carbon materials that combine the compressive strength of carbon steel (180–360 MPa) with the low density of Styrofoam (125–215 kg/m³). This advancement holds promise for applications in aviation and other industries where strength-to-weight ratio is critical. The materials were created using a multi-objective Bayesian optimization algorithm and fabricated through two-photon polymerization photolithography.Google's AI Summaries Face Legal Battle Over Search TrafficChegg has sued Google, claiming its AI-generated search summaries are cutting traffic to its site and harming revenue. The lawsuit argues that Google's AI Overviews provide direct answers, discouraging users from clicking external links. This case highlights rising tensions between content creators and AI-driven search models. 

How do our beliefs about ourselves and the world influence the way we learn and make decisions? Dr. Hayley Dorfman joins us to discuss how internal representations like sense of control shape our learning processes. We discuss the interplay between reinforcement learning and Bayesian inference, how agency beliefs impact learning from positive and negative feedback, and how these processes change across different developmental stages/

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Marketing analytics is crucial for understanding customer behavior.PyMC Marketing offers tools for customer lifetime value analysis.Media mix modeling helps allocate marketing spend effectively.Customer Lifetime Value (CLV) models are essential for understanding long-term customer behavior.Productionizing models is essential for real-world applications.Productionizing models involves challenges like model artifact storage and version control.MLflow integration enhances model tracking and management.The open-source community fosters collaboration and innovation.Understanding time series is vital in marketing analytics.Continuous learning is key in the evolving field of data science.Chapters:00:00 Introduction to Will Dean and His Work10:48 Diving into PyMC Marketing17:10 Understanding Media Mix Modeling25:54 Challenges in Productionizing Models35:27 Exploring Customer Lifetime Value Models44:10 Learning and Development in Data ScienceThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz,...

Cruel, petty and occasionally magnanimous, fates rule our lives, determining everything from our careers and romances to our financial success. Despite a burgeoning academic literature studying luck and the occasional theoretical probabilist complaining about Bayesian statistics, we haven't brought the chance of chaotic complex systems into the classroom, and that's particularly true in political science and international relations. That should change, and play-based learning offer new forms of education for future generations.Joining host Danny Crichton and Riskgaming director of programming Laurence Pevsner is Nicholas Rush Smith, director of the Master's Program in International Affairs at The City College of New York and its Graduate Center. His students graduate into plum assignments across international organizations like the United Nations, and he has been increasingly utilizing simulations and experiential learning to transform how future international civil servants learn their craft.We talk about Nick's recent experience playing “Powering Up,” our Riskgaming scenario focused on China's electric vehicle market. Then we talk about the power of play, how dopamine affects the learning cycle, why losing is the best education for winning, David Graeber's ideas around the balance between rules and play, and finally, how play-based learning can teach principles used in even the most bureaucratic institutions like the United Nations and the U.S. Army.Produced by ⁠⁠⁠⁠Christopher Gates⁠⁠⁠⁠Music by ⁠⁠⁠⁠George Ko

This episode is sponsored by Thuma. Thuma is a modern design company that specializes in timeless home essentials that are mindfully made with premium materials and intentional details. To get $100 towards your first bed purchase, go to http://thuma.co/eyeonai   In this episode of the Eye on AI podcast, Pedro Domingos, renowned AI researcher and author of The Master Algorithm, joins Craig Smith to explore the evolution of machine learning, the resurgence of Bayesian AI, and the future of artificial intelligence. Pedro unpacks the ongoing battle between Bayesian and Frequentist approaches, explaining why probability is one of the most misunderstood concepts in AI. He delves into Bayesian networks, their role in AI decision-making, and how they powered Google's ad system before deep learning. We also discuss how Bayesian learning is still outperforming humans in medical diagnosis, search & rescue, and predictive modeling, despite its computational challenges. The conversation shifts to deep learning's limitations, with Pedro revealing how neural networks might be just a disguised form of nearest-neighbor learning. He challenges conventional wisdom on AGI, AI regulation, and the scalability of deep learning, offering insights into why Bayesian reasoning and analogical learning might be the future of AI. We also dive into analogical learning—a field championed by Douglas Hofstadter—exploring its impact on pattern recognition, case-based reasoning, and support vector machines (SVMs). Pedro highlights how AI has cycled through different paradigms, from symbolic AI in the '80s to SVMs in the 2000s, and why the next big breakthrough may not come from neural networks at all. From theoretical AI debates to real-world applications, this episode offers a deep dive into the science behind AI learning methods, their limitations, and what's next for machine intelligence. Don't forget to like, subscribe, and hit the notification bell for more expert discussions on AI, technology, and the future of innovation!    Stay Updated: Craig Smith Twitter: https://twitter.com/craigss Eye on A.I. Twitter: https://twitter.com/EyeOn_AI (00:00) Introduction (02:55) The Five Tribes of Machine Learning Explained   (06:34) Bayesian vs. Frequentist: The Probability Debate   (08:27) What is Bayes' Theorem & How AI Uses It   (12:46) The Power & Limitations of Bayesian Networks   (16:43) How Bayesian Inference Works in AI   (18:56) The Rise & Fall of Bayesian Machine Learning   (20:31) Bayesian AI in Medical Diagnosis & Search and Rescue   (25:07) How Google Used Bayesian Networks for Ads   (28:56) The Role of Uncertainty in AI Decision-Making   (30:34) Why Bayesian Learning is Computationally Hard   (34:18) Analogical Learning – The Overlooked AI Paradigm   (38:09) Support Vector Machines vs. Neural Networks   (41:29) How SVMs Once Dominated Machine Learning   (45:30) The Future of AI – Bayesian, Neural, or Hybrid?   (50:38) Where AI is Heading Next  

01:00 What makes Stephen Miller successful with Donald Trump 16:30 My 3am AI nightmare 25:00 "Ties bind and blind." I am triply inclined not to criticize Ben Shapiro because I share his outlook, his sacred values, his Orthodox Judaism, and many of his friends. 28:00 60 Minutes did nothing wrong in its Kamala Harris interview edit 37:00 Hoover: Apocalypse Now? Peter Thiel on Ancient Prophecies and Modern Tech, https://www.youtube.com/watch?v=qqHueZNEzig 48:00 Peter Thiel: The Techno-Apocalypse is Nigh, https://decoding-the-gurus.captivate.fm/episode/peter-thiel-the-techno-apocalypse-is-nigh 1:12:30 Can a barking dog be racist? 1:23:00 The Eagles are bigger, stronger, faster and more talented than the Chiefs, but Kansas City has a better coach 1:45:00 Ungoverning: The Attack on the Administrative State and the Politics of Chaos, https://www.amazon.com/Ungoverning-Attack-Administrative-State-Politics/dp/0691250529 2:19:00 Andrew Gelman: “Florida man eats diet of butter, cheese, beef; cholesterol oozes from his body”: How much am I to blame for this?, https://statmodeling.stat.columbia.edu/2025/02/06/florida-man-eats-diet-of-butter-cheese-beef-cholesterol-oozes-from-his-body/ 2:27:00 Andrew Gelman: What do I think of this Bayesian analysis of the origins of covid?, https://statmodeling.stat.columbia.edu/2025/02/03/bayesian-analysis-of-origins-of-covid/ 2:29:30 Andrew Gelman: Dan Ariely: “Why Louisiana's Ten Commandments law is a broken moral compass”, https://statmodeling.stat.columbia.edu/2025/01/22/ariely-why-louisianas-ten-commandments-law-is-a-broken-moral-compass/ 2:33:30 Andrew Gelman: The “delay-the-reckoning heuristic” in pro football?, https://statmodeling.stat.columbia.edu/2025/01/20/the-delay-the-reckoning-heuristic-in-pro-football/ 2:38:00 My disdain for RFK Jr and Trump's health picks 2:45:00 My character flaws of lying and cheating 2:49:30 Virtue Signalling Is Virtuous, https://lukeford.net/blog/?p=146676 2:54:00 Decoding Decoding The Gurus, https://lukeford.net/blog/?p=144554 4:15:30 The rise of the far-right 4:11:00 The Great American Bank Robbery: The Unauthorized Report About What Really Caused the Great Recession by Paul Sperry https://odysee.com/@LukeFordLive, https://rumble.com/lukeford, https://dlive.tv/lukefordlivestreams Superchat: https://entropystream.live/app/lukefordlive Bitchute: https://www.bitchute.com/channel/lukeford/ Soundcloud MP3s: https://soundcloud.com/luke-ford-666431593 Code of Conduct: https://lukeford.net/blog/?p=125692 http://lukeford.net Email me: lukeisback@gmail.com or DM me on Twitter.com/lukeford, Best videos: https://lukeford.net/blog/?p=143746 Support the show | https://www.streamlabs.com/lukeford, https://patreon.com/lukeford, https://PayPal.Me/lukeisback Facebook: http://facebook.com/lukecford Book an online Alexander Technique lesson with Luke: https://alexander90210.com Feel free to clip my videos. It's nice when you link back to the original.

Evan Wimpey joins me to chat about working as a professional comedian, some Bayesian jokes, and other stuff that happened before ;)

Die Themen in den Wissensnachrichten: Warum wir uns selbst nicht kitzeln können +++ Deutsch kommt wohl vom Schwarzen Meer +++ Ungewöhnliches Gruppenkuscheln bei Koalas beobachtet +++**********Weiterführende Quellen zu dieser Folge:Modelling sensory attenuation as Bayesian causal inference across two datasets, in: PLOS ONE 24.01.2025The genetic origin of the Indo-Europeans, In: Nature 05.02.2025Social affiliation among sub-adult male koalas in a high-density population, In: Australian Mammology 30.01.2025ADAC Staubilanz 2024Medienbericht vom 06.02.2025Alle Quellen findet ihr hier.**********Ihr könnt uns auch auf diesen Kanälen folgen: TikTok auf&ab , TikTok wie_geht und Instagram .

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Intro to Bayes Course (first 2 lessons free)Advanced Regression Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Thank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin Caprani, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Hugo Botha, Vinh Nguyen, 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, Michael Hankin, Cameron Smith, Tomáš Frýda, Ryan Wesslen, Andreas Netti, Riley King, Yoshiyuki Hamajima, Sven De Maeyer, Michael DeCrescenzo, Fergal M, Mason Yahr, Naoya Kanai, Steven Rowland, Aubrey Clayton, Jeannine Sue, Omri Har Shemesh, Scott Anthony Robson, Robert Yolken, Or Duek, Pavel Dusek, Paul Cox, Andreas Kröpelin, Raphaël R, Nicolas Rode, Gabriel Stechschulte, Arkady, Kurt TeKolste, Gergely Juhasz, Marcus Nölke, Maggi Mackintosh, Grant Pezzolesi, Avram Aelony, Joshua Meehl, Javier Sabio, Kristian Higgins, Alex Jones, Gregorio Aguilar, Matt Rosinski, Bart Trudeau, Luis Fonseca, Dante Gates, Matt Niccolls, Maksim Kuznecov, Michael Thomas, Luke Gorrie, Cory Kiser, Julio, Edvin Saveljev, Frederick Ayala, Jeffrey Powell, Gal Kampel, Adan Romero, Will Geary, Blake Walters, Jonathan Morgan, Francesco Madrisotti, Ivy Huang, Gary Clarke, Robert Flannery, Rasmus Hindström, Stefan, Corey Abshire and Mike Loncaric.Takeaways:The evolution of sports modeling is tied to the availability of high-frequency data.Bayesian methods are valuable in handling messy, hierarchical data.Communication between data scientists and decision-makers is crucial for effective model use.Models are often wrong, and learning from mistakes is part of the process.Simplicity in models can sometimes yield better results than complexity.The integration of analytics in sports is still developing, with opportunities in various sports.Transparency in research and development teams enhances decision-making.Understanding uncertainty in models is essential for informed decisions.The balance between point estimates and full distributions is a...

Sports analytics is a booming industry with new technologies allowing for the parsing of ever more sophisticated statistics. Analysts can now examine the height and the force of a gymnast tumbling pass, the probability of going for it on a 4th down in football, actually working out, and the arc of the best swing for a baseball player. Analytics are also used in the conditioning of athletes, particularly for all the baseball players preparing for the start of the MLB's spring training. Analytics is the focus of this episode of stats and stories with guest Alexandre Andorra. Alexandre Andorra is a Senior Applied Scientist for the Miami Marlins as well a Bayesian modeler at the PyMC Labs consultancy firm that he cofounded as well as the host the podcast dedicated to Bayesian inference “Learning Bayesian Statistics” His areas of expertise include Hierarchical Models, Gaussian Processes and Causal Inference.

Part one of our two-part investigation into the Rationalist cult “The Zizians.” We start with the killing of a border patrol officer and make our way back into the belly of the beast: Silicon Valley. Featuring: Harry Potter fanfic, samurai swords, Guy Fawkes masks, Blake Masters, Bayesian probability, and Eliezer Yudkowsky. Infohazard warning: some of your least favs will be implicated. Discover more episodes at podcast.trueanon.com

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Bayesian statistics offers a robust framework for econometric modeling.State space models provide a comprehensive way to understand time series data.Gaussian random walks serve as a foundational model in time series analysis.Innovations represent external shocks that can significantly impact forecasts.Understanding the assumptions behind models is key to effective forecasting.Complex models are not always better; simplicity can be powerful.Forecasting requires careful consideration of potential disruptions. Understanding observed and hidden states is crucial in modeling.Latent abilities can be modeled as Gaussian random walks.State space models can be highly flexible and diverse.Composability allows for the integration of different model components.Trends in time series should reflect real-world dynamics.Seasonality can be captured through Fourier bases.AR components help model residuals in time series data.Exogenous regression components can enhance state space models.Causal analysis in time series often involves interventions and counterfactuals.Time-varying regression allows for dynamic relationships between variables.Kalman filters were originally developed for tracking rockets in space.The Kalman filter iteratively updates beliefs based on new data.Missing data can be treated as hidden states in the Kalman filter framework.The Kalman filter is a practical application of Bayes' theorem in a sequential context.Understanding the dynamics of systems is crucial for effective modeling.The state space module in PyMC simplifies complex time series modeling tasks.Chapters:00:00 Introduction to Jesse Krabowski and Time Series Analysis04:33 Jesse's Journey into Bayesian Statistics10:51 Exploring State Space Models18:28 Understanding State Space Models and Their Components

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Also, be sure to stick around to the end of the episode for an upcoming change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Also, be sure to stick around to the end of the episode for an upcoming change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Also, be sure to stick around to the end of the episode for an upcoming change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Also, be sure to stick around to the end of the episode for an upcoming change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Also, be sure to stick around to the end of the episode for an upcoming change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Read the official blog post about the change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Read the official blog post about the change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

On Hands-On Tech, Mikah helps Lars, who is seeking help for a friend who is being flooded with spam emails and is looking for a way to filter out the spam emails! Mikah strongly recommends transitioning away from ISP-provided email addresses, as they often get shuffled between companies and can face service disruptions There are also some options to consider when moving from an old email account, such as setting up auto-responses directing to the new address or configuring your new email account to pull from your old account. Mikah suggests two things to do. The first is to create a Gmail account to leverage its excellent spam filtering capabilities, then configure it to pull mail from the Cox address and connect it to Outlook. The second option is to use local spam filtering software like Mailwasher, which offers Bayesian filtering that learns from user input to improve spam detection over time. Other software options to consider are Spambully, Spamfighter, and clean.io, though some may not work without MX record access. Also, be sure to stick around to the end of the episode for an upcoming change to Hands-On Tech. Send in your questions for Mikah to answer! hot@twit.tv Host: Mikah Sargent Download or subscribe to Hands-On Tech at https://twit.tv/shows/hands-on-tech Get episodes ad-free with Club TWiT at https://twit.tv/clubtwit

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:BART models are non-parametric Bayesian models that approximate functions by summing trees.BART is recommended for quick modeling without extensive domain knowledge.PyMC-BART allows mixing BART models with various likelihoods and other models.Variable importance can be easily interpreted using BART models.PreliZ aims to provide better tools for prior elicitation in Bayesian statistics.The integration of BART with Bambi could enhance exploratory modeling.Teaching Bayesian statistics involves practical problem-solving approaches.Future developments in PyMC-BART include significant speed improvements.Prior predictive distributions can aid in understanding model behavior.Interactive learning tools can enhance understanding of statistical concepts.Integrating PreliZ with PyMC improves workflow transparency.Arviz 1.0 is being completely rewritten for better usability.Prior elicitation is crucial in Bayesian modeling.Point intervals and forest plots are effective for visualizing complex data.Chapters:00:00 Introduction to Osvaldo Martin and Bayesian Statistics08:12 Exploring Bayesian Additive Regression Trees (BART)18:45 Prior Elicitation and the PreliZ Package29:56 Teaching Bayesian Statistics and Future Directions45:59 Exploring Prior Predictive Distributions52:08 Interactive Modeling with PreliZ54:06 The Evolution of ArviZ01:01:23 Advancements in ArviZ 1.001:06:20 Educational Initiatives in Bayesian Statistics01:12:33 The Future of Bayesian MethodsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy, Robin Taylor,, Chad Scherrer, Zwelithini Tunyiswa, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Ian Moran, Paul Oreto, Colin...

In this episode of the St. Emlyn's podcast, Rick Body and Greg Yates delve into the concept of likelihood ratios, an advanced yet practical tool for diagnosing patients in the emergency department. Building on the previous episode about predictive values, they explain how likelihood ratios help compare the probability of test results between diseased and non-diseased patients. They provide examples, like evaluating chest pain and using the Smith Calculator for Anterior ST Elevation, to show how likelihood ratios can change clinical decision-making. Rick and Greg also discuss Bayesian reasoning and how pretest and post-test probabilities are used in practice. 00:00 Introduction to the Podcast 00:34 Understanding Likelihood Ratios 02:05 Practical Example: Chest Pain Case 03:53 Calculating Likelihood Ratios 07:17 Applying Bayesian Reasoning 09:50 Recap and Conclusion

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In this episode of Vanishing Gradients, the tables turn as Hugo sits down with Alex Andorra, host of Learning Bayesian Statistics. Hugo shares his journey from mathematics to AI, reflecting on how Bayesian inference shapes his approach to data science, teaching, and building AI-powered applications. They dive into the realities of deploying LLM applications, overcoming “proof-of-concept purgatory,” and why first principles and iteration are critical for success in AI. Whether you're an educator, software engineer, or data scientist, this episode offers valuable insights into the intersection of AI, product development, and real-world deployment. LINKS The podcast on YouTube (https://www.youtube.com/watch?v=BRIYytbqtP0) The original podcast episode (https://learnbayesstats.com/episode/122-learning-and-teaching-in-the-age-of-ai-hugo-bowne-anderson) Alex Andorra on LinkedIn (https://www.linkedin.com/in/alex-andorra/) Hugo on LinkedIn (https://www.linkedin.com/in/hugo-bowne-anderson-045939a5/) Hugo on twitter (https://x.com/hugobowne) Vanishing Gradients on twitter (https://x.com/vanishingdata) Hugo's "Building LLM Applications for Data Scientists and Software Engineers" course (https://maven.com/s/course/d56067f338)

When billionaire British entrepeneur Mike Lynch drowned during the sinking of the superyacht Bayesian in August, it sent shockwaves around the world.Having just successfully fought off the US Justice Department on fourteen counts of fraud and conspiracy, he was celebrating his newfound freedom when he was tragically killed during a freak storm.After months of work by our senior reporter, Henry Bodkin, the Daily T investigates what might have caused a boat that was previously described as unsinkable to vanish beneath the waves.Clips in this episode from: BBC NewsnightBBC NewsUniversity of Cambridge Judge Business SchoolBBC Radio 4Sky NewsAPPlanning Editor: Venetia RaineyExecutive Producer: Louisa WellsSound Design: Elliot LampittSocial Media Producer: Niamh WalshStudio Operator: Meghan Searle Hosted on Acast. See acast.com/privacy for more information.

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Effective data science education requires feedback and rapid iteration.Building LLM applications presents unique challenges and opportunities.The software development lifecycle for AI differs from traditional methods.Collaboration between data scientists and software engineers is crucial.Hugo's new course focuses on practical applications of LLMs.Continuous learning is essential in the fast-evolving tech landscape.Engaging learners through practical exercises enhances education.POC purgatory refers to the challenges faced in deploying LLM-powered software.Focusing on first principles can help overcome integration issues in AI.Aspiring data scientists should prioritize problem-solving over specific tools.Engagement with different parts of an organization is crucial for data scientists.Quick paths to value generation can help gain buy-in for data projects.Multimodal models are an exciting trend in AI development.Probabilistic programming has potential for future growth in data science.Continuous learning and curiosity are vital in the evolving field of data science.Chapters:09:13 Hugo's Journey in Data Science and Education14:57 The Appeal of Bayesian Statistics19:36 Learning and Teaching in Data Science24:53 Key Ingredients for Effective Data Science Education28:44 Podcasting Journey and Insights36:10 Building LLM Applications: Course Overview42:08 Navigating the Software Development Lifecycle48:06 Overcoming Proof of Concept Purgatory55:35 Guidance for Aspiring Data Scientists01:03:25 Exciting Trends in Data Science and AI01:10:51 Balancing Multiple Roles in Data Science01:15:23 Envisioning Accessible Data Science for AllThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim

Contributor: Ricky Dhaliwal MD Educational Pearls:  Etomidate was previously the drug of choice for rapid sequence intubation (RSI) However, it carries a risk of adrenal insufficiency as an adverse effect through inhibition of mitochondrial 11-β-hydroxylase activity A recent meta-analysis analyzing etomidate as an induction agent showed the following: 11 randomized-controlled trials with 2704 patients Number needed to harm is 31; i.e. for every 31 patients that receive etomidate for induction, there is one death The probability of any mortality increase was 98.1% Ketamine is preferable due to a better adverse effect profile Laryngeal spasms and bronchorrhea are the most common adverse effects after IV push Beneficial effects on hemodynamics via catecholamine surge, albeit not as pronounced in shock patients 2023 meta-analysis compared ketamine and etomidate for RSI Ketamine's probability of reducing mortality is cited as 83.2% Overall, induction with ketamine demonstrates a reduced risk of mortality compared with etomidate The dosage of each medication for induction Etomidate: 20 mg based on 0.3 mg/kg for a 70 kg adult Ketamine: 1-2 mg/kg (or 0.5-1 mg/kg in patients with shock) Patients with asthma and/or COPD also benefit from ketamine induction due to putative bronchodilatory properties References  Goyal S, Agrawal A. Ketamine in status asthmaticus: A review. Indian J Crit Care Med. 2013;17(3):154-161. doi:10.4103/0972-5229.117048 Koroki T, Kotani Y, Yaguchi T, et al. Ketamine versus etomidate as an induction agent for tracheal intubation in critically ill adults: a Bayesian meta-analysis. Crit Care. 2024;28(1):1-9. doi:10.1186/s13054-024-04831-4 Kotani Y, Piersanti G, Maiucci G, et al. Etomidate as an induction agent for endotracheal intubation in critically ill patients: A meta-analysis of randomized trials. J Crit Care. 2023;77(April 2023):154317. doi:10.1016/j.jcrc.2023.154317 Summarized & Edited by Jorge Chalit, OMS3 Donate: https://emergencymedicalminute.org/donate/  

Clinical research is undergoing a revolution in light of new demands for speed and opportunities from a technological standpoint. These trends have given rise to a debate about the quality and clinical meaning of traditional methods of investigations versus modern types of clinical studies to collect real world evidence. This debate at the 3rd annual Medical Affairs Innovation Olympics #MAIO2024 in a unique and exciting format with a live poll at the conclusion, features an animated discussion from three speakers: Rashad Massoud, MD, MPH, CEO of Rashad Massoud Associates, LLC., globally recognized healthcare quality expert, physician, formerly visiting faculty at the T.H. Chan School of Public Health; Suzanne Pavon (moderator), Doctor of Pharmacy, Board Member at Iethico, former Vice President of Pharmacovigilance and Quality at Argenx; and Sana Syed, Senior Medical Director - Clinical Lead at Sanofi and public health expert formerly at T.H. Chang School of Public Health. Debate Objectives: ● To discuss the utility of RCTs in research and learning ● To discuss the challenges in translating RCT findings into the real-world environment ● To review the utility of the RCT approach to facilitate real world implementation ● To review the impact of the RCT approach for impact and limitations ● To discuss alternative research methods for research and learning ● To conclude with the research approaches that fit best for clinical trials and the real world; indicating a need for an adaptive, dual approach. 0:00 Alloutcoach Intro Music 0:09 Episode Highlight 3:09 Innovation Olympics Introduction 4:44 Debate Rules & Introduction 6:30 RCTs are the Gold Standard for Research and Learning - For the Motion - Sana Syed 8:12 The Scientific Method - Standard RCT Design 9:46 Rare Disease Case Study 11:38 Translating Biology vs Translating Real World Factors 14:34 Diversity of patients critical for data to represent populations 18:50 RCTs are NOT the Gold Standard for Research: Against the Motion - Rashad Massoud 20:27 Properties of an RCT 21:19 Other Research Questions to Eliminate Other Factors that may influence the results 24:13 Access Questions and Outcomes of Interest - Discovery and Delivery 24:48 Agency for Healthcare Research and Quality (AHRQ) - ~17 yrs to translate data into real world 26:33 Efficacy vs. Effectiveness Research 31:02 Concluding Remarks - case study in which RCT designs are not beneficial 35:30 Question: Health Avatar and AI to create real and virtual control arm Using virtual control arm using real world databases using Bayesian statistical methods 39:23 Case study to emphasize Harnessing Tacit knowledge 42:02 Comment: Weaknesses in generating data we can translate into populations 43:44 Question: Are we creating RCTs from virtual patients or classical RCT design? 47:34 Final Comments - For the Motion, Sana Syed Clinical Studies and Scientific Method - adjustments in diverse patient recruitment tactics 49:31 Final Comments - Against the Motion, Rashad Massoud 53:14 Live Voting Results

Reporting bias, not external focus: A robust Bayesian meta-analysis and systematic review of the external focus of attention literature McKay B, Corson AE, Seedu J, et al. Psychol Bull. 2024;150(11):1347-1362. doi:10.1037/bul0000451 Due to copyright laws, unless the article is open source we cannot legally post the PDF on the website for the world to download at will. Brought to you by our sponsors at: CSMi – https://www.humacnorm.com/ptinquest Learn more about/Buy Erik/Jason/Chris's courses – The Science PT Support us on the Patreons! Music for PT Inquest: “The Science of Selling Yourself Short” by Less Than Jake Used by Permission Other Music by Kevin MacLeod – incompetech.com: MidRoll Promo – Mining by Moonlight Koal Challenge – Sam Roux

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:CFA is commonly used in psychometrics to validate theoretical constructs.Theoretical structure is crucial in confirmatory factor analysis.Bayesian approaches offer flexibility in modeling complex relationships.Model validation involves both global and local fit measures.Sensitivity analysis is vital in Bayesian modeling to avoid skewed results.Complex models should be justified by their ability to answer specific questions.The choice of model complexity should balance fit and theoretical relevance. Fitting models to real data builds confidence in their validity.Divergences in model fitting indicate potential issues with model specification.Factor analysis can help clarify causal relationships between variables.Survey data is a valuable resource for understanding complex phenomena.Philosophical training enhances logical reasoning in data science.Causal inference is increasingly recognized in industry applications.Effective communication is essential for data scientists.Understanding confounding is crucial for accurate modeling.Chapters:10:11 Understanding Structural Equation Modeling (SEM) and Confirmatory Factor Analysis (CFA)20:11 Application of SEM and CFA in HR Analytics30:10 Challenges and Advantages of Bayesian Approaches in SEM and CFA33:58 Evaluating Bayesian Models39:50 Challenges in Model Building44:15 Causal Relationships in SEM and CFA49:01 Practical Applications of SEM and CFA51:47 Influence of Philosophy on Data Science54:51 Designing Models with Confounding in Mind57:39 Future Trends in Causal Inference01:00:03 Advice for Aspiring Data Scientists01:02:48 Future Research DirectionsThank you to my Patrons for making this episode possible!Yusuke Saito, Avi Bryant, Ero Carrera, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, William Benton, James Ahloy,

When we gain new information about beliefs we hold, it’s good practice to update our viewpoints accordingly to avoid incoherence in our thinking. On today’s ID The Future, host Jonathan McLatchie invites professor and author Dr. Tim McGrew to the show to discuss how Bayesian reasoning can help us maintain coherence across our set of beliefs. The pair also apply Bayesian logic to the debate over Darwinian evolution to show that a confidence in design arguments can be mathematically rigorous and logically sound. Bayesian logic provides a mathematical way to update prior probabilities with new information to produce a more realistic likelihood ratio. And when it comes to evaluating different hypotheses, small pieces of evidence can add up. “Even evidence Read More › Source

Send us a textIn this episode, Ben and Daphna are joined by Dr. Lehana Thabane, professor of biostatistics at McMaster University, to discuss principles of success for young researchers. Dr. Thabane shares five guiding principles: fostering collaboration, navigating challenges with a positive mindset, practicing kindness, honing power skills like communication and time management, and seeking mentorship. The conversation also touches on the benefits of Bayesian analysis in research, its practicality, and its ability to provide direct answers to clinical questions. This inspiring episode offers invaluable insights for aspiring researchers and clinicians alike.As always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below. Enjoy!

Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!My Intuitive Bayes Online Courses1:1 Mentorship with meOur theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)-------------------------Love the insights from this episode? Make sure you never miss a beat with Chatpods! Whether you're commuting, working out, or just on the go, Chatpods lets you capture and summarize key takeaways effortlessly.Save time, stay organized, and keep your thoughts at your fingertips.Download Chatpods directly from App Store or Google Play and use it to listen to this podcast today!https://www.chatpods.com/?fr=LearningBayesianStatistics-------------------------Takeaways:Epidemiology focuses on health at various scales, while biology often looks at micro-level details.Bayesian statistics helps connect models to data and quantify uncertainty.Recent advancements in data collection have improved the quality of epidemiological research.Collaboration between domain experts and statisticians is essential for effective research.The COVID-19 pandemic has led to increased data availability and international cooperation.Modeling infectious diseases requires understanding complex dynamics and statistical methods.Challenges in coding and communication between disciplines can hinder progress.Innovations in machine learning and neural networks are shaping the future of epidemiology.The importance of understanding the context and limitations of data in research. Chapters:00:00 Introduction to Bayesian Statistics and Epidemiology03:35 Guest Backgrounds and Their Journey10:04 Understanding Computational Biology vs. Epidemiology16:11 The Role of Bayesian Statistics in Epidemiology21:40 Recent Projects and Applications in Epidemiology31:30...