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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...
The causal graph is at the core of Wolfram Physics.It's crucial to the derivations of Special Relativity, General Relativity and Quantum Mechanics.And if that's not enough to convince you that you need to know about the causal graph, how about this:The causal graph is a reflection of the nature of causality, the nature of objectivity, the nature of reality itself.—Einstein's train thought experimentWhat is the multiway graph? video ⋅ podcast ⋅ articleWhat precisely is causal invariance? video ⋅ podcast ⋅ articleCausality ain't what you think it is video ⋅ podcast ⋅ article—The Last Theory is hosted by Mark Jeffery, founder of Open Web MindI release The Last Theory as a video too! Watch here.The full article is here.Kootenay Village Ventures Inc.
In this episode of Peace of Mind, hosts John and Eoin look at Causal depression which is the result of a traumatic event and ways to deal with it and ways that do not help but make matters worse. L'articolo E64 | Peace of Mind – John and Eoin – Causal depression and how to deal with it proviene da Radio Maria.
Send us a textWhat if nonprofits could measure their impact without breaking the bank or succumbing to funder demands for costly trials? Join us on "The Nonprofit Fix" as we unravel the complexities of impact measurement, challenging the dominance of randomized control trials (RCTs) and exploring more accessible alternatives. With three decades of experience in program evaluation, we dissect the limitations of RCTs and the pitfalls of non-experimental studies, which often inflate success rates without truly reflecting causality. Our conversation doesn't stop there—we navigate the promising terrain of AI and machine learning, which holds the potential to revolutionize evaluation methods by leveraging existing data for more accurate and cost-effective assessments.We then examine the paradigm shift in nonprofit data management, advocating for a transition from compliance-driven collection to harnessing data for real-time insights and program enhancement. Imagine reducing evaluation lag time while simultaneously fostering a dynamic, responsive service delivery—technology makes this possible. By repurposing administrative data and adopting machine learning, nonprofits can create feedback loops that enhance decision-making and program effectiveness. This approach not only transforms service delivery but also generates a culture of continuous improvement that benefits both practitioners and beneficiaries.Finally, we delve into the nuances of differentiating raw data from genuine impact within the nonprofit sector. With a critical eye, we address the challenges of using simplistic surveys and the role of sophisticated program administrative data systems. The conversation extends to the potential of machine learning to build comprehensive models that reflect true program efficacy. We explore key resources like Project Evident and training opportunities that open new horizons for nonprofits seeking to refine their impact measurement. As we wrap up, we offer a glimpse into our podcast's evolution and tease future discussions on the effects of changes in federal administration on nonprofits.
In this episode of Daily Value, we look at the latest research on B vitamins and their in neuropsychiatric disorders. A newly published meta-analysis suggests a causal relationship between B vitamin deficiencies and neuropsychiatric disorders. We will break down the scientific findings on B6, B12, and folate, shedding light on their roles in conditions like Parkinson's disease.Discussion Points:How recent genetic studies support a causal link between B vitamin deficiencies and mental health conditions.The role of B vitamins in reducing neurotoxicity and slowing brain atrophy.How vitamin B12 may protect against dopamine neuron loss and disease progression.Evidence linking low B6 levels to neurotransmitter imbalances and schizophrenia risk.The impact of folate on one-carbon metabolism and its protective role in neurodegeneration.https://www.sciencedirect.com/science/article/abs/pii/S0149763425000685#:~:text=In a meta-analysis of,beneficial for certain specific diseases.https://pubmed.ncbi.nlm.nih.gov/26757190/https://pubmed.ncbi.nlm.nih.gov/32257364/https://pubmed.ncbi.nlm.nih.gov/30858560/https://pubmed.ncbi.nlm.nih.gov/32424116/https://pubmed.ncbi.nlm.nih.gov/33941768/Support the show
In this Episode:Catholic Teaching and Final Salvation Diocesan Staff Apologist and Speaker for Catholic Answers, Dr. Karlo Broussard, explains the Why's behind Catholic Beliefs from Faith, Morality, and Culture. Providing the Reasons behind the claims made by the Catholic Church. Send your questions to...Karlo@stmichaelradio.comA Production of St. Michael Catholic RadioThe Catholic Reason Airs Every Thursday on 94.9 St Michael Catholic Radio at 4 p.m. CST.
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Send us a textFrom Quantum Causal Models to Causal AI at SpotifyCiarán loved Lego.Fascinated by the endless possibilities offered by the blocks, he once asked his parents what he could do as an adult to keep building with them.The answer: engineering.As he delved deeper into engineering, Ciarán noticed that its rules relied on a deeper structure. This realization inspired him to pursue quantum physics, which eventually brought him face-to-face with fundamental questions about causality.Today, Ciarán blends his deep understanding of physics and quantum causal models with applied work at Spotify, solving complex problems in innovative ways.Recently, while collaborating with one of his students, he stumbled upon a new interesting question: could we learn something about the early history of the universe by applying causal inference methods in astrophysics?Could we? Hear it from Ciarán himself.Join us for this one-of-a-kind conversation!------------------------------------------------------------------------------------------------------Video version and episode links available on YouTubeRecorded on Nov 6, 2024 in Dublin, Ireland.------------------------------------------------------------------------------------------------------About The GuestCiarán Gilligan-Lee is Head of the Causal Inference Research Lab at Spotify and Honorary Associate Professor at University College London. He got interested in causality during his studies in quantum physics. This interest led him to study quantum causal models. He published in Nature Machine Intelligence, Nature Quantum Information, Physical Review Letters, New Journal of Physics and more. In his free time, he writes for New Scientist and helps his students apply causal methods in new fields (e.g., astrophysics).Connect with Ciarán:- Ciarán on LinkedIn: https://www.linkedin.com/in/ciaran-gilligan-lee/- Ciarán's web page: https://www.ciarangilliganlee.com/About The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entreSupport the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4
On this episode, host Sima Vasa talks to George London, Chief Technology Officer of Upwave. George shares insights into the complexities of the advertising ecosystem, the role of data in campaign optimization and the parallels between financial and ad markets. Key Takeaways: (02:14) From philosophy to data leadership.(05:34) Persistence and adaptability shaped George's path to CTO.(08:15) Causal inference helps Upwave provide reliable insights for large media investments.(11:32) Rigorous measurement turns $10M in ad spend into $20M in value.(13:10) Daily insights from Upwave simplify complex national ad campaigns.(15:12) Actionable insights drive Upwave's mission to optimize brand investments.(16:23) Targeted surveys reveal brand impact across specific ad campaigns.(17:59) The ad ecosystem spans brands, publishers and a complex chain of intermediaries.(20:36) Generative AI powers Upwave's automated ad reporting with Trade Desk.(22:47) Advertising shares dynamics with financial markets, including bidding and price discovery. Resources Mentioned: UpWaveTrade Desk Thanks for listening to the Data Gurus podcast, brought to you by Infinity Squared. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show, and be sure to subscribe so you never miss another insightful conversation. #Analytics #MA #Data #Strategy #Innovation #Acquisitions #MRX #Restech
After his recent trip to Atlanta for a speaking gig, Keith's got some fresh insights about how the way we see ourselves can really shape our success. He's breaking down the difference between a scarcity mindset that holds you back and an abundant mindset that drives success. Keith shares some personal stories and drops some knowledge bombs on how shifting your identity and aligning with the right energy can totally change your game. If you want to level up in business or life, this episode is packed with tips and wisdom you won't want to miss. Let's dive in! Check out these episode highlights: 00:00 - Overcoming Self-Limiting Beliefs and Burnout 06:03 - Spiritual Alignment Attracts Positive Opportunities 07:53 - Elevating Identity to Overcome Scarcity Mindset 11:12 - "Exploring Purpose and Seeking Guidance" 14:41 - "Embrace Clarity Through Higher Perspective" Key Takeaways: Identity Dictates Perception: The same piece of information can lead to vastly different outcomes depending on one's mindset. Whether you're stuck in a scarcity mindset or thriving in an abundance-focused one, your identity steers the ship. The Power of Conviction: Aligning with high beliefs and developing strong convictions in your actions can attract the right people and opportunities. Conviction is an unparalleled influence that can't be easily replicated. Faith and Action: Faith isn't just belief; it's action. It's about courage and the commitment to move forward, trusting in your ability to figure things out. This approach opens up new perspectives and higher levels of opportunity. Resources and Websites:
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
reference: Sri Aurobindo and the Mother, Powers Within, Chapter XIX Occult Powers of the Subliminal, pp. 151-152 This episode is also available as a blog post at https://sriaurobindostudies.wordpress.com/2025/01/18/the-quest-to-establish-the-connection-to-the-higher-subtler-causal-planes-of-existence/ Video presentations, interviews and podcast episodes are all available on the YouTube Channel https://www.youtube.com/@santoshkrinsky871 More information about Sri Aurobindo can be found at www.aurobindo.net The US editions and links to e-book editions of Sri Aurobindo's writings can be found at Lotus Press www.lotuspress.com
Send us a textStefan Feuerriegel is the Head of the Institute of AI in Management at LMU.His team consistently publishes work on causal machine learning at top AI conferences, including NeurIPS, ICML, and more.At the same time, they help businesses implement causal methods in practice.They worked on projects with companies like ABB Hitachi, and Booking.com.Stefan believes his team thrives because of its diversity and aims to bring more causal machine learning to medicine.I had a great conversation with him, and I hope you'll enjoy it too!>> Guest info:Stefan Feuerriegel is a professor and the Head of the Institute of AI in Management at LMU. Previously, he worked as a consultant at McKinsey & Co. and ran his own AI startup.>> Episode Links:Papers- Feuerriegel, S. et al. (2024) - Causal machine learning for predicting treatment outcomes (https://www.nature.com/articles/s41591-024-02902-1)- Kuzmanivic, M. et al. (2024) - Causal Machine Learning for Cost-Effective Allocation of Development Aid (https://arxiv.org/abs/2401.16986)- Schröder, M. et al. (2024) - Conformal Prediction for Causal Effects of Continuous Treatments (https://arxiv.org/abs/2407.03094)>> WWW: https://www.som.lmu.de/ai/>> LinkedIn: https://www.linkedin.com/in/stefan-feuerriegel/Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4
New Casual Catch-Up episode is now available! In this episode, we discuss our recent trip to Philadelphia for PAX Unplugged. You'll also hear from the designers and developers of several stand-out SM:TT games that we spoke with at the con.
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,
Send us a textCausal Bandits at cAI 2024 (The Royal Society, London)The cAI Conference in London slammed the door on baseless claims that causality cannot be used in industrial practice.In the episode of Causal Bandits Extra we interview participants and speakers at Causal AI Conference London, who share their main insights from the event, and the challenges they face in applying causal methods in their everyday work.Time codes:00:29 - Eyal Kazin (Zimmer Biomet)01:44 - Athanasios Vlontzos (Spotify)04:02 - Mimie Liotsiou (Dunnhumby)06:13 - Fernanda Hinze (Croud)09:00 - Clara Higuera Cabañes (BBVA)10:28 - Javier Moral Hernández (BBVA)11:25 - Álvaro Ibraín Rodríguez (BBVA)12:10 - Hugo Proença (Booking.com)13:21 - Debora Andrade (Seamless AI)15:09 - Puneeth Nikin (Croud)17:54 - Puneet Gupta (Cisco)19:43 - Arthur Mello (Sephora)=============================
Scott Hebner, an experienced AI analyst and former IBM executive, brings a wealth of knowledge on the rapidly evolving field of artificial intelligence. After transitioning from a corporate role to advisory work, Hebner has focused on how AI is transforming industries, particularly small businesses. He highlights the shift from traditional predictive AI models to powerful generative AI tools that are democratizing access to cutting-edge technology. Small businesses, in particular, are benefiting from AI's ability to streamline operations, improve decision-making, and increase productivity, leveling the playing field with larger enterprises. As AI continues to evolve, the next big leap is goal-oriented AI agents—systems that autonomously pursue objectives and solve complex business problems. Hebner emphasizes how businesses must adapt quickly to this fast-paced development and invest in AI talent to stay competitive. The demand for skilled AI professionals is growing, but the supply is lagging, creating an urgent need for investments in education and training to build the workforce of the future. The intersection of AI and decision-making is also transforming industries, with causal AI emerging as a powerful tool for understanding cause and effect in business scenarios. To stay up-to-date on the latest AI trends and developments, follow Scott Hebner and explore theCUBE Research newsletters for in-depth and expert insights on the cutting edge of AI and technology. Whether you're a small business owner or a tech enthusiast, these resources are a great way to stay informed and ahead of the curve in the AI revolution. We're happy you're here! Like the pod? Visit our website! Start your trial on Simplified. Schedule a consult, get on the mailing list, and learn more about my favorite tools and programs via https://www.yourbrandamplified.com
Do language models understand the causal structure of the world, or do they merely note correlations? And what happens when you build a big AI society out of them? In this brief episode, recorded at the Bay Area Alignment Workshop, I chat with Zhijing Jin about her research on these questions. Patreon: https://www.patreon.com/axrpodcast Ko-fi: https://ko-fi.com/axrpodcast The transcript: https://axrp.net/episode/2024/11/14/episode-38_0-zhijing-jin-llms-causality-multi-agent-systems.html FAR.AI: https://far.ai/ FAR.AI on X (aka Twitter): https://x.com/farairesearch FAR.AI on YouTube: https://www.youtube.com/@FARAIResearch The Alignment Workshop: https://www.alignment-workshop.com/ Topics we discuss, and timestamps: 00:35 - How the Alignment Workshop is 00:47 - How Zhijing got interested in causality and natural language processing 03:14 - Causality and alignment 06:21 - Causality without randomness 10:07 - Causal abstraction 11:42 - Why LLM causal reasoning? 13:20 - Understanding LLM causal reasoning 16:33 - Multi-agent systems Links: Zhijing's website: https://zhijing-jin.com/fantasy/ Zhijing on X (aka Twitter): https://x.com/zhijingjin Can Large Language Models Infer Causation from Correlation?: https://arxiv.org/abs/2306.05836 Cooperate or Collapse: Emergence of Sustainable Cooperation in a Society of LLM Agents: https://arxiv.org/abs/2404.16698 Episode art by Hamish Doodles: hamishdoodles.com
How often do you assume you know WHY something happened? How quickly do you land on a conclusion about why something DIDN'T happen? Turns out, we humans are great at assigning "causes" to things. We're just not great at getting it right! In this quick episode, I clue you into the cognitive tendency of Causal Attribution, including where it comes from, how to spot it, and how to make conscious decisions in spite of it! LINKS! What's your DECISION STYLE? Take the Quiz! Need to say NO? Here's your 11-minute Crash Course on Saying NO Guilt-Free. Ready to talk about getting clear, intentional & sure of your choices? Book your free consultation Join the Alignment and Accountability Club Hire me for two weeks to consciously, confidently work through a decision with the Make a Decision Package
Send us a textWhich models work best for causal discovery and double machine learning?In this extra episode, we present 4 more conversations with the researchers presenting their work at the CLeaR 2024 conference in Los Angeles, California.What you'll learn:- Which causal discovery models perform best with their default hyperparameters?- How to tune your double machine learning model?- Does putting your paper on ArXiv early increase its chances of being accepted at a conference?- How to deal with causal representation learning with multiple latent interventions?Time codes:00:24 Damian Machlanski - Hyperparameter Tuning for Causal Discovery08:52 Oliver Schacht - Hyperparameter Tuning for DML14:41 Yanai Elazar - Causal Effect of Early ArXiving on Paper Acceptance18:53 Simon Bing - Identifying Linearly-Mixed Causal Representations from Multi-Node Interventions=============================
There is a growing mandate among researchers and VCs to provide proof of causal human biology for new targets. On the latest BioCentury This Week podcast, BioCentury's editors discuss the different strategies being deployed to identify causal links to disease using observational patient data or human cell models, including the challenges that come with each approach and the various computational methodologies companies are using.They also discuss the outcome of FDA's advisory committee meeting on Barth syndrome candidate elamipretide from Stealth Biotherapeutics, and the implications of the discussion for review of ultrarare disease therapies more broadly.Diving into the deal of the day, the editors review the proposal by H. Lundbeck to acquire Longboard Pharmaceuticals for $2.6 billion, and discuss how the biotech's therapy for developmental epilepsies may stack up against competitors.View full story: https://www.biocentury.com/article/65384300:00 - Introduction00:34 - Causal Biology and Big Data17:52 - FDA's Ultra-Rare Decision27:29 - Lundbeck Acquires LongboardTo submit a question to BioCentury's editors, email the BioCentury This Week team at podcasts@biocentury.com.Reach us by sending a text
Send us a textRoot cause analysis, model explanations, causal discovery.Are we facing a missing benchmark problem?Or not anymore?In this special episode, we travel to Los Angeles to talk with researchers at the forefront of causal research, exploring their projects, key insights, and the challenges they face in their work.Time codes:0:15 - 02:40 Kevin Debeire2:41 - 06:37 Yuchen Zhu06:37 - 10:09 Konstantin Göbler10:09 - 17:05 Urja Pawar17:05 - 23:16 William OrchardEnjoy!Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/military-history
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/political-science
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/world-affairs
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/national-security
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/science-technology-and-society
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices
If you seek a compelling exploration of contemporary armed conflict, then Conflict Realism: Understanding the Causal Logic of Modern War and Warfare (Howgate Publishing, 2024) by Amos C. Fox is for you. It delves into the intricate web of causation to unveil five pivotal trends shaping the landscape of war and warfare - urban warfare, sieges, attrition, precision strike strategy, and proxy wars - revealing a stark reality: wars remain far more attritional than anticipated by policymakers, military practitioners, and analysts alike. What's more, just as attritional wars are becoming quite common, conflict elongation – wars of extended duration – are also becoming the norm. Through insightful analysis and a keen understanding of geopolitical intricacies, Amos Fox navigates the reader through the intricate interplay of these trends, shedding light on their profound implications for global security. This riveting work challenges conventional wisdom, offering readers a thought-provoking perspective on the contemporary nature of armed conflicts, ultimately urging a reconsideration of strategies and policies in the face of an ever-evolving battlefield. Amos C. Fox, PhD, is a Fellow with Arizona State University's Future Security Initiative. Amos is also a lecturer in the Department of Political Science at the University of Houston. He hosts the Revolution in Military Affairs, Soldier Pulse and WarCast podcasts, serves as an editorial board member with the Journal of Military Studies and is a senior editor with Small Wars Journal. Amos is also a retired US Army officer, where he served more than 24 years, retiring at the rank of Lieutenant Colonel. Stephen Satkiewicz is an independent scholar whose research areas are related to Civilizational Sciences, Social Complexity, Big History, Historical Sociology, military history, War studies, International Relations, Geopolitics, as well as Russian and East European history. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/book-of-the-day
Send us a text *Causal Bandits at AAAI 2024 || Part 2*In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada.Time codes: 00:12 - 04:18 Kevin Xia (Columbia University) - Transportability4:19 - 9:53 Patrick Altmeyer (Delft) - Explainability & black-box models9:54 - 12:24 Lokesh Nagalapatti (IIT Bombay) - Continuous treatment effects12:24 - 16:06 Golnoosh Farnadi (McGill University) - Causality & responsible AI16:06 - 17:37 Markus Bläser (Saarland University) - Fast identification of causal parameters17:37 - 22:37 Devendra Singh Dhami (TU/e) - The future of causal AI Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4
STRONGER BONES LIFESTYLE: REVERSING THE COURSE OF OSTEOPOROSIS NATURALLY
Welcome back to the Stronger Bones Lifestyle Podcast! In Episode 79, your host Debi Robinson dives deep into the topic of bone health, specifically focusing on osteopenia and how it differs from osteoporosis. Drawing from her extensive experience working with women through yoga classes, online programs, and direct consultations, Debi sheds light on these often misunderstood conditions.Debi embarks on a detailed exploration of how bone loss begins long before most women are aware, starting around age 35. She explains the importance of understanding bone health terminology, the metabolic processes contributing to bone loss, and the role of lifestyle factors such as diet, stress, and exercise. By dissecting statistics and providing practical insights, Debi empowers her listeners to take control of their bone health.Listen to learn more about the stages of bone loss, the central role of calcium and vitamin D, and the statistics regarding bone health in postmenopausal women. Debi emphasizes that osteoporosis and osteopenia are not inevitable parts of aging and offers actionable steps for prevention and reversal through lifestyle adjustments.This episode provides a comprehensive guide for women looking to understand and improve their bone health, arming them with the knowledge needed to live a stronger, more empowered life.Key Takeaways:[1:54] Osteopenia 101[2:46] Building our bones and loosing bones[4:16] Acidic Blood[5:42] First Stage of Bone Loss[7:02] The Stats of Bone Loss[8:31] The disease state of Bone Loss [8:54] What to look at in your DEXA scan[9:45] How the term Osteopenia came about[10:56] Being in control of our bone health[12:27] Metabolic and biochemical components[13:57] Medications[14:41] Causal factors of OsteopeniaMemorable Quotes:"It's not because you're not taking enough Calcium and Vitamin D supplements, it's because your body is not properly digesting and absorbing those nutrients." [6:02] - Debi"We look at bone loss as a disease and there's a pill for disease. Well that's not necessarily true." [14:04] - Debi"You are in control of you, so learn what you can do to build stronger bones." [20:28] - DebiTo learn more about me and to stay connected, click on the links below:Instagram: @debirobinsonwellnessWebsite: DebiRobinson.comHealthy Gut Healthy Bones Program
Send us a text Causal Bandits at AAAI 2024 || Part 1In this special episode we interview researchers who presented their work at AAAI 2024 in Vancouver, Canada and participants of our workshop on causality and large language models (LLMs)Time codes:00:00 Intro00:20 Osman Ali Mian (CISPA) - Adaptive causal discovery for time series04:35 Emily McMilin (Independent/Meta) - LLMs, causality & selection bias07:36 Scott Mueller (UCLA) - Causality for EV incentives12:41 Andrew Lampinen (Google DeepMind) - Causality from passive data15:16 Ali Edalati (Huawei) - About Causal Parrots workshop15:26 Adbelrahman Zayed (MILA) - About Causal Parrots workshop Support the showCausal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4
http://www.coachmiked.com Subscribe: Apple Podcasts: https://podcasts.apple.com/us/podcast/impact-and-fulfillment-with-coach-mike-d/id1230596918 Spotify: https://open.spotify.com/show/4oNGxgmdwaTOpWwdf1BGyR YouTube: https://www.youtube.com/@coachmikeD Be Blessed, Well and Wise! -Coach Mike D
WATCH: https://youtu.be/_Kj2OgkxGa0 Terrence Deacon is Professor and Chair of the Department of Anthropology and member of the Helen Wills Neuroscience Institute at the University of California, Berkeley. His research combines developmental evolutionary biology and comparative neuroanatomy to investigate the evolution of human cognition, and is particularly focused on the explanation of emergent processes in biology and cognition. Terrence is a Harvard Lehman Fellow, a Harvard Medical School Psychiatric Neuroscience Fellow, a Western Washington University Centenary Alumni Fellow, and the 69^th James Arthur Lecturer for the American Museum of Natural History. He has published over 100 research papers in collected volumes and scholarly journals, and his acclaimed book, "The Symbolic Species: The Co-evolution of Language and the Brain" (1997) was awarded the I. J. Staley Prize for the most influential book in Anthropology in 2005 by the School of American Research. His other books include "Incomplete Nature: How Mind Emerged from Matter" (2011) and "Homo Sapiens: Evolutionary Biology and the Human Sciences" (2012). TIMESTAMPS: (0:00) - Introduction (1:29) - The Mind-Body Problem (10:50) - Universal Grammar (18:10) - Linguistic Prosthesis & Shared Cognition (27:49) - Teleology vs Teleonomy (35:29) - Absential Causal Powers (39:58) - Thermodynamics, Morphodynamics & Teleodynamics (44:20) - The Role of Constraints & Causal Emergence (1:00:55) - Self-Organization, Self-Assembly & Self-Repair (1:24:17) - The Origin of Life on Earth & Proto-Life in the Cosmos (1:33:50) - Pre-LUCA (Last Universal Common Ancestor) Evolution Problem (1:46:45) - "Falling Up: How Inverse Darwinism Catalyzes Evolution" (Terrence's Next Book) (2:06:50) - Incomplete Nature & Mind's Emergence (2:20:06) - Mind-Body Solution & Landscape of Consciousness (2:28:06) - Implications of Terrence's Work (2:37:10) - Artificial Intelligence (2:44:30) - Terrence's Major Influences (Peirce etc.) (3:01:30) - Importance of Development in Evolution ("EvoDevo") (3:06:40) - Conclusion EPISODE LINKS: - Terrence's Website: https://tinyurl.com/2zchenan - Terrence's Publications: https://tinyurl.com/4tctx9ve - Terrence's Books: https://tinyurl.com/yrxt72dh - Keith Frankish: https://youtu.be/jTO-A1lw4JM - Michael Levin: https://youtu.be/1R-tdscgxu4 - Mark Solms: https://youtu.be/rkbeaxjAZm4 CONNECT: - Website: https://tevinnaidu.com - Podcast: https://podcasters.spotify.com/pod/show/drtevinnaidu - Twitter: https://twitter.com/drtevinnaidu - Facebook: https://www.facebook.com/drtevinnaidu - Instagram: https://www.instagram.com/drtevinnaidu - LinkedIn: https://www.linkedin.com/in/drtevinnaidu ============================= Disclaimer: The information provided on this channel is for educational purposes only. The content is shared in the spirit of open discourse and does not constitute, nor does it substitute, professional or medical advice. We do not accept any liability for any loss or damage incurred from you acting or not acting as a result of listening/watching any of our contents. You acknowledge that you use the information provided at your own risk. Listeners/viewers are advised to conduct their own research and consult with their own experts in the respective fields.
Today, a different episode. I started offering coaching, and to give you the insight into my method, I invited a Commander legend, Shivam. He told me he was struggling a bit with BLB draft (aren't we all) and wanted a second pair of eyes on his drafting. We go through his draft and try to identify where the potential problems lay. As it is Shivam, plenty of anecdotes in between, but eventually we identify some problems with his lane and hopefully - his next drafts will be better for it. You can find Shivam 24.7 on twitter - and it is a joy. A true oasis of positivity in the bitter world of Magic content creation. https://x.com/ghirapurigears If you are interested in coaching - ping me a message. On Discord. On Twitter. You can find my socials on https://linktr.ee/sierkovitz Join the Discord, sign up for Patreon, and use this Linktree for everything else! Watch this episode and see the slides: https://www.youtube.com/watch?v=TbviD8zbFaU This podcast is sponsored by mtgazone.com - get your reading fix from the best and brightest Magic writers in the business. You can get the BulkBox at https://www.bulkbox.co.uk/store/ if you are in the UK. Remember to use SIERKO10 code for a 10% discount! If you are outside of UK, you can find your local distributor on the BulkBox website. Theme song: You Do You, Mana Junkie by essesq (c) copyright 2020 Licensed under a Creative Commons Attribution (3.0) license. http://dig.ccmixter.org/files/essesq/61332
PhaseV is pioneering the use of reinforcement learning, causal ML, and adaptive trial design to optimize resource utilization and time efficiency in drug development.
Come dive into the transformative world of Cross Discipline Causal Loop Diagramming on this episode of the Mob Mentality Show! Unveil the principles and practices behind generating organization-wide understanding and breaking down cross-functional misunderstandings that often lead to missed opportunities. **Key Highlights:** **1. The Cost of Misunderstanding:** Discover how cross-functional misunderstandings can lead to missed opportunities and inefficiencies. We explore real-world examples and offer practical solutions to bridge these gaps. **2. Insights from "The 5th Discipline":** Learn from the wisdom of Peter Senge's seminal book, "The 5th Discipline". Understand how systems thinking can revolutionize your organizational dynamics and foster a culture of continuous improvement. **3. Causal Loop Diagramming 101:** Get a clear definition of causal loop diagramming and its critical role in understanding complex adaptive systems within organizations. See how quantities in a system impact each other through balancing and reinforcing loops. **4. Breaking Down Knowledge Silos:** Address the dangers of over-reliance on specialists and knowledge silos. Learn how causal loop diagramming can facilitate better communication and collaboration across different functions and departments. **5. Visualizing Complex Systems:** Grasp the importance of visualizing exponential effects and cause-and-effect patterns that enhance the performance of typically separately managed systems. **6. Practical Examples and Tools:** Dive into examples from a Fifth Discipline book club and understand how to draw and use causal loop diagrams. Explore tools and techniques that make the process accessible and effective. **7. Real-World Applications:** Hear about a causal loop diagram example related to bad code quality and how pairing with each part of the system can build a comprehensive system diagram. Understand the impact of psychological safety on system self-awareness. **8. Interdepartmental Impact:** Discuss the importance of seeing the whole system to understand interdepartmental impacts. Compare the efficacy of having the whole system in the room with and without formal tools. **9. Addressing Second-Order Effects:** Identify and address second-order effects and human shortcomings in seeing side effects of side effects. Learn how diagramming can help deal with the chunking problem of a large system. **10. Feedback and Future Learning:** Hear feedback from Big Agile session participants and understand the learning path for drawing and using causal loop diagrams. Discover how mob/pair causal loop diagramming can enhance understanding and problem-solving. Join us as we uncover the power of causal loop diagramming to aid decision making, improve communication, and foster a culture of collaboration and continuous improvement. Don't miss out on this edifying episode! Ensure you stay engaged in the world of organizational development and systems thinking by tuning in to this episode. Your learning path for causal loop diagramming and breaking down knowledge silos starts here! Video and Show Notes: https://youtu.be/Oe75SzPeCSs
Please join us for this helpful healing session to take you to the next level of what is possible. Here the Arcturian Energies and Consciousness assist us in clearing those beliefs and patterns which are holding us back from accomplishing what is possible in our lives.We use the energies and frequencies to clear all seven subtle body levels of patterns which are holding us back.Physical-clear deeply embedded patterns in our muscles and ligaments which still hold the belief in lack or poverty consciousness.Etheric-clear those patterns also from the chakras and channels that don't allow us to take things to the next level.Emotional-clear self sabotage and self doubt from the emotional field.Mental-clear mental patterns that limit who we think we are and what we are capable of.Causal/karmic-clear karmic patterns where we may have held others back from their full potential.Spiritual-Be able to hear clearing the pattern and call of our soul and soul plan.Divine-Remember our True Self and the wholeness that already is. --- Support this podcast: https://podcasters.spotify.com/pod/show/gene-ang/support
Send us a Text Message.Can we say something about YOUR personal treatment effect?The estimation of individual treatment effects is the Holy Grail of personalized medicine.It's also extremely difficult.Yet, Scott is not discouraged from studying this topic.In fact, he quit a pretty successful business to study it.In a series of papers, Scott describes how combining experimental and observational data can help us understand individual causal effects.Although this sounds enigmatic to many, the intuition behind this mechanism is simpler than you might think.In the episode we discuss:
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Dialogue on What It Means For Something to Have A Function/Purpose, published by johnswentworth on July 16, 2024 on LessWrong. Context for LW audience: Ramana, Steve and John regularly talk about stuff in the general cluster of agency, abstraction, optimization, compression, purpose, representation, etc. We decided to write down some of our discussion and post it here. This is a snapshot of us figuring stuff out together. Hooks from Ramana: Where does normativity come from? Two senses of "why" (from Dennett): How come? vs What for? (The latter is more sophisticated, and less resilient. Does it supervene on the former?) An optimisation process is something that produces/selects things according to some criterion. The products of an optimisation process will have some properties related to the optimisation criterion, depending on how good the process is at finding optimal products. The products of an optimisation process may or may not themselves be optimisers (i.e. be a thing that runs an optimisation process itself), or may have goals themselves. But neither of these are necessary. Things get interesting when some optimisation process (with a particular criterion) is producing products that are optimisers or have goals. Then we can start looking at what the relationship is between the goals of the products, or the optimisation criteria of the products, vs the optimisation criterion of the process that produced them. If you're modeling "having mental content" as having a Bayesian network, at some point I think you'll run into the question of where the (random) variables come from. I worry that the real-life process of developing mental content mixes up creating variables with updating beliefs a lot more than the Bayesian network model lets on. A central question regarding normativity for me is "Who/what is doing the enforcing?", "What kind of work goes into enforcing?" Also to clarify, by normativity I was trying to get at the relationship between some content and the thing it represents. Like, there's a sense of the content is "supposed to" track or be like the thing it represents. There's a normative standard on the content. It can be wrong, it can be corrected, etc. It can't just be. If it were just being, which is how things presumably start out, it wouldn't be representing. Intrinsic Purpose vs Purpose Grounded in Evolution Steve As you know, I totally agree that mental content is normative - this was a hard lesson for philosophers to swallow, or at least the ones that tried to "naturalize" mental content (make it a physical fact) by turning to causal correlations. Causal correlations was a natural place to start, but the problem with it is that intuitively mental content can misrepresent - my brain can represent Santa Claus even though (sorry) it can't have any causal relation with Santa. (I don't mean my brain can represent ideas or concepts or stories or pictures of Santa - I mean it can represent Santa.) Ramana Misrepresentation implies normativity, yep. In the spirit of recovering a naturalisation project, my question is: whence normativity? How does it come about? How did it evolve? How do you get some proto-normativity out of a purely causal picture that's close to being contentful? Steve So one standard story here about mental representation is teleosemantics, that roughly something in my brain can represent something in the world by having the function to track that thing. It may be a "fact of nature" that the heart is supposed to pump blood, even though in fact hearts can fail to pump blood. This is already contentious, that it's a fact the heart is supposed to pump blood - but if so, it may similarly be a fact of nature that some brain state is supposed to track something in the world, even when it fails to. So teleology introduces the possibility of m...
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Causal Graphs of GPT-2-Small's Residual Stream, published by David Udell on July 10, 2024 on LessWrong. Thanks to the many people I've chatted with this about over the past many months. And special thanks to Cunningham et al., Marks et al., Joseph Bloom, Trenton Bricken, Adrià Garriga-Alonso, and Johnny Lin, for crucial research artefacts and/or feedback. Codebase: sparse_circuit_discovery TL; DR: The residual stream in GPT-2-small, expanded with sparse autoencoders and systematically ablated, looks like the working memory of a forward pass. A few high-magnitude features causally propagate themselves through the model during inference, and these features are interpretable. We can see where in the forward pass, due to which transformer layer, those propagating features are written in and/or scrubbed out. Introduction What is GPT-2-small thinking about during an arbitrary forward pass? I've been trying to isolate legible model circuits using sparse autoencoders. I was inspired by the following example, from the end of Cunningham et al. (2023): I wanted to see whether naturalistic transformers[1] are generally this interpretable as circuits under sparse autoencoding. If this level of interpretability just abounds, then high-quality LLM mindreading & mindcontrol is in hand! If not, could I show how far we are from that kind of mindreading technology? Related Work As mentioned, I was led into this project by Cunningham et al. (2023), which established key early results about sparse autoencoding for LLM interpretability. While I was working on this, Marks et al. (2024) developed an algorithm approximating the same causal graphs in constant time. Their result is what would make this scalable and squelch down the iteration loop on interpreting forward passes. Methodology A sparse autoencoder is a linear map, whose shape is (autoencoder_dim, model_dim). I install sparse autoencoders at all of GPT-2-small's residual streams (one per model layer, 12 in total). Each sits at a pre_resid bottleneck that all prior information in that forward pass routes through.[2] I fix a context, and choose one forward pass of interest in that context. In every autoencoder, I go through and independently ablate out all of the dimensions in autoencoder_dim during a "corrupted" forward pass. For every corrupted forward pass with a layer N sparse autoencoder dimension, I cache effects at the layer N+1 autoencoder. Every vector of cached effects can then be reduced to a set of edges in a causal graph. Each edge has a signed scalar weight and connects a node in the layer N autoencoder to a node in the layer N+1 autoencoder. I keep only the top-k magnitude edges from each set of effects NN+1, where k is a number of edges. Then, I keep only the set of edges that form paths with lengths >1.[3] The output of that is a top-k causal graph, showing largest-magnitude internal causal structure in GPT-2-small's residual stream during the forward pass you fixed. Causal Graphs Key Consider the causal graph below: Each box with a bolded label like 5.10603 is a dimension in a sparse autoencoder. 5 is the layer number, while 10603 is its column index in that autoencoder. You can always cross-reference more comprehensive interpretability data for any given dimension on Neuronpedia using those two indices. Below the dimension indices, the blue-to-white highlighted contexts show how strongly a dimension activated following each of the tokens in that context (bluer means stronger). At the bottom of the box, blue or red token boxes show the tokens most promoted (blue) and most suppressed (red) by that dimension. Arrows between boxes plot the causal effects of an ablation on dimensions of the next layer's autoencoder. A red arrow means ablating dimension 1.x will also suppress downstream dimension 2.y. A blue arrow means ...
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: What and Why: Developmental Interpretability of Reinforcement Learning, published by Garrett Baker on July 9, 2024 on LessWrong. Introduction I happen to be in that happy stage in the research cycle where I ask for money so I can continue to work on things I think are important. Part of that means justifying what I want to work on to the satisfaction of the people who provide that money. This presents a good opportunity to say what I plan to work on in a more layman-friendly way, for the benefit of LessWrong, potential collaborators, interested researchers, and funders who want to read the fun version of my project proposal It also provides the opportunity for people who are very pessimistic about the chances I end up doing anything useful by pursuing this to have their say. So if you read this (or skim it), and have critiques (or just recommendations), I'd love to hear them! Publicly or privately. So without further ado, in this post I will be discussing & justifying three aspects of what I'm working on, and my reasons for believing there are gaps in the literature in the intersection of these subjects that are relevant for AI alignment. These are: 1. Reinforcement learning 2. Developmental Interpretability 3. Values Culminating in: Developmental interpretability of values in reinforcement learning. Here are brief summaries of each of the sections: 1. Why study reinforcement learning? 1. Imposed-from-without or in-context reinforcement learning seems a likely path toward agentic AIs 2. The "data wall" means active-learning or self-training will get more important over time 3. There are fewer ways for the usual AI risk arguments to fail in the RL with mostly outcome-based rewards circumstance than the supervised learning + RL with mostly process-based rewards (RLHF) circumstance. 2. Why study developmental interpretability? 1. Causal understanding of the training process allows us to produce reward structure or environmental distribution interventions 2. Alternative & complementary tools to mechanistic interpretability 3. Connections with singular learning theory 3. Why study values? 1. The ultimate question of alignment is how can we make AI values compatible with human values, yet this is relatively understudied. 4. Where are the gaps? 1. Many experiments 2. Many theories 3. Few experiments testing theories or theories explaining experiments Reinforcement learning Agentic AIs vs Tool AIs All generally capable adaptive systems are ruled by a general, ground-truth, but slow outer optimization process which reduces incoherency and continuously selects for systems which achieve outcomes in the world. Examples include evolution, business, cultural selection, and to a great extent human brains. That is, except for LLMs. Most of the feedback LLMs receive is supervised, unaffected by the particular actions the LLM takes, and process-based (RLHF-like), where we reward the LLM according to how useful an action looks in contrast to a ground truth regarding how well that action (or sequence of actions) achieved its goal. Now I don't want to make the claim that this aspect of how we train LLMs is clearly a fault of them, or in some way limits the problem solving abilities they can have. And I do think it possible we see in-context ground-truth optimization processes instantiated as a result of increased scaling, in the same way we see in context learning. I do however want to make the claim that this current paradigm of mostly processed-based supervision, if it continues, and doesn't itself produce ground-truth based optimization, makes me optimistic about AI going well. That is, if this lack of general ground-truth optimization continues, we end up with a cached bundle of not very agentic (compared to AIXI) tool AIs with limited search or bootstrapping capabilities. Of course,...
Send us a Text Message.Video version of this episode is available here Causal personalization?Dima did not love computers enough to forget about his passion for understanding people.His work at Booking.com focuses on recommender systems and personalization, and their intersection with AB testing, constrained optimization and causal inference.Dima's passion for building things started early in his childhood and continues up to this day, but recent events in his life also bring new opportunities to learn.In the episode, we discuss:What can we learn about human psychology from building causal recommender systems?What it's like to work in a culture of radical experimentation?Why you should not skip your operations research classes?Ready to dive in? About The GuestDima Goldenberg is a Senior Machine Learning Manager at Booking.com, Tel Aviv, where he leads machine learning efforts in recommendations and personalization utilizing uplift modeling. Dima obtained his MSc in Tel Aviv University and currently pursuing PhD on causal personalization at Ben Gurion University of the Negev. He led multiple conference workshops and tutorials on causality and personalization and his research was published in top journals and conferences including WWW, CIKM, WSDM, SIGIR, KDD and RecSys.Connect with Dima: Dima on LinkedInAbout The HostAleksander (Alex) Molak is an independent machine learning researcher, educator, entrepreneur and a best-selling author in the area of causality (https://amzn.to/3QhsRz4).Connect with Alex:- Alex on the Internet LinksThe full list of links is available here#machinelearning #causalai #causalinference #causality Should we build the Causal Experts Network?Share your thoughts in the surveySupport the Show.Causal Bandits PodcastCausal AI || Causal Machine Learning || Causal Inference & DiscoveryWeb: https://causalbanditspodcast.comConnect on LinkedIn: https://www.linkedin.com/in/aleksandermolak/Join Causal Python Weekly: https://causalpython.io The Causal Book: https://amzn.to/3QhsRz4
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: Applying Force to the Wrong End of a Causal Chain, published by silentbob on June 23, 2024 on LessWrong. There's a very common thing that humans do: a person makes an observation about something they dislike, so they go ahead and make an effort to change that thing. Sometimes it works, and sometimes it doesn't. If it doesn't work, there can be a variety of reasons for that - maybe the thing is very difficult to change, maybe the person lacks the specific skills to change the thing, maybe it depends on the behavior of other people and the person is not successful in convincing them to act differently. But there's also one failure mode which, while overlapping with the previous ones, is worthy to highlight: imagine the thing the person dislikes is the outcome of a reasonably complex process. The person observes primarily this outcome, but is partially or fully ignorant of the underlying process that causes the outcome. And they now desperately want the outcome to be different. In such a situation they are practically doomed to fail - in all likelihood, their attempts to change the outcome will not be successful, and even if they are, the underlying cause is still present and will keep pushing in the direction of the undesired outcome. Three Examples Productivity in a Company A software company I worked for once struggled with a slow development cycle, chronic issues with unmet deadlines, and generally shipping things too slowly. The leadership's primary way of addressing this was to repeatedly tell the workforce to "work faster, be more productive, ship things more quickly". In principle, this approach can work, and to some degree it probably did speed things up. It just requires that the people you're pushing have enough agency, willingness and understanding to take it a step further and take the trip down the causal chain, to figure out what actually needs to happen in order to achieve the desired outcome. But if middle management just forwards the demand to "ship things more quickly" as is, and the employees below them don't have enough ownership to transform that demand into something more useful, then probably nothing good will happen. The changed incentives might cause workers to burn themselves out, to cut corners that really shouldn't be cut, to neglect safety or test coverage, to set lower standards for documentation or code quality - aspects that are important for stable long term success, but take time to get right. To name one very concrete example of the suboptimal consequences this had: The company had sent me a new laptop to replace my old one, which would speed up my productivity quite a bit. But it would have taken a full work day or two to set the new laptop up. The "we need to be faster" situation caused me to constantly have more pressing things to work on, meaning the new, faster laptop sat at the side of my desk, unused, for half a year. Needless to say, on top of all that, this time was also highly stressful for me and played a big role in me ultimately leaving the company. Software development, particularly when multiple interdependent teams are involved, is a complex process. The "just ship things more quickly" view however seems to naively suggest that the problem is simply that workers take too long pressing the "ship" button. What would have been a better approach? It's of course easy to armchair-philosophize my way to a supposedly better solution now. And it's also a bit of a cop-out to make the meta comment that "you need to understand the underlying causal web that causes the company's low velocity". However, in cases like this one, I think one simple improvement is to make an effort for nuanced communication, making clear that it's not (necessarily) about just "working faster", but rather asking everyone to keep their eyes open for cause...
Focusing on cardiovascular health is essential in aging. On today's episode, we dive deep with Donald Lloyd-Jones, MD, to discuss cardiovascular disease risk factors, learn different biomarkers and their ideal target levels, understand the role of screening tests, and Dr. Lloyd-Jones' essential tips for preventing cardiovascular disease.Donald Lloyd-Jones, MD, is a world-class cardiologist and epidemiologist who recently served as president of the American Heart Association. His research interests include cardiovascular health and healthy aging, and cardiovascular disease epidemiology and prevention. He has served on the faculty and in leadership roles at Harvard Medical School and Northwestern. He received his MD from Columbia University College of Physicians and Surgeons and his master's in epidemiology from the Harvard School of Public Health.(03:17) – Introducing Dr. Don Lloyd Jones(06:01) – Looking at evidence(08:34) – The demographics of cardiovascular disease(11:30) – Causal risk factors(13:29) – Creating longer-term risk estimates(17:00) – Cumulative risk(21:10) – Addressing risk at a younger age(23:31) – Monitoring blood pressure(29:00) – Sodium(32:03) – Cholesterol(36:27) – Screening for plaque(42:51) – Coronary calcium scores(44:49) – Total cholesterol numbers(51:39) – Apolipoprotein(a)(56:00) – Screening children(59:14) – Homocysteine levels(1:00:25) – Exercise (1:04:25) – Heart rate variability(1:05:40) – Hormone replacement therapy for women(1:08:05) – THC (1:10:40) – Microplastics(1:13:15) – COVID-19's impact on cardiovascular diseaseIf there are topics that you are interested in learning more about, please visit MichaelJLeeMD.com.If you'd like to receive new episodes as they're published, please follow I'd Love to Know in Apple Podcasts, Spotify, or wherever you get your podcasts. If you enjoyed this episode, please consider leaving a review on Apple Podcasts or Spotify. It really helps others find the show.The information from this podcast does not constitute medical advice and is meant for basic informational purposes only. If you're interested in pursuing any of the therapies, supplements, or medications discussed here, please consult with your physician.Podcast episode production by Dante32.
Erick Scott is founder of cStructure, a causal science startup. Erick has expertise in medicine, public health, and computational biology. info@cStructure.io “A causal roadmap for generating high-quality real-world evidence” https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10603361/ Follow along on Twitter: The American Journal of Epidemiology: @AmJEpi Ellie: @EpiEllie Lucy: @LucyStats
Summary Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human" Interview Introduction How did you get involved in machine learning? Can you start by unpacking the idea of "human-like" AI? How does that contrast with the conception of "AGI"? The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment? The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models? What are the opportunities and limitations of causal modeling techniques for generalized AI models? As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability? What are the practical/architectural methods necessary to build more cognitive AI systems? How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications? What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems? When is cognitive AI the wrong choice? What do you have planned for the future of cognitive AI applications at Aigo? Contact Info LinkedIn (https://www.linkedin.com/in/vosspeter/) Website (http://optimal.org/voss.html) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Aigo.ai (https://aigo.ai/) Artificial General Intelligence (https://aigo.ai/what-is-real-agi/) Cognitive AI (https://aigo.ai/cognitive-ai/) Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_graph) Causal Modeling (https://en.wikipedia.org/wiki/Causal_model) Bayesian Statistics (https://en.wikipedia.org/wiki/Bayesian_statistics) Thinking Fast & Slow (https://amzn.to/3UJKsmK) by Daniel Kahneman (affiliate link) Agent-Based Modeling (https://en.wikipedia.org/wiki/Agent-based_model) Reinforcement Learning (https://en.wikipedia.org/wiki/Reinforcement_learning) DARPA 3 Waves of AI (https://www.darpa.mil/about-us/darpa-perspective-on-ai) presentation Why Don't We Have AGI Yet? (https://arxiv.org/abs/2308.03598) whitepaper Concepts Is All You Need (https://arxiv.org/abs/2309.01622) Whitepaper Hellen Keller (https://en.wikipedia.org/wiki/Helen_Keller) Stephen Hawking (https://en.wikipedia.org/wiki/Stephen_Hawking) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)
If a person believed in UFO's, the existence of aliens, and certain Native American spiritual beliefs, you may think they would also trust in the possibility of ghosts too? But that is not always the case. Someone may have to make the hard decision of letting their favorite furry friend cross the Rainbow Bridge but with that loss discovers their abilities of trance mediumship after hearing their lost pet greet them at home from The Beyond. And a humble farmer may be kicking himself in the pants for being “too modest” in the possible presence of gift offering Djinns. These and other enlightening tales are the topic of this Sunday's Listener's Stories. Tune in for an enlightening time as Liv re-tells the stories sent in by listeners just like you. Providing insight as a psychic medium to unwrap any further spiritual secrets.TELL US YOUR PARANORMAL STORIES HERE: https://www.metapsyckicks.com/extrasFOR MORE CHECK OUT OUR YOUTUBE CHANNEL: https://www.youtube.com/@MetaPsycKicksOR READ THE BLOG: https://www.metapsyckicks.com/journalOR JOIN OUR PATREON: https://www.patreon.com/metapsyckicks——-BOOK A PSYCHIC MEDIUM READING:Olivia the Medium: https://www.metapsyckicks.com/our-servicesBOOK A TAROT READING:Emily the Intuitive: https://www.metapsyckicks.com/our-services-----Sources to dive down a rabbit hole -https://en.wikipedia.org/wiki/Causal_planehttps://en.wikipedia.org/wiki/Anima_Sola#:~:text=The%20Anima%20Sola%20is%20taken,in%20chromolithographs%2C%20sculptures%20and%20paintings.-----RECOMMENDED PRODUCTS:Our YouTube Setup ►► https://kit.co/metapsyckicks/meta-psyckicks-youtube-setupOur Podcast Setup ►► https://kit.co/metapsyckicks/meta-psyckicks-podcasting-setupEm's Tarot Collection ►► https://kit.co/metapsyckicks/em-s-tarot-card-collectionOther Divination Tools: ►► https://kit.co/metapsyckicks/other-divination-toolsDISCLAIMER: This description might contain affiliate links that allow you to find the items mentioned in this video and support the channel at no cost to you. While this channel may earn minimal sums when the viewer uses the links, the viewer is in NO WAY obligated to use these links. Thank you for your support!-----ARE YOU A PSYCHIC QUIZ: https://www.metapsyckicks.com/extrasCHECK OUT OUR WEBSITE AND BLOG:www.metapsyckicks.comEMAIL US: metapsyckicks@gmail.com——-SAY HI ON SOCIAL:YouTube: https://www.youtube.com/channel/UC-Np1K0QH8e-EDHhIxX-FaAInstagram: https://www.instagram.com/metapsyckicksTikTok: https://www.tiktok.com/@metapsyckicks?lang=enFacebook: https://www.facebook.com/Meta-PsycKicks-107812201171308Em's Pet Channel - Chin Villain: https://www.youtube.com/chinvillainOlivia The Medium:Instagram - https://www.instagram.com/oliviathemedium/Twitter - https://twitter.com/OliviaTheMediumSupport this podcast at — https://redcircle.com/meta-psyckicks/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy
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