Podcasts about why the new science

This episode is sponsored by 'Deel. Hire, manage and pay – anyone, anywhere: https://www.deel.com/nickdayhr/In this episode of the HR L&D Podcast, Nick Day is joined by Don Yaeger, Hall of Fame speaker, 12-time New York Times bestselling author, and co-author of The New Science of Momentum.Drawing on six years of research and more than 250 interviews across business, sport, politics, and the military, Don breaks down how great leaders create momentum before it appears, how they sustain it under pressure, and how poor leadership decisions can destroy it overnight.This conversation explores why momentum is a mindset, not a moment, how storytelling shapes belief inside organizations, why hiring A-players who can cheer for other A-players is critical, and what leaders must do when momentum starts moving in the wrong direction.A must-listen for HR leaders, talent professionals, L&D teams, and executives responsible for culture, engagement, and performance at scale.Connect with Don: https://www.linkedin.com/in/donyaeger?utm_source=chatgpt.comThe New Science of Momentum: https://donyaeger.com/shop/new-science-of-momentum/?utm_source=chatgpt.comNick Day's LinkedIn: https://www.linkedin.com/in/nickday/Find your ideal candidate with our job vacancy system: https://jgarecruitment.ck.page/919cf6b9eaSign up to the HR L&D Newsletter - https://jgarecruitment.ck.page/23e7b153e7(00:00) Preview(02:52) What Human Resources Really Means(04:06) The Difference Between a Spark and Real Momentum(08:21) Recruiting A-Players Who Can Celebrate Each Other(10:26) The Scottie Pippen Lesson on Ego and Leadership(14:57) Why The New Science of Momentum Was Written(22:02) Peaking at the Right Time as a Leader(28:05) Conviction When the Odds Are Against You(33:41) The One Trait Shared by All Great Leaders(36:01) Team Chemistry vs Culture(39:04) Building a Leadership Story Bank(43:04) Why Employees Leave When Momentum Is Lost(45:18) A Defining Leadership Failure and Lesson Learned(47:33) The Advice Don Wishes He'd Received Earlier

AI is becoming ubiquitous in our lives. It shapes how we work, play, interact, create, and even manage our health—and this is only the beginning. To understand where we are and where we might go, we first need to understand how we got here. By tracing the evolving nature of machine intelligence, we can appreciate how today's AI differs from its past and how it is likely to evolve. With that in mind, we can begin to ask the big questions: When should we trust AI over human judgment? How should we govern its development? How will it change what it means to be human? And what roles will humans play in the future of work?To help us through this journey, I'm delighted to welcome back to TRIUM Connects Professor Vasant Dhar, the Robert A. Miller Professor at NYU's Stern School of Business and Professor of Data Science at NYU. Vasant is one of the world's leading thinkers on the impact of AI on society. He was present at the birth of AI and has been involved in every step of its evolution—both as an entrepreneur and as a scholar. He also hosts the acclaimed podcast Brave New World, which explores how machines are transforming humanity in the post-COVID era.In this episode, we discuss his newest book, Thinking With Machines: The Brave New World of AI. It's a remarkable hybrid: part autobiography, tracing how his professional life has intertwined with the development of AI; part user's guide, offering a lucid framework for deciding when to trust machines over human control; and part deep dive into the philosophical and policy implications of creating an alien intelligence.It was a real pleasure to welcome Vasant back onto the show. I learned a lot during our conversation, and I hope you will enjoy it as much as I did.CitationsDawid A, LeCun Y. Introduction to Latent Variable Energy-Based Models: A Path Towards Autonomous Machine Intelligence. arXiv. June 5, 2023.Dennett DC. Intentional systems. J Philos. 1971;68(4):87-106.Dhar V. Thinking With Machines: The Brave New World of AI. Galloway S, foreword. Hoboken, NJ: Wiley; 2025.Frank, R. H., & Cook, P. J. The winner-take-all society: Why the few at the top get so much more than the rest of us. Penguin Books; 1995.Ganguli D, Askell A, Henighan T, et al. Alignment faking in large language models. arXiv. December 20, 2024.Harari YN. Nexus: A Brief History of Information Networks from the Stone Age to AI. New York, NY: Random House; 2024.Kauffmann J, Dippel J, Ruff L, et al. Explainable AI reveals Clever Hans effects in unsupervised learning models. Nat Mach Intell. 2025;7:1–10.Pearl J, Mackenzie D. The Book of Why: The New Science of Cause and Effect. New York, NY: Basic Books; 2018.Pfungst O. Clever Hans (The Horse of Mr. Von Osten): A Contribution to Experimental Animal and Human Psychology. Rahn H, trans. New York: Henry Holt; 1911.Popper KR. The Logic of Scientific Discovery. London, UK: Hutchinson; 1959Suleyman M, Bhaskar M. The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma. New York, NY: Crown; 2023.Yudkowsky E, Soares N. If Anyone Builds It, Everyone Dies: Why Superhuman AI Would Kill Us All. New York, NY: Little, Brown and Company; 2025. Hosted on Acast. See acast.com/privacy for more information.

$37 billion. That's how much gets wasted annually on marketing budgets because of poor attribution and misunderstanding of what actually drives results. Companies' credit campaigns that didn't work. They kill initiatives that were actually succeeding. They double down on coincidences while ignoring what's actually driving outcomes.   Three executives lost their jobs this month for making the same mistake. They presented data showing success after their initiatives were launched. Boards approved promotions. Then someone asked the one question nobody thought to ask: "Could something else explain this?" The sales spike coincided with a competitor going bankrupt. The satisfaction increase happened when a toxic manager quit. The correlation was real. The causation was fiction. This mistake derailed their careers.   But here's the good news: once you see how this works, you'll never unsee it. And you'll become the person in the room who spots these errors before they cost millions.   But first, you need to understand what makes this mistake so common—and why even smart people fall for it every single day. What is Causal Thinking? At its core, causal thinking is the practice of identifying genuine cause-and-effect relationships rather than settling for surface-level associations. It's asking not just "do these things happen together?" but "does one actually cause the other?"   This skill means you look beyond patterns and correlations to understand what's actually producing the outcomes you're seeing. When you think causally, you can spot the difference between coincidence, correlation, and true causation—a distinction that separates effective decision-makers from those who waste millions on solutions that were never going to work. Loss of Causal Thinking Skills Across every domain of professional life, this confusion costs fortunes and derails careers.   A SaaS company sees customer churn decrease after implementing new onboarding emails—and immediately scales it company-wide. What they missed: they launched the emails the same week their biggest competitor raised prices by 40%. The competitor's pricing reduced churn. But they'll never know, because they never asked the question. Six months later, when they face real churn issues, they keep doubling down on emails that never actually worked.   This happens outside of work too. You start taking a new vitamin, and two weeks later your energy improves. But you started taking it in early March—right when days got longer and you began going outside more. Was it the vitamin or the sunlight and exercise? Most people credit the vitamin without asking the question.   But here's the good news: once you understand how to think causally, these mistakes become obvious. And one of these five strategies can be used in your very next meeting—literally 30 seconds from now. Let me show you how. How To Master Causal Thinking Mastering causal thinking isn't about becoming a statistician or learning complex formulas. It's about developing five practical strategies that work together to reveal what's really driving results. These build on each other—starting with basic tests you can apply right now, and progressing to a complete system you can use for any decision. Strategy 1: The Three Tests of True Causation Think of these as your checklist for evaluating any causal claim.   The Three Tests:   Test #1 - Timing: Confirm the supposed cause actually happened before the effect. If traffic spiked Monday but you launched the campaign Tuesday, that campaign didn't cause it. The cause must always come before the effect.   Test #2 - Consistent Movement: When the supposed cause is present, does the effect reliably occur? When the cause is absent, does the effect disappear? Document instances where they occur together. Then examine situations where the cause is absent. If the effect happens just as often without the cause, you're looking at correlation, not causation.   Test #3 - Rule Out Alternatives: Think carefully about what else could explain what you're seeing. Actively try to disprove your idea rather than only looking for supporting evidence. If you can't eliminate other explanations, you don't have causation. Strategy 2: Ask "Could Something Else Explain This?" Here's a technique you can implement in the next 30 seconds that will immediately improve your causal thinking: whenever someone presents a causal claim, ask out loud: "Could something else explain this?"   This single question is remarkably powerful. It forces the speaker to consider hidden factors they ignored. It reveals whether they've actually done causal analysis or just noticed a correlation and declared victory. It shifts the conversation from assumption to examination.   Try it in your next meeting when someone says "We did X and Y improved." Watch how often they haven't considered alternatives. Watch how often their confident causal claim becomes less certain when forced to address this simple question.   Most people present correlations as causations without even realizing it. Your question makes that leap visible. Suddenly they have to justify it with evidence or back down. It's not confrontational—it's curious. And curiosity is the foundation of good causal thinking.   Use it today. Use it every time someone attributes an outcome to a cause without ruling out alternatives.   That question leads us naturally to our next strategy—learning to identify what those "something elses" actually are. Strategy 3: Hunt for Hidden Causes A confounding variable is a third factor that influences both your suspected cause and your observed effect. It creates the illusion of a direct relationship where none exists.   Here's a simple example: ice cream sales and drowning deaths both increase during summer months. Does ice cream cause drowning? Obviously not. The confounding variable is warm weather, which causes both more ice cream purchases and more swimming.   Now here's the business version: A retail company sees both customer satisfaction and sales increase after renovating their stores. Does the renovation cause higher satisfaction? Maybe—but both also increased because they renovated during the holiday shopping season when people are generally happier and spending more anyway. Same logical structure. Same expensive mistake if they conclude renovations always boost satisfaction.   Map the Relationship: When you observe a correlation, write down your suspected cause and your observed effect. This visualization helps you spot gaps in your logic immediately.   Ask "What Else Changed?": Think carefully about what other factors were present or changed during the same period. Make a written list so your brain doesn't skip over these hidden causes.   Search for Common Causes: Identify factors that could influence both variables at the same time. For instance, if both employee satisfaction and productivity increased, could several toxic managers have left the company?   Consider Time-Based and Environmental Factors: Examine seasons, business cycles, economic trends, reorganizations, leadership changes, and industry shifts that could affect multiple outcomes at once.   Test by Controlling Variables: If possible, create scenarios where you can control or account for potential hidden causes. Try analyzing subgroups where the hidden cause is absent, or run controlled A/B tests.   Once you can spot these hidden causes, you're ready to understand why your brain makes these mistakes in the first place. And this next one? It's probably happening in your head right now without you realizing it. Strategy 4: Outsmart Your Brain's Shortcuts Your brain is wired to see causal connections everywhere, even where none exist. This isn't a design flaw—it's a survival mechanism that kept your ancestors alive. But in the modern business world, this pattern-seeking instinct can mislead you.   Your brain wants simple causal stories. Reality is usually more complex. Once you know what to watch for, you can catch yourself before making these errors.   Catch Your Instant Explanations: When you observe a pattern, pause before declaring causation. Ask yourself: "Am I seeing causation because it's really there, or because my brain desperately needs an explanation?"   Fight Confirmation Bias: Actively search for information that challenges your causal idea, not just data that supports it. If you can't find contradicting evidence, you haven't looked hard enough.   Here's how this plays out: A manager believes remote work hurts productivity. She notices every time someone's late to a Zoom call. But she doesn't notice the three on-time people. She remembers the one missed deadline but forgets the five delivered early. Her brain is filtering reality to confirm what she already believes.   Question Your Compelling Stories: Be wary of explanations that sound too neat. If your causal explanation reads like a perfect success story, double-check it.   Don't See Patterns in Randomness: Three successful quarters in a row doesn't mean you've discovered a winning formula. It might just be a lucky streak. Always ask "Could this pattern occur by chance?"   Watch the 'After Therefore Because' Trap: Every time you catch yourself thinking "we did X and then Y happened," force yourself to consider alternative explanations. Ask yourself "What would I need to see to know this isn't causal?"   Now that you understand how your brain works, let's put this all together into a practical system you can use every time you need to make a high-stakes decision. Strategy 5: The Five-Question Causation Check Mastering causal thinking requires more than understanding principles—it demands a clear approach you can apply when the stakes are high and the pressure is on.   The Five-Question Causation Check:   Define the Relationship Clearly: Write out the specific causal claim you're evaluating with precision. "Social media advertising increases qualified leads by X%" is better than "marketing works."   Verify the Basics: Does the cause come before the effect in time? Are they consistently related across different contexts? Are there possible alternative explanations?   Look for or Create Tests: Find situations where the supposed cause varies while other factors stay constant. The goal is isolation—can you isolate the variable you're testing from everything else that's changing?   Check if More Causes More: Does more of the cause lead to more of the effect? If doubling your ad spend doubles your conversions, that's stronger evidence than if the relationship is erratic.   Test Reversibility: If you remove the cause, does the effect disappear? If you reinstate the cause, does the effect return? This is why pilot programs and controlled rollbacks are so valuable. Put It Into Practice You now have the complete framework for causal thinking—five strategies that work together to reveal what's really causing what.   But here's what separates people who learn this from people who actually use it—one simple practice you can do this week that makes this framework automatic. Practice Exercise: The Causation Audit A practical and effective way to internalize these strategies is through practice with real-world scenarios from your actual work.   Here's how to conduct your own causal analysis:   Identify a Correlation from Your Work: Choose a recent pattern or causal claim that affects budgets or strategy.   State Your Causal Hypothesis: Write out your causal claim explicitly. Be specific about the supposed cause and the supposed effect.   Brainstorm Alternative Explanations: List at least five alternatives. Force yourself beyond the obvious first three.   Apply Your Three Tests: Evaluate whether your idea meets all three tests for causation. Did the cause come first? Do they consistently move together? Have you actually ruled out alternatives?   Design a Simple Test: If possible, design a test to isolate the variable you're testing. For example, have some account managers follow one approach while others don't, with otherwise similar conditions.   Share Your Analysis: Explain your reasoning to a colleague or manager. Teaching forces clarity and demonstrates analytical rigor.   With practice, you'll become skilled at spotting false causation and identifying true cause-and-effect relationships. This skill compounds over time, making you more valuable with every analysis you conduct.   So what does this actually get you? Let me paint the picture of what changes when you master this skill. The Rewards The rewards of mastering causal thinking are well worth the effort and will compound throughout your career.   You become immune to the most expensive mistakes in business—the ones where you solve the wrong problem perfectly. When everyone else is celebrating a correlation as success, you'll be asking the questions that reveal what's really driving outcomes. Imagine being in a meeting where leadership is about to allocate $2 million to scale an initiative, and you're the one who asks the question that reveals a competitor's bankruptcy actually caused the results. That's career-defining value.   Your strategic recommendations carry weight because they're based on actual causation rather than hopeful patterns. Leaders who can distinguish between correlation and causation make decisions that actually work. When your predictions prove accurate while others' fail, your credibility compounds—you become the person everyone turns to when stakes are high.   You develop the intellectual humility that marks exceptional leaders. Causal thinking teaches you to question your initial judgments, seek alternative explanations, and change your mind when evidence demands it. These qualities don't just make you a better thinker—they make you someone others trust with important decisions.   So take these strategies and practice them. Apply them in your daily work. Question causal claims, hunt for hidden causes, check your biases, and use the systematic process. This makes you a more effective decision-maker, a more credible advisor, and someone who spots opportunities and avoids disasters that others miss entirely.   And you'll become the person in the room everyone listens to when the stakes are high. Your Thinking 101 Journey In Episode 1, "Why Thinking Skills Matter Now More Than Ever," we exposed the crisis: your thinking ability is collapsing, AI dependency is creating cognitive debt, and those who can't think independently will be left behind.   In Episode 2, "How To Improve Your Logical Reasoning Skills," you learned to distinguish deductive certainty from inductive probability, calibrate your confidence to match your evidence, and stop treating patterns as proven facts.   Today, you learned how to distinguish true causation from mere correlation—saving yourself from expensive mistakes where you solve the wrong problem perfectly.   Up next—Episode 4: "Analogical Thinking—The Power of Comparison." Your brain doesn't learn through pure logic—it learns by comparison. Every breakthrough idea came from someone who made an unexpected connection. You'll learn how to generate insights through analogy, recognize when comparisons break down, and spot when others use false analogies to manipulate you.   Hit that subscribe button so you don't miss future episodes. Also—hit the like and notification bell. It helps with the algorithm so others see our content. Why not share this video with a colleague who you think would benefit from it?   Because right now, while you've been watching this, someone just approved a million-dollar budget based on a correlation they mistook for causation. The only question is: will you be the one who catches it?       SOURCES CITED IN THIS EPISODE Pathmetrics – Marketing Attribution Waste 5 Common Marketing Attribution Mistakes to Avoid. (2025). Pathmetrics. (Citing Proxima research on global marketing waste) https://www.pathmetrics.io/attribution/5-common-marketing-attribution-mistakes-to-avoid/   Harvard Business Review – Correlation vs Causation in Leadership Luca, M. (2021). Leaders: Stop Confusing Correlation with Causation. Harvard Business Review. https://hbr.org/2021/11/leaders-stop-confusing-correlation-with-causation   The CEO Project – Correlation vs Causation in Business Correlation vs Causation in Business. (2024). The CEO Project. https://theceoproject.com/correlation-vs-causation-in-business/   Nature Communications – Causality in Digital Medicine Glocker, B., Musolesi, M., Richens, J., & Uhler, C. (2021). Causality in digital medicine. Nature Communications, 12, 4993. https://www.nature.com/articles/s41467-021-25743-9   Stanford Social Innovation Review – The Case for Causal AI Sgaier, S. K., Huang, V., & Charles, G. (2020). The Case for Causal AI. Stanford Social Innovation Review. https://ssir.org/articles/entry/the_case_for_causal_ai       ADDITIONAL READING On Causation and Decision-Making Pearl, J., & Mackenzie, D. (2018). The Book of Why: The New Science of Cause and Effect. Basic Books.   On Thinking Clearly Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.   On Statistical Reasoning Angrist, J. D., & Pischke, J. S. (2009). Mostly Harmless Econometrics: An Empiricist's Companion. Princeton University Press.         Note: All sources cited in this episode have been accessed and verified as of October 2025.  

The world is changing fast. Technology can be used to empower us -- and also to hack our brains & our lives. What laws do we need to protect our freedoms? Rahul Matthan joins Amit Varma in episode 360 of The Seen and the Unseen to share his work on privacy -- and on a new, subtle approach towards data governance. (FOR FULL LINKED SHOW NOTES, GO TO SEENUNSEEN.IN.)   Also check out: 1. Rahul Matthan on Twitter, Instagram, LinkedIn, Trilegal, Substack and his own website. 2. Privacy 3.0: Unlocking Our Data-Driven Future -- Rahul Matthan. 3. The Third Way: India's Revolutionary Approach to Data Governance -- Rahul Matthan. 4. The Life and Times of KP Krishnan -- Episode 355 of The Seen and the Unseen. 5. Sudhir Sarnobat Works to Understand the World -- Episode 350 of The Seen and the Unseen. 6. Roam Research. 7. Zettelkasten on Wikipedia. 8. Tana, Obsidian and Notion. 9. Getting Things Done -- David Allen. 10. The Greatest Productivity Mantra: Kaator Re Bhaaji! -- Episode 11 of Everything is Everything. 11. Hallelujah (Spotify) (YouTube) -- Leonard Cohen. 12. Hallelujah (Spotify) (YouTube) -- Jeff Buckley. 13. The Holy or the Broken: Leonard Cohen, Jeff Buckley, and the Unlikely Ascent of "Hallelujah" -- Alan Light. 14. Hallelujah on Revisionist History by Malcolm Gladwell. 15. Bird by Bird: Some Instructions on Writing and Life -- Anne Lamott. 16. The New Basement Tapes. (Also Wikipedia.) 17. Kansas City -- Marcus Mumford. 18. The Premium Mediocre Life of Maya Millennial -- Venkatesh Rao. 19. Vitalik Buterin Fights the Dragon-Tyrant — Episode 342 of The Seen and the Unseen. 20. Paul Graham on Twitter and his own website. (His essays are extraordinary.) 21. Ribbonfarm by Venkatesh Rao. 22. The Network State --  Balaji Srinivasan. 23. Marc Andreessen on Twitter. 24. The Techno-Optimist Manifesto -- Marc Andreessen. 25. Siddhartha Mukherjee and Carlo Rovelli on Amazon. 26. For the Lord (Spotify) (YouTube) -- Rahul Matthan. 27. Predicting the Future -- Rahul Matthan (on Asimov's concept of Psychohistory etc). 28. Gurwinder Bhogal Examines Human Nature — Episode 331 of The Seen and the Unseen. 29. The Looking-Glass Self. 30. Panopticon. 31. Danish Husain and the Multiverse of Culture -- Episode 359 of The Seen and the Unseen. 32. A Scientist in the Kitchen — Episode 204 of The Seen and the Unseen (w Krish Ashok). 33. We Are All Amits From Africa — Episode 343 of The Seen and the Unseen (w Krish Ashok and Naren Shenoy). 34. Nothing is Indian! Everything is Indian! — Episode 12 of Everything is Everything. 35. The Right to Privacy -- Samuel D Warren and Louis D Brandeis. 36. John Locke on Britannica, Stanford Encyclopedia of Philosophy, Wikipedia and Econlib. 37. Build for Tomorrow -- Jason Feifer. 38. Ex Machina -- Alex Garland. 39. Arrival -- Denis Villeneuve. 40. The Great Manure Crisis of 1894 -- Rahul Matthan. 41. Climate Change and Our Power Sector — Episode 278 of The Seen and the Unseen (w Akshay Jaitley and Ajay Shah). 42. The Book of Why: The New Science of Cause and Effect -- Judea Pearl. 43. The New World Upon Us — Amit Varma on Alpha Zero. 44. Brave New World -- Vasant Dhar's podcast, produced by Amit Varma. 45. Human and Artificial Intelligence in Healthcare -- Episode 4 of Brave New World (w Eric Topol). 46. The Colonial Constitution -- Arghya Sengupta. 47. Beyond Consent: A New Paradigm for Data Protection -- Rahul Matthan. 48. The Puttaswamy case. 49. Judicial Reforms in India -- Episode 62 of The Seen and the Unseen (w Alok Prasanna Kumar.) 50. Accidental Feminism: Gender Parity and Selective Mobility among India's Professional Elite --  Swethaa S Ballakrishnen. 51. Magic Fruit: A Poetic Trip -- Vaishnav Vyas. 52. Hermanos Gutiérrez and Arc De Soleil on Spotify. 53. The Travelling Salesman Problem. 54. The Twenty-Six Words That Created the Internet -- Jeff Kosseff. 55. Code: And Other Laws of Cyberspace -- Lawrence Lessig. 56. Financial Inclusion and Digital Transformation in India -- Suyash Rai. 57. No Time for False Modesty -- Rahul Matthan. 58. In Service of the Republic: The Art and Science of Economic Policy -- Vijay Kelkar and Ajay Shah. 59. Once Upon a Prime -- Sarah Hart. 60. The Greatest Invention -- Silvia Ferrara. 61. Surveillance State -- Josh Chin and Liza Lin. 62. Surveillance Valley -- Yasha Levine. 63. Sex Robots and Vegan Meat -- Jenny Kleeman. 64. How to Take Smart Notes -- Sönke Ahrens. 65. The Creative Act -- Rick Rubin. 66. How to Write One Song -- Jeff Tweedy. 67. Adrian Tchaikovsky and NK Jemisin on Amazon. 68. Snarky Puppy. on Spotify and YouTube. 69. Empire Central -- Snarky Puppy. 70. Polyphia on Spotify and YouTube. 71. The Lazarus Project on Jio Cinema. This episode is sponsored by the Pune Public Policy Festival 2024, which takes place on January 19 & 20, 2024. The theme this year is Trade-offs! Amit Varma and Ajay Shah have launched a new video podcast. Check out Everything is Everything on YouTube. Check out Amit's online course, The Art of Clear Writing. And subscribe to The India Uncut Newsletter. It's free! Episode art: ‘Protocol' by Simahina.

Steven Forth is Ibbaka's Co-Founder, CEO, and Partner. Ibbaka is a strategic pricing advisory firm. He was CEO of LeveragePoint Innovations Inc., a SaaS business designed to help companies create and capture value. In this episode, Steven advocates for proactive scenario planning, encouraging businesses to identify critical uncertainties and fortify their pricing strategies for the uncertainties of the future.   Why you have to check out today's podcast: Understand the significance of pricing as a strategic element often overlooked in planning, and recognize its pivotal role in post-COVID economic landscapes Acknowledge the shift to a sounder economic period, where capital has a tangible cost, emphasizing the importance of net present value as a cornerstone of planning assumptions Prioritize fixing issues strategically, considering both short-term and long-term plays, and embrace scenario planning for effective pricing strategies in a dynamic environment   "I think we are settling into a sounder economic period where capital has a cost, net present value matters, and we need to have that as a planning assumption." - Steven Forth   Topics Covered: 01:38 - An observation about pricing being overlooked in strategic planning for 2024 and pricing being just an afterthought 04:20 - The need to strategically approach pricing in the context of the next three years post-COVID and thoughts on the monetization of generative AI 07:24 - Important thoughts on what kind of impact will AI have in businesses in the years ahead in comparison to what blockchain years ago 09:32 - From low interest rates to normal range, the importance of capital costs and net present value as part of planning assumptions. 13:05 - The need to take realistic steps to investments in AI, impact of non-zero interest rates on capital costs, the stabilization of buying behaviors into 2024 and how all these are considered in pricing planning in 2024 18:47 - Prioritizing what needs to be fixed first rather than fixing all at once and risk messing up everything 19:52 - How often should one conduct a pricing strategy 22:25 -Two key things in mind when planning for 2024: first establish baselines and trends, then aligning pricing with the overall strategic goals of the company 27:13 - What it means to have a portfolio point of view when making pricing planning and how to implement a faster cadence to reach your pricing goals 30:09 - Attributing business results to pricing changes and introducing the concept of causal analysis   Key Takeaways: "I think we are settling into a sounder economic period where capital has a cost, net present value matters and we need to have that as a planning assumption." - Steven Forth "You can't really do strategic planning if you don't understand where you are and how you got there." - Steven Forth "I would encourage people to at least consider looking at scenario planning where you plan for more than one scenario. You identify critical uncertainties and you plan for each of the critical uncertainties. That approach would make a lot of sense for pricing." - Steven Forth   People / Resources Mentioned: Judea Pearl: https://en.wikipedia.org/wiki/Judea_Pearl The Book of Why: The New Science of Cause and Effect: https://www.amazon.com/Book-Why-Science-Cause-Effect/dp/046509760X   Connect with Steven Forth: LinkedIn: https://www.linkedin.com/in/stevenforth/ Email: steven@ibbaka.com   Connect with Mark Stiving: LinkedIn: https://www.linkedin.com/in/stiving/ Email: mark@impactpricing.com  

With a background as a classroom teacher, a master's in educational neuroscience, and a doctorate in special education, Dr. Neena Saha has seen all facets of education. In her work, she noticed a gap in the research-to-practice workflow for early literacy and dedicated herself to streamlining the process of finding and disseminating the best educational research for educators. Together, Susan Lambert and Neena discuss the need for reading researchers to work together and collaborate in a more focused and concerted group effort, the challenges of implementation, and how educators can best keep up with research that often feels overwhelming.Show notes:Listen: Our recent episode with Claude GoldenbergRead: Neena's monthly reading research updateWatch: Neena's July video about a Bayesian network meta-analysisWatch: Celebrating the Legacy of Dr. Bud RoseWebsite: Center for Research Use in EducationRead: “Survey of Evidence in Education for Schools Descriptive Report”Read: “The Book of Why: The New Science of Cause and Effect” by Judea PearlRead: Reading Research Recap—If you want to start receiving monthly notifications for this series, please register or sign in to your Lexile & Quantile Hub account and join the Reading Research mailing list.Quotes:"What I did was focus really on dissemination, right? Getting rid of that hurdle of, you know, there's so many journals out there." —Dr. Neena Saha"You have to look at the full body, you're like cherry picking stuff if you're going to social media and the person with the biggest megaphone wins or whoever has the most interesting way of presenting it." —Dr. Neena Saha"We need a more concerted effort. There needs to be a bunch of researchers that come together and hash it out. It can't just be single ones here and there." —Dr. Neena Saha"Teachers or educators out there right now, when you're feeling overwhelmed and you can't figure out how to find the evidence, or some evidence, guess what? We're affirming for you that there's no easy way to do it...this is more of a systemic problem." —Dr. Neena Saha"It's not enough to do the science. You have to make sure it gets out there." —Dr. Neena Saha

In this week's episode, host Kristin Hayes talks with Nafisa Lohawala, a fellow at Resources for the Future who researches the effects of government policies on the transportation sector. Lohawala discusses the findings of a recent report that explores efforts to electrify medium- and heavy-duty vehicle fleets, the opportunities and challenges of electrification as a pathway toward lower transportation-sector emissions, and policies that could aid electrification. References and recommendations: “Medium- and Heavy-Duty Vehicle Electrification: Challenges, Policy Solutions, and Open Research Questions” by Beia Spiller, Nafisa Lohawala, and Emma DeAngeli; https://www.rff.org/publications/reports/medium-and-heavy-duty-vehicle-electrification-challenges-policy-solutions-and-open-research-questions/ Special series on the Common Resources blog: Electrifying Large Vehicles by Emma DeAngeli, Nafisa Lohawala, and Beia Spiller; https://www.resources.org/special-series-electrifying-large-vehicles/ “The Book of Why: The New Science of Cause and Effect” by Judea Pearl and Dana Mackenzie; https://www.hachettebookgroup.com/titles/judea-pearl/the-book-of-why/9780465097616/

Not many forces on Earth are comparable in power to a resilient spirit combined with a strong work ethic, and Dr. Glen Ferguson embodies this dynamic! Tune-in to this episode to hear how Dr. Ferguson went from a young man whose high school guidance counselor discouraged him from even applying to college, to enlisting in the U.S. Navy, to completing a PhD in physical chemistry, to becoming a data science individual contributor, to his present day role as Director of AI & ML at Huckleberry Labs! It is a remarkable story of resilience in the face of adversity and triumph against daunting odds!   FEATURED GUESTS: Name: Glen Ferguson Email: gallenferguson@gmail.com LinkedIn: https://www.linkedin.com/in/glenferguson/   SUPPORT THE DATA CANTEEN (LIKE PBS, WE'RE LISTENER SUPPORTED!): Donate: https://vetsindatascience.com/support-join   EPISODE LINKS: The Book of Why: The New Science of Cause and Effect (book recommendation): https://www.amazon.com/The-Book-of-Why-audiobook/dp/B07CYJ4G2L Deep Learning with Python 2nd Ed (book recommendation): https://www.amazon.com/Audible-Deep-Learning-Python-Second/dp/B09RN7QLT3 Deep Learning (book recommendation): https://www.deeplearningbook.org/ Pattern Recognition and Machine Learning (book recommendation): https://www.amazon.com/Pattern-Recognition-Learning-Information-Statistics/dp/0387310738 Introduction to Statistical Learning 2nd Ed (book recommendation): https://hastie.su.domains/ISLR2/ISLRv2_website.pdf   PODCAST INFO: Host: Ted Hallum Website: https://vetsindatascience.com/thedatacanteen Apple Podcasts: https://podcasts.apple.com/us/podcast/the-data-canteen/id1551751086 YouTube: https://www.youtube.com/channel/UCaNx9aLFRy1h9P22hd8ZPyw Stitcher: https://www.stitcher.com/show/the-data-canteen   CONTACT THE DATA CANTEEN: Voicemail: https://www.speakpipe.com/datacanteen   VETERANS IN DATA SCIENCE AND MACHINE LEARNING: Website: https://vetsindatascience.com/ Join the Community: https://vetsindatascience.com/support-join Mentorship Program: https://vetsindatascience.com/mentorship   OUTLINE: 00:00:07​ - Introduction 00:01:22 - Glen's first touch point with VDSML 00:03:15 - Glen's military background, college, & grad school 00:12:10 - Glen's first civilian career as a physical chemist working in materials science 00:15:31 - The shift to pursue data science 00:17:37 - A sabbatical in winemaking 00:20:25 - Glen's experience attending the NYC Data Science Academy 00:28:18 - Glen publishes about one of his data science projects in a peer reviewed journal 00:29:15 - Glen's first roles in data science as an individual contributor 00:38:37 - Glen moves into data science management 00:44:12 - Glen's overview of roles in the datasphere and military occupational specialties that map well to those roles 00:55:58 - Glen's current learning focus 00:57:29 - Glen's favorite learning resources 00:59:01 - The best way to contact Glen 01:00:03 - Farewells

Por que algo acontece? O que causou isso ou aquilo? Como entender a causa dos fenômenos que acontecem na natureza e na nossa sociedade? Para explorar o mundo complexo da causalidade trouxemos um especialista na área, o pesquisador Marcel Ribeiro-Dantas. Neste episódio, exploramos o que seria causalidade, se a inferência causal varia conforme a complexidade de cada área, se ela é sempre probabilística, se a plausibilidade é importante para entender a causalidade e os perigos de se politizar a ciência. Marcel é engenheiro de Computação e Automação, especialista em Big Data e mestre em Bioinformática pela Universidade Federal do Rio Grande do Norte. Aluno de doutorado na Universidade Sorbonne, em Paris, onde estuda causalidade no contexto de pacientes com câncer. Atualmente é pesquisador no Instituto Curie, mas foi membro co-fundador do Laboratório de Inovação Tecnológica em Saúde (LAIS) do Hospital Universitário Onofre Lopes (HUOL-UFRN), onde participou de atividades de pesquisa por 9 anos nas áreas de informática em saúde, dispositivos médicos, telemonitoramento de pacientes, telerradiologia, sistemas de recursos humanos em saúde e inteligência artificial. Participou também de atividades de pesquisa em âmbito internacional frutos de cooperações, como com a Universidade de Harvard e o MIT. Atualmente, tem interesse nos seguintes temas: inferência causal, redes biológicas, bioinformática e inteligência artificial. -----------REFERÊNCIAS DO EPISÓDIO---------- Mais informações sobre Marcel RIbeiro-Dantas: http://mribeirodantas.me/ Artigos Publicados por Marcel RIbeiro-Dantas: Google Scholar Livro - The Book of Why: The New Science of Cause and Effect https://amzn.to/3snKnGM ------------Cursos com Desconto------------ http://www.universogeneralista.com.br/curadoria-de-cursos/ ------------------Apoie o Canal------------ https://apoia.se/universogeneralista ------------------Youtube------------------ https://www.youtube.com/c/UniversoGeneralista ------------------Redes Sociais------------ https://www.instagram.com/universogeneralista/ https://twitter.com/UGeneralista -------- Tratamento de áudio ----------- Allan Spirandelli - https://www.instagram.com/allanspirandelli/ Spotify - https://sptfy.se/7mFh --------ASSUNTOS DO EPISÓDIO------- (0:00) Introdução (1:37) Currículo do convidado (2:42) Histórico do convidado (9:15) De onde vem a “causalidade”? (15:39) O que é causalidade e inferência causal? (22:24) Probabilidade e interdisciplinaridade (33:18) Método quantitativo através da história (38:58) Tipos de estudo, causalidade e correlação (45:05) Estudo clínico randomizado e seus desafios (1:05:07) Importância da Plausibilidade (1:17:25) Plausibilidade baixa em estudo positivo: o que acontece? (1:25:32) Critérios de Hill e suas limitações (1:33:16) Modelos qualitativos e quantitativos (1:37:59) Causalidade e estudos escassos (1:42:21) As limitações da ciência e o raciocínio evolutivo (1:45:33) Plausibilidade extrema (1:48:22) A popularização da ciência de dados (1:54:09) Identificação e estimação de causas (1:56:57) Politização da ciência e seus perigos --- Send in a voice message: https://anchor.fm/universogeneralista/message

To say that event A causes event B is to not only make a claim about our actual world, but about other possible worlds — in worlds where A didn't happen but everything else was the same, B would not have happened. This leads to an obvious difficulty if we want to infer causes from sets of data — we generally only have data about the actual world. Happily, there are ways around this difficulty, and the study of causal relations is of central importance in modern social science and artificial intelligence research. Judea Pearl has been the leader of the “causal revolution,” and we talk about what that means and what questions remain unanswered.Support Mindscape on Patreon.Judea Pearl received a Ph.D. in electrical engineering from the Polytechnic Institute of Brooklyn. He is currently a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. He is a founding editor of the Journal of Causal Inference. Among his awards are the Lakatos Award in the philosophy of science, The Allen Newell Award from the Association for Computing Machinery, the Benjamin Franklin Medal, the Rumelhart Prize from the Cognitive Science Society, the ACM Turing Award, and the Grenander Prize from the American Mathematical Society. He is the co-author (with Dana MacKenzie) of The Book of Why: The New Science of Cause and Effect.Web siteGoogle Scholar publicationsWikipediaAmazon author pageTwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Dr. Jerry Smith welcomes you to another episode of AI Live and Unbiased to explore the breadth and depth of Artificial Intelligence and to encourage you to change the world, not just observe it!   Dr. Jerry is talking today about questions and answers in the world of data science machinery and artificial intelligence.   Key Takeaways: What are Dr. Jerry's favorite AI design tools? Dr, Jerry shares his four primary tools: MATLAB. Is a commercial product. It has a home, academic, and enterprise version. MATLAB has toolkits and applications. The Predictive Maintenance Toolbox at MATLAB, especially the preventive failure model is of great value when we want to know why things fail, also by measuring systems performance and predicting the useful life of a product. Mathematical Modeling with Symbolic Math Toolbox is useful for algorithm-based environments. It is built on solid mathematics. R Programming is Dr. Jerry's favorite free tool for programming with statistical and math perspectives. R is an open and free source and comes with a lot of applications. Python is a great tool for programming and is as capable as R programming to assist us in problem-solving. Python is very useful when you know your work is directed to an enterprise level. Does Dr. Jerry have any recommended books for causality? The Book of Why is foundational for both the businessperson and the data scientist. It provides a historical review of what causality is and why it is important. For a deeper understanding of causality, Dr. Jerry recommends Causal Inference in Statistics: A Primer.   Counterfactuals and Causal Inferences: Methods and Principles it is a great tool to think through the counterfactual analysis.   Behavioral Data Analysis with R and Python is an awesome book for the practitioner who wants to know what behaviors are, how they show up in data, the causal characteristics, and how to abstract behavioral aspects from data. Dr. Jerry recommends Designing for Behavior Change, it talks about the three main strategies that we use to help people change their behaviors. The seven rules of human behavior can be found in Eddie Rafii's latest book: Behaviology, New Science of Human Behavior. Dr. Jerry shares his favorite tools for casual analysis: Compellon allows us to do performance analysis, showing the fundamental causal chains in your target of interest. It can be used by analysts. It allows users to do “what-if” analysis. Compellon is a commercial product.   Causal Nexus is an open-source package in Python that has a much deeper look at causal models than Compellon. BayesiaLab is a commercial tool that is one of the higher-end tools an organization can have. It allows you to work on casual networks and counterfactual events. It is used in AI research.   What skills are needed for data science machinery and AI developers? Capabilities can be segmented into Data-oriented, Information-oriented, Knowledge, and Intelligence. These different capabilities are used in many roles according to several levels of maturity.   Stay Connected with AI Live and Unbiased: Visit our website AgileThought.com Email your thoughts or suggestions to Podcast@AgileThought.com or Tweet @AgileThought using #AgileThoughtPodcast!   Learn more about Dr. Jerry Smith   Mentioned in this episode: MATLAB MATLAB Mathematical Modeling Python Artificial Intelligence with R Compellon Causal Nex BayesiaLab   Dr. Jerry's Book Recommendations: The Book of Why: The New Science of Cause and Effect, Judea Pearl, Dana Mackenzie   Causal Inference in Statistics: A Primer, Madelyn Glymour, Judea Pearl, and Nicholas P. Jewell   Counterfactuals and Causal Inferences: Methods and Principles,  Stephen L. Morgan and Christopher Winship   Behavioral Data Analysis with R and Python: Customer-Driven Data for Real Business Results, Florent Buisson   Designing for Behavior Change: Applying Psychology and Behavioral Economics, Stephen Wendel   Behaviology, New Science of Human Behavior, Eddie Rafii

新型コロナウイルスの世界的蔓延により、これまで以上に様々な変化が生まれた2021年。 そんな変化の大きい時代において、次にくるものとは一体何か。 パロアルトインサイトCEOである石角友愛と、CTOの長谷川貴久、そして日本を代表する投資ファンドであるインテグラルで 多くの企業支援を行ってきた山崎壯が、それぞれの角度から2022年を予測する。 ※本エピソードを記事化したものはこちらから※ 【出演者】 石角友愛 / 長谷川貴久 / 山崎壯 【2022年はこれがくる！】 ・テスラの自動運転 ・Apple M1チップ ・Web3と仮想通貨 ・非接触で冷凍食品販売できる自販機「ど冷えもん」 【今週のおすすめコンテンツ（長谷川貴久）】 「The Book of Why: The New Science of Cause and Effect by Judea Pearl」 統計的および哲学的な観点から、因果関係と因果推論についてまとめた一冊。 DX推進担当者や経営者の方にもおすすめです。 =================================== その他、ご質問や感想、取り上げて欲しいテーマなどあればお気軽にご連絡ください。 メール : info@paloaltoinsight.com 石角友愛のTwitter : @tomoechama （ハッシュタグ「#レベル5」をつけて投稿お願いします） パロアルトインサイトHP : www.paloaltoinsight.com =================================== 楽曲提供：Atsu (beatmaker and rapper from Zenarchy) https://twitter.com/atsu_izm 「Transform」Level 5 テーマソング https://m.soundcloud.com/atsuizm/transform --- Send in a voice message: https://anchor.fm/level5/message

Show notes:- On the Measure of Intelligence by François Chollet - Part 1: Foundations (Paper Explained) [YouTube](https://www.youtube.com/watch?v=3_qGr...)- [2108.07258 On the Opportunities and Risks of Foundation Models](https://arxiv.org/abs/2108.07258)- [2005.11401 Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://arxiv.org/abs/2005.11401)- Negative Data Augmentation: https://arxiv.org/abs/2102.05113- Beyond Accuracy: Behavioral Testing of NLP models with CheckList: [2005.04118 Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://arxiv.org/abs/2005.04118)- Symbolic AI vs Deep Learning battle https://www.technologyreview.com/2020...- Dense Passage Retrieval for Open-Domain Question Answering https://arxiv.org/abs/2004.04906- Data Augmentation Can Improve Robustness https://arxiv.org/abs/2111.05328- Contrastive Loss Explained. Contrastive loss has been used recently… | by Brian Williams | Towards Data Science https://towardsdatascience.com/contra...- Keras Code examples https://keras.io/examples/- https://you.com/ -- new web search engine by Richard Socher- The Book of Why: The New Science of Cause and Effect: Pearl, Judea, Mackenzie, Dana: 9780465097609: Amazon.com: Books https://www.amazon.com/Book-Why-Scien...- Chelsea Finn: https://twitter.com/chelseabfinn- Jeff Clune: https://twitter.com/jeffclune- Michael Bronstein (Geometric Deep Learning): https://twitter.com/mmbronstein https://arxiv.org/abs/2104.13478- Connor's Twitter: https://twitter.com/CShorten30- Dmitry's Twitter: https://twitter.com/DmitryKan

Ekrem Aksoy joins the adventure to discuss transformers and the method of helping Machine Learning algorithms focus on the important parts of an image to determine what to do. Panel Ben Wilson Charles Max Wood Daniel Svoboda Francois Bertrand Guest Ekrem Aksoy Sponsors Dev Influencers Accelerator Links Attention to Transformers Attention in the Human Brain and Its Applications in ML See, Attend and Brake: An Attention-based Saliency Map Prediction Model for End-to-End Driving Ekrem Aksoy - Medium Ekrem Aksoy, PhD - Gradient LinkedIn: Ekrem Aksoy Picks Ben- Read all the blog posts in this episode Charles- Accounting software | Xero Charles- The Prosperous Coach Daniel- Debt - Updated and Expanded: The First 5,000 Years  Ekrem- The Book of Why: The New Science of Cause and Effect Ekrem- Attention in Psychology, Neuroscience, and Machine Learning Contact Ben: Databricks GitHub | BenWilson2/ML-Engineering GitHub | databrickslabs/automl-toolkit LinkedIn: Benjamin Wilson Contact Charles: Devchat.tv DevChat.tv | Facebook Twitter: DevChat.tv ( @devchattv ) Contact Francois: Francois Bertrand GitHub | fbdesignpro/sweetviz

Jessica Young, PhD is a biostatistician in the Department of Population Medicine at Harvard Medical School who joins the show to discuss the ins and outs of her interesting and important work. Tune in to learn the following: How confounding factors in a study can influence the findings of the study, and how/why the gold standard of randomized trials can address this What is meant by the “fundamental challenge of causal inference” and how this explains why assumptions are always necessary in order to claim that a statistical analysis is unbiased Why large subject numbers or data points can't overwhelm biases; why bias is a function of the thing being studied Dr. Young's job is two-fold: she works on both the applications of statistical methods for public health and clinical medicine, and also on the development of methods in these areas. She focuses on causal inference, which is the formal process of understanding how to estimate causal effect from data collected in real-world studies. Through examples including a longitudinal study on nurses starting in the 1970s to present day studies revolving around the coronavirus pandemic, Dr. Young discusses confounding factors in studies and the effect they have on interpretations of findings, the importance of randomization, the presence of bias regardless of how statistically significant a finding is, meta-analyses, where she sees the field of biostatistics heading in the near future, and more.   To learn about the basics of causal inference, Dr. Young recommends reading The Book of Why: The New Science of Cause and Effect. Visit https://www.populationmedicine.org/JYoung to learn more about her work and publications.

So, what’s the deal with chiropractors? Are they all just full of it? Ian Kaplan is the COO of Hybrid Performance Method (Stefi Cohen’s training company) and soon to be doctor of chiropractic, and he’s also one of the most thoughtful and skeptical people in the fitness space. In this conversation, Ian breaks down how he thinks about uncertainty and providing treatment options when a lot of the research shows that most things that we talk about in the fitness and rehab space - well - don’t actually work. He also lays into some of the most common issues he sees with other chiros. And, of course, we spend awhile getting into the weeds and discussing pain science, Bayesian reasoning, and the future of artificial intelligence in guiding treatment protocols. Check out the full conversation with Ian if you want to hear two dorks talk about things like "epistemic humility" - or if you want to hear one of the smartest fellas in fitness explain how he thinks about pain. Check out more from Ian and Hybrid Performance Method here: Website: www.hybridperformancemethod.com Instagram: @kaplanfitness.hybrid | @hybridperformancemethod | @hybridperformancemethod | @steficohen Podcast: Hybrid Unlimited If you're enjoying the show, the best way to support it is by sharing with your friends. If you don't have any friends, why not a leave a review? It makes a difference in terms of other people finding the show. You can also subscribe to receive my e-mail newsletter at www.toddnief.com. Most of my writing never makes it to the blog, so get on that list. Show Notes: [01:04] Ian’s beefs with the field of chiropractic – and what it means to be “evidence-based” in a field with so much uncertainty. [11:50] How should someone actually think about treatment given the inherent uncertainty in dealing with complex systems? How does Ian weigh the costs and benefits of a potential treatment? [18:46] Why are clinicians so easy to fool: regression toward the mean and threshold effects. And, how to give patients hope without lying to them. [25:46] Is it better to try to treat pain with targeted tissue interventions or is it better to focus on the brain? [33:31] The role of artificial intelligence in developing precision medicine models for treating pain patients [39:51] What are the barriers to effectively analyzing treatment data from chiropractors and physical therapists? [44:50] A brief summary of Bayesian inference and its value for treatments, pain science and making business decisions [56:56] How does Ian think about Bayesian inference as a unifying principle for weird stuff we see in pain science like placebo effects and extreme pain sensitivity [01:07:54] How to check out more from Ian Links and Resources Mentioned Humorism What’s the Difference Between a “Straight” Chiropractor and a “Mixer”? from Gutierrez Chiropractic Sensitivity and specificity Threshold effect Opportunity cost Regression toward the mean Bean machine “Arthroscopic Partial Meniscectomy versus Sham Surgery for a Degenerative Meniscal Tear” from the New England Journal of Medicine “Null hypothesis significance testing: A short tutorial” from F1000 Research Confidence interval “Learn About Lookalike Audiences” from Facebook Tempus Externality Introduction to Bayesian networks Information theory “The Bayesian brain: the role of uncertainty in neural coding and computation” from CellPress Frequentist probability “Frequentist And Bayesian Approaches In Statistics” from Probabilistic World “How to take the ‘outside view’” from McKinsey “The Book of Why: The New Science of Cause and Effect” by Judea Pearl Neural network “To Make Sense of the Present, Brains May Predict the Future” from Quanta Magazine Greg Lehman

In this episode, I talk with Irineo Cabreros about causality. We discuss why causality matters, what does and does not imply causality, and two different mathematical formalizations of causality: potential outcomes and directed acyclic graphs (DAGs). Causal models are usually considered external to and separate from statistical models, whereas Irineo’s new paper shows how causality can be viewed as a relationship between particularly chosen random variables (potential outcomes). Links: Causal models on probability spaces (Irineo Cabreros, John D. Storey) The Book of Why: The New Science of Cause and Effect (Judea Pearl, Dana Mackenzie)

Sam Harris speaks with Judea Pearl about his work on the mathematics of causality and artificial intelligence. They discuss how science has generally failed to understand causation, different levels of causal inference, counterfactuals, the foundations of knowledge, the nature of possibility, the illusion of free will, artificial intelligence, the nature of consciousness, and other topics. Judea Pearl is a computer scientist and philosopher, known for his work in AI and the development of Bayesian networks, as well as his theory of causal and counterfactual inference. He is a professor of computer science and statistics and director of the Cognitive Systems Laboratory at UCLA. In 2011, he was awarded with the Turing Award, the highest distinction in computer science. He is the author of The Book of Why: The New Science of Cause and Effect (coauthored with Dana McKenzie) among other titles. Twitter: @yudapearl

Mark Leonard speaks with Mark Schieritz from Die Zeit and ECFR's Sebastian Dullien about a new framework for transatlantic relations. The podcast was recorded on 6 September 2018. Bookshelf: Crashed by Adam Tooze https://www.penguinrandomhouse.com/books/301357/crashed-by-adam-tooze/9780670024933/ The Book of Why: The New Science of Cause and Effect by Judea Pearl https://www.penguin.co.uk/books/289825/the-book-of-why/#mJDZe5QqZFKGwC7k.99 The German barrier to a global euro by Sebastian Dullien https://www.ecfr.eu/article/commentary_german_barrier_global_euro_maas Weg vom Dollar by Mark Schieritz https://www.zeit.de/wirtschaft/2018-09/transatlantische-beziehungen-zahlungsverkehr-europa-usa-heiko-maas Es reicht! by Tina Hildebrandt, Kerstin Kohlenberg, Jörg Lau, Mark Schieritz und Michael Thumann https://www.zeit.de/2018/36/aussenpolitik-handelsstreit-donald-trump-heiko-maas Picture credit: Dollars and euros background by Petr Krachtovil via Public Domain Pictures https://www.publicdomainpictures.net/en/view-image.php?image=20851&picture=dollars-and-euros-background, CC-BY-0.

A new project out of the Center for Open Science in Charlottesville, Virginia, found that of all the experimental social science papers published in Science and Nature from 2010–15, 62% successfully replicated, even when larger sample sizes were used. What does this say about peer review? Host Sarah Crespi talks with Staff Writer Kelly Servick about how this project stacks up against similar replication efforts, and whether we can achieve similar results by merely asking people to guess whether a study can be replicated. Podcast producer Meagan Cantwell interviews Emily Brodsky of the University of California, Santa Cruz, about her research report examining why earthquakes occur as far as 10 kilometers from wastewater injection and fracking sites. Emily discusses why the well-established mechanism for human-induced earthquakes doesn't explain this distance, and how these findings may influence where we place injection wells in the future. In this month's book podcast, Jen Golbeck interviews Judea Pearl and Dana McKenzie, authors of The Book of Why: The New Science of Cause and Effect. They propose that researchers have for too long shied away from claiming causality and provide a road map for bringing cause and effect back into science. This week's episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. About the Science Podcast [Image: Jens Lambert, Shutterstock; Music: Jeffrey Cook]

A new project out of the Center for Open Science in Charlottesville, Virginia, found that of all the experimental social science papers published in Science and Nature from 2010–15, 62% successfully replicated, even when larger sample sizes were used. What does this say about peer review? Host Sarah Crespi talks with Staff Writer Kelly Servick about how this project stacks up against similar replication efforts, and whether we can achieve similar results by merely asking people to guess whether a study can be replicated. Podcast producer Meagan Cantwell interviews Emily Brodsky of the University of California, Santa Cruz, about her research report examining why earthquakes occur as far as 10 kilometers from wastewater injection and fracking sites. Emily discusses why the well-established mechanism for human-induced earthquakes doesn't explain this distance, and how these findings may influence where we place injection wells in the future. In this month's book podcast, Jen Golbeck interviews Judea Pearl and Dana McKenzie, authors of The Book of Why: The New Science of Cause and Effect. They propose that researchers have for too long shied away from claiming causality and provide a road map for bringing cause and effect back into science. This week's episode was edited by Podigy.

A new project out of the Center for Open Science in Charlottesville, Virginia, found that of all the experimental social science papers published in Science and Nature from 2010–15, 62% successfully replicated, even when larger sample sizes were used. What does this say about peer review? Host Sarah Crespi talks with Staff Writer Kelly Servick about how this project stacks up against similar replication efforts, and whether we can achieve similar results by merely asking people to guess whether a study can be replicated. Podcast producer Meagan Cantwell interviews Emily Brodsky of the University of California, Santa Cruz, about her research report examining why earthquakes occur as far as 10 kilometers from wastewater injection and fracking sites. Emily discusses why the well-established mechanism for human-induced earthquakes doesn’t explain this distance, and how these findings may influence where we place injection wells in the future. In this month’s book podcast, Jen Golbeck interviews Judea Pearl and Dana McKenzie, authors of The Book of Why: The New Science of Cause and Effect. They propose that researchers have for too long shied away from claiming causality and provide a road map for bringing cause and effect back into science. This week’s episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. About the Science Podcast [Image: Jens Lambert, Shutterstock; Music: Jeffrey Cook]

Billions of years ago, Mars probably hosted many water features: streams, rivers, gullies, etc. But until recently, water detected on the Red Planet was either locked up in ice or flitting about as a gas in the atmosphere. Now, researchers analyzing radar data from the Mars Express mission have found evidence for an enormous salty lake under the southern polar ice cap of Mars. Daniel Clery joins host Sarah Crespi to discuss how the water was found and how it can still be liquid—despite temperatures and pressures typically inhospitable to water in its liquid form. Read the research. Sarah also talks with science journalist Katherine Kornei about her story on changing athletic performance after gender transition. The feature profiles researcher Joanna Harper on the work she has done to understand the impacts of hormone replacement therapy and testosterone levels in transgender women involved in running and other sports. It turns out within a year of beginning hormone replacement therapy, transgender women plateau at their new performance level and stay in a similar rank with respect to the top performers in the sport. Her work has influenced sports oversight bodies like the International Olympic Committee. In this month's book segment, Jen Golbeck interviews Andrew Lawler about his book The Secret Token: Myth, Obsession, and the Search for the Lost Colony of Roanoke. Next month's book will be The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie. Write us at sciencepodcast@aaas.org or tweet to us @sciencemagazine with your questions for the authors. This week's episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. [Image: Henry Howe; Music: Jeffrey Cook]

Billions of years ago, Mars probably hosted many water features: streams, rivers, gullies, etc. But until recently, water detected on the Red Planet was either locked up in ice or flitting about as a gas in the atmosphere. Now, researchers analyzing radar data from the Mars Express mission have found evidence for an enormous salty lake under the southern polar ice cap of Mars. Daniel Clery joins host Sarah Crespi to discuss how the water was found and how it can still be liquid—despite temperatures and pressures typically inhospitable to water in its liquid form. Read the research. Sarah also talks with science journalist Katherine Kornei about her story on changing athletic performance after gender transition. The feature profiles researcher Joanna Harper on the work she has done to understand the impacts of hormone replacement therapy and testosterone levels in transgender women involved in running and other sports. It turns out within a year of beginning hormone replacement therapy, transgender women plateau at their new performance level and stay in a similar rank with respect to the top performers in the sport. Her work has influenced sports oversight bodies like the International Olympic Committee. In this month’s book segment, Jen Golbeck interviews Andrew Lawler about his book The Secret Token: Myth, Obsession, and the Search for the Lost Colony of Roanoke. Next month’s book will be The Book of Why: The New Science of Cause and Effect by Judea Pearl and Dana Mackenzie. Write us at sciencepodcast@aaas.org or tweet to us @sciencemagazine with your questions for the authors. This week’s episode was edited by Podigy. Download a transcript of this episode (PDF) Listen to previous podcasts. [Image: Henry Howe; Music: Jeffrey Cook]