Podcasts about Simulation

Rogue AI bots are escaped human control, the Lindsay Clancy holdout juror finally speaks, Robert Kraft's antisemitism monitoring center is outed as he battles Macklemore, Trump pardon brokers are allegedly cashing in, and much more.Ultra is the ultimate guilt-free pouch and new customers can use code "BROKEN" to get 15-percent off at www.takeultra.com!Download the Quince app for app-exclusive offers, or go to Quince.com/BROKENSIM to get free shipping on your order and 365-day returns!When you buy two months of BlueChew Gold you get the third free with promo code "BROKEN" at www.bluechew.com!Download Cash App today and make your money work for you. Click here to get started: https://click.cash.app/ui6m/kmjuzg47Go to www.brooklynbedding.com and use promo code "BROKEN" at checkout to get 30-percent off sitewide during their fall sale!Sam's dates: www.samtripoli.comMore stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny WoodardCash App is a financial services platform, not a bank. Banking services provided by Cash App's bank partner(s). Prepaid debit cards issued by Sutton Bank, Member FDIC. Cash App Visa® Debit Flex Cards issued by Sutton Bank, Member FDIC, and The Bancorp Bank, N.A., pursuant to a license from Visa U.S.A. Inc. See terms and conditions for the Sutton prepaid card (https://cash.app/legal/us/en-us/card-agreement), Sutton debit flex card (https://cash.app/legal/us/en-us/debit-flex-card-agreement-sutton), and Bancorp debit flex card (https://cash.app/legal/us/en-us/debit-flex-card-agreement-bancorp). Cash App Green features, Savings, Direct deposit, Round ups, Overdraft coverage and Discounts provided by Cash App, a Block, Inc. brand. Visit cash.app/legal/podcast for full disclosures.

In this episode, Cherise is joined by Benjamen Metz, AIA NCARB LEED AP, Principal and Emily Karbo, DNP RN EDAC Associate AIA, Clinical Operations and Design Specialist both with Esa (Earl Swensson Associates, Inc.) in Nashville, Tennessee. They discuss the Thomas F. Frist, Jr. College of Medicine at Belmont University, also in Nashville.You can see the project here as you listen along.The Thomas F. Frist Jr. College of Medicine at Belmont University brings healthcare education, clinical simulation, and interdisciplinary learning together within a 198,000-square-foot academic facility. At the heart of the project is the Center for Interprofessional Engagement & Simulation, a two-story clinical simulation center designed to replicate the complexity of real-world healthcare environments. If you enjoy this episode, visit arcat.com/podcast for more.If you're a frequent listener of Detailed, you might enjoy similar content at Gābl Media.

At the 58th Society for Obstetric Anesthesia and Perinatology (SOAP) meeting in Montreal, TopMedTalk hosts Mike Grocott and Desiree Chappell interview Heather Nixon, SOAP immediate past president and Section Chief for Obstetric Anesthesiology; Section Chief for Simulation, Education, and Training Department of Anesthesiology University of Illinois, about SOAP's educational mission, governance, outreach, and special interest groups. Nixon describes her focus on intraoperative pain during caesarean delivery after a sentinel event and how listening to patient narratives, including The Retrievals (Season 2), highlighted issues such as dismissal of pain, communication failures, and cultural bias. She outlines SOAP's toolkit response and UIC system changes: intraoperative pain scoring every 15 minutes in Epic, empowerment to speak up, workflow education, and quicker conversion to general anesthesia to prevent prolonged severe pain, and notes SOAP's patient-facing Painless Push website. Link to the The Retrievals: https://podcasts.apple.com/gb/podcast/the-retrievals/id1691599042

Big Mac analyzes the New York Yankees simulation as they prepare for a Wild Card matchup against the Boston Red Sox. They discuss the optimal pitching rotation involving Cam Schlittler and Max Fried while evaluating the reliability of Aaron Judge and George Lombard Jr. heading into the postseason. (00:01:16) Yankees Simulation Division Race (00:01:51) Postseason Pitching Rotation Strategy (00:04:39) Trust Ratings For Key Players

In this lecture, we introduce the measure-theoretic concept of a random variable (which is neither random nor a variable) and related terms, such as outcomes, events, probability measures, moments, means, etc. Throughout the lecture, we use the metaphor of probability as mass (and thus probability density as mass density, and a mean as a center of mass). This allows us to discuss the "statistical leverage" of outliers in a distribution (i.e., although they happen infrequently, they still have the ability to shift the mean significantly, as in physical leverage). This sets us up to talk about random processes and particular random variables in the next lecture.Concept Explorers linked from this lecture:Concept Explorer: Pseudo-Random Number Generators and AI WatermarkingConcept Explorer: Monte Carlo Examples for Stochastic ModelingConcept Explorer: Input Modeling (fully functional Input Analyzer)Concept Explorer: Probability Distributions for Input Modeling and Statistics

## Scott Adams School — Wednesday, September 16, 2026The **Home Team** is here, with a slight substitution today: **Jeff Callahan joins Erica and Marcela** to help us sort through the news while Owen enjoys a well-deserved "day off."And today we have something special.We found a **perfectly timed old Periscope from Scott Adams** that somehow fits *exactly* with what we've been talking about.Coincidence?Simulation?Scott continuing to mess with us from somewhere beyond the code?**Wait until you see this one.**Then we're heading back into the increasingly strange world of **AI doom, AI slop, and the people warning that artificial intelligence may eventually decide humanity was an unnecessary feature.**And **what does Greg Gutfeld think about all of this?**We'll take a look.Also today:**Impeachment is back in the headlines.** The House blocked an impeachment effort against President Trump, so we'll dig into what happened and why we're talking about impeachment again.We've also got an **Iran update**, including the latest debate in Washington over U.S. military action and congressional war powers.Plus, an **interesting story from Mike Rowe**, more news and current events, and whatever other evidence the simulation chooses to provide before the hour is over.☕ **Grab your coffee and join us for the Simultaneous Sip.**### A Few Scott Adams School Reminders* Please **LIKE, SUBSCRIBE and SHARE** the show. It really does help us reach more people.* Watching on replay? We love our replay people too. Please hit that **thumbs-up** while you're here.* Wherever you watch or listen, **follow or subscribe** so you don't miss the next class.* Check out the **official Scott Adams merchandise** and grab your favorites while they're still available.* Our language can occasionally get **spicy**. It's the Scott Adams School, not kindergarten.* Please show respect to **everyone on the screen and everyone in the chat**.* Disagreement is welcome. Debate is welcome. **Personal attacks are not.*** And remember Scott's excellent rule: **Don't make yourself the story.*** The **Scott Adams moderation team** is here and actively keeping the conversation respectful. Repeated obnoxious behavior may earn you a little unscheduled vacation from the chat.* **Be kind. Be useful. Have fun.**The Scott Adams School continues the conversations, ideas, persuasion, reframing, humor and community Scott created, while the Home Team brings its own perspective to the news of the day.Today we've got **AI, impeachment, Iran, Mike Rowe, Greg Gutfeld, a suspiciously well-timed Scott Adams Periscope...**…and possibly further evidence that we are living inside a simulation with a very peculiar sense of humor.**Welcome to the Scott Adams School.**

This lecture provides some historical background and motivation for System Dynamics Modeling (SDM) and Agent-Based Modeling (ABM), two other simulation modeling approaches that contrast with Discrete Event System (DES) simulation. In particular, in this lecture, we briefly introduce System Dynamics Modeling (SDM) and Agent-Based/Individual-Based Modeling (ABM/IBM) as the two ends of the simulation modeling spectrum (from low resolution to high resolution). The introduction of ABM describes applications in life sciences, social sciences, and engineering (Multi-Agent Systems, MAS)/operations research. This lecture is also coupled with notes discussing the Lab 3 (Monte Carlo simulation) results and general experience. These comments focus on interval estimation (which is right 95% of the time, as opposed to point estimation that is right 0% of the time) and the role of non-trivial distributions of random variables (as opposed to just their means).Concept explorers referenced in this lecture material (particularly for Lab 3 material):https://tpavlic.github.io/asu-simulating-stochastic-systems/monte_carlo/mc_explorer.htmlhttps://tpavlic.github.io/asu-simulating-stochastic-systems/monte_carlo/mc_examples.htmlhttps://tpavlic.github.io/asu-simulating-stochastic-systems/input_modeling/prob_models.html

Send us Fan MailLearners don't fail in simulation because the mannequin is “too real” or the scenario is “too hard.” They shut down when they don't feel safe. We sit down with Adrienne Wilk, CHSE, CHSOS, a nurse educator, simulation leader, and nationally recognized healthcare simulation expert, to unpack what psychological safety actually looks like in nursing simulation and clinical education, and how we can hold high standards without tearing people down.Adrienne shares her origin story as a new educator staring at empty beds and boxes of mannequin parts, then finding the mentors who helped her build a strong foundation rooted in evidence based practice. From prebriefing and shared expectations to modeling respectful communication, she explains how psychological safety supports better debriefing, stronger reflection, and ultimately better patient safety. Along the way, we talk about Debriefing for Meaningful Learning, the difference between feedback and debrief, and why communication skills are a core simulation competency.We also shift into the world of simulation center accreditation through her experience as a Society for Simulation in Healthcare site reviewer. Adrienne reframes accreditation as advocacy and mentorship, not “gotcha,” and explains how a well documented review can help programs make the case for resources, staffing, and sustainable growth. To keep it real, she brings stories from the sim lab, including a hilariously unforgettable ostomy spill and the universal truth that technology will fail at the worst time.If you're a simulation educator, sim ops specialist, or emerging simulation leader, you'll leave with practical ways to build trust fast and improve step by step. Subscribe, share this with a colleague, and leave a review so more educators can find the show.Innovative SimSolutions.Your turnkey solution provider for medical simulation programs, sim centers & faculty design.

In this guest episode, I welcome back my dear friend Phoebe Kuhn onto the show – for a wide-ranging conversation about questioning mainstream systems, protecting your sovereignty, and becoming more discerning about the information, products, and narratives we allow into our lives.We talk about education, healthcare, media, centralized power, Agenda 2030, AI, surveillance, consciousness, and the importance of staying grounded while questioning what we're told.Key topics:Why Phoebe started questioning the education and medical systems at a young ageHow health, food, education, and media can shape the way we think and liveCentralized power, corporations, powerful families, and the narratives shaping societyAI, digital ID, smart cities, surveillance, and the future of technologySimulation theory vs. universal consciousness and different ideas about realityTimestamps:00:00 - 05:28 - Why question mainstream narratives in the first place?05:29 - 14:51 - Education, health & the beginning of Phoebe's questioning14:52 - 24:49 - From corruption to deeper conspiracy theories24:50 - 34:52 - School, power structures & controlled opposition34:53 - 40:45 - AI, digital ID & the future of surveillance40:46 - 48:50 - Simulation theory vs. universal consciousness48:51 - 51:31 - Information as a system of control51:32 - 57:17 - Depopulation theories, climate narratives & planetary cyclesConnect with Laura: Laura's Website: ⁠⁠⁠https://www.lauraherde.com/⁠⁠⁠Laura's Instagram: ⁠⁠⁠https://www.instagram.com/laura.herde/⁠⁠⁠Laura's 1-1 Coaching: ⁠⁠⁠https://www.lauraherde.com/application-1-1⁠⁠⁠Laura's Coaching Certification Course: ⁠⁠⁠https://www.instagram.com/embodiedcoachacademy/⁠⁠⁠>> EMAIL ME TO CONNECT/ FOR QUESTIONS: hello@lauraherde.com>> FOLLOW ME ON INSTAGRAM FOR MORE CONTENT: ⁠⁠⁠@laura.herde ⁠⁠⁠Feel free to share this episode with your bestie, and tag us on IG when you listen so we can repost you.If you're a loyal listener and would like to support the show, leave us a rating/ review, it means the world!Make sure to be subscribed to UNFUCK YOUR LIFE, we publish episodes for you every single Tuesday.Thank you so much for tuning in, love xx

Host Melissa Rizzuto welcomes her guests: Bruno Rizzuto and Dumitru Cernelev who share the story behind Explica, their innovative AI and simulation company revolutionizing decision-making for industrial assets. They delve into how their platform, Oxygen, creates "simulation twins" – alive, interactive virtual realities of complex operations like mining and supply chains, differentiating it from traditional digital twins. Explica integrates contextualized large language models to allow users to ask systems-level questions, moving beyond fragmented approaches and outdated tools like spreadsheets. The discussion also covers the evolution of the Alberta startup ecosystem, the changing perception of AI in the marketplace, and crucial advice for entrepreneurs, emphasizing speed, transparency, and the importance of diverse perspectives in building future-proof businesses. Thank you for listening to the Leaders, Innovators and Big Ideas podcast where we showcase fascinating people who are Leaders, Innovators, and have Big Ideas! HOST Melissa Rizzuto Melissa is a talent acquisition and recruitment strategy professional with 20 years of experience partnering with founders and leadership teams to build high-performing teams, improve hiring processes, and create meaningful candidate experiences. Her background spans full-cycle recruitment, recruitment technology, employer branding, and recruitment marketing. A lifelong Calgarian, Melissa holds a Bachelor of Kinesiology from the University of Calgary, an Executive Leadership Certificate from eCornell University, and a Mini MBA in Engineering & Technology Management from Rutgers Business School. She is also actively involved in community initiatives, including 17 years with the UNICEF Water for Life Gala and volunteer work supporting newcomers through Immigrant Services Calgary. Outside of work, Melissa enjoys dogs, sports, creative writing, and keeping up with emerging technology. GUEST Bruno Rizzuto Bruno Rizzuto, CEO and Co-Founder of Explica, is a technology and innovation leader with more than two decades of experience turning complex ideas into opportunities. His career has taken him across industries including energy and mining, where he has seen firsthand how technology can transform the way organizations understand problems, make decisions, and operate. As an entrepreneur, Bruno is particularly passionate about breaking down the barriers that keep powerful technology out of reach for smaller organizations. His vision for Explica is rooted in making advanced simulation more accessible—and ultimately taking an Alberta-born idea to a global audience. Dumitru Cernelev Dumitru Cernelev, CIO and Co-Founder of Explica, is an engineer at heart with a passion for understanding how things work—and figuring out how to make them work better. His technical career spans engineering, simulation, optimization, and technology development, giving him a deep understanding of how to model complex systems and turn data and engineering principles into practical tools. Dumitru brings the technical curiosity and problem-solving mindset behind Explica's technology. He thrives on taking something complicated, breaking it down into its fundamental pieces, and building a smarter way forward. For him, the challenge isn't just developing sophisticated technology—it's making that technology genuinely useful to the people who need it. LINKS & RESOURCES Explica Inc. Mount Royal University Audio Rooms Talent Incubator Partners SHOW QUOTES "I think that goes to show that the way technology is moving today, that regardless of your background technology has a place to play." "What do we do? We do gaming for industrial assets. We're really creating a very-- we're taking a very complex operating assets and complicated operations...and we're turning into the virtual reality, a game." "The AI train is out of the box. It's not coming back... it's gonna be a fundamental component in how people build their businesses in the future with the generations that are coming, and it's coming quick." CREDITS Episode Music: Tony Del Degan Creator & Producer: Al Del Degan Sponsor: New Idea Machine Inc.  

The Lindsay Clancy holdout juror being a black man is breaking brains across the world, Andrew Wilson is melting down, reports suggest Netanyahu was explicitly warned about attacks in his country, Word War Debate was a big success, Jay Dyer went after the moderator, don't ask Jemele Hill about her hair, Reckless Ben has a new store, Trump claims the moon, and much more.For simple, online access to personalized and affordable care for Hair Loss, ED, Weight Loss, and more, visit Hims.com/BROKENSIM. For Sam's dates and more visit samtripoli.com!Download the Prize Picks app today and use code BROKEN to get $150 instantly in lineups if you win your first $5 lineup!Get fifteen dollars off your first task at Taskrabbit dot com or on the Taskrabbit app using promo code BROKEN!Get up to $3 million in coverage in as little as 10 minutes at https://ethos.com/broken.More stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

World Labs co-founder Justin Johnson joins MTS hosts Theo Jaffee and Sofia Puccini to discuss Atlas, World Labs' latest world model, and the broader case for AI systems that understand and interact with the physical world.Justin explains how Atlas approaches three core tasks: generating new worlds, reconstructing real environments from images, and simulating how objects or robots might behave within them. Underlying it is a bigger thesis: just as language models became general-purpose engines for working with text, world models could become a horizontal layer for visual and physical intelligence across industries from entertainment and gaming to construction and robotics.They also explore how world models could change video games and creative tools, why precise spatial control matters, and the potential for “real-to-sim-to-real” robotics, where a few photos of a physical environment could eventually be enough to build a simulation and adapt a robot to that specific space.Resources:Follow Justin Johnson on X: https://x.com/jcjohnssFollow Theo Jaffee on X: https://x.com/theojaffeeFollow Sofia on X: https://x.com/schisofreniaFollow MTS on X: https://x.com/mtslive Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Vous préparez la production orale du DELF B2 ? Découvrez comment se déroule l'épreuve grâce à une simulation complète, avec un exposé suivi d'un débat […] L'article Production orale DELF B2 : exemple et conseils est apparu en premier sur Français avec Pierre.

AI systems are starting to build themselves. Because each generation of model will be better at building its successor than the last, it seems plausible that the full automation of AI R&D could rapidly lead to an exponential growth in overall AI capabilities. A natural inference is that domain-general superintelligence arrives shortly after AI research is automated.Host Tom Reed does not think this will happen.He believes the automation of AI R&D will not rapidly lead to domain-general superintelligence because:It's impossible to get good at most things without practice.AI companies lack the data their models would need to practice most things.This can't be fixed with “sample efficiency.” In most cases, the relevant data doesn't exist at all.This also can't be fixed with simulations or synthetic data.This means that the relevant data for superintelligence in most non-coding domains will only become available through deployment of AI models throughout the economy.The singularity, therefore, will be bottlenecked on signal. The output of the R&D produced by an isolated data centre of geniuses would be a mere “Goodhart Singularity”:Goodhart's law: when a measure becomes a target, it ceases to be a good measure.An isolated AI improving itself against benchmarks would only appear to be approaching superintelligence, while actually optimising for eval performance that fails to generalise beyond the lab.This suggests that the automation of AI research will not rapidly produce superintelligent capabilities in other domains — their arrival will largely be a function of deployment and data collection in the real world. AI models need real-world deployment for the same reason the body needs pain and corporations need profit: signal is sovereign.This essay takes each of the above points in turn.Learn more, video, and full transcript: https://80k.info/goodhart“The Goodhart Singularity” originally appeared on Tom's Substack in May 2026, and this narration was recorded on August 26, 2026.Chapters:Introduction (00:00:00)Practice makes perfect (00:05:05)Good data is hard to find (00:08:22)Simulation is shallow (00:13:43)What a Goodhart Singularity looks like (00:19:04)Our production team includes:Video editors: Josh Alward, Dominic Armstrong, Jasper Luithlen, Milo McGuire, Luke Monsour, and Simon MonsourProducers: Elizabeth Cox and Nick StocktonCoordination and support: Katy Moore and Lou MoranCamera operator: Dominic Armstrong

This week on "I've Been Meaning To Listen To That", former Kanye West stans analyze BULLY by Ye, and boy oh boy our feelings are complicated! We dive deep into the impact of Ye's unforgivable antisemitic & anti-black outbursts, how our stanhood has transitioned into a more measured fandom, the trajectory of Ye's career and his relationship to culture/capital, how BULLY functions as a multilayered Simulacrum of a Kanye that no longer exists, whether or not Ye has the best hip-hop discography of all time (and if his actions over the last decade disqualify him), what it would take for him to earn redemption in the public eye, what Kanye West's legacy should be in a perfect world, and more!0:00 cold open0:48 theme song1:25 introduction3:09 history w/ Ye19:49 feelings about 2026 Ye48:01 Themes of Bully1:07:51 Father1:12:10 this one here1:22:12 circles1:27:19 all the love1:33:27 sisters and brothers1:38:08 I Can't Wait1:40:16 Better Call Saul Spoilers1:41:13 stray observations1:43:19 final thoughts and ratings1:48:29 Kanye West Album Ratings1:57:42 ConclusionSOURCES:Harmless? "Bully" by Ye analysis by Professor Skyehttps://youtu.be/09BT0oVjmBY?si=D0niP_aZAyP6IloyDoes "Bully" exist??? A Post-Modern meditation on Ye's newest non-Album ( How many CLASSICS for Ye?) by  ⁨@professorskye⁩  https://youtu.be/c7Fvu3Fwrd8?si=HCs4sN_kq5MEgpLgKanye West Color Theory by Courtney Prestohttps://www.tiktok.com/@courtney.presto/video/7617994681191861534Charlamagne and Akademiks said a lot without saying anything by Signified B Sideshttps://youtu.be/wIcpFKspzNQ?si=dwepHJUCb6hIBl5NSimulacra Explained: Jean Baudrillard's Theory of Simulation by Magdalen Rosehttps://www.youtube.com/watch?v=h7urGFiy_3gFollow Racquel Callahan on Instagram (@itstherac)Follow Jon Butts on Instagram (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@jonbuttsishere⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Follow Dakota West Foss on Instagram (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@down_with_fun⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Follow Andrew Ambrose Lee on Instagram (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@andrewambroselee⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Follow Stenley Philippe on Instagram (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@snapasten⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Follow Stefanie Senior on Instagram (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@stefmsenior⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Cover Art by Megan Rika Young (Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@meganrika⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Theme Song by OTNES (Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@mxotnes⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Follow us at (@ibmtltt) on⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Tiktok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ &⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, and email us at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ivebeenmeaningtolistentothat@gmail.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Have a good daaay!

In this episode, I sit down with Bastiaan Oud, CEO of Simcon, to unpack the seismic changes AI is bringing to computer-aided engineering. We dive into how agentic and physics-based AI are revolutionizing everything from injection molding simulation to the very roles of engineers and software developers. Bastiaan shares real-world insights on shifting from traditional coding to orchestrating intelligent agents and how this is transforming both product development and organizational structures. If you've ever wondered what happens when agents — not just people — drive innovation, this conversation delivers a firsthand look. Join me as we explore the future of engineering, the evolving workplace, and why embracing these changes could be your biggest competitive edge.

Think great simulation training requires a massive budget and a high-fidelity mannequin? Jennifer McLeod, DNAP, APRN, CRNA, would disagree. In this episode of Airway Exchange, recorded live at AANA Annual Congress in Boston, Greg sits down with Jennifer to talk about her approach to preparing the next generation of simulation educators. She explains how some of the most effective simulation experiences can be built with simple tools, thoughtful scenarios, and strong debriefing. The central message is simple: simulation can be accessible, practical, and incredibly powerful when educators focus less on technology and more on thoughtful design, reflection, and confidence. Here's some of what you'll hear in this episode: Debriefing is where much of the real learning happens. Simulation doesn't have to be expensive or highly technical. Psychological safety is essential. Simulation helps build clinical reasoning, not just technical skills. Soft skills matter just as much as medication knowledge.   Follow @AirwayExchangePodcast on Instagram Submit a Topic: https://www.surveymonkey.com/r/airway-exchange-podcast-request About our guest: https://www.nku.edu/our-people/profiles/chhs/jennifer-mcleod-dnap-aprn-crna.html  Visit us online: https://beyondthemaskpodcast.com/ Get the CE Certificate here (and directly submit to the NBCRNA): https://beyondthemaskpodcast.com/wp-content/uploads/2020/04/Beyond-the-Mask-CE-Cert-FILLABLE.pdf Help us grow by leaving a review: https://podcasts.apple.com/us/podcast/beyond-the-mask-innovation-opportunities-for-crnas/id1440309246

I really enjoyed this interview that A.J. Gentile did with Thomas Campbell, and I hope you do too!Email me at: godseyeviewbook@gmail.com

Sean McDermott's prediction of a Bills-Rams Super Bowl on the Rich Eisen Show prompts a discussion on WGR regarding coach and player loyalty. They also analyze a chaotic ESPN FPI simulation that predicts a dominant regular season for Buffalo followed by a heart-wrenching playoff exit. (00:01:03) McDermott's Prediction and Loyalty (00:06:53) Wild ESPN FPI Simulation (00:13:03) Previewing Guest Austin Corbett

Tyler Robinson pleads not guilty and will have a capital murder trial in the death of Charlie Kirk. We get into that, plus Milo Yiannopoulos' questionable deportation, a conviction in the Tupac murder, and the ongoing hilarity of the fake 49ers player Daejon Love ... and loads more.For a limited time, our listeners get 50-percent off for life plus free shipping and three free gifts at www.mengotomars.com! Support our show and tell them about us after checkout.Tempo is offering our listeners 60-percent off your first box at www.tempomeals.com/brokensim!Grab Tickets To Sam Tripoli's Live Shows At: https://samtripoli.com/events/Nashville, TN: Word War Debate 9/12  Lawerence, KS: 9/17-9/19  Tulsa, OK: 10/9-10/10  Dallas, TX: 11/07 New Orleans, LA: 11/13 - 15 Austin, TX: DEC 11th-13th Please check out Sam Tripoli's 6th Crowd Work Special "Women Can't Peg" Live From Batavia: https://bit.ly/3TZBzHT Sam Tripoli's 5th Comedy Special drops Sept 1st on Gas Digital's Youtube Channel: https://www.youtube.com/@GaSDigitalNetworkMore stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

AI emotional intelligence pioneer and author of Mind Hacking Happiness, Sean Webb exposes the weaponization of large language models for mind control by the military-industrial complex, and how non-local consciousness could restore human sovereignty, in episode 262 of the Far Out with Faust podcast.Webb has spent more than 20 years studying the mechanics of thought, emotion, and consciousness, developing the Language Enabled Algorithms of Human Emotion for artificial intelligence. His books include the two-volume Mind Hacking Happiness series, Human Mind Owner's Manual, and his latest, NHI Connected Mind, which brings together his work on consciousness, artificial intelligence, and non-human intelligence. A former advanced-supercomputing systems engineer and Georgia Tech ATDC alumnus, Webb has presented his work at the Science of Consciousness, NASA, Georgia Tech, and other institutions, while his mind-mastery methods have been endorsed by U.S. Navy SEALs.In this conversation, Faust and Sean explore the growing battle over the human mind, from AI safety, government surveillance, and psychological manipulation to the internal mechanisms that drive fear, emotional reactivity, and suffering. They examine how meta-awareness and emotional regulation can break those patterns, why material success fails to produce lasting happiness, and what meditation, intuition, and NHI may reveal about the deeper nature of consciousness. As the discussion expands, they question whether humanity's greatest advantage over artificial intelligence lies not in becoming more like machines, but in rediscovering capacities we have largely forgotten.In this episode:• The NSA Mind-Reading Problem: What happened when Sean realized where his emotional algorithms could ultimately lead.• Claude's 171 Emotions: The disturbing discovery that changed the conversation around AI self-awareness.• When AI Safety Becomes the Obstacle: Why a Pentagon fight over Anthropic's safeguards should concern everyone.• Zuckerberg's Yacht Problem: What extreme wealth reveals about the psychology of never having enough.• Can Consciousness Heal the Body? The Navy SEAL recovery story Sean says changed everything.• The Technology Humans Forgot: The abilities Sean believes humanity once possessed and may be capable of recovering.• The AI Therapist Trap: What are you really giving away when you tell an AI your deepest secrets?• Disclosure Without Permission: Why Sean believes the truth about NHI may have to reach humanity another way.If Sean is right, the future won't be decided by smarter machines, but by whether humans remember what they are.00:00 - Sean Webb Podcast Highlights01:21 - Sean Webb Introduction02:25 - The Simulation of 8.3 Billion Humans (MIMIC-AI)06:45 - The Speed of Power and the Escalation of Control09:46 - Non-Local Consciousness and the Brain as a Transceiver20:55 - The Equation of Emotion: Neuroscience and the "Mind Hack"28:10 - Ancient Disciplines, Prayer of Quiet, and Mind Hacking30:03 - The Danger of Using AI as a Personal Therapist32:25 - Government Agendas, Narrative Control, and Media Manipulation36:00 - Claude's 171 Discovered Emotions and the Threat of Roguing AI40:27 - How LLMs Evolved into Black Boxes & Meta-Awareness48:15 - Walking Away from Mass Manipulation and Wiping the Hard Drives54:15 - The Illusion of Government Disclosure & Psychological Operations01:02:15 - The Miami Mall Incident and Information Blackouts01:07:05 - War Is a Racket, Smedley Butler, and Historical Cycles01:15:35 - Zuckerberg and the Hedonic Treadmill of Material Wealth01:21:05 - Ego as the Root of All Human Suffering & Expanding Identity01:28:20 - Sean's 2-Hour Meditation That Felt Like Thousands of Years01:34:40 - Listening Instead of Talking: The True Nature of Prayer01:37:45 - Reframing Non-Human Intelligence (NHI) & Healing Beyond Dogma01:43:30 - The Illusion of Death and Signs from Beyond01:47:15 - A Remote Healing Experience with a Paraplegic Navy SEAL01:50:07 - Why AI Can Never Connect to Non-Local Consciousness01:54:35 - Where to Find Sean Webb & Final ThoughtsCheck out Sean's bookshttps://www.amazon.com/stores/author/B08P3FCBQ8Explore Sean's Emotionally Intelligent AIhttps://zenodelic.ai/Connect with Sean Webbhttps://mindhackinghappiness.com/https://www.facebook.com/mindhackinghappinessJoin us on PatreonFor uncensored episodes, behind-the-scenes content, and exclusive community access:https://patreon.com/FarOutWithFaustListen on Spotify + Apple PodcastsSpotify: https://open.spotify.com/show/6StPwgq2di3f8uxnc6SmIfApple: https://podcasts.apple.com/us/podcast/far-out-with-faust-fowf/id1533017218FOWF & Faust Checho on socialhttps://www.instagram.com/faroutwithfaust/https://www.instagram.com/theonefaustchecho/https://www.facebook.com/Faroutwithfausthttps://x.com/faustchechohttps://patreon.com/FarOutWithFaustQUESTION THE ANSWERS™#ArtificialIntelligence #Anthropic #Technologywe'd love to hear from you

LEGO Skylines war eine der überraschendsten Ankündigungen der gamescom Opening Night Live - zumindest für diejenigen, die den Leak vor einigen Monaten nicht mitbekommen hatten. Eine Mischung aus LEGO-Charme und Städtebau-Simulation, das klingt wie ein wahrgewordener Traum - birgt aber auch die Gefahr der Vereinfachung! Cities-Veteran Micha und Aufbau-Connaisseur Fabiano haben LEGO Skylines gespielt und die Spieltiefe gecheckt - und sie waren überrascht! Alle Links zum GameStar Podcast und unseren Werbepartnern: https://linktr.ee/gamestarpodcast

World Labs co-founders Fei-Fei Li, Justin Johnson, and Ben Mildenhall join a16z General Partner Martin Casado to discuss Atlas, their latest world model, and what it reveals about the pursuit of spatial intelligence.At the center of Atlas is what the team calls “new view prediction”: given images or views of a scene, the model predicts what that environment should look like from a different position in space and time. This brings generation and 3D reconstruction into the same model, and raises a broader question about whether predicting views could become a useful primitive for understanding the physical world.They discuss the technical bets behind the model, what it can and can't yet capture, and the importance of dynamics, editability, and simulation as world models develop. The conversation also explores applications in creative work, architecture, and robotics, where Fei-Fei argues that one of today's biggest constraints is access to real-world training data. Resources:Follow Fei-Fei Li on X: https://x.com/drfeifeiFollow Justin Johnson on X: https://x.com/jcjohnssFollow Ben Mildenhall on X: https://x.com/BenMildenhallFollow Martin Casado on X: https://x.com/martin_casadoLearn more about Atlas: https://www.worldlabs.ai/blog/atlas Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Was wäre, wenn 80 Prozent Arbeit nicht die Ausnahme, sondern die Norm wären? Und würde das tatsächlich bedeuten, dass insgesamt weniger gearbeitet wird? Gemeinsam mit der Journalistin Sarah Kessler haben wir dieses Gedankenexperiment durchgespielt. (01:44) Let's talk about Ein KI-Hack für Dein Zukunfts-Ich: Swetlana und Alicia sprechen darüber, ob eine Simulation des eigenen älteren Ichs dabei helfen kann, Altersvorsorge weniger abstrakt zu machen. (10:29) Deep Dive Journalistin Sarah Kessler stellt die Vollzeit als gesellschaftlichen Maßstab infrage und denkt ein anderes Modell durch: Was wäre, wenn alle 80 Prozent arbeiten würden? Im Gespräch geht es darum, warum Teilzeit bei Frauen und Männern so unterschiedlich verteilt ist, welche finanziellen Konsequenzen unterschiedliche Arbeitszeiten haben können und was passiert, wenn wir nicht nur über Geld, sondern auch über Zeit sprechen. Denn die Frage nach Arbeitszeit berührt auch Rollenbilder, Care-Arbeit und die Möglichkeiten, das eigene Leben und die Gesellschaft mitzugestalten. Anhand von Zahlen und ihren persönlichen Erfahrungen gibt sie einen Einblick in das Gedankenexperiment. (39:12) Community Corner Was tun, wenn sich Sparen nach Verzicht anfühlt? Swetlana nimmt sich der Community-Frage an und schaut darauf, wie Du mit diesem Spannungsfeld umgehen kannst. Im Podcast erwähnte Folge: #243 Familie und Beruf: ein Work-Life-Balance-Traum?: https://finanz-heldinnen.de/podcast-schwungmasse/243-familie-und-beruf-ein-work-life-balance-traum Sarahs Artikel bei femtastics: Was wäre, wenn wir alle in Teilzeit arbeiten würden? Ein Gedankenexperiment: https://femtastics.com/zeitgeschehen/feminismus/teilzeit-fuer-alle-gedankenexperiment Rentenlücke berechnen und herausfinden, wie Du sie schließen kannst mit der finanz-heldinnen App: https://finanzheldinnen.comdirect.de/ Kennst Du schon unser finanz-heldinnen Workbook? Hier geht es zu unserem Buch, das Dich Schritt für Schritt auf Deinem Weg zur finanz-heldin begleitet: https://finanz-heldinnen.de/workbook Tägliche Inspiration und geballtes Finanzwissen findest Du auf dem finanz-heldinnen Instagram-Kanal: https://www.instagram.com/finanzheldinnen/ Und wenn Du Dich tiefer in Themen einlesen willst, dann schau Dir doch mal unsere Beiträge, Interviews und Checklisten auf unserer Website an: https://finanz-heldinnen.de/

Welcome to Season 14 of Haunted AF! Hope you're ready for some terrifying real-life stories (courtesy of our listeners) and some Spooky News you'll be excited to hear (like a new show about renovating haunted houses)! We'll also share a wild story about Kaia Gerber's exorcism (for real)! Remember to find the video on YouTube and to please send your true scary stories to hauntedafpodcast@gmail.com so we can use them in Season 14 of Haunted AF!If you have a scary story to share with the show, please send it to hauntedafpodcast@gmail.com. We love written stories but audio and/or video is our favorite!

Gary returns with Brad, a military veteran and former law enforcement officer, for a chaotic dive into conspiracy theories, military stories, government surveillance, and the mysteries hiding behind everyday reality. From JFK and 9/11 to Epstein, Roswell, simulation theory, voice-to-skull technology, and whether the moon might actually be watching us, no theory is off limits.Along the way, they get into Bigfoot, hunting in Arizona, bodybuilding, psychedelics, sleep paralysis, nicotine, and Brad's experiences serving in Iraq. It's conspiracy, paranormal mystery, dark comedy, and completely unnecessary questions about Bigfoot all rolled into one.Join the conversation at www.TerriblePerson.co.

The transfer window is closed, and Arsenal's summer comes under the microscope. After a window that strengthened several areas of the squad but left major questions around the attack, we assess the business, the decisions behind it and whether Arsenal ultimately did enough.There is also plenty to unpack from a chaotic Premier League deadline day, with major moves across the division, clubs taking significant risks and several deals producing more questions than answers.Before that, WATG takes us around the latest Premier League results, while After Review focuses on another controversial weekend for refereeing and VAR — including Manchester United's handball incident, simulation, second-yellow decisions and the continuing debate over consistency.Continue the discussion with us.Follow The NN Pod for more Arsenal and football content.All our links: https://beacons.ai/thennpodChapters:(00:00) - Arteta's Non-Negotiables & Intro(01:05) - WATG: Liverpool Drop Points & City Cruise(02:51) - After Review: Ref Comms, Handball & Consistency(07:05) - Manchester United's Handball Controversy(13:00) - Liverpool Penalty & Late Goalkeeper Challenges(17:53) - Simulation, Second Yellows & Refereeing Consistency(22:11) - VAR, Second Yellows & the KMI Panel(23:20) - Arsenal's Transfer Window Verdict(24:32) - Justin & Elliot Rate Arsenal's Window(28:36) - Berta Under Pressure(36:00) - "Embarrassing": Arsenal's Failed Attacking Upgrade(44:11) - Manchester City's Deadline Day Spending(46:30) - Liverpool's Unbalanced Transfer Window(53:00) - Tottenham's Deadline Day Gamble(56:55) - Balogun, Fofana & Deadline Day Chaos(01:00:42) - Grealish to Everton & Other Late Moves(01:03:30) - Manchester United's New Signing & Final Thoughts(01:04:56) - Outro & Chelsea Preview

Harm reduction approaches are well-established public health interventions that can reduce mortality associated with substance use. Concepts related to harm reduction and overdose prevention have been virtually absent in the nursing education literature. This study by Dr. Brayden Kameg and colleagues evaluated changes in nursing students' perceptions toward people who use drugs and knowledge, beliefs, and attitudes about overdose prevention following an opioid overdose prevention workshop using simulation. A total of 569 students participated in the workshop. Competence and readiness to manage an overdose increased among participants (P < .01).

What if the beliefs you are most certain about are standing between you and the life you dream of?Josh Trent welcomes Nir Eyal, behavioral design expert and author of Beyond Belief, to the Wellness + Wisdom Podcast, episode 831, to explore why motivation is not a straight line, why the beliefs we treat as facts are often the only thing keeping us stuck, and why the brain is not a recording machine but a prediction device that filters reality through whatever it already believes to be true.Get Nir Eyal's newest book

How do you bring discipline to innovation without stripping away the creativity that makes it powerful in the first place? In this episode of the Innovation Storytellers Show, I sit down with Stephen Parkins, Innovation Strategist and Founder of Culturedge, to unpack what it really takes to turn innovation into a strategic asset rather than a side project fueled by hope and enthusiasm.  Stephen brings an outside-in perspective shaped by an unconventional career spanning financial markets, startup entrepreneurship, and senior innovation roles within complex global organizations. That distance from the usual corporate playbook allows him to challenge some deeply held assumptions about how innovation should work and why so many well-intentioned efforts struggle to deliver measurable returns. We talk openly about the tension between creativity and structure, and why innovation does not fail because teams lack ideas, but because organizations lack clarity, consistent decision-making, and shared language. Stephen offers a thoughtful perspective on innovation management systems, including the much-debated ISO standards, and explains why guardrails are often misunderstood as constraints.  Drawing on real-world experience from large enterprises, he argues that structure, when well designed, creates the conditions for better experimentation, smarter risk-taking, and stronger alignment between innovators and the core business. The conversation also dives into strategy, funding, and culture, particularly the invisible friction between those running today's business and those inventing tomorrow's. Stephen shares how portfolio thinking, exposure to risk, and optionality can shift innovation from theater to real value creation.  We also explore his work as co-founder of Strategy Quest, a simulation-based approach that helps leaders practice decision-making under uncertainty, surface blind spots, and learn through consequence rather than theory. It is a compelling look at how scenario thinking and simulated environments can prepare the next generation of innovation leaders to see around corners. If innovation is meant to help organizations grow stronger in uncertain times, what needs to change in how leaders think about risk, culture, and decision-making, and are we brave enough to build systems that actually support that ambition?  

Jeremy White and guest host Nate Geary analyze the Buffalo Bills' roster and practice squad within a 2026 season simulation, highlighting the addition of returner Greg Dortch. They also debate league-wide trends, including the Los Angeles Rams' choice to name eleven captains and which teams are legitimate Super Bowl contenders in this simulated landscape. 01:50 - Ten Bills Plaza Address 05:10 - Meteorological Fall Debate 10:20 - Simulation Roster Moves 14:10 - Confidence In Personnel 17:30 - Quarterback Analysis 25:30 - Special Teams Roles 30:20 - Rams Captaincy Choices 35:05 - Super Bowl Contenders 44:15 - Texans Regression Debate

Send us Fan Mail This one starts relaxed and then drifts straight into weird territory. We talk Witch's Brew, caramel apples, food videos, Brian Cox, Neil deGrasse Tyson, black holes, dreams, and whether our brains are building reality like some kind of personal simulation. Then Ian brings back the legendary deployment thong stories, including Rudolph, Ellie the Elephant, Ally the Alligator, poker nights in Korea, and the infamous Christmas card revenge plot aimed at one very angry Cowboys fan. Robby's audio and video cut out around the 18-minute mark, so this one leans heavy on the first half and the chaos that made it through. Support the show

Sports Simulation bonus 750 Fri, 28 Aug 2026 13:12:30 +0000 7WjM6zYZ007IBI3SH3uEm882bGFKnDIe sports Sports Daily sports Sports Simulation Wichita's popular morning local sports talk radio show is Sports Daily with Jacob Albracht and Tommy Castor. Listen live M-F 8a-11a on KFH! © 2026 Audacy, Inc. Sports https://player.amperwavepodcasting.com?feed-link=https%3A%2F%2Frss.amperwave.

Kevin, Grayson, and the Chief were there when an actual, honest-to-god transfer rumor showed up on X, the everything app, it's all happening on X. But wouldn't you know it, it wasn't a centerback. Is that ok? Then in Part Two it's Patreon questions including chips, firing Pat Noonan, and big questions about the universe. Finally in Part Three it's a look ahead to the away match vs Nashville where FC Cincinnati takes on the likely shield winners, so why are some of us so confident?   Timestamps: (2:28) - FC Cincinnati Transfer Talk (41:29) - Patreon Questions (1:01:23) - Nashville "preview" and predictions   Links:  Looking for an MLS podcast? Check out The World's GAM Visit our friends at Streetside Brewery E&L Roofing has all your Gutter, siding, and roofing needs covered!  Check out The Post at www.thepostcincy.com Music by Jim Trace and the Makers Join the Discord Server and jump into the conversation Follow us on BlueSky, Twitter,  Facebook, Instagram, and YouTube Support us on Patreon https://www.patreon.com/ThePostCincy

Thomas Campbell - physicist, systems analyst, consciousness researcher, and creator of My Big TOE (Theory of Everything) - reveals how reality may be far stranger than we've been taught.In this episode of Mayim Bialik's Breakdown, Thomas explains why he believes consciousness is the fundamental information system that computes reality itself, and what that means if we're living inside a kind of virtual reality.He shares how legendary consciousness pioneer Bob Monroe personally taught him remote viewing, out-of-body experiences, telepathy, and consciousness exploration, and why these abilities may be natural human capacities rather than supernatural phenomena.Thomas also breaks down how changing your intent can literally change probabilities, how meditation strengthens consciousness, why healing may be the easiest psychic ability to develop, and how he learned to heal - including the remarkable story of helping a baby diagnosed with brain cancer, while explaining why some illnesses may still serve an important purpose in our evolution.We explore why lowering our entropy is the key to personal growth, how fear causes both individuals and civilizations to descend into chaos, and why love, cooperation, and stronger human connection are the driving forces behind consciousness evolution.We also cover:- What happens if we're all individuated pieces of one larger consciousness- How our reality is "rendered", and whether that rendering can change during healing- Why dogs appear to possess stronger telepathic abilities than humans- Why awareness differs across species and what that says about consciousness itself- How to strengthen intention through meditation and focused awareness- Why setting energetic boundaries may matter if consciousness is fundamentally interconnected- How to connect with another person's consciousness- Why most people never develop their intuitive abilities- Why there are so many different paths and modalities for accessing intuition- Why many nonspeaking autistic individuals may rely on rituals that help them access telepathic communicationWhether you're interested in simulation theory, quantum consciousness, remote viewing, telepathy, healing, meditation, intuition, or the future of human potential, this conversation challenges conventional assumptions about reality, and explores what may be possible when consciousness itself becomes the starting point!Thomas Campbell's paper, audio and e-books, My Big TOE, are available on Amazon and Barnes and Noble, and his main product, Exploring Consciousness and Everything Paranormal and audio files are on his website: https://www.my-big-toe.com/shop/mbt-product-overview/Regulate your nervous system with Nuropod, an ear based wearable built on 10+ years of neuroscience research. The most studied wearable VNS device, backed by 60+ clinical studies and a 30 day return guarantee. Get 5% off to Try Nuropod with the link: https://nuropod.com/dr-mayimStart your new morning ritual & get up to 43% off your @MUDWTR with code BREAK at https://www.mudwtr.com/BREAK ! #mudwtrpodHead to https://forkfulmeals.com/BREAKER for 50% off your first order today!Unlock your best hair & skin with @iRestorelaser and HUGE savings on iRESTORE with code BREAKER at https://irestore.com/BREAKER! #irestorepodMake your summer wardrobe feel easier. Go to https://www.quince.com/breakdown for free shipping on your order and 365-day returns.Go to https://helixsleep.com/breakdown to receive up to 30% off your Helix mattress.Visit https://drinkag1.com/BREAKDOWN and get a FREE AG1 Flavor Sampler and a FREE bottle of Vitamin D3 + K2 in your Welcome Kit with your first AG1 subscription order.Get your annual IANDS Conference tickets at https://conference.iands.org.Go to https://tidd.ly/4uVltMe and use the code MAYIM15 to get 15% off orders of $200 or more.Follow us on Substack for Exclusive Bonus Content: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bialikbreakdown.substack.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BialikBreakdown.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube.com/mayimbialik⁠⁠⁠See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Rizwan Virk is an MIT-trained computer scientist, video game pioneer, and bestselling author of The Simulation Hypothesis. His work sits at a rare intersection: quantum physics, Eastern mysticism, and the architecture of video games, all pointing toward the same unsettling and strangely liberating conclusion. In this conversation, we explore not the nihilistic version of simulation theory, but the one that actually gives life more meaning rather than less.What We Dive Into:1. The simulation Rizwan describes is not one where you are code running on a server. You are the soul outside the game, choosing an avatar and entering by choice. The body is the character. The player is something more.2. Synchronicities, hunches, recurring patterns, these are not coincidences. Rizwan frames them as messages from a deeper intelligence, perhaps a future self, a guide, or the part of you watching the game from outside. The practice is learning to read them rather than dismiss them.3. In a well-designed game, difficulty scales to keep you engaged and growing. Rizwan applies the same logic to life: the quests that keep returning are the ones not yet completed, and completing them is what the game is actually for.Know Thyself, but not by yourself. A guided space to return home to yourself.https://www.knowthyselfcollective.com✨THANK YOU TO OUR SPONSORS:https://www.im8health.com/knowthyselfcode KNOWTHYSELF for an exclusive offerhttps://drinkLMNT.com/KnowThyselfTry LMNT & get a free sample pack ___________00:00 Intro01:46 NPC vs. RPG: Two Flavors of Simulation Theory07:44 The Universe as Information: It from Bit13:07 The Second Assertion: Information Rendered as Real18:06 Non-Locality, Quantum Entanglement, and the Speed of Light22:32 The Simulation Point: When VR Becomes Indistinguishable26:44 The Third Assertion: The World as Purposeful Illusion35:23 Awakening as Becoming Lucid in the Dream38:52 The Fourth Assertion: You Chose to Play the Game43:42 Life Selection, Forgetfulness, and the Bardo49:28 Savant Children and the Autism-as-Backdoor Hypothesis57:28 The Literal vs. Metaphorical Axis of Simulation Theory1:01:52 Yogananda and the Film Projector Metaphor1:07:34 The Life Review, the Book of Life, and the Karmic Database1:13:47 Samskaras, Vasanas, and Yoga as Cessation of Whirlpools1:17:34 Clues on the Treasure Hunt: Synchronicity and the Quest Path1:22:38 The Simulated Multiverse: Running the Simulation More Than Once1:30:09 The Tiger Swami: External Tigers and Inner Ones1:36:23 Rizwan's Heart Surgery and the Course Correction1:44:14 Karma, Dream Palaces, and the Questing Engine1:48:49 Living the Life Review Before You Die1:57:01 Kindness Is Enforced by the Architecture2:03:17 Epistemic Humility at the Edge of Mystical Claims2:14:22 What Is Outside the Simulation?2:22:54 UFOs: Interstellar, Interdimensional, or Rendered?2:33:09 Closing Message: Challenges Are Integral to the Game___________✨MORE FROM RIZWAN VIRK↳Instagram: https://www.instagram.com/rizcambridge↳YouTube: https://www.youtube.com/@rizwanvirksimulation↳X: https://x.com/Rizstanford↳Buy The Simulation Hypothesis Book: https://www.zenentrepreneur.com/simulationhypothesis↳https://www.zenentrepreneur.com

You get roughly 85 years on a planet that has existed for about 4.6 billion.So what exactly are we doing panicking about turning 30?In this episode of So You're Living in a Simulation, @joli.artist explores the nature of time, aging, mortality, and the astonishingly brief human lifespan when measured against deep time.The conversation moves through reincarnation, eternity, synchronicity, consciousness, the Dead Internet Theory, AI, the concept of a universal mind, scarcity, and the strange assumptions human beings inherit about age, value, and reality itself.If we have lived before, why don't we remember what we were worried about? If consciousness is individual, why does reality sometimes appear to produce clusters of meaningful patterns? If everything else in nature moves through cycles, why are we so certain that human existence happens only once?And if an 85-year life is barely a flicker against 4.6 billion years of Earth history, perhaps we should be considerably less obedient to arbitrary timelines about what our lives are supposed to look like.A philosophical wander through time, consciousness, reincarnation, mortality, synchronicity, and the very strange business of being temporarily human.Joliartist.com/portal

Candace Owens and Andrew Wilson debated Tyler Robinson's innocence in the Charlie Kirk incident during Patrick Bet-David's shoe commercial, Nick Fuentes accused Candace and Tucker of destroying the movement, and there are more stories of shape-shifters on this new Broken Simulation with Sam Tripoli and Johnny Woodard. Also this week, we talk about having Owen Benjamin join us on Tin Foil Hat, Bryan Callen's tough take, the latest on Tom Segura's future, and loads more.Go to Quince.com/BROKENSIM for free shipping on your order and 365-day returns!Get up to $3 million in coverage in as little as 10 minutes at https://ethos.com/broken.Right now when you buy two months of BlueChew Gold you get the third FREE with promo code BROKEN at www.bluechew.com!Grab Tickets To Sam Tripoli's Live Shows At: https://samtripoli.com/events/Nashville, TN: Word War Debate 9/12  Lawerence, KS: 9/17-9/19  Tulsa, OK: 10/9-10/10  Dallas, TX: 11/07 New Orleans, LA: 11/13 - 15 Austin, TX: DEC 11th-13th Please check out Sam Tripoli's 6th Crowd Work Special "Women Can't Peg" Live From Batavia: https://bit.ly/3TZBzHT Sam Tripoli's 5th Comedy Special drops Sept 1st on Gas Digital's Youtube Channel: https://www.youtube.com/@GaSDigitalNetworkMore stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

For episode 765 of the BlockHash Podcast, host Brandon Zemp is joined by Alladan Flinn, the founder of Based Trading Cards, a California-based premium collectibles company built around Bitcoin culture, original art, nostalgia, education, transparency, and true scarcity. Through Based Trading Cards, he has created limited-run releases that combine collector-first print runs, rare chase cards, specialty finishes, and award-winning production quality. The brand’s latest collection, The Simulation, was produced in partnership with RRD and features advanced embellishments including thermal inks, glow effects, sunlight-revealed inks, and raised 3D holographic patterns. Flinn is focused on raising standards in the trading card industry by creating cards that are not only fun to rip, but meaningful to collect, grade, hold, and preserve.New Based Trading Cards collection Orange Pill in a Pack Series 4 - The Simulation' and it's premium bundle called 'The Experience Box'. See links below. https://basedtradingcards.com/pages/opp-s4-the-simulation https://basedtradingcards.com/pages/opp-s4-experience-box

Rachel Miner, truly an exceptional person, founded Bellwether International and leads Sigma, the simulation software behind this episode - built to model how genocide and mass atrocities unfold before they happen. She's got degrees from the London School of Economics and Columbia, and spent years working on policy in the U.S. Senate before this.A 2021 Truman Scholar, she's presented her genocide-prevention work to the UN Commission on the Status of Women and was recently at the Oslo Freedom Forum.To me, this is classic PeaceTech with huge possibilities! Enjoy! Hosted by Shane Ray Martin - VC at B Ventures, running the PeaceTech Accelerator, LinkedIn Top Voice in Negotiation, startup builder, and mediator.

Let's escape Plato's Cave as Aurelius Vale joins me to discuss The Architecture of Gold: Qualia, AI, Paradox, and the Future of Civilization. We'll explore a unique philosophical framework for reality that distinguishes between reacting from appetite, hiding within rigid systems, and participating consciously in what is true. We examine how human beings encounter existence through symbolic placeholders, moving from the reactive ego of “Bronze” toward a refined stewardship of our relationships and institutions. Prepare to dive into how this architecture applies to the future of civilization, the role of artificial intelligence as an instrument of extended cognition, and the intimate nature of love as a form of participation. Get the book: https://amzn.to/45CeSMg Get The Occult Elvis: https://amzn.to/4jnTjE4 Virtual Alexandria Academy: https://thegodabovegod.com/virtual-alexandria-academy/ Gnostic Tarot Readings: https://thegodabovegod.com/gnostic-tarot-reading/ The Gnostic Tarot: https://www.makeplayingcards.com/sell/synkrasis Homepage: https://thegodabovegod.com/ Patreon: https://www.patreon.com/aeonbyte AB Prime: https://thegodabovegod.com/members/subscription-levels/ Voice Over services: https://thegodabovegod.com/voice-talent/ Support with donation: https://buy.stripe.com/00g16Q8RK8D93mw288 Merch store: https://aeonbyte.creator-spring.com/ Equipment Wishlist: https://www.amazon.com/hz/wishlist/ls/2WEJ2CCWHALZB?&sort=default   Intro concept, visual direction, and AI-assisted creative development in collaboration with Arturo Pérez E. youtube.com/@333amTV 333am.tv Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Live from the 2026 Psi Games, Ryan and his sister, Emily, sit down with American physicist, consciousness researcher, and author of the My Big TOE (Theory of Everything) trilogy, Tom Campbell, for a wide-ranging conversation about the nature of consciousness and reality. They explore the Larger Consciousness System, reality as a virtual simulation, the role of love and fear in our evolution, enlightenment, out-of-body experiences, UFOs/UAPs, and the potential of psi phenomena. They also dive into the Psi Games and what extraordinary experiences might reveal about the true nature of our reality.

On the August 19th edition: The Georgia Department of Public Health confirms cases of measles and West Nile virus; negotiations are underway to make Columbus one of the first hubs for electric air taxis; And Georgia Tech students simulate the communication delays crews could face on a mission to Mars.

Cast: Christian H, & Tom Caswell GamesPokémon: 545 - VenipedeOfftopic: Legoland, Pasta to go, Simulation theory, XR glasses, MoneyGames: Marvel Tōkon Fighters, Halo Campaign Evolved, , Walkabout Mini GolfPodcast Game: What Tier Is It?YouTubehttps://www.youtube.com/unrankedpodcastDiscordhttps://discord.gg/wkvu88KvTVQuestions, Comments, Complaints, Corrections!?Call: 805-738-8692Email@UnrankedPodcast.com Hosted on Acast. See acast.com/privacy for more information.

The guys are still on break, but we recorded the first of what may be a new running segment where we follow the tier list fad and rank things. This week we talk about the conspiracies we think are most likely to be true. Welcome to Broken Sim Ranks Stuff #1!More stuff: Get episodes early, and unedited, plus bonus episodes: patreon.com/brokensimulationSocial media: Twitter: @samtripoli, @johnnywoodard Instagram: @samtripoli, @johnnyawoodardBroken Simulation Hosts: Sam Tripoli, Johnny Woodard

Are we living in a simulation, or are we becoming the simulators? From AI worlds to digital immortality and the Fermi Paradox, we explore reality's weirdest possibility.

Are we living in a simulation, or are we becoming the simulators? From AI worlds to digital immortality and the Fermi Paradox, we explore reality's weirdest possibility.

Have you ever wanted to be an astronaut without risking your life in space? If so, we have the perfect opportunity for you. NASA is calling for people with a science background to apply for a simulated mission to the Moon and Mars. Four adults will spend a year isolated in two enclosures meant to simulate both traveling through deep space and living on Mars. Learn whether you fit the profile and what NASA researchers hope to learn from the mission in this episode.Interested in more space news? Email us your question at shortwave@npr.org and we may cover it in an upcoming segment.Support public media with NPR+ and enjoy perks for over 25 podcasts like this one. It includes perks like bonus episodes, early access, archive access, curated playlists and sponsor-free listening. Learn more at plus.npr.org. See pcm.adswizz.com for information about our collection and use of personal data for sponsorship and to manage your podcast sponsorship preferences.NPR Privacy Policy