Podcasts about digital twins

Explore the evolving world of reusable packaging in the produce industry with Vonnie Estes and guest Tim Debus, President and CEO of the Reusable Packaging Association. Delve into recent advancements in public policy, the impact of European regulations, and the role of technology in enhancing packaging systems. Unpack the economics of closed-loop systems, the significance of data in supply chain efficiency, and the potential of AI models fueled by tech-enabled reusable packaging. Gain insights into the strategic alignment required among stakeholders to achieve sustainability and operational excellence in fresh produce logistics.Key TakeawaysPolicy Impact: Recent advancements in public policy are significantly influencing the packaging industry, pushing it towards sustainable practices through regulations.Technology Integration: The integration of tracking and sensor technologies transform reusable packaging into smart systems that provide real-time data essential for supply chain optimization.System Efficiency: Successful reusable packaging systems require stakeholder alignment, continuous operational improvement, and scale to achieve desired efficiencies.Economic Metrics: Key metrics such as volume, turns, and loss prevention are crucial in determining the economic viability of reusable packaging systems.Closed Loop Challenges: Expanding reuse systems across broader supply chains involves addressing complexities that require innovative solutions and infrastructure development.Guest ResourcesTim Debus is the President and CEO of the Reusable Packaging Association. With over two decades of experience in the packaging industry, Tim has been at the forefront of advocating for sustainable packaging solutions. His work focuses on promoting reusable packaging systems that enhance supply chain efficiency and reduce environmental impact. Tim's leadership has positioned him as a key influencer in transitioning global markets toward more sustainable practices through policy development and technological advancement.Show LinksInternational Fresh Produce Association - https://www.freshproduce.com/Fresh Takes on Tech - https://www.freshproduce.com/resources/technology/takes-on-tech-podcast/Facebook - https://www.facebook.com/InternationalFreshProduceAssociation/Twitter - https://twitter.com/IntFreshProduce/LinkedIn - https://www.linkedin.com/company/international-fresh-produce-association/Instagram - https://www.instagram.com/intlfreshproduceassn/

En este episodio hablamos de los eventos más relevantes relacionados a los mercados financieros de Estados Unidos durante la semana laboral que terminó el viernes 25 de septiembre de 2026.En la empresa de la semana hablamos de Siemens $SIEGY (07:53)Y en la sección educativa hablamos de Digitl Twins o Gemelos Digitales (12:04)Les dejo la liga a nuestro canal de youtube donde podrán encontrar los audiogramas y videos educativos: https://www.youtube.com/channel/UC6thsV8Y_m2DgYPOqjLVfSQY también dejo la liga del blog donde estaremos subiendo las transcripciones de los episodios: www.ramonlog.com#finanzas #bolsadevalores #inversiones #SIEGY #DigitalTwins

I'm really excited about the potential for Personal AI Agents. While GrokBot and Muse and Microsoft Autopilot (Scout) are very young (and not at all mature yet), we can now clearly see how personal AI is going to transform and disrupt our business lives. In this podcast I explain the architecture of HR 2030, our reference blueprint for Agentic HR. You can see how personal agents, action agents, rules agents, monitoring agents, data agents, and Superagents all work together. And it's important to think this through so you can plan your vendor selection, tech architecture, security architecture, and AI staffing and “management” plan. Some important concepts: HR “agents” do more than HR – they enable dynamic growth, productivity, and cost savings – so take a problem-oriented view Clearly defined agents won't misbehave, as you'll see in our architecture Companies will implement Superagents based on business priority, not just for ideation SaaS incumbents are likely to deliver many of these agents, so SaaS Apocalypse may be over Agent training, context, and maintenance is critical – and this gives you scale and growth – so new jobs are needed Frontier labs may or may not survive this enterprise model Personal AI agents will be massive and likely disrupt many HR tech vendors. Lots to learn about here, read more on HR 2030 and call us or get Galileo to learn more. Chapters (00:00:00) - Introducing HR 2030 Architecture(00:01:02) - Three Tiers: Super Agents, Agents, Personal Agents(00:02:20) - Building the Agent Database and Use Cases(00:03:23) - Strategic Planning vs Buying Point Tools(00:04:13) - The SaaS Apocalypse Myth(00:04:47) - Rise of Personal Agents (Muse, Grokbot, Scout)(00:05:43) - What a Personal Assistant Could Do For You(00:06:24) - Why Personal Agents Beat Vendor Platforms(00:07:28) - How Personal Agents Know Everything About You(00:08:06) - Positive Uses and Privacy Considerations(00:09:34) - The Digital Twin in Practice Today(00:10:14) - Personal Agents Helping Employees Grow(00:11:01) - This Is Coming - Being Realistic(00:11:25) - How Agents Will Interact With Employees(00:12:09) - Impact on HR Software Vendors and Coaching Tools(00:13:42) - How to Access and Engage With HR 2030(00:14:17) - Galileo's Role and Ongoing Development(00:15:48) - Falling AI Costs and Open Source Models(00:16:21) - Copilot and Running Open Source Models Cheaply(00:16:50) - Business 2030: Dynamic Enablement for Growth(00:17:12) - Not About Headcount Reduction(00:17:36) - Upcoming Conferences and Closing

Meta rilancia la propria strategia puntando alla cosiddetta Personal SuperIntelligence attraverso l’assistente virtuale Muse da usare su smartphone, computer, occhiali e su un nuovo dispositivo, ancora in forma di prototipo. Con Roberto Pezzali, esperto di tecnologia della redazione di Dday.it parliamo in particolare di Ray-Ban Meta Audio e Meta VR Glasses.Ci occupiamo di sicurezza dei dispositivi fisici. L’italiana Exein (che ha annunciato un finanziamento da 270 milioni di dollari con una valutazione di 1,7 miliardi) guarda alla transizione verso la Physical AI come naturale evoluzione della propria tecnologia pensata per proteggere un dispositivo. Ne parliamo con Giovanni Alberto Falcione, CTO e co fondatore di Exein.STMicroelectronics ha presentato un progetto che si svilupperà nei prossimi anni alle porte di Milano (nel centro ricerche di Castelletto) per creare un nuovo concetto di laboratorio di ricerca basato su un Digital Twin: un ecosistema digitale connesso capace di integrare tutti le fasi: modellizzazione, emulazione, simulazione, fino alla progettazione e al collaudo di nuovi prodotti. Enrico Pagliarini ne ha parlato con Fabio Gualandris, responsabile Ricerca, Produzione e Qualità di STMicroelectronics.E come sempre le notizie di tecnologia e innovazione più importanti della settimana.

In this episode, we launch our new Airline Digital Innovation Series, where we'll bring new concepts, technologies and ideas to our audience through conversations with people who are developing and applying them across the airline industry. We start with digital twins and my guest Will Bryan, CEO of Autonoma, a company built around data science, simulation and innovation. Before bringing this technology to aviation, Will worked in the high-speed world of autonomous vehicles and driverless racing, including the Indy Autonomous Challenge. Now, he is applying what he learned there to the complex physical world of airline and airport operations. For years at Diggintravel, we've talked about experimentation, scientific decision-making and de-risking ideas. Digital twins take those principles beyond websites and digital channels. They give airlines and airports a way to simulate real operations, explore possible scenarios and test decisions before introducing them into the live environment. What if an airport could see the consequences of an operational decision before making it? What if an airline could simulate different responses to a disruption before it happens? And what if experienced teams could safely test ideas that would otherwise be too risky or expensive to try? Put simply, digital twins could allow airlines and airports to see and test the future before it happens. In our conversation, Will explains what digital twins really are, why they are more than attractive 3D visualizations, how they are already being applied in aviation and where this technology could take airlines and airports next. In the following episode, we'll move from the concept to the practical details and explore what it takes to build a digital twin in the real airline and airport world. RESOURCES: Read the companion article with key digital-twin concepts, aviation use cases and highlights from this episode: https://diggintravel.com/airline-digital-twins/

Watch the YouTube version of this episode HEREMichael McCready has spent years building McCready Law across seven states, and now he runs the firm from Puerto Rico. In this episode, he joins Tyson to unpack one of his most interesting systems: a digital twin trained on decades of his podcasts, presentations, SOPs, employee materials, deposition transcripts, opening statements, and closing arguments. His team can ask, “What would Michael do?” and get an answer rooted in his management style, firm culture, values, and legal thinking.Michael also breaks down how his firm uses internal LLMs and AI agents across intake, case opening, 30-day, 90-day, and 180-day reviews, medical records, demands, litigation, and more, all with human oversight and audit trails. He and Tyson discuss why law firms are becoming technology companies, how owners become bottlenecks, what “delegate, don't abdicate” looks like in practice, and how a firm can start using AI without building an advanced tech stack on day one. They also talk about how Michael built a 180-person, 24-lawyer firm that can operate across seven states while he lives in Puerto Rico.What You'll LearnHow Michael trained a digital twin using podcasts, presentations, SOPs, employee materials, and legal transcripts.Why data permissions, guardrails, human oversight, and audit trails matter when AI touches law firm work.How internal LLMs can turn SOPs and employee handbook material into direct, usable answers for a team.How AI agents can review intake decisions and case files throughout the lifecycle of a matter.Why law firm owners often become the pinch point, and what “delegate, don't abdicate” looks like in practice.How Michael built a seven-state law firm that he can continue leading while living in Puerto Rico.Highlights 00:01  Meet Michael McCready and his AI digital twin02:39  How the “What Would Michael Do?” digital twin started07:37  Internal LLMs, data permissions, guardrails, and human oversight16:24  Why law firms are becoming technology companies21:13  Training AI on voice, legal thinking, and trial work24:26  Getting the owner out of the way without abdicating responsibility29:54  AI agents across the case lifecycle and practical places to start44:26  How Michael runs a seven-state firm from Puerto RicoConnect with MichaelWebsiteLinkedIn

How can digitalization help scale circular construction? In this episode of circularity.fm, Kilian Eckle, founder of RoofUz, and Timo Walraven, researcher at the University of Applied Sciences Utrecht, discuss how digital twins, building data, standardization, and modular construction can make circular construction more economically feasible. What you'll hear: • How digital twins can be used to identify and compare buildings with potential for rooftop extensions • Why standardization, modular construction, and bundling similar buildings can improve the financial feasibility of projects • How building data can connect projects across a city and help planners and owners l Using rooftop extensions as a case, the conversation looks at how digitalization can support more standardized processes, better project economics, and the transfer of knowledge between construction projects.

Matt Risinger sits down with the digs.com team at Build Show Live to unpack a new partnership shaping how custom and production builders document construction. The conversation traces digs.com's evolution into an AI-powered platform that captures takeoffs, diagramming, and selections, then converts that data into a 3D digital twin for homeowners—often compared to a "Carfax for homes." Talk turns to the newly launched Build HD standard, a durability-focused building certification addressing water intrusion and construction defects, and how it integrates with an upcoming AI tool trained on years of building-science content. The discussion also touches on data ownership, homeowner privacy controls, a builder-supply partnership expanding distribution, and where artificial general intelligence might fit into future construction workflows. Watch full episodes of Matt on Facebook, Instagram and Build Show Network. https://www.facebook.com/buildshownetworkhttps://www.instagram.com/risingerbuild/https://buildshownetwork.com/go/mattrisinger Don't miss a single episode of Build Show content. Sign up for our newsletter.

Live from ONS 2026 in Stavanger, Jim sits down with Nicolay Ryste, VP of Product Management at Aize, the digital twin software company spun out of the Aker family. Nicolay traces Aize's path from an internal BP pilot to today's V2 platform running across Aker BP, ExxonMobil, and SBM Offshore, then previews V3: extending digital twins beyond topside to subsea and onshore assets, a rebuilt 3D and 2D engine built for tablets and lower-power devices, and a new MCP server that lets users bring their own AI agents into the twin. They also dig into how Aize is running a phased, side-by-side rollout so existing customers can adopt V3 without disrupting daily operations.

Only 4% of Benedikt Bösel's customers scanned the QR code on their meat — even though that code would have shown them the life of the specific cow they were eating, its pasture, its movement, everything. The other 96% either trusted the brand completely, or simply didn't care. That number sits at the centre of this conversation.This episode was recorded live at Groundswell 2026, the UK's largest regenerative agriculture festival. Koen moderated a 55-minute panel on a question that matters as much to investors as to farmers: who actually benefits when data is collected from farms?The four panellists: Ichsani Wheeler, soil scientist at the OpenGeoHub Foundation and satellite data researcher building an open-access soil registry; Benedikt Bösel, farmer of a 3,000-hectare operation in eastern Brandenburg running a digital twin alongside the physical farm; Antony Yousefian, co-founder of The First Thirty, an investment firm building the data infrastructure to turn farms into preventative healthcare systems; and Matthijs Westerwoudt, co-founder of Wilder Land, building what he calls a Strava for biodiversity using AI-powered acoustic monitoring.Video credit @Commonland.In this session:— Why Benedikt says most farm data has been useless — and what is finally changing— Why Antony believes the cost of communicating with plants has collapsed— The NHS values one quality-adjusted life year at £30,000 — and why that number should reframe the entire food system— The 2013 US case where aggregated farm data was used to set surveillance pricing on inputs against the farmers who generated it— Why only 4% of customers scanned the QR code with full cow data — and what that tells us about the real entry point to consumer behaviour change— How Wilder Land is turning bird species counts into a biodiversity score farmers can put on a product label— What Benedikt's wife's iron levels after birth have to do with all of this-------This session is part of the AI 4 Soil Health project which aims to help farmers and policy makers by providing new tools powered by AI to monitor and predict soil health across Europe. For more information visit ai4soilhealth.eu.Thoughts? Ideas? Questions? Send us a message!Find out more about our Generation-Re investment syndicate:https://gen-re.land/ Thank you to our Field Builders Circle for supporting us. Learn more hereSupport the show=======In Investing in Regenerative Agriculture and Food podcast show we talk to the pioneers in the regenerative food and agriculture space to learn more on how to put our money to work to regenerate soil, people, local communities and ecosystems while making an appropriate and fair return. Hosted by Koen van Seijen.

Explore the dynamic realm of digital twins in agriculture with Vonnie Estes and Dr. Ziynet Boz from the University of Florida. Delve into how digital twins can transform complex systems like reusable packaging supply chains, enhancing decision-making by simulating real-world scenarios. Discover the distinction between digital twins and traditional models, the role of AI, and the challenges of implementing digital twins in produce logistics. With insights on optimizing costs, reducing environmental impacts, and improving supply chain dynamics, this episode unveils the future of sustainable agriculture driven by digital innovation.Key TakeawaysDigital twins are dynamic digital representations that remain connected with real-world counterparts to enhance understanding and decision-making.Unlike traditional lifecycle assessment tools and spreadsheets, digital twins can adapt to real-time data and simulate different operational scenarios.Reusable packaging presents a complex challenge well-suited for digital twins due to the numerous feedback loops and supply chain requirements involved.A practical digital twin implementation can integrate and balance economics, environmental impact, and operational efficiency.Collaboration between industry and academia can bridge knowledge gaps, ensuring robust interoperability across varied systems and stages in agriculture.Guest ResourcesDr. Ziynet Boz is an Assistant Professor at the University of Florida, specializing in the intersection of sustainable food systems, packaging modeling, and digitization. With a prolific career focused on systems questions, she is renowned for her expertise in digital twins and their application in agricultural and biological engineering. Ziynet's research interests integrate systems engineering, industrial engineering, and environmental sustainability.Show LinksInternational Fresh Produce Association - https://www.freshproduce.com/Fresh Takes on Tech - https://www.freshproduce.com/resources/technology/takes-on-tech-podcast/Facebook - https://www.facebook.com/InternationalFreshProduceAssociation/Twitter - https://twitter.com/IntFreshProduce/LinkedIn - https://www.linkedin.com/company/international-fresh-produce-association/Instagram - https://www.instagram.com/intlfreshproduceassn/

Vieillissement de la population, baisse de la natalité, vagues de chaleur plus fréquentes, pression sur les ressources... Les collectivités doivent aujourd'hui composer avec des transformations profondes qui redessinent peu à peu les territoires. Ces évolutions interrogent aussi bien l'aménagement urbain que l'organisation des services publics, la gestion des ressources ou encore l'adaptation au changement climatique. Pour mieux les comprendre et s'y adapter, les collectivités s'appuient de plus en plus sur la donnée, les jumeaux numériques ou encore l'intelligence artificielle, capables d'aider à anticiper les besoins futurs et à éclairer la décision publique.Mais la technologie ne suffit pas à elle seule. Son efficacité repose aussi sur la qualité des données, les compétences mobilisées et la capacité à instaurer un cadre de confiance pour les citoyens.Dans cet épisode d'Écoutons le Futur, nos invités explorent les mutations démographiques et climatiques qui transforment les villes, le rôle croissant des jumeaux numériques et de la donnée dans l'anticipation des risques et des besoins, ainsi que les questions d'éthique, de souveraineté et de place du citoyen dans la ville de demain.Présents sur notre plateau :- Prune Bonnivard, Directrice de l'information Géographique et de l'Innovation Territoriale chez Grand Paris Seine Ouest- Sophie Houzet, Directrice de la Fabric'O au Cerema- Gwenaëlle Carfantan, Présidente de la Commission Smart Aménagement chez Smart Building Alliance- Nina Bouchet, Directrice Secteur Public & Services chez Capgemini InventUne émission animée par Valère Corréard

As systems become more software-defined, the traditional development model of waiting for hardware before serious software validation begins is no longer sustainable. Software teams need platforms earlier. Hardware teams need realistic workloads sooner. Verification teams need better ways to expose system-level issues before tapeout. We will look at how digital twins are becoming the shared foundation for this new HW/SW co-development model. Using virtual platforms, hardware emulation, and FPGA prototyping, teams can start earlier, collaborate more closely, and design/optimize/validate with greater confidence. We will also discuss new system-level verification challenges in power, performance, and DFT, showing how pre-silicon Hardware platforms can help teams find critical issues earlier and deliver more robust software-defined systems.

The risk function’s fundamental purpose has not changed in light of AI. What has changed is the nature and pace of risks amplified by AI, as well as the tools available to manage them. Anke Raufuss, Björn Nilsson, and Laura Webanck join us to discuss the role of the digital twin and how it is changing the future of risk management. Related Insights Evolving model risk management in the age of AI How companies can strengthen their geopolitical risk readiness Global risk productivity survey: Four themes shaping risk management The future of risk: How global trends are reshaping risk management From risk to resilience: New approaches to navigating disruption in Asia Risk and Resilience on McKinsey.com McKinsey Risk & Resilience on LinkedIn Support the show: https://www.linkedin.com/showcase/mckinsey-strategy-&-corporate-finance/See www.mckinsey.com/privacy-policy for privacy information

September 2, 2026: I share key lessons from my CHRO session with iFood CEO Diego Barreto, including why he is investing in AI even with negative ROI, how he built a digital twin, and how iFood is rethinking reviews, meetings, and change management. Then I get into the Wall Street Journal's report on employers using new interview rituals to fight AI-enabled candidate fraud. Finally, I unpack Uber cutting 10% of its workforce and why the real story may be work redesign, bureaucracy, and coordination, not AI replacing jobs.

Peggy kicks off her five-part series of predictions for 2027 by looking at how businesses will use technology to make better decisions. For her first prediction, Peggy says digital twins will move beyond visualization and become increasingly important tools for understanding what could happen next. She also encourages listeners to: · See digital twins as decision tools and to use data to look forward. · Ask more what if questions and explore scenarios before acting. · Keep human judgment in the loop and always start with the business problem. https://peggysmedleyshow.com

Niantic Spatial CTO Brian McClendon on how the best engineers solve problems most give up on — from a 4D model of the world to shipping research in months. He built Google Earth and ran Google Maps for over a decade, and he's been there, done that. What he's building now is harder, and it's already live for customers. Along the way: who makes it on his team and who doesn't, and the one piece of advice he'd give every engineer using AI.In this video, we cover:- The 4D model of the world: visual positioning, change detection, and treating a pile of photos like a database- Gaussian splats and real-to-sim: capturing a room and loading it into Nvidia Isaac to train robots- Turning a research idea into a production service in six months- What Google Maps taught him about building for robots instead of humans- Designing problems AI can self-check, and why token maxing is a wasteFor engineers and engineering leaders who want to work on problems that don't have a known answer yet — and who want to know what a CTO who's built the definitive product in his field looks for in the people he hires.Recorded at the AI4 conference 2026. Timestamps:00:00:00 - Google Earth? Been There, Done That00:00:46 - Turning a Research Idea Into Production in 6 Months00:02:37 - How Any Photo Gets Located Within Half a Meter00:06:37 - The Long-Term Goal: A 4D Model of the World00:08:37 - Treating a Pile of Photos Like a Database00:11:58 - What Google Maps Taught Him About Training Robots00:15:22 - Gaussian Splats Explained in Plain Terms00:17:42 - The Unsolved Problem: Scale and Semantic Change00:20:34 - Why Google Earth Is Good Enough00:22:09 - Who Makes It on His Team and Who Doesn't00:23:38 - Designing Problems AI Can Self-Check00:26:03 - The Insights Hidden in the Physical World00:28:18 - Digital Twins, Cities, and Ready Player One00:31:35 - Visual Positioning When GPS Gets Spoofed00:33:24 - Token Maxing Is Bullshit: Advice for EngineersGuest: Brian McClendon, CTO at Niantic Spatial. The engineer behind Google Earth; ran Google Maps for over a decade.https://www.linkedin.com/in/brianmcclendon#NianticSpatial #GoogleEarth #SoftwareEngineering

In this episode of HSS Presents, radiologist Dr. Harry G. Greditzer IV speaks with Dr. Andrew D. Pearle, Chief Emeritus of the Sports Medicine Institute at HSS, about the evolution of computer-assisted surgery and the emerging role of digital twins in orthopedics. Dr. Pearle explains how patient-specific MRI models, computational biomechanics, and additional data inputs may help clinicians assess ACL-injury risk, simulate surgical approaches, and personalize treatment planning. The conversation highlights neuromuscular training—a combination of strength, agility, plyometric, and landing-stabilization exercises—as an important strategy for reducing injuries among young athletes. Looking ahead, they also discuss how digital twins could support more individualized prevention, informed decision-making, and treatments designed around each patient's anatomy, movement patterns, and activity goals.

Episode 6 of Rethinking EHS, Season 3 focuses on water-related infrastructure resilience and how organisations can prepare for more frequent and severe climate-induced water risks. Across the UK, India and Switzerland, the discussion shows that resilience challenges take different forms — from surface-water and coastal flooding to drought, intense downpours, glacier melt and slope instability. A recurring lesson is that infrastructure can no longer be planned as isolated assets or designed only around historical climate records; connected systems and cascading failures must be considered. Looking ahead, the guests emphasise proactive, pre-event planning: integrating climate risk assessments into asset management, combining engineered and nature-based solutions, improving forecasting and monitoring, learning across regions, and mapping supply-chain exposure. The goal is not simply to withstand the next event, but to keep essential services operating, recover quickly and remain fit for purpose over the full design life. --- Guest quotes: Alex Perryman: “So I'd say in the UK we're talking about the whole spectrum, like too much water, too little water and everything in between and the quality of it, the availability of it, how efficiently we use it and how we reuse it even as a resource.” Michalis Lellis: “Climate conditions are changing and infrastructure that was once considered resilient may no longer provide the same level of protection in the future.” --- Sponsor Copy Rethinking EHS is brought to you by the Inogen Alliance. Inogen Alliance is a global network of 70+ companies providing environment, health, safety, and sustainability services, working together to provide one point of contact to guide multinational organizations to meet their global commitments locally. Visit inogenalliance.com to learn more. Produced by https://madcontent.co.nz/

Explore the evolving role of digital twins in the fresh produce industry in this episode of Fresh Takes on Tech. Host Vonnie Estes is joined by Erik Larsen from the International Fresh Produce Association, who delves into the definition, significance, and implementation challenges of digital twins. Discover how this innovative technology is transforming supply chain data management, enhancing traceability, and optimizing logistics. Erik also shares insights into the importance of data sharing and collaboration among supply chain partners. Tune in for a fresh perspective on leveraging technology for better data-driven decisions.Key TakeawaysDigital twins are virtual models reflecting real-world objects or systems, enhanced with real-time data, pivotal for optimizing supply chain management in the fresh produce industry.Erik Larsen emphasizes the critical shift from focusing on compliance to leveraging robust data infrastructures for optimization, safety, and profitability across the supply chain.Enabling the seamless sharing and interoperability of data among all stakeholders in the supply chain is crucial for successful implementation.Industries are increasingly recognizing that data sharing is a crucial business strategy rather than just a technological endeavor.To adopt digital twins successfully, the industry must prioritize standardized product identification, traceability, and a robust foundational data strategy.Guest ResourcesErik Larsen: Erik Larsen is the Director of Supply Chain at the International Fresh Produce Association (IFPA). With over 25 years of experience, he has an extensive background in technology, data, and supply chain operations within the produce industry. His career began in the grower-shipper sector with companies like Gargiulo and Naturipe, where he served as head of IT. Larsen also worked with L&M companies and Duda Farm Fresh Foods, gaining a wealth of experience across various levels of produce supply chains. His involvement in industry committees and chairing the Produce Supply Organization demonstrates his leadership and dedication to advancing the sector.Show LinksInternational Fresh Produce Association - https://www.freshproduce.com/Fresh Takes on Tech - https://www.freshproduce.com/resources/technology/takes-on-tech-podcast/Facebook - https://www.facebook.com/InternationalFreshProduceAssociation/Twitter - https://twitter.com/IntFreshProduce/LinkedIn - https://www.linkedin.com/company/international-fresh-produce-association/Instagram - https://www.instagram.com/intlfreshproduceassn/

Can AI simulate your exact biology to predict how long you will live? Today we sit down with Dr. Dmitri Chebanov, computational biologist from Memorial Sloan Kettering Cancer Center and Chief Scientist at Holivita, to explore how AI digital twins and multi-omics are turning reactive medicine into proactive longevity.For decades, health optimization has relied on trial and error: try a new supplement, change your diet, wait three months, and hope your blood work improves. But biology is complex, and standard medical care only reacts after disease develops. Dr. Dmitri Chebanov shares how cutting-edge transformer neural networks, trained on multi-omic patient data, can create a high-dimensional "Digital Twin" of your body. Originally developed to simulate clinical trials in oncology and reduce the 90% trial failure rate, this technology is now transitioning to personal longevity. We break down the three critical layers of a digital twin (whole genome sequencing, dynamic biomarkers like DNA methylation, and real-time symptoms), how to simulate lifestyle interventions before taking a single pill, and what the future of real-time predictive healthcare looks like

Andrew of the 6G Agenda joins Nephilim Death Squad for a deep dive into Flock cameras, AI surveillance, predictive policing, Palantir, Oracle, Neuralink, smart dust, smart cities, digital twins, and the accelerating technocratic system being built around everyday life.The conversation begins with Flock camera networks and expands into a much larger discussion about automated license-plate recognition, mobile surveillance, drones, police militarization, data aggregation, and predictive policing. Andrew lays out his research and claims involving Flock, Axon, Oracle, Palantir, Peter Thiel, Total Information Awareness, Palantir Gotham, the Internet of Things, and the idea of a “digital twin” built from personal data.From there, the discussion turns toward Neuralink, brain-computer interfaces, immigrant detention-center allegations, smart dust, MEMS, graphene, body-activity data, Wi-Fi tracking, LED infrastructure, smart cities, and Microsoft patent WO2020060606. The hosts also examine the rapid growth of artificial intelligence, the concept of technological singularity, and whether these systems could eventually resemble technologies associated with the Biblical Mark of the Beast.The second half shifts toward the Christian response to surveillance, fear, propaganda, and social control. Rather than allowing conspiracy research to produce paralysis, anger, or fear, the discussion emphasizes discernment, trust in the Lord, action, community, and the Biblical principle of being in the world without becoming part of its system.The episode closes with a broader prophecy discussion involving the “little season,” Gog and Magog, the millennial reign, Isaiah 11, the wolf and the lamb, Osiris, Odin, Hitler imagery, Antichrist symbolism, and the idea that modern technological and political systems may be converging toward something much larger.Which part of this system concerns you most: Flock cameras, Palantir, Neuralink, smart dust, AI, or smart-city infrastructure? Drop your thoughts in the comments.► Subscribe for more Biblical analysis, Christian worldview discussions, and conspiracy research through a Biblical lens.Listen on Spotify, Apple Podcasts, and everywhere podcasts are available.Apple Podcasts: 6G Agenda on Apple PodcastsSpotify: 6G Agenda on SpotifySpreaker: 6G Agenda on SpreakeriHeartRadio: 6G Agenda on iHeartRadioX / Twitter: @6gagendaInstagram: @6gagendaTikTok: @6gagendapodcastPatreon: 6G Agenda Patreon6gagendapod@gmail.com.00:00 Intro02:23 Andrew of the 6G Agenda05:42 Flock Cameras and the Surveillance Network10:18 Oracle, AI and Total Data Collection13:05 Police Militarization and the Panopticon18:28 Drones, Palantir and Predictive Policing19:44 Digital Twins and the Internet of Things24:40 Blacklists, the NDAA and Domestic Surveillance28:34 Christianity, Freedom and the Coming Pressure31:23 What Happens to the Church Under Persecution?35:31 Neuralink and Brain-Computer Interfaces38:54 Neuralink and Detention Center Allegations46:47 Peter Thiel, Palantir and Total Information Awareness50:38 Palantir Gotham and the Surveillance System54:49 Smart Cities vs. Rural Communities60:31 Building Communities With Like-Minded People65:42 AI Acceleration, Singularity and the Mark of the Beast67:38 Smart Dust, MEMS and Graphene69:25 Body Activity Data and the Microsoft Patent75:11 Smart Dust, Consent and Predictive Programming80:48 Fear, Faith and Christian Action88:17 Being in the World, But Not of It94:37 Electric Gloves and the Expanding Surveillance State99:15 The “Little Season,” Gog and Magog104:46 Osiris, Odin and Antichrist Symbolism110:04 Isaiah 11: The Wolf and the Lamb112:10 ClosingBecome a supporter of this podcast: https://www.spreaker.com/podcast/nephilim-death-squad--6389018/support.☠️ Nephilim Death Squad — New episodes 5x/week.Join our Patreon for early access, bonus shows & the private Telegram hive.Subscribe on YouTube & Rumble, follow @NephilimDSquad on X/Instagram, grab merch at toplobsta.com. Questions/bookings: chroniclesnds@gmail.com — Stay dangerous.

An imperfect drink with a perfect story. My reflections from Black Hat USA 2026 An Analog Brain In A Digital Age — A Newsletter by Marco Ciappelli No time to read? Let TAPE3 read it to you.

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

This week we're diving into a challenge that's becoming increasingly important: how do we make sure our engineering workflows, software, data, and AI can all work together reliably? My guest is Jonathan Wright, Chief Technologist at Keysight. Jonathan and I chat about the industry's shift toward multi-physics and application-specific workflows and how that evolution is changing the way engineering teams tackle system complexity. We also explore the biggest 'hidden' risks engineering teams face today and delve into that "AI Assurance" void that is slipping under the radar of so many engineering teams today.

What happens when legendary venture capital investors and pioneering media entrepreneurs replace traditional studio infrastructure with AI digital twins? In this throwback episode of our Edge of AI Podcast, host Ron Levy sits down with Tim Draper and Mark Scarpa.Mark breaks down how Defiance.TV operates as the first AI-powered television network, utilizing digital twins, automated script writing, AI fact-checking, and decentralized IPFS blockchain distribution to maintain journalistic integrity across 160 million households. Tim shares how his own AI doppelganger on Draper TV broadcasts daily news updates on several hundred portfolio companies in up to 22 native languages, surprisingly serving as his own daily intelligence briefing.Discover Tim Draper's vision for decentralized governance using AI to streamline bloated government bureaucracies, aligning performance incentives with GDP growth, and why Bitcoin and blockchain ledgers will eliminate modern accounting and auditing friction.Support us through our Sponsors! ☕ Want to make content like ours? Sign up with Castmagic to make your creative process easy: https://bit.ly/CastmagicReferral Work smarter, grow faster. Automate your SEO, get AI insights, and manage all your clients in one place with Helm. Start today 50% off your first month at helmseo.comDouble your team's efficiency with COCO. Hire dedicated AI employees for copywriting, research, and CRM. Use code REF-W8CBVH for an exclusive 5% off your first order: https://coco.xyz/dashboard/hire/plan?ref=REF-W8CBVH Do you want to grow a business? Go from an idea to livebusiness in minutes. Use our Referral code: edgeof to 50% off your first month at https://www.willo.ai/When you purchase through these links, we may earn a commission. ____

Artificial intelligence is changing the way facilities operate, but successful adoption starts long before implementing new technology. Recorded live at World Workplace Asia-Pacific in Hong Kong, Dr. Matt Tucker, IFMA's Director of Knowledge and Research, sits down with Serena Pau, Chief Commercial Officer at Neuron Digital, to discuss digital transformation across the region, the importance of breaking down data silos and why curiosity may be one of the most valuable skills facility managers can develop. They also explore how AI can improve reporting, energy management and decision-making while keeping people at the center of the workplace.  Timestamps: 0:00 - AI should enhance, not replace, people 0:24 - Introduction to Serena Pau and Neuron Digital 2:18 - Digital maturity across Asia-Pacific 4:08 - Balancing AI with the human side of facility management 6:11 - Why change management matters more than technology 7:00 - World Workplace 2026 7:35 - Where AI can make the biggest impact today 8:46 - Turning AI insights into actionable recommendations 9:02 - Why curiosity is the most important AI skill 10:21 - The rise of the FM analyst 11:02 - Using AI to improve reporting and tell better data stories 12:18 - Building a future-ready digital strategy 13:17 - Mindset before technology 13:43 - Closing remarks Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

Send me a messageHeavy equipment electrification has a hidden problem: the machinery itself can waste most of the energy you put into it. That means bigger batteries, higher capital costs and a weaker business case before the machine has even started work.My guest is Hiten Sonpal, CEO of Rise Robotics, who is working on replacing conventional hydraulics with belt-driven actuation. We get into why hydraulic systems can be roughly 25% efficient, how that inefficiency drives battery and charging requirements, and why downtime and maintenance failures may matter even more than the emissions case.We examine why better batteries are not always the answer, what changes when heavy machinery becomes drive-by-wire, and why industrial AI and autonomy depend on something far less glamorous: machines that can actually generate useful operational data. We also look at prognostics, digital twins, teleoperation and the prospect of one operator supervising several machines rather than controlling just one.Listen now to understand what is really constraining heavy-equipment electrification — and where better engineering can cut cost, downtime and operational friction.Could your supply chain take the hit? Download my free 15-minute resilience scorecard to uncover hidden vulnerabilities, calculate your score and turn the results into a practical 30-day action plan: tomraftery.com/scorecard  If disruption hit tomorrow, would you know where your supply chain was most exposed? In 15 minutes my free scorecard helps you assess 27 resilience statements, calculate your score, and turn the result into three priorities and a 30 day action plan. You can download the scorecard free at tomraftery.com/scorecard.Support the showPodcast supportersI'd like to sincerely thank this podcast's generous Subscribers:Alicia FaragKieran OgnevGary LynchAnd remember you too can become a Resilient Supply Chain+ subscriber  - it is really easy and hugely important as it will enable me to continue to create more excellent episodes like this one and give you access to bonus episodes of topical, timely supply chain resilience analysis.

University of Michigan Pass/Fail Policy: Sean and Scott critique UMich's decision to shift first-semester freshmen to pass/fail grading, arguing that lowering academic expectations fails to fix root mental health issues or prepare students for real-world accountability. AOC & Egg Freezing Trends: The hosts discuss Rep. Alexandria Ocasio-Cortez sharing her egg-freezing experience, highlighting the moral distinction between unfertilized eggs and human embryos while raising downstream ethical concerns about IVF practices. AI-Designed Viruses & Biosecurity: The hosts evaluate Stanford's synthetic virus research to stress the urgent need for human moral wisdom and accountability as AI technology advances. AI Pastor "Digital Twin": The hosts analyze a Bay Area pastor who built an AI duplicate trained on two million words of his sermons and communications, warning that relying on AI efficiency undermines face-to-face pastoral care and genuine human discipleship. Canadian Military Prayer Ban: The hosts discuss new directives by the Canadian Armed Forces barring chaplains from making references to God or offering faith-specific prayers at public military ceremonies, examining how mandated "spiritual reflections" relegate religious expression to the private sphere.Audience Question: Moral Disagreement & Objective Morality: In response to a listener asking if widespread disagreement over issues like capital punishment undermines objective morality, the hosts explain that people generally agree on core moral principles while differing on their practical applications or specific facts. Pushing Back in Interfaith Dialogue: Addressing a question about an interview with an Orthodox Jewish guest who rejected Jesus as Messiah, Sean discusses the ongoing tension between defending theological truth and preserving personal relationships to win the person rather than just the argument.==========Think Biblically: Conversations on Faith and Culture is a podcast from Talbot School of Theology at Biola University, which offers degrees both online and on campus in Southern California.   Find all episodes of Think Biblically at: https://www.biola.edu/think-biblically.   To submit comments, ask questions, or make suggestions on issues you'd like us to cover or guests you'd like us to have on the podcast, email us at thinkbiblically@biola.edu.  

Keeping the "LIVE" in Alive! — Wellness Wednesday Every week I meet someone who's helping us all Keep the "LIVE" in Alive! This week, I met a guy who made me realize I may be seriously behind. Tony Medrano is 55 years old, training for his FOURTH Ironman triathlon — and apparently works out four, five, sometimes SEVEN OR EIGHT HOURS A DAY. I had one immediate thought: Tony, you're making me feel horrible about myself. LOL. But that's not actually why I invited him on the show. Tony is the CEO and co-founder of LongevityPlan.AI, and what really got my attention was something I'd never heard of before: Your Own AI Digital Twin Yes. A digital version of YOU. The concept is that your personal health information — things like lab work, genetics, DEXA scans, VO2 max, wearable data, supplements and health goals — can help create a digital representation of you. Why? So you can learn more about how different wellness strategies might affect you before experimenting on your actual body. As Tony put it: "Why not do it digitally first?" Now THAT got my attention. Especially because I keep hearing more and more about peptides, biohacking, longevity treatments and all kinds of things people are trying in the quest to feel younger, stronger and better. And some of it sounds fascinating. Some of it also makes me think: Who the heck knows what they're doing? Tony himself stressed an important point: people interested in peptides should learn about them, consult their own doctor who knows their medical history, and proceed from there. The NFL Connection Made Me Go… "Wait, WHAT?" Tony told me he first became fascinated with digital-twin technology while working with the NFL, where he says the concept was being used to help protect valuable players from injury. His thought was basically: Why should professional athletes be the only ones who get this kind of technology? He wanted to bring the concept to regular people, too. And that led to what I thought was the most important message of our conversation. Tony said: "When people are engaged in their health, there is a cascading effect of positive outcomes." I LOVE THAT. Because underneath all the AI, digital twins, peptides and futuristic stuff is a very simple idea: Pay Attention to Your Health BEFORE Something Goes Wrong. Tony admits that for years as a busy tech executive, he wasn't particularly engaged with his own health. Now? He says his energy is through the roof, he feels better, looks better, performs better at work — and has discovered that being active has actually expanded his social life. Apparently, the daytime workout people are a pretty good crowd. I asked him if they were nicer. Tony said: "They're nicer, they're prettier, they're safer." LOL. Okay Tony. Point taken. So maybe I don't need to start training for a 140-mile Ironman tomorrow. But I probably should get on the bike. And maybe that's the real Wellness Wednesday takeaway: You don't have to become obsessed with longevity. You don't have to try every new wellness trend. And you certainly don't have to work out eight hours a day. But becoming a little more curious, a little more informed and a little more involved in your own health? That might be one of the smartest investments you can make in keeping the LIVE in alive. And Tony? We're done here and you can get back on the bike pal. But just know that you're making the rest of us look bad. LOL. — Debbie Nigro Keeping the "LIVE" in Alive! Wellness Wednesday The Debbie Nigro Show

In this episode of Investor Connect, we welcome Hemanth Sheelvant, who currently works in manufacturing consulting with Bosch Manufacturing Co-Intelligence. He discusses how digital twins and AI are reshaping industrial operations—and why even well-funded projects still fail. Hemanth explains the "iceberg problem" in enterprise and AI deployments, where teams focus on the visible technology while underestimating the governance, cybersecurity, regulatory compliance, integration, and ongoing operations that drive the total cost. He shares how to avoid surprises by starting with customer discovery, aligning early with IT and key decision-makers, defining KPIs and budget for pilot-to-scale upfront, and assessing plant digital maturity through pre-discovery studies. The conversation also covers using AI first as a human-assist system, choosing simpler models when they meet the business outcome, building balanced teams with domain experts, and scaling via modular, deployable "Lego block" solutions and SaaS-friendly business models. Visit Bosch Manufacturing Co-Intelligence at https://www.manufacturing-co-intelligence.com/ Reach out to Hemanth at https://www.linkedin.com/in/hemanth-sheelvant/ ________________________________________________________________________ For more episodes from Investor Connect, please visit the site at: http://investorconnect.org Check out our other podcasts here: https://investorconnect.org/ For Investors check out: https://tencapital.group/investor-landing/ For Startups check out: https://tencapital.group/company-landing/ For eGuides check out: https:/_/tencapital.group/education/ For upcoming Events, check out https://tencapital.group/events/ For Feedback please contact info@tencapital.group Please follow, share, and leave a review. Music courtesy of Bensound.

How do you take a model that works in process development and get it accepted for use in GMP manufacturing? That question stalls most bioprocess modeling projects before they start. Ignasi Bofarull-Manzano, Senior Data Scientist and CMC Consultant at Körber Pharma, pushes back on the premise: the process you run today is already governed by a mathematical model, fitted once at small scale during process characterization and then left untouched for years, even as the process shifts.Part 1 separated digital models from digital shadows and digital twins, and made the case for starting with the decision rather than the data. Part 2 goes into the plant: what regulators actually require, what the numbers looked like on a real biologics process, and where a team should start on Monday morning.Topics covered:Core differences—and surprising similarities—between modeling in development versus manufacturing (02:35)Regulatory requirements: credibility assessments, model risk, and validation steps for digital twins (05:07)Real-world example: How deploying an end-to-end process model led to 35% yield increase for Takeda, and considerations for ROI in manufacturing (08:34)Advice for startup leaders on when to invest in modeling and how to scale efforts case-by-case (11:42)Steps for scientists new to modeling: identifying bottlenecks, starting simple, and proving value offline before scaling up (12:26)The importance of understanding basic statistics before relying on AI-generated models (15:22)A stepwise summary for deploying digital modeling effectively in biotech (16:01)Smart insight: The digital twin is the last step, not the first. Identify the bottleneck, build the simplest model that supports the decision, and concatenate it end to end so you can see how a parameter moves final drug substance quality rather than one unit operation's output. Prove the value offline. Only then connect interfaces, because that is where the cost and the validation burden live. Teams that lead with the twin arrive at the C-level with a proof of concept and no evidence. Teams that lead with the offline model arrive with a number.Before a digital twin can earn its keep, you need connected data, the right model, and a clear decision for it to support. These four episodes cover that ground — data silos, hybrid and mechanistic modeling, and twins built to survive regulatory scrutiny.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del ValEpisodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David HardyConnect with Ignasi Bofarull-Manzano:LinkedIn: www.linkedin.com/in/ignasi-bofarullKörber Pharma website: www.koerber-pharma.comSupport the show

Recorded at HRS2026, this episode of The Lead features host Christopher Kowalewski, MD, in conversation with Junaid A.B. Zaman, MA, MD, PhD, CCDS, and Patrick Boyle, BS, PhD, FHRS, about the journal article, Digital Twin–Guided Ablation for Ventricular Tachycardia. Together, they discuss the study's findings and explore the use of digital twin technology to guide ventricular tachycardia ablation and its potential implications for clinical electrophysiology. Learning Objectives Review the design and key findings of the study evaluating digital twin–guided ablation for ventricular tachycardia. Discuss the use of digital twin technology as a tool to guide ventricular tachycardia ablation. Explore the potential clinical implications of digital twin–guided ablation for the management of ventricular tachycardia. Host: Christopher Kowalewski, MD   Guests: Junaid A.B. Zaman, MA, MD, PhD, CCDS  Patrick Boyle, BS, PhD, FHRS    Disclosures: C. Kowalewski No relevant disclosures   J Zaman Honoraria/Speaking/Consulting Fee: Acutus Medical Inc., Johnson and Johnson, American Medical Association, American College of Physicians, Bylis Medical Company Office/Trustee/Director/Other Fiduciary Role: Society of Bedside Medicine Travel/Entertainment: Boston Scientific   P Boyle Research: Seattle Foundation, Catherine Holmes Wilkins Charitable Foundation, NIH/NHLBI, American Heart Association Other Non-Financial Relationship: Cardiovascular Engineering and Technology Office/Trustee/Director/Other Fiduciary Role: The Cardiac Electrophysiology Society

Amazon keeps raising the bar. Steve Dennis and Michael LeBlanc open with the retailer's blockbuster quarter: massive gains in AWS and advertising, staggering AI capex, and retail revenue up 16% year over year, up from 12% last year. Third-party sellers now drive more than 60% of the business, and Citi pegs underlying GMV growth near 10%, roughly double the industry average. Amazon's B2B division alone now tops $60 billion, bigger than all but eleven U.S. retailers. Then the hosts dig into the agentic commerce numbers Amazon disclosed as Rufus folds into Alexa shopping, why sample bias demands caution here, and how grocery momentum is turning Amazon into a mega market-share threat.  Then: Shopify's revenue up 32%, and the tale-of-two-cities reality that a handful of giants now drive nearly all e-commerce growth. A mid-year check on Steve's Prediction #8 — luxury's future won't be evenly distributed. LVMH, Kering, and Capri lag on China softness and war induced Gulf weakness, while Hermès, Ralph Lauren, Richemont, and Zegna keep delivering outsized results. Live from the CommerceNext Growth Show: Kartik Hosanagar. He's Wharton's John C. Hower Professor of Technology and Digital Business, author of A Human's Guide to Machine Intelligence, and co-founder of Bliss Labs. His argument: the biggest shift in retail isn't a new technology or channel. It's a new customer. Treating AI as another distribution channel is the same mistake movie studios made with Netflix. There's no marketing science for AI yet. $9.99 pricing, scarcity, social proof — a model may not respond to any of it. Kartik walks through the Ridge wallet case: invisible in ChatGPT's "gifts for men" results, then the default recommendation within three weeks, complete with a hallucinated promotion. Every retailer faces the same fork: efficiency or meaning. The middle is the most dangerous place to stand. Kartik also details the simulation sandboxes that let brands test counterfactuals in a day instead of eight weeks, why you can't just ask an LLM why it picked a brand, and the three attitudes retailers need now: curious, experimental, collaborative Back in studio: Wayfair's encouraging quarter and Steve's change of heart, Warby Parker's mixed report as store count passes 400, tariff rebates ($100 billion of $160 billion already repaid, more tariffs looming), and whether live selling has hit its tipping point with QVC out of bankruptcy and Whatnot valued at $20 billion. About UsSteve Dennis is a strategic advisor and keynote speaker focused on growth and innovation, who has also been named one of the world's top retail influencers. He is the bestselling author of two books: Leaders Leap: Transforming Your Company at the Speed of Disruption and Remarkable Retail: How To Win & Keep Customers in the Age of Disruption. Steve regularly shares his insights in his role as a Forbes senior retail contributor and on social media.Michael LeBlanc is a senior retail advisor, keynote speaker and media entrepreneur. Michael has delivered keynotes, hosted fire-side discussions hosted senior retail executive on-stage in 1:1 interviews worldwide. Michael produces and hosts a network of leading retail trade podcasts, including The Remarkable Retail Podcast, The Voice of Retail, The Food Professor, The FEED powered by Loblaw and the Global eCommerce Leaders podcast. He has been recognized by the NRF as a global Top Retail Voice for 2025 and 2026 and continues to be a ReThink Retail Top Retail Expert for the fifth year in a row.

Most bioprocess teams believe a digital twin demands vast datasets and sophisticated models. Ignasi Bofarull-Manzano argues both assumptions are wrong, and that the data already sitting in your Excel files, historians and ELNs is probably enough to start.Ignasi Bofarull-Manzano, Senior Data Scientist and CMC Consultant at Körber Pharma, breaks down what a digital twin actually is, where modeling pays back fastest across the product lifecycle, and how to tell a real business case from an expensive proof of concept.In this episode:Misconceptions about data requirements for digital twins—why quality and context of data matter more than sheer quantity (02:40)Ignasi's journey from curiosity in biology to a career in data science, modeling, and digital twins (04:31)Clear distinctions between digital models, digital shadows, and digital twins, explained with real-world analogies (06:42)How to approach digital development when faced with legacy data silos and scattered analytics (09:56)The importance of starting with a focused business need instead of chasing trends or buzzwords (12:28)Insights into where modeling truly delivers value in the product lifecycle—development versus manufacturing (13:11)Strategies for small companies to leverage digitalization and data from the ground up (15:56)An accessible overview of physics-informed AI, physical AI, and hybrid modeling—and their application in bioprocessing (18:15)The comparative advantages of physics-informed AI versus hybrid models in different bioprocessing contexts (24:45)Smart insight: Do not start with the model. Start with the bottleneck. Identify the business need first, then the decision the model must support, then the minimum data required for that context of use. Build the model offline, concatenate it end to end across unit operations rather than optimizing one in isolation, and prove the value before connecting a single interface. Teams that skip this sequence end up building models because models sound impressive, and those projects get expensive before they get useful.Before a digital twin can earn its keep, you need connected data, the right model, and a clear decision for it to support. These four episodes cover that ground — data silos, hybrid and mechanistic modeling, and twins built to survive regulatory scrutiny.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del ValEpisodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David HardyConnect with Ignasi Bofarull-Manzano:LinkedIn: www.linkedin.com/in/ignasi-bofarullKörber Pharma website: www.koerber-pharma.comSupport the show

MySpace makes a comeback & Disney partners with TikTok… It's social media's 2 destinies.Your next Pringle may be perfect… because AI perfected the potato with “Digital Twins.”Ousted Uber founder Travis Kalanick is back with a new startup… Atoms is building vertiports with Joby.Plus, the hot new way to find love… is in a group-shared Excel spreadsheet for dating.$DIS $PEP $MSFTGrab your Tickets to the IPO Tour: Our In-Person OfferingSan Francisco 9/23: https://www.ticketmaster.com/event/1C0064AFB5F688BDBoston 10/14: https://tickets.citywinery.com/event/tboy-the-ipo-tour-in-person-offering-8cdhupSeattle 11/4 (21+): https://www.axs.com/events/1446394/the-best-one-yet-ticketsNEWSLETTER:https://tboypod.com/newsletter OUR 2ND SHOW:Want more business storytelling from us? Check our weekly deepdive show, The Best Idea Yet: The untold origin story of the products you're obsessed with. Listen for free to The Best Idea Yet: https://wondery.com/links/the-best-idea-yet/NEW LISTENERSFill out our 2 minute survey: https://qualtricsxm88y5r986q.qualtrics.com/jfe/form/SV_dp1FDYiJgt6lHy6GET ON THE POD: Submit a shoutout or fact: https://tboypod.com/shoutouts SOCIALS:Instagram: https://www.instagram.com/tboypod TikTok: https://www.tiktok.com/@tboypodYouTube: https://www.youtube.com/@tboypod Linkedin (Nick): https://www.linkedin.com/in/nicolas-martell/Linkedin (Jack): https://www.linkedin.com/in/jack-crivici-kramer/Anything else: https://tboypod.com/ About Us: The daily pop-biz news show making today's top stories your business. Formerly known as Robinhood Snacks, The Best One Yet is hosted by Jack Crivici-Kramer & Nick Martell. Hosted on Acast. See acast.com/privacy for more information.

Artificial intelligence is accelerating change across every industry, but simply adding AI to existing processes won't deliver lasting results. Recorded live at World Workplace Europe, Erik Jaspers is joined by Peter Hinssen and Mikis Waschl to explore how AI is reshaping facility management, why digital transformation was only the beginning, and what organizations must do to build stronger data foundations, embrace digital twins and rethink how value is delivered. They also discuss the economics of AI, Europe's role in the future of technology and why today's leaders must prepare for a world that is never normal.  This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Timestamps 00:00 Introduction 01:45 Meet The Guests 03:39 Digital Was Just Start 05:36 AI Goes Geopolitical 07:52 FM Outcomes And Twins 11:02 Data Foundations Matter 12:15 CarPlay For Buildings 16:12 Construction Productivity Gap 17:39 AI Economics And Pricing 22:26 Stop Pilot Snacking 23:49 Europe Digital Autonomy 26:07 Wrap Up And Outro Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

Matt Risinger is joined by Ryan and Ty from Digs to explore practical ways artificial intelligence is improving residential construction. Rather than focusing on AI hype, the conversation centers on how builders can use connected project data, digital twins, and construction-specific tools to streamline estimating, organize documentation, simplify homeowner handoffs, and improve warranty support. The discussion also looks at how better information management can strengthen the client experience, support long-term home maintenance, and create lasting value for builders and homeowners alike. Huge thanks to our episode sponsor, Pella. To learn more visit: https://www.pella.com/ Watch full episodes of Matt on Facebook, Instagram and Build Show Network. https://www.facebook.com/buildshownetworkhttps://www.instagram.com/risingerbuild/https://buildshownetwork.com/go/mattrisinger Don't miss a single episode of Build Show content. Sign up for our newsletter.

Imagine a future where healthcare consists of engineers working alongside medical researchers and clinicians to build better ways to measure the body, model disease, predict risk and design more effective diagnostics and treatments. That's what Dr. Kristin Myers is doing in the field of women's health.Digital twins have transformed manufacturing by allowing engineers to simulate systems, predict failures and optimize performance before making changes in the real world. Dr. Kristin Myers believes those same engineering principles could fundamentally reshape healthcare. As a mechanical engineering professor at Columbia University, Myers is applying computational modeling, AI and biomechanics to one of medicine's most complex frontiers.In this episode, Myers explains why women's health has historically been difficult to study, how engineering disciplines are beginning to fill decades-long research gaps, and why technologies like digital twins, wearable sensors, machine learning and computational models may dramatically improve diagnosis, treatment and long-term patient outcomes. She also explores what this emerging field means for engineers, educators and the next generation of healthcare innovation.In this episode:Why digital twins could become as important in healthcare as they already are in manufacturing.The engineering challenges that have slowed progress in women's health research for decades.How AI, wearable devices and longitudinal patient data could transform diagnosis and personalized medicine.Why mechanical, electrical and software engineers all have a role to play in the future of healthcare.What engineering educators should teach today to prepare students for tomorrow's biomedical breakthroughs.3 Big Takeaways from this Episode:1. Engineering is becoming a core driver of healthcare innovation. The future of medicine won't be built by clinicians alone. Myers explains how mechanical engineers, computational modelers, AI researchers and device designers are bringing new tools and ways of thinking to problems that traditional medical research has struggled to solve.2. Digital twins are moving from factories to patients. The same technologies manufacturers use to simulate equipment and optimize production are beginning to model organs, pregnancies and disease progression. While clinical implementation remains years away in many applications, digital twins are already accelerating biomedical research and medical device development.3. Tomorrow's engineers will need both technical fundamentals and AI fluency. As AI reshapes engineering education, Myers argues that foundational engineering principles remain essential. Students must still learn how systems work from first principles while using AI to accelerate analysis, design and innovation rather than replace critical thinking.Resources in this Episode:ERVA (Engineering Research Visioning Alliance - NSF)Report: Transforming Women's Health Outcomes through EngineeringConnect with our guest online:ERVA Facebook  |  ERVA LinkedIn  |  Connect with Kristin on LinkedInMore notes & resources on the episode page: https://techedpodcast.com/columbia/We want to hear from you! Send us a text.Instagram - Facebook - YouTube - TikTok - Twitter - LinkedIn

Did you like the episode? Send me a text and let me know!! Episode Title: The Skill Nobody Teaches You: Unlearning, Failing Forward & Building Brain Digits Episode Summary: What happens when an entrepreneur from Beirut, Lebanon starts a digital agency in 2011 — when Facebook was three years old and mobile websites were a novelty — and then bootstraps his way through banking crises, regional conflict, and the AI revolution to build a company now operating across the GCC? You get Jack Jendo, founder of Brain Digits, and one of the most globally minded conversations in the show's history. In this episode of Undiscovered Entrepreneur: Get Across the Start Line, Jack unpacks why bootstrapping beats fundraising, why unlearning is a skill most entrepreneurs never develop, why failure needs to be called failure before you can learn from it, and why the future of nations depends on what humans choose to do with the most powerful tool ever created. What You'll Learn: Why Jack left a remote work opportunity in 2010 — before remote work existed — to start his first ventureWhat Brain Digits does: AI enablement, VR training, and emerging technology solutions across hospitals, banking, and governmentWhy bootstrapping forces you to learn every step and makes you unbreakable in a crisisWhy getting investor funding can turn a founder into an employee — and how to know when funding is actually rightThe fire analogy that explains AI's dual nature better than any tech policy paperWhy "unlearning" is the most underrated skill in entrepreneurshipWhy you need to admit failure before you can learn from it — not relabel it as "a step"Jack's 10 entrepreneurial power cards: the priority framework he shares with every founder he meetsThe concept of co-opetition — and why competing less and cooperating more grows the whole marketWhy plans are useless but planning is everythingTimestamps: [00:00:00] – Introduction & Welcome[00:01:00] – Jack's Entrepreneur Origin: Not Built for 9-to-5 in 2010[00:02:00] – Starting a Digital Agency When Facebook Was Three Years Old[00:04:00] – What Brain Digits Does: AI, VR & Emerging Technology Across the GCC[00:05:30] – From Delaware to Dubai to Saudi Arabia: How Brain Digits Scaled[00:07:00] – AI as a Tool, Not a Replacement: The Fire Analogy[00:09:00] – PhD Research: The Future of Nations with Emerging Technologies[00:10:00] – AI as a Second Brain — And the Danger of Losing Critical Thinking[00:11:30] – Human Skills in an AI World: What Schools Should Be Teaching[00:12:30] – How to Bootstrap a Business From Zero[00:14:00] – Why the Founders Who Raised Millions Wished They Had Bootstrapped[00:15:30] – The Lebanon Banking Crisis: Why Bootstrapping Made Jack Unbreakable[00:17:00] – When to Get Funding — and Why Early Funding Makes You an Employee[00:19:00] – Job Security Is a Myth: Personal Accountability in Any Economy[00:20:30] – Why Even Employees Should Start a Small Venture Now[00:22:30] – Admit Failure Before You Can Learn From It[00:23:30] – The Skill of Unlearning: Why Your Brain Needs to Delete Old Files[00:25:00] – Zone of Genius: Working Light, Fast & Free[00:27:30] – Jack's One Piece of Advice: Don't Fall in Love With Your Idea[00:29:30] – The 10 Entrepreneurial Power Cards Framework[00:31:30] – Co-opetition: Cooperating to Compete on a Larger Scale[00:33:30] – Jack's Six-Month Goal: Leading the AI Conversation in Government, Education & Health[00:35:00] – How to Find Jack & Brain DigitsConnect with Jack Jendo:

Episodio 624 con Luca e Gabriele Intermite, una nuova entry alla conduzione e parte della divisione social della nostra redazione. Episodio tutto su mare e polimeri, una strana combinazione. Luca ci parlerà di un nuovo articolo uscito su Nature, che tratta si una "pelle sintetica" che prende ispirazione dalle capacità di camouflage dei cefalopodi come polpi e seppie. La pelle è costituita da un polimero conduttore in grado di rigonfiarsi in maniera selettiva, formando strutture superfciali oridnate, e rispondrere alla luce, colorandosi in base alla dimesione delle strutture. Nel nostro intervento esterno, Leonardo intervista Ion Turcanu, che ci parla di “Digital Twin” per guida autonoma e guida da remoto. Torniamo in studio con la barza brutta, dove Gabriele da il meglio di se e dimostra di essere degno membro della redazione. Nella seconda parte dell'episodio, Gabriele ci parla di alcuni polimeri altamente specializzati come neoprene, kevlar e nanofibre di polietilene, in grado di poter essere impiegati nella realizzazione di mute "antisqualo", ma anche altre più interessanti e importanti applicazioni nella vita di tutti i giorni. Per supportarci iscrivetevi al supporters club di spreaker, oppure potete contibuire con una donazione su paypal!Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/scientificast-la-scienza-come-non-l-hai-mai-sentita--1762253/support.

The Deep Ponder Podcasthttps://open.spotify.com/show/6m5KWVuwZJxj13Eh9BsAtY?si=qyolfJQdSFGIX1LkYVkltw&utm_source=copy-linkForbidden Knowledge Network https://forbiddenknowledge.news/ FKN Link Treehttps://linktr.ee/FKNlinksMake a Donation to Forbidden Knowledge News https://www.paypal.me/forbiddenknowledgenehttps://buymeacoffee.com/forbiddenFKN Merch!https://galactecfire.com/fkn/We are back on YouTube! https://youtube.com/@forbiddenknowledgenews?si=XQhXCjteMKYNUJSjBackup channelhttps://youtube.com/@fknshow1?si=tIoIjpUGeSoRNaEsDoors of Perception is available now on Amazon Prime!https://watch.amazon.com/detail?gti=amzn1.dv.gti.8a60e6c7-678d-4502-b335-adfbb30697b8&ref_=atv_lp_share_mv&r=webDoors of Perception official trailerhttps://youtu.be/F-VJ01kMSII?si=Ee6xwtUONA18HNLZListen to Forbidden Knowledge News on clearair.fm every Tuesday, Thursday, and Saturday 12:15pm CSThttps://clearair.fm/Pick up Independent Media Token herehttps://www.independentmediatoken.com/Be prepared for any emergency with Prep Starts Now!https://prepstartsnow.com/discount/FKNStart your microdosing journey with BrainsupremeGet 15% off your order here!!https://brainsupreme.co/FKN15Book a free consultation with Jennifer Halcame Emailjenniferhalcame@gmail.comFacebook pagehttps://www.facebook.com/profile.php?id=61561665957079&mibextid=ZbWKwLWatch The Forbidden Documentary: Occult Louisiana on Tubi: https://link.tubi.tv/pGXW6chxCJbC60 PurplePowerhttps://go.shopc60.com/FORBIDDEN10/or use coupon code knowledge10Johnny Larson's artworkhttps://www.patreon.com/JohnnyLarsonSign up on Rokfin!https://rokfin.com/fknplusPodcastshttps://www.spreaker.com/show/forbiddenAvailable on all platforms Support FKN on Spreaker https://spreaker.page.link/KoPgfbEq8kcsR5oj9FKN ON Rumblehttps://rumble.com/c/FKNpGet Cory Hughes books!Lee Harvey Oswald In Black and White https://www.amazon.com/dp/B0FJ2PQJRMA Warning From History Audio bookhttps://buymeacoffee.com/jfkbook/e/392579https://www.buymeacoffee.com/jfkbookhttps://www.amazon.com/Warning-History-Cory-Hughes/dp/B0CL14VQY6/ref=mp_s_a_1_1?crid=72HEFZQA7TAP&keywords=a+warning+from+history+cory+hughes&qid=1698861279&sprefix=a+warning+fro%2Caps%2C121&sr=8-1https://coryhughes.org/Our Facebook pageshttps://www.facebook.com/forbiddenknowledgenewsconspiracy/https://www.facebook.com/FKNNetwork/Instagram @forbiddenknowledgenews1@forbiddenknowledgenetworkXhttps://x.com/ForbiddenKnow10?t=uO5AqEtDuHdF9fXYtCUtfw&s=09Email Forbidden Knowledge News forbiddenknowledgenews@gmail.comsome music thanks to:https://www.bensound.com/ULFAPO3OJSCGN8LDDGLBEYNSIXA6EMZJ5FUXWYNC6WJNJKRS8DH27IXE3D73E97DC6JMAFZLSZDGTWFIBecome a supporter of this podcast: https://www.spreaker.com/podcast/forbidden-knowledge-news--3589233/support.

On this episode of the SeventySix Capital Sports Leadership Show, Wayne Kimmel interviewed Vala Dormiani, Founder of Ludis. Dormiani is the Founder of Ludis, an AI-native platform transforming how organizations develop talent through personalized coaching and performance intelligence. Prior to founding Ludis, he held senior product and strategy leadership roles at Cloudera, GO1, and Slice, which was acquired by Rakuten.In addition to his operating experience, Vala has worked as a venture capital investor in both the United States and Australia, backing and advising emerging technology companies. He matriculated at Stanford University at the age of 14 and has earned numerous undergraduate and graduate degrees.Chapters:01:11 Making Data Science Easy for Everyone02:25 Applying Ludis Across Industries04:22 Data Collection and Integration in Sports06:11 AI-Generated Code and Deployment08:40 Using Data for Injury Prediction and Performance12:49 AI Democratization and Industry Evolution17:04 Service as Software and Function Automation20:32 The Impact on Data Science Jobs22:55 Digital Twins and Human Augmentation27:04 Future of AI and Industry Transformation35:02 Democratization of Data in Sports37:02 The Future of AI Ecosystems and Partnerships

Every pollster knows the problem: for every hundred voters you ask to take a poll, only one or two actually respond. So what if AI could just answer the questions for them? It's a seductive idea — and Ben Leff has put it to the test.Ben is co-founder and CEO of Verasight, a survey research firm founded by academics and trusted by leading institutions and media organizations. He and his colleagues G. Elliott Morris and Peter Enns recently published a series of papers asking the question the industry is buzzing about: can AI digital twins replace human survey respondents? Their verdict, after rigorous real-world testing, is a firm — for now.In this conversation with host Eric Wilson, Ben breaks down exactly what synthetic sampling is, how Verasight built and stress-tested it, and where it consistently fell apart. Ben sees a narrow lane where synthetic data could earn a legitimate spot in the toolkit — as a directional pre-screen, a quick and cheap starting point before committing to a full sample. But for anything requiring precision in close races, the humans still have to show up.Visit our website: CampaignTrend.com

Will AI bots replace humans in the workforce? Could one replace Evan… right now? That’s what we tackle on this week’s Shell Game, in which Evan sees just how much of his job his voice agent can handle on his behalf. Shell Game is made by humans. More specifically, it's made by three humans: Evan Ratliff (host and writer), Sophie Bridges (producer), and Samantha Henig (executive producer). Visit shellgame.co to find out more and support the show.See omnystudio.com/listener for privacy information.

What if there were a copy of you at work, answering emails and joining meetings while you slept? After Anna read a BBC article about people who are building AI versions of themselves, she brought the idea to Andrew, and the two of them talk about what these “digital twins” could mean for the rest of us. They look at the same question from three sides, the worker, the boss, and the business owner, and they keep coming back to one worry. If a company has a copy of you, do they still need you? Read the original BBC article on digital twins here: The Best Way to Learn with This Episode: Culips members get an interactive transcript, a helpful study guide, and ad-free audio for this episode. Take your English to the next level by becoming a Culips member. Become a Culips member now: Click here Members can access the ad-free version: Click here. Join our Discord community to connect with other learners and get more English practice. Click here to join. Keep an ear out for these phrases during the episode: The bottom line To not sit right with someone Joe Blow The cat’s out of the bag M.O. (modus operandi) Dog-eat-dog

On this episode of CoinDesk's Public Keys at the New York Stock Exchange, Jennifer Sanasie is joined by CoinDesk Indices President Dave LaValle to unpack a $2.97 billion outflow streak from Bitcoin ETFs and what it really means for institutional adoption.Bloomberg Intelligence Senior ETF Analyst Eric Balchunas joins the show to explain why the recent outflows may be more noise than signal, share his bullish outlook on the fast-rising HYPE ETFs, and discuss how firms like Morgan Stanley, Goldman Sachs, and BlackRock are expanding access to Bitcoin through new investment products. In this week's 10X segment, LaValle breaks down the fundamentals of margin trading, explaining what separates professional traders from retail investors when it comes to managing leverage, risk, and conviction. Plus, Stellar Development Foundation CEO and Executive Director Denelle Dixon discusses DTCC's decision to select Stellar as the first public blockchain connected to its upcoming tokenized securities settlement platform, and what it means for the future of tokenization and institutional blockchain adoption. - This episode of Public Keys is brought to you by Kraken. For more: ⁠https://pro.kraken.com/⁠ - Timecodes: 00:00 Welcome to Public Keys 00:54 Jamie Dimon vs Brian Armstrong on Stablecoin Yields 03:21 Bitcoin ETFs Shed $2.97B in Outflows 05:50 BTC ETFs Post Worst Week Since January 06:50 Grayscale Amends HYPE ETF Filing 08:36 Bloomberg Intelligence's Eric Balchunas Joins Public Keys 09:39 Why BTC ETF Outflows Are Just 'Noise' 13:00 Wall Street's New BTC Products: Goldman, Morgan Stanley, iShares 15:33 HYPE Is the 'Hansel from Zoolander' of Crypto ETFs 17:57 Will SpaceX ETFs Pull Capital from Crypto? 20:42 10X: What Separates Pro Traders from Retail 22:25 Knowing Your 'Out': The Biggest Mistake in Margin Trading 25:06 Stellar Development Foundation's Denelle Dixon on the DTCC Tokenization Deal 26:14 Stellar Hits $3B in Tokenized Assets in Five Months 28:46 Can Blockchains Handle DTCC-Level Volume? 30:21 Digital Twins and the Issuer-Led Tokenization Question 31:50 Will One Blockchain Win the RWA Race? - This episode was hosted by Jennifer Sanasie.