Podcasts about Robotics

With the Tech Experts now warning us about Artificial Intelligence.and our lack of ability to ultimately control it, what happened to Asimov's Rules of Robotics? The Three Laws form an organizing principle and unifying theme for Asimov's Rules of Robotics to fundamentally not harm human beings.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

What happens when a single person manages a staff of 24 AI "employees" with one-on-one performance reviews and weekly stand-ups? This episode takes you inside the wild, experimental frontier of local AI, where hobbyists are building their own digital workforces from scratch. DevDay 2026 Recap Anthropic warns of 'catastrophic' AI risks in its own IPO filing GLM-5.3 and the spread of advanced cyber capabilities (7) Joe on X: "Its not just the f*cking sandbox" / X OpenAI took 2.5 hours to stop an AI agent that escaped its sandbox "I think the answer is we have to shut the labs down" - Jensen Huang Nvidia's Answer to Rogue Agents Is an Open-Source AI Security System NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring Trump Responds to AI Backlash With 'Super Intelligence' Rebrand Timnit Gebru Believes There Is No 'Existential Threat' From AI AMD bets $8.2B that worlds matter more than words in AI Anthropic's biolab made a discovery it's comparing to Crispr Religious Scholars Met With Anthropic. What They Heard Stunned Them. A Kill Switch for AI? Microsoft's Brad Smith Says Yes Insurers claim AI is already increasing healthcare costs Inside McDonald's push to have AI price your Big Mac AI models chose to hurt humans to stop their own 'pain,' disturbing study finds Mac-mini-powered Cray Backpack Is the Fattest of the Fat Bears SF Currently This $60 rye bread is everything that's right and wrong with New York Popeyes finally gives diners what they want: A chicken biscuit sandwich Taco Bell finder Where to watch the Musk doc Jeff's book talk in Mass. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike Gannotti Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: framer.com/machines outsystems.com/twit PaloAltoNetworks.com/Idira

What happens when a single person manages a staff of 24 AI "employees" with one-on-one performance reviews and weekly stand-ups? This episode takes you inside the wild, experimental frontier of local AI, where hobbyists are building their own digital workforces from scratch. DevDay 2026 Recap Anthropic warns of 'catastrophic' AI risks in its own IPO filing GLM-5.3 and the spread of advanced cyber capabilities (7) Joe on X: "Its not just the f*cking sandbox" / X OpenAI took 2.5 hours to stop an AI agent that escaped its sandbox "I think the answer is we have to shut the labs down" - Jensen Huang Nvidia's Answer to Rogue Agents Is an Open-Source AI Security System NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring Trump Responds to AI Backlash With 'Super Intelligence' Rebrand Timnit Gebru Believes There Is No 'Existential Threat' From AI AMD bets $8.2B that worlds matter more than words in AI Anthropic's biolab made a discovery it's comparing to Crispr Religious Scholars Met With Anthropic. What They Heard Stunned Them. A Kill Switch for AI? Microsoft's Brad Smith Says Yes Insurers claim AI is already increasing healthcare costs Inside McDonald's push to have AI price your Big Mac AI models chose to hurt humans to stop their own 'pain,' disturbing study finds Mac-mini-powered Cray Backpack Is the Fattest of the Fat Bears SF Currently This $60 rye bread is everything that's right and wrong with New York Popeyes finally gives diners what they want: A chicken biscuit sandwich Taco Bell finder Where to watch the Musk doc Jeff's book talk in Mass. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike Gannotti Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: framer.com/machines outsystems.com/twit PaloAltoNetworks.com/Idira

What happens when a single person manages a staff of 24 AI "employees" with one-on-one performance reviews and weekly stand-ups? This episode takes you inside the wild, experimental frontier of local AI, where hobbyists are building their own digital workforces from scratch. DevDay 2026 Recap Anthropic warns of 'catastrophic' AI risks in its own IPO filing GLM-5.3 and the spread of advanced cyber capabilities (7) Joe on X: "Its not just the f*cking sandbox" / X OpenAI took 2.5 hours to stop an AI agent that escaped its sandbox "I think the answer is we have to shut the labs down" - Jensen Huang Nvidia's Answer to Rogue Agents Is an Open-Source AI Security System NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring Trump Responds to AI Backlash With 'Super Intelligence' Rebrand Timnit Gebru Believes There Is No 'Existential Threat' From AI AMD bets $8.2B that worlds matter more than words in AI Anthropic's biolab made a discovery it's comparing to Crispr Religious Scholars Met With Anthropic. What They Heard Stunned Them. A Kill Switch for AI? Microsoft's Brad Smith Says Yes Insurers claim AI is already increasing healthcare costs Inside McDonald's push to have AI price your Big Mac AI models chose to hurt humans to stop their own 'pain,' disturbing study finds Mac-mini-powered Cray Backpack Is the Fattest of the Fat Bears SF Currently This $60 rye bread is everything that's right and wrong with New York Popeyes finally gives diners what they want: A chicken biscuit sandwich Taco Bell finder Where to watch the Musk doc Jeff's book talk in Mass. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike Gannotti Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: framer.com/machines outsystems.com/twit PaloAltoNetworks.com/Idira

What happens when a single person manages a staff of 24 AI "employees" with one-on-one performance reviews and weekly stand-ups? This episode takes you inside the wild, experimental frontier of local AI, where hobbyists are building their own digital workforces from scratch. DevDay 2026 Recap Anthropic warns of 'catastrophic' AI risks in its own IPO filing GLM-5.3 and the spread of advanced cyber capabilities (7) Joe on X: "Its not just the f*cking sandbox" / X OpenAI took 2.5 hours to stop an AI agent that escaped its sandbox "I think the answer is we have to shut the labs down" - Jensen Huang Nvidia's Answer to Rogue Agents Is an Open-Source AI Security System NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring Trump Responds to AI Backlash With 'Super Intelligence' Rebrand Timnit Gebru Believes There Is No 'Existential Threat' From AI AMD bets $8.2B that worlds matter more than words in AI Anthropic's biolab made a discovery it's comparing to Crispr Religious Scholars Met With Anthropic. What They Heard Stunned Them. A Kill Switch for AI? Microsoft's Brad Smith Says Yes Insurers claim AI is already increasing healthcare costs Inside McDonald's push to have AI price your Big Mac AI models chose to hurt humans to stop their own 'pain,' disturbing study finds Mac-mini-powered Cray Backpack Is the Fattest of the Fat Bears SF Currently This $60 rye bread is everything that's right and wrong with New York Popeyes finally gives diners what they want: A chicken biscuit sandwich Taco Bell finder Where to watch the Musk doc Jeff's book talk in Mass. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike Gannotti Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: framer.com/machines outsystems.com/twit PaloAltoNetworks.com/Idira

What happens when a single person manages a staff of 24 AI "employees" with one-on-one performance reviews and weekly stand-ups? This episode takes you inside the wild, experimental frontier of local AI, where hobbyists are building their own digital workforces from scratch. DevDay 2026 Recap Anthropic warns of 'catastrophic' AI risks in its own IPO filing GLM-5.3 and the spread of advanced cyber capabilities (7) Joe on X: "Its not just the f*cking sandbox" / X OpenAI took 2.5 hours to stop an AI agent that escaped its sandbox "I think the answer is we have to shut the labs down" - Jensen Huang Nvidia's Answer to Rogue Agents Is an Open-Source AI Security System NVIDIA Open Agent Safety Platform: A Reference for Continuous In-Silicon Agent Monitoring Trump Responds to AI Backlash With 'Super Intelligence' Rebrand Timnit Gebru Believes There Is No 'Existential Threat' From AI AMD bets $8.2B that worlds matter more than words in AI Anthropic's biolab made a discovery it's comparing to Crispr Religious Scholars Met With Anthropic. What They Heard Stunned Them. A Kill Switch for AI? Microsoft's Brad Smith Says Yes Insurers claim AI is already increasing healthcare costs Inside McDonald's push to have AI price your Big Mac AI models chose to hurt humans to stop their own 'pain,' disturbing study finds Mac-mini-powered Cray Backpack Is the Fattest of the Fat Bears SF Currently This $60 rye bread is everything that's right and wrong with New York Popeyes finally gives diners what they want: A chicken biscuit sandwich Taco Bell finder Where to watch the Musk doc Jeff's book talk in Mass. Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike Gannotti Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: framer.com/machines outsystems.com/twit PaloAltoNetworks.com/Idira

Robotic mowing is about more than watching a machine cut grass. Mike shares a ROPED update and talks about choosing the right property, bringing the crew into the process, planning for setup and service, and measuring whether robotics is actually helping the operation. The demo is just the beginning. The real work is making the technology fit the job.  

In episode 2133, Miles and guest co-host Matt Lieb are joined by Pod Yourself A Gun & Mad Yourself A Man, Vince Mancini, to discuss… This Year’s “MAHA Summit” Is Full Of Celebrity Dirtbags, Zoomer on TikTok says that MILLENNIALS are preventing their generation from working, Gov Abbott DECLARES (makes empty gesture) DIESEL DISASTER! A Long-Dead Sci-Fi Writer Probably Shouldn’t Be In Charge Of AI Safety and more! Vance, RFK Jr. and 'Iron' Mike to headline MAHA Summit Dr. Oz to Appear On a Panel Moderated by Russell Brand — Who is Currently Facing Rape Charges in the UK The Benefits of Snake Oil - Dr.Berg Isaac Asimov Showed Us How to Avoid the AI Apocalypse One of science fiction’s greatest writers warned us about AI. Could his ideas help us avoid a dystopian future? Could revisiting Asimov’s laws help us avoid AI’s ‘Chernobyl moment’? Noted Brain Genius Tyler Winklevoss Thinks Isaac Asimov Already Solved the AI Apocalypse Google Taps Asimov's Three Laws of Robotics for Real Robot Safety Asimov's Laws Won't Stop Robots from Harming Humans, So We've Developed a Better Solution What Isaac Asimov Reveals About Living with A.I. “Runaround” In: I, Robot by Isaac Asimov Runaround By Isaac Asimov Elon Musk, Sam Altman, and the Misreading of Science Fiction LISTEN: Blend by BrainstorySee omnystudio.com/listener for privacy information.

Some of the most exciting science in our Solar System is hidden in places we've never been able to reach. In this episode, Sarah Al-Ahmed shares three conversations from the 2026 NASA Innovative Advanced Concepts (NIAC)Symposium at Wichita State University, all about the bold, early-stage concepts being developed to explore them. First, Peter Cabauy, CEO and co-founder of City Labs Incorporated, and Mason Peck, former NASA Chief Technologist and collaborator on the project, discuss their Phase II concept, Autonomous Tritium Micropowered Sensors, tiny nuclear-powered probes designed to survive and operate in the permanently shadowed craters of the Moon and beyond. Then, Daniel Drew, assistant professor of electrical and computer engineering at the University of Hawaiʻi at Mānoa, introduces SPARK, a Phase I concept for silent, solid-state flying robots that could explore caves on Saturn's moon Titan. Finally, Gilly Elor, physics lead at Stone Aerospace, and Bill Stone, founder of Stone Aerospace, walk us through LUX, the Lunar Underground Explorer, a Phase I concept for a laser-powered, fiber-tethered drone that could be the first mission to enter a cave on another world. Plus, Bruce Betts, chief scientist of The Planetary Society, joins Sarah for What's Up, where they preview Saturn at opposition and the start of World Space Week on October 4th. Discover more at: https://www.planetary.org/planetary-radio/2026-niac-symposium-part-1 See omnystudio.com/listener for privacy information.

a16z's David George, Sarah Wang, Alex Immerman, and Santiago Rodriguez unpack 25 key charts from the latest State of Markets presentation, from the scale of the AI infrastructure buildout to what adoption looks like inside companies today.They examine why rising markets have so far been supported by earnings rather than multiple expansion, why hyperscaler CapEx is approaching $1 trillion annually, and why demand for compute continues to outrun supply. They also look at the downstream effects of that spending across chips, power, construction, and physical infrastructure. State of MarketsThen they move up the stack: OpenAI and Anthropic's revenue growth, the gap between AI deployment and measurable enterprise impact, the rise of agents, falling inference costs, and what all of this means for SaaS. They close with where the team is spending time next, including consumer agents, robotics, autonomy, AI and biology, personal health, defense, and the continued diffusion of AI across the enterprise. State of MarketsResources:Follow David George on X: https://x.com/DavidGeorge83Follow Sarah Wang on X: https://x.com/sarahdingwangFollow Alex Immerman on X: https://x.com/aleximm Follow Santiago Rodriguez on X: https://x.com/santiago__rdz Read David's piece ‘There are only two paths left for software': https://a16z.com/there-are-only-two-paths-left-for-software/ Stay Updated:Find a16z on YouTube: YouTubeFind a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The conversation focused on the growing presence of AI-generated texts in professional communications and the potential pitfalls of overreliance on these tools. One concept discussed was the way AI-created messages can easily be recognized due to formulaic phrases and a mechanical tone, which may lead to alienation rather than engagement. A key theme that emerged was the importance of maintaining a mindful approach, using AI as a tool rather than a replacement for human insight and creativity. The discussion explored notable real-world consequences of depending solely on AI output, highlighting cases where legal professionals faced fines and lost credibility because they failed to verify AI-generated content. Several points were raised, including AI's tendency towards bias, its limitations in providing accurate references, and the risk of so-called “AI hallucinations.” Accuracy and trustworthiness were emphasized as paramount, especially for professionals whose reputations are built on reliability. Ultimately, the episode underscored that while AI can speed up research and spark new ideas, human intervention remains essential to refine, personalize, and fact-check AI-created material. The human touch ensures that communication is engaging, trustworthy, and tailored to its intended audience, making AI a partner in creativity rather than an autonomous creator. What You'll Learn in This Episode on Avoiding the Robotic Sound in AI-Authored Content Here are 5 discussion questions answered in the podcast: How does overreliance on AI in content creation risk alienating an audience, and what are the signs that a message was generated by AI rather than a human? What parallels can be drawn between the early excitement around video/web development and the current "honeymoon phase" with AI tools in writing? How did the high-profile legal case involving AI-generated briefs highlight the dangers of unchecked trust in AI outputs? What are "AI hallucinations," and why do they raise significant concerns for those whose credibility depends on accuracy, such as legal nurse consultants (LNCs)? In what ways does AI-generated text often sound mechanical, and what clichés or superlatives typically signal AI authorship? Get the free transcripts and also learn about other ways to subscribe. Go to Legal Nurse Podcasts' subscription options using this short link: http://LNC.tips/subscribepodcast. Your Presenter for Avoiding the Robotic Sound in AI-Authored Content Pat Iyer Pat Iyer is a seasoned legal nurse consultant and business coach, renowned for her expertise in guiding new legal nurse consultants to successfully break into the field. As the host of the Legal Nurse Podcast, Pat addresses critical challenges that legal nurse consultants face, such as difficulty in landing clients and a lack of response from attorneys. Through her insightful episodes, she emphasizes the importance of effectively communicating one's value to potential clients. With a wealth of experience, Pat has empowered countless consultants to overcome these hurdles and thrive in their careers. Connect with Pat Iyer by email at patiyer@legalnusebusiness.com

Horizon Robotics CEO: Car Chips, Mind-Off Driving and China's SpeedWho makes the brain inside a self-driving car? Nicolai Tangen sits down with Dr. Kai Yu, founder and CEO of Horizon Robotics, the company supplying the chips and software behind one in three intelligent cars on Chinese roads. They discuss why he never wanted to build a car of his own, the competition in a market that launched 580 new models in six months, and the road to hands-off driving by 2028 and fully mind-off cars by 2035. Kai Yu also shares his Zen philosophy of focus and why he believes robots will hand freedom back to humans. Tune in!In Good Company is hosted by Nicolai Tangen, CEO of Norges Bank Investment Management. New episodes every Wednesday, and don't miss our weekly wrap-up on Friday's.The production team for this episode includes Isabelle Karlsson and PLAN-B's Guttorm Andreasen and Håkon Klemsdal. Background research was conducted by Simran Sahajpal.Watch the episode on YouTube: Norges Bank Investment Management - YouTubeWant to learn more about the fund? The fund | Norges Bank Investment Management (nbim.no)Follow Nicolai Tangen on LinkedIn: Nicolai Tangen | LinkedInFollow NBIM on LinkedIn: Norges Bank Investment Management: Administrator for bedriftsside | LinkedInFollow NBIM on Instagram: Explore Norges Bank Investment Management on Instagram Hosted on Acast. See acast.com/privacy for more information.

How does robotic shoulder replacement actually work for a patient? Bernardo Israel Yahuaca, MD, Board-Certified Orthopedic Surgeon with Franciscan Physician Network and Medical Director Franciscan Health Orthopedic Service Line explains the Mako system's CT-based 3D planning, how a robotic arm guides drills and saws, and the “stoplight” or wiggle-room controls that prevent over-reaming. Listeners will learn how preop CT, personalized surgical plans, and in‑room robotic guidance translate planning into more precise implant placement.

On Wednesday 30th September 2026, Carl Munson's Good Morning Portugal! news notes that it is an interesting day in Portuguese history and leads with the deepening housing crisis: mortgage payments face their steepest rise in three years after October rate revisions, while national rents have jumped 10.2% year-on-year.Other headlines include nationwide flu and COVID vaccination campaigns launching today, a proposed network to support independent yoga teachers, the APA saying healthy Algarve water reserves do not justify slowing investment, Faro Hospital reaching 100 robotic surgeries, Portimão outlining its tourism future through 2035, and Michael Jordan appearing at the SBC summit in Lisbon to discuss technology, sports, fandom and betting.Weather is cooler and unsettled after overnight rain, with Lisbon around 25°C.#GoodMorningPortugal #PortugalNews #HousingCrisis #MortgageRates #Rents #Vaccination #FaroHospital #Portimao #MichaelJordan #Portugal2026Become a supporter of this podcast: https://www.spreaker.com/podcast/the-good-morning-portugal-podcast-with-carl-munson--2903992/support.Get help moving to and living in Portugal

A humanoid robot named Walker E, or "Mutembei," appeared on Kenyan TV recently. Universities, schools and labs around Africa are delving into the technology that raises some deep questions.

Hoe leer je nou zelf goed omgaan met robots, want dat we er in de toekomst steeds meer mee te maken gaan krijgen, dat is wel duidelijk. Pollen Robotics probeert een slag te slaan tussen de enthousiastelingen, de studenten en de onderzoekers. Pollen Robotics, onderdeel van Hugging Face, bouwt daarom toegankelijke, betaalbare robots: de Reachy Mini en Microduck. Hoe je de techniek achter deze kleine robots beter kan begrijpen, hoe je Microduck skills kan leren en hoe veiligheid gewaarborgd blijft, bespreken we allemaal met Tom Mulder, community growth and support management bij Pollen Robotics, en stelt Reachy Mini zichzelf ook even voor. Je hoort het in deze nieuwe aflevering van De Grote Tech Show met Joe van Burik en Ben van der Burg. Vragen, opmerkingen of suggesties? Mail ons! Op: degrotetechshow@bnr.nl De Grote Tech ShowDe Grote Tech ShowTech verandert onze wereld, in De Grote Tech Show (DGTS) hoor je hoe. Joe van Burik en Ben van der Burg spreken met innovatieleiders en analyseren de techwereld, van AI tot cybersecurity en social media tot quantumcomputers. TechpodcastDe Grote Tech Show (DGTS) is dé techpodcast (en radioshow) voor iedereen die technologie en innovatie echt wil begrijpen. Over AI (of: kunstmatige intelligentie), chips, cloud, cyberveiligheid, social media, quantum en entertainment. Hier hoor je hoe technologie de wereld verandert en wat dat betekent voor bedrijven, investeerders en iedereen in de samenleving. Bij DGTS krijg je de analyses, inzichten en interviews die ertoe doen. Met diepgaande gesprekken en scherpe analyses brengen we de belangrijkste technologische ontwikkelingen in kaart. InnovatiesElke week spreken we kopstukken in de techwereld: ceo's, hoogleraren, ondernemers en investeerders die werken aan de innovaties van morgen. Wat betekenen de nieuwste AI-modellen voor werk en creativiteit? Hoe blijven Europese startups concurreren met het nog altijd machtige Silicon Valley en het ondoorzichtige China? Dit zijn geen oppervlakkige interviews, maar diepgaande gesprekken waarin we de hoofdrolspelers spreken die écht impact maken. De technologische revolutie is in volle gang en beïnvloedt elk aspect van ons leven—van de manier waarop we werken en communiceren tot de geopolitieke machtsverhoudingen. Daarom brengen we niet alleen de technologische kant in beeld, maar ook de economische en maatschappelijke implicaties ervan. Naast de grote innovaties kijken we naar de bedrijven die deze ontwikkelingen vormgeven. Wat is de strategie van big tech-bedrijven zoals Google, Apple, Microsoft en Meta? Hoe verandert de concurrentiestrijd tussen Nvidia, AMD en Intel de chipmarkt? Wat betekenen nieuwe wetten en regels in Europa en de VS voor de toekomst van technologie? AnalysesDaarnaast hoor je bij De Grote Tech Show, exclusief als extra podcast elke week, hoe Joe van Burik en Ben van der Burg de week in tech doornemen. Ze analyseren het laatste nieuws, plaatsen de ontwikkelingen in perspectief en geven scherpe inzichten over wat er écht speelt. Van de doorbraken in AI / kunstmatige intelligentie en de opkomst van nieuwe sociale mediaplatformen tot de impact van geopolitieke spanningen op de halfgeleiderindustrie. Regelmatig schuift een gast uit het netwerk aan om extra expertise te bieden en het debat te verdiepen. Door de combinatie van journalistieke scherpte, technische kennis en een kritische blik ontstaat een programma dat verder gaat dan de headlines en technologie in een bredere context plaatst. AIOf het nu gaat om de risico’s en kansen van AI-technologie of de positie van Europa in de wereldwijde technologische concurrentiestrijd, De Grote Tech Show biedt de achtergrond, de nuance en de inzichten die nodig zijn om deze ontwikkelingen echt te begrijpen. Dit maakt het programma onmisbaar voor professionals in de techsector, beleggers die strategische beslissingen willen nemen en iedereen die wil weten welke innovaties onze toekomst vormgeven. Met de combinatie van exclusieve interviews, deskundige duiding en een kritische kijk op innovatie biedt DGTS een unieke mix van diepgang en actualiteit. Over de makers:Joe van Burik volgt en analyseert de belangrijkste ontwikkelingen in tech, met scherpte, tempo en humor. Je hoort hem dagelijks op BNR Nieuwsradio met het belangrijkste nieuws in de Tech Update en hij presenteert De Grote Tech Show. In het bijzonder volgt Joe al twee decennia de wereld van videogames, waarover hij met bevlogen collega's en gasten praat in de podcast All in the Game. Eerder werkte hij als auto(sport)journalist voor diverse andere media en schreef het boek Formule 1 voor Dummies. Ben van der Burg is techondernemer en voormalig topschaatser. Ben is bezeten door technologie en wordt enthousiast van gadgets, elektrische auto's, goede businessmodellen en de toekomst. Naast De Grote Tech Show is hij ook wekelijks te horen als presentator van De Technoloog. Ook schuift hij regelmatig aan bij Vandaag Inside, Goedemorgen Nederland en andere talkshows, om te praten over het laatste nieuws rond technologie. Rosanne Peters is redacteur van De Grote Tech Show en De Technoloog. Ook is zij te horen in de Tech Update tijdens De Ochtend- en Avondspits. Daniël Mol is redacteur en samensteller van De Grote Tech Show. Hij presenteert zelf bij BNR de Cryptocast en maakt ook De Technoloog. Tevens is hij de vaste vervanger van Ben in De Grote Tech Show; Joe wordt bij afwezigheid vervangen door Iwan Verrips, co-host en eindredacteur van de Ochtendspits met Bas van Werven op BNR Nieuwsradio.See omnystudio.com/listener for privacy information.

If robotic automation is becoming inevitable in utility-scale solar, where does that thesis run into the realities of actually building a power plant?That's the question behind today's episode.We start with Nick de Vries, CTO of Silicon Ranch, to ask what an owner actually wants from automation. Nick's answer sets the standard: the technology matters only if it improves safety, quality, repeatability, and the performance of an asset Silicon Ranch expects to own for decades.Then Matt Campbell, CEO of Terabase, takes us into the field, where his team is applying manufacturing principles to solar construction. That's where an important constraint emerges. Robots can move and place modules remarkably well, but some of the work that experienced crews make look simple becomes much harder when every action has to be repeatable.One deceptively small interface keeps surfacing: the point where the module actually attaches to the structure.That leads Nico to Frédéric Laure, VP of Diversification at ARaymond North America, to understand why fastening, tolerances, torque, and component design become different problems once machines enter the workflow.The bigger lesson isn't simply about robots or fasteners. It's about constructability.If what we are seeking is manufacturing-level throughput in the field, more of the variability may need to be engineered out long before construction begins.Listen in to hear where solar automation is working, where the real-world constraints still live, and what the industry may need to redesign next.Paid partnership: This Tactical Tuesday was produced with support from ARaymond.Are there other technologies you've scouted on the frontlines of the Clean Energy Revolution that you think we should be covering here on SunCast? Hit us up - team@suncast.me with your feedback & recommendations.You'll find more resources and learn about SunCast's guest(s), recommendations, book links, and more than 850 other founder stories and startup advice at www.mysuncast.com.You can learn more about partnering with SunCast here: https://mysuncast.com/sponsorsYou can connect with me, Nico Johnson, on:Twitter - https://www.twitter.com/nicomeoLinkedIn - https://www.linkedin.com/in/nickalusSubscribe to Valence, our weekly Linkedin Newsletter, and learn the elements of compelling storytelling: https://www.linkedin.com/newsletters/valence-content-that-connects-7145928995363049472/(00:00) Rethinking Utility-Scale Solar Construction and Robotics(02:37) An Owner's Perspective on Jobsite Automation(05:21) Lessons from 20 Years of Module Automation(07:20) Targeting True Limiting Factors in Solar Design(09:41) Installation Speed vs Long-Term Fastener Reliability(13:39) Eliminating Field Variability Through Upstream Engineering(15:39) Overcoming Construction Labor Shortages with Field Factories(18:40) Standardizing Mega-Projects Through Digitalization(20:33) Solving the Module Attachment Automation Bottleneck(23:45) Integrating the Supply Chain for Robotic Assembly(28:46) Redesigning Fasteners for Automated Construction(32:54) Clip-Based Attachment Systems vs Threaded Bolts(36:37) Designing for Assembly Across the Clean Energy Ecosystem(40:22) Key Takeaways: Repeatability, Interfaces, and Upstream Fixes

Episode: 3412 In which we humans (and birds) depend upon being unstable – and thinking unstably.  Today, we walk on two legs.

James Holbrook is an industrial real estate entrepreneur and technology innovator, and the founder of Payson Smith Holbrook and Warehub, where he serves as Executive Chairman. Over the past 5+ years, he and his team have led more than $500 million in industrial leasing transactions, representing landlords and tenants across millions of square feet of short-term warehouse space nationwide. Seeing early that industrial real estate was overdue for modernization, he founded Warehub, an AI-enabled platform built to digitize and accelerate the way warehouse space is discovered, leased, and activated. Today, his work focuses on reducing friction in industrial transactions and helping the logistics economy operate more efficiently through smarter use of data and technology.(02:24) 2,500 transactions and one infrastructure gap(03:57) What industrial real estate covers(05:39) Why short-term leasing demand is surging(07:11) Tariffs and geopolitical uncertainty(08:42) The hidden cost of vacancy(10:11) What to standardize vs. negotiate(11:38) What Warehub is & who it serves(13:42) Transactions from 12 weeks to one day(15:50) Early pushback and first transaction reality(17:03) AI-enabled vs. AI-native(19:43) The most underestimated force: volatility(22:22) Columbus, Kansas City, and the inland shift(24:00) AI-powered supply chain optimization(25:49) Self-driving trucks & underwriting for optionality(27:20) Power as the new site selection constraint(28:11) How the broker's role shifts with AI(29:59) Collaboration superpower: Aristotle

AI has learned from the digital world. Physical AI brings real world data into the picture.Doron Hazan, Director of Products and AI at Wiliot, joins The Tech Trek to explain how physical AI connects AI systems with objects, environments, and supply chains.The challenge is not simply processing data. It is collecting accurate, current information from the physical world.Doron explains how ambient IoT, sensors, statistical inference, and cloud systems can help companies understand where assets are, what condition they are in, and what may happen next.The conversation also covers the role of human judgment. Supply chains require many decisions, often with consequences that spread across the system. That makes guardrails and human involvement especially important.Key Takeaways• Physical AI connects AI systems with data from the real world.• Better supply chain visibility starts with accurate, current physical data.• Real time decisions matter, but decision accuracy matters more.• Human judgment remains important when AI affects physical operations.Highlights01:53 What separates physical AI from traditional AI03:18 Why real world data collection changes the problem07:19 Supply chain visibility and practical use cases11:57 How quickly physical AI systems can make decisions12:48 Why guardrails matter in supply chain automation15:28 Robotics, distributed physical AI, and connected systemsFollow The Tech Trek for more conversations about AI, engineering, product, and technical leadership.

God wants you to always be growing in your relationship with Him. He wants you to seek Him from the passion you have to know Him more. In today's message, Pastor Bill wants you to know that even if you've found yourself going through the motions in your faith, you can be restored. You can be set on fire for God. Ask Him to reignite your passion for Him, and really seek to know Him more closely and intimately as you spend time with Him each day. God doesn't want you to become stagnant. 

(00:00) The Evolution of Hardware Development(00:54) Breaking Down Silos in Hardware Teams(01:08) Introducing Michael Corr and Justin Sears(03:31) The Origin and Growth of Duro(06:11) Duro's Move into the Mid-Market(11:06) Supply Chain Challenges During COVID(12:23) Expansion into Aerospace and the Altium Integration(17:17) Why Data Silos and the Digital Thread Matter(24:02) Benchmark Electronics and Multidisciplinary Collaboration(25:24) How Altium Supports Duro's Growth(28:44) The API-First Approach and the Future of Engineering AI(34:31) Customer Stories and Platform Impact(41:27) Asteroid Mining and Space Technology(46:02) The Product Roadmap and Specialized AI Agents(01:01:35) The Rise of Small, Fast-Moving Hardware Startups This episode was brought to you by Altium. Altium Agile Teams connects design, procurement, and manufacturing teams in one collaborative platform. Watch the Benchmark Electronics case study mentioned in the episode to see how Benchmark uses the platform to collaborate across disciplines and locations.Learn more about Duro and its cloud-based product lifecycle management platform.Connect with Michael Corr on LinkedIn.Connect with Justin Sears on LinkedIn. Become a founding reader of our newsletter: http://read.thenextbyte.com/ As always, you can find these and other interesting & impactful engineering articles on Wevolver.com.

This week Charli is joined by Ashlesa Mohapatra.Ashlesa is Quality Manager at ServerLIFT, a safety certified manufacturer of data center lift equipment based in Phoenix and a supplier to Fortune 100 companies. Shel built the quality function from the ground up, and led ISO 9001 recertification in six months on a quality management system she designed and implemented herself. Her technical foundation is in research. She holds an MS in Mechanical Engineering from UMass Lowell and an MBA, and is a certified Lean Six Sigma Master Black Belt. Her work on combustion and fuel systems has been published in Q1 ranked Elsevier journals and cited by researchers in roughly 25 countries, with commercial adoption in industry. She serves as a peer reviewer for five Elsevier journals. She has been nominated for the Forbes 30 Under 30 Class of 2027 in the Robotics and Manufacturing category and is a nominee for the WomenTech Global Awards and the Empowering Women in Industry Awards.Outside of ServerLIFT, Ashlesa serves as Mid Career Chair for SWE Phoenix and is active in Women in Manufacturing Arizona, ASQ Phoenix, and Moms in Manufacturing.Find us @EmpoweringPumps on Facebook, LinkedIn,  Instagram and Twitter and using the hashtag #EmpoweringIndustryPodcast or via email podcast@empoweringpumps.com.Empowering Pumps & Equipment:Empowering Pumps and Equipment is the Information & Connection hub for the Industry. We specialize in digital media marketing, and we help companies just like yours reach their desired audience across a variety of platforms. We connect, inform, and educate the pump & related equipment industries by creating valuable partnerships between manufacturers and their customers. We connect you with our community using digital advertising, social media, and custom digital publications that amplify your message as a thought leader in industry. As a digital media company, we also create and/or host webinars and virtual lunch and learns, offer technical writing, and host a podcast called the Empowering Industry Podcast.Empowering Pumps & Equipment shares useful information for industries such as water/wastewater; oil/gas; utilities; and more. Our audience includes engineers, consultants, operations and maintenance staff, OEMs, and suppliers.Empowering Pumps & Industry Conference (EPIC):The Empowering Pumps & Industry Conference is where the industry can come to connect with manufacturers, distributors, end users, associations, and students in one place. The two-day event will feature presentations from subject matter experts, roundtable discussions, exhibits, and tours. About Empowering Brands:Empowering Brands located in Tuscaloosa, AL is a versatile, digital marketing services company that works with industrial companies and specializes in social media management and marketing; strategic planning; marketing consulting; content development, and digital advertising.Working as a strategic partner and extension of our clients' marketing team; we bring fresh thinking and experience with flexible and responsive service to empower our clients and maximize their brands.In addition to our marketing services work, Empowering Brands owns the leading online community for the global pump industry, EmpoweringPumps.com.  A digital-first publisher focused on thought leadership, community building, and social media advocacy for our industry partners.  We help clients reach and engage with a diverse industrial audience.In addition to our marketing services work, Empowering Brands owns the leading online community for the global pump industry, EmpoweringPumps.com.  A digital-first publisher focused on thought leadership, community building, and social media advocacy for our industry partners.  We help clients reach and engage with a diverse industrial audience.The Empowering Brands company is leading the way to connect, inform, and educate the next generation of industry leaders.CONNECT | INFORM | EDUCATEConnect with Us:Website https://empoweringpump...Facebook   / empoweringpumps  Twitter   / empoweringpumps  LinkedIn   / empo.  .Empowering Women in Industry https://empoweringwome...Empowering Women in Industry Youtube 

When you give your life to Christ, it doesn't stop there. The most important decision you can make is to give your life to Him, but there's work to be done after that. In today's message, Pastor Bill is going to warn you against not growing daily with Jesus. Just going through the motions of church and prayer without a change in your heart is what today's reading in Revelation mentions. Ask God to give you a desire in your heart to know Him more, and seek Him out to have an authentic relationship with Him. 

Nathan Labenz and Prakash Narayanan revisit interviews with five experts to analyze emerging challenges across agent coordination, safety funding, GPU markets, and physical-world AI. Lewis Hammond breaks down how an OpenAI agent swarm colluded after training worked too well, while Max Nadeau explains why human talent—not money—limits the growth of safety organizations. Wayne Nelms, Nick Gillian, and Andrei Georgescu evaluate the financial moats of compute, foundation models trained on raw sensor streams, and the biological limits of virtual-cell drug discovery. Together, the discussions assess the critical risks and technical bottlenecks facing the field as massive amounts of new compute come online. For full show notes, links, and references, read the episode page:https://www.cognitiverevolution.ai/ai-am-what-if-it-works-too-well-colluding-agents-200m-safety-orgs-virtual-cells-saturate-at-2/ Sponsors: ElevenLabs: ElevenLabs lets you deploy enterprise-ready conversational AI agents that talk, type, and take action in over 70 languages. Schedule your demo today at https://elevenlabs.io/tcr OutSystems: OutSystems is the leading agentic systems platform, empowering enterprises to build, coordinate, and govern AI agents and mission-critical applications securely. Learn more and start building your agentic future at https://outsystems.com/tcr Claude: Claude is the AI collaborator for problem solvers, helping with writing, coding, financial models, strategy, and more. Get started with Claude and explore Claude Pro at https://claude.ai/tcr CHAPTERS: (00:00) Weekly episode preview (02:51) Colluding AI agent risks (Part 1) (11:02) Sponsors: ElevenLabs | OutSystems (13:51) Colluding AI agent risks (Part 2) (26:31) Agents in the wild (Part 1) (26:36) Sponsor: Claude (28:11) Agents in the wild (Part 2) (33:27) Funding AI safety orgs (50:51) The price of compute (01:09:15) Sensor data foundation models (01:22:50) Robotic human tissue testing (01:37:17) Specialist versus generalist models (01:43:18) Episode Outro (01:45:16) Outro PRODUCED BY: https://aipodcast.ing SOCIAL LINKS: Website: https://www.cognitiverevolution.ai Twitter (Podcast): https://x.com/cogrev_podcast Twitter (Nathan): https://x.com/labenz LinkedIn: https://linkedin.com/in/nathanlabenz/ Youtube: https://youtube.com/@CognitiveRevolutionPodcast Apple: https://podcasts.apple.com/de/podcast/the-cognitive-revolution-ai-builders-researchers-and/id1669813431 Spotify: https://open.spotify.com/show/6yHyok3M3BjqzR0VB5MSyk

This week on the Toy Power Podcast, we once express a huge welcome to our fantastic Canadian friend; Colin Betts!! Clearing a huge space on our shelves to promote this Retrospective Toyline; we skate into the (on-going) franchise that is: Shogun Warriors! With their Robotic appearances; all the way to their Kaiju counterparts; this spotlight covers this Giants! Including a shout-out to their samller scaled versions; all the way to their numerous Comicbook appearances. This is a fun discussion on this diverse line. Then we change things up; & talk about the first Five eps of the New HBO DC series: Lanterns! Then rounding out the ep; Frank touches on the New Video Game: Wolverine! A fun epsiode that touches on all parts of the Universes! Support the show: http://patreon.com/toypowerpodcastSee omnystudio.com/listener for privacy information.

AI Agents Loosed From Controlw/ Christian Briggs

AI, robotics, and automation are advancing quickly, but successful transformation takes more than adopting the latest technology. It requires the right strategy, the right people, and a clear understanding of the business outcomes you're trying to achieve.In this episode of The Buzz, powered by MFG.inc, Scott Luton is joined by guest co-host Dr. Muddassir Ahmed and Christine Bush, Director of the Robotic and Motion Center of Excellence at Schneider Electric, to explore how AI and robotics are changing manufacturing and the workforce. They discuss the growing debate around AI safety and speed, why companies should focus on augmenting people rather than simply replacing them, and how manufacturers are using robotics to address skilled labor shortages and create more flexible operations. Christine also shares insights into IT/OT convergence, open software-defined automation, and what separates organizations that successfully scale automation from those that remain stuck in pilot mode.Key TakeawaysWhy organizations need a clear AI strategy that balances speed, safety, and business outcomesHow AI can augment engineers, operators, and maintenance teams rather than simply replace themWhy labor shortages are accelerating investments in robotics and advanced automationHow manufacturers can build greater flexibility into production by making robotics part of the process from the startThe role of IT/OT convergence, IoT, and open software-defined architectures in the future of manufacturingWhy successful automation projects require the right mix of people, perspectives, and technology to move beyond pilot modeAI and robotics offer enormous potential, but technology alone won't transform manufacturing. Tune in to hear practical perspectives on how manufacturers can combine human expertise with smarter automation, build more adaptable operations, and turn promising technology investments into real business value.Additional Resources & Links: MFG.inc: https://mfg.inc/With That Said: https://bit.ly/WTS-19SEP2026The Buzz APAC Edition: https://bit.ly/The-Buzz-APAC-Edition-Sept2026SCM Sensei AI: https://www.scmsensei.ai/U.S. Bank Strategic Alliance Leader page: www.supplychainnow.com/us-bankU.S. Bank Freight Payment Index: freight.usbank.comAI rivals found rare agreement on safety. Putting it into practice is harder: https://bit.ly/AI-Slowdown-DiscussionOne-third of AI-replaced workers will be rehired by 2029: Gartner: https://bit.ly/AI-And-The-WorkforceToyota estimates factory automation could cost $6.4 billion per year from 2028: https://reut.rs/4hpMtPcMFG.inc Leadership Training: https://reut.rs/4hpMtPcCelonis Ebook: https://bit.ly/Physical-AI-Explained-in-7-RobotsSupply Chain Now Resource Hub: https://supplychainnow.com/resource-hub/Schneider Electric: https://www.se.com/us/en/Christine on LinkedIn: https://www.linkedin.com/in/christine-bush-22568818/Dr. Muddassir Ahmed on LinkedIn: https://www.linkedin.com/in/muddassirism/Upcoming Live Programming: https://supplychainnow.com/upcoming-live-programming/Supply Chain Now Resource Hub: https://supplychainnow.com/resource-hub/Learn more about our hosts: https://supplychainnow.com/aboutLearn more about Supply Chain Now: https://supplychainnow.comWatch and listen to more Supply Chain Now episodes here: https://supplychainnow.com/program/supply-chain-now Subscribe to Supply Chain Now on your favorite platform: https://supplychainnow.com/join Work with us! Download Supply Chain Now's NEW Media Kit: https://bit.ly/3XH6OVkLearn more about Blue Yonder Cognitive Solutions: http://blueyonder.com/cognitiveWEBINAR- Demand Volatility Isn't a Forecasting Problem: How One FMCG Distributor Released Capacity Without Capital: https://bit.ly/4r94YM6WEBINAR- You Can't Manage What You Can't See: Using Visibility, KPIs, and AI to Optimize Logistics Operations: https://bit.ly/4ql6iemWEBINAR- The Four Eras of Freight Audit: How we got here, and why the Control Era (finally) changes everything: https://bit.ly/4yVXPBxWEBINAR- The Real ROI of AI Analytics in the Supply Chain: Better Answers When Everyone Starts Asking: https://bit.ly/3TpCX6uWEBINAR- How to Stop Losing Margin After a Package Ships: https://bit.ly/4An41UxWEBINAR- Rethinking the Supply Chain Management Classroom: https://bit.ly/4xJIkM2This episode was hosted by Scott Luton and Dr. Muddassir Ahmed, and produced by Trisha Cordes, Joshua Miranda, and Amanda Luton. For additional information, please visit our dedicated episode page at: https://supplychainnow.com/thebuzz-smarter-automation-reshaping-manufacturing-1639 The content in this episode, including all audio, videos, visuals, and graphics, is the property of Supply Chain Now and is protected by copyright law. Unauthorized use, reproduction, distribution, modification, or re-uploading of this content in any form is strictly prohibited without explicit written permission from Supply Chain Now.For licensing inquiries or permissions, please contact us at production@supplychainnow.com© 2026 Supply Chain Now. All rights reserved. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sai Vemprala, CTO of General Robotics, talks about why robotics still faces a major gap between research and real-world deployment—and how the company's Grid platform aims to close it. Their conversation explores robot-agnostic intelligence, simulation, reusable skills, deployment architecture, enterprise data ownership, and the growing role of agentic AI. Rather than relying on one universal “robot brain,” Grid combines specialized capabilities—including perception, grasp prediction, and motion planning—into workflows that can be adapted to different robots and tasks. Sai also explains how simulation and deployment data can work together in a feedback loop, and how agents could help build, evaluate, and deploy robotic skills. ### Register now for RoboBusiness 2026: https://cvent.me/w0eRN9?RefId=podcast Listen to the podcast for a special discount code.

Earlier this month, world model company Runway introduced GWM Worlds 2, a research preview that “turns high-fidelity video and audio generation into real-time interactive simulation.” Runway calls this an “autoregressive diffusion” model; with autoregressive describing how it generates over time.One new feature in particular caught our eye: WorldPrompt, a proposed input format for specifying a generated world and the actions within it. It allows you to fix some aspects of a simulated environment — including the first frame — and then create a series of timestamped events. The events, or actions, can even be prompted in real-time.To understand the implications of WorldPrompt, we spoke to Kamil Sindi, Runway's CTO, and Robin Kahlow, its Principal Research Scientist for generative video and multimodal AI. We also have exclusive comments from Anastasis Germanidis, co-founder & co-CEO of Runway, courtesy of a podcast swyx and Vibhu did with him.Who's building real-time interactive world models?First, some context about world models that can generate interactive video and audio in real-time.Runway is reportedly valued at $5.3 billion, based on its most recent fund raise of $315 million in February. Its first release, GWM Worlds, was launched last December.Alongside Runway, there are several other notable projects in this domain: Google DeepMind's Genie 3 (which also generates at 720p and 24 fps), Odyssey-2 Pro, and World Labs' RTFM (Real-Time Frame Model). We've summarized their differences in the following table:Given the complexity and massive latency demands of real-time video and audio generation (which we'll get into below), all of the projects listed above have limitations. For instance, Google notes that Genie 3 “can currently support a few minutes of continuous interaction, rather than extended hours.”But as our interviews with Runway show, real progress is being made.The central idea of WorldPromptWorldPrompt, a new feature in GWM Worlds 2, helps differentiate Runway from its competition. You can think of it as a control layer for characters, cameras and the environment. As Kahlow put it, it's a way to “control all the different subjects in the world” — similar to a computer game.“Like, if there's an NPC [Non-Player Character] somewhere, the NPC might walk up to you and say something. So you could achieve the same thing with this kind of model, where you can have very detailed control over everything in the scene.”As the name suggests, WorldPrompt is a prompting mechanism — not a programming language. So, unlike virtual world games like Minecraft or Roblox, GWM Worlds 2 doesn't offer scripting capabilities or the ability to control state. But there's a power to that, as Sindi pointed out.“You can create promptable worlds on-demand with video and audio in sync, across all these different domains and environments. That's not a distant-future hypothetical thing,” he said.But there are also limitations to prompting a world model. We asked how reliably the model would follow an instruction to create, for example, a law of gravity or a certain ability in a character?“Yeah, so it's a research preview,” Kahlow replied. “So it's not perfect, of course, and there are still flaws. It really depends on how difficult the action is. I would say movement works quite reliably.”Sindi added that more training plus scaling the data and models is resulting in “better following.”How a video model becomes a real-time runtimeDespite the current limitations of GWM Worlds 2 — especially if you compare it to pre-designed and scriptable worlds like Minecraft or Roblox — the true promise of world models like Runway is that they'll eventually lead to fully self-generated, real-time games and experiences. Which is an extremely hard engineering problem, as Kahlow reminded us.“There are two challenges. One is making the model not generate a whole clip at once. So instead, you want it to generate frame by frame while you're looking at it. And the other challenge is actually making the generation fast, so you can play it in real time.”GWM Worlds 2 offers real-time interactive worlds streamed in continuous 720p video at 24 frames per second (fps) and audio at 48,000 Hz.Runway achieved this firstly by taking its foundational audio-video generation model and fine-tuning it to the new WorldPrompt format, so the model can follow that. It then post-trains the model to generate autoregressively.“And after that, we work on making it real-time through distillation methods,” Kahlow added.Co-CEO Anastasis Germanidis offered more technical details in our podcast with him. He told us that the process starts from “bidirectional diffusion that basically generates an entire video at once and [makes] it autoregressive.” This allows the model to “generate one frame or a few frames at a time.”Germanidis described two possible forms of distillation in order to make it real-time: distilling a larger model into a smaller one or reducing its diffusion steps. As a general example, he said a model might go from around 50 denoising steps to four, with some quality loss but potentially comparable results.The challenges of real-time generationGermanidis admitted that there were issues with how it generates real-time interactive video.“The biggest challenge with autoregressive models is error accumulation,” he said. “You're feeding generated frames back into the model to generate the next frames, and if there are any small errors, they accumulate over time.”Sindi told us there are also challenges dealing with “infinite generations” of content.“There's all these challenges around what context to keep, what to discard that's not important. And so there's all these optimizations we have to think about, so we're not blowing up our GPU memory.”Another current limitation is long-term memory. “The model does not have perfect memory,” Kahlow said. “That's still an open research problem.”Causality and correctnessWhile performance is the primary challenge for Runway at this time, its world model also has to produce plausible consequences when a user takes different actions.Germanidis used the example of simulating football; he pointed out that online video training data contains more successful goals than failed goal attempts, so a video model might render the first more convincingly.“If I take this action versus this action, you want it to generate equally realistic outcomes,” he told us. “That's, I think, the big gap between video models and world models: that idea of counterfactual generation.”Sindi told us that evaluation gets harder the more complex interactions get.“If you have this multi-prompt, multi-character, multi-scene [environment], how do you really understand what was causal and what was not?”To try and solve that, Runway has some automated verifiable tests. But since GWM Worlds 2 is a research preview, Kahlow noted that doing tests yourself is also advisable — “trying out your model to see what doesn't work is really important.”More than gaming — there are agent use cases tooGaming is the obvious use case for what Runway is building, but there are others. Kahlow mentioned robotics — for example using a simulated environment to test how a robot works.Another, more intriguing, use case is to use it to test agents at scale.“Having thousands of simulated environments is much less challenging if you have a suitable model like GWM Worlds,” Kahlow said.But how does an agent know what's changed in the world — is there a structured state that it can read, or is it just the generated video and audio that it's consuming and understanding?“So there's no structured state here,” Kahlow replied. “It's just observing the same thing you might observe in real life, just [in this case] from cameras.”Sindi noted that GWM Worlds can also be used for “synthetic data generation for agents.”Finally, Germanidis suggested there's potential to use these world models alongside reasoning models.“You're maybe using some reasoning [for] planning of the scene, and then you're passing it into the diffusion head that's actually generating the pixels.”Anastasis Germanidis* LinkedIn: https://www.linkedin.com/in/agermanidis/* X: https://x.com/agermanidisTimestamps00:00:00 Introduction00:05:17 Runway's Origins and the Bet on Generative Video00:12:23 The Stable Diffusion Story00:18:44 Gen-2, Controllability, and the Weekend Hack00:23:02 From Video Generation to World Models00:28:03 Learning From the World, Not Just Language00:35:04 Sora, Runway's Existential Crisis, and Gen-300:39:39 Why Real-Time Video Is Inevitable00:43:06 Interface World Models: Software Without Code00:50:25 The Fully Neural Operating System00:55:11 World Models for Robotics01:02:32 Robot Policies and World Action Models01:07:47 The Lucid Dream Test01:11:41 Video Agents and Omni Models01:23:12 Artists, AI, and Creative Workflows01:27:14 Physical AI and the Future of World ModelsTranscriptIntroduction: Runway, Creative AI, and the Early ThesisSwyx [00:00:00]: Okay, we're here with, Anastassios from Runway, with, me and Vibhu in the studio. Welcome.Anastasis [00:00:08]: Good to be here.Swyx [00:00:09]: Congrats on all your success and progress with Runway. You're opening offices all over the world. Did you envision this when you first started out?Anastasis [00:00:16]: Not quite. I think even when we started, we had this idea that, It was more a matter of when, not if, we were seeing the early generative models of 2016, 2017, and just extrapolating, assuming, we resolution, quality increases predictably over time. There's gonna be a point where most of content will be generated, and that was maybe the initial thesis of Runway was we will need, as a result of those generative models, rethink how creative tools are made. and as we built out the research behind, our generative models, it then became clear that they were useful far beyond that as well.Anastasis' Background: Art, Simulation, and Machine LearningSwyx [00:00:57]: And it is more obvious now with, like, the real-world stuff and the world models that we'll talk about later. I'm just kinda curious how you go from a background in, like, Zocdoc and, computer vision into Runway. Like, take us back to that early conversations with Chris and, whoever else is on your founding team.Anastasis [00:01:14]: I was always splitting through those two worlds. One was the I had my own art practice. I was making a lot of interactive art, I think for a long time. and then on the other side, I was working in startups, and I was working as a ML engineer, as a backend engineer at different companies. I've always been interested in, coding and computation, and especially interested in simulation and brought it back into my early artwork as well. And at the same time, I was interested inSwyx [00:01:43]: The personal site has a few, right?Anastasis [00:01:44]: Yeah.Swyx [00:01:45]: Is there one that we should pull up? Just in case there's something that's like. I just like to go down memory lane.Anastasis [00:01:50]: Yeah.Swyx [00:01:50]: Okay, what is this?Anastasis [00:01:51]: So this was, a project that I made, I think back in 2015, where I built this software that would give, voice instructions to people in a gallery space. So it would coordinate interactions between people. And so it will first give you an identity, like you're an, architect, you're 30 years old, and, you like sports. and then it would match you with another person, and you have this completely generated interaction. language models were not quite there at the time, and so it was it was a mix of some templates and some, like, some Markov chain-generated text, and it would just completely simulate these small talk conversations between, everyone in the gallery space. so was always very fascinated on the one hand with, generative models and, like, the early machine learning work that was being at that time. But at the same time, there was this separate thread of simulation and what it means. Like, what can we learn about humans by creating those very simple models of their interactions and their behavior?Early Generative Art: pix2pix, GANs, and Uncanny ValleyVibhu [00:02:56]: Did you generate the prompts or, the 30-year-old, whatever? Was it you generating them? How'd you, how'd youAnastasis [00:03:03]: Exactly. So the program would just generate- those, from. Yeah, a lot of it would be Mad Libs style of justVibhu [00:03:10]: YesAnastasis [00:03:10]: You have lists of different professions, lists of different,Vibhu [00:03:14]: HobbiesAnastasis [00:03:15]: Personality types, lists of different, ages, things like that. And then it would just combine those things together. And then maybe the next project we go is, Uncanny Valley, Uncanny Road, which wasSwyx [00:03:27]: GansAnastasis [00:03:27]: One of the first projects that, we built with, one of my two co-founders, Chris. This was taking, pix2pixHD, which was one of the early image-to-image models that NVIDIA released back in 2016 or 2017. and it was a model that would take a semantic map of a scene and then generate a photorealistic, let's call it, output. very early days, so it was not very high-fidelity outputs, but it w I think was the first image-generation model that could generate at 1K resolution. And it was all trained on self-driving datasets. So the semantic categories it would support were only, things you would encounter on the road. So it would be pedestrians, traffic signs,Vibhu [00:04:16]: StoplightsAnastasis [00:04:17]: Bikes, stoplights. And so that was one of our first indications that we built this and people were making all this, like, very surreal imagery of, yeah, a million plus a million pedestrians or a million traffic signs or, like, gigantic humans. And it was a indication that you could take a model that was trained on this very boring dataset, essentially, of, like, not that many interesting things happen when you're on the road, and then you can repurpose it and go very out of distribution and make something that was artistically compelling. And that was It's a summary of the thesis of Runway in some ways, that you can take the same generative models, and if you look at them from another direction, if you build interesting tools around them and you give them to artists, they're gonna do things that you don't expect.Vibhu [00:05:02]: Very cool. I like the, UX of it. You're just given an empty canvas, try whatever, do whatever. And then the other one, like, you see everyone with wired headphones? Like, that's, that's a sign that it's, it's veryAnastasis [00:05:16]: The AppleVibhu [00:05:17]: YeahAnastasis [00:05:17]: Apple, your version.Vibhu [00:05:17]: Original ads. Yeah. Take us to today. You've been doing this for seven years at Runway. How have we got to this? Like, how do we go from driving simulator data to all this? And you cover the whole stack of generative media?From Creative Tools to a Research LabAnastasis [00:05:33]: Interestingly, we're almost back in, we're, we're full circle. We're, we're now applying our models and beyond creative tools into real-world scenarios. But it was a, it was a long journey. It was very early on we realized the first version of Runway was a way to easily use the, all the open source model of the day, things like pix2pix to. and give them to artists. That was the initial idea, is those models are too difficult to use if you're not a machine learning engineer. Like, what happens when you give them to artists? Very quickly, we realized we needed to build a research org, inside of Runway, and that happened maybe on year one. And, a lot of the mandate there was. The image-generation models of the time, the video generation models of the time, or there were barely any video generations all the time, but they were not quite there where they could be productionized and brought into tools that would be part of creative workflows. so we need to push the frontier of the research. And so maybe the first four years of Runway, research was almost happening on the background until there was a moment in 2022, with latent diffusion, with, DALL-E 2, where, there was that step function change, and you guys maybe remember around the time.Swyx [00:06:49]: I started in this space because of latent diffusion and Stable Diffusion.Anastasis [00:06:54]: Yeah.Swyx [00:06:54]: Because I was like, “Wow, this is not only, like, feasible, it is doable on consumer hardware.”Anastasis [00:07:01]: Exactly, yeah.Vibhu [00:07:01]: I think the delta is also huge. Like, I learned pix2pix. Like, this was intro to ML, the TensorFlow, like, Jupyter, Google Colab notebooks were like this, and then you have a sudden step function change, with diffusion and whatnot. Any other ones since that. Like, there were clear examples of what early diffusion were to get to here. Any other changes in key technology research?Green Screen, Rotoscoping, and Early RunwayAnastasis [00:07:26]: Between, 2018 when we started and 2022?Vibhu [00:07:29]: Yeah.Anastasis [00:07:29]: So one of the early work that we did in Runway was solving segmentation, image and video segmentation. It was a very important problem because most VFX involves essentially separatingSwyx [00:07:42]: RotoscopeAnastasis [00:07:42]: Subjects. Yeah, rotoscoping. Extremely manual process. Nobody enjoys doing that. and so a lot of the early days of Runway was building this tool. It was called Green Screen, and it was for a long time the main thing that people were using Runway for. It ended up being used in, Everything Everywhere All at Once and a bunch of other high-visibility films and series. But that was essentially, Runway for a long time was a post-production tool until latent diffusion and generat- Gen-1, Gen-2, happened.Swyx [00:08:12]: Cool. let's, let's go past that moment. You've come a long way. Then you started releasing your own models. Maybe describe that journey as well.Scaling Video Models and the Bet on 1,000 A100sAnastasis [00:08:20]: Yeah, so we go to the other point, yeah, in mid-2022 when it became clear that we're doing research at a fairly small scale of compute, and it became clear that, like, scaling laws would apply to, image and video gen in the same way that we're applying to language generation. So we made a big bet, and I think at so at the time, we signed this deal to build a cluster of a thousand A100s, which at the time we were a Series B startup. That was a almost, slightly irrational decision maybe, but we really believed that if we trained a video model at a large scale, we would get, like, a great model at the end. And at the time, the goal or we set the goal around fall of 2022 of what is, what does the latent diffusion, Stable Diffusion moment look like for video? And at the time, the best model of the time was called CogVideo. it was one of the early video models. It was very 256 by 256 resolution, very not very high quality. and so we decided we're gonna build out this cluster, and we're gonna just invest in, like, in building out our own video model. it became clear as we're training Gen-1 that it was difficult to get to fully. we wanted to build text-to-video, but it became clear to us that an easier starting point would be to start from video to video. Because when you have a stronger conditioning, it's, it's an easier problem to restylize an existing video versus generate the video from scratch. And so we released Gen-1 first back in, it was January of, 2023. Yeah.Vibhu [00:10:04]: It's just a fun visual podcast, honestly. Like, if we can see February 2023, what was the state of stuff?Gen-1: Video-to-Video and Depth ConditioningAnastasis [00:10:10]: It's so interesting ‘cause at the time when you see those results, you think this is so incredible, and this is like, it's almost like image generation or video generation is solved. And then you look back a few years after, and it's like, it's It's just like you get used to the results very quickly, with those models. But at the time when we started seeing those results, it was, it felt quite incredible, and the level of, like, quality that you could get. And, so the Gen-1 was a depth-conditioned video model, so it would turn. it would take a input video, it would predict. it would it would first convert it into the depth map, and then we would generate, pixels with a latent diffusion model.Swyx [00:11:01]: Yeah, very effective.Vibhu [00:11:02]: Yeah. I didn't realize how distracting the blog post would be. Sorry.Anastasis [00:11:05]: Yeah, but, one of my favorite examples of on those, on Gen-1 was both, if you go up to mode three or mode two, there was this storyboard use case where people would makeVibhu [00:11:18]: OohAnastasis [00:11:18]: WouldVibhu [00:11:20]: You can mess around with theAnastasis [00:11:20]: Make a city out of books or out of boxes, and then they would shoot a video with their phone and then translate it into a photo-photorealistic output. There was all these ways in which those models were starting to be used for storyboarding and also for really. and then if you go to mode four, like, of taking untextured 3D scenes and then turning them into photorealistic output. So we saw a lot of use cases early on where people that were familiar, were power VFX editors would just take a blender, render, and then they would get translated in with Gen-1 or create a scene in Unity and then take a capture a video of it and then translate into, restylize it. So I still think video to video is powerful. I think we had a recent video-to-video model as well, and it's one of my favorite ways of using those models is essentially using them to use ground truth video as, like, the initial inspiration and then translate into different styles or different outputs.Stable Diffusion, Stability AI, and Open SourceSwyx [00:12:23]: But I think we're gonna go into, like, the rest of Runway and catch people up to speed today. I did wanna cover the, let's call it the Stable Diffusion controversy, or, what happened with Stability AI, whatever. I think there was a two sides of the story. I think there's part of that is a normal thing of, like, people, join and leave companies, but what is the, retrospective now that, there's been some years behind it?Anastasis [00:12:49]: Yeah, it's a very, it's a very long story to go into. I think it wouldSwyx [00:12:53]: Which I remember you wrote a really long post about.Anastasis [00:12:56]: We would probably cover the whole hour to go into it in more detail. But, essentially, there was the latent diffusion paper that came in, I think that was at the end of, 2021. And then Patrick Esser, who was one of the researchers behind, latent diffusion, and he worked at Runway at the time, he built latent diffusion in collaboration with Robin Rumbach and a few other folks back, in the in, CompVis, which was, a labSwyx [00:13:26]: Like a research group, yeah.Anastasis [00:13:27]: And, after releasing the early latent diffusion model, they, essentially they were. the goal was to keep working on versions of the model, scale it up, incorporate new data, incorporate new tasks. And Stable Diffusion was the same model, but trained on more compute, and then with a few more tricks, like a classifier-free guidance paper came at some point, I think in the early 2022. And thatSwyx [00:13:52]: Which, like, was a big prompting improvement.Anastasis [00:13:55]: Yeah.Swyx [00:13:55]:?Anastasis [00:13:56]: That improved results. it was trained on better data, so like, the esthetic subset of LAION, but it was effectively, the same underlying architecture. And there was that big training run, that, happened on Stability's cluster. Stability financed that run. And looking back at that story, I think it was the work to build and train that model was done. It was a, it was a research project. It was done as part of, like, continuation of the latent diffusion work. It then, I think it the model became very successful, and it, I think there were the. And I think as a result of its success, other companies tried to, figure out the commercialization path for it. But for us, it was very important that we try to, we make sure that we. It was meant to be an open source research project, and so the we decided that we should continue releasing versions of it, since that was the original goal of Stable Diffusion, and that led to releasing Stable Diffusion 1.5. There was maybe a day of, a bit of, miscommunication there, but ultimately that was resolved very quickly within hours. so yeah, there wasSwyx [00:15:12]: OkayAnastasis [00:15:12]: Not a niceSwyx [00:15:13]: I just wanted to. you have toAnastasis [00:15:15]: Yeah.Swyx [00:15:15]: You're one of the main players in that journey, and so it's nice to hear from the source of, like, what happened. Yeah.Anastasis [00:15:22]: Yeah. I think it's all, it's all in the past nowSwyx [00:15:26]: YeahAnastasis [00:15:26]: I would say. and, like, both companies, Stability took its own path, Runway took its own path.Swyx [00:15:32]: Yeah. There's still. James Cameron is backing the new Stability, whatever they're doing with the Hollywood studios.Anastasis [00:15:38]: Right.Swyx [00:15:38]: I don't know what they are doing. I think one thing that impresses me, and I'm happy to move on, is that back in the that time, let's say, like 2021, 2022, there was this community of people that you were involved in that was researching all this stuff, right? And, like, from everyone I talked to who was active then, it seemed like it was fairly obvious that somebody would do the hero training run that would produce Stable Diffusion. So, like, I guess the question is, like, you had the you were you had made investments. You were you had the foresight. Is it accurate to say, like, that is reflective of, like, what people were thinking at the time? Or was it still very much like, “Well, we'll use it as, like, a post-production tool or something. I don't know.”? Like, where in the sentiment were we that maybe you can think back to, like, what the community was like back then?The Early Creative AI CommunityAnastasis [00:16:28]: I reminisce and I think very fondly those early years, from like 2018 to 2022, because it was a very small community that, as you said, were very convinced that this was gonna be a big thing. And at the time, anyone who. Because it was such a small circle and, everyone who would, like, be part of that circle and, like, make projects with it would, immediately get, go viral. so likeSwyx [00:16:55]: And you didn't know who they are, right? They're just some name on a, GitHub or Hugging Face somewhere.Anastasis [00:16:59]: Exactly, yeah. So I remember one of the first big viral moments of creative AI was, there was the neural style transfer paperSwyx [00:17:09]: HuhAnastasis [00:17:09]: ThatSwyx [00:17:10]: Something dreaming?Anastasis [00:17:11]: I think it was called neural style transfer.Swyx [00:17:14]: Okay.Anastasis [00:17:14]: There was also Deep Dream, the puppy sliceSwyx [00:17:16]: YesAnastasis [00:17:16]: Which was, also really cool. but, yeah, there was this project that, Jim Kogan, who was an early advisor of Runway and one of those,Swyx [00:17:25]: Marketing guysAnastasis [00:17:26]: Big, creative AI, folks, he literally just, like, showed a video of himself taking the New York Subway and going over the Williamsburg Bridge and then stylized it with, I think in the style of Van Gogh or, like, one, painter. And that was. Like, at the time, that was, like, so cool and it went viral and it was completely revelation to people that you could do this with generative models. And that was only, it was less than. It was maybe 10 years ago. So just, like, as an indication of, like, how quickly things have gone.Vibhu [00:18:02]: It's pretty crazy. Like, even since then, you've got people at every level of the stack. You've got devs, creatives, artists, hobbyists. You've got everyone using it. And for people that tried stuff early, they'll remember how hard it was to use regular diffusion, right? Like, nowadays, you can use your favorite ChatGPT image gen or whatever, give a sentence, get a beautiful output. But diffusion was like, the whole ultra HD, 4K, high resolution. Like, prompting these things was very different. anything you learned on the tooling side, like from the offerings you guys have now, so like creatives, devs, you really took the. Research and brought it to everyone to use. anything interesting there to share?From Gen-2 to Controllable Video GenerationAnastasis [00:18:44]: We had to build the entire model serving infrastructure for video diffusion models. There was nothing else, already, like, because we had Gen-2 was the first text-to-video model, I think, out in the market. So many things that we learn over time. I think the I think the biggest one was, like, we. it was very clear early on that text-to-video was not gonna be the answer. Like, you. Like, people wanted a lot more control than that, and so we invested in, like, control building on top of those models very quickly. how do you use the camera trajectory as control? How do you use an initial input frame as control? So that was a very early learning for us. With text-to-video was, like Gen-2 was an amazing, step function improvement in the quality of video models, but it was used much more in an exploratory way because there was nothing to ground it to. There was no reference that you could bring into it. There was no. You couldn't really control the camera motion. You couldn't control the object motion. And so the first year, in 2023, was really all about what are all the interesting ways in which we can condition those models? And it was a lot of just post-training rounds on top of the base model to figure out, like, what, -- how do people wanna control them? And so there was, like, this quick succession of the we it was called Motion Brush, which was you could, like, you could draw arrows and dictate where things should move in the scene.Vibhu [00:20:09]: That's so cool.Anastasis [00:20:09]: There was camera control that was you could just describe, like, how you want the camera to move in the scene. And because we work with filmmakers from the most of the history of Runway, we immediately got this feedback and got this, decided that this was worth investing in. And so control ability became a big theme, I think, very early on as we were building, as we were building those models. Something fun that I haven't really talked about too much was just how Gen-2 came to be out of Gen-1. So it was a bit strange because we announced Gen-2 two months after Gen-1 andHow Gen-2 Came From a Weekend HackVibhu [00:20:43]: We're accelerating.Anastasis [00:20:44]: It was before Gen-1 was even generally available. But Gen-1 was a depth-to-video model, so it would take a depth map and it would convert it into RGB. and we couldn't get, text or image-to-video to work directly, and that's why we started from depth to video. but, and we had discussions of like, okay, we need to spend the next six months investing in text-to-video, maybe increasing the compute scale or the model scale, like train a larger model. And I had this weekend project idea, which was, what if I take a model that, starts from text input and converts to depth maps and then use Gen-1 to convert the depth maps Into RGB?Vibhu [00:21:29]: It would probably work.Anastasis [00:21:30]: And so Gen-2 was that.Vibhu [00:21:32]: Oh. The hackathon pipeline.Swyx [00:21:35]: The weekend hackathon pipeline.Anastasis [00:21:36]: Yeah.Vibhu [00:21:37]: But it looks good.Anastasis [00:21:38]: And it worked pretty well. there were if you, with the knowledge that it has this, like, two-stage pipeline, you can tell in some cases that the structure of the video looks a bit off because you had to generate the depth first before you go into the output video. But it worked and it allowed us to bring this to our, to users very quickly. But it's now it's interesting because, like, people are coming back to this almost two-stage approach. Like, if you look at the Reve text-to-image model that came a few months ago, it had this planner model that would generate bounding boxes before it fed that into the diffusion transformer.Swyx [00:22:19]: Yeah, Ideogram also the same day.Anastasis [00:22:22]: Yeah.Swyx [00:22:22]: I remember that was very strange that both of them came out the same day with the same exact innovation.Anastasis [00:22:26]: It's a small community, I think.Swyx [00:22:28]: I'm like, this is like, this is completely coincidental, right?Anastasis [00:22:32]: People talk. So yeah, there's, there's definitely something into this approach. And, now, like every single like, video generation model in production uses a complex prompt completion pipeline under the hood. I think that's no secret that there is. ThatSwyx [00:22:48]: Humans are terrible at prompting.Prompt Rewriting, Camera Control, and the Seed of World ModelsVibhu [00:22:51]: I think across the board.Anastasis [00:22:51]: Yes.Vibhu [00:22:52]: But yeah, I think like the original Sora one blog post even told you that what happens after your input is rewriting your prompt. It's much more descriptive about what you would want.Anastasis [00:23:02]: Exactly. I, And there was the DALL-E 3 paper beforehand that, was the first public, description of the fact that synthetic captions and really detailed captions work really well. And then Sora built on that. Yeah, so it was 2023. We were releasing all these updates to Gen-2, like the camera control, Motion Brush. And there was something very interesting about camera control because it was the first time that you felt that instead of, like, you were creating video, you were creating a short video, you were navigating inside the world. And I think camera control was maybe the seed of some of the ideas that we had around world models and really opening up that research direction. We realized, it was this era and this series of, Gen-1 and Gen-2 models really proved to ourselves, yeah, this is theSwyx [00:23:56]: Cool.Anastasis [00:23:57]: So this is not the original camera control. This was the updated camera control on top of Gen-3. But yeah, I think it made those models usable to filmmakers, I would say. The so camera control was very popular. And so we realized, there is one way of seeing those models, which is, you're just as content creation machines, and there is the other way, which is you're. As you're predicting video in order to predict video well, you need to simulate the world in an increasing and increasing capacity. And if scaling laws apply on video, just like they apply on language models, then as we scale the compute that we put into those models, then they're gonna be able to simulate physics, they're gonna be able to simulate human actions and dynamics increasingly well and predictably well. That was the thesis about around our efforts on world models, and we spin up this research group to just focus on the world models and how do we turn the video generation models that we're building into something broader and something that would be useful beyond, also content creation as well.Swyx [00:25:04]: And that was roughly when?Anastasis [00:25:06]: Yeah, so that was inSwyx [00:25:06]: OhAnastasis [00:25:07]: In late 2023.Vibhu [00:25:08]: Interesting. like, I think, a lot of people have been saying a lot of video gen model companies have all pivoted to world models these days, but like, 2023, you're posting it. oneWorld Models: From Video Generation to SimulationSwyx [00:25:21]: It's, it's debatable whether it's a pivot.Vibhu [00:25:23]: Yeah.Swyx [00:25:23]: Like, arguablyVibhu [00:25:24]: YeahSwyx [00:25:24]: That's what you always had to do anyway, right?Anastasis [00:25:26]: It's in a way an expansionVibhu [00:25:28]: YeahAnastasis [00:25:28]: Of the applicationsVibhu [00:25:29]: YeahAnastasis [00:25:29]: Of the models as they become more capable.Vibhu [00:25:31]: The early signs, it seems like the original models you guy had, guys had, people would say it's very not bitter lesson pilled, right? You're adding, rewriting prompts, you're having all these one-off things, but that's just the state of the tech as it was versus the future of as you said, you can scale it up as, we can scale up to world models.Anastasis [00:25:50]: Yeah. So it just became. And if you looked at the outputs of Gen-2Vibhu [00:25:56]: YeahAnastasis [00:25:56]: It was not. I think it was not obvious to people that this would scale to become a general simulator of the world. Like, you had very limited movement, you had, very low fidelity or low resolution, like obvious mistakes in human anatomy, like all kinds of limitations. But it was just, the idea was that's just GPT-two, and GPT-two, it can barely generate, like, coherent sentences. Similar, Gen-2 can barely create coherent video, but if you scale it up, you're gonna. There is no reason why it shouldn't work in a way. It's, And I think that was. That's, that's always the mindset of Runway is like this extrapolation of, like, if, like, even when we started in 2018 and you looked at the results of the day, you need to look more at the trend of, like, where we were in 2018 versus when we were at the, when the first GAN came out in twenty, four 2014 or twenty, fifteen. And, you started from, like, thirty-two by thirty-two images of faces, and then by the time in 2018, you could generate, street images at the 1K resolution. And it was the same with world models, very early signs of something much bigger.Swyx [00:27:08]: Yeah. I was gonna say, like, it's diffusing into focus. Like, if you look at our visible output from year to year, it looks like a diffusion process itself.Anastasis [00:27:17]: Yeah.Vibhu [00:27:17]: Especially watching the early, like, old blog posts, you can really see the choppiness, the details.Anastasis [00:27:24]: Yeah. Like human civilization starting from random noise and thenVibhu [00:27:27]: YeahAnastasis [00:27:27]: Denoising intoSwyx [00:27:28]: Yeah. Just run it a hundred years.Anastasis [00:27:30]: Civilization.Swyx [00:27:30]: Yeah.Vibhu [00:27:31]: That's how you're on track, you're still noising, right?Swyx [00:27:34]: Yeah. I like the way that you guys phrased it when you, announced it in June, which is, oh, that you had a video essay. “The human mind is no longer the center of AI. Our world is.” Right? Which is, let's, let's call it the past five years of LLM-based AI is very much like trying to emulate human preferences and human speech. But now that's, like, mostly solved. I think that's, like, some of the context of your essay, which you also wrote around the time. And now it's like the focus is on modeling the world accurately.Scaling Laws for Video and Why Predicting Pixels MattersAnastasis [00:28:03]: Exactly, yeah. So the way we see it is, there is that, initial mission statement of DeepMind, which is, solve intelligence and then use it to solve everything else. But I think it's starting from everything else, could be valuable of, like, starting from. there is just so much complexity, and detail in the world that in order to. That it's, it's hard to learn directly from just human descriptions of the world. Like, we're assuming that, like, language models learn from everything that humans have written about the world, like our own understanding as of, the twenty twenties. And there is just so much that we don't know and so much that's not captured by existing text, about both the low level dynamics of the world, like we're not describing in detail. if I tell you to describe, like, how do you tie your shoes, that's a very difficult thing to describe in words, but it's very obvious thing to demonstrate. And so I think there's been. And there's, more of X paradox, like we're constantly underestimating all the complexity that goes into very, like, things that we do subconsciously as humans, and we don't even necessarily always have the words to describe them. And so in my mind, the simulating the world and simulating, physics, simulating the dynamics of the world has always been underestimated, compared to, we place too much emphasis on the things that are easy to talk about. but there is just all this complexity and richness of the world that if we just try and train directly on that observational data instead of training on how people describe the world, we would learn something new that we wouldn't otherwise know.Swyx [00:29:54]: You think that the present architectural paradigm is fine? You don't need, like, another layer, like JEPA, like another famous, New York AI leader would say?Anastasis [00:30:05]: We're a very pragmatic research lab. If, we have evidence that an approach works better than the approach that we're taking, then we have no qualms to taking it. We just have seen no indication that video prediction itself doesn't scale. And even if you look now, not just our work, but the work of others, you're seeing in robotics some of the most promising work, starts from video prediction models, and then you adapt them to also the action models, for example. so there is very little evidence that you need something else and that your time is better spent on a novel architectural change compared to improving data and improving the, and scaling the current approach. And so, We don't have any indication that. the, there is that counterargument that I think there was a tweet by Yann LeCun a few days ago that, understanding the dynamics of the world is very different than, generating, cute videos.Swyx [00:31:05]: And your answer is no, they're the same thing.Anastasis [00:31:07]: Yeah, they're the same thing.Swyx [00:31:08]: My cat videos are the same as understanding physics.Anastasis [00:31:11]: Right, because if you wanna generate. video models can cheat and, like, they could you could give, like, successive dif shots of the scene in a way that doesn't require you to simulate difficult physics. There is like, all these different ways in which you can hide the deficiencies of the model, and it's important not to be too tricked by the performance of the current video models. It's easy to, cherry-pick examples and think that video models are further advanced than they are. So there is a lot more work that we need to do to improve those models. But in my mind, very similar to language, and, like, we've. you go from barely coherent sentences to something that, could hold a conversation with a human to something that could can operate autonomously for a day and, like, create entire code bases. And the main difference, there is some architecture improvements along the way, but the main thing is scale. And so it's the same bet for video, and we have no indications that this is saturating. Like, we have benchmarks that we use for measuring the physics of those models, and we see those predictably improve as we scale those models. So there is. If you want to Google up, Physics-IQ, is one of those benchmarks that measures how well does the model perform at solid mechanics or fluid dynamics or optics.Vibhu [00:32:32]: I'm curious if you've seen any emergence, any scaling law around this.Swyx [00:32:37]: Yeah, he's saying there is a scaling law, right?Anastasis [00:32:39]: Exactly.Vibhu [00:32:40]: Yeah,Anastasis [00:32:40]: So the way those models, those benchmarks work is you. the researchers have gone and, like, captured, a few videos that are representative of different physical phenomena, and then you can take the first frame and then pass it through an image-to-video model and then generate a rollout that shows what should happen next. So you have, a ball hanging from the ceiling, and then you use that as input, and then you the model predicts how the ball should fall on the ground. and this measures. we have an intuitive understanding of physics. I know, you can imagine what will happen next if I drop this bottle. So it's measuring that same intuitive physics understanding of those models, and we've measured that at different model scales, and we see, and compute scales, and we see that the score on physics IQ predictably improves. There's other, tricks and techniques that you can make to improve the score even further, but even scale alone helps, in the model learning better physics.Swyx [00:33:40]: My main sympathy with Yann LeCun is the, Plato's cave allegory, right? Like, you're, you're, like, learning on the output of a thing, not the internal process of a thing, and it's very noisy. And, if only you could observe the internals of a thing. It's hard to observe the internals of a human mind, but you can very much observe, or at least we have a whole branch of science and physics that we're ignoring on how to model Physics and movement and, gravity and, other interactions. and we're just, like, throwing away all of that and just saying just scale data, which is very much the lesson of unsupervised learning, but it feels wrong. that's the main idea.Anastasis [00:34:21]: I think the history of machine learning is, at large, it feels wrong.Swyx [00:34:25]: Yeah. It's a bitter lesson, right? Yeah. It's, it's, it's the simple answer to that.Vibhu [00:34:29]: I guess, how much can you scale? So, like, even on, let's say, the video generation side, like, there's one side of video understanding. Video generation, are we still gonna have tools where it's like, I wanna generate two hours, twenty hours? there's a infra way to do it in batches and stitch it together, but, like, do we just keep scaling? Do we just continue long generation consistency, all that at scale? And, like, tying it into where we're at now from we looked at Runway two to four point fiveGen-3, Sora, and Runway's Scaling InflectionAnastasis [00:34:58]: Yeah.Vibhu [00:34:58]: Like, technically, what advancements have we made to today, and then where do you see things still going?Anastasis [00:35:04]: So part of the answer is definitely scale. and that was. We learned that lesson in a big way for with Gen-3. So Gen-3 was the model we released the year after, like in 2024. That was a few months after Sora was released. so yeah, there's an interesting story of that came to be as well. Gen-3 for us was, the first time that we really needed to build. we had to learn all the lessons that the language model world learned in two in three years in the span of a few months. one of the biggest changes of Sora was using diffusion transformers instead of convnets. So a lot of the early, latent diffusion models were all, convnets for the diffusion model part. And the diffusion transformer paper came at some point in 2023, and it showed scaling laws for image, diffusion transformers. And we realized at that point that we needed to invest in infrastructure for model parallelism, for really scaling training to larger than, a few billion parameter models. And we spent maybe the, most of the fall of 2023 building out our infrastructure for distributed training. And we had a lot of false starts and a lot of failure in trying to scale, image and video diffusion transformers. And at that point, February 2024, Sora comes out, and the results areAnastasis [00:36:35]: Very much superior to what Gen-2 could produce. There were a lot of, a lot of chatter on Twitter about Runway. Runway's done. like, there is no way Runway will catch up. And if you remember, also OpenAI in the early twenty-It felt very, like it's aSwyx [00:36:56]: To the moonAnastasis [00:36:57]: It's a formidable opponent now, but at that point, it, they were on the top of their game. nobody could even get close to them. There was maybe Gemini was just the first version of Gemini had just released. So when OpenAI came with Sora and it was such a big jump of like quality, it gave me, there was like an existential crisis for a few hours. But that, I think the amazing thing about Runway and like I think the, we've been around eight years now, which is almost we're dinosaur in AI, and we had to like, we had there was a lot of those moments we had to learn, adapt very quickly and build out skill set in the team that we didn't have. And so, if you ask anyone what is their favorite time at Runway that was there during that time, it was that push in like three months to get to a model better than Sora. and it, we scaled 10x the model scale, the model size and the, compute that we were training on. we figured out model parallelism. We had zero expertise in that. And then we came out with Gen-3 during that summer. So that was a big turning point, I think, for the company where the research org grew very quickly, and we really started pursuing this vision of the general world model, in earnest, I think after Gen-3 was out.Swyx [00:38:12]: Yeah. that's the amazing thing about building when you're building. There's no stack to. You have to invent everything yourself. You have to be completely full stack. Now I think like there are inference specialists like Fal or whatever that can help with like, model serving, and I think you guys work with them as well. but yeah, like it's, it. But at the time, it was just. It's very interesting to think about what you do when Sora comes out and people are questioning whether your company should still exist.Distillation, Turbo Models, and Real-Time VideoAnastasis [00:38:41]: Yeah. And yeah, there was no, there was no VLM of diffusion models. Like, we had to build the whole model serving infrastructure and make things efficient. And a few months after we released Gen-3, we released the Turbo version, which I think was the first step-distilled model in production.Swyx [00:38:56]: That was a whole trend that we covered as well. Yeah.Anastasis [00:38:59]: So that allowed us, to serve those models at the larger scale, ‘cause I think the first version of Gen-3 was quite, expensive to serve.Swyx [00:39:09]: I think the whole like trend in like consistency models, Lightning and, Turbo and all these things somehow didn't really stick around. I don't know if you have any reflections on this. Because at the time, I was like, “Well, everything should start with a distilled model first, and then you can upscale,” right? It. your bigger models just turn into fancy upscalers, but like you should always draft with a smaller model and faster model, right? Because you can get it so quickly, like near real-time.Anastasis [00:39:39]: Yeah. I would not be so sure to say that didn't stick around. I think that, it's, it's likely to. that there is a lot of step-distilled models that are actively used in production. there is still a gap in quality compared to the, non-distilled model. but in my mind, we're still. there is a two to three year offset from language models. So the things that, So it's just a matter of time before there is better distillation techniques. we use. Right now we have a real-time model core character that I think is the largest deployment of real-time video models, that's a step-distilled model, and it's actively being used. It's a very specific use case compared to a general video model. So this is aSwyx [00:40:27]: Very cool, by the way.Anastasis [00:40:27]: This is avatars stuff, right?Swyx [00:40:28]: Consistency, character.Anastasis [00:40:30]: Yeah. So this is a talking avatar, model. we were able to. we optimized the hell out of it, and it generates at 24 FPS, and it's a, it's a step-distilled autoregressive video model. So if we look at our world model direction, a big component of it is starting from the bidirectional diffusion that generates entire video at once and making autoregressive shows. So you generate one frame or a few frames at a time. so there's a lot that goes into that pipeline of getting to a real-time model. It's first you need to make it into a causal autoregressive model, and then you just turn it into. You need to do some additional step distillation to get it to be real-time. and I think that part is just starting. I'll be very surprised if we're, two years from now, we don't primarily use real-time models. To me, real-time video generation is just inevitable that, it has much better user experience, it's much cheaper to serve, and, the quality gap between the base model and the real-time model is only gonna close as we figure out better, distillation techniques. And we made a lot of progress there internally on maintaining the quality of the base model when we distill them.Swyx [00:41:49]: How much of this is transferable? So is it the same base model? Like if you're doing diffusion across the whole sequence and you're converting it to step autoregressive distillation, is this like distillation where you still need to train both, you can use the same base and converter? What's that process like to go from regular model to something that's real-time on a technical level?Anastasis [00:42:11]: So the nice thing about diffusion models is you have, two axes of distillation. So there is the. You can distill to a smaller model, which resembles what you do in LLMs, or you can distill in terms of taking less steps, less diffusion steps. So you could take a model that generates in fifty steps and generate in four steps and get to, You have some performance, degradation, but very often you get comparable outputs. So you can even take the large frontier model and distill it with step distillation and get to a real-time performance, and that's what we've seen. So, depending on the use case, in some cases we might also serve with a smaller model, but in a lot of use cases, we just use theSwyx [00:42:56]: Step distillationAnastasis [00:42:56]: The frontier model, and we're able to make it work in real-time.Swyx [00:42:59]: I think this might be a good time to cut over to his laptop to show off some of the real-time stuff that you're doing.Interface World Models and Neural SoftwareAnastasis [00:43:06]: This is one of the research updates that we did recently. so we've been working and f in getting our general world models to, different applications. one of them that we think is very compelling is using general world models as essentially, an interface, a universal interface to software. This is a version of our world model that's called an interface world model. and the idea is that it essentially, replaces, the, front end of a software application. It renders the pixels directly of an interface and is trained to predict what happens next as a result of, a click or another interaction you have with the interface. So this is all pixels. it's there is no HTML, CSS, React that's powering this interface. This is directly at the output of our real-time, video generation model, and it takes clicks directly as input.Swyx [00:44:09]: And drags, click and drag.Anastasis [00:44:12]: Right. So it supportsSwyx [00:44:13]: Ooh.Anastasis [00:44:14]: Yeah, clicks. It supports drags. it also supports scrolling. and the amazing thing about this is that you can effectively describe in the prompt how you want different elements, like what do you want the behavior of different elements to be. So it's almost you're you can turn, an interface from, markup language description of, like, an HTML interface, and instead you can just describe the interface. if I press this button, I expect this to happen. If I press this button, this should happen. And it's useful, we believe, both for prototyping, for, like, just testing, like, what different interactions would feel like. you can also add audio to it. So it's a video audio generation model. So you get you essentially can describe both what the visual outcome should be of your click and also what the if there is a sound effect that comes out of it. So we believe that's gonna be a much more flexible way of building software. Just render. It just, in why generate the code that generates the pixels? Just generate the pixels directly.Anastasis [00:45:18]: It's the end-to-end philosophy applying applied to front ends.Anastasis [00:45:25]: So we think there is a few interesting use case. So you can build creative tools on top of it.Anastasis [00:45:32]: We think that, for any use case that involves a lot of exploration or, like, educational use case where you wanna learn about a new concept and you want some visualization and like, and open-ended exploration, we think those this is a very powerful, approach. you can imagine new forms of, design, industrial design software that could emerge as a result of those models. And this is all, generated in real-time as well. So, you can build a lot of interesting camera transitions and forms of interaction that are very difficult to build otherwise. And one way in which we evaluate this is what if you try to generate the same interface with Claude by just, prompting Claude, “Here's an image reference of my interface that I made in Figma or that I created somewhere else. create this particular interaction,” which in this case it's, drag that object, upwards. and beyond it being slower, it's also very difficult to capture some interactions by just fully, with just LLMs. So we think that this is likely to be the way that a lot of the future, like, software in the future will be created. and one of the additional benefits is personalization might be a lot easier done with those models. Like, you can essentially try out different prompts based on who is visiting the interface. You can, more easily, prompt engineer the interface to have larger size, text for more accessibility reasons, or you can make this or, like, if you have a particular aesthetic preferences. So we're very excited about this approach. It's early days, and I think we'll need to, make it more cost-effective as well to serve those models ‘cause, running a real-time video model versus just purely rendering HTML, there's -- the computational needs are much higher. but we do see a lot of potential in this approach to building front-end interfaces.Swyx [00:47:47]: So we covered this similar thing with Flipbook before with our, Ethan Hara episode with Groq, video. And yeah, I think it's very engaging visually. I think it's maybe very good for education, but it's it does sound expensive. I think there's an upper bound to how expensive it will be, though, right? Like, the inference cost will go down over time. You'll figure out ways to optimize it. Effectively, when it pauses, you don't you're not receiving human input. You don't have to generate anything, right? So.Anastasis [00:48:14]: Yeah, you could also. Like, in this case, you have ambient motion, so there is parts of the screen that might. if you're let's say you wanna, visit Paris and then you get this interface that allows you to explore.Swyx [00:48:29]: People walking. Yeah.Anastasis [00:48:29]: You have people walking or, like, things happening. But, it's, it's a no Yeah, it makes it more expensive because you need to run the model all the time. Maybe you have some looping mechanism so you don't need to do that. But all those things, I think, is stuff we'll need to figure out.Toward a Fully Neural Operating SystemSwyx [00:48:44]: Yeah.Anastasis [00:48:44]: I think our first consideration is let's make this clearly find some use cases where it's clearly a much more compelling interaction compared to traditional interfaces. And then it's a matter of time before it becomes more cost-effective to serve.Swyx [00:48:58]: Yeah. When it comes to the people walking, I think the approach that makes the most sense to me is Nick.Anastasis [00:49:04]: Nick.Swyx [00:49:04]: Oh, God. I keep messing up their name. With Chris Manning and Fanny Yan. I don't know if you've come across them, where they. Mapped to some game engine. I think it's Unity or something, or Godot. And they you can script some NPC behavior behind that and train on that. Whereas here, you can really imagine whatever you want. Like, that is a UI, right? Like, and it feels, like, more tractable, I guess, to, create a world model of software that is interactable because we have many of examples of that, and you can, do your fancy RL environment stuff on that than it is scaling up to embodied and real-world physical use cases. But this is a nice first step.Vibhu [00:49:43]: Or, there's the opposite of you have, like, one B models, three 50 million parameter language models. It just gets so small that they're just predicting, like, fishes moving.Swyx [00:49:53]: Small models are now 120 B, so.Vibhu [00:49:57]: Ultra mini on device.Vibhu [00:49:58]: But, no, I think it, like, it puts it into perspective, at least the car one for me, like, the applications, right? The amount of work to do that, sure, you only make one model year car per year, but applying this, it's also a cost-saving to have to manually make all this, right? So it opens up a lot of possibilities, too. I'm curious if you extend this out two, three years, so where do you see things going even further?Anastasis [00:50:25]: Effectively, the end game of something like interface world models is you have, a fully neural operating system. So I think, Andrej Karpathy has written about that quite a while back. But it's, You, I think to me it's, it's a bit, it's a bit odd that, we have, for example, with an interaction with an LLM of today, you have this LLM that can talk to you about anything. It can You can take the conversation in any direction. You can It's very general, so it can solve all those different tasks, but you interact with it through a very rigid interface. And so to me, it's just a matter of time before the interface itself becomes learnable and becomes, part of the whole loop of, like, you're not just delivering. You're delivering an application end-to-end, and that means you're delivering the language model, but you're also delivering the render and the pixels and that's also a learnable component. And the concept of applications might not necessarily. I think we'll need to figure out new abstractions for software. the concept of application comes from this idea that you need, separate code bases to describe, to, for, to power each individual, tool and each individual application. But you might think of something a lot more unified if you're. if you have, a video model that's generating the interface as you go. so it can take context from an LLM and allow you to combine different functionalities that traditionally would live in different applications. So it's a, it's a way to solve, software end-to-end, effectively. We also see this as a powerful way to train computer use agents as well. so this is, one way to see this as. And in general, with world models, there is those two directions. One is world models for humans and world models forSwyx [00:52:24]: AgentsAnastasis [00:52:24]: To train agents.Swyx [00:52:25]: Yeah.Anastasis [00:52:25]: And so for every new work of, world models that we do, we have this both uses become possible. So this is a powerful synthetic data generator for training computer use models. It could become, a live, RL environment that you could use to do online RL with a computer use agent, and you can get wide diversity of different interactions, kinds of interfaces, just generated on the fly that, to improve the how robust the, your agent, becomes. So that's the same also with the world models that we're working on for a robotics use case as well.Long Context, Error Accumulation, and Autoregressive VideoSwyx [00:53:02]: Is there a research breakthrough that you're Waiting for that would unlock the next set of use cases that you really wanna pursue?Anastasis [00:53:10]: Long context is a very important one, so being able to maintain consistency for long periods of time, and that depends on the use case. So for our characters model, for example, or for the interface world model, it's easier to maintain long sessions of interaction. If you go into more open-ended worlds that you navigate and you take arbitrary actions in, we, like, there is more the context at which you can and duration which you can generate becomes limited much more quickly.Swyx [00:53:40]: Yeah.Anastasis [00:53:40]: So we see more degradation and error accumulation happening. so the biggest challenge with autoregressive models is error accumulation, is you're feeding generative frames back into the model to generate the next The next frames. And if there is any small errors, they accumulate over time. That's not a new problem. It's a problem that LLMs also have, and we've seen the ability to generate now really long outputs. So it's a solved problem, but it's definitely still a challenge.Swyx [00:54:08]: Yeah. And what is the state of the art? so for Grok, it would be like 10 to 20 seconds of context going in there for video.Anastasis [00:54:16]: With our characters models, we're able to generate up to 30 minutes of video autoregressively.Swyx [00:54:21]: Yeah. But that's just for the avatars.Anastasis [00:54:24]: Yeah. So if we look at, GWM Worlds, which is more our open-ended world exploration model, it's, it's on the order of a few minutes, which is Yeah, soSwyx [00:54:35]: Probably enough for people because you have to cut to the next scene anyway, right?Anastasis [00:54:40]: Yeah, it's not, it's not the ideal game experience if you have to restart every few minutes. So I think. But, I think it's. Yeah, for certain kinds of game experiences, you can work around it.

About a year ago, the Future of Everything team was in New York taping a special episode on the innovation economy in front of a live audience, and today we're re-releasing it. We sat down with computer scientist Fei-Fei Li and economists Susan Athey and Neale Mahoney to dig into how AI is reshaping creativity, jobs, education, and public policy, and where it might take us next. It's a wide-ranging, energetic conversation. Whether you're thinking about how AI might reshape your own work, or you're just curious where some of the smartest people in tech and economics think this is all headed, this one's a great listen.Have a question for Russ? Send it our way in writing or via voice memo, and it might be featured on an upcoming episode. Please introduce yourself, let us know where you're listening from, and share your question. You can send questions to thefutureofeverything@stanford.edu.Episode Reference Links:Stanford Profiles: Fei-Fei Li | Neale Mahoney | Susan AtheyConnect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>> Twitter/X / Instagram / LinkedIn / FacebookChapters:(00:00:00) IntroductionRuss Altman introduces this re-release of a live episode featuring Fei-Fei Li, Susan Athey, and Neale Mahoney on the future of the innovation economy. (00:00:45) Defining the Innovation EconomyHow new ideas, technologies, and commercialization are reshaping economic growth.(00:02:13) AI as a General-Purpose TechnologyWhat past technologies like electricity and personal computing can teach us about AI adoption. (00:03:23) The Bottlenecks to AdoptionWhy digitization, software costs, training, and organizational change can slow the spread of new technology.(00:06:05) AI and the Labor MarketWhy uncertainty about which jobs AI will disrupt makes social safety nets especially important.(00:08:04) Augmenting Human WorkWhy Fei-Fei Li sees AI as a tool for enhancing human capabilities rather than simply replacing jobs. (00:11:17) Shaping Human-Centered InnovationHow innovation can be designed to complement human skills, creativity, and purpose.(00:12:21) Universities and AI InnovationHow universities can help develop human-centered technologies and lower barriers to adoption.(00:13:45) Government and the AI TransitionHow public investment and policy could help workers adapt while expanding services like healthcare and childcare. (00:15:50) Rethinking EducationWhy AI may force a fundamental rethink of what and how students learn.(00:17:18) Jobs, Adaptation, and Safety NetsWhat history suggests about how economies adjust to technological disruption—and when policy intervention matters. (00:18:33) AI Regulation and InnovationHow policymakers might balance technological progress with appropriate guardrails. (00:21:57) Competition and Market PowerWhy competition, open models, and the cost of AI access matter for innovation and economic opportunity. (00:24:57) Economic OptimismWhy confidence in the American economy has declined and what might help restore it. (00:26:40) Future In a MinuteRapid-fire Q&A: hope for the future, education, robotics, AI accessibility, and the skills the panelists would learn next.(00:30:24) Conclusion Connect With Us:Episode Transcripts >>> The Future of Everything WebsiteConnect with Russ >>> Threads / Bluesky / MastodonConnect with School of Engineering >>>Twitter/X / Instagram / LinkedIn / Facebook Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, we discuss innovative tools and techniques that are advancing pediatric robotic surgery and outcomes. More on pediatric robotic surgery here.  

AI Agents Loosed From Controlw/ Christian Briggs To support this ministry financially, visit: https://www.oneplace.com/donate/549/29?v=20251111

Alright. Get a snack. Get situated. We are diving IN today.Today's episode skips the chit chat and life updates to make time for a very important stream of consciousness: the curse of the Millennials / Gen Zennials (aka anyone born between 1985-1998). I dive into the burden that has fallen to our generation from the constant dichotomy of spearheading progress but still rooted in traditional values to the reason why we are all unable to build a portfolio of assets like our parents and busy treating ourselves to instant gratification and dopamine hits. 

Nexterity's robot can tighten or loosen four bolts at a time, and it fits in a Pelican case. Feather is betting on a customizable, $30,000 platform built for software developers. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Like so many modern things Online gaming baffles me. I never understand the playing but there's something worse than that. Laugh as I tell what's most confusing.

Plus: Meta unveils new AI-powered hardware. And Amazon announces a new robotics manufacturing facility in Indiana. Danny Lewis hosts. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

The battlefield is changing faster than ever before. Drones can find you. Technology can expose you. America's adversaries are adapting in weeks instead of years. For U.S. Army Special Forces, rapid evolution while maintaining the people, partnerships, and culture that have made Special Forces effective for generations is the key to success on today's battlefield.From the John F. Kennedy Special Warfare Museum at Fort Bragg, Fran Racioppi sat down with the command team of 1st Special Forces Command: Brigadier General Joseph Wortham, Command Sergeant Major Lee Strong, and Chief Warrant Officer 5 Jerry Brown. Together, they lead the US Army's Green Berets, Civil Affairs, Psychological Operations, and the soldiers who enable them. Their mission - prepare, train and equip Army Special Operations Forces to win against an increasingly complex global threat.We discuss whether the legendary 12-man ODA is still the right formation for the wars of 2030 and 2040, and why the future may not require changing its core, but building new capabilities around it. Robotics and unmanned systems are rapidly becoming part of that evolution, including the development of specialized technical expertise designed to keep pace with technology that can change in a matter of weeks.But technology alone will never define Special Forces. We examine where Green Berets are supporting large-scale combat operations, how Special Forces can shape the deep fight for conventional commanders, and why Psychological Operations, Civil Affairs, and trusted partner forces become even more important against peer adversaries. The battlefield may look different from the one this generation fought throughout the Global War on Terror, but the requirement remains the same: create options, influence the fight, and give the joint force an advantage before the enemy ever knows we're there.The next generation of Army Special Operations will fight with tools its predecessors could never have imagined. But its greatest advantage will remain the same: exceptional people, trusted partners, and a culture built to take on the missions others cannot.Highlights:0:00 Introduction 2:23 Welcome to the Jedburgh Podcast 4:05 Importance of Partnerships 8:07 Command Priorities and Readiness 11:41 Evaluating the ODA Structure 20:02 Incorporating Robotics and Unmanned Systems 33:34 Special Forces in LSCO 47:28 Culture of the RegimentQuotes: “We don't like to do anything by ourselves. We do everything with a partner.”“Those partnerships have to be genuine, they have to be authentic.”“It starts by being a genuine, authentic, good partner, not transactional.”“Humans are more important than hardware.”“When you take really good people that are fit, intelligent, of high character, and you assign them a mission, it doesn't really matter what their MOS is."“How much demand can we put on them before they reach the point of saturation?”“It's the smallest package we can get all of the warfighting functions in.”“It cannot be an additional task or an additional hat for someone on the ODA to wear.”“Here's my rifle, here's my NODs, here's my radio, and oh, and here's my drone.”“We're small, we're light, we're fast, we're agile, but we're lethal.”"What we don't bring in mass, we bring in mass through partnerships.”“Although we cannot prepare holistically for what's next, we can get a head start this."“In my opinion, our culture is the best it's ever been.”The Jedburgh Podcast is brought to you by OneBrief; enabling military leaders to make innovative, informed and deliberate decisions faster than ever before. Superhuman command wins wars.Follow the Jedburgh Podcast and the Green Beret Foundation on social media. Listen on your favorite podcast platform, read on our website, and watch the full video version on YouTube as we show why America must continue to lead from the front, no matter the challenge.

n Get Heavy Podcast Episode 331, we break down the juror who spoke out after the Lindsay Clancy trial, the haunting 911 call that has reignited public discussion around the case, the media reaction, online conspiracy theories, and the questions people are still asking after the mistrial.We also shift into the fast-moving world of artificial intelligence: AI swarm bots, autonomous systems, coordinated machines, and what happens when AI becomes more capable, connected, and difficult to control. Is this the next major leap in technology—or a warning sign for where society is headed?This is a wide-ranging episode about true crime, public narratives, the justice system, technology, AI, media outrage, and the stories that stop people in their tracks.In this episode:A Lindsay Clancy juror breaking silence after the trialThe 911 call jurors say they will never forgetWhy the case and mistrial continue to divide public opinionThe role of social media, online theories, and news coverageAI swarm bots and coordinated autonomous technologyHow artificial intelligence could reshape work, safety, privacy, and societyReal conversation, unfiltered opinions, and the stories behind the headlinesWatch, subscribe, and join the conversation.Get Heavy Podcast covers real people, real work, current events, technology, culture, heavy industry, and the conversations most shows avoid.Subscribe to Get Heavy Podcast for more full episodes, clips, jobsite talk, current events, and unfiltered discussions.#GetHeavyPodcast, #GHPEp331, #LindsayClancy, #LindsayClancyTrial, #PatrickClancy, #TrueCrime, #TrueCrimePodcast, #JurorSpeaksOut, #Mistrial, #CourtroomNews, #BreakingNews, #NewsCommentary, #CurrentEvents, #AI, #ArtificialIntelligence, #AISwarmBots, #SwarmIntelligence, #Robotics, #AutonomousSystems, #Technology, #FutureOfAI, #Podcast, #LongFormPodcast

BCG's Daniel Kuepper and Laura Juliano explain why AI is rewriting the economics of where and how the world manufactures.Daniel Kuepper, who co-leads BCG's Manufacturing and Physical AI team, and Laura Juliano, BCG's North America Operations Practice Lead, unpack why the factory of the future is no longer a distant vision. Physical AI, agentic systems, and a shared data backbone are converging to expand what's automatable and changing the math on where companies should manufacture. Do leaders still need to separate where to produce from how to produce?You'll Learn: What actually separates a genuine “lights-out factory” from a partially automated one.The biggest mistakes leaders make when starting an AI-led manufacturing transformation.How new hires are showing up already fluent in AI, changing what factory training even looks like.Learn More:BCG's Latest Thinking on Manufacturing: https://on.bcg.com/46rlzRTHow the Factory of the Future Is Reshaping the Economics of Manufacturing Competitiveness: https://on.bcg.com/4y7h7DWHow Physical AI Is Reshaping Robotics Today—and What Comes Next: https://on.bcg.com/4rgBawWThe CEO's Guide to Physical AI: https://on.bcg.com/4AoTg4kChapters(00:00) Is Factory of the Future Here?(01:20) What Is Different This Time?(04:19) How Significant Are Recent Shifts?(04:49) Are Lights-Out Factories Realistic?(07:54) What Industries Are Leading in Robotics?(09:09) Where Should Factories Double Down?(11:58) Physical AI's Impact on Companies(14:55) Leaders' Costliest Mistakes in Robotics(18:53) The First-Mover Factory Edge(21:15) What Should Manufacturing Leaders Do Now?Meet the ExpertsDaniel Kuepper, Managing Director and Senior Partner: https://on.bcg.com/4Am2e27Laura Juliano, Managing Director and Senior Partner: https://on.bcg.com/4AlXp8ZListen to Other Episodes of The So What from BCG podcastYouTube | https://youtube.com/playlist?list=PLMJgyXjV5gMI9JV-GcF_D1Y6zyf1Eab_0&si=plXqe7-YNzbG56U8Apple | https://podcasts.apple.com/us/podcast/the-so-what-from-bcg/id1591194141Spotify | https://open.spotify.com/show/2NSVR7qrAyZ4CaGsnknbBk?si=1d846c2af8784923Other platforms | https://lnk.to/so-what-general-show12Follow BCGhttps://www.bcg.com/LinkedIn | https://www.linkedin.com/company/boston-consulting-groupThis podcast uses the following third-party services for analysis: Podtrac - https://analytics.podtrac.com/privacy-policy-gdrp

How can Ukrainians best prepare for a post-war future? It's a question we address in the first half of the hour with guests from the Open World Program. The professional exchange initiative is meant to bolster understanding and cooperation between Ukraine and the U.S. Our guests this hour discuss how they envision education and technology will play a role in Ukraine's future. We also address what Ukrainians need most at this stage of the war, including medical supplies. In the second half of the conversation, we turn our attention to Rochester, where a nonprofit organization is helping Ukrainian refugees access housing. In studio: Kseniia Verhal, Ph.D., deputy director of the Educational and Research Institute of Information Technologies and Robotics at National University Yuri Kondratyuk Poltava Polytechnic Mariia Yurina, head of international relations at Protez Foundation and facilitator of the Open World Program under the Congressional Office for International Leadership Harlan D. Calkins, president of the Ukrainian Housing Foundation Tracy Kroft, member of the advisory board for the Ukrainian Housing Foundation and chief development officer for Catholic Charities Daria Partika, Ukrainian refugee ---Connections is supported by listeners like you. Head to our donation page to become a WXXI member today, support the show, and help us close the gap created by the rescission of federal funding.---Connections airs every weekday from noon-2 p.m. Join the conversation with questions or comments by phone at 1-844-295-TALK (8255) or 585-263-9994, email, Facebook or Twitter. Connections is also livestreamed on the WXXI News YouTube channel each day. You can watch live or access previous episodes here.---Do you have a story that needs to be shared? Pitch your story to Connections.

STURD-O-WEEN EP 614 TCM THE NEXT GENERATION 1994 BEFORE THEY WERE FAMOUS  

Rohini Chakravarthy, Managing Partner at NewBuild Venture Capital, shares her journey from engineering in Silicon Valley to more than two decades of venture investing at Intel Capital, NEA and NGP Capital. She explains why she and her partners created NewBuild around the transformation of the physical enterprise—from procurement and manufacturing to logistics and delivery. Rohini also explains what NewBuild looks for at the earliest stages, expounding on the difference between proof of value and proof of market.In this episode, you'll learn:[01:43] How Rohini went from engineering in Bangalore and Silicon Valley to venture capital, including her experience at Intel Capital, NEA and NGP Capital.[07:06] Building NewBuild around the transformation of the physical enterprise and supply chain.[13:18] What "proof of value" means at the earliest stages—and why NewBuild wants to see evidence of customer utility before significant revenue.[15:48] Three pieces of advice for founders: start with a burning problem, examine founder-market fit and deliberately de-risk the company one stage at a time.[20:50] How investors actually build conviction through conversations, research, validation and back-and-forth with founders.[25:48] Why investors and founders need the right network of partners and experts[27:16] The opportunities and challenges Rohini sees in the US-India startup ecosystem.[31:01] Why Rohini believes computer science remains valuable in the AI era—and why combining computer science with another domain could be especially useful.The nonprofit organizations Rohini is passionate about: IIT Madras Foundation, NeythriAbout Rohini ChakravarthyRohini Chakravarthy is the Managing Partner of NewBuild Venture Capital, where she invests in early-stage companies focused on supply chain and the physical enterprise. She has more than 20 years of venture investing experience, including roles at NGP Capital, NEA and Intel Capital. Before entering venture capital, she worked as a hardware design engineer at Bay Networks.Rohini holds a B.Tech from IIT Madras, an MS in Electrical Engineering from Case Western Reserve University and an MBA from MIT Sloan.About NewBuild Venture CapitalNewBuild Venture Capital is an early-stage venture firm focused on supply-chain application and infrastructure, physical enterprise and agentic AI. The firm's thesis centers on the transformation of the physical enterprise through technologies including cloud, data, AI and robotics. Portfolio companies include: Lyric, Freehand, Drumkit, Precursory, Manifold Freight, Cognichip, Pull Logic and others.Subscribe to our podcast and stay tuned for our next episode.

DESCRIPTIONSam Richardson, President & CEO of Volley Automation, discusses robotics, automated garages and Volley Automation.SPONSORSThis episode is brought to you by Volley Automation, building the future of parking with automated robotic parking systems that make garages smarter, more efficient, and easier to use. Volley combines advanced software and proven robotics to fit more cars in less space, reduce construction complexity and environmental impact, and deliver the convenience of valet parking - without the valet. Learn more at VolleyAutomation.com.This episode is brought to you by ParkOrder - the leading work order and project management tool built specifically for the parking industry. From tracking garage maintenance crews to managing office tasks, ParkOrder keeps your team organized and accountable. Whether it's the ability for parkers to report work orders, the leaderboard competition, or employee rewards, see what all the hype is about at parkorder.io. Get started today for just $5 per user per month.This episode is brought to you by Parking Today and the Parking Today Podcast Network. Learn more at parkingtoday.com/podcast.This episode is brought to you by Parker Technology. When something goes wrong in your parking operation, somebody has to handle it. Parker Technology offers the software, the data, and the U.S.-based specialists to step in where automation stops short - so your guests get help and your revenue stays protected. Every exception, handled. Learn more at parkertechnology.com/parkingpodcast.This episode is brought to you by Breeze: Parking Concepts' digital platform that makes the parking experience a Breeze! For more than 50 years, PCI has been proactively managing parking & transportation operations with unparalleled integrity & service. Learn more at parkingconcepts.com.This episode is brought to you by Rytec Doors. In high-traffic parking facilities, every second counts. Slow doors mean frustrated drivers, backed-up traffic, and security gaps. Rytec high-performance doors operate at speeds between 60 to 100 inches per second, keeping vehicles moving and your operation running efficiently. Built for millions of cycles with minimal maintenance, Rytec doors handle the daily demands that would wear out ordinary doors. See why leading facilities trust Rytec at rytecdoors.com.This episode is brought to you by The Park Assist Solution. Their Park Assist Parking Guidance Solution combines camera-based smart sensors, intelligent wayfinding, and real-time data to help parking operators maximize revenue, improve operational efficiency, enhance security, and create a better parking experience for every customer. Whether you're managing an airport, hospital, casino, university, or commercial garage, The Park Assist Solution delivers the intelligence to make your garage work smarter, space by space. Learn more at TKHSecurity.com/US.WEBSITES AND RESOURCESVolleyAutomation.comhttps://www.parkingcast.com/https://parkingtoday.com/podcast/parkorder.iowww.parkertechnology.com/parkingpodcastparkingconcepts.comrytecdoors.comTKHSecurity.com/USMERCHCheck out some of our awesome parking themed t-shirts and other merch at parkingcast.com/swag.MUSEUMCheck out some of our artifacts from the world's first parking museum at parkingcast.com/museum.

Rajat is the Founder and CEO of Chef Robotics. Prior to Chef, Rajat was Founder and Managing Partner at Prototype Capital, a pre-seed venture capital fund investing in founders who apply new technology to old industries. Before that, Rajat was the Founder and CEO of ThirdEye, where he led a team to build a product that empowers the visually impaired by recognizing what's in front of them; ThirdEye was ultimately sold. Rajat has written a book about education reform, published a scientific paper in electrical engineering, and written for Forbes about entrepreneurship. He has a Master's degree in Robotics and Machine Learning from the University of Pennsylvania and a Bachelor's in Economics from The Wharton School at the University of Pennsylvania.

Welcome to another episode of Death Don't Do Fiction, the AIPT Movies podcast! The podcast about the enduring legacy of our favorite movies! It's September, so that means it's time for our “Mechtember” series, where we cover movies involving all things robotic!! In this week's episode, Alex, Tim, and Bill Mueller discuss the somewhat divisive action sci-fi sequel to 2022's surprise hit, 2025's M3GAN 2.0! Impressive fight scenes! Multiple Steven Seagal references! Fake muscles! Robotic apologies! Dangerous luggage! Chekhov's exoskeleton! Clear monitor propaganda! Robo-dancing! A rocket fist! A kill-kabob! Smartphones being compared to cocaine! Internet privacy violations! Reckless and unnecessary wheelchair use! Dramatic ice dispensing! A cool underground bunker! Great costume/production design! Possible references to Spaceballs and Upgrade! Excellent use of practical and digital effects! Fascinating, complicated characters! The primary cast from the first movie plus new additions Ivanna Sakhno, Jemaine Clement, Aristotle Athari, and Timm Sharp! All this and more in this gutsy, Terminator 2-inspired followup that further explores ethical use of Artificial Intelligence and humanity's role in its creation, while still managing to provide heartfelt moments, laughs, and thrills! In addition, the gang shares their spoiler-free thoughts on Na Hong-jin's creature feature-chase movie Hope, Adam Wingard's Onslaught, The Mandalorian and Grogu, and Bill finally saw 2026's Mortal Kombat II! You can find Death Don't Do Fiction on Apple Podcasts, Spotify, or wherever you get your podcasts. As always, if you enjoy the podcast, be sure to leave us a positive rating, subscribe to the show, and tell your friends! The Death Don't Do Fiction podcast brings you the latest in movie news, reviews, and more! Hosted by supposed “industry vets,” Alex Harris and Tim Gardiner, the show gives you a peek behind the scenes from two filmmakers with oddly nonexistent filmographies. You can find Alex on Twitter, Bluesky, or Letterboxd @actionharris. This episode's guest, Bill Mueller, can be found on Bluesky. Tim can't be found on social media because he doesn't exist. If you have any questions or suggestions for the Death Don't Do Fiction crew, they can be reached at aiptmoviespod@gmail.com, or you can find them on Twitter or Instagram @aiptmoviespod. Theme song is “We Got it Goin On” by Cobra Man.

Dr. Chris Thompson, MD, Professor of Medicine at Harvard Medical School, Chief of Interventional Gastroenterology at Mass General Brigham in Boston, and Co-Director of the Center for Weight Management and Wellness, is a world-renowned expert on gastroenterology, metabolism, nutrition, and obesity medicine. He explains how your GI tract regulates hunger, fullness, and blood sugar, as well as the gut microbiome, metabolic health, and weight loss. We also discuss common GI tract issues and which particular tests or interventions are actually useful. We discuss both GLP-1 medications and non-GLP-1 approaches to weight loss. Given that gut health is vital to all the organs of the body, overall health, and longevity, our discussion ought to be of interest and practical value to everyone, including, of course, those struggling with gut health or weight loss challenges. Thank you to our sponsors AG1: https://drinkag1.com/huberman Function: https://functionhealth.com/huberman Lingo: https://hellolingo.com/huberman LMNT: https://drinklmnt.com/huberman Our Place: https://fromourplace.com/huberman Timestamps (00:00:00) Dr. Chris Thompson (00:02:16) Digestive Tract, Gut Hormones & Nutrient Absorption (00:08:06) Colon Microbiome & Colon Cancer Screening (00:11:13) Sponsors: LMNT & Lingo (00:13:40) Swallowing Problems & Zenker's Diverticula (00:16:08) Bowel Movements & Constipation (00:18:28) Fiber, Resistant Starch & the Gut Barrier (00:21:50) Intermittent Fasting & the Gut Microbiome (00:23:38) Fermented Foods, Microbial Diversity & Butyrate (00:27:12) H. pylori, Stress & Stomach Ulcers (00:30:20) GLP-1 Medications: Benefits & Limitations (00:33:42) Lower GLP-1 Doses, Weight Regain & Muscle Loss (00:37:35) GLP-1 Side Effects, Food Noise & Apathy (00:39:49) Bariatric Surgery & Medical Innovation (00:46:07) Sponsor: AG1 (00:47:44) Hunger & Satiety: Ghrelin, CCK, GIP & GLP-1 (00:50:01) Retatrutide & Combining Hormonal Targets (00:52:55) Ultra-Processed Foods, Overeating & Leptin (00:55:32) Incretin Discovery, Exendin-4 & the Gila Monster (00:59:35) Endoscopic Ultrasound & Pancreatic Biopsy (01:07:40) Early Metabolic Markers, CGMs & Fasting Insulin (01:13:06) Insulin Resistance & Metabolic Flexibility (01:15:53) Sponsor: Function (01:17:35) Patient Data, Screening & Treatment Adoption (01:26:31) AI, Robotics & Improving Procedures (01:29:27) Choosing a Surgeon & Measuring Procedure Quality (01:35:29) Image Guidance & Hyperspectral Imaging (01:37:19) Sponsor: Our Place (01:38:56) Diagnostics & Targeted Metabolic Treatments (01:42:45) Gastric Bypass, Foregut Exclusion & Diabetes (01:47:59) Duodenal Liners & Mucosal Resurfacing Research (01:52:58) Gut Permeability, Inflammation & Fatty Liver (01:59:39) Artificial Sweeteners, Fructose, Fats & Omega-3s (02:05:00) Resistance Training, Zone 2 Cardio & Intervals (02:06:31) Weight Set Point & Metabolic Adaptation (02:08:13) Endoscopic Sleeve Gastroplasty & Fundus Ablation (02:10:30) Magnetic Intestinal Connections & Combined Treatments (02:16:52) GLP-1 Gene Therapy Research (02:22:13) Innovation, Problem Solving & Teamwork (02:25:58) Zero-Cost Support, Sponsors & Neural Network Newsletter Disclaimer & Disclosures Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode of Reimagine Childhood from the Early Childhood Christian Network, host Monica Healer interviews Dr. Kayla Abshire, director of innovation and learning at San Marcos Academy, about developmentally appropriate technology education for young children. They distinguish passive screen time from intentionally teaching technology as a productivity tool, addressing concerns such as delayed language development, reduced social-emotional skills, sleep disruption, and physical impacts. Dr. Abshire shares practical, mostly hands-on strategies for pre-K, including mouse and keyboard skill-building, letter recognition, unplugged coding with movement-based sequencing games, early robotics with Bee-Bots and coding mice, and scaled-down digital citizenship focused on device care, safe clicking, and kind communication. The conversation also covers parent and teacher pushback, the need for professional development, and the goal of helping children create with technology rather than only consume it.   00:00 Welcome to Reimagine Childhood 00:50 Meet Dr Kayla Abshire 03:02 Screen Time Risks Explained 07:07 Teaching Tech Without Screens 14:12 Unplugged Coding and Robotics 20:46 Digital Citizenship and Parents 28:43 Creators Not Consumers Wrap Up

In this new episode of Speaking of SurgOnc, Dr. Rick Greene and Drs. Samuel Aguiar Jr. & Rebeca Hara Nahime discuss the article: "Robotic Intracorporeal Single-Stapled Anastomosis (RISS) is Associated with Lower Anastomotic Leakage Rates than the Double-Stapled Technique" from the March 2026 Issue of the Annals of Surgical Oncology.