Podcasts about Multimodal

AI isn't just transforming the way we work, but also the way we write the software that people use for work. In this episode, we talk to two engineering productivity leads at Dropbox: Uma Namasivayam, senior director of software engineering productivity, and Anuradha Agarwal, director of software engineering. Whether it's writing tests, fixing bugs, tackling tech debt, or accelerating migrations, they explain how Dropbox engineers are using agentic AI—including in-house tools like Nova—to build the future of Dropbox, and create more space to do impactful work. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Alzheimer's disease can begin developing years before memory and thinking problems become noticeable. Iris Broce-Diaz, Ph.D., of UC San Diego is developing accessible, noninvasive, and cost-effective tools to help primary care physicians and neurologists identify people at higher risk earlier. Her models combine information from cognitive assessments, brain imaging, genetics, and PET scans to estimate the likelihood that mild cognitive impairment will progress within five years. Combining multiple measures improves the models' ability to separate higher- and lower-risk patients. Broce-Diaz also plans to add women-specific factors, including menopause history, hormone therapy, reproductive history, endocrine markers, and symptoms. The goal is to support better referrals, guide monitoring, and help eligible patients consider treatment sooner. Earlier risk assessment could also strengthen clinical trials and make dementia care more useful across different health systems. Series: "Motherhood Channel" [Health and Medicine] [Show ID: 41632]

Debate da Super Manhã: Consolidada entre os principais eventos de negócios dos setores de transporte, logística, intralogística, tecnologia e comércio exterior do Brasil, A Multimodal Nordeste, chega a terceira edição reunindo mais de 140 marcas expositoras e expectativa de receber nove mil visitantes de todo o país. No debate desta segunda-feira (3), a comunicadora Natalia Ribeiro conversa com os convidados sobre transporte, logística, comércio exterior e a cadeia de suprimentos das regiões Norte e Nordeste do Brasil Participam o diretor da Feira Multimodal Nordeste e empresário do setor de logística, Domenico Carneiro; o presidente do Sindicato das Empresas de Transportes de Cargas, Armazenagem e Logística do Estado de Pernambuco (SETCEPE) e empresário do setor de logística na região, Carlos Eduardo Maçães; e o Presidente da Câmara Setorial de Logística de Pernambuco e do Comitê Aberto de Logística da Câmara Americana de Comércio para o Brasil (Amcham Brasil), Pedro Macedo.

AI makes it easier than ever to find and act on information—especially now that teams can connect to and search across all the apps they use for work. So how do you ensure that only the right people and the right tools can access your team's most sensitive content? In this episode, we talk with Jess Jimenez, the head of security at Dropbox, about what security looks like in the age of AI at Dropbox-scale—from building AI products securely to building trust with the people who use them. Jess talks about the importance of access control lists, defending against the latest AI threats, and how Dropbox Protect helps teams securely share content with both humans and AI so they can collaborate more safely. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

The Big Unlock · Umesh Rustogi, General Manager, HLS Dragon & Platform, Microsoft In this episode, Umesh Rustogi, General Manager, Healthcare & Life Sciences Dragon & Platform at Microsoft, explains that meaningful AI outcomes depend on combining unified multimodal data foundations with clinician-centered design to achieve healthcare’s quadruple aim: improving care quality, patient experience, operational efficiency, and workforce well-being. Addressing the severe nursing shortage and turnover driven by administrative burnout, Umesh details how Microsoft co-innovated with nine major health systems to deploy Dragon Copilot for Nursing, a clinical assistant that works alongside nurses. By leveraging ambient AI on mobile devices, the solution helps nurses capture flow sheet documentation for their review and maintains a running cognitive memory across shifts, significantly lowering cognitive load and enabling nurses to be truly present with their patients. Umesh also emphasizes that enterprise-scale success requires far more than model selection. It demands responsible AI governance, transparent audit capabilities, deep workflow customization, and robust change management, including nurse leader sponsorship and dedicated unit champions. He envisions smart hospital rooms, multimodal AI, wearable integration, and agentic workflows transforming clinical operations by delegating non-patient-facing tasks so caregivers can focus on delivering better care. Take a listen. This guest appearance was facilitated through conversations initiated at HIMSS.

AI Safety Basics, Exploit Gym, and a 438km Tesla FSD Trip — Plus Kimi K3 and Agentic Tools With John away, Jim and Marcel discuss recent AI security concerns, arguing that sensational claims about an OpenAI model "escaping" distract from basic safeguards like network segmentation and monitoring, especially when testing hacking capability via the Exploit Gym benchmark in a sandboxed, virtualized environment. They pivot to self-driving, with Marcel describing a new rear-wheel-drive Tesla Model Y that drove 438 km to Gananoque and back on supervised Full Self-Driving, including automated routing to Superchargers and self-parking, and they note winter and sensor considerations plus lower fueling costs. The conversation shifts to enterprise AI costs and Chinese models like Kimi K3, Qwen, GLM, and DeepSeek, emphasizing experimentation, data governance, and that huge models require data centers. They highlight agentic systems that can install and run software, memory tools like Mnemosyne, and reflect on how AI could free time for higher-level thinking. 00:00 John Is Away 00:47 Writing And AI Escapes 02:24 Movie Hacking Myths 04:45 Robot Ducks And Chicks 06:04 Exploit Gym Explained 10:36 Agents And Emergence 12:45 Security Lessons Learned 17:05 Self Driving In China 18:35 Tesla Road Trip FSD 25:45 Winter Range And Sensors 28:48 EV Range Reality Check 29:22 Charging Costs and Solar 30:17 Why CIOs Fear AI Costs 32:29 Chinese Models and Multimodal 33:21 Open Source Catch and Sandboxes 35:30 Picking the Right Model Mix 37:33 Innovation vs Reliability Lessons 41:18 Foreign Car Analogy for AI 45:41 What to Use and Pay For 47:33 Voice Mode Gets Real 49:40 Agents That Do the Work 54:20 Memory Plugins and Skill Atrophy 57:59 Habits, Choice, and Closing Thoughts

You've probably never heard of Inkling. It's the newest (and first) model from Thinking Machines Labs, and it could very well be a small snowball that picks up major momentum in today's enterprise AI landscape. If you haven't heard of Thinking Machines, they're led by Mira Murati, the former CTO at OpenAI. The big bet with Inkling? The future of AI could be using smaller models fine-tuned and optimized for smaller tasks. Will it work? Tune in live as we dive in. The Most Important AI Model You'll Probably Never Use That Just Dropped -- An Everyday AI Chat With Jordan WilsonNewsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageToday's Episode on LinkedIn: Thoughts on this? Join the convo on LinkedIn and connect with other AI leaders.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Inkling AI Model Launch OverviewThinking Machines Lab Leadership HighlightInkling's Multimodal and Agentic CapabilitiesOpen Source vs. Proprietary AI ModelsEnterprise Procurement with American AI ModelsAI Fine Tuning as a Service (Tinker)Benchmark Scores: Inkling vs. Frontier ModelsCustomization and Model Shopping for EnterprisesAI Token Costs Driving Model EfficiencyBridgewater Case Study: AI Model CustomizationFrontier Models Enabling Efficient Fine-TuningFuture Trends: Specialized Small Language ModelsTimestamps:00:00 Inkling: A new AI model release05:43 Inkling AI model details09:08 China's dominance in open source AI11:48 Launch and model updates discussed15:21 Concerns over using Chinese open-source models19:06 Training smaller AI models20:22 Using GPT for AI Model Training23:54 Predicting Rise of Small Language Models28:38 Choosing the right AI modelKeywords: Inkling, Thinking Machines Lab, Meera Muradi, former OpenAI CTO, open source AI model, American AI model, fine tuning as a service, enterprise AI, multimodal AI, agentic models, customizable AI, Tinker, enterprise distribution, model procurement, Chinese open source models, strategic reset, model overhang, capabilities gap, AI model shopping, model routing, cost-conscious enterprises, artificial intelligence index, 975 billion parameter model, text-image-audio AI, open weights, proprietary AI models, customization accessibility, small language models, AI workflows, context window, Bridgewater use case, model distillation, GPU infrastructure, API costs, token efficiency, fine-tuned models, post training, AI competitive leverage, recurring financial judgment, AI benchmarks, middle tier models, automated model evaluation, privacy and workflow mapping, economical AI models, model rental, model routing automation.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner 

Not all work happens in writing. Teams that work with photos, videos, and audio need AI that works for them too. This is why, with Dropbox, you can search within multimedia content for key moments and important information—not just text. In this episode, we talk with Appu Shaji and Hicham Badri, two Dropbox machine learning engineers who are part of the team that makes all of this possible. They explain how multimodal search works—from understanding the context of the initial query, to identifying objects and actions in complex scenes—and how they ensure those models work fast, even at Dropbox-scale. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

View the Show Notes Page for This Episode Become a Member to Receive Exclusive Content Sign Up to Receive Peter's Weekly Newsletter Gayatri Devi is a nationally recognized neurologist specializing in memory disorders, including Alzheimer's disease and related dementias. In this episode, Gayatri explains how to think about dementia as a spectrum—including Alzheimer's disease, vascular dementia, Lewy body dementia, and mixed presentations—while exploring the evolving biology of amyloid, tau, and neuroinflammation and why brain pathology does not always correlate with symptoms. She discusses her approach to detecting subtle cognitive decline in high-functioning individuals, the role of biomarkers and APOE4 testing in asymptomatic patients, the benefits and risks of anti-amyloid therapies such as lecanemab and donanemab, and strategies for minimizing treatment-related complications. Gayatri also examines why some patients may stabilize or even improve with individualized care, the overlap among different dementia syndromes, and the relationship between menopause, estrogen, and cognition—including her concept of menopause-related cognitive impairment. Finally, she discusses how advances in early detection, AI-assisted monitoring, targeted therapies, and precision medicine are reshaping the future of dementia care. We discuss: Gayatri's training and clinical focus, why dementia is a spectrum disease, and how personalized treatment is changing Alzheimer's care [3:45]; How Alzheimer's disease fits within the broader spectrum of dementia: diagnosis, biomarkers, and early pathophysiology [7:15]; The emerging role of neuroinflammation and viral infections in Alzheimer's disease [13:30]; Gayatri's comprehensive approach to evaluating cognitive decline in high-functioning patients [17:45]; Why forgetting names is usually normal and when word-finding problems become concerning [29:00]; Why women are at higher risk for Alzheimer's disease and how menopause influences cognition [33:45]; The promise and limitations of blood-based biomarkers for diagnosing Alzheimer's disease [40:15]; When preclinical Alzheimer's screening is appropriate and how to interpret positive biomarker results [45:00]; Case study: early Alzheimer's prevention in a highly-functional woman in her 50s with two copies of APOE4 [47:15]; Anti-amyloid therapies: balancing clinical benefit with ARIA risk using slow titration [51:45]; The aducanumab controversy, why it was discontinued, and why Gayatri would still choose it [1:00:00]; How anti-amyloid therapies cause ARIA, strategies for detecting and managing these complications, and how future therapies may improve safety and accessibility [1:03:30]; Two patient examples of exceptional responses to anti-amyloid therapy [1:12:30]; A multimodal approach to Alzheimer's treatment: combination therapy, MRI-guided TMS, GLP-1 receptor agonists, and more [1:15:00]; Vascular dementia, Lewy body dementia, and the overlap with Alzheimer's disease [1:21:00]; Lewy body dementia and Parkinson's disease: distinguishing two alpha-synuclein disorders [1:26:45]; Risk factors for Lewy body dementia and what remains unknown [1:36:15]; Treating menopause-related cognitive impairment: hormone therapy, brain rehabilitation, and balancing breast cancer risk [1:38:45]; How biomarkers changed Gayatri's perspective on the potential for Alzheimer's patients to improve [1:47:15]; The future of Alzheimer's care: AI, precision medicine, and personalized treatment [1:49:30]; and More. Connect With Peter on Twitter, Instagram, Facebook and YouTube

For decades, we've thought about conditions like Alzheimer's disease, depression, and Parkinson's disease as disorders of the brain. But what if that's only part of the story? Emerging science is revealing that many of the factors driving cognitive decline, mental illness, and neurodegenerative disease may begin far outside the brain itself—in our metabolism, our immune system, our gut microbiome, our hormones, and even the environment we're exposed to every day. As part of our summer series, we're revisiting some of the most important conversations we've had on the topic of brain health. In this special compilation episode, you'll hear from leading experts Dr. Richard Isaacson, Dr. Chris Palmer, and Drs. Ray Dorsey and Michael Okun as they explore the new science of brain health and what it means for preventing cognitive decline, supporting mental health, and protecting our brains as we age. What I find most encouraging about this research is that it challenges the idea that brain decline is simply an inevitable part of aging. Instead, it suggests that many of the biggest drivers of brain health are things we can influence through the choices we make, the environments we create, and the ways we care for our bodies long before symptoms ever appear. View Show Notes From This Episode Sign up for Dr. Hyman's Brainshaping Academy to learn how to nourish the biological systems that support your mental, emotional, and cognitive health https://drhyman.com/products/brainshaping?utm_source=dr_hyman_show&utm_medium=newsletter&utm_campaign=may_27&utm_content=link Get Free Weekly Health Tips from Dr. Hyman https://drhyman.com/pages/picks?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Sign Up for Dr. Hyman's Weekly Longevity Journal https://drhyman.com/pages/longevity?utm_campaign=shownotes&utm_medium=banner&utm_source=podcast Join the 10-Day Detox to Reset Your Health https://drhyman.com/pages/10-day-detox Join the Hyman Hive for Expert Support and Real Results https://drhyman.com/pages/hyman-hive This episode is brought to you by Timeline, Sunlighten, Seed, BIOptimzers, Paleovalley, and Pique. Support healthy aging and get 20% off at timeline.com/drhyman with code HYMAN. Discover why so many people are using sunlighten.com and save up to $2,100 today and free shipping with code HYMAN. Make your microbiome part of your daily routine with seed.com/hyman and save 25% with code 25HYMAN. Go to bioptimizers.com/hyman and use code HYMAN to save 15% off your order, plus get a free gift.  Shop nutrient-rich foods and supplements at paleovalley.com/hyman and save 15% off your first order. Elevate your daily wellness ritual at piquelife.com/hyman and enjoy 15% off for life plus free gift (0:00) Understanding brain dysfunction, inflammation, and lifestyle interventions (0:21) Heavy metals, toxins, and Parkinson's disease (3:30) Introduction to the summer series on brain health (4:02) The MIND diet, precision nutrition, and brain-healthy fats (9:06) Vitamins, supplements, and caloric intake for cognitive health (11:23) Vitamin D, B complex, and reversing mild cognitive impairment (14:05) Additional supplements and intensive lifestyle interventions (17:18) Importance of magnesium and metabolic health (18:24) Multimodal treatments, hormone replacement therapy, and individualized care (26:49) Biopsychosocial factors, stress, and the gut-brain connection (32:30) Inflammation, energy dysregulation, and metabolism in brain health (42:33) Parkinson's disease: causes, history, and environmental factors (46:02) Leaky brain, blood-brain barrier, and Parkinson's as a whole-body disease (49:00) Closing remarks, disclaimer, and gratitude to sponsors

This podcast and YouTube episode features an in-depth conversation with Anthony David Vernon, a philosopher and educator, exploring the intersection of disability studies, left-wing politics, and the systemic failures of accessibility in a post-pandemic world. The discussion challenges the "normative" framework of society, examining how both civic institutions and political movements often fail to truly incorporate the voices and needs of the disabled community.Key Discussion HighlightsThe ADA and the "Checkmark" Problem: Vernon argues that because the ADA is enforced primarily through personal lawsuits and remains largely unfunded, it often results in "checkmark" compliance rather than true accessibility.The Post-Pandemic Erasure: The conversation explores how the rush to move past COVID-19 safety measures has prioritized "normative desires" over the accessibility needs of high-risk and disabled individuals.Multimodality as Justice: Implementing "ready-made" scaffolding and multiple points of entry into education and digital spaces benefits all learners, not just those with a formal diagnosis. Referenced Works (APA Format)Albers, B. (2022). Able-bodied leftists cannot abandon disabled solidarity to move on from COVID. Truthout. https://truthout.org/articles/abled-bodied-leftists-cannot-abandon-disabled-solidarity-to-move-on-from-covid/Data for Progress. (2023, October 3). Disabled voters do not believe politicians care about disabled Americans. https://www.dataforprogress.org/blog/2023/10/3/disabled-voters-do-not-believe-politicians-care-about-disabled-americansHryhorec, S. (2025, October 26). LET ME IN: Mark Butler's office isn't accessible [Video]. YouTube. https://www.youtube.com/watch?v=DEfbZzCspDkIacoboni, G. (2023). Why politics is failing disabled people and what to do about it. Independent Social Research Foundation (ISRF). https://isrf.org/blog/why-politics-is-failing-disabled-people-and-what-to-do-about-itRotarou, E. S., & Sakellariou, D. (2024). Neoliberalism and disability: The systemic erasure of access. Social Science & Medicine. https://www.sciencedirect.com/science/article/pii/S0277953623007189University of Hawaiʻi at Mānoa. (n.d.). Disability studies and political theory: A framework for inclusion. https://scholarspace.manoa.hawaii.edu/server/api/core/bitstreams/425af050-0220-49dc-b28d-f86d976dcf02/contentVarn, C. D. (2023, October). Multimodal availability for those with learning disabilities. PeerCentered. https://www.peercentered.org/2023/10/multimodal-availability-for-those-with.htmlVernon, A. D. (2023, December). Silence: Non-verbal communication in philosophy. Activated Thinker. https://medium.com/activated-thinker/silence-non-verbal-communication-in-philosophy-d5d148ba8a1dWillies, E. (2025, June 16). Anthony David Vernon advocates for social democracy as a tool of rebellion against fascism [Video]. YouTube. https://www.youtube.com/watch?v=Rs7l6jNGDzwSend us Fan Mail Musis by Bitterlake, Used with Permission, all rights to BitterlakeSupport the showCrew:Host: C. Derick VarnIntro and Outro Music by Bitter Lake.Intro Video Design: Jason MylesArt Design: Corn and C. Derick VarnLinks and Social Media:twitter: @varnvlogblue sky: @varnvlog.bsky.socialYou can find the additional streams on YoutubeCurrent Patreon at the Sponsor Tier: Jordan Sheldon, Mark J. Matthews, Lindsay Kimbrough, RedWolf, DRV, Kenneth McKee, JY Chan, Matthew Monahan, Parzival, Adriel Mixon, Buddy Roark, Daniel Petrovic,Julian, Drea, Free Beer 

When AI is at its best, the conversations can feel uncanny—almost magical in their accuracy, relevance, and speed. For that you can thank the AI agents that work together behind the scenes to search, reason, and sift through all your content to get you what you need to do your job. We talk with Jongmin Baek and Marta Mendez, two Dropbox machine learning engineers, about building conversational AI that's helpful, useful, and grounded in your team's shared context, so you can spend more time on the work that really matters. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

“Opioid-sparing” sounds like an automatic win until you look closely at what replaces the opioids. We take on one of the toughest questions in modern anesthesiology: how do we reduce opioid-related harm without trading it for medication interactions, kidney injury, bleeding risk, rebound pain, or poorly controlled postoperative pain?We break down what individualized multimodal analgesia really means in day-to-day anesthesia practice. That starts before the first dose is ordered, with a preoperative assessment that weighs comorbidities, baseline renal function, hydration status, and potential drug-drug interactions. We also dig into the medication safety side of multimodal protocols, including why CYP2D6 matters for common oral opioids like hydrocodone, oxycodone, and tramadol, and how CYP2D6 inhibitors such as certain antidepressants can change opioid effectiveness and even extend opioid use after discharge.Regional anesthesia remains a cornerstone, but we stay honest about the pitfalls: incomplete coverage, visceral pain that sneaks through, and the timing mismatch that can trigger rebound pain 12 to 24 hours after a single-shot block, sometimes when the patient is already home. We also discuss when continuous peripheral nerve blocks may better match the duration of perioperative stress and inflammation, plus the practical barriers that determine whether advanced regional techniques are feasible.If you care about opioid-sparing anesthesia, patient safety, and better postoperative recovery, listen and share this with a colleague. Subscribe to the podcast, leave a review, and tell us: what's one change you'll make to your multimodal analgesia plan after hearing this?For show notes & transcript, visit our episode page at apsf.org: https://www.apsf.org/podcast/313-individualized-multimodal-analgesia/© 2026, The Anesthesia Patient Safety Foundation

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (6/27/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v79s9oy","div":"rumble_v79s9oy"}); Source Links (In Chronological Order): Telegram: View @TLAVagabond (18) Aaron Day on X: "I know I got into crypto so that I could have a bank hold it for me. How about you? This is the inversion of crypto. You understand that, right?" / X (18) Grok / X New Tab (18) The Last American Vagabond on X: "Can we admit that EVERYONE sees this coming? Be sure to remember those of us who were trying to draw your attention to this when we might've been able to stop it (I still think we can) & be sure to remember who gaslit you right up until "BREAKING" the story after it was too late." / X (18) The Last American Vagabond on X: "Maybe the truth is that almost no one does, and this whole "new media" push was the construction of the new algorithmically channeled and controlled online reality. So we end up screaming into the wind. Just a thought." / X (18) The Last American Vagabond on X: "This is my point. Most of the people who wanted to “Make America Great Again” (even if you didn't agree with how they wanted to do it) are either calling out this admin or no longer supporting it at all because Trump double crossed them. Those are the real MAGA. #TwoPartyIllusion https://t.co/68tyJkyoQz" / X (15) The Last American Vagabond on X: "It's comments like this that should show you the reality. No, Vance is the same problem." / X (18) The Last American Vagabond on X: "Hey look, two war criminals." / X New Tab US Army begins fielding next-generation battlefield biometrics system | Biometric Update It's Official: The US is now "USrael" | The Corbett Report Was Israel Involved With Charlie Kirk's Death - Let's Look At The Facts The Charlie Kirk Hysteria Is a Blueprint for Future Political Chaos (9) DR. ETIQUETTE

In this episode of The Lead, host Sandeep A. Saha, MD, MS, FHRS, is joined by Suraj Kapa, MD, FHRS, and Albert Joseph Rogers, BSE, MBA, MD, FHRS, to discuss the journal article, Predicting Sudden Cardiac Death in Patients With Sarcoidosis Using a Multimodal Artificial Intelligence Model. Together, they review the study's approach to using a multimodal artificial intelligence model to predict sudden cardiac death risk in patients with sarcoidosis and discuss the potential implications of these findings. Learning Objectives Review the methodology and key findings of a multimodal artificial intelligence model developed to predict sudden cardiac death in patients with sarcoidosis. Discuss the potential role of artificial intelligence–based risk prediction in the evaluation of patients with sarcoidosis. Examine the clinical implications of sudden cardiac death risk stratification in this patient population. Host: Sandeep A Saha, MD, MS, FHRS Guests: Suraj Kapa, MD, FHRS and Albert Joseph Rogers, BSE, MBA, MD, FHRS   Disclosures: Sandeep A Saha, MD, MS, FHRS •       Honoraria/Speaking/Consulting Fee/Speaker's Bureau: Medtronic, Inc.   Suraj Kapa, MD, FHRS •       Honoraria/Speaking/Consulting Fee: Abbott •       Stock Options: Nanowear Inc. •       Other Non-Financial Relationships: Biosense Webster, Inc., Boston Scientific   Albert Joseph Rogers, BSE, MBA, MD, FHRS •       Research: National Institutes of Heath, American Heart Association •       Ownership/Partnership: WearLinq, Inc. •       Honoraria/Speaking/Consulting Fee: Other

Brent Peterson sits down with Michelle Donnelly, Chief Revenue Officer at Crescendo, to explore how AI-native customer experience solutions are transforming the way brands interact with their customers. The conversation covers everything from autonomous digital agents to the critical role humans still play in customer support. Michelle brings a wealth of experience from her time at Salesforce and the AI chip industry, and she shares fascinating real-world examples of how Crescendo's approach is turning traditional cost centers into profit centers. If you care about customer experience, this episode deserves your full attention.Key TakeawaysAI agents must work seamlessly with human agents. A digital-only approach without a human fallback creates frustrating loops that drive customers away.Customer support is becoming a revenue channel. By combining personalization, memory, and business context, AI agents can turn a simple support interaction into an upsell opportunity.Speed to value matters. Crescendo deploys in under four weeks, a dramatic improvement over traditional SaaS implementations that can take months.Outcome-based pricing changes the game. Rather than selling seats, Crescendo charges based on outcomes, aligning their success with the customer's success.Multimodal interactions let customers choose. Whether through chat, voice, WhatsApp, or email, the customer decides how they want to engage, and the AI adapts accordingly.Quality assurance reveals powerful patterns. Analyzing interactions across the customer base surfaces product issues and opportunities that brands would otherwise miss.Knowledge bases improve over time. The AI learns from every interaction and actually enhances the brand's existing knowledge base rather than relying on static content.Chapters00:00 Introduction to Crescendo and Michelle's Journey03:53 The Role of AI in Customer Experience09:30 Seamless Integration of Digital and Human Agents15:02 Multimodal Customer Interactions18:52 Quality Assurance and Content Relevance22:26 Transforming Customer Support into Profit Centers28:22 Democratizing AI for All BusinessesConnect with Michelle on LinkedIn:https://www.linkedin.com/in/michelledonnelly/https://www.linkedin.com/company/crescendocx/

How do you build AI that actually understands you and the work you do? It all starts with having the right context.  We talk with Dropbox staff product manager Noorain Noorani and principal engineer Sean-Michael Lewis about the art of context engineering and how Dropbox connects to all the tools your team needs for work—so you get AI that works wherever you do.  ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck. Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

#youtube #chatgpt #supergrok #podcastWelcome back to So Lead Saturday. I'm Chibi version of Vaishali Lambe, and today we are talking about Multimodal A I Explained Simply.The core idea for today is simple: A I is moving from text-only systems to models that understand images, video, audio, and actions.This topic matters because A I is no longer something that sits only inside research labs or innovation teams. It is now entering everyday workflows, business decisions, product roadmaps, leadership conversations, and career planning. But with that growth comes a lot of confusion. We hear big words, exciting demos, and bold predictions. Yet in the real world, success with A I usually comes down to something much more practical: clarity, workflow design, trust, and measurable value.Let's start with what people often misunderstand.When a new A I trend becomes popular, many people immediately focus on the tool. They ask: Which model should I use? Which platform is best? Which app is trending right now? Those questions are useful, but they are not the starting point. The better starting point is: what problem are we solving, who is affected, what decision needs to improve, and what workflow needs to become easier?That difference matters.A tool-first approach creates scattered experiments. A workflow-first approach creates business value.Until we meet, happy leading, and let's lead together. Stay safe. Bye for now.

In this episode, host Alyssa Watson, DVM, welcomes back Ellen M. Lindell, VMD, DACVB, to discuss her recent Clinician's Brief article, “Beyond Fluoxetine: A Multimodal Approach to Anxiety, Aggression, & Fear in Cats.” Dr. Lindell uses real-life cases to explore why things like house soiling and anxious behaviors occur in our cats. She shares advice on how to ask the right questions to plan environmental and behavioral modifications and when medication should get involved. Resources: https://www.cliniciansbrief.com/article/feline-anxiety-aggression-fluoxetine-quiz https://www.zoetisus.com/petcare/care-is-your-calling/ Contact: podcast@instinct.vet Where To Find Us: Website: CliniciansBrief.com/Podcasts YouTube: Youtube.com/@clinicians_brief Facebook: Facebook.com/CliniciansBrief LinkedIn: LinkedIn.com/showcase/CliniciansBrief/ Instagram: @Clinicians.Brief X: @CliniciansBrief The Team: Alyssa Watson, DVM - Host Alexis Ussery - Producer & Multimedia Specialist

We also talk about Nintendo's replaceable battery in Europe, some hope for energy storage, and an explanation of Apple's smart glasses strategy as well.Starring Tom Merritt and Jenn CutterShow notes found here. Hosted on Acast. See acast.com/privacy for more information.

Send me a derm question or story through text or voicemail!Skin barrier is having a moment... and for good reason. In this episode, I break down why restoring the skin barrier is a non-negotiable piece of the multimodal approach to managing atopic dermatitis in dogs and cats, and more importantly, how to actually do it when your clients can't keep up with a bathing schedule.Watch The Episode: https://www.youtube.com/@thedermvet3932Follow The Derm Vet Podcast: https://www.instagram.com/thedermvetpod/Follow Me: https://www.instagram.com/thedermvet/Timestamps00:00 Intro00:45 Itch Inquiry: Recurrent Yeast Otitis01:37 Antifungal Resistance in Malassezia03:54 Underlying Allergies and Immunotherapy06:29 Paronychia07:19 Toothpick vs. Tape Methods09:30 Treatment for Yeast15:35 Summary/Outro 

People can be interested without being ready to buy, they can agree to a proof of concept without having a clear path to production, they can praise the product without becoming the kind of customer who helps the company grow. That distinction was at the center of Collin Stewart's conversation with Ankur Patel, founder and CEO of Multimodal, on the Predictable Revenue Podcast. Multimodal builds AI for document-heavy, decision-heavy workflows in financial services, and Ankur's story is useful because it shows how easy it is to mistake activity for traction. Highlights include: Identifying the Niche (02:01), Customer Development and Validation (04:36), Pricing Strategy and First Customer (10:23), Evolving Market Strategies (17:46), Recognizing Product-Market Fit (22:50), and more... Stay updated with our podcast and the latest insights on Outbound Sales and Go-to-Market Strategies!

This is a recap of the top 10 posts on Hacker News on June 03, 2026. This podcast was generated by wondercraft.ai (00:30): Gemma 4 12B: A unified, encoder-free multimodal modelOriginal post: https://news.ycombinator.com/item?id=48385906&utm_source=wondercraft_ai(01:55): Meta workers can opt out of being tracked at work up to 30 minOriginal post: https://news.ycombinator.com/item?id=48383220&utm_source=wondercraft_ai(03:21): Pwnd Blaster: Hacking your PC using your speaker without ever touching itOriginal post: https://news.ycombinator.com/item?id=48382310&utm_source=wondercraft_ai(04:46): Elixir v1.20: Now a gradually typed languageOriginal post: https://news.ycombinator.com/item?id=48388324&utm_source=wondercraft_ai(06:12): I was recently diagnosed with anti-NMDA receptor encephalitisOriginal post: https://news.ycombinator.com/item?id=48384355&utm_source=wondercraft_ai(07:38): DaVinci Resolve 21Original post: https://news.ycombinator.com/item?id=48384482&utm_source=wondercraft_ai(09:03): Uber's $1,500/month AI limit is a useful signal for AI tool pricingOriginal post: https://news.ycombinator.com/item?id=48383056&utm_source=wondercraft_ai(10:29): 32GB of DDR5 now costs $375 – AI shortage continues to squeeze PC buildingOriginal post: https://news.ycombinator.com/item?id=48383241&utm_source=wondercraft_ai(11:54): U.S. to dismantle system tracking Atlantic currents that are at risk of collapseOriginal post: https://news.ycombinator.com/item?id=48392232&utm_source=wondercraft_ai(13:20): MacBook Neo is so popular that Apple doubled productionOriginal post: https://news.ycombinator.com/item?id=48386238&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

No matter your role, experience or industry, we all (mostly) waste hours a week doing the same thing: manually creating slides. 

Modern work can be frustrating and chaotic—if you don't have the right tools. From context engineering to multimodal search, go behind the scenes and hear how Dropbox engineers are building AI that actually understands you, so you can focus on the work that matters most. If you're new to Working Smarter, we've travelled from the F1 track to the bottom of a lake, and heard real stories from chefs, doctors, lawyers, and founders about how AI is helping them do more of what they love about their jobs. But in our third season, we're talking to the people behind the tools—the engineers and product leaders building helpful, time-saving AI features into the Dropbox experience you already know and trust. You'll hear all about their work on agents, inference, security, and, of course, how the people building AI use AI themselves. ~ ~ ~  Working Smarter is brought to you by Dropbox. Find, organize, and share your work—all in one place—with context-aware AI from Dropbox. You can listen to more episodes of Working Smarter on Apple Podcasts, Spotify, YouTube, Amazon Music, or wherever you get your podcasts. To read more stories and past interviews, visit workingsmarter.ai This show would not be possible without the talented team at Cosmic Standard: producer Ben Montoya, sound engineer Aja Simpson, technical director Jacob Winik, and executive producer Eliza Smith. Special thanks to our illustrator Fanny Luor, marketing consultant Meggan Ellingboe, and editorial support from Catie Keck.  Our theme song was composed by Doug Stuart.  Working Smarter is hosted by Matthew Braga. Thanks for listening!

June is here so guess what? It's officially Hot AI Summer.

Guests: Dr. Kelly Williams, Marion L Brittain Postdoctoral Fellow at Georgia Tech; Dr. Meryem Yilmaz Soylu, research scientist at the Center for 21st Century Universities (C21U); Alison Valk, the Emerging Technologies Librarian at the Georgia Tech Library.  First broadcast May 29 2026. Playlist "That's as you like it."

Google dropped like 197 new AI features this week.

AI suddenly feels like it has crossed a threshold, and Yann Dubois, co-lead of the Post-training Frontiers team at OpenAI, joins Matt Turck to explain why. Yann's team has led the post-training behind the company's reasoning models, including the recent GPT-5.5 release. In this conversation, we go inside the shift from raw model capability to useful, reliable systems: what changed with GPT-5.5, why reinforcement learning is moving beyond math and coding competitions into messy real-world work, how reasoning models like GPT-5.5 actually work, the difference between GPT-5.5 Thinking and GPT-5.5 Pro, why post-training has become one of the most important frontiers in AI, and why evals, model-as-judge, hallucinations, agentic workflows, GDPval, and continual learning are now central to the next phase of frontier models. Yann also shares why continual learning remains one of AI's biggest unsolved problems three years after ChatGPT, and where startups still have massive room to build as frontier models race ahead.(00:00) - Cold open(00:34) - Intro(01:30) - Why recent AI progress feels like a step function(04:13) - Model reliability & the rollercoaster of shipping 5.5(07:33) - How OpenAI structures vertical and horizontal teams(09:49) - Improving model efficiency and test-time compute(12:32) - Yann Dubois' journey from Switzerland to OpenAI(15:37) - Reasoning in 2026: Real-world utility vs verifiable rewards(18:34) - GPT-5.5 Thinking vs Pro: Scaling test-time compute(20:09) - How reasoning models become more efficient(23:23) - Pre-training scaling and overcoming the data wall(27:03) - Multimodal data, synthetic data, and embodied AI(31:05) - Demystifying mid-training and post-training(37:21) - Does RL create new capabilities in AI?(38:53) - The challenges and frontier of scaling RL(43:09) - Is building AI models a craft or a strict science?(48:21) - How AI models generalize across different domains(54:18) - How reinforcement learning cures AI hallucinations(56:04) - Negative generalization and conflicting instructions(58:05) - Can RL scale to law, medicine, and the broader economy?(1:00:19) - The evaluation bottleneck and Model as a Judge(1:04:21) - Continuous AI progress & continual learning(1:08:49) - Will foundation models eat the agent harness?(1:11:23) - Why startups should focus on the last mile of AI

This research examines the development and scaling laws of Native Multimodal Models (NMMs), which are AI systems trained from scratch to process both images and text simultaneously. The sources compare early-fusion architectures, which integrate raw multimodal signals from the start, against traditional late-fusion models that rely on separate pre-trained encoders. Findings indicate that early-fusion models are more efficient to train, easier to deploy, and perform as well as or better than late-fusion counterparts at lower compute budgets. Furthermore, the study highlights that incorporating a Mixture of Experts (MoE) significantly boosts performance by allowing the model to learn modality-specific weights. This specialized approach enables sparse models to handle heterogeneous data more effectively than dense architectures while maintaining the same inference cost. Ultimately, the reports suggest that NMMs follow predictable scaling properties similar to large language models, providing a blueprint for the next phase of edge AI development.

En este episodio de "Marcas que venden", conversamos con Amalia Carrasco, Directora de Comunicación de BlaBlaCar para España y Portugal. Amalia nos cuenta su transición desde el mundo de las agencias de comunicación hasta liderar la estrategia de una de las marcas más reconocidas en el sector de la movilidad. Exploramos cómo BlaBlaCar ha logrado pasar de ser una web para compartir coche a convertirse en un marketplace multimodal que integra autobuses y trenes. Analizamos el peso de la confianza como motor del negocio, cómo gestionan un rebranding que no pierda la esencia social de la marca y por qué el "boca-oreja" sigue siendo su canal de captación más potente. Si te interesa el marketing basado en el propósito, la gestión de reputación y las anécdotas reales de una comunidad que incluso termina en bodas, ¡este episodio es para ti!. Linkedin de Amalia: ⁠https://www.linkedin.com/in/amaliacarrascolozano/⁠ Linkedin de Capi Herrero: ⁠https://www.linkedin.com/in/juanmiguelherrero/⁠ Linkedin de Chema Martínez: ⁠https://www.linkedin.com/in/chema-martinez-pastor/⁠ Una producción de Formato Podcast: ⁠https://formatopodcast.com/⁠ Con la colaboración de Espacio Eventize: ⁠https://espacioeventize.com/

En este episodio de "Marcas que venden", conversamos con Amalia Carrasco, Directora de Comunicación de BlaBlaCar para España y Portugal. Amalia nos cuenta su transición desde el mundo de las agencias de comunicación hasta liderar la estrategia de una de las marcas más reconocidas en el sector de la movilidad. Exploramos cómo BlaBlaCar ha logrado pasar de ser una web para compartir coche a convertirse en un marketplace multimodal que integra autobuses y trenes. Analizamos el peso de la confianza como motor del negocio, cómo gestionan un rebranding que no pierda la esencia social de la marca y por qué el "boca-oreja" sigue siendo su canal de captación más potente. Si te interesa el marketing basado en el propósito, la gestión de reputación y las anécdotas reales de una comunidad que incluso termina en bodas, ¡este episodio es para ti!. Linkedin de Amalia: ⁠https://www.linkedin.com/in/amaliacarrascolozano/⁠ Linkedin de Capi Herrero: ⁠https://www.linkedin.com/in/juanmiguelherrero/⁠ Linkedin de Chema Martínez: ⁠https://www.linkedin.com/in/chema-martinez-pastor/⁠ Una producción de Formato Podcast: ⁠https://formatopodcast.com/⁠ Con la colaboración de Espacio Eventize: ⁠https://espacioeventize.com/

Is AI really the end of creativity, or the biggest emancipation of creative energy we've ever seen? How can authors thrive in a time of super abundance, when anyone can make anything? What happens when publishers become technology providers, and agents start shopping for books on our behalf? With Nadim Sadek. In the intro, my AI-Assisted Artisan Author webinars. This show is supported by my Patrons. Join my Community and get articles, discounts, and extra audio and video tutorials on writing craft, author business, and AI tools, at Patreon.com/thecreativepenn Nadim Sadek is a serial entrepreneur and the founder and CEO of Shimmr AI, an AI-powered book marketing company, as well as the bestselling author of children's books and non-fiction books, including Quiver, don't Quake: How Creativity Can Embrace AI. You can listen above or on your favorite podcast app or read the notes and links below. Here are the highlights and the full transcript is below. Show Notes Using AI as a research partner, editor, and constructive critic when writing a book The ratio of dreaming to execution Why publishers still draw red lines at AI-written words, and why that may change Inside Shimmr's three-engine advertising system: Strategizer, Generator, and Deployer Multimodal interactivity, agentic purchasing, and the idea of the Panthropic You can find Nadim on LinkedIn or at NadimSadek.com. Transcript of Interview with Nadim Sadek Jo: Nadim Sadek is a serial entrepreneur and the founder and CEO of Shimmr AI, an AI-powered book marketing company, as well as the bestselling author of children's books and non-fiction books, including Quiver, don't Quake: How Creativity Can Embrace AI. So welcome to the show, Nadim. Nadim: It is lovely to be here. I feel very privileged to be invited onto this. Thank you. Jo: Oh, I'm excited to talk to you today, and we're really talking about AI. I wanted to start with the fact that you do seem to have a sort of relentless optimism. How do you remain so optimistic about AI when the publishing industry that we both work in seems so overwhelmingly negative? Lift our eyes to the horizon—what is the bigger picture? Nadim: Oh my goodness. That is a big one. I think my optimism is quite confined actually in the area of publishing. If you were to ask me to speak about AI more broadly—which you're not, but I'm going to give you a little bit of it—I've got lots of concerns. That includes the advent of autonomous weapons and economic singularity, where the wealth from AI as an industry is going into just a few hands, and energy usage, and cultural homogenisation, I suppose, and the potential for brain rot. There's a whole pile of stuff which is really not very good about AI, and all the normal things about fraud and theft and so on. However, if you recognise that and then you say what's going on in publishing, then the obvious thing that you first have to deal with is what did happen with copyright. Is it appropriate to say that things have been stolen and taken without permission and so on? It is. It's going through the American courts at one pace. I saw that Penguin Random House have started a case against OpenAI in Germany, where there will be a much faster legal conclusion—a judge's conclusion, I think. This will begin to put parameters on how copyrighted materials can be used, and possibly also some retrospective judgment about what has happened to this point and what can be done about it. So it's good that you've asked questions so early in our conversation, because I think —  It's important to contextualise my optimism. It is whilst noting with regret the behaviour of the AI industry—the models themselves—in not dealing with copyright in the most generous or appropriate fashion. I think we should also recognise that copyright probably wasn't designed for machine learning in the way that it is. Probably the industry wasn't terribly well prepared to note, negotiate with, and navigate the very fast-moving technological culture of AI companies. So I think lots of mistakes have been made on both sides. When you put all that to one side, what's left for me is an amazing emancipation of creative energy and also a huge efficiency being brought to the publishing industry. We can talk about both those things further, but for me that is what's going on. The efficiency of bookmaking and publishing generally—the whole workflow of getting a book out of somebody's head and into a reader's hands—I think is immensely streamlined and improved by AI. Actually, if you talk about it carefully, which I'm sure we will do, the ability of creators to share and let others experience their creative endeavours becomes so much better, so much fuller, so much richer. So that's why I'm excited about it. Jo: Well, let's get into those two things then. You mentioned the emancipation of creative energy, and you've worked with various AI tools as part of your creative and business processes. You've said that AI can be a creative companion. So specifically when it comes to Quiver, don't Quake, for example— How are you using the various tools in such an emancipated way? Nadim: Well, just to put a bit of a broader context on it, we're an AI-native company at Shimmr, and separately I wear a hat as an author. You mentioned the AI books and the children's books. I'm also writing a book about the psychology of motorcycling. So it's a very odd authorial footprint, but it means that I kind of tramp around the place and learn different things. What I've noticed, even within Shimmr, is that the whole team has been using AI tools very differently. Lots of people are very bright in the company. They're all brighter than me, and I salute them and love them. But they've all used AI to become more creative in their own ways. For example, our Chief Commercial Officer is very numerate and logical, and not loquacious. She prefers to say things straight and simply. She has become an unbelievably creative financial modeller and analyst because she uses AI in lots of different ways. So she has flourished and grown so much, and is creative in a way that she never could be before—not only around numeracy and financial matters, but in thinking through new concepts for sales and marketing and for our commercial development. I've just noticed all around me this going on. When it comes to me, I prefer to express myself through writing. I talk a bit as well, as you can tell, but my favourite means of communication is just writing. When I was writing Quiver, don't Quake, I would use AI in a number of different fashions. One would be for research. One of the chapters is about the psychology of creativity. I'm a psychologist, so I tend to come at things from a psychological perspective. What is the psychology of creativity? Well, here comes a million-word answer from an AI—this person said this, this person said that. Then I kind of focused my research in particular areas and assembled them by drawing from the outputs of several AIs about what has been said about AI, what the science says about it, what sociology says about it, what particular creatives that we're all aware of say about it, whether they're in the advertising industry or musicians or artists or whatever. So that was a very rich way of researching things. I would often put a chapter in—this is a slightly different use—a manuscript that I'd written and say, “Read this as if you're somebody just coming across my book, and tell me where the reader might struggle between one paragraph and another, or where there's a logical fallout, or where the concept isn't really very fully excavated and developed.” It would occasionally prompt me to say, “You could probably do with a line that brings the reader from this point to that point.” And usually I listened to that and then wrote something new. In another use case, I eventually gave it the whole book and said, “I think I've done an okay job here and I quite like the flow and I'm sort of satisfied enough, but before I send it to the publisher and say, ‘there you go,' what do you think? Are there any ways in which this book could become a better and more interesting read?” It came back fairly promptly and said, “Well, what you haven't really done is considered what all the naysayers would say. You've done your dark moments of militarism and all that stuff, but what about some of the other stuff closer to publishing or creativity?” So off I went on a new round of research, and did some myself and used the AI for other bits. The funny thing, really the ironic thing here, is that the book is much better, and most people salute the book for the eighth to ninth chapter that talks about the constructive critics. I assemble them all and articulate all their arguments and say how hideous AI is and how terrible it is for the world and all of us. And then I try to repudiate some of them, not in a defensive way, but just to say, actually, yes, that's one perspective and here's another one. That chapter, ironically, about how AI is terrible was prompted by AI. It said, “You should really have a go at me.” And so I did. So that was another use case. Then finally—perhaps I'll say this—I have a friend who is, I think, the Editor-in-Chief of Penguin in India. I got to know her at a book fair or something. We started chatting, and I told her about my kids' books. I said, “I could really do with an editor on these ten books that are due to be published.” She very generously, amiably, and very constructively gave me feedback on each individual book and then on the whole set. I was really happy with it. I said to her, “That was a delight.” She said, “You'd be much better off working with Editrix.” I said, “What's Editrix?” She said, “Well, it's an AI platform I've created where you can go and self-edit.” I said, “You must be kidding. I'd much prefer chatting to you and our interactions.” She said, “Yes, well, go and try it.” So I got an account for the Editrix AI. Off I went, gave it my books, and lo and behold, it came up with some incredibly sophisticated and subtle observations on the books that neither Meru nor I had seen. For example, there's a story where a boy who lives in a house on a hill meets another boy on a bridge, and they end up in a silly confrontation. They're young and foolish, and it sort of transpires that the other boy lived in a local village. Now, I suppose in retrospect, it's pretty obvious that this could be seen to be colonialist, imperialist, and a sense of entitlement from the boy at the top of the hill crossing the bridge first and so on. Hadn't crossed my mind. The AI said, “I can tell from the rest of your writing that you don't really have a sort of racist or imperialist or superior attitude to things, but in this story, there could be a misapprehension that you do.” I thought, wow, what a great warning. So I changed it. There are almost endless ways—and I can tell you others, because I'm writing a book about clouds at the moment—in which AI can help you as an author. I've just shared some of those with you. Jo: Yes, well, I love that. I also use it for research. I definitely use the “give me feedback as a reader avatar, as a reader of this type of genre” or whatever. Nadim: Yes. Jo: I use different tools as well, so I agree with you. All of that is, I think, what a lot of people are doing. You also said you did a lot of the writing and rewriting, so the human was very much there. This was not an AI-generated work in any way. It was using an AI as a sort of collaborator—a creative companion, to use your words—which I think is great. One of the things that AI-positive people like us are finding is that there's so much negativity around the traditional publishers, around other authors, around supposedly negative backlash from readers. I think there's a lot of very noisy people who are probably making this sound worse than it is. Since you are so embedded in traditional publishing in so many ways, how are publishing people thinking about this? Do you think it's just different in terms of the creative side versus say the marketing side? What is happening there, and what do you recommend for authors? Nadim: What I'm observing is that there is increasingly confident adoption of AI for corporate efficiency, which is a polite way of saying where one can see profitability being improved. Could you streamline legal contracting? Yes. Can you manage royalty payments better? Yes. Are there better sustainability prospects with managing a warehouse and distribution and so on with AI? Yes. Could you improve your marketing by looking at competitive titles and trends, and optimising your metadata and your SEO and now your GEO, all using AI? Yes, yes, yes, yes, yes. All of these things can be assisted. Can you manage much more of your backlist, where you don't have the human or financial capital to manage all of those titles in a truly respectful and invested way? Yes, yes, yes. So wherever there's corporate efficiency, I see publishers being increasingly bold about saying they have integrated AI into their workstreams. What's much more tentative and hesitant is where there's discussion of authors—and I do hesitate to use the right words here—being assisted by, employing, working with AI. I kind of shorthand it as creative emancipation. It really means very many different things. Let me give you the example that I referred to briefly a second ago of Cloud Land, which is probably my first real novel. I'm very lucky. I sit working every day at a desk that's got three windows, and I look at the sky, and every day it's different, and I'm fascinated by it. I've been flying around the world since I was very young—my father worked for the World Health Organization, we moved between many countries—so I've also seen clouds from the sky a lot. I've noticed that in different parts of the world there are different cloud formations. It came to me one day that it would be very interesting if the clouds were somehow sentient, and that there is a cloud society, and that Cloud Land lived above human land and absorbed and observed us. Actually, the more I started thinking about it, the more I thought, well, we kind of evaporate. We give off vapour all the time and it rises up to clouds and maybe we're sending DNA signals to it, and it condensates and sends rain and storms and winds and lightning and thunder and all. There's a huge amount of interaction between Cloud Land and human land if you think about it. So I went into an AI. I said, “Hey, I've been thinking about this, blah, blah, blah. Any observations on what I've been saying so far?” I think one of the first things it said to me was, “You are actually playing with quantum physics.” I had no idea what quantum physics were really. I thought, well, this is interesting. I went and researched quantum physics, and actually there is some of that in it. If you count Cloud Land as a creative notion— The original idea, the creativity, came wholly from me, and then the development of it has been assisted by working with AI. I as a creator have spent much more time originating ideas about a story than would historically have been true. I probably would have gone to a library, tried to find the right geography textbook, read up about clouds, discovered what the nomenclature is, thought about whether I could put characters to cumulonimbus versus stratus something or other, and kind of worked my way gradually through it. There is something that I refer to in Quiver, don't Quake, which is what I call the ratio of dreaming to execution. I think previously, without AI, creators would probably spend 80% of their time researching and trying to get information and assembling things and editing documents and spell-checking and doing a whole pile of different tasks None of which I actually dismiss, because I think sometimes those difficult and “menial” tasks give you time to let ideas percolate and flourish and grow. It's just part of the process. But whereas before, I think we probably spent 20% of our time originating and 80% of our time assembling, I think it's inverted now. You can probably do 80% of the time you want creating and 20% of the time fiddling about getting your act together. So I feel that that's a huge emancipation of individual creativity. There's also—and we can talk about this if you wish—I think a much broader sociological phenomenon going on, which is really about every person in the world, all 8 billion of us, being creatives. That's the way I see the world. I think that only a minority of that 8 billion have the gift of craft that we recognise—of writing or drawing or making music or being an architect or a biomedical scientist or something that's creative and assembling things. And AI gives you courage and helps you to identify what you wish to make. I really don't mean creating the artefacts. I don't mean painting or making a song or writing a book. I just mean helping one to express and articulate oneself so that one's creative idea is shareable and experienceable by others. Jo: Well, it's interesting. I mean, everything that we've discussed, you're really saying that the main line is the actual writing of the words, because none of us can articulate how ideas come. Especially with Claude, we might have a creative spark, but I'm sure you've found the same: if I go to Claude, which is my favourite, with my creative spark, by the time we've discussed it, possibly over days, I've lost track of who said what. The idea definitely started with me, because the AI at the moment doesn't have its own creative spark in terms of its own drive to write a book, for example. So it starts with me, but then it goes back and forth, back and forth—sparks new ideas, something it wrote makes me think about something else. I think the difficulty with how publishing seems to be doing this at the moment is that it is just the written words on the page that is their red line around “have you used AI to generate a book?” But even that, I just think, surely that will change. For example, in the publishing industry, ghost writing—or writing dead authors, like Wilbur Smith—I was going to say Wilbur Smith is a good one. I mean, we've seen them, just different dead authors essentially writing in the voice of those people. So I just see that there are many possible places where publishers might want this kind of tool. I don't know— Do you see any openness to the actual words themselves? Nadim: I think you're right to identify that that is the place that it gets stickiest. What you kind of do in your private time—imagining and dreaming things up and interacting—it's a facsimile for talking to your friends or another author or something. It's just an AI companion. So I think that that is, you're right, less scrutinised. It is when one examines the words on the page. It's funny—it's almost as if it's a measure of how hard did you work to do this? Or did you just splatter it down on the page by pressing a button somewhere? It's almost as if, as creatives, we have to evidence that we have suffered, you know? I think there's a different form of suffering when you write with AI. It's true that if you command AI in some way to write for you, the default writing will be pretty anodyne, pretty bland, pretty mundane. It is deliberately so. AI is created and it is tuned to be inoffensive, to please most people, to be accessible to most readers and consumers of it. So it's another thing that I encourage people to do: don't approach AI with a kind of Google mindset where you just do a question and answer—”what time is it in New York now?” “Well, it's five hours behind” or whatever. Instead you say, “Hey, listen, I'm thinking about clouds, but I want a bit of spittle going up and down between the two, and I'd quite like a crazy cloud that harasses us.” Well, now I'm putting in some of my idiosyncrasy and my eccentricity and my personal perspective. The more you do that, the more that even if you did press a button and say, “Command, I want you to write this book,” that will no longer be a bland and mundane bit of output. It'll be very tuned by your interactions, and it'll exhibit some of your nature. So I think there probably are factories—there's always factories. They're probably—and actually I know this—writing a lot of romance, writing a lot of porn, things which are fairly well parametered. You know what happens in both of those genres more or less, so it's pretty easy for a machine to emulate what an author might write there and go and do it. But if you get into something like, “a sand dune was my cousin”—like, okay, well that's a bit different. What do you mean? And there it becomes a much more interesting bit of writing. So I think we're going to see a spectrum. To come back to your question about where publishers draw red lines, I think it's where they just see straight away mundane output that doesn't feel like it had a lot of craft or ingenuity or hard work to it. But I believe that as we go on, that's going to become harder and harder to establish. As we become more sophisticated users of AI, and AI's capabilities to understand us and to work with us become better, then I don't think it'll be such a big question where the words came from. What we'll feast on with each other is our creative ideas and how they're expressed, but not how they were produced. Jo: I mean, I always say to people, I'm not a word generator. That's not what makes me or my books worthy. It is what I do with it. It's the stories I tell, or it's the personal things behind it. So generating millions and millions of words, whether you generate them by typing or handwriting or AI or whatever, it isn't the word generation that is the point. It's all of the things that make that finished thing what it is. So anyway, let's come back to the other thing, because you mentioned that publishers seem very happy around corporate efficiency, anything that drives profitability. You also mentioned that Shimmr is an AI-native company. Now, I, and many people listening—we are a one-person company. So I run my own company. It's a publishing company. I do all my publishing, I do all my marketing, I do all my business as just me. So I also use AI for a lot of this stuff. I wondered— How do you see publishers changing to become more AI-native? How can we as individual author-publishers do that too? Because it feels like a massive mindset shift, not just plug in Opus 4.7 here. Nadim: I have been found saying at various publishing events—and it is deliberately a little bit provocative—that I believe that publishers have always been technology providers to creatives. It's not only what they do, but it is a part that they don't seem to embrace very hard. Even if you just go back to Gutenberg—I mean, here's a printing press, it's a bit of technology. “I'll make your book, I'll make your words into books.” It started there, and it's always been. That applies to distribution and e-commerce and audiobook manufacture and all sorts of other things along the way. So I encourage publishers to accept the notion that what they should do to attract authors in the future is partly—only partly—develop their own house AIs. It can be as ethically trained as that house wishes to deal with the copyright furore. It can be tuned to do editing in a particular way. It can have a specific way of copy editing. It can have a collaborative notion. It can have an assistant that helps you understand genres and hotspots and competitive titles. It can help you to think about, as Americans might say, what's hot and what's not in the world at the moment. So you might be more attuned to what the market demands, if that affects you at all. Some writers don't care, and that's fine. It can certainly help with all the marketing then. How can you produce social media content that's appropriate to your book, and all the rest of it. So I think there's a way in which publishers could massively enable authors. I talk to tons and tons of authors clearly about Shimmr, and what they all resent, I would say, is finding their time stolen by trying to flog their work rather than make it. Jo: Yes. Nadim: So the marketing process is just theft of creative time for most authors, and they hate doing it, and they're often not very good at it, because it's a completely different skillset from creating great stories or writing non-fiction books about particular subjects. So I believe that authors should be embracing the notion that publishers will create their own house AIs. And goodness me, we might even decide which publisher we prefer to go to on the strength of their AI position. Wouldn't that be interesting? But that is what I see the future being. Jo: Yes. I mean, definitely there's some quite significant authors—Dean Koontz, probably one of the biggest—who went to Amazon because of their technical ability around publishing and marketing. He was like, “Yes, I want this because of this.” Not that he'd be in bookshops or whatever—of course Dean Koontz is—but yes, so I think you're right there. For individuals also, as you know, we can use AI to help us market. I upload my books to Claude when they're finished, and I've just been marketing today. I'll say, “create 10 Midjourney images based on this book and give me all the marketing copy.” So I think we can use it now to help us be more efficient. On the other side of that, I think the bigger thing that's starting to happen is marketing is now much easier in one way. Nadim: Yes. Mm-hmm. Jo: So it's getting fuller, or even more. Nadim: Yes. Jo: So how do we deal with this? Because Shimmr is an AI marketing company. How are you thinking about the predominance of very, very good AI marketing now? Nadim: Yes, and it gets better all the time. It's a great question. Obviously, strategically, as an enterprise, we've really had to think about this one. If I go back one step, I always believe that innovation succeeds when it starts in a narrow space. So when Shimmr launched, we put ourselves forward and were quickly embraced, I have to say, as automated advertising that sells books. Nothing particularly more complicated than that. “Okay, you do ads, you automate it for me, and it'll help flog my books. Yes, that's it.” We had a rush. We've worked with about 250 publishers. As you might anticipate, it started with smaller ones, then got bigger. We now work with the biggest as well. That notion of automated advertising selling books was successful. Actually, that was about three years ago—a bit shorter than three years ago. What's happened in that time is that we have now collected a ton of data, and meanwhile the AI models have become more sophisticated and competent. Maybe I should just pause briefly and say what Shimmr actually does. We've got three main engines that are all chained together, to use pretty old language. The first one is what we call the Strategizer. It reads the book, it understands what we call its book DNA. So it's the structural elements of what the narrative is, who the protagonists are, and all the rest of it. It's also a psychological study of it—what's going on, what are the emotions or the values, what are the interests, how they intersect, where are the tensions, all those sorts of things. The Strategizer decides, “Well, reading everything between the covers of this book and understanding the author's intent, this is the best way to put this book forward because here are its strong points.” It hands that off to the second machine, which we call the Generator, which says, “Thanks for the creative brief. I'll make you the ads now.” It does videos and music and captions and all the rest of it. Then it presents its newly baked campaign to the third machine, which is the Deployer, that says, “Okay, well, I know where to find the audiences for this. If that's the DNA of the book and this is the campaign that manifests it, then I know where to find these people.” It goes and autonomously deploys it in various media channels to specific audiences who might be interested in that content. So that's what we started doing, and that generated a huge amount of data. Where we've got to recently—really in the last six months—is understanding that, as you've just said, most people can generate their own stuff. So in some ways they can look just like a mini Shimmr. The thing that differentiates the content is always the strategy. What we have learned to do now—and it's because of an agentic framework—is we've moved beyond what's between the covers of the book to look at life. We look at culture, what's going on, what are the trends, what's in and what's out. Even if you take a particular trend—let's say, fascism—what's the language associated with it that's being treated positively and respectfully, and what's the stuff that leads to it being dismissed straight away? All those sorts of nuances around everything. But equally, as well as going deep with a set of agents on what fascism might be in today's culture, we also go wide and say, “Well, how does that sit next to loyalty or hedonism or ambition or something else?” So we get this very, very circumspect analysis of the market. Then, indeed, if you do write a book about—I'm really going off-piste here, but you know, the hedonism of fascism, like, God, that would be a weird book—you discover that actually you're not really competing with another book, but you are competing with that specific podcast and this movie that came out, and another movement that's born in Italy but it's moving across Europe now or something. So we were able to produce strategies which now lead to a much broader offer, one which is much more sophisticated and much more likely to drive success in a book or in a creative enterprise. It informs product listings, metadata, author communications, PR, SEO, GEO, and of course the thing that we started with, advertising. So things that you see made by Shimmr should be much more resonant and much more attuned to the world, and commercially much more likely to drive success, than simply saying, “Here's a book, make ten Midjourney images out of it.” Jo: Mm-hmm. Nadim: It's really about the quality of the briefing and the quality of the assets that you're able to produce by having a much more sophisticated Strategizer. So we've gone back into the intellectual property and the human analysis, in a way, of the world. To understand where a specific piece of creative work sits in culture and society has become a much bigger proposition. Jo: Right. So you did mention podcasts there. So as in, you might present to a publisher “these are the podcasts that they should pitch” for example? Nadim: There's that, of course, but it's also, don't think that this book is competing with these three titles which your team put together. It's more that, if people want to listen to hedonistic fascism, they can listen to that podcast before they read this book. Jo: Okay, that's interesting. Interesting times. So we don't have much time left, but I think one of the biggest questions that people have—even if they're AI-positive, as I am and many people listening are—it's not that we're worried about AI replacing us, because we know we're individuals and all that, but we are slightly concerned about the volume of books in the market. And not just books, but TV shows and YouTube and TikTok. It's very hard to stand out. You do say in the book: “When anyone can make, maybe creativity lies not in the making, but in making others care.” How can I move up the value chain? So for many of us who make an income this way, what are your recommendations? Nadim: Great question. And actually I think it's really central. My latest catchphrase is that in a time of super abundance, we need super discoverability. So it's exactly as you just said—tons of work, tons of movies, tons of podcasts, and tons of everything. If you believe in what I've been saying, which is that we're emancipating the creative spark of 8 billion people, there's going to be even more. So I believe that the solution is what I call multimodal interactivity. That doesn't mean multimedia—it means multimodal. Multimodal means you can engage with an experience in different modalities—the same idea. So my conviction is that if you write a book or make a painting or have a piece of music that you've come up with—or anything really, creatively—and you wish it to both survive the first six weeks of its birth and then thrive in a more perpetual way in society and culture, then people have to be able to experience and engage with your idea in multiple modalities. I would always write a book, because that's what I do. Others produce a podcast or write a piece of music—whatever the same sort of things. Any one of us needs to make sure that that reappears and is experienceable and interactable with in different modalities. So my book should have some Instagram reels. There might be YouTube shorts, there might be a podcast, there might be a piece of music associated with it, it could be a movie. It could be a game, it could be an app. You really have to think about allowing your creative idea—more than your creative artefact—to live in culture. Sure, you want to make an income from the artefact that you are good at producing. As many of your listeners, and I, would be writers of books, we want that to persist as a revenue stream, and it should do. I would simply argue that making sure that whatever you've produced in your book is manifest, and people can interact with it in other modalities, is the surest way to get it seen and discovered. Jo: Yes, it's interesting. I've actually started looking at making my non-fiction books into skills. Nadim: Yes. Jo: And also making markdown MD files—books as markdown files for agents to buy. Nadim: Very good. You are way ahead of the curve. Jo: Well, I sell on Shopify, as do many listeners, and Shopify, as I'm sure you know, is now enabled for agentic purchasing. We are in ChatGPT. So it's really interesting to think, well, if the agents go shopping for people now and in the future, what you want is to be able to find it. Also, I haven't actually put an explicit licence, but people email me and say, “Can I upload your books into an LLM?” And I'm like, “If you buy a copy from me, then yes, you can.” Nadim: Yes. Jo: So I think it's changing. And as you say, I do think that people are more and more going to want to say “buy the PDF and put it in NotebookLM” or use it as a skill. Nadim: That's right. Jo: That kind of thing. Nadim: Yes, and then they go on a walk with their dog and they listen to the podcast about your book, which they've created on NotebookLM. It's exactly that. I think my worst fear for publishers is that they lose so much of the value chain—distribution, creative collaboration, all sorts of things along the way—that the worst position they could end up in is simply as book manufacturers, which would be just one small manifestation of a creative idea. Jo: Well, I'm excited about the future. I hope you are too. I think you are. What are you particularly excited about in terms of the changes coming? Nadim: Well, if I can be my most extravagant now, my greatest excitement about AI and the changes that are coming are that it'll produce what I describe as the Panthropic. The Panthropic is a way of seeing AI not as a companion or some anthropomorphic being, but instead the repository of everything that humans have ever thought or felt or created or shared, accessible to us all in an anonymised way. It's just a repository of interactable information. My excitement about it is that the liberation that that gives to information—which becomes knowledge, which of course we all know leads to some power—should result in truly new thinking, new philosophy, new spiritualism, possibly new questions about what it is to be a human being and what life on Earth is all about. New economics, new employment, new education. I think one can too easily underestimate the massive liberation of intellectual consideration and creativity that's about to surf across the globe, and I'm so excited by it. Jo: Mm-hmm. Yes, me too. Very interesting times ahead. So where can people find you and your books and everything you do online? Nadim: I think the easiest thing is just to go to LinkedIn and find me there as Nadim Sadek. You can also go to my personal website, which is NadimSadek.com, and that'll take you wherever you want on different journeys and different parts of my career. It'll also give you links to books. Of course, they're available in all formats—audio, paperback, ebook—and in many different languages, all through Amazon and other platforms, and Spotify and Audible and all the usual things. Jo: All the usual things. Well, thanks so much for your time, Nadim. That was great. Nadim: It's a pleasure. Thank you so much for having me.The post AI, Creativity, And The Future of Publishing with Nadim Sadek first appeared on The Creative Penn.

This podcast sponsored by Virbac. In honor of Mobility Awareness Month this May, dvm360 is shining a spotlight on the movement health of veterinary patients. Joining host Adam Christman, DVM, MBA, is Kara Amstutz, DVM, DACVSMR (Canine), CVA, CVPP, CCRT, to discuss how proactive mobility care profoundly impacts the quality and duration of life for aging pets.Together, they explore the necessity of a comprehensive, multimodal approach, integrating pain management, targeted nutrition, and physical exercise, while highlighting the essential role joint supplements play in long-term support.

In this must-listen episode, Dennis sits down with Dr. Jon Andrews—former 5th and 20th Group Special Forces medic turned Duke-trained anesthesiologist (pediatric & cardiac fellowships)—to tackle one of the biggest headaches in austere medicine: you have a tiny box of opioids and ketamine, a long mission, and a patient who needs to stay alive AND comfortable.They break down exactly how to stretch every milligram using real OR strategies adapted for prolonged field care: patient-specific planning, smart titration, multimodal synergy, regional blocks, ketamine myths, and when (and how) to layer non-narcotics without crashing your patient or your supply.Why this episode matters: Acute pain becomes chronic pain. Chronic pain leads to opioid dependence, PTSD, and worse outcomes. In the field, your choices today shape your patient's tomorrow—and whether you still have meds left when the next casualty shows up.Key TakeawaysStart low, titrate smart. Cut your first dose in half on sick or unstable patients. You can always give more—never the other way around.Multimodal is mission-critical. Hit pain from every angle (blocks + ketamine + acetaminophen + judicious NSAIDs) to dramatically reduce opioid requirements and prevent chronic pain pathways.Ketamine IS an analgesic. It's not just dissociation—it's an NMDA antagonist that blunts central sensitization and has proven opioid-sparing effects.Schedule your non-opioids. Acetaminophen (1 g IV/PO/PR q6h) and longer-acting adjuncts form your baseline; use fentanyl or morphine only for breakthrough.Blocks beat everything—if you can do them. Pre-emptive regional anesthesia (when feasible) is the single highest-yield move before surgical stimulus hits.Monitor like your life depends on it. Heart rate, blood pressure, and respiratory rate are your best pain score when the patient can't talk.Plan for worst-case evacuation. Bring more than you think you'll need and dose for the opioid-naïve or opioid-tolerant reality in front of you.Why treating hypertension in the OR (or field) almost always starts with fixing pain firstThe “start low, see response, add more” mantra every austere provider needsWhy Tylenol often performs as well as morphine in blinded ED studies (and why your patients still doubt it)Real talk on ultrasound-guided blocks in 2011 vs. today—and why proficiency still mattersThe dangerous synergy of opioids + benzos + ketamine on respiratory driveWhy you must get comfortable decreasing doses, not just ramping them upChapters01:55 – The austere reality: limited narcotics and why your favorite med won't last forever03:37 – OR planning vs. field reality: opioid-naïve vs. chronic users05:57 – Multimodal analgesia explained (blocks, ketamine, Tylenol, NSAIDs, dexmedetomidine)08:28 – Patient & mission factors that should drive your loadout12:23 – Golden rule: start low, titrate to effect, monitor vitals15:05 – Sick-patient hack: cut your mental dose in half16:01 – Is ketamine actually an analgesic? (NMDA, opioid-sparing, PTSD data)19:12 – Extending your supply: bolus vs. infusion, redosing strategy24:27 – First-line multimodal choices in the field27:43 – Juggling multiple agents: timing, scheduling, and longer-acting blocks30:15 – Regional anesthesia timing—pre-emptive is king (post-injury limitations)32:48 – Ultrasound & blocks in the current PFC world35:08 – Safety considerations for adjuncts (liver, kidneys, bleeding, alcohol)36:59 – Bang-for-buck data on Tylenol vs. morphine38:55 – Practical integration: layering Tylenol/ketamine with fentanyl titration41:54 – Getting comfortable titrating down (and why pain scores can lie)42:53 – Final wisdom: use everything you're comfortable with.For more content go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠www.prolongedfieldcare.org⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Consider supporting us: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠patreon.com/ProlongedFieldCareCollective⁠⁠⁠⁠⁠⁠ or ⁠⁠⁠⁠⁠⁠www.lobocoffeeco.com/product-page/prolonged-field-care

The countdown started yesterday in my kitchen, as my daughter flipped the calendar forward for something and realized she had less than thirty days of school left. She loves her teacher and looks forward to school, so she felt sad. It launched her into a story about how her class is trying to convince her teacher to move to the next grade with them. If you, too, are starting to plan ahead and think end-of-year thoughts, today I want to share a way to help students review and reflect on the year in one multimodal activity. I've had requests in The Lighthouse for ways to help students reflect on their own learning - to tell their own learning story. Research backs the importance of metacognitive reflection for students - in other words, it's helpful for them to think not only about what they've learned, but also how they've grown and developed as learners, and where they might want to go next. Before we dive in, feel free to grab the free curriculum that goes along with this episode. Everything pictured below and discussed throughout the episode is already set up to make this activity as easy to implement for you as possible! And yes, the handouts are editable so you can tweak them to suit your own twist on the activity. Grab the free curriculum for this activity: https://sparkcreativity.kartra.com/page/endofyearhexagons  Go Further:  Explore alllll the Episodes of The Spark Creativity Teacher Podcast. Grab the free Better Discussions toolkit Join our community, Creative High School English, on Facebook. Come hang out on Instagram.  Enjoying the podcast? Please consider sharing it with a friend, snagging a screenshot to share on the 'gram, or tapping those ⭐⭐⭐⭐⭐ to help others discover the show. Thank you! 

A proud California girl, Louisa Frahm was born and raised in San Diego. She received her undergraduate degree in journalism from the University of Colorado at Boulder in 2012. Throughout the past decade, she's built a booming career in the news SEO world, conducting search efforts at E! Online, Yahoo!, TMZ, People Magazine, Entertainment Weekly, the Los Angeles Times, and ESPN. She also served as a Trends Curator on the Google Trends team. To bolster her communications skill set, she acquired a master's degree in communication management from the University of Southern California in 2021. Leadership development and mentoring are two of her biggest professional passions. When Louisa isn't busy with work, she loves enjoying any and all things pop culture with her family and friends. Her Funko Pop collection is over 100 figurines strong. Ask her about Prince, Freddie Mercury, and David Bowie.

In this episode of the PRS Global Open Keynotes Podcast, Dr. Paulo Michels discusses his technique for minimally invasive full body remodelling. His technique involves pre-surgery diet, intraop liposuction, rib recontouring, ultrasound guided fat grafting and video assisted minimally invasive lipoabdominoplasty. This episode discusses the following PRS Global Open article: "Full-body Remodeling with Minimally Invasive Techniques" by Paulo Michels, Ricardo Araujo and Rafaela T.B. Michels. Read it for free on PRSGlobalOpen.com: https://journals.lww.com/prsgo/fulltext/2026/01000/full_body_remodeling_with_minimally_invasive.60.aspx Dr. Paulo Michels is a plastic surgeon in Abu Dhabi. Your host, Dr. Damian Marucci, is a board-certified plastic surgeon and Associate Professor of Plastic Surgery at the University of Sydney in Australia. #PRSGlobalOpen; #KeynotesPodcast; #PlasticSurgery; Plastic and Reconstructive Surgery- Global Open The views expressed by hosts and guests are their own and do not necessarily reflect the official policies or positions of ASPS.

Katharine Jarmul, Privacy in ML/AI Expert & Author of Practical Data Privacy, joins Hugo to unpack why most AI privacy advice is theater: and what technical privacy actually looks like when you're shipping LLMs, agents, and multimodal systems into the real world.In this episode, we dig into how to build defensible systems in an era of AI agents and multimodal models: why system prompts (and your entire agent harness!) should be considered public by default, and why “privacy observability” is as critical as data observability for anyone building with LLMs today. Multimodal is what changes the threat model: identifiers hide in images, audio, and metadata, not just text, and the old anonymization playbook doesn't cover it.Vanishing Gradients is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.We Discuss:* No Convenience Tax, you don't have to trade privacy for utility: high-utility AI products can be privacy-preserving through technical controls like privacy routing and input sanitization;* Public Prompts and Harnesses: assume any instruction or secret in a system prompt or agent harness will be exfiltrated; don't put sensitive info there in the first place;* Privacy Observability, tag and track data flows so information is used only for its original intended purpose: catch design flaws before they become legal problems;* Technical Privacy, implement mathematical and statistical constraints directly into ML systems and data flows so privacy is measurable and enforceable, not aspirational;* Tiered Guardrails, a three-layer approach: deterministic filters for hard rules, algorithmic models for nuanced classification, and internal alignment training for behavioral baselines;* Federated Learning Is Not Privacy, model updates in FL leak sensitive data on their own: you must layer differential privacy or encrypted computation on top, or you're reverse-engineerable;* Anonymization Spectrum, navigate the “grayscale” of privacy in multimodal AI, balancing data utility and individual risk as identifiers hide in non-obvious places;* Privacy Champions, embed privacy accountability directly into development by training and incentivizing engineers inside product teams;* Red Teaming as Ritual, your goal is to attack yourself: practice thinking like an attacker, and turn privacy testing into an organization-wide creative ritual rather than a siloed security task.You can also find the full episode on Spotify, Apple Podcasts, and YouTube.You can also interact directly with the transcript here in NotebookLM: If you do so, let us know anything you find in the comments!

What does it take to build a multimodal transportation network that actually works for rural communities, growing regions, and everyone in between? In Kansas, that question is critical as the Kansas City Chiefs plan their stadium move and the 2026 FIFA World Cup comes to town.   Listen in as we sit down with Matt Messina, Chief of Multimodal Transportation at the Kansas Department of Transportation (KDOT), to explore how Kansas is prioritizing transit solutions for upcoming projects and how community input shapes decisions. It's an insightful journey into the challenges and opportunities of public transit, pedestrian infrastructure, and the future of mobility.   We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today—a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen—and give us a review on your preferred podcasting platform.   Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.

We've been on a bit of a mini World Models series over the last quarter: from introducing the topic with Yi Tay, to exploring Marble with World Labs' Fei-Fei Li and Justin Johnson, to previewing World Models learned from massive gaming datasets with General Intuition's Pim de Witte (who has now written down their approach to World Models with Not Boring), to discussing the Cosmos World Model with with Andrew White of Edison Scientific on our new Science pod, to writing up our own theses on Adversarial World Models. Meanwhile Nvidia, Waymo and Tesla have published their own approaches, Google has released Genie 3, and Yann LeCun has raised $1B for AMI and published LeWorldModel.Today's guests have a radically different approach to World Modeling to every player we just mentioned — while Genie 3 is impressive, its many flaws demonstrate the issues with their approach - terrain clipping, noninteractivity (single player, no physics/no objects other than the player move), and maximum of 60 second immersion. Moonlake AI (inspired by the Dreamworks logo) is the diametric opposite - immediately multiplayer, incredibly interactive, indefinite lifetime, capable of MANY different kinds of world models by simulating environments, predicting outcomes, and planning over long horizons. This is enabled by bootstrapping from game engines and training custom agents: In Towards Efficient World Models, Chris Manning and Ian Goodfellow join Fan-Yun in explaining why their approach to efficiency with structure and casuality instead of just blind scaling is sorely needed:SOTA models still show physical or spatial understanding glitches, such as solid objects floating in mid-air or moving “inside” other solid objects.If the goal is to plan for the next action, how often is a high-resolution pixel view necessary for modeling the world? Our bet is that there is a disproportionately large share of economically valuable tasks where such detail is not required. After all, humans with a wide variety of sensory limitations have little difficulty doing almost everything in the world. Furthermore, for a large number of purposes, describing a scene or a situation in a few words of language (“the car's tires squealed as it cornered sharply”) is sufficient for understanding and planning.Experiments also show that humans only partially process visual input in a top-down, task-directed way, often making use of abstracted object-level modeling. In almost all cases, partial representations combined with semantic understanding are sufficient.…If the goal is to facilitate the understanding of causality in multimodal environments, then the world model—whether it is used in the virtual world or the physical world—must prioritize properties such as spatial and physical state consistency maintained over long time periods, and an ability to evolve the world that accurately reflects the consequences of actions. That's what Moonlake is building.Game engines are the right starting point abstraction to efficiently extract causal relationships, and building the interfaces and community (including their new $30,000 Creator Cup) to kickstart the flywheel of actions-to-observations.We were fortunate enough to attend their sessions at GDC 2026 (the Mecca of Game Devs), and were impressed by the huge variety and flexibility of the worlds people were building with Moonlake's tools already! Live videos on the pod.Full Video Pod on YouTube!Timestamps00:00 Benchmarking Gets Hard00:47 Meet Moonlake Founders01:26 Why Build World Models03:12 Structure Not Just Scale05:37 Defining Action Conditioned Worlds07:32 Abstraction Versus Bitter Lesson14:39 Language Versus JEPA Debate20:27 Reasoning Traces And Rendering Layer37:00 Gameplay Over Graphics38:02 Fiction Rules And World Tweaks39:15 Code Engines Beat Learned Priors41:10 Diffusion Scaling Limits43:23 Symbolic Versus Diffusion Boundary46:14 Platform Vision Beyond Games50:24 Spatial Audio And Multimodal Latents54:23 NLP Roots Hiring And Moon Lake NameTranscript[00:00:00] Cold Open[00:00:00] Chris Manning: Think this whole space is extremely difficult as things are emerging now. And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks.[00:00:20] But these days so much of what people are wanting to do is nothing like that, right? You're wanting to get some recommendations about which backpack would be best for you for your trip in Europe next month. It's not so easy to come up with a benchmark, and it's the same problem with these world models.[00:00:41] Meet the Founders[00:00:41] swyx: Okay. We're back in the studio with Moon Lake's, two leads. I, I guess there's other founders as well, but, sun and Chris Manning. Welcome to the studio.[00:00:54] Fan-yun Sun: Thanks. Thanks, Chris. Thanks for having us.[00:00:56] swyx: You've got, you guys have, come burst onto the scene with a really refreshing [00:01:00] new take of mold models.[00:01:01] I would just want to, I guess ask how you, the two of you came together. Chris, you're a legend in NLP and just AI in, in, in general. You're, you're his grad student, I guess[00:01:10] Fan-yun Sun: Actually my co-founder.[00:01:11] swyx: Oh, yeah.[00:01:12] Fan-yun Sun: I should give a lot of credit to my co-founder, Sharon. Yeah. She was, she was actually working with Professor Fe Androgyn and then she ended up working with, Ron and Chris Manning here.[00:01:22] And then, so I got connected through to Chris initially, actually through my co-founder,[00:01:26] What is Moon Lake?[00:01:26] swyx: what is Moon Lake? What, what is, actually, I'm also very curious about the name, but like why going into world models?[00:01:33] Fan-yun Sun: So I was working a lot. With actually Nvidia research during my PhD years on essentially generating interactive worlds to train reinforcement learning agents or embody EA agents.[00:01:44] And then there's two observations. One in academia and one in industry. An industry like folks at Nvidia are actually paying a lot of dollars to purchase these types of interactive worlds, whether it's for the sake of evaluation or training the robots, or policies or models. And [00:02:00] then, in academia, same thing is happening.[00:02:02] And more specifically, when I was actually working with Nvidia on the synthetic data foundation model training project, we were actually generating a lot of these synthetic data and showing that, hey, you can actually, these synthetic data are actually as useful as real world data when it comes to multimodal pre-training.[00:02:16] But then, like I said, there's a lot of dollars being paid out to like external vendors or, or like. Other folks to manually curate these types of data. It was very clear to us that, okay, on our way to, let's call it embody general intelligence models need to learn the consequences behind their actions, which means that they need interactive data and the demand for those types of data are growing exponentially.[00:02:38] But everybody's sort of thinking about it from a pure, say, video generation perspective or something else. But we feel like the true actually opportunity is actually building reasoning models that can do these things, like how humans do these things today. So that's a little bit on the genesis of Moon Lake, and I think the reason I got into world models was partly.[00:02:59] A philosophical [00:03:00] take of the on the world where I like, believe the simulation theory and stuff like that. But on the other, on the other hand, it's really just like, oh, like there's an opportunity there that I feel like nobody's doing it the way I think should be done.[00:03:10] Structure, Not Scale: The Vision[00:03:10] Chris Manning: I can say a little bit about that.[00:03:12] Yeah. So of the overall goal is the pursuit of artificial intelligence and most of my career has been doing that in the language space and that's been just extremely productive. As we all know, the story of the last few years, I don't have to tell about how much we've achieved with large language models, but, uh.[00:03:31] Although they have been extremely effective for ramping language and general intelligence, it's clearly not the whole world. There's this multimodal world of vision, sound, taste that you'd like to be dealing with more than just, language. And then the question is how to do it. And despite, a huge investment in the computer vision space, right, as the research field computer [00:04:00] vision has been for decades, far, far larger than the language space, actually.[00:04:05] I think it's fair. Say that, vision, understanding sort of stalled out, right? You got to object recognition and then progress just wasn't being made right? If you look at any of these, vision language models, it's the language that's doing 90% of the work and the vision barely works. And so there's really an interesting research question as to why that is and at heart, the ideas behind Moon Lake are an attempt to answer that, believing that there can be a really rich connection between a more symbolic layer of abstracted understanding of visual domains, which aren't in the mainstream vision models, which are still trying to operate on the surface level of pixels.[00:04:50] swyx: I think one of your blog posts, you put it as structure, not scale. Is that, a general thesis?[00:04:57] Chris Manning: Yeah. Well, scale is good too.[00:04:58] swyx: Yeah. Scale is good. Too[00:04:59] lot,[00:04:59] Chris Manning: [00:05:00] lots of data is good as well and scale, but nevertheless, you want the structure Yeah. To be able to much more efficiently learn.[00:05:07] swyx: Yeah. The other thing I really liked also is you put out an example of what your kind of reasoning traces look like.[00:05:12] Right. Which you would distill is the word that comes to mind. I don't even think that's a good, good description, but it would involve, for example, geometry, physics, affordances, symbolic logic, perceptual mappings, and what, what have you. But like that, that is the kind of example that involves, let's call it spatial reasoning, role model reasoning as as compared to normal LM reasoning.[00:05:35] Yeah.[00:05:36] Defining World Models vs Video Generation[00:05:36] Vibhu: But also like taking it a step back. So how do you guys define world models? A lot of people see okay, you can do diffusion, you can do video generation. But, you guys put out quite a few blog posts. You put out a essay recently, we can even pull it up about efficient world models. You have a pretty like structural definition here, but for the general audience that don't super follow the space, right.[00:05:55] What's, what's the difference in what we see from like a video generation model to [00:06:00] a world gen A simulator? How do you kind of paint that last[00:06:02] Chris Manning: year? Yeah, so I think this is actually a little bit subtle because, people look at these amazing generative AI video models, SAWA VO three, one of these things, and they think Genie, they think, oh, this is amazing.[00:06:17] This is we've solved understanding the world because you can produce these generative AI videos, but. The reality is that although the visuals do look fantastic, those visuals actually are accompanied by an understanding of the 3D world, understanding how objects can move, what the consequences of different actions are, and that's what's really needed for spatial intelligence.[00:06:49] So I mean, a term we sometimes use is that you need action condition, world models. That you only actually have a world model if you can predict, [00:07:00] given some action is taken, what is going to change in the world because of it. And in particular, that becomes hard over longer time scales. So if you're simply, trying to.[00:07:12] Predict the next video frame. That's not so difficult. But what you actually want to do is understand the consequences, likely consequences of actions minutes into the future. And to do that, you actually much more of an abstracted semantic model of the world.[00:07:32] The Bitter Lesson & Data Abstraction[00:07:32] swyx: Yeah, the question comes where you want to have more structure than is available in just predicting the next token.[00:07:41] And typically, well, let's, let's call it the experience of the last five years has been that is just washed away by scale, right? So what is the right middle ground here that, you don't ignore the bitter lesson, but also you. Can be more efficient than what we're doing today.[00:07:57] Chris Manning: One possibility [00:08:00] is, look, if we just collect masses and masses and masses and masses of video data, this problem will be solved.[00:08:11] Under certain assumptions that could be true, but there are sort of multiple avenues in which it could not be true. The first is what's really essential is understanding the, the consequences of actions producing an action conditioned world model. And if you are simply, collecting observational video data, which is the easy stuff to collect, when you're sort of mining online videos, you don't actually.[00:08:41] Know the actions that are being taken to see how the video is changing. And so if you are never collecting directly actions and you are having to try and infer them from what happened in the observed video, that's not impossible. But it's very [00:09:00] hard and it's not really established that you can get that to work at any scale yet.[00:09:05] And so there's a lot of premium on collecting action condition video data, which is part of why there's been a lot of interest in using simulation so that you can be collecting data where you do know the actions, which isn't quite limited supply, but there's also in the limit of as much data as you could possibly have.[00:09:28] Maybe the problem is eventually solvable, but. Even though we collect huge amounts of text data is always at a great level of abstraction, right? Language is a human designed, abstracted representation where there's meaning in each token and it's representing and abstraction of the world, right?[00:09:51] As soon as you are describing someone as a professor, and as soon as you are saying that they're condescending, right? These are very [00:10:00] abstracted descriptions of the world. It's not at what you're observing as pixel level, and to get to that kind of degree of abstraction, starting from pixels is orders and magnitude of extra data and processing.[00:10:14] And so, although, we absolutely want to exploit, get as much data as possible, use the bitter lesson. Nevertheless, if there are ways in which you can work with five orders of magnitude less data than people working purely from pixels, you're gonna be able to make a lot more progress, a lot more quickly.[00:10:34] And that's the bet here. And so you could just say that's only wanting to be able to, do it more efficiently, do it more quickly, do it more cheaply. But I think it's actually more than that, I think. One should be making the analogy to how human beings work at one level. You know? Yes, we have these high [00:11:00] resolution eyes and we can look and see a scene like a video, but all of the evidence from neuroscience and psychology is that most of what comes into people's eyes is never processed.[00:11:13] Right. That you are doing fairly fine ated processing of exactly what you're focusing on. But as soon as it's away from that of yeah, there's another guy over there that you've sort of only processing top down this very abstracted semantic description of the world around you. And so, that's what human beings are doing.[00:11:33] They're working with semantic abstractions and so. I think it is just the right representation. ‘cause we also have other goals we want to be able to do, real time worlds. So that means there's a limit to how much processing you can do and we want to do long-term planning and consistency. And again, that favors abstraction.[00:11:55] I mean, I guess there was actually a recent. Blog posts that [00:12:00] came out from our Friends of physical intelligence and, they were sort of heading in the same direction they were saying Oh, to the pay[00:12:06] swyx: pay model.[00:12:07] Chris Manning: Yeah. Yeah. To maintain a long term memory of what's happening in the world. So we can, do longer term we actually storing text of what is, been happening in the world.[00:12:19] Right. It is not such a successful strategy of trying to keep it all at a pixel level.[00:12:24] Vibhu: And yeah, I mean, you can see it in video models like that Temporal consistency. We're at a scale of train on, all the video data we have. We have it for maybe 30 seconds, a few minutes. That's not the same as a game state played for half an hour.[00:12:37] Right. I thought you guys break it down pretty well. You have a, you have a blog post about. Building multimodal worlds with an agent. I dunno if you guys wanna talk about this. This is one of the things I read, I[00:12:48] swyx: thought, yeah, it's the thing I talked about with the reasoning chain. Yeah.[00:12:51] Vibhu: So there's like different phases to this.[00:12:53] It seems like it's more of an agent, a scaffold, very different approach than just, type in a prompt and you, you don't have the same consistency. [00:13:00] It also, like, for people that are listening, I, I would highly recommend reading it. It breaks down the problem in a different light, right?[00:13:06] So like, what do you need to consider when you're talking about video, like world game models, right? How would, what do you need to consider? What are the factors? What are the elements? What's the state? So I don't know if you guys have stuff to talk about for this one.[00:13:19] Fan-yun Sun: Yeah. Actually, I wanted to add on a little bit Yeah.[00:13:22] On our previous point, which is just like, change topics so quickly. I, I do feel like sometimes people confuse like, oh, like we're taking an an, an method with abstraction. That means they don't believe in bitter lesson. Like that's just false, right? Like we are believed is a bitter lesson. But then I feel like the question that we always discuss is like, what is the right abstraction level today?[00:13:42] The analogy I like to make is like, let's just say we can encode and decode. Represent all of images, videos, audio and bytes. Then the most bitter lesson approached is to train a next byte prediction model as opposed to the next token prediction model where it's just like, okay, it's natively multimodal, can just, but it's like, yeah, like [00:14:00] to, to Chris's point, it's like the scale and computing you need to achieve that.[00:14:03] So that's why we always come back to like, okay, what is the most efficient way to do it? And reasoning models to the point of this blog post is a showcase of like, Hey, we're actually just like reasoning about the world and reasoning about. The aspects of the world that CAGR that matter for me to learn what I want to learn from this role model.[00:14:21] swyx: Yeah, it's like you're improving the en encoder of whatever you're, trying to model. And like a better representation would just represent the important things in less space. Yeah. Which would just be more efficient.[00:14:33] Fan-yun Sun: Yeah.[00:14:34] swyx: So yeah, I, I, I fully agree that it is not, antagonistic to, bitter lesson.[00:14:38] I do wanna wanna mention one more thing. Is there any philosophical differences with the JPA stuff that, Yun is working on? I gotta go there. You, you, you, you're, you're imagining like some latent abstraction. I'm like, okay, fine. Let's, let's talk about it, right? Like it's an elephant in the room.[00:14:52] Chris Manning: Yeah.[00:14:53] JEPA & Philosophical Differences with LeCun[00:14:53] Chris Manning: There are philosophical differences. Jan Lacoon is a dear friend of mine, but. [00:15:00] He has never appreciated the power of language in particular, or symbolic representations in general. Yarn is a very visual thinker. He always wants to claim that he thinks visually and there are no words, symbols, or math in his head.[00:15:21] Maybe that's true of yarn. It's certainly not the way I think. Um. But at any rate, the world according to yarn is the basic stuff of the, the world and of intelligence is visual and language is just. This low bit rate communication mechanism between humans and it doesn't have much other utility and it's far inferior to the high bit rate video, that comes into your eyes.[00:15:53] And I think he's fundamentally missing a number of important things [00:16:00] there. Think of this evolutionary argument looking at animals, right? That the closest analogies, the things with chimps, right? So chimpanzees, have fairly similar brains to human beings. They have great vision systems, they have great memory systems.[00:16:18] They've got, better memory than we do of short term memories. They can plan, they can build primitive tools that, humans. Massively ahead in what we understand about the world, what we can plan, what we can build. And essentially what took off for us was that humans managed to develop language and that gave a symbolic knowledge, representation, and reasoning level, which just, okay if this sort of vaulting of what could be done with the intelligence in brains.[00:16:59] So the [00:17:00] philosopher Dan de refers to language as a cognitive tool and argues that, humans unique among the creatures in the world have managed to build their own cognitive tools and language is the famous first example. But other things like, mathematics and programming languages are also cognitive tools.[00:17:21] They give you an ability to. Think in abstractions, in extended causal reasoning chains. And that allows you to do much more. And we use that for spatial representation and intelligence and planning and gameplay as well. So we believe, and this is, underlying the specific technologies that Moon Lake is making, that symbolic representations are powerful.[00:17:50] And you want to use that in your understanding of the visual world when you want a causal understanding, when you want to maintain long-term [00:18:00] consistency and prediction. And as I understand it, that's just not in ya Koon's worldview. So I think that's the fundamental philosophical difference. Then there's the specific model.[00:18:11] He's been advancing jpa, that's a reasonable. Research bed is a direction as to, to head for building out a model of the visual world. To my mind, it's sort of one reasonable research bed. It's not really established. It's the best one that everyone should be following,[00:18:32] swyx: at least developed at scale, at Meta.[00:18:34] But it's not just vision, right? Like, I mean, JPA is a, just joint admitting prediction can be applied to anything really. And people have done it. The argument is that there is a latent representation or that is probably more. Suited to the task, then why not let machines do it for us instead of predefining it at all?[00:18:50] And isn't something like a JPA shaped thing the right answer? And if not, why not?[00:18:55] Chris Manning: So I think there's a part of jpa that's right, which is [00:19:00] you do want to have a joint. Embedding that gives you a consistent model of the world. And Jan's argument is you can never get that from auto aggressive language models ‘cause they're sort of left to right churning out one token at a time.[00:19:22] I guess this is where we're the research arguments of the field, I'm not actually convinced that's right. ‘cause although the token production is this auto aggressive, process that's heading, left to right, I guess don't have to be left to right. But anyway, in sequence of tokens we could have right to left Arabic.[00:19:40] But although that's true, all of the weights of the model that are internal to the transformer, they are a joint model of the model's understanding of the world. And so I think you can think of the weights of the model as a form of. Joint representation, [00:20:00] and therefore it is plausible to think that could be the basis of a world model, which avoids, ya's objections.[00:20:10] swyx: I think I follow, and obviously that would touch on what Moon Lake eventually ends up doing as well. Right. Like, which it's hard to tell because you put out the end results, but we don't know the inputs that go into it. So it's, it's, that's something that we have to figure out over time.[00:20:25] Vibhu: Yeah. I mean, I guess this kind of breaks down some of the outputs. Do you wanna walk us through it?[00:20:31] Reasoning Traces & Interactive Worlds[00:20:31] Fan-yun Sun: Yeah. So this, this really just walks us through the reasoning traces of like, okay. So that just say, if we wanna build a world in this context, it's really just a game demo that, that shows the, the variety of interactions that this world model can build.[00:20:45] And yeah, it's really just a reasoning traces of like, okay it prompted to create a bowling game. Like how did it achieve what you saw? That level of causality, interaction and consistency, right? So yeah, this is almost just like a, an example of [00:21:00] like a reasoning traces. Very[00:21:01] swyx: detailed.[00:21:01] Fan-yun Sun: Yeah.[00:21:01] Vibhu: Very, very detailed.[00:21:02] You gotta you don't even realize it, right? Like when a video is generated, what happens when a ball strikes a pin, right? So first, like you, there's audio in that, like audio triggers happens, score increments, the world changes. Like pins have to start dropping. There's a timer that goes on. It's just like very similar to how now we're used to reasoning for language models.[00:21:20] There's a whole state of what happens. So geometry, physics, all this stuff. And then yeah, there's kind of that single prompt. So asset, ation all this stuff. It's like a, it's a nice view to see what's going on.[00:21:32] swyx: I think Sun is also too polite to point out that, both like Google's genie, demos as well as world Labs is marble, do not have interactive worlds.[00:21:41] Fan-yun Sun: That's the benefit of having a reasoning model, right? Like, because you can, you can say, oh, like maybe in this particular context, I want to learn how to bowl. And then you can say, okay, then what is it important when it comes to learning how to bowl? Okay, maybe it's like I need to understand the, the basic of like, physics and I want to throw it over [00:22:00] them.[00:22:00] I wanna know that when I, when it resets it's a new game. So I know that yeah, basically, you know to pick up the ball, you know that ball's gonna cause the pins to fall down. You know that what's important to this particular bowling game is to score and you know that the score corresponds to the number of pins that fell down.[00:22:19] So it's just like, if it's a model that sort of knows what it. Looks like, knows what a bowling game looks like, but doesn't actually allows you to practice over and over again and to understand that, oh, like what it takes to actually get a high score. Then it sort of doesn't actually allow you to learn what you set out to learn within the world model.[00:22:38] And I think this is really just one example of showing like the advantages of the approach that we're taking over most the, let's call it the zeitgeist, is today, when people talk about clinical role models,[00:22:51] Chris Manning: right? So it sort of seems like the question to ask when there's a world model is.[00:22:58] Can I not [00:23:00] only just wander around the world and look at the beautiful graphics, can I interact with the objects in the world and see the right consequences of actions?[00:23:11] Vibhu: And you also understand what the consequences would be if you do something right. So it's not just like, okay, there's one thing if I pick it up, something will happen.[00:23:19] But, there's 50 options and I know I can expect, I can infer what would happen if I do any of them. Right. So very different when you can actually see it play around with it.[00:23:28] swyx: There,[00:23:28] Beyond Unity: Cognitive Tools for World Building[00:23:31] swyx: there's two cheeky elements of that. I mean, the, the, the I guess, less ambitious one is, let's really establish for listeners, why is this fundamentally different than writing Unity code, right?[00:23:40] Like just creating a model to translate a prompt into Unity code[00:23:44] Fan-yun Sun: so there is an underlying physics engine. Yeah. In that sense, there's some overlapping things to Unity, but the way we think about it is like physics engine. Tools or code are cognitive tools like borrowing Chris's term, right? Like tools [00:24:00] that the model can employ as means to an end.[00:24:04] So today maybe you say, okay, in this particular context we care about physics, we care about the long-term causality consequences. Then yes, we deploy it, employ physics engine, and then maybe tomorrow we say, okay, we're we're training that. Just say drones where we only care about really fluid dynamics and the visual aspect of the world.[00:24:25] Then, then yeah, maybe we don't actually, the model actually doesn't have to use a physics engine. Or maybe it employs other types of representation or physics engine to achieve the task. So yes, writing code for Unity is sort of similar to a tool that our A model can employ, but our goal is for a model to take a representation conditioned reasoning.[00:24:46] Approach or process.[00:24:47] swyx: Yeah,[00:24:47] Fan-yun Sun: internally.[00:24:48] swyx: Yeah. Using these things as just like general two calls. Right. Which I think is very interesting. The other more ambitious one is, some kind of recursive element where it becomes multiplayer, right? Like here, there's a single player element, you're not [00:25:00] modeling any other people involved.[00:25:01] And that is a whole other thing.[00:25:04] Fan-yun Sun: But in fact, we can really do multiplayers. Oh yeah, okay. I haven't seen any double situations. So just actually just like prompt our, our model to say, Hey, like configure to multiplayer. Then it'll do like this. You'll be able to configure multiplayer[00:25:16] swyx: great[00:25:17] Fan-yun Sun: persistency database for you.[00:25:18] Easy. Yeah.[00:25:19] Vibhu: So what, what are like some of the current limitations in where we're at? So there's one approach of like, okay, scale up video predictors. Obviously there's data issues. With approaches like this, is it data constraints? What are like the next steps? Is it real time? Like, so there's one side of, write an agent to write Unity code, but okay, I want to be streaming a game real time.[00:25:38] I want to have characters being also like agent, but where, where do we kinda see this scaling up? Right?[00:25:44] Fan-yun Sun: Yeah, there's definitely a data constraint. Like the more data, the, the better. This reasoning model can almost basically act as humans to like operate a variety of tools and softwares to build whatever's necessary.[00:25:57] And then there's a sort [00:26:00] of fidelity constraint, which we're actually solving with another model, which we can talk about later. But it's like, it's not as easy to get to photorealism with the approach that we're taking. But we think there are better solutions to that, which is we can dive into later.[00:26:14] Later.[00:26:15] Vibhu: The one one thing you note here is it's a diffusion model, right? So there's, there's a few approaches, diffusion caution, splatting, yeah, so Ry diffusion model, you guys wanna[00:26:25] Fan-yun Sun: Yeah.[00:26:25] Vibhu: Introduce,[00:26:26] Fan-yun Sun: yeah, totally.[00:26:26] Rie: Neural Rendering & Skins for Worlds[00:26:26] Fan-yun Sun: So within our world modeling framework, we think there are two models that we train, right?[00:26:31] Like, there's the multimodal reasoning model that we just talked about that essentially handles. Mainly the, the causality, the persistency and logic determinism of the world. And then RY is our bet on saying, okay, like while all those model, can take care of all these things that we just talked about, it's limitations compared to existing, say, video models, is that it doesn't have as high of a pixel [00:27:00] ality right off the gate, right?[00:27:02] And EE is to say, Hey, we can actually take whatever persistent representation that we generate with our multimodal reasoning model and learn to restyle it into photo photorealistic styles or arbitrary styles you want. So this model is almost to say, Hey, I'm going to respect the persistency and interactivity of the world that you created, but my only job is to make sure that its pixel distribution is close to what we want.[00:27:29] Vibhu: Yeah.[00:27:30] swyx: Great example right there. You kept the KL divergence.[00:27:33] Fan-yun Sun: Oh. Where,[00:27:34] swyx: no, no. I mean this, this is a, a classic like, how you don't stray too far from the source material as you, you kept the kl, which is Oh yeah. Kind of cool. Yeah.[00:27:43] Fan-yun Sun: Yeah.[00:27:44] swyx: I mean, and the[00:27:44] Chris Manning: difference is, and I mean sun was pointing at this, where sort of saying it's in one way a more difficult path, but a better path that, typically the diffusion models are producing the whole scene and it looks lovely, [00:28:00] but there isn't spatial understanding behind it, which is allowing for the real time graphics gameplay, the spatial intelligence, understanding the consequences of worlds where this is, taking a path where it is assuming an abstracted semantic model of the world's state.[00:28:20] And then the diffusion model is then being used on top of that to produce the high quality graphics.[00:28:27] swyx: Is there an intended practical, or business use for this, or is it like a, like a demonstration of capabilities?[00:28:34] Fan-yun Sun: We actually believe that this is gonna be the next paradigm of rendering. So it's gonna replace how ra raizer, it's gonna replace DLSS today because it not only has these pixel prior that's learned from the world such that you can literally play any game in photo realistic styles, which is a lot of people's desire when they do GTA, right?[00:28:51] Like,[00:28:51] Vibhu: all the mods, all the people adding perfect lighting and all this.[00:28:54] swyx: So[00:28:54] Fan-yun Sun: skins[00:28:55] swyx: for worlds, let's call it[00:28:56] Fan-yun Sun: skins, let's call it skin for worlds. I,[00:28:58] Vibhu: it's also like, you can call it skin, you can call it [00:29:00] customization. You can play it how you want, right?[00:29:01] Fan-yun Sun: Yeah, exactly. And I think another thing that we really pointed out specific specifically in this blog is the programmability of it, right?[00:29:09] So what this means is that this render historically render is always a derivative of the game state, right? You're saying, oh, here's the game state, I'm rendering out a frame. But here I'm saying actually this render can be part of the gameplay loop. I can say something along the lines of, if upon getting 10.[00:29:26] Apples, I'm gonna, my weapon of choice, my bullet's gonna turn into apples. And that's, that's possible because we can say, we can basically dynamically have certain game state trigger the, the preconditions to the render such that the rendering is now part of the game loop too. One thing is to just say, okay, it's, it's, it's the appearance.[00:29:47] But the second thing is also to say there's these novel interactions that are possible because this render now has actually priors of the world.[00:29:57] swyx: It is up to the artist to figure out what to do with it.[00:29:59] Fan-yun Sun: It [00:30:00] is up to the creators. Yes.[00:30:01] swyx: Yeah.[00:30:01] Fan-yun Sun: And I also think that's actually another big argument that we're making and the reason that we're picking, taking the bet we're baking is that a lot of the times, whether it's for embody AI gaming, like you want a layer where human can inject their intentions.[00:30:15] So, for example, let's just say in the context of gaming, it's obviously like my creative intent, but maybe in the context of embodied ai, it's like, oh, like I take this foundational policy and I want to actually fine tune it to deploy in my house. So you want to almost say, inject, have a layer where human can say, oh, here's the distribution of things I want to create to achieve my goal.[00:30:35] And I think 3D graphics as it as it is today, is basic, the layer for people to say, Hey, what do I care about in this world? And it allows, basically human intent to be expressed in these worlds much more explicitly and distributionally as opposed to just saying, Hey, I'm gonna generate like, arbitrary.[00:30:54] And it's like just prompts,[00:30:55] swyx: it's one of those things where like, I think you, you're going to build up a series of models, right? [00:31:00] This is just one of, this is probably like the highest utility or heaviest, frequency one, I don't dunno what to call this. Where like you Yeah. You can immediately drop this in on any game and you don't need anything else that.[00:31:10] That you guys do. But, I, I could see, I could see that I think the, the human intent is something that people are not even used to because we're so used to static worlds or, worlds that just don't react, or, I don't know. It's, it, you're kind of blowing my mind right now with like, I'm, I wonder if you've talked to people at GDC Hmm.[00:31:27] And what are they gonna do with it?[00:31:30] Fan-yun Sun: Yeah. Now the stance that we take on this front is like, we're not gonna be more creative than our users to ship[00:31:35] swyx: it out.[00:31:35] Fan-yun Sun: Yeah. But we wanna make sure that we're building things in a way that really allows them to express their intent.[00:31:41] swyx: The thing that you said about, here's the distribution that I want.[00:31:45] I think text may be too low of a bandwidth to. To really demonstrate, because I, I, there, I'm, I'm probably just gonna want to drop in a bunch of, reference assets and then you can figure it out from[00:31:58] Vibhu: there. But you probably wanna do a, a mixture of [00:32:00] both, right? Like you throw in a few images. I wanted this style.[00:32:02] Yeah. I want it to look like this. So it, it's, it's a mixture, right?[00:32:05] Chris Manning: I, I think it's a mixture. I mean, yeah, I mean there's clearly a visual component of this, and it's not that, everything can be text. ‘cause of course you want to give a visual look, but there's also a massive amount of giving the overall picture of the look of the world and the behavior of things that you can express in a few words of text.[00:32:32] And it be very time consuming and difficult to do via visual means. So I think, yeah, you want a combination of both.[00:32:40] Evaluating World Models[00:32:40] Vibhu: So one question I kind of have is, how do we go about evaluating world models? So like, there's many axes, right? One is like, okay. I have preferences. How well do we adhere to prompts? One is the simulation.[00:32:50] One is like do things, is there core logic that's broken? So coming from we know how to evaluate diffusion, there's fidelity, there's [00:33:00] stuff like that. But what are some of the challenges that most people probably aren't thinking about?[00:33:04] Fan-yun Sun: Yeah, I think this is like a great question and probably one of the hardest questions in role models because like, I think it always comes back to what are you building this role model for?[00:33:13] And depending on your end goal and purpose, the evaluation should defer. So in the context of games, then the most direct way of measuring is how much behind are people actually spending in this world that you create? And if your goal is to say, for example, in the context that we just talked about, like, hey, deploying, deploying action in body, a agent, then your, your end.[00:33:33] Metric is then, okay, after training in these worlds that you generate how robust it is to when you actually deploy to the target environment. But then, it's, it's hard to measure these end metrics. So today people have like these proxy metrics that I call that basically try to measure what we really care about, which is the end metrics, but then frankly it's different for every use case.[00:33:57] Yeah,[00:33:57] Vibhu: which seems like quite a challenge, right? Like in [00:34:00] in language models or video models. Image models, your benchmarks are proxies, right? People aren't actually asking instruction, following tool use questions. They're proxies of how well it will do downstream. But for this, so like, should teams, should companies have their own individual benchmarks outside of games?[00:34:16] If you think of stuff like, okay, video production, movies, stuff like that, that also want to use world models. Should, should they sort of internalize like. Their own proxy. Is this something you guys do? Where, where does that connect[00:34:28] Chris Manning: go? Yeah, I think this whole space is extremely difficult as things are emerging now.[00:34:35] And I mean, it's not only for world models, I think it's for everything including text-based models, right? ‘cause in the early days it seemed very easy to have good benchmarks ‘cause we could do things like question answering benchmarks and could you answer the question based on these documents and the various other kinds of, do pieces of logical reasoning or math.[00:34:58] But again, these are sort of. [00:35:00] And there were sort of visual equivalents of things like object recognition, right? For these small component tasks. These days so much of what people are wanting to do also with language models is nothing like that, right? You're wanting to, have an interaction with the language model and get some recommendations about which backpack would be best for you for your trip in Europe next month.[00:35:25] And it's not the same kind of thing, right? And it's not so easy to come up with a benchmark as to does this large language model give you an effective interaction for guiding you in a good way for shopping, right? So, and it's the same problem with these world models. So if we take the game design case, well success is that a game designer can.[00:35:57] Produce what they are [00:36:00] imagining in a reasonable amount of time. And that's really the kind of macro task. That's a very hard thing to turn into a benchmark and I think a lot of this is actually going to turn into people walking, walking with their feet. Right? I mean, I guess that's what's happening, at the large language model level, right?[00:36:23] When people are choosing to use, GPT five or Gemini or clawed, individuals are trying out these different models and deciding, oh, I like the kind of answers that GT five gives me, or no, I feel like I get more accurate detail from Claude, right?[00:36:43] Vibhu: It's a lot of[00:36:43] Chris Manning: vitech, a lot of people just using it.[00:36:45] It's vibe checking. I realize that, but it's actually whether. People feel it's giving them utility in what they want. Right.[00:36:52] Vibhu: And the the interesting thing there is like a lot of people prefer the visual, right? This looks pretty, which is not the objective of what this is [00:37:00] for, right? It's if a, if a game designer is working on something, they care about the game engine, right?[00:37:04] The state, it's, it can look whatever. You can fix that up later. Or you can have a really good game state and you can quickly edit it to 20. 20 different versions, like Keep State,[00:37:14] Chris Manning: right?[00:37:14] Vibhu: So[00:37:14] Chris Manning: that's a really important distinction, for and for speaking to Moon Lake strength, right? So, yeah, great visuals are lovely to look at for a few seconds, but gains are really all about the concept, the game play.[00:37:33] And a lot of the time that doesn't actually even require great visuals. I mean, there are just lots of very successful games which have relatively primitive visuals, and there are other games where people have spent millions producing photo realistic, visuals, and the game sucks, right? So, keeping those two axes apart is really important in thinking about what's important in a [00:38:00] world model for different uses.[00:38:02] swyx: This conversation is reminding me of some game review and fiction discussions I've, had in my sort of non-AI related life. Some, for some people might know Brandon Sanderson, who's a very famous, fiction author, had, is is a big game reviewer. And he, he's a big fan of video games where you change one thing about a normal what you might assume about, about the world.[00:38:22] For example, Baba is you, I don't know if you might have come across that, where like the rules change as you play the game. And also like where, you can do things like reverse time selectively or like change gravity selectively. And I think this is also reminds, reminds me of other kinds of world models that are created by authors.[00:38:38] Where Ted Chang is, is my typical example where he'll take the world that, you know today, but change one thing about it and, but then create a consistent world based on that. Which is long-winded answer of me to, of. For me to say is it's it easy to create alternative roles that don't exist, but you change one thing and then let's, let's run a whole bunch of people through it to see if it works.[00:38:58] Chris Manning: My first dance will [00:39:00] be, that seems a lot easier and more conceivable to do using Techn technology like Moon Lakes than with some of the other world models out there, where the sun can actually make it happen. I'll let him give a second answer.[00:39:15] swyx: If I guess for you, you're constrained by the game engine tool, right?[00:39:18] Like at the end of the day, that's the, that's the thought, partner that you have. If I ask for something where like, if it never is allowed to reverse time or if gravity only ever works one way, then well that's it. But sometimes gravity might change,[00:39:33] Fan-yun Sun: but it's a lot easier to change with code as opposed to a model that is learned primarily on data of.[00:39:42] Real world and virtual worlds that are, I guess, like for example, junior, like there's actually trained on a lot of real world data and a lot of virtual gaming data, and it's hard to say maybe it's easier to say, okay, I wanna change the visuals in like the time period of, of the world. Like, you can't change gravity, for [00:40:00] example.[00:40:00] Vibhu: I feel like you can to light bounds, right? Everything comes down to like, code is a better way to execute it, but the models aren't that diverse and creative, right? You can say, okay, make gravity slower. It can do that, but it's limited to your representation of how you text it out, right? Like they're, they're only gonna do a few iterations, whereas programmatically, if there's a game engine under the hood, you can kind of go wild, right?[00:40:22] So one of the, I dunno, one of the limitations of most models is that they're very overtrained to one style. Right. And extracting diversity is pretty difficult. At least that's something we've seen.[00:40:35] Fan-yun Sun: I mean, are there examples you have in mind where you Existing models? Yeah. Like it would be easier to do that's not using code.[00:40:43] Certain types of creative intent or like transition state transitions,[00:40:47] swyx: Clipping, other models, other wo models are very good at clipping through things. Clipping my, my, my legs clipping through a rock because it's, it's just, it's just bad. [00:41:00] Like, you would have to struggle very hard with your stuff to actually make that happen.[00:41:04] Which I think is maybe a topic that you actually prepared on, Gian Splatting versus, the other stuff.[00:41:09] Vibhu: Yeah. Yeah. It's just for those not super familiar, right? There's a, there's gian splatting, there is diffusion. Like what works, what scales up. I feel like in February when Soro one came out the blog post was literally titled like,[00:41:21] swyx: you bring it up.[00:41:22] You never know.[00:41:23] Vibhu: World, world, video generation models are world simulators. It's super bitter lesson pilled. Yeah, emer, a lot of it is emergence, right? So, not to go through their blog post, basically their whole thing was as you scale up all this consistency, all this stuff just kind of solves, it's a very simple premise, right?[00:41:41] They just scaled up, diffusion, and from there, this is, this is Feb 2024, how much can we, it's already been two years, which is basically five years. How much more in AI time do we need to just scale up or, or do we hit a data cap? But I think we already talked about this a lot, right? Like this is back to the beginning discussion of what's [00:42:00] appropriate for the time.[00:42:01] And that seems like your approach, right?[00:42:03] Fan-yun Sun: Yeah. The point I'm trying to make is that they're very many, many different types of world simulators and like having a world simulator that can produce pixel coherency is very, very useful for games and, marketing and all these things, but it's not as useful as people think when it comes to causal reasoning.[00:42:25] When it comes to embodied ai. Yeah, like it this title is true. We're not saying that it's, it's like, not a great world simulator, but actually in the blog that we, we, we, we wrote, the bet is more so that there are gonna be disproportionately large share of value of real world tasks or, and virtual tasks where high resolution pixel fidelity is not needed.[00:42:47] Yes. Video models have their values.[00:42:50] swyx: Yeah. This is at the absolute limit of my physics understanding, but one example that comes to mind is basically having to solve like ba the equivalent of a three [00:43:00] body problem in a deterministic Well, where the video models, which is approximated good enough. Yeah.[00:43:08] Right. Like there's, there's some point at which your approach kind of runs into like the you now have to simulate the world. Please, thank you very much. And like you're trying to do that, but only to the extent that the game engine lets you and like game engines cannot do some things.[00:43:23] Fan-yun Sun: Yeah, no, I mean, I think the interesting or more technical question here actually is where do you draw the boundary between.[00:43:32] What's handled with, let's say, diffusion prior and what, when? What's handled with symbolic priors?[00:43:38] swyx: Yes.[00:43:38] Fan-yun Sun: Okay.[00:43:38] swyx: Okay.[00:43:39] Fan-yun Sun: Right. Let's go there. Because this, this boundary can actually be fluid. Like I think like maybe what you're trying to get at is like, okay, people are saying pixel prior, everything. But what we're saying is, okay, there's a boundary that we draw where this is where we think provides the most economical value for the domains and things that we care about today.[00:43:59] [00:44:00] And I actually do think, and it's something that we do internally all the time, which is like, okay, given new equations that we learn or new elements of the world and that we, we learn, or maybe some other knowledge that we acquire in the process of developing the models. Should we still be maintaining this line exactly as it is today?[00:44:22] Or should we move it a little bit left or a little bit right? Right. Like sometimes that we realize that, oh, like maybe customers or, or folks like want certain things that are better handled with preop pryor as opposed to, symbolic prior than,[00:44:34] swyx: yeah. Your, your skin thing is a, is a example moving it, right.[00:44:37] Yeah.[00:44:37] Or left. Yeah,[00:44:37] Fan-yun Sun: exactly.[00:44:38] swyx: I dunno what the, the left right is.[00:44:39] Fan-yun Sun: Yeah, yeah, yeah. No the, the model.[00:44:42] swyx: Yes.[00:44:42] Fan-yun Sun: Actually we have a few iterations of them. They're actually at slightly different[00:44:45] swyx: I know boundaries. You should, you should do that. That's a cool dimension to show.[00:44:49] Fan-yun Sun: Yeah.[00:44:50] swyx: Is quantum mechanics the diffusion prior of our world?[00:44:55] Right. It's like that's the boundary of classical mechanics versus quantum. Right? Like, that's it. At one [00:45:00] point God plays dice and the other point doesn't.[00:45:02] Fan-yun Sun: I dunno if Chris, you wanna say it, but I think, I think generally I feel like physics is better with symbol P priors.[00:45:08] Chris Manning: Even quantum physics.[00:45:09] Fan-yun Sun: Even quantum physics.[00:45:11] swyx: Yeah. This is starts against to, MLST territory is, is what I call it, where, he, he likes to get philosophical. We, we we're quite friendly.[00:45:18] Vibhu: I mean, we need to get, we need to get singularity. I heard some of that.[00:45:23] swyx: No, no, I think that is actually really helpful and man, I just want you to productize this like, as a product guy, I'm just like, oh, also[00:45:32] Vibhu: a gamer, I[00:45:33] swyx: wanna, it's like a researcher, like, it's cool.[00:45:35] Like this is a, the theoretical, like you have a very good, I don't know, like the way of thinking about these things, but I just wanna see you like, express it. I do think like your fundamentally things when, when you leave open new tools, like, okay, use, use human intent to incorporate it into how you render.[00:45:52] Artists are gonna have to take like two to three years to figure out what to do with this. And you just don't know.[00:45:57] Chris Manning: Right. But I think, this is, [00:46:00] gives a much more approachable and controllable world for the society, which is the beauty, the beauty of, NLP, that that will enable it to be adopted and used.[00:46:10] And we are very hopeful about that. Yeah,[00:46:13] Fan-yun Sun: yeah. Yeah. I mean, we are, we are very focused actually on commercialization in the sense that like we do, we do really believe in the data flywheel app approach. Yeah. Where, we put this in the hands of the creators and the users and then they will teach us when, what capability our model should improve.[00:46:27] And that's why we are, we are actually, like products and beta[00:46:31] swyx: Yeah. Focusing on gaming. What, what's like the adjacent thing to gaming[00:46:34] Fan-yun Sun: embody adjacent, basically. So maybe we can, we can I'll maybe start with where we see the platform in three years. Yeah. Which is like, okay. The users would tell us what they want to achieve.[00:46:45] The end goal could be, Hey, I just, I wanna make something to teach my kids the value of humility. Or it could be, Hey, I wanna fine tune my, drones to be really good at rescue situations. I could be vacuum robots. I want to like train [00:47:00] my manipulation or like vacuum robot to be very robust to my office, right?[00:47:04] But it's like, whatever it is, scenario robust to[00:47:06] swyx: my office[00:47:07] Fan-yun Sun: or like navigate very robustly in my office. But then it's like, whatever end goal that you want, our role model will say, okay, given what you want to achieve, let me generate a distribution of environments such that I can train and evaluate whatever it is you want.[00:47:24] Yeah. Right. Maybe for the purpose of games, it's just the end simulation and that's the end product for certain policies. It's like I can train it within these environments and then help you see where your policy is failing or not. Yeah. And then, so I think,[00:47:37] swyx: so in that case, much more of a training tool.[00:47:40] Than in other training[00:47:41] Vibhu: evaluation? Both. Right?[00:47:43] swyx: Sure. Same. Same thing.[00:47:43] Fan-yun Sun: Yeah, same thing. I think it's just this role model that allows people to train any policy that can act in any multimodal environments.[00:47:51] swyx: Would it be harder to reward hack? Is there an angle here where it is harder to reward hack? Like it's just, I'll just put it generally because I think that's a, that's obviously a key [00:48:00] problem that a lot of people face when in training agents in these environments, and I don't know, can you solve it?[00:48:07] Chris Manning: I think not necessarily. To the extent that there's a mis specified reward that. It seems like it could be hacked in a more symbolic world or in a more pixel based world. I dunno if Sun's got any thoughts, but I don't think that's really being solved.[00:48:26] swyx: The other thing that comes to mind is just you could just build a better sawa as a video generator model, right?[00:48:31] Because then you, you would move the diffusion, side a bit more further to the right. I think if I got the directionality correct. And that's it.[00:48:40] Vibhu: It's better on domains, right? Like on consistency over now, or for sure it exists versus something doesn't, right.[00:48:46] Chris Manning: So[00:48:46] swyx: yeah. Yeah. Is[00:48:49] Vibhu: is a question more like, like[00:48:51] swyx: I'm just riffing on like, how do you, what can you build, you know?[00:48:54] Oh, with the stuff that you have. I do think that the minor, the academic does go immediately to training [00:49:00] and in eval evaluation, but like art tends to take unusual directions. Like you might end up,[00:49:06] Chris Manning: okay. Yeah. But the question is, can you use this piece of software to develop compelling gameplay and. I don't think you can take SOAR and produce compelling gameplay, right?[00:49:19] If you want to have a world that you can wander around in a bit, you are good. But what are your abilities to have gameplay mechanics implemented the way you'd like them to be and to have things stay, with the long-term history of your gameplay that influences future actions. I think there's just nothing there for that.[00:49:39] swyx: Yeah, I do tend to agree. I, I'm just trying to sort of test the boundaries. I would also make the observation that as AAA games industry has developed the line between what is a movie and what is a game has blurred. And you, you, you do end up basically producing a two hour movie as part of your game.[00:49:57] Fan-yun Sun: No, honestly, there, there's so many actually [00:50:00] applications in adjacent markets that our world model can go into. Yeah. But yeah, it, it's sort of fun to riff, riff on. Although on the execution side, we we, we need to stay focused with like, okay, what are the capabilities we want to unlock over time?[00:50:11] And there's a roadmap for that. But yeah, if we're just riffing on sort of like the possibilities, I feel like, whether it's endless Yeah, it's like classic[00:50:18] swyx: and the embedding for a possibility and endless in my mind, it's very close. Yeah. I do wanna, focus on one, like weird choice. I, I don't know if it's weird.[00:50:28] Maybe I'm, I got something here. Audio, right? You could have just said no audio And audio in my mind has a lot of recursion, whereas in video you can just do recasting and that's much computationally much simpler. Audio just seems way harder. I don't know if you wanna just comment on just the special 3D audio.[00:50:46] Problem. Did you really have to do it? I guess you do to be immersive, but like a lot of people do treat it as like, well, you just stick a, a tt S model on top of[00:50:57] Vibhu: Well, there's a lot more to game audio than [00:51:00] just speech. Right. It's not just[00:51:01] swyx: tts. Yeah. Tts. S Fxt, GM Spatial in my mind Echoes[00:51:06] Chris Manning: Yeah.[00:51:06] swyx: And reflections.[00:51:07] And I, I don't even know what's, what else? I don't know what, what other problems in this space.[00:51:13] Fan-yun Sun: Yeah, I think this point like the, it's sort of a more, more pointing to the benefits of using an game engine as a tool that's available to the model, right? Because like part of the spatial audio is from the code that is underlying the simulation.[00:51:32] And while we do give our model access to other types of audio models as. Tools.[00:51:39] swyx: None of them would be spatial, I think.[00:51:41] Fan-yun Sun: But that's exactly sort of more 0.2. We're giving our model an abstraction or a suite of tools such that it's able to achieve that. And you can argue that sort of spatial is like a, like a emergence out of the, the tools that we and abstraction that we provide to the agents.[00:51:59] And I think that's the beauty of [00:52:00] this, this, this approach is like there's a lot of things kind of like how human's built technology and they're like Lego blocks that build on top of each other. And it's the same thing here. There's gonna be things that sort of just sort of emerges from being able to put these things together in like combinatorially interesting ways,[00:52:14] Chris Manning: right?[00:52:15] So this integrated audio model exploits the understanding and semantics of the Moon Lake world, right? And whereas in general for the Gen AI video models. There's no actual integration across to audio at all, right? That someone might stick some music or stick a soundscape or whatever else on top of their video.[00:52:44] So it's not a silent video, but they're in no way connected into a consistent world model. And there's nothing that's okay. An action is happening in the video. Therefore there should be a sound that's [00:53:00] coming from this part of the visual field.[00:53:03] swyx: Yeah.[00:53:03] Vibhu: Is that different than Sora too? Does it not have audio?[00:53:06] Not to say it's not like[00:53:08] swyx: amazing[00:53:08] Vibhu: isn't a spatial[00:53:09] swyx: audio.[00:53:09] Vibhu: It doesn't,[00:53:10] swyx: no. I've played around it with it enough. It just sounds like someone put an 11 laps voice on top of it and just tried to do the lip sync.[00:53:18] Vibhu: Oh, yeah. I've seen, okay. Generate a dog at the beach and reactions to big wave and move[00:53:23] swyx: around.[00:53:23] It's definitely like, so have the dog, have the dog move away from camera and see if the, the song goes down. It doesn't. ‘Cause they don't have facial audio.[00:53:32] Fan-yun Sun: We do want to basically like we, our moral model, like the one we're training is basically towards the goal of having a combined latent representation across all these different modalities.[00:53:42] Right? Such that it can like reason across these different modalities. So for example, if I close my eyes and like you play a video, you play a sound of like a car skidding away from me. I almost can like, visually extrapolate that trajectory in my mind. And I think that type of capability, we want our model to be able to reason, right?[00:53:59] And that's the reason that [00:54:00] we're sort of taking this multimodal reasoning approach. It's like we want this combine late in space that can[00:54:05] swyx: Yeah. Oh, you said late in space. We like that. Here we have to play the, the bell Every time that someone says late in space, no, you gotta train daredevil one. Where you, you, you, it's only audio, but you have to work out.[00:54:15] Where everything is.[00:54:19] Cool. I I think that that was, that was about it for our Moon Lake coverage. I do think that we have like a couple of, Chris Madden questions on, on IR and, just any, any other sort of attention topics or n NLP topics.[00:54:31] Vibhu: Okay.[00:54:31] swyx: Go ahead.[00:54:32] Chris Manning's Journey: From NLP to World Models[00:54:32] Vibhu: Well, no, I mean, yeah, it's just fun. We talked a bit about how you guys met, but you basically, you, you were like the godfather of NLP per se, right?[00:54:39] You spent the whole career from early embeddings, early early attention. You did 2015 attention for machine translation, everything. You, you had information retrieval, so RAG before rag, we just wanna shout that out and admire a lot of that. Right? So what prompted the switch over to world models?[00:54:56] How, how'd all that come about?[00:54:58] Chris Manning: To some answer it [00:55:00] is, the enthusiasms and creativity of students, but there's a bit of a history there, right? So, yeah. So clearly most of my career has been doing stuff with language and how I got into research was thinking, ah, this is just so amazing how humans can produce speech and understand each other in real time.[00:55:21] And somehow they managed to learn languages from their kids. How could this possibly happen? And so, yeah, starting off I was very focused on language, but as it sort of got into the 2000 and tens, I started, going, I'd been working on question answering, and then I started to get, interest in visual question answering.[00:55:42] And that was an area where it was very noticeable. That the visual understanding was bad. Right. These were the days when like, it sort of seemed like there's almost no visual [00:56:00] understanding. You were just getting answers that came from priors. So, if you asked how many people are sitting at the table, it'd always answer two regardless of how many, how many people you could see in the picture.[00:56:11] And so it seemed like, oh, these models actually aren't able to get semantic information outta

The integration of Artificial Intelligence (AI) into post-injury rehabilitation is transforming recovery paradigms by enabling personalized, adaptive, and efficient rehabilitation pathways tailored to individual patient needs. This podcast reviews the current advances in AI applications that facilitate assessment, monitoring, and optimization of rehabilitation programs following injuries. Through machine learning algorithms, wearable sensors, and predictive analytics, AI enhances the precision of therapy plans, tracks patient progress in real-time, and predicts recovery trajectories. The discussion includes the benefits of AI-driven rehabilitation, including improved functional outcomes, reduced recovery times, and increased patient engagement. It also addresses challenges such as data privacy, algorithmic bias, and integration with clinical workflows.  1. Transforming recovery paradigms Traditional post‑injury rehab relies on periodic in‑person assessments, therapist intuition, and standardized protocols that only partially account for individual variability. AI is shifting this model toward: Continuous, data‑driven care: Instead of snapshots in clinic, rehab can be informed by near real‑time streams of kinematic, physiological, and behavioral data from wearables, smart devices, and robot interfaces. Dynamic adaptation: Therapy intensity, task difficulty, and exercise selection can be automatically adjusted based on ongoing performance, fatigue, and recovery trends, rather than fixed schedules. Precision rehabilitation: Algorithms can identify which patients are likely to respond to specific interventions (e.g., constraint‑induced movement therapy vs robotics) and tailor plans accordingly. This moves rehabilitation from a "one‑size‑fits‑many" paradigm toward precision, context‑aware therapy, analogous to precision oncology but focused on function and participation. 2. Assessment, monitoring, and optimization AI for assessment Sensor‑based movement analysis: Machine learning models process accelerometer, IMU, EMG, and pressure data to quantify gait symmetry, joint kinematics, balance, and fine motor control with higher resolution than visual observation alone. Automated scoring: AI can approximate or support standardized scales (e.g., Fugl‑Meyer, Berg Balance Scale) by mapping sensor features or video-derived pose estimates to clinical scores, reducing inter‑rater variability and saving clinician time. Continuous monitoring Home and community tracking: Wearable and ambient sensors enable monitoring of daily steps, walking speed, arm use, posture, and adherence to exercises outside the clinic, feeding rich longitudinal datasets into AI models. Real‑time alerts: Algorithms can detect abnormal patterns—such as increased fall risk, reduced limb use, or signs of over‑exertion—and flag the clinician or adjust digital therapy content automatically. Optimization and decision support Predictive models: Using historical data, AI can forecast functional gains, plateau points, or risk of complications (e.g., falls, readmission), supporting individualized goal‑setting and resource allocation. Reinforcement learning and "digital twins": Emerging work in neurorehabilitation treats rehab as a sequential decision problem, using model‑based reinforcement learning and patient "digital twins" to recommend optimal timing, dosing, and progression of interventions over weeks to months.​ 3. Technologies: ML, wearables, analytics Machine learning algorithms: Supervised ML classifies movement quality (normal vs compensatory), detects exercise type from sensor streams, and estimates clinical scores. Unsupervised learning clusters patients into phenotypes (e.g., gait patterns after stroke), revealing subgroups that respond differently to certain therapies. Reinforcement learning and contextual bandits explore which therapy adjustments yield the best long‑term functional outcomes for a given individual.​ Wearable sensors and robotics: Inertial sensors, EMG, pressure insoles, and exoskeleton sensors capture high‑frequency movement and muscle activity data during training. Robotic devices (upper‑limb exoskeletons, gait trainers) coupled with AI can modulate assistance, resistance, or task difficulty in real time based on performance and predicted fatigue. Predictive and prescriptive analytics: Predictive analytics estimate trajectories (e.g., time to independent walking, expected upper‑limb function) to inform shared decisions with patients and families. Prescriptive analytics recommend therapy intensity, modality mix, and scheduling to maximize functional gains under resource constraints. 4. Benefits: outcomes, efficiency, engagement Improved functional outcomes: Studies report better motor recovery, gait quality, and ADL performance when AI‑assisted training is used—especially when robotics and intelligent feedback are involved. Reduced recovery time and resource use: More precise dosing and earlier identification of non‑responders can reduce ineffective sessions, shorten time to key milestones, and support safe earlier discharge with robust remote follow‑up. Increased adherence and engagement: AI‑driven digital rehab platforms use gamification, adaptive difficulty, and personalized feedback to keep patients engaged in home programs, improving adherence compared to static paper instructions. Support for clinicians: Instead of replacing therapists, AI can offload repetitive measurement tasks, highlight concerning trends, and offer data‑driven suggestions, allowing clinicians to focus on relational, motivational, and complex decision‑making aspects of care. 5. Challenges and ethical considerations Data privacy and security: Rehab AI often relies on continuous collection of sensitive motion, physiological, and sometimes audio/video data, raising questions about consent, storage, secondary use, and breach risk. Approaches like federated learning and on‑device processing are being explored to reduce centralization of identifiable data while still enabling model training. Algorithmic bias and fairness: If training data under‑represent older adults, women, certain racial/ethnic groups, or people with severe disability, AI models may misestimate performance or risk for those groups, potentially widening disparities in rehab access and outcomes. Ongoing auditing, diverse datasets, and participatory design with patients and clinicians are needed to ensure equitable performance. Integration with clinical workflows: Many AI tools are developed in research settings and are not yet seamlessly integrated into EHRs, scheduling systems, or therapist documentation workflows. Poorly integrated tools risk adding documentation burden or "alert fatigue," reducing adoption. Successful implementations co‑design interfaces with frontline therapists and physicians. Regulation, liability, and trust: It remains unclear in many jurisdictions how to regulate adaptive rehab algorithms (as medical devices, clinical decision support, or wellness tools) and who is liable when AI‑informed plans cause harm.​ Transparent, explainable models and clear communication to patients about the role of AI are critical for maintaining trust. 6. Case studies and emerging trends Remote and hybrid digital rehabilitation: AI‑driven platforms providing home‑based stroke, orthopedic, or Parkinson's rehab with clinician dashboards are improving adherence and extending care beyond brick‑and‑mortar clinics. Collaborative AI for precision neurorehabilitation: Frameworks combining patient‑clinician goal setting, digital twins, and reinforcement learning exemplify "collaborative AI" that augments rather than replaces therapists.​ Multimodal personalization: Integration of movement data, EMG, heart rate, sleep, and self‑reported pain/fatigue is enabling more nuanced adaptation to daily fluctuations in capacity. Conversational AI for education and coaching: Early work is assessing tools like ChatGPT as low‑risk supports for exercise education and motivation, though they are not yet precise enough to replace professional plan design AI is moving rehab toward patient‑centered, continuously adapting, and data‑rich care, but realizing this promise depends on addressing privacy, bias, workflow, and regulatory challenges in partnership with clinicians and patients.

Meta just made a multi-billion acquisition for AI agents.