Podcasts about Embedding

Betsy Helmuth welcomes special guest Zandra Zuraw, who shares her journey from a varied professional background to interior design. Zandra discusses her transition to podcasting and the challenges she faced, recounting her early podcasting days and shift towards teaching and design retreats. They talk about adapting to COVID with online courses and launching "Slow Style Home." The conversation explores evolving career paths, partnerships, and finding one's voice. Zandra explains the principles and mindset of the Slow Style framework, embedding values and subconscious interpretations in home decor, and furniture arrangement. Timestamps: 0:00 Introduction and welcome with special guest Zandra Zuraw 1:42 Zandra's journey from varied professional background to interior design 4:16 Transitioning to podcasting and dealing with challenges 7:00 Early podcasting days and shift towards teaching and design retreats 8:12 Adapting to COVID with online courses and launching "Slow Style Home" 10:22 Evolving career paths, partnerships, and finding one's voice 12:51 Principles and mindset of the Slow Style framework 19:58 Embedding values and subconscious interpretations in home decor 24:44 Deep dive into values in home decor and furniture arrangement 29:51 Exploring the Slow Style philosophy and room context changes 33:32 More from Zandra Zuraw: podcast and book details 35:41 Betsy's closing remarks and episode credits - You can achieve a luxe look in your home without hiring a high-end designer by embracing your unique style and being intentional about your design choices. - Developing a signature style involves experimenting and playing with different elements in your space to find what truly resonates with you over time. - Incorporating personal values into your home decor can make your space more meaningful and reflective of who you are, whether through sustainable choices or creating a welcoming environment for family and friends. Don't forget to subscribe for more design tips and inspiration! Links: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Uploft.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AffordableInteriorDesign.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Submit your design questions⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to be featured on the show ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Become a Premium Member⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ and access the bonus episodes Click ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to become an interior designer with Uploft's Interior Design Academy. Get Betsy's book: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠betsyhelmuth.com/book⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠For more about our residential interior design services, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ModernInteriorDesign.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ For our commercial interior design services, visit ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠OfficeInteriorDesign.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Us: Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@uploftinteriordesign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠facebook.com/UploftIntDes⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ TikTok: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠tiktok.com/@uploftinteriordesign⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/company/uploft-interior-design⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you enjoy the show, please spread the word and leave a review on iTunes! Learn more about your ad choices. Visit podcastchoices.com/adchoices

In 2010, producer David Gutnick travelled to Haiti soon after the devastating earthquake there. Embedding with a local family, he slept on the ground alongside hundreds of thousands of others, now homeless after the earthquake. The 7.3 magnitude earthquake killed more than 220,000 and some 300,000 were injured. Those survivors were left eking out survival in their already impoverished and insecure nation. David spent a day and a night living with The Merisier family, Nadine, her husband Madsen, their five children, a son and daughter-in-law, and two grandchildren. From our special summer archival doc series, this is 24 in Delmas 56

Are YOU curious about self publishing? Wondering how much you can make from it and how it differs from traditional publishing? In this episode, prolific self-published author Matt Rogers shares his experience writing three or four novels a year and selling more than a million copies! He also talks about his move to traditional publishing in 2025 – including his latest novel, The Damned, as well as some unique writing methods he employs in his routine and much much more! 00:00 Welcome03:39 Graduate spotlight07:55 Writing tip: Don’t give up after your start!12:12 WIN: Murder Most Delicious by Danielle Postel-Vinay14:20 Word of the Week: ‘Accoutre’15:07 Interview with: Matt Rogers16:19 Matt’s early writing origins18:28 Self-publishing breakthrough22:02 Transcribing for craft25:33 Prolific output and bookstores29:15 Influences and endorsements32:47 Self-publishing hustle35:41 From indie to bookstores37:55 Matt describes his new books41:02 Embedding philosophy inside thrillers44:21 Ideas and writing routine47:24 Editing workflow and wordcounts50:13 Self versus traditional publishing53:33 Writing advice and reading recommendations57:22 What’s next?58:20 Final thoughts Read the show notes Connect with Valerie and listeners in the podcast community on Facebook Visit WritersCentre.com.au | ValerieKhoo.comSee omnystudio.com/listener for privacy information.

Aaron Alva is a Harvard Berkman Klein Center fellow and the Founder of Alva Strategy Center, advising organizations and enforcers on privacy, security, and AI governance. Previously, Aaron was a lead tech advisor at the FTC, where he was instrumental in driving the agency's approach to privacy and security enforcement. In this episode… Privacy risks often hide in how companies collect, use, and share personal information. Smart TVs, health-related websites, and location data have all drawn regulatory scrutiny when data is used in ways consumers did not reasonably expect. A decade of FTC privacy enforcement shows companies what regulators consider unfair or deceptive. So, what can companies learn from these cases to strengthen their privacy practices? Reducing privacy risk starts when companies understand the data they collect, where it goes, why it's being used, and whether that use is necessary in the first place. Companies should pay close attention to handling sensitive data with care, including health information, location data, children's and teens' data, and driver behavior data. Embedding stronger privacy practices often comes down to establishing clear purpose limitations, thoughtful data minimization measures, limited retention, and privacy-enhancing defaults. It also requires a regular and thorough review of AdTech tools, like pixels and tags. Getting these practices right can help companies reduce regulatory risk. Yet when companies fall short, the FTC and state privacy regulators can impose remedies that reach beyond fines, requiring companies to delete data, stop certain data uses, change platform default settings, or build a stronger privacy program.  In this episode of She Said Privacy/He Said Security, Jodi and Justin Daniels talk with Aaron Alva, Founder of Alva Strategy Center, about what companies can learn from a decade of FTC privacy enforcement. Aaron explains the role technologists play in helping enforcement agencies work through technically complex privacy issues during investigations. He delves into lessons from major enforcement actions involving smart TVs and social media platforms and shares insights on the FTC's privacy remedies. Aaron also explains how companies can strengthen their privacy practices by setting clear limits on data use, treating sensitive data with care, and aligning privacy controls with consumer expectations.

This episode of The New Abnormal podcast features Clare Stark, Head of Futures Literacy and Foresight at UNESCO - the United Nations Educational, Scientific and Cultural Organization.They're a specialized UN agency dedicated to promoting world peace and security through international collaboration in education, arts, sciences, and culture. Headquartered in Paris, its mission spans 194 member states and encompasses several focus areas. Key Initiatives include World Heritage Sites, Global Education, Scientific & Ethical Standards, and Cultural Heritage Safeguarding.Clare is a dedicated professional with more than 20 years of experience in international development cooperation, she's passionate about ensuring that technology leads to positive social transformations, and using futures literacy and foresight to inspire collective action to shape the futures we want, using evidence-based approaches to enhance anticipatory capacities.She covers a lot of ground in the podcast, including UNESCO's role in advancing futures literacy and anticipatory governance / Embedding foresight into national and int'l policymaking / The Global Anticipatory Policy Coalition / AI governance, AI readiness and responsible deployment / Climate anxiety & information anxiety / Trust, democracy and misinformation / Leadership through collective intelligence and collaboration / The importance of culture, history and indigenous knowledge in shaping future policy / Participatory foresight involving young people, educators and citizens / The future of education, creativity and public policy in an AI-enabled world. So, I hope you enjoy listening to her as much as I did, in what I hope you'll agree is a fascinating conversation re: the growing importance of anticipatory governance and futures thinking in addressing today's global challenges.

What does it take to make football a place where everyone belongs? This week on The Jack Murley Sports Show, we're joined by Brandon Gregory – football coach, campaigner, LGBTQ+ advocate and one of the driving forces behind the growth of Cardiff Dragons, one of the UK's leading inclusive football clubs. Football has always been at the heart of Brandon's life. As a player, coach and lifelong fan, he's dedicated himself to opening up the beautiful game for LGBTQ+ people and anyone who has ever felt excluded from the sport they love. Now, as he prepares to compete at the Gay Games in Valencia before beginning the next chapter of his football journey, Brandon reflects on the impact of grassroots football, the importance of representation, and why creating welcoming spaces in sport has never mattered more. A winner at the Football v Homophobia Awards, Brandon is one of the unsung heroes helping to change football for the better—and we're delighted to share his story. In this episode, we discuss: ⚽ Brandon's journey into coaching and football leadership

Das Thema KI übt auf nicht wenige Menschen die gleiche Anziehung aus wie gratis Nagelpilz. Ein exklusiver Spaß für Menschen, die einen Laserpointer am Schlüsselbund haben und sich Witze in Programmiersprache erzählen. Für die meisten andern ist KI einfach ist eine ungebetene Erinnerung an die eigene Inkompetenz. Und das Miststück ist dabei, sich Stück für Stück unsere liebe Welt unter den Nagel zu reißen. Zum Beispiel unsere Jobs, und da muss man doch was tun! Das Wichtigste vorweg: Man muss wirklich gar nichts können, um Chatbots zu nutzen. Nicht einmal schreiben. Prompts, Vibe Coding, Embedding - das sind in erster Linie Begriffe, mit denen Leute Ahnung simulieren, aber nichts, was uns davon abhalten sollte, sich jetzt ein paar Chatbots zu unseren willigen Gehilfen zu machen. Der Zukunfts- und Trendforscher Oliver Leisse berät Unternehmen zu gesellschaftlichen und wirtschaftlichen Trends. In den letzten Jahren hat sich Oliver vor allen Dingen mit der Auswirkung von KI auf die Bereiche des täglichen Lebens beschäftigt. Er geht davon aus, dass unser Berufsleben, wie wir es heute kennen, mehr als angezählt ist. Vor allen Dingen recht einfache Einsteigerjobs werden wohl sehr bald von KI-Systemen übernommen, sagt Oliver. Andere Tätigkeiten sind mit KI viel schneller erledigt. Da bleibt dann mehr Zeit für einen frisch aufgebrühten Flat White und eine lange Gassirunde oder ihr nutzt die freien Stunden, um euch mithilfe von KI noch unentbehrlicher fürs Unternehmen zu machen. KI stattet einen mit Fähigkeiten aus, die euch bisher nicht mal im Traum erschienen sind. Wichtig ist nur, dass alle mitmachen. Der Techanalyst und Podcaster Philipp Klöckner warnt vor einer gesellschaftlichen Spaltung, getriggert durch KI-Trainingsdaten. KI hat auf weißer Mann gelernt, deshalb benachteiligen KI Modelle, alle, die nicht dazu gehören. Schade, wenn KI zukünftig Personalentscheidungen trifft, zum Beispiel. Link zur Folge: https://www.tagesschau.de/wirtschaft/technologie/jobverlust-ki-shumer-warnung-arbeitsmarkt-100.html FLEXIKON LIVE am 3.11. in Köln beim Cologne Comedy Festival: https://comedy.cologne/events/flexikon-der-podcast/ Link zur unserer Untenrum-Empfehlung der Woche: Liebt Euch! Der Dating Podcast von DASDING: https://www.ardsounds.de/sendung/liebt-euch-der-dating-podcast/urn:ard:show:178a60183a5a9d80/

In this session from DX Annual, Rebecca Fitzhugh, Lead Principal Engineer at Atlassian, moderates a panel featuring Nidhi Allipuram, Vice President, Enterprise Developer Experience and Platform at Nationwide, Jai Schniepp, Senior Director, DevX Product Management at Comcast, Brent Foster, Vice President and Head of Architecture and Strategy at TD Bank, and Praveena Patchipulusu, Vice President of Engineering at HPE.Together, they discuss how large enterprises are approaching AI adoption, what it takes to build an AI-first software development lifecycle, and how engineering leaders are balancing speed, security, governance, and developer experience. They also share their perspectives on the changing role of engineers, human accountability, and how organizations can prepare for the future of software engineering.Where to find Rebecca Fitzhugh: • LinkedIn: https://www.linkedin.com/in/rmfitzhugh • X: https://x.com/RebeccaFitzhugh Where to find Jai Schniepp:• LinkedIn: https://www.linkedin.com/in/jessicaschnieppWhere to find Nidhi Allipuram: • LinkedIn: https://www.linkedin.com/in/nidhi-allipuramWhere to find Brent Foster: • LinkedIn: https://www.linkedin.com/in/engineeringthefuture• Website: https://brentfoster.meWhere to find Praveena Patchipulusu: • LinkedIn: https://www.linkedin.com/in/praveena-patchipulusu-158741In this episode, we cover:(00:00) Intro(02:28) The AI journey across TD Bank, Comcast, and HPE(05:59) Inside Nationwide's AI-assisted development lifecycle(10:04) Reimagining the software development lifecycle with AI(11:32) Security, governance, and human accountability(15:27) Embedding security and guardrails into AI workflows(17:55) How AI is changing the role of an engineer(21:52) What developer experience looks like in the AI era(26:55) What software engineering may look like in 2030(32:47) How to prepare for the AI-driven futureReferenced:• Atlassian• TD Bank• Comcast Corporation• Hewlett Packard Enterprise (HPE)• Nationwide • GitHub Spec Kit• Abi Noda

Welcome to The Inner Game of Change.  where we explore the thinking that shapes how change really happens. Why is it that people can sit in a room, agree with a strategy, support a transformation and nod enthusiastically at a presentation, only to struggle when the change finally arrives?Today's guests suggest the answer may lie in a concept called psychological distance.Terri Block and Susan Bartlett from Workomics spend their days helping organisations bring customers, stakeholders and teams together to solve complex problems through co creation.In this conversation we explore Construal Level Theory, why humans think differently about things that feel distant versus things that feel immediate, and why co creation may be one of the most powerful ways of helping people move from abstract ideas to tangible action.Along the way we discuss ownership, expertise, accountability, skin in the game, whether facilitators can ever truly be neutral, and even whether artificial intelligence can become a co creator.I thoroughly enjoyed this conversation and I think you will too.I am grateful to have Susan and Terri chatting with me today. About The GuestsSusan BartlettI am a principal at Workomics, where we help biotechs bring life-changing therapies to patients. I contribute to projects from the perspectives of go-to-market strategy, operational effectiveness, human-centred design, and strategic communications. I treasure my colleagues and my clients, and feel fortunate to be able to work with them every day. I write a monthly Substack newsletter (please subscribe!). Through the newsletter, I explore how we make work and workplaces better for people — customer-centric, inclusive and equitable, focused on employee well-being, meaningfully integrated with emerging technology. In the past, I have been a Rhodes Scholar, a CEO, and a licensed propane dispenser, only one of which involved an objective assessment of my abilities. I love to solve specific, pragmatic problems by drawing on a variety of disciplines and traditions — my university degrees span English literature, software design, philosophy, politics, and economics, and computer science. At various points in my career, I have devoted myself to: — Communicating complex medical concepts to patients. — Architecting the data, software, technology, and IT governance structures of large enterprises. — Embedding human-centred design capabilities to enable customer experience. — Applying machine learning techniques to natural language problems.Terri BlockI co-lead Workomics where we help biotechs and pharma bring life-changing medications to patients who need it. We focus on patient experience including go-to-market strategies and campaigns, creating impactful educational experiences for patients and their care teams, and empowering internal teams to champion patient-centricity across their organization. My career in human-centered design in the life sciences industry spans 10 years and is underpinned by a whole other career in theatre and teaching. The red thread is bringing the best out in people and imagining better possibilities for our work-at-hand. I am a creative at heart and author of Words of Wonder www.wordsofwonderbook.com.Contactworkomics.comSend us Fan MailExecutive Wins PodcastThe Executive Wins Podcast features inspiring Executives who share their biggest wins.Listen on: Apple Podcasts SpotifyAli Juma @The Inner Game of Change podcastFollow me on LinkedIn

Kelle Fontenot, Chief Digital Officer at KPMG, joins me to talk about how one of the world's largest professional services firms is embedding AI into the way work gets done. Kelle shares how KPMG is approaching enterprise AI adoption through its AIQ program, why AI requires close partnership between digital, HR, technology, and the business, and what it takes to drive change across 250,000 people globally. We also get into the realities of AI adoption inside a large, highly regulated organization: digital teammates, AI agents, tool overload, trust, security, and why traditional training alone doesn't change behavior. Kelle offers a practical look at how leaders can move beyond experimenting with AI and start making it part of the everyday flow of work without losing human judgment along the way.

Kelly Anne Pipe is Head of Developer Experience at Vanguard, and Nicole Scribner is a Director in the firm's Chief Technology Office focused on engineering enablement and advancement.In this session from DX Annual, Kelly Anne and Nicole share how Vanguard is expanding its AI strategy beyond software engineering to the entire product development lifecycle. While the company initially focused on tools like GitHub Copilot for engineers, they found that faster coding alone did not significantly improve delivery speed. Product managers, designers, QA teams, and organizational processes were still operating at a different pace.To address this challenge, Vanguard developed a product team maturity model built around three stages: Augmented, Accelerated, and Autonomized. The framework spans six dimensions, from AI-powered delivery and AI-ready codebases to team autonomy, operations, and responsible AI.Kelly Anne and Nicole explain how Vanguard is applying the model across more than 800 product teams, the behaviors they believe will enable faster delivery, and the lessons they have learned about measurement, organizational change, dependencies, and scaling AI across the product development lifecycle.In this episode, we cover:(00:00) Intro(02:16) The state of AI one year ago at Vanguard(02:54) The engineering bubble(05:05) Building an AI maturity model for 800 product teams(08:24) Dimension 1: AI-powered product delivery(10:00) Dimension 2: AI-ready codebase(12:20) Dimension 3: Autonomous agent utilization (13:00) Dimension 4: AI-augmented operations(14:00) Dimension 5: Team autonomy and enablement(16:11) Dimension 6: Responsible AI(18:15) The people problem: role evolution (20:00) The measurement problem (22:55) Lessons learned from rolling out the maturity model (26:46) What's ahead (30:10) Q&A #1: Getting your codebase ready for AI(32:22) Q&A #2: Audit trails and responsible AI(34:16) Q&A #3: Vanguard's maturity model progress(36:15) Q&A #4: Measuring cycle time across 800 teamsReferenced:• Vanguard• Jennifer St Pierre - Dell Technologies | LinkedIn• Mercari

Insurance organizations unlock the greatest value from AI not by improving algorithms alone, but by embedding AI into customer education, data intake and analysis, and workflow guardrails that … Read More » The post Embedding AI Into Insurance Workflows: Where the Real Transformation Happens appeared first on Insurance Journal TV.

Insurance organizations unlock the greatest value from AI not by improving algorithms alone, but by embedding AI into customer education, data intake and analysis, and workflow guardrails that … Read More » The post Embedding AI Into Insurance Workflows: Where the Real Transformation Happens appeared first on Insurance Journal TV.

Insurance organizations unlock the greatest value from AI not by improving algorithms alone, but by embedding AI into customer education, data intake and analysis, and workflow guardrails that … Read More » The post Embedding AI Into Insurance Workflows: Where the Real Transformation Happens appeared first on Insurance Journal TV.

Embedding batteries into appliances to bypass big bottlenecks: home electrical upgrades. Instead of rewiring buildings, Copper turns induction stoves into distributed energy assets that can also support the grid.Copper is building appliances with integrated energy storage, starting with Charlie, a 30” induction stove with a built-in battery. The company focuses on making electrification cheaper, faster, and easier for multifamily buildings and older housing stock.They've received $60M in equity funding and government contracts so far.Before co-founding Copper, CEO Sam Calisch helped launch Rewiring America, was an Activate Fellow, co-authored Electrify, and previously founded Elmworks. He earned his PhD from MIT's Center for Bits and Atoms.Here's what we discussed:Installation arbitrage that changes adoption economics – Traditional induction stoves often require expensive 240V upgrades and panel work, while Charlie plugs into an existing 110V outlet behind most gas stoves using an onboard 5kWh LFP battery to deliver high-power cookingMultifamily as the wedge market – Buildings facing costly gas infrastructure repairs can avoid six-figure retrofit costs, with some projects saving over $100k by switching directly to Copper's battery-enabled electric appliancesAppliances as grid assets – Aggregated stoves participate in California's DSGS virtual power plant program, providing dispatchable capacity during peak demand and potentially offsetting future appliance costsLicensing instead of building everything alone – Copper is pursuing partnerships with incumbent appliance manufacturers rather than vertically integrating every product category itselfFounder operating system – Weekly written goals, deliberate “play time” for experimentation, outdoor activity, and separating business problems from personal identity to sustain long-term decision quality--Join our confidential CEO community.Private CEO group for VC/PE-backed climate tech founders navigating capital, strategy, and scale. Capped at 45 CEOs. See if you're a fit → entrepreneursforimpact.comJoin 40,000 professionals who get our newsletter.Climate tech finance, strategy, leadership. 2-min read. → entrepreneursforimpact.substack.comLeave a podcast review.If you got value, take 30 seconds and do the community a favor. It helps push more capital and talent toward scalable climate solutions.

Is your messaging making you memorable, or just visible?You could have the best content engine in your industry, be publishing consistently across multiple channels, and focusing on quality. But if it isn't memorable, none of it sticks.Core messaging and positioning is one of those things that sounds simple until you try to do it well. If you asked ten people in your organization what you are all about and why customers should choose to work with you, would they all broadly answer in the same way?In most organizations, the answer is no.That inconsistency shows up everywhere. In your content, on your website, in sales conversations, and in ways that are hard to trace back to the source.In part four of our seven-part B2B content strategy series, Amy Woods digs into what messaging is, what goes into a messaging framework, and how to know whether yours is working for you.Find out:What messaging is and why consistency is what makes it stickWhether you can have a B2B content strategy without clear messagingThe five components every B2B messaging framework needsHow to define your messaging and positioning through internal conversationsHow to use your messaging framework on a daily basis, including how to embed it into your AI toolsThe signals that tell you your messaging is workingWhen to review your messaging and how to update it without doing a hard pivotImportant links & mentions:Blog post about this episode: https://www.content10x.com/357Part one of the B2B content strategy series - What Is a B2B Content Strategy (And Why Does It Matter)?: https://www.content10x.com/354Part two of the B2B content strategy series — How Do You Align a B2B Content Strategy to Business Goals?: https://www.content10x.com/355Part three of the B2B content strategy series — How Does Competitor Analysis Fit Into Your B2B Content Strategy?: https://www.content10x.com/356B2B content strategy series on YouTube: https://youtube.com/playlist?list=PLVwaHzx-z4d4Rcnrh2VsUN9d47rrdMypp&si=NjYvp1Ilo26KnIF3Amy on LinkedIn: https://www.linkedin.com/in/amywoods2/Content 10x website: https://www.content10x.com/Amy's book: www.content10x.com/book (Content 10x: More Content, Less Time, Maximum Results)Timestamps:01:57 Free B2B Content Operations Benchmark Assessment02:35 What is messaging?03:44 Why a great content strategy can't exist without clear messaging04:33 Who owns messaging?05:10 Core components of a messaging framework06:55 How to define messaging through internal conversations08:35 Where messaging shows up in real-world execution09:16 Opinionated content and category positioning10:38 Embedding messaging into AI tools and workflows11:09 How to know your messaging is working (or not working)13:08 Quantitative vs qualitative signals from customers and sales13:47 Revisiting and adjusting your messaging framework16:07 Recap and what's next17:55 Wrap upAbout the host:Amy Woods is the CEO and founder of Content 10x, a creative agency that provides specialist content strategy, creation and repurposing support to B2B organizations.She's also a best-selling author, hosts two content marketing podcasts (The Content 10x Podcast and B2B Content Strategist), and speaks on stages all over the world about the power of content marketing.Join thousands of business owners, content creators and marketers and get the latest content marketing tips and advice delivered straight to your inbox every week https://www.content10x.com/newsletter

The ethics issues that arise in neuroscience research are usually novel, unresolved and understudied. Embedding ethicists in labs helps scientists navigate these challenges and develop strategies in real time to prevent harm.

In this episode, Kate Webber, Chief Solutions Officer at the PRI, is joined by Claudia Wearmouth, Global Head of Responsible Investment at Columbia Threadneedle Investments, and Travis Antoniono, Investment Director for Sustainable Investments at CalPERS.Together, they explore how responsible investment is being applied in practical, financially material ways, including how it is embedded into investment processes, how transparent dialogue between asset owners and managers supports long-term outcomes, and the role evidence plays in sustainable investment decision-making.Overview:Responsible investment is increasingly moving from a specialist function to a core part of investment decision-making. Across public and private markets, sustainability and governance considerations are being integrated into due diligence, portfolio construction, stewardship and long-term risk management.This episode explores how investors are building practical frameworks around financial materiality, balancing quantitative tools with qualitative judgement, and adapting to rapidly evolving risks such as climate change and AI disruption.Detailed coverage:Embedding sustainability into investment processesBoth guests explain how sustainability considerations are now integrated throughout the investment lifecycle, from initial due diligence through to ongoing monitoring and exit decisions.Financial materiality and fiduciary dutyThey explore how responsible investment supports long‑term, risk‑adjusted returns and helps meet fiduciary responsibilities to beneficiaries.The role of dedicated expertiseTravis Antoniono discusses embedding dedicated sustainability specialists directly into investment due diligence teams, while Claudia Wearmouth outlines how sustainable investment analysts can better work alongside fundamental research teams.Data, evidence and judgementThe conversation explores how responsible investment relies on a growing evidence base. While data is still evolving, investors increasingly combine quantitative tools with qualitative insight and real-world case studies.Explore real-world examples of how investors are combining data and judgement in practice in the PRI's investment case database: https://public.unpri.org/investment-tools/investment-case-databaseHow AI is changing investment researchAI is beginning to transform investment analysis itself, helping teams assess sector disruption, and emerging financial impacts more dynamically.Building organisational buy-inBoth guests highlight that embedding responsible investment depends on strong leadership and clear direction, with teams working together to apply it in practice.The importance of asset owner–manager relationshipsTransparency, trust and detailed communication are highlighted as essential for aligning investment objectives, stewardship expectations and long-term strategy execution.Practical lessons for investorsThe episode concludes with practical recommendations on how investors can improve governance and decision-making through more consistent use of evidence and ongoing dialogue.Chapters:00:08 - Introduction and the investment case for responsible investment01:29 - Embedding sustainability into investment processes05:14 - Sustainability, fiduciary duty and long-term returns10:56 - Building the evidence base for responsible investment13:39 - How AI is changing investment analysis20:15 - Creating organisational buy-in and investment alignment22:18 - Climate solutions, strategy and total portfolio thinking27:12 - Asset owner and investment manager collaboration35:15 - Key lessons on transparency, trust and detail37:04 - Practical recommendations for investorsDisclaimer:This podcast and material referenced herein is provided for information only. It is not intended to be investment, legal, tax or other advice, nor is it intended to be relied upon in making an investment or other decision. PRI Association is not responsible for any decision made or action taken based on information on this podcast. Listeners retain sole discretion over whether and how to use the information contained herein. PRI Association is not responsible for and does not endorse third parties featured on in this podcast or any third-party comments, content or other resources that may be included or referenced herein. Unless otherwise stated, podcast content does not necessarily represent the views of signatories to the Principles for Responsible Investment. All information is provided “as is” with no guarantee of completeness, accuracy or timeliness, or of the results obtained from the use of this information, and without warranty of any kind, expressed or implied. PRI Association is committed to compliance with all applicable laws. Copyright © PRI Association 2026. All rights reserved. This content may not be reproduced, or used for any other purpose, without the prior written consent of PRI Association.

Chinese consumer brands are rapidly expanding across Southeast Asia, moving beyond electronics and electric vehicles into sectors such as beauty, food service and home appliances, according to a report by Euromonitor International.市场研究机构欧睿国际的一份报告显示，中国消费品牌正在东南亚迅速扩张，其业务已从电子产品和电动汽车拓展至美妆、餐饮和家用电器等领域。Its “Rise of Chinese Brands in Southeast Asia” report found that the ASEAN economies of Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam account for 95 percent of the region‘s $4 trillion GDP. The region has become the largest and fastest-growing export destination for Chinese goods.该机构发布的《中国品牌在东南亚的崛起》报告指出，在东南亚地区4万亿美元的经济总量中，印度尼西亚、马来西亚、菲律宾、新加坡、泰国和越南这六个东盟经济体合计占95%。该地区已成为中国商品最大且增长最快的出口目的地。In 2024, China's exports to Southeast Asia reached $587 billion, up 12 percent year-on-year. More than 70 percent of Chinese companies operating in ASEAN plan further expansion, citing strong performance and untapped consumer demand, said the China Council for the Promotion of International Trade.据中国国际贸易促进委员会数据，2024年中国对东南亚出口额达到5870亿美元，同比增长12%。超过70%在东盟经营的中国企业表示将进一步扩大业务，这得益于其强劲的业绩表现以及尚未充分开发的消费市场。With a population exceeding 650 million, 63 percent under 40 and a median age of 31, Southeast Asia‘s consumer market is thriving. This demographic fuels demand for e-commerce, livestreaming shopping, fintech solutions and affordable premium products.东南亚人口超过6.5亿，其中63%在40岁以下，中位年龄为31岁，消费市场充满活力。这一人口结构推动了对电子商务、直播购物、金融科技解决方案以及高性价比优质产品的充分需求。Countries like Vietnam and Indonesia are outpacing China in GDP growth, offering Chinese brands a rapidly expanding consumer base with rising disposable incomes and accelerating urbanization, said the report.报告称，越南、印度尼西亚等国的经济增速已领先中国，这为中国品牌提供了一个蓬勃发展的消费市场——那里的居民收入不断增长，城市化步伐也在加快。Chinese companies have long dominated sectors such as EVs, consumer electronics and home appliances. In EVs, BYD is now the top brand in most Southeast Asian markets and the number-one car brand in Singapore, surpassing Toyota. In home appliances, Chinese brands‘ share of the air conditioner market rose from 9 percent in 2015 to 25 percent in 2024. Haier, Midea and Gree have become household names. In smartphones, Chinese brands' market share has increased from 21 percent in 2014 to over 60 percent today.长期以来，中国企业在电动汽车、消费电子产品和家用电器等领域占据主导地位。在电动汽车领域，比亚迪现已成为大多数东南亚市场的头号品牌，并在新加坡超越丰田成为第一大汽车品牌。在家电领域，中国品牌在空调市场的份额从2015年的9%上升至2024年的25%。海尔、美的和格力已成为家喻户晓的名字。在智能手机领域，中国品牌的市场份额已从2014年的21%提升至如今的60%以上。Now, Chinese companies are breaking into sectors once considered difficult for foreign entrants. In beauty and personal care, mass-market skincare brands achieved a 115 percent compound annual growth rate (CAGR) from 2019 to 2024. In the consumer food and beverage sector, chains such as Mixue, Luckin Coffee and Chagee are expanding aggressively. Mixue outlets grew 80 percent between 2019 and 2024, and by April 2026, Mixue had 4,153 overseas stores, while Chagee reached 262, said the China Chain Store and Franchise Association.如今，中国企业正挺进昔日外资难以进入的领域。美妆个护方面，大众护肤品品牌2019—2024年复合年增长率高达115%。餐饮消费方面，蜜雪冰城、瑞幸咖啡、霸王茶姬等品牌正加速扩张。中国连锁经营协会数据显示，2019至2024年，蜜雪冰城门店增长80%，截至2026年4月，其海外门店达4153家，霸王茶姬海外门店达262家。Nathanael Lim, APAC insight manager for beverages at Euromonitor International, said: “Chinese coffee and tea chains maintain consumer interest through relentless product innovation, often unveiling new menu items monthly. Significant investment in research and development and direct ingredient sourcing allows them to craft unique flavors that resonate with local palates.”欧睿国际亚太地区饮料行业洞察经理纳撒尼尔·林（音译）表示：“中国咖啡和茶饮连锁品牌通过不断的产品创新来维持消费者的兴趣，每月都会推出新品菜单。对研发和原材料直接采购的大量投入，使他们能够打造出与当地口味产生共鸣的独特风味。”Partnerships with local players are also central to expansion. In January, Eastroc Beverage signed a cooperation agreement with Indonesia‘s Salim Group to establish a joint venture, with investments of up to $200 million. Since 2021, Eastroc has exported products to 30 countries and regions.与当地企业建立合作伙伴关系对扩张同样至关重要。今年1月，东鹏饮料与印尼三林集团签署合作协议，共同成立合资公司，投资额高达2亿美元。自2021年以来，东鹏饮料已向30个国家和地区出口产品。Euromonitor said deep localization is key to Chinese brands' success, surpassing mere price competition. Many beauty and F&B companies register as local entities, adapt products for tropical climates and employ local teams for livestreaming and marketing activities.欧睿国际表示，深度本土化是中国品牌取得成功的关键，其重要性超越了单纯的价格竞争。许多美妆和餐饮企业在当地注册为本土实体，针对热带气候调整产品，并聘请本地团队从事直播带货和营销活动。“To move beyond transactional entry points, Chinese companies must transition from exporters to long-term ecosystem participants. Embedding within local value chains, adapting to cultural and economic contexts, and cultivating trust — through local manufacturing, customer service and community engagement — will be essential to sustaining growth,” the report said.报告指出：“为了超越交易性进入方式，中国企业必须从出口商转型为长期的生态系统参与者。通过本地制造、客户服务和社区参与等方式，融入当地价值链、适应文化与经济环境并建立信任，对于实现持续增长至关重要。”Euromonitor International /ˌjʊərəʊˈmɒnɪtər ˌɪntəˈnæʃənəl/欧睿国际untapped /ʌnˈtæpt/未开发的，未利用的fintech /ˈfɪntek/金融科技affordable premium products /əˈfɔːdəbəl ˈpriːmiəm ˈprɒdʌkts/高性价比优质产品disposable income /dɪˈspəʊzəbəl ˈɪnkʌm/可支配收入unveil /ʌnˈveɪl/推出，公布partnership /ˈpɑːtnəʃɪp/合作伙伴关系

Introduction What if the real bottleneck in commercial insurance isn't distribution or pricing—it's the workflow itself? Nearly $100 billion of SME P&C insurance is placed every year using manual processes, disconnected systems, and data that lives in spreadsheets and email threads. Hamesh Chawla has spent the last four years building the infrastructure to change that. Before founding Mulberri in 2021, Chawla led product and technology at Edelman Financial Engines and Asurion. He came to insurance not as a lifer but as a technologist who saw an industry still running on 20th-century tooling. Mulberri is his answer: an AI operations platform connecting PEOs, brokers, SMEs, and carriers—from smart submission and risk scoring to quote-and-bind and certificate of insurance. In this conversation, Josh Hollander and Chawla dig into why the MGA market was the right pivot, what AI governance looks like when binding decisions carry real capital risk, and why the SME segment is the most underserved frontier in commercial insurance. Guest Bio Hamesh Chawla is the Co-Founder and CEO of Mulberri, an AI operations platform for MGAs, PEOs, brokers, and carriers serving the SME market. Before Mulberri, he was EVP and Chief Product & Technology Officer at Edelman Financial Engines, with prior roles at Asurion and Zephyr (acquired by SmartBear). He holds an MS in Computer Science from Texas A&M University. Mulberri has raised $10.8M from Eos Venture Partners, Altamont Capital Partners, MS&AD Ventures, and Hanover Technology Management. Key Topics • The $100B manual workflow problem — Nearly $100B of SME P&C is placed annually using ACORD forms emailed back and forth, loss runs parsed by hand, and decisions made without the data that exists in the market. Mulberri automates this stack. • From embedded insurance to AI operating system — Chawla explains why he pivoted from embedded distribution to building the workflow layer MGAs actually run on—ingesting unstructured data, structuring it through a GenAI OS, and routing decisions with full context. • AI governance when capital is at stake — When AI is binding real policies, black-box models get rejected. Mulberri surfaces claim propensity, frequency, severity, and loss ratio so underwriters can interrogate and trust the output. • The PEO channel as data and distribution — PEOs sit on firmographic and workforce data directly predictive of workers' comp risk. Embedding into that channel is both a data strategy and a go-to-market strategy. • Building for carriers, brokers, and SMEs simultaneously — Carriers need loss ratio visibility, brokers need submission efficiency, SMEs need straightforward access. Aligning all three is the hardest product problem in the space. Notable Quotes "Our mission since day one has been to leverage technology to complement underwriters' expertise—simplifying and streamlining the business insurance process while ensuring transparency." "The Risk Engine puts the information underwriters need at their fingertips to make fast, accurate decisions—not replacing them, but making them dramatically more effective." Resources Guest: • Mulberri: https://www.mulberri.io • Hamesh Chawla on LinkedIn: https://www.linkedin.com/in/hameshchawla/ Host & Organization: • Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ • Horton International (USA): https://www.horton-usa.com/ • Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Apple Podcasts, and Spotify.

Clinicians do not have time to switch screens to search for medical evidence. Forcing them to open another application to find answers just adds to their cognitive load.Healthcare IT Today sat down with Derrick Leung from the BMJ Group. We discussed how his organization is rethinking the delivery of medical evidence. You will learn why they are moving their knowledge base directly into the clinical workflow via an API and using human curation to ground AI tools.

As sustainability conversations increasingly center on regulation, compliance deadlines and investment in new data systems, something risks getting lost: the need to invest in people who drive the change. Todd Corley's perspective emphasizes the importance of inspiring and developing individuals across all levels of an organization. Rooted in a career shaping social, philanthropic, sustainability and belonging initiatives in corporate workplaces, he now holds one of the most distinctive roles in the industry as Chief People and Impact Officer at Carhartt. From that vantage point, he builds strategy bottom-up, and asks a different question: What happens when you treat people and culture not as a support function to your people and impact strategy, but as its foundation?   Integrating this strategic framework within global organizations requires persistence, adaptability, and a willingness to accept that there is no one-size-fits-all approach to building a purpose-driven culture. In this webinar, we explored: - How Todd's journey to Chief People and Impact Officer shapes his approach to sustainability - What it looks like to structure teams and governance for real impact, and why capacity building is a strategic investment - Carhartt's people-first approach in practice: examples of community connection, skilled trade development and accessible circularity - Making the internal business case: navigating pushback and keeping purpose at the core - Reasons for optimism, and what the industry needs to do next to act on them

Joyce talks about how radical Islam are and have been quietly embedding themselves into American institutions with a plan to take over despite our values and culture. See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Read the unfiltered memos I send my team as we scale Acquisition.com to $1B+:https://leilahormozi.com/subscribe Acceptance is power. Growth starts when people stop resisting discomfort and start moving with reality instead of against it. In this episode, Leila explains why anxiety, fear, and negative thoughts only control people who treat them like they were facts. She also shares the mental frameworks that helped her stop spiraling. Learning that thoughts are not facts might be the difference between staying trapped and finally moving forward.In this episode00:00 The power of active acceptance02:25 Why you must audit your beliefs04:06 Identifying and swapping irrational beliefs05:32 Embedding new beliefs with actionMore Value:Get your personalized $100m scaling roadmap: https://www.acquisition.com/roadmap Read the unfiltered memos I send my team as we scale Acquisition.com to $1B+: https://leilahormozi.com/subscribeReceive a curated set of internal memos from the past year at Acquisition.com: https://leilahormozi.com/acq Watch my latest YouTube videos: https://www.youtube.com/@leilahormozi/featuredLearn how to scale your business to millions of dollars in annual revenue: https://www.acquisition.com/ DISCLOSURE Information shared here is for educational purposes only. Individuals and business owners should evaluate their own business strategies, and identify any potential risks. The information shared here is not a guarantee of success. Your results may vary. Copyright © 2026.

In this episode, Paul Turner discusses his journey in integrating climate and nature education into the core school curriculum through the Ministry of Eco Education. Paul gives examples of places which are becoming more sustainable using practical measures. He also gives advice to students who are thinking of entering a green career.

“If you're not growing, you're dying.” – Brent W. RempeIn this week's episode, Carol Schultz sits down with Brent Rempe (President & CEO of First Alliance Credit Union) to unpack what actually drives workplace evolution—and why most companies fail to make their values meaningful. Brent shares how his team redefined their mission, vision, and values after realizing the old ones didn't resonate, and how simplifying them into something tangible changed the direction of the organization.Brent explains why having too many values makes them forgettable, how organizations can embed values into hiring and performance systems, and why alignment matters more than raw output. They explore how behavioral interviews reveal real character, why pairing HR with hiring managers improves decision-making, and how growth can expose weaknesses inside a team. The conversation also touches on leadership realities—like imposter syndrome—and why purpose and storytelling are critical to keeping employees engaged. The episode closes with a practical look at how companies can create workplaces where people feel connected to the impact of their work, not just the job itself.TakeawaysMission, vision, and values only work if they are simple and actionableToo many values make culture harder to understand and applyValues should be embedded into hiring, performance, and daily decisionsBehavioral interviews help uncover genuine alignment—not rehearsed answersHR involvement improves hiring consistency and reduces biasGrowth without alignment can create internal frictionEmployees stay engaged when they feel their work has real impactStorytelling helps teams connect to purpose and meaningEven experienced leaders deal with imposter syndromeStrong culture creates momentum, not just complianceChapters00:03 Intro: What it means to evolve a workplace04:13 Rethinking mission, vision, and values05:14 Why simplicity in values matters07:33 The three core tools: mission, vision, values08:17 Embedding values into hiring and performance09:10 How to interview for alignment10:28 The role of HR in better hiring decisions15:58 Defining the ideal member and growth focus18:05 Looking beyond credit scores: human-centered decisions20:26 Growth challenges and team development26:19 Imposter syndrome among leaders31:25 Purpose, storytelling, and employee motivationConnect With Host Carol SchultzFind more information about our host Carol Schultz and her company at Vertical Elevation, LinkedIn, YouTube, and Instagram.Want to be our next guest expert? Email cat.gloria@verticalelevation.com with your information.And of course, click "follow" to stay up-to-date on new episodes and leave an honest review/rating letting us know what you thought!

In this episode of the AI Agent & Copilot Podcast, host Giuseppe Ianni speaks with Parmesh Rajan, VP of Technology for HSO US, live from the AI Agent & Copilot Summit NA in San Diego, California.Rajan explains how organizations are moving beyond experimentation and embedding AI directly into business processes, implementation delivery, and workforce productivity. The discussion highlights a shift from standalone copilots toward operational, production-grade AI agents that deliver measurable outcomes. Key Takeaways AI Embedded in Core Operations, Not Add-On Tools: Organizations are moving from experimenting with AI to embedding it directly into enterprise systems and workflows. As Parmesh Rajan explains, the goal is to shorten complex ERP implementations and integrate AI into core business processes like finance, operations, and customer engagement rather than treating it as a standalone capability. Automation Across the Entire Implementation Lifecycle: HSO is applying AI across the full delivery stack—from gap-fit analysis and solution design to coding assistance and data transformation. This is helping reduce manual effort in traditionally lengthy 12–18 month implementations while improving accuracy and accelerating time to value for customers. Adoption Depends on Workflow Fit, Not Agent Quantity: Industry-specific AI agents are critical for real-world adoption because they align with how users actually work. Examples like automated timesheets and AI-driven expense processing show that success comes from embedding agents into daily workflows, with effectiveness ultimately measured by usage rather than the number of agents deployed. Visit Cloud Wars for more.

Employee engagement continues to be one of the most talked-about topics in HR, but also one of the most misunderstood. While many organisations measure it, far fewer truly understand what drives it or how to improve it in a meaningful way. In this episode of the HR Insights Podcast, Stuart Elliott sits down with industry experts Paul Knight, Group Chief People Officer at PA Media, and Rebecca Saunders Jones, Managing Director of Loopin, to explore what employee engagement really looks like today, why it may be stalling in many organisations, and what HR and business leaders can do differently. From leadership accountability to real-time data and shifting workforce expectations, the conversation offers a practical and honest view of where engagement stands today, and where it needs to go next. Key timestamps03:52 – What does employee engagement actually mean? 05:20 – Where is engagement today? 08:05 – What's ‘job hugging'? 10:29 – Why traditional engagement surveys fall short 16:36 – Who really owns engagement?24:42 – Transparency vs psychological safety28:28 – The biggest risk to engagement35:12 – Can you over-measure engagement?41:43 – Embedding engagement into cultureYou can listen to and download HR Insights from Apple Podcasts, Google Podcasts, Spotify and other popular podcast apps. Please subscribe so the latest episodes are directly available! You can also join our HR Community by following us on LinkedIn.Thank you for listening and please do review and rate us wherever you listen! 

In today's episode, we're speaking with Lisa Lawson, founder of Scotland's Dear Green Coffee Roasters.Lisa started in the coffee industry working alongside Toby Smith in the early days of creating Toby's Estate in Sydney, and went on to launch Dear Green in Glasgow in 2011, with a mission to bring sustainably sourced coffees to her local community.  Since then, Dear Green has become a cornerstone of the Scottish specialty coffee scene – not only producing the Glasgow Coffee Festival, but also setting the benchmark for what a responsible coffee business should look like.In this inspiring conversation, Lisa shares her philosophy of embedding sustainability into every aspect of her business. She also offers practical advice for greener operations – from measuring and reducing carbon emissions, to investing in renewable energy and working towards zero-to-landfill waste goals.Credits music: "Dust of a Star" by Daisy Chute in association with The Coffee Music Project and SEB Collective. Tune into the 5THWAVE Playlist on Spotify for more music from the showSign up for our newsletter to receive the latest coffee news at worldcoffeeportal.comSubscribe to 5THWAVE on Instagram @5thWaveCoffee and tell us what topics you'd like to hear

In this episode, I'm joined by Drs. Brandon May and Maggie Pavone, and Kate Heersink to talk about how we can better support healthier lifestyles for individuals with developmental disabilities. We start by digging into how each of them came to this work. Maggie shares some early experiences working as a direct support professional, where she began to notice patterns between food-related variables and challenging behavior. Brandon talks about coming into behavior analysis through the health and fitness world, and seeing firsthand how difficult it was to support individuals in building healthier routines without a clear behavioral framework. Kate adds her perspective from working with individuals with brain injury, where the connection between physical health and overall functioning is hard to ignore. We also spend some time acknowledging that this isn't entirely new territory. There's a solid body of work—both within and outside of behavior analysis—focused on physical activity and health for individuals with disabilities. At the same time, there's still a gap when it comes to practical, easy-to-implement tools that can be used by the people doing the day-to-day work. From there, we get into the early development of the Fit 4 All program and how it's currently being implemented in a day program setting for adults with developmental disabilities. Kate walks through what a typical session looks like, including: Starting the day by ensuring wearable tech (e.g., a Fitbit) is in place Using a token system tied to individualized goals (hydration, movement, functional fitness, and nutrition skills) Embedding physical activity throughout the day (walking, fitness videos, etc.) Teaching basic nutrition concepts using structured learning trials Incorporating functional skills like cooking where appropriate One of the things I appreciated about this approach is how integrated it is. Rather than treating exercise or nutrition as separate, isolated targets, they're woven into the flow of the day and supported through clear contingencies and reinforcement systems. We also talk about the importance of working within real-world environments. This isn't about creating tightly controlled, clinic-based interventions—it's about meeting people where they are and building systems that can be implemented by direct support staff, teachers, and caregivers in the settings where individuals actually live and spend their time. This is very much a "boots on the ground" application of behavior analysis—figuring out how to increase things like step count, heart rate, and water consumption in ways that are practical, sustainable, and individualized. And like a lot of good ABA work, it involves ongoing problem-solving—adjusting activities, testing different approaches, and using data to guide decisions. If you're a BCBA, or someone working directly with individuals with developmental disabilities, this conversation is a good reminder that health and wellness is an area where our science has a lot to offer—and probably more room to grow.

At kdc/one, learning is more than a support function—it's a driver of business performance. In this episode, kdc/one's Director of Learning and Development Sharron Northern shares how she's building a global strategy that simplifies complexity, aligns with business goals, and creates real demand for development.Show Notes:Kdc/one's Sharron Northern focuses on simplifying systems, focusing on leaders and creating meaningful learning experiences to drive engagement and performance. Her top takeaways include: Start with what leaders care about. Align learning initiatives to real business priorities to quickly build trust and demonstrate value.Create a “pull” for learning—not just push. When learning solves real problems, leaders actively seek it out, increasing engagement and impact.Simplify to scale. Breaking down complex systems and focusing on clear priorities enables global organizations to move faster and more effectively.Integrate learning into performance systems. Embedding development into performance management ensures learning is reinforced, measured, and sustained.Design for engagement and application. Interactive elements like role play, peer discussion, and even gamification—when used intentionally—drive retention and behavior change.Powered by Learning earned Awards of Distinction in the Podcast/Audio and Business Podcast categories from The Communicator Awards and a Gold and Silver Davey Award. The podcast is also named to Feedspot's Top 40 L&D podcasts and Training Industry's Ultimate L&D Podcast Guide. Learn more about d'Vinci at www.dvinci.com. Follow us on LinkedInLike us on Facebook

Favour Obasi-ike, MBA, MS breaks down why every business website needs an active, well-structured blog. He introduces content pillars — long-form foundational articles around 3,000 words — and content clusters, shorter supporting articles around 700 words that link back to the pillar to build semantic authority. The session also covers how embedding multimedia like YouTube videos and infographics increases "in-view" time and reduces bounce rates. It closes with Favour revealing his background as a music producer and playing an original instrumental track live.Who is this for?Business owners, content creators, and digital marketers who want to turn their website blog into a long-term traffic and authority asset — especially anyone publishing content inconsistently or without a proper content structure.Key Moments & Timestamps01:33 — Why every business website needs an active blog and a structured sitemap.04:21 — How embedding YouTube videos retains traffic and intellectual property on your domain.65:01 — Understanding content pillars (3,000 words) vs. content clusters (700 words).68:00 — How infographics increase content shares by up to 300% and lower bounce rates.143:10 — Favour reveals his music production background and plays an original instrumental track live.FAQsQ: Why embed a YouTube video instead of sharing the link?A: Embedding keeps traffic and intellectual property on your domain, increasing "time on page" and sending positive ranking signals to search engines.Q: What is the difference between a content pillar and a cluster?A: A pillar is a comprehensive long-form article on a broad topic. A cluster is a shorter article that links back to the pillar, building semantic authority over time.Q: Do people still read blogs in 2026?A: Yes. While many people skim, search engine bots read everything — and AI tools like ChatGPT, Siri, and Alexa pull answers directly from published blog content.Action StepsAudit Your Sitemap: Confirm your blog is active and properly indexed in your XML sitemap.Embed Your Media: Keep traffic on-site by embedding YouTube videos and podcast episodes directly into blog posts.Build Content Pillars: Write one comprehensive pillar article, then support it with 3–5 shorter cluster articles that link back to it.Use Infographics: Add visual elements to increase screen time and lower your bounce rate.Refresh Old Content: Update popular older posts with new information to keep them evergreen and re-indexable by search engines.Ready to Rank? Book Your SEO & Web Dev Services Today

Episode 131 How to design read aloud lessons that build understanding—not just engagement The difference between read aloud that supplements vs. supplants your instruction Using read aloud to teach reading skills like character motivation and author's craft How to connect knowledge building and accountable talk into one cohesive lesson Embedding learning science strategies like retrieval practice and interleaving into read aloud Designing literacy instruction so students remember and apply what they learn over timePractical Strategies Mentioned• Modeling character motivation during read aloud using sentence stems • Using repetition in a text to teach author's craft • Retrieval prompts like “What happened yesterday?” • Interleaving skills (character traits + motivation in one question) • Echo, choral, and partner reading followed by comprehension checks • Planning intentional stopping points and think-alouds • Using text sets (poems, articles, videos) to deepen understandingThese are all strategies grounded in the science of reading and learning science that help students move from understanding in the moment to learning that actually sticks.As you listen, consider this question:What is my read aloud actually doing in my literacy block?Is it:Filling time?Reinforcing skills?Or driving instruction and building understanding over time?Instructional leadership starts with teachers who are willing to move from doing the lesson to designing the learning experience.Earthquake Terror (used as a mentor text example for author's craft)Wonder by R.J. Palacio (used for text connections and deeper thinking)Episode 129: Why Read Aloud Still Matters in Upper Elementary Episode 130: How Accountable Talk Builds Thinking in Your Literacy ClassroomIf you're ready to strengthen your instruction and design literacy lessons that actually stick, you can learn more about coaching and professional development below:In This Episode We DiscussSelf-Leadership ReflectionResources MentionedPrevious Episodes ReferencedWork With EvaGrab my free guide: How to Keep Your Mini Lesson Mini  Book a discovery call for 1:1 coaching or school professional development

What does it really take to build a successful business that creates both financial returns and meaningful social impact?In this episode of Mirror Talk: Soulful Conversations, we sit down with Brent Freeman, Founder and President of Stealth Venture Labs, who has helped brands like Crocs, Poo-Pourri, and Home Chef generate over $500M in revenue.Brent shares the deeper principles behind sustainable entrepreneurship, including how to build a business rooted in purpose, why social impact should be part of a company's DNA, and how joy can become a real metric for success. He also opens up about his powerful Return Of Joy principle and how reconnecting with the things he truly loved transformed his health, mindset, impact, and results.If you are building something meaningful, navigating challenges, or trying to grow without losing yourself in the process, this conversation will give you wisdom, clarity, and practical encouragement.In this episode, you will learn:How to build a business that creates both profit and positive social impactWhat it truly means to be a social entrepreneurImportant habits every successful entrepreneur should buildCommon obstacles entrepreneurs face and how to overcome themHow to lead by example in business and in lifeBrent Freeman's Return Of Joy principle and how it can transform your lifeHow to create an abundance mindsetThe future of digital marketing in an AI-driven worldBrent's advice for anyone who wants to become truly successfulTimestamps:00:00 - Introduction to Brent Freeman02:22 - Brent's background and entrepreneurial journey05:26 - Embedding social impact into your business DNA09:09 - Scarcity mindset vs abundance mindset11:39 - The power of giving and community impact14:16 - Turning obstacles into opportunities for growth16:21 - Building resilience through hardship20:07 - Lessons from challenges and setbacks22:20 - Leading by example and building strong teams25:43 - Creating a high-performance culture with emotional intelligence27:01 - Brent's Return Of Joy principle30:33 - Reconnecting with joy through daily practices35:19 - The activities that bring Brent joy37:53 - Stealth Venture Labs and the future of digital marketing40:05 - AI and the evolution of marketing43:02 - Brent's advice for aspiring entrepreneursResources and Links:Stealth Venture LabsBrent Freeman on LinkedInBrent Freeman on InstagramThink and Grow Rich by Napoleon HillConnect with Brent Freeman:Website: https://www.stealthventurelabs.com/Profile: https://speakonpodcasts.com/brent-freeman/If this episode encouraged you, share it with someone building a business, pursuing purpose, or trying to grow without losing joy along the way.Ask what is on your heart. Mirror Talk will reflect back what may help you see more clearly. Try it here: https://mirrortalkpodcast.com/ask-mirror-talk/Thank you for joining me on this MIRROR TALK podcast journey. Please subscribe to any platform and remember to leave a review and rating.Stay connected: https://linktr.ee/mirrortalkpodcast More inspiring episodes and show notes are here: https://mirrortalkpodcast.com/podcast-episodes/ Your opinions, thoughts, suggestions, and comments are important to us. Please share them here: https://mirrortalkpodcast.com/your-opinion-matters/ Could you support us by becoming a Patreon? Please consider subscribing to one or more of our offerings at http://patreon.com/MirrorTalk All proceeds will help enhance the quality of our work and outreach, enabling us to serve you better.We use and trust these podcasting tools, software, and gear. We've partnered with amazing platforms to give our Mirror Talk community exclusive deals and discounts: https://mirrortalkpodcast.com/best-podcasting-tools/

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Watch every episode ad-free & uncensored on Patreon: https://patreon.com/dannyjones David Holthouse is a gonzo journalist, writer & filmmaker. His documentaries include Operation Odessa, the Last Narc, Sasquatch & Krishnas. https://davidholthouse.com SPONSORS https://amentara.com/go/dj - Use code DJ22 for 22% off your first order. https://shopify.com/dannyjones - Sign up for your one-dollar-per-month trial today. https://liquid-iv.com - Use code DANNY for 20% off your first order. https://mengotomars.com - Use code DANNY for 50% Off & 3 Free Gifts. https://whiterabbitenergy.com/?ref=DJP - Use code DJP for 20% off. EPISODE LINKS https://davidholthouse.com FOLLOW DANNY JONES https://www.instagram.com/dannyjones https://twitter.com/jonesdanny OUTLINE 00:00 - Operation Odessa 05:38 - Surveillance in Russia 12:07 - Cartel's access to technology & intel 14:44 - Cartel Influencers 18:13 - Why Chihuaua City, Mexico is terrifying 21:06 - Gonzo journalism 24:39 - The Last Narc & who killed Kiki Camarena 30:53 - Felix Rodriguez responds to Kiki Camerana rumors 35:59 - The trauma of Vietnam veterans 39:18 - More veterans die at home than at war 42:07 - Felix Rodriguez's relationship with CIA 45:03 - California's unsolved Sasquatch murder 49:13 - The scariest moment of filming Sasquatch documentary 53:32 - The scariest part of California 01:01:31 - "Mirroring" for good documentary filmmaking 01:04:51 - What Chinese cartels are up to 01:06:19 - Narco Mennonites 01:11:25 - Crazy story about El Chapo 01:15:14 - Staying up for 72 hours with meth heads 01:20:34 - Embedding with Skinheads 01:29:48 - Visiting Aryan Fest 01:37:59 - How to spot Scientologists 01:41:14 - The Hare Krishna movement 01:48:39 - David's production style 01:55:44 - David's secret to finding new projects 02:02:13 - Supernatural beliefs in Mendocino, CA 02:07:53 - Interdimensional portals in the woods 02:08:55 - David saw the Pheonix Lights 02:15:17 - Link between Epstein Files & UFOs 02:19:33 - California's energy policy relies on Iran oil 02:26:17 - Why we need nuclear power Learn more about your ad choices. Visit podcastchoices.com/adchoices

In this episode, Sharona and Boz welcome back Matt Townsley to dig into a critical—and often overlooked—truth about grading reform: if leaders don't understand and support it, it simply won't scale. Drawing on both research and real-world experience, Matt explains why grading reform is a “second-order change” that requires deep philosophical commitment from administrators, not just technical adjustments from teachers. The conversation explores the upcoming Iowa based leadership-focused standards-based grading conference, the role of systems-level support, and emerging frameworks like multi-tiered support for teacher implementation. Along the way, the trio connects these ideas to broader challenges in both K–12 and higher education, from structural barriers to the growing urgency of reform in the age of AI. The takeaway is clear: isolated classroom innovation isn't enough—lasting change requires aligned leadership, intentional systems, and a shared purpose for what grades are meant to communicate.LinksPlease note - any books linked here are likely Amazon Associates links. Clicking on them and purchasing through them helps support the show. Thanks for your support!Standards-Based Grading Conference: The 3rd Annual Collaborative Assessment Conference for Leadership TeamsAll Things Standards-Based Grading, by Matt TownsleyGrading Reform Isn't Options Anymore - Here's Why, with Matt TownsleyTop 5 standards-based grading articles for 2025, by Matt TownsleyWhen standards-based grading feels dark…and reassessments become the flashlight everyone reaches for later, by Matt TownsleyWalking the talk: Embedding standards-based grading in an educational leadership course The 4 Common Myths about Grading Reform, Debunked, by Matt Townsley and Sarah MorrissPrevious Episodes MentionedEpisode 18 - Sportscaster of Alternative GradingEpisode 46 – Extinguishing the Fires within Assessment and Grading Reform: Welcoming Back Dr. Matt TownsleyEpisode 48 - Implementation Challenges and Opportunities: A Conversation with Becky Peppler and Don Smith on Working with K-12 School Districts to implement Alternative GradingEpisode 59 - Leaning Into ROI and Communication in Leading Grading Reform: An Interview with Dr. Chad LangEpisode 74 - Exploring Alt Grading in Physical Education (in more detail) with Josh OgilvieResourcesThe Center for Grading Reform - seeking to advance education in the United States by supporting effective grading reform at all levels through conferences, educational workshops, professional development, research and scholarship, influencing public policy, and community building.The Grading Conference - an annual, online conference exploring Alternative Grading in Higher Education & K-12.Some great resources to educate yourself about Alternative Grading:The Grading for Growth BlogThe Grading ConferenceThe Intentional Academia BlogRecommended Books on Alternative Grading:Grading for Growth, by Robert Talbert and David ClarkSpecifications Grading, by Linda NilsenUndoing the Grade, by Jesse StommelFollow us on Bluesky, Facebook and Instagram - @thegradingpod. To leave us a comment, please go to our website: www.thegradingpod.com and leave a comment on this episode's page.If you would like to be considered to be a guest on this show, please reach out using the Contact Us form on our website, www.thegradingpod.com.All content of this podcast and website are solely the opinions of the hosts and guests and do not necessarily represent the views of California State University Los Angeles or the Los Angeles Unified School District.MusicCountry Rock performed by Lite Saturation, licensed under a Attribution-NonCommercial-NoDerivatives 4.0 International License.

Healthcare doesn't have a technology problem; it has a workflow problem. In this episode, Kshitij Jaggi discusses why healthcare's digital tools have failed to improve efficiency and how agentic AI can transform operations by completing work rather than creating more of it. He explains the critical difference between task automation and system-level orchestration, and why administrative bottlenecks, such as prior authorizations, delay care. He also explores how governance, traceability, and new oversight roles are essential for responsible AI adoption. Finally, he shares how throughput should define ROI and unlock better outcomes across the healthcare system. Tune in to learn how AI can eliminate friction, improve access to care, and reshape the future of healthcare operations.  About Kshitij Jaggi: Co-founder and CEO of RISA Labs, Kshitij (KJ) leads the company's mission to accelerate oncology innovation through data-driven collaboration and transformative technology. Things You'll Learn: Most healthcare software fails because it adds work for users instead of reducing it, making time the primary barrier to adoption. True transformation in healthcare requires system-level orchestration rather than isolated task automation. Embedding clinical intelligence into administrative workflows reduces errors, delays, and inefficiencies in care delivery. Administrative processes are the biggest source of friction in healthcare systems. Integrating agentic AI with EMR systems can significantly increase throughput in a labor-constrained healthcare environment. Long-term success depends on platform-based solutions, governance and oversight of AI, and measuring ROI through throughput, timeliness of care, and reduced treatment leakage while enabling more seamless care and faster innovation. Resources: Connect with and follow Kshitij Jaggi on LinkedIn. Follow RISA Labs on LinkedIn and visit their website.

In this episode, Jeff Mains sits down with Stanley Leong — former IBM/Agilent engineer turned bestselling author and private wealth advisor — to explore what it truly means to engineer your finances. Stanley brings his analytical, systems-driven engineering background to personal wealth building, and the result is a refreshingly practical framework for tech founders and high-income professionals who are great at running businesses but often treat their personal finances as an afterthought.Stanley shares how getting laid off the day after buying his first house sent him on an unexpected 20-year journey into financial planning. He explains why concentration risk (too much wealth in one stock or one company) is the #1 mistake he sees among tech professionals, why investment management is really risk management, and how the key question every investor should ask first is "What if I'm wrong?" The conversation also dives deep into underutilized tax strategies — including the Mega Backdoor Roth and the HSA as a stealth retirement account — and wraps with a powerful discussion on aligning money with purpose and preparing emotionally for life after a liquidity event.Key Takeaways4:10 — From Chips to Cashflow: Stanley's Origin Story Stanley was laid off the day after buying his first house. Frustrated by conflicting advice and no clear answers, he pivoted from engineering to financial planning — and discovered he could serve others facing the same confusion.7:24 — What "Engineering Your Finances" Actually Means Stanley applies the same systematic, process-oriented thinking he used as an engineer to personal finance. His "Wealth Focus Model" structures client meetings around specific, scheduled topics — goal tracking, protection planning, taxes, and investment strategy.9:02 — Concentration Risk: The #1 Mistake Tech Founders Make Too much net worth tied up in a single stock, employer equity, or your own company is the most common and dangerous financial mistake. Tech founders are especially vulnerable — success can quietly become massive exposure.15:19 — How to Think About When to Diversify Start with your goal (e.g., retire at 60), work backward to determine how much you need to set aside in diversified investments, and then let the rest work harder in higher-risk/higher-reward vehicles. This keeps you on track even if the concentrated bet doesn't pay off.17:10 — Investment Management Is Really Risk Management Most people think investing is about making money. Stanley reframes it: the job is to manage risk first, then optimize returns. That mindset shift is what separates investors from gamblers.18:10 — The Investor's First Question: "What If I'm Wrong?" Before committing capital to anything, ask what happens if the investment doesn't go your way — and whether you can live with that outcome. Gamblers ask "How much can I make?" Investors ask "What's the downside?"20:34 — Tax Diversification: Build Three Buckets Prepare for an uncertain tax future by spreading wealth across three types of accounts: pre-tax (traditional 401k), after-tax Roth (tax-free growth and withdrawals), and taxable brokerage. Having optionality across tax buckets is just as important as investment diversification.22:44 — The Mega Backdoor Roth: A Largely Unknown Strategy High earners who can't contribute directly to a Roth IRA can use a little-known third 401k contribution type — after-tax contributions — to funnel an additional $20–40K/year into a Roth position. The key: don't forget to actually convert the after-tax contributions to Roth.27:45 — The HSA: The Most Tax-Efficient Account Nobody Maxes Out The Health Savings Account beats every other tax-advantaged vehicle: pre-tax contributions, tax-deferred growth, and tax-free withdrawals. The strategy: don't use it for current healthcare costs — let it grow, save your receipts, and reimburse yourself decades later tax-free.32:44 — The Retirement Tax Window Many Miss Many high earners experience a brief "tax valley" in early retirement — income drops before RMDs and Social Security kick in. Use that window to convert pre-tax retirement accounts to Roth at a very low (sometimes 0%) rate before required minimum distributions force higher taxes.36:19 — Money Without Purpose Has No Value Stanley's first question to every new client: "What is the purpose of this money?" Clear goals — not just "retire someday," but where, with whom, doing what — make risk evaluation real and decisions intentional.39:10 — Life After a Liquidity Event: The Emotional Preparation The financial transition is only part of the story. Founders who retire or exit without a clear vision for what comes next often struggle. Start forming that post-exit identity before the event — read, talk to others, explore — so you're moving toward something, not just away from work.42:17 — Financial Independence ≠ Retirement The better framing is "financial independence" — the freedom to work on your own terms. One of Stanley's clients realized he loved his job the moment he knew he didn't have to be there anymore. The ability to walk away is sometimes more valuable than walking away.Tweetable Quotes"You should want to pay more capital gains tax than anyone you know — because that means you've made more money than anyone you know." — Stanley Leong"Investment management sounds cooler, but we're really risk managers. The focus on risk is what defines an investor versus a gambler." — Stanley Leong"A gambler's first question is 'How much money am I going to make?' A good investor's first question is always 'What if I'm wrong?'" — Stanley Leong"Money without purpose has no value." — Stanley Leong"Success can quietly turn into massive exposure. Diversification isn't about fear — it's about freedom." — Stanley Leong"Don't be afraid to pay capital gains tax. It means you made money. The more you pay, the more you made." — Stanley Leong"Financial independence doesn't mean you stop. It means you're still living your life — just maybe in a different way." — Stanley Leong"Start forming your post-retirement vision while you're still working — it's a lot easier to dream when you're not already in it." — Stanley LeongSaaS Leadership Lessons1. Engineer Your Systems, Not Just Your Product The same discipline you apply to software architecture belongs in your financial life. Build repeatable, scheduled processes around your wealth — don't wing it. A systematic approach to finances compounds over time just like good code.2. Concentration Is a Silent Risk As founders, your identity and your net worth are often tied to one thing: your company. That's a risk management problem, not a success story. The most dangerous financial position isn't losing — it's winning so much in one place that you forget you're exposed.3. Reframe Risk Before You Reach for Returns Before you invest in anything — a new product line, a strategic hire, a side bet — ask "What if I'm wrong?" Not just "What's the upside?" Embedding this question into your leadership culture protects the company as much as the balance sheet.4. Build Optionality Into Everything — Including Taxes High-growth founders often optimize for today's tax savings and ignore tomorrow's flexibility. Diversifying across tax buckets (pre-tax, Roth, taxable) gives you options in an unpredictable future. The same principle applies to your cap table, your customer base, and your revenue streams.5. Purpose Drives Better Decisions at Every Stage Vague goals produce vague results. Whether you're managing a P&L or a portfolio, specificity creates accountability. "Retire at 60 to travel Europe with my family" is a strategy. "Someday retire" is a wish. Build toward something concrete.6. Financial Independence Is a Better Goal Than Exit The most underrated outcome of building a great company isn't the exit — it's the freedom to choose. Many founders discover they love the work once they no longer have to do it. Design your financial life so you work because you want to, not because you have to.Guest ResourcesStan@engineeringyourfinancesbook.comwww.engineeringyourfinancesbook.comEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn -

Healthcare payments are often discussed as a transparency problem, but the deeper issue is structural fragmentation across contracts, claims, remittances, and workflows. In this episode, Ted Ferrin, Senior Vice President of Payments Innovation at Zelis, explains how the acquisition of Rivet is bringing provider-facing payment intelligence into Zelis's broader infrastructure. He discusses why achieving financial clarity between payers and providers has been so difficult due to fragmented systems and legacy technology. Ted highlights that true transparency goes beyond simply displaying data and requires meaningful, actionable insights. He also shares how tools like Claims Insights and Zap Edge embed intelligence into payment workflows to reduce rework, improve visibility, and create a smoother experience for providers, payers, and patients. Tune in and learn how better payment intelligence could help turn transparency from a buzzword into real operational trust.  About Ted Ferrin: Ted Ferrin is Senior Vice President of Payments Innovation at Zelis, where he focuses on building solutions that improve healthcare payments and strengthen financial clarity for providers. He joined Zelis through its acquisition of Rivet, the company he founded and led as CEO for more than eight years. Before Rivet, Ted held leadership and sales roles at Canopy, Instructure, and Qualtrics. His work has centered on building organizations, products, and customer-focused growth strategies, with a particular passion for making healthcare more efficient and easier to use for providers. He studied psychology and business management at Brigham Young University.  Things You'll Learn: Healthcare payment transparency breaks down when contracts, claims, remittances, and analytics all live in disconnected systems. True transparency requires clean, normalized data delivered in real time within workflows, not just static reporting. Providers still face a major administrative burden because the old payment infrastructure often forces manual reconciliation and rework. Shared financial clarity can improve trust by reducing disputes, errors, delays, and unnecessary administrative effort for both providers and payers. Embedding payment intelligence at the point of transaction can help organizations move from passive visibility to more actionable decision-making.  Resources: Connect with and follow Ted Ferrin on LinkedIn. Follow Zelis on LinkedIn and visit their website. 

Raul Parquet is the Director of Ecommerce at Princess Cruises, where he's helping to lead them into a more digital future where visa requirements, multi-destination itineraries, and endless customization options are something customers can actually complete online. In this episode, Raul shares: The unglamorous but vital elements of a complete eCommerce analytics stack, and the table-stakes things teams often skip Why an Analytics team embedded inside product is a requirement, and the deployment discipline that brings with it And how Princess Cruises is using AI behind the scenes to help their team work smarter — and why, when it comes to customers, simplicity will always matter more than technology Links LinkedIn: https://www.linkedin.com/in/raul-parquet/ Princess Cruises: https://www.princess.com/ Chapters 00:00 Introduction 02:00 Why cruises are one of the hardest ecommerce problems to solve 6:00 Embedding analytics teams into product 8:00 The analytics stack: What "table stakes" actually looks like 13:30 How AI is already helping analytics teams work smarter 15:00 The gaps most teams don't know they have 19:00 Simplifying complex bookings: The Tesla analogy 21:00 100% of Princess Cruisers have been on the website 25:00 Where AI actually fits in the customer journey 29:00 Outro Follow LaunchPod on YouTube We have a new YouTube page! Watch full episodes of our interviews with PM leaders and subscribe! What does LogRocket do? LogRocket's Galileo AI watches user sessions for you and surfaces the technical and usability issues holding back your web and mobile apps. Understand where your users are struggling by trying it for free at LogRocket.com.Special Guest: Raul Parquet.

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

Text a Message to the ShowPolice Social Workers are not a new concept but until now there's not been a nationwide standard for how they are trained or how they are employed alongside police officers.  Caroline Ban teaches at Valparaiso University and she has started the first public safety social work certificate program in the nation for people who have their masters in social work or are getting their MSW.  Caroline talks about the how social workers can add something valuable to the police team that I know you'll appreciate.Music is by Chris HaugenHey Chaplain Podcast Episode 137Tags:Social Workers, Co-responders, Education, Embedding, Mental Health, Officer Wellness, Police, Professor, Training, University, St. Louis, Valparaiso, Indiana, Missouri Support the showThanks for Listening!  And, as always, pray for peace in our city.Subscribe/Follow here:Apple Podcasts: https://podcasts.apple.com/us/podcast/hey-chaplain/id1570155168Spotify: https://open.spotify.com/show/2CGK9A3BmbFEUEnx3fYZOYEmail us at: heychaplain44@gmail.comYou can help keep the show ad-free by buying me a virtual coffee!https://www.buymeacoffee.com/heychaplain

On this episode of The Jeff Dornik Show, Jeff Dornik and Karen Kingston warned that the Trump administration is fast tracking AI with no real guardrails, that Elon Musk, Peter Thiel, Alex Karp, Sam Altman, and the broader Silicon Valley machine are pushing a system that centralizes power, replaces workers, and strips away accountability when AI causes harm. They argued that Palantir is embedding itself across corporate America, that Americans are being forced to train the very AI that will replace them, and that the end result is a dystopian system where constitutional rights, human autonomy, and even access to the internet can be controlled by those who own the technology.Follow Karen Kingston on Pickax - https://pickax.com/karen_kingston Follow Jeff Dornik on Pickax - https://pickax.com/jeffdornikSPONSOR:The Deep State and the Globalists don't want you owning precious metals… which is exactly the reason you should get the FREE Gold and Silver Guide from My Gold Guy today to see if investing in gold and silver is right for you. https://mygoldguy.com/jeff Tune into The Jeff Dornik Show LIVE daily at 7pm ET on Rumble. Subscribe on Rumble and never miss a show. https://rumble.com/c/jeffdornikBig Tech is silencing truth while farming your data to feed the machine. That's why I built Pickax… a free speech platform that puts power back in your hands and your voice beyond their reach. Sign up today: https://pickax.com/?referralCode=y7wxvwq&refSource=copyBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-jeff-dornik-show--4788100/support.Follow The Jeff Dornik Show on Apple Podcasts and leave a 5-star review. That's how we reach more people and bypass Big Tech suppression.Watch LIVE daily at 7pm ET on Rumble and subscribe so you never miss a show:https://rumble.com/c/jeffdornikBig Tech is silencing truth while harvesting your data to feed the machine. That's why I built Pickax, a free speech platform where creators own their content and your voice isn't controlled. Join now:https://pickax.com/?referralCode=y7wxvwq&refSource=copy

Trust is difficult enough in an environment with strict controls and security; AI adds dimensions that make establishing trust even more challenging in the public sector. Today, we sat down with three experts who share insights into achieving this elusive trust. Leaders must evaluate how to trust three elements: the model, the data, and the monitoring processes. Tim Willging from Rocket software suggests that model choice matters for transparency.  A federal leader will need to document the system thoroughly, the training data, and known risks. One way to accomplish that is with a "model card."  This document provides details on AI's performance and training data. Model choice matters for transparency and risk documentation. Even if we assume the data we use to train a model is good, we must consider the concept of "context of use."   One data set may be safe for one context, but not another. Users need a deep understanding of data that includes hybrid governance, legal concerns, and ethical considerations. If we have learned anything in the past decade, it is that checkbox solutions never work. For example, if a data source is examined and deemed safe, this can change. Legacy data pipelines may not be secure, and the data may not be encrypted in transit or at rest. Continuous monitoring is mandatory for any valid AI application. The panel also explored the role of AI in improving collaboration, data integrity, and public service, as well as the need for continuous monitoring and agile governance to ensure trustworthy AI deployments.  

Education and our ability to respond to climate change are inexorably linked. Major international studies have shown that education is the single strongest predictor of whether or not someone is aware of climate change. In the US, while 74% of Americans support climate action, support is typically 10–20 points higher among those with a college education. It's not about perceptions on climate change; a more educated workforce is better able to innovate, accelerate the climate transition, and adapt in a less stable world – especially if that education builds climate resilient skills.One could almost imagine a university designed around this need – and that is exactly what the team at Unity Environmental University are building. Today, we're joined by Unity President Dr. Melik Khoury who is creating not just a new curriculum, but a new, more inclusive approach to higher ed. Dr. Khoury argues powerfully against the elitism that has underpinned our educational system and climate narratives. We spoke about his background, the role of education in addressing climate change, how Unity is different, and the influence it could have. Dr Khoury's energy is contagious and we're sure he'll get you thinking. Enjoy.On today's episode, we cover:03:06 – Dr. Khoury's upbringing in West Africa and awakening to environmental issues04:24 – Discovering the real impacts of resource exploitation06:01 – Choosing higher education transformation as the main lever08:32 – The core climate problem: beyond politics and single-issue framing09:04 – Climate as transdisciplinary: food, energy, people, commerce11:05 – History of Unity Environmental University13:07 – Transforming Unity's model and unbundling education15:14 – New operational model and rethinking the faculty role15:23 – Scaling Unity and redefining what a university is19:42 – Preparing students to operate in complex, uncertain systems20:15 – Embedding climate and sustainability across the curriculum23:14 – AI's challenge to traditional notions of knowledge and learning23:48 – What Unity is learning from its students and their needs28:14 – What success looks like for a climate-focused university28:30 – Influencing the broader higher-ed ecosystem31:47 – How AI is changing higher education and climate learning32:11 – Why Unity embraces rather than bans AI35:58 – Concrete AI experiments at Unity (UNA, tutors, automation)39:31 – Is climate momentum fading? Perception vs. reality39:57 – Climate's “brand problem” and the real enemy: ignorance42:58 – Depoliticizing climate and making the economic case43:16 – How we broadly attack ignorance through education reform45:52 – Call for partners and funders to back scalable climate education45:52 – Closing thanks and episode wrap-upResources MentionedUnity Environmental UniversityConnect with usDr. Melik KhouryJason RissmanKeep up with Invested In ClimateSign up for our NewsletterLinkedInInstagramIf you like what you hear, subscribe and rate to support the show! Have feedback or ideas for future episodes, events, or partnerships? Get in touch!

Text a Message to the ShowThis is a special bonus episode of Hey Chaplain recorded on site in New Orleans, Louisiana, at the International Association of Chiefs of Police (IACP) Officer Safety and Wellness Conference.  Chaplain Phil Reeves from the Los Angeles Sheriff's Department was my partner in giving a presentation on embedding chaplains.  We recorded that talk and the Q&A that followed and I want to give you a few select cuts of what we did.  I also had the opportunity to hang out with some chaplains from California, Massachusetts, Colorado, and other states, so I'm going to throw in a few clips of those police chaplains saying hi to the Hey Chaplain audience.Music is by LesFMHey Chaplain Bonus Episode 49Tags:Chaplaincy, Chaplains, Culture, Embedding, Expectations, IACP, Leadership, Podcasting, Police, Relationships, Standards, Kansas City, New Orleans, California, Colorado, Kansas, Louisiana, MassachusettsSupport the showThanks for Listening!  And, as always, pray for peace in our city.Subscribe/Follow here:Apple Podcasts: https://podcasts.apple.com/us/podcast/hey-chaplain/id1570155168Spotify: https://open.spotify.com/show/2CGK9A3BmbFEUEnx3fYZOYEmail us at: heychaplain44@gmail.comYou can help keep the show ad-free by buying me a virtual coffee!https://www.buymeacoffee.com/heychaplain