Podcasts about google labs

The tools we use shape how we work, what we see, and how we think. Dmitri Glazkov, Strategy Lead at Google Labs, initiated Breadboard and helped launch Opal—tools that let people connect prompts into systems that think together like Tinkertoys for the mind. His passion is building technology that makes creativity easier and more human. In this episode, Dart and Dmitri explore how AI can capture tacit knowledge, why strategy gets embedded in culture, and how to design “tiny brains” that think with us, not for us.Dmitri Glazkov is Strategy Lead at Google Labs and the initiator of Breadboard, the open-source foundation for Google's Opal project. He is a longtime Google engineer and an early contributor to Chrome and Web Components.In this episode, Dart and Dmitri discuss:- How AI tools reshape the experience of work- Why Breadboard and Opal make creativity easier- How AI can help capture and share tacit knowledge- The difference between dandelion and elephant growth strategies- How strategy becomes embodied in company culture- What “lensical thinking” means and how to use it- Why Dmitri calls Opal a cognitive WYSIWYG- How chains of prompts can act as “tiny brains”- And other topics…Dmitri Glazkov is the Strategy Lead at Google Labs and the initiator of Breadboard, the open-source project that underpins Google's Opal tool for creative AI experimentation. Over nearly two decades at Google, Dmitri has shaped how people interact with technology—from helping build Chrome and pioneering Web Components to exploring how artificial intelligence can amplify human thought. His work focuses on designing systems that think with us, not for us, making creativity more accessible to everyone. Resources Mentioned:Opal: https://opal.withgoogle.comDmitri's Blog: https://glazkov.comDart and Dmitri's article, “The Unvarying Infrastructure of Variation”: https://read.fluxcollective.org/p/69Connect with Dmitri:LinkedIn: https://www.linkedin.com/in/dglazkov Work with Dart:Dart is the CEO and co-founder of the work design firm 11fold. Build work that makes employees feel alive, connected to their work, and focused on what's most important to the business. Book a call at 11fold.com.

Most people spend over 30 hours a year dealing with customer service—on hold, repeating account numbers, and navigating endless phone trees. But what if AI could fix that without losing the human touch?Clay Bavor, co-founder of Sierra (now valued at $10B) and former VP at Google, joins us to explore how AI agents are reshaping how companies interact with customers and what that means for the most complex service industry in the world: healthcare.We cover:

Freeplay AI emerged from a precise timing insight: former Twitter API platform veterans Ian Cairns and Eric Schade recognized that generative AI created the same platform opportunity they'd previously captured with half a million monthly active developers. Their company now provides the observability, evaluation, and experimentation infrastructure that lets cross-functional teams—including non-technical domain experts—collaborate on AI systems that need to perform consistently in production. Topics Discussed: Systematic customer discovery: 75 interviews in 90 days using jobs-to-be-done methodology to surface latent AI development pain points Cross-functional AI development: How domain experts (lawyers, veterinarians, doctors) became essential collaborators when "English became the hottest programming language" Production AI reliability challenges: Moving beyond 60% prototype success rates to consistent production performance Enterprise selling to technical buyers: Why ABM and content worked where ads and outbound failed for VPs of engineering Category creation without precedent: Building thought leadership through triangulated insights across hundreds of implementations Offline community building: Growing 3,000-person Colorado AI meetup with authentic "give first" approach GTM Lessons For B2B Founders: Structure customer discovery with jobs-to-be-done rigor: Ian executed a systematic 75-interview program in 90 days, moving beyond surface-level feature requests to understand fundamental motivations. Using Clay Christensen's framework, they discovered engineers weren't just frustrated with 60% AI prototype reliability—they were under career pressure to deliver AI wins while lacking tools to bridge the gap to production consistency. This deeper insight shaped Freeplay's positioning around professional success metrics rather than just technical capabilities. Exploit diaspora networks from platform companies: Twitter's developer ecosystem became Ian's customer research goldmine. Platform company alumni have uniquely valuable networks because they previously interfaced with hundreds of technical teams. Rather than cold outreach, Ian leveraged existing relationships and warm introductions to reach heads of engineering who were actively experimenting with AI. This approach yielded higher-quality conversations and faster pattern recognition across use cases. Target sophistication gaps in technical buying committees: Traditional SaaS tactics failed because Freeplay's buyers—VPs of engineering at companies building production AI—weren't responsive to ads or generic outbound. Instead, Ian invested in deep technical content (1500-2000 word blog posts), speaking engagements, and their "Deployed" podcast featuring practitioners from Google Labs and Box. This approach built credibility with sophisticated technical audiences who needed education about emerging best practices, not product demos. Build authority through cross-portfolio insights: Rather than positioning as AI experts, Ian built trust by triangulating learnings across "hundreds of different companies" and sharing pattern recognition. Their messaging became "don't just take Freeplay's word for it—here's what we've seen work across environments." This approach resonated because no single company had enough AI production experience to claim definitive expertise. Aggregated insights became more valuable than individual case studies. Time market entry for the infrastructure adoption curve: Ian deliberately positioned Freeplay for companies "3, 6, 12 months after being in production" rather than competing for initial AI experiments. They recognized organizations don't invest in formal evaluation infrastructure until they've proven AI matters to their business. This patient approach let them capture demand at the moment companies realized they needed serious operational discipline around AI systems. //  Sponsors:  Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role.    Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Productie zonder mensen klinkt als sciencefiction. Toch bestaan deze zogeheten dark factories al. Deze aflevering in het kort:☑️ Wat dark factories zijn en hoe ze werken☑️ Waarom datacenters essentieel zijn voor de maakindustrie☑️ Zelf AI mini-apps bouwen met OpalStel je een fabriek voor waar de lichten uit kunnen blijven. Robots draaien dag en nacht door, zonder pauze, zonder fouten en zonder menselijke handen op de werkvloer. Dit is de dark factory: een productielocatie die volledig autonoom opereert. Wat ooit toekomstmuziek leek, is in China al realiteit. Neem een bedrijf als Xiaomi, dat een al volledig geautomatiseerde smartphonefabriek runt. Maar hoe ver is Nederland eigenlijk in deze ontwikkeling? En wat betekent dit voor de toekomst van werk?

Google is bringing Gemini into Windows territory, taking on Microsoft Copilot right from your desktop. Is the world's most popular browser about to change how you work with AI on Windows? Host: Paul Thurrott Download or subscribe to Hands-On Windows at https://twit.tv/shows/hands-on-windows Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Google is bringing Gemini into Windows territory, taking on Microsoft Copilot right from your desktop. Is the world's most popular browser about to change how you work with AI on Windows? Host: Paul Thurrott Download or subscribe to Hands-On Windows at https://twit.tv/shows/hands-on-windows Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Google is bringing Gemini into Windows territory, taking on Microsoft Copilot right from your desktop. Is the world's most popular browser about to change how you work with AI on Windows? Host: Paul Thurrott Download or subscribe to Hands-On Windows at https://twit.tv/shows/hands-on-windows Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Google is bringing Gemini into Windows territory, taking on Microsoft Copilot right from your desktop. Is the world's most popular browser about to change how you work with AI on Windows? Host: Paul Thurrott Download or subscribe to Hands-On Windows at https://twit.tv/shows/hands-on-windows Want access to the ad-free video and exclusive features? Become a member of Club TWiT today! https://twit.tv/clubtwit Club TWiT members can discuss this episode and leave feedback in the Club TWiT Discord.

Ask a question on our Youtube Channel: https://www.youtube.com/@GenerativeAIMeetup Mark's Travel Vlog: https://www.youtube.com/@kumajourney11 Mark's Personal Youtube Channel: https://www.youtube.com/@markkuczmarski896 Attend a live event: https://genaimeetup.com/ Shashank Linked In: https://www.linkedin.com/in/shashu10/  Mark Linked in: https://www.linkedin.com/in/markkuczmarski/  In this episode of the Generative AI Meetup Podcast, hosts Shashank (a software engineer at Google Labs working on the AI vibe design tool Stitch) and Mark (a former Amazon engineer now building a stealth startup) dive into the world of "vibe coding"—a revolutionary approach to programming inspired by AI researcher Andrei Karpathy. Vibe coding lets you focus on the big picture and product vision while letting large language models (LLMs) handle the nitty-gritty details, melting away traditional coding hurdles. Shashank walks through his weekend project, Convo (convochat.io), an AI-powered app that analyzes exported chat backups from WhatsApp, Telegram, Facebook Messenger, or SMS. It delivers fun stats (like 11,000 messages sent to a friend over four years), conversation summaries, sentiment analysis, and even tips for better chats—all built with minimal manual coding. The duo shares practical tips for vibe coding success: brainstorming ideas with Claude, designing UIs in Stitch, building with tools like Cursor or Replit, using Git for checkpoints, picking popular frameworks (e.g., Tailwind CSS), writing tests, debugging with logs, optimizing SEO, and branding with AI-generated logos. They discuss pros (rapid prototyping, low costs—Shashank spent just $18), cons (scaling challenges, bug fixes), and best practices for beginners, including modularity, documentation, and refactoring. Whether you're a seasoned dev or a total newbie, this episode shows how AI tools can turn ideas into launched MVPs in days, not months. Tune in for inspiration, real-world examples, and motivation to vibe code your next project!

What if, thanks to AI, you can now research and write a book two, three, or even four times faster? For authors and AI pioneers Steven Johnson (Editorial Director, NotebookLM and Google Labs) and Ethan Mollick (Wharton professor and creator of One Useful Thing), that's the new reality. In this episode, they crack open their personal toolkits to reveal the prompts and workflows they use to supercharge their creativity. What you'll learn: How Steven used AI to write 40,000 words in 72 hours. The specific AI tools Steven and Ethan rely on for researching and writing. Whether AI will ever write better than humans. How the very concept of a "book" may morph into an interactive, personalized experience that readers can query, customize, and even turn into a game. Further listening: BILL GATES: Superhuman AI May Be Closer Than You Think SAL KHAN: How AI Will Revolutionize the Way We Learn MARYANNE WOLF: Are We Forgetting How To Read? STEVEN JOHNSON & DAVID CHALMERS: Artificial Intelligence Meets Virtual Worlds ADAM BROTMAN & ANDY SACK: The AI Tsunami Is Already Here ——— This episode is brought to you by AUTHOR INSIDER, our exclusive community and learning platform for ambitious creators. What's Inside: ✅ Innovative strategies from bestselling authors and industry experts ✅ Audience growth tactics to expand your readership and revenue ✅ Vibrant creator community for networking and collaboration ✅ Exclusive content not available anywhere else

There's millions of people who want to vibe code -- but don't know where to get started.After all... vibe coding tools often are still full-stack enterprise powerhouses with a steep uphill learning curve. If only there were a simpler vibe coding platform that didn't even have code. That's Google Opal. And for this rendition of AI at Work Wednesday, we show you how to use Google's Opal to create simple apps that tackle some of your most repetitive, redundant tasks.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Google Opal Vibe Coding Tool OverviewHow to Use Google Opal: Step-by-StepOpal vs. Cursor, Replit, Copilot ComparisonBuilding No-Code AI Apps with OpalGoogle Opal Natural Language Workflow CreationOpal for Task-Based AI App DevelopmentGoogle Opal Beta Features and LimitationsPrebuilt Google Opal Apps and TemplatesVisual Editor and App Sharing in OpalOpal's Integration with Gemini AI ModelsTimestamps:00:00 "Everyday AI for Business Leaders"04:34 "Opal: Easy No-Code AI Apps"07:33 "Validate Ideas Before Full Development"11:25 Interactive Canvas with Chat Integration14:36 "Podcast: Interactive Instruction Feature"19:13 Efficient Research for Timely Episodes22:16 "Real-Time Deep Research Process"24:20 "Trends in Smaller Language Models"29:05 Improving Podcast Visual Strategies30:58 Remixing Apps with Google Opal35:45 "No-Code Opal Revolutionizes MVP Development"37:11 "Opal Tool Demos for Users"Keywords:Google Opal, Opal vibe coding, vibe coding tool, no code AI, Google AI, Google Labs, natural language app builder, AI app generator, AI workflow automation, task apps, visual app builder, Google Gemini, Gemini app, AI chaining, Google AI models, Gemini 2.5, Gemini Pro, Gemini Flash, AI image generation, VO3, audio LM, Lyria 2, multimodal AI, deep research, interactive web application, app editor, Opal gallery, prebuilt AI apps, app remixing, AI output customization, AI for nontechnical users, AI chaining tools, Google Jules, Cursor, GitHub CopilotSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info)

Is AI just better software? Or something completely different that requires a new paradigm to understand? Today we sit down with Bret Taylor and Clay Bavor, two of the best product builders in the world to tackle that question. Bret and Clay are the co-founders of the AI company Sierra.Brett's resume reads like a greatest hits of Silicon Valley: co-creator of Google Maps, founder of FriendFeed (acquired by Facebook where he became CTO), founder of Quip (acquired by Salesforce where he became co-CEO), former Chairman of the Board at Twitter, and current Chairman of the Board at OpenAI. Clay spent 18+ years at Google, starting as an APM alongside Brett and eventually running product for Gmail, Drive, Docs (all of Google Workspace), Google Labs, and the company's AR/VR efforts.In addition to AI, today's conversation has some great tech industry history discussion and old Google stories, perfect to tide us all over between Google Part I and Part II!Additional Topics:The accelerating adoption curves of technology waves, and if we'll ever see an app that gets a billion users in one daySecond- and third-order effects of agents on the internet economy and customer experienceMaking predictions on which AI terminology will stick and what won'tNew pricing models in the era of AI, like “outcome-based pricing”What it's like to build teams in this new AI eraLinks:SierraSponsors:Plaid: https://plaid.com

Todd Ogasawara and Jon Westfall covered a range of interesting topics, from real-world natural disasters to the cutting edge of AI development and personal tech. Todd shared his recent experience during a statewide tsunami alert in Hawaii, triggered by an 8.8 magnitude earthquake off Russia. While initial information was well-managed, he highlighted significant issues with traffic chaos during evacuation and a concerning lack of information post-wave impact. On the technology front, Todd discussed Google Notebook LM, praising its ability to create succinct summaries and slideshows with voiceovers from source material. He also introduced Google Opal, a new experimental tool from Google Labs that allows users to build and share powerful AI mini-apps using natural language and visual editing, describing it as a "step beyond Visual Basic" for accelerating AI prototyping and workflows. Jon Westfall also shared his recent tech purchases and an exciting new project. He acquired an 8Bitdo Micro Bluetooth Gamepad, a pocket-sized mini-controller weighing just 24.8 grams with 16 buttons. Its versatility allows it to function as a game controller for Switch, Android, and Raspberry Pi, or as a keyboard mode device for various applications, including as a remote for his new Kobo Libra Colour eReader. The Kobo Libra Colour features a 7" E Ink Kaleido™ 3 color display and Kobo Stylus 2 compatibility for colorful mark-ups and note-taking, with notebooks backed up to Kobo Cloud, Dropbox, or Google Drive. Jon also unveiled his open-source project, Uncle John's Bank, a self-hostable banking system for parents and kids designed to teach financial literacy, notably incorporating daily compounding interest and Certificates of Deposit (CDs). This sophisticated project was developed remarkably fast (75 hours) thanks to extensive use of OpenAI Codex, which integrated directly with his GitHub repository, even writing developer documentation. However, Jon noted a peculiar issue where GitHub Copilot (AI) reviewing Codex (AI)-generated code sometimes caused new problems, suggesting limitations in AI-to-AI code interaction. Finally, Jon shared intriguing results from asking various AIs (Google Gemini, ChatGPT, Microsoft Copilot, Anthropic Claude) for investment advice, observing their diverse recommendations and risk appetites.

The NATO Innovation Fund is entering a new chapter, marked by the arrival of two new partners and the departure of its penultimate founding team partner. Also, Google is testing a new vibe-coding tool called Opal, available in the U.S. through Google Labs, that lets users quickly spin up web apps with just a few prompts. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Could GPT-5 only be weeks away?Why are Microsoft and Google going all in on vibe coding?What's the White House AI Action Plan actually mean?Don't spend hours a day trying to figure out what AI means for your company or career. That's our job. So join us on Mondays as we bring you the AI News That Matters. No fluff. Just what you need to ACTUALLY pay attention to in the business side of AI. Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo and connect with other AI leaders on LinkedIn.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:GPT-5 Release Timeline and FeaturesGoogle Opal AI Vibe Coding ToolNvidia B200 AI Chip Black Market ChinaTrump White House AI Action Plan DetailsMicrosoft GitHub Spark AI Coding LaunchGoogle's AI News Licensing NegotiationsMicrosoft Copilot Visual Avatar (“Clippy” AI)Netflix Uses Generative AI for Visual EffectsOpenAI Warns of AI-Driven Fraud CrisisNew Google, Claude, and Runway AI Feature UpdatesTimestamps:00:00 "OpenAI's GPT-5 Release Announced"04:57 OpenAI Faces Pressure from Gemini07:13 EU AI Act vs. US AI Priorities12:12 Black Market Thrives for Nvidia Chips13:46 US AI Action Plan Unveiled19:34 Microsoft's GitHub Spark Unveiled21:17 Google vs. Microsoft: AI Showdown25:28 Google's New AI Partnership Strategy29:23 Microsoft's Animated AI Assistant Revival33:52 Generative AI in Film Industry38:55 AI Race & Imminent Fraud Crisis40:15 AI Threats and Future InnovationsKeywords:GPT 5 release date, OpenAI, GPT-4, GPT-4O, advanced reasoning abilities, artificial general intelligence, AGI, O3 reasoning, GPT-5 Mini, GPT-5 Nano, API access, Microsoft Copilot, model selector, LM arena, Gemini 2.5 Pro, Google Vibe Coding, Opal, no-code AI, low-code app maker, Google Labs, AI-powered web apps, app development, visual workflow editor, generative AI, AI app creation, Anthropic Claude Sonet 4, GitHub Copilot Spark, Microsoft GitHub, Copilot Pro Plus, AI coding tools, AI search, Perplexity, news licensing deals, Google AI Overview, AI summaries, click-through rate, organic search traffic, Associated Press, Condé Nast, The Atlantic, LA Times, AI in publishing, generative AI video, Netflix, El Eternauta, AI-generated visual effects, AI-powered VFX, Runway, AI for film and TV, job displacement from AI, AI-driven fraud, AI voice cloning, AI impersonation, financial scams, AI regulation, White House AI Action Plan, executive orders on AI, AI innovation, AI deregulaSend Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

Join me as I chat with Josh Woodward, VP of Google Labs & Gemini, as he showcases Google's latest AI products and their capabilities. The conversation covers Gemini's advanced features including personalized context integration and video generation, Flow's AI filmmaking capabilities, Notebook LM's research and knowledge exploration tools, Stitch's UI design automation, and Project Mariner's autonomous web task execution. Throughout the demonstration, Josh highlights how these tools can empower both professionals and individuals to create high-quality content and automate tasks. Timestamps: 00:00 - Intro 01:57 - Gemini app features and capabilities 06:53 - Video generation with Gemini 13:53 - Flow AI filmmaking tool demonstration 21:17 - Notebook LM research and knowledge exploration 26:47 - Stitch UI design tool overview 31:04 - Project Mariner autonomous web agent demo Checkout Google's AI Product Suite: https://labs.google Key Points: • Josh Woodward demonstrates five Google AI products: Gemini, Flow, Notebook LM, Stitch, and Project Mariner • Gemini features include scheduled actions, personalized context, and video generation capabilities • Flow is an AI filmmaking tool that allows users to create and edit high-quality videos with simple prompts • Project Mariner enables AI agents to perform web-based tasks autonomously with human oversight The #1 tool to find startup ideas/trends - https://www.ideabrowser.com LCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/ Boringmarketing - Vibe Marketing for Companies: http://boringmarketing.com/ The Vibe Marketer - Join the Community and Learn: http://thevibemarketer.com/ Startup Empire - a membership for builders who want to build cash-flowing businesses https://www.skool.com/startupempire/about FIND ME ON SOCIAL X/Twitter: https://twitter.com/gregisenberg Instagram: https://instagram.com/gregisenberg/ LinkedIn: https://www.linkedin.com/in/gisenberg/ FIND JOSH ON SOCIAL X/Twitter: https://x.com/joshwoodward LinkedIn: https://www.linkedin.com/in/joshwoodward/

David Webster is the head of UX at Google Labs, the company’s experimental AI division. When he stepped into the role in 2022, the tech world was scrambling to respond to the rise of ChatGPT — and Google Labs was no exception. Since then, the team has launched several high-profile projects, including the viral NotebookLM. Webster joins Oz to share his philosophy on human-centered design and how it shapes Google’s AI experiments.See omnystudio.com/listener for privacy information.

Most leaders learn on the fly—and Kim knows the bruises that come with it. In this episode she joins longtime Google Distinguished Designer Ryan Germick to discuss the innovative "Kim Scott Portrait," an AI-powered tool designed by Google Labs (and trained by the real Kim) to scale Kim's expertise and deliver Radically Candid advice 24/7. Discover how this new technology aims to humanize AI, free authors from the burden of answering repetitive questions, and foster more productive communication in the workplace. Get all of the show notes at ⁠⁠⁠⁠⁠⁠⁠⁠RadicalCandor.com/podcast⁠⁠⁠⁠⁠⁠⁠⁠. Episode Links: Transcript Now You Can Talk Radical Candor 24/7 With the Kim Scott Portrait Google Portrait | Kim Scott Ryan Germick - Google | LinkedIn Connect: ⁠⁠⁠⁠⁠⁠⁠⁠Website⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠Bluesky⁠⁠⁠⁠⁠⁠⁠ Chapters: (00:00:00) Introduction Kim and Ryan Germick introduce the “Portrait” collaboration—an AI version of Kim designed to scale her coaching. (00:01:33) Live Coaching Demo Kim's Portrait answers a tough management question. (00:03:36) Why the Portrait Matters How the Portrait helps Kim reach more people and free up time for writing. (00:05:38) Kim's Next Book A look into Kim's upcoming optimistic novel set in 2070. (00:06:30) Family Interactions with the Portrait Funny and revealing story of Kim's son debating the AI. (00:08:10) The “Automated Kim” Origin Story How a team joke at Google inspired the Portrait concept. (00:09:29) Coaching at Scale Why books and AI scale Kim's message better than 1:1 coaching. (00:11:41) Personalized vs Generic AI The value of expert-driven Portraits over average LLM responses. (00:12:57) Training the Portrait Kim explains her hands-on role in fine-tuning its responses. (00:14:44) Solving Repetitive Questions How Portraits provide patient, consistent answers to FAQs. (00:16:07) Productive Disagreement Through Portraits The vision for AI-facilitated, respectful debates. (00:17:26) Expanding Globally Plans for multi-language and international Portrait availability. (00:17:48) Real-World Use Cases The ways Portraits support work, life, and social media decisions. (00:20:23) Empathy-Driven AI AI as a personal board of directors, with lived-experience expertise. (00:23:51) Empowering Creators Portraits can be embedded on creators' own platforms—no lock-in. (00:26:19) Lived Experience as Research Kim defends storytelling as a valid path to truth and insight. (00:28:24) Supporting New Managers Portraits offer guidance during the lonely transition into leadership. (00:31:11) Navigating Difficult Bosses Portraits can help employees manage up with empathy and agency. (00:33:30) Changing Workplace Culture Helping people shift from silence or aggression to Radical Candor. (00:36:17) Personality Extenders Portraits as scalable human touchpoints for the future. (00:38:51) Creating Your Own Portrait How to create your own Portrait and scale your voice. (00:39:48) Conclusion Learn more about your ad choices. Visit megaphone.fm/adchoices

In this eclectic and geek-fueled episode of AwesomeCast, Michael Sorg and Katie Dudas (with a quick cameo from Dave Podnar) dive into everything from espresso martinis and new podcasts to the latest in airport infrastructure and retro gaming. Plus, get a firsthand review of Google's experimental AI fashion app and marvel at micro-sized McDonald's nostalgia. This week's Chachi Says Video Game Minute also brings a science twist to gaming's effects on stress and autism research. From Lego X-Files to Denver's Eye of Sauron, it's an awesome mix of tech, nostalgia, and the downright bizarre.

In this eclectic and geek-fueled episode of AwesomeCast, Michael Sorg and Katie Dudas (with a quick cameo from Dave Podnar) dive into everything from espresso martinis and new podcasts to the latest in airport infrastructure and retro gaming. Plus, get a firsthand review of Google's experimental AI fashion app and marvel at micro-sized McDonald's nostalgia. This week's Chachi Says Video Game Minute also brings a science twist to gaming's effects on stress and autism research. From Lego X-Files to Denver's Eye of Sauron, it's an awesome mix of tech, nostalgia, and the downright bizarre.

Wenn du gute Inhalte für Menschen machst, dann machst du auch gute Inhalte für Suchmaschinen, für KI-Tools etc. In dieser spannenden Episode sprechen Klausi und Patrick mit unserem lieben Gast Michael Weckerlin, auch bekannt als „Mr. SEO“. Michael bezeichnet sich selbst als KI-Enabler und ist der perfekte Ansprechpartner, um über die aktuellen und zukünftigen Auswirkungen von Künstlicher Intelligenz auf Suchmaschinenoptimierung, Webseiten und besonders Webtexte zu sprechen. Die Jungs und Michael diskutieren, wie wichtig es ist, weiterhin nutzerzentrierte und datenbasierte Inhalte zu erstellen, die mit deinen eigenen Erfahrungen (First-Hand-Experience) und lokalen Besonderheiten angereichert sind. Außerdem bekommst du Einblicke, wie KI dir dabei helfen kann, hochwertige Texte zu erstellen, ohne dass die menschliche Note verloren geht. Du lernst auch, was die sogenannten AI-Overviews von Google für deine Klickzahlen bedeuten könnten und warum dein Google Business Profil sowie Kundenbewertungen wichtiger denn je werden. Wir werfen einen Blick in die Zukunft der Suche, sprechen über hyperpersonalisierte Assistenten und die mögliche Rückkehr von Printmedien. Vor allem aber bekommst du das wichtigste Learning mit auf den Weg: Bleib ruhig, informier dich und handle nicht aus Panik! Deine 5 wichtigsten Erkenntnisse aus dieser Episode: 1. SEO ist nicht tot, sondern im Wandel: Die Online-Marketing-Branche erlebt durch generative KI einen massiven Umbruch, doch nutzerzentrierte und datenbasierte SEO bleibt relevant, wenn auch mit neuen Spielregeln. 2. Qualität des Contents ist König: Generische Inhalte funktionieren nicht mehr gut; setze auf hochwertige Texte, die du durch eigene Erfahrungen und lokale Aspekte anreicherst. KI kann dabei ein starkes Werkzeug sein, wenn du im Prozess bleibst und die Qualität sicherstellst. 3. Neue Suchgewohnheiten erfordern Anpassung: Nutzer stellen spezifischere Fragen (Longtail-Suchen), und die AI-Overviews von Google werden zu einem Rückgang von Klicks auf Webseiten führen, besonders bei rein informationellen Anfragen. 4. Stärke deine lokale Präsenz und baue Vertrauen auf: Dein Google Business Profile, Kundenbewertungen und aktives Community Management auf verschiedenen Plattformen (wie z.B. Reddit) sind entscheidend, um Vertrauen aufzubauen und lokal relevant zu bleiben. 5. Blick in die Zukunft ist Pflicht: Hyper-Personalisierung durch persönliche KI-Assistenten und neue Google-Formate wie der AI-Mode werden kommen. Nutze schon jetzt multimediale Inhalte und beobachte Entwicklungen in den Google Labs, um am Ball zu bleiben. Hier findest Du die Shownotes / Links zur heutigen Episode: Mr. SEO Website > https://mister-seo.com/ Michael auf LinkedIn > https://www.linkedin.com/in/mister-seo Kontakt zu Patrick und Klaus: - [Patrick > LinkedIn](https://www.linkedin.com/in/patrick-neumann-3bb03b128) - patrick.neumann@parsmedia.info - [Klaus > LinkedIn](https://www.linkedin.com/in/klausschenkmann) - klaus.schenkmann@parsmedia.info - Telefonat mit Klaus: [Buche gerne einen Termin](https://doodle.com/bp/klausschenkmann/marketing-talk-mit-klaus) Immer für Dich am Start: - [parsmedia Website](https://parsmedia.info) - [Praxismarketing-Blog](https://parsmedia.info/praxismarketing-blog) - [parsmedia Instagram ](https://www.instagram.com/parsmedia.praxismarketing) - [parsmedia Facebook](https://www.facebook.com/parsmedia.praxismarketing) - [parsmedia YouTube](https://www.youtube.com/@die.praxismarketing.agentur/podcasts) - [parsmedia alle Episoden auf einen Blick](https://parsmedia.info/marketing-podcast/) - Intro-Stimme: [Annette Hardinghaus](https://annettesprecherin.de) - Soundfiles: [DJ ActiMax](https://www.instagram.com/actimaxdj) – Produktion: [Podcast-Agentur Podcastliebe](https://podcastliebe.net/)

Brian Brushwood joins us to explain how he uses camera robots to help him with his podcasting setup. Is it too soon to call Microsoft Copilot+ PCs a failure? Google Labs launches Doppl, a new experimental iOS and Android app that lets you virtually try on outfits via AI generated images of yourself. Starring Sarah Lane, Tom Merritt, Robb Dunewood, Brian Brushwood, Roger Chang, Joe. To read the show notes in a separate page click here! Support the show on Patreon by becoming a supporter!

Dans ce 119 ème épisode, je vous ai glissé une suprise.Pourquoi NotebookLM va changer votre manière de travaillerDans cet épisode, j'explore NotebookLM, l'outil d'intelligence artificielle de Google Labs qui transforme vos documents en véritables assistants de productivité. Ce que vous allez découvrirNotebookLM, c'est quoi ?Un assistant de recherche et de synthèse intelligent développé par Google, basé sur Google Gemini, capable d'interagir avec vos documents (PDF, Google Docs, Slides, URLs…) pour générer :Des résumés intelligentsDes podcasts audio personnalisésDes réponses argumentées avec citationsCe que NotebookLM permet concrètement :Gagner du temps en extrayant l'essentiel de rapports longsCentraliser et explorer vos contenus pour créer une bibliothèque de connaissancesAccéder à des formats audio pour apprendre où que vous soyezCollaborer facilement avec vos équipes6 étapes clés expliquées dans l'épisode :Connexion à votre compte GoogleCréation de carnets thématiquesImport de sources variéesQuestions via interface chatGénération de réponses instantanéesOrganisation et export collaboratifBonus : Comparatif des alternatives à NotebookLML'épisode passe aussi en revue des solutions concurrentes comme :Notion AI : workspace collaboratif enrichi par l'IAMem AI : notes connectées par machine learningEvernote AI : pionnier de la note augmentéeMicrosoft OneNote + Copilot : puissant allié de l'environnement 365Saurez-vous reconnaître ma vraie voix ?Ce podcast est aussi une expérience immersive. Vous pensiez reconnaître une voix humaine à coup sûr ? Détrompez-vous. Cet épisode a été entièrement généré avec la dernière version d'ElevenLabs V3, un outil de synthèse vocale ultra-réaliste qui repousse les limites du contenu audio généré par intelligence artificielle. Plus vrai que nature, il offre un rendu bluffant… au point de se demander : entendez-vous vraiment ma vraie voix ?Soutenez le podcast :✅ Abonnez-vous à DigitalFeeling sur LinkedIn✅ Rejoignez ma newsletter : substack.com/@elodiechenol✅ Laissez 5 ⭐ sur Apple Podcasts ou Spotify

In this episode of Project Synapse, Jim and Marcel navigate a rapidly evolving AI landscape while John appears intermittently through AI-generated clips, with his permission. They delve into intriguing developments at Google Labs, including a mysterious AI model that briefly appeared and vanished. The discussion highlights the differing ambitions of Google and OpenAI, examining Google's focus on creating a comprehensive information hub versus OpenAI's broad-reaching aspirations, like the recent OpenAI for Business initiative. A central theme is the persistent rivalry in AI, specifically between Gemini and ChatGPT, while also touching on niche players like Anthropic's Claude and Perplexity. The conversation takes a deep dive into the complexities of integrating AI into daily life, the potential benefits, and the significant risks, including issues surrounding privacy and identity. Counterpoint to their technical musings is a look at the societal impacts of AI, including job displacement and the need for universal basic income. Finally, the hosts ponder the unsettling yet fascinating future where personal identity might be verified through biometric scanning, as proposed by World ID. 00:00 Introduction and Setting the Scene 00:40 Unexpected Developments in AI 01:53 Google vs. OpenAI: The Rivalry 03:55 AI Integration in Everyday Life 04:36 The Rise of Niche AI Players 05:42 Personal Experiences with AI Tools 12:10 The Future of AI and Privacy Concerns 17:20 The Evolution of AI and Robotics 26:53 Smart Home Integration and Standards 34:21 The Illusion of Choice in Technology 36:13 The Privacy Paradox 36:31 The Integration of AI in Daily Life 38:52 The Rise of Deep Fakes and Identity Theft 41:35 The Future of Personal Data and Security 44:51 The Debate on Universal Identification 46:52 The Acceleration of Technological Change 52:19 The Need for Intelligent Design in AI 53:34 The Role of Governments and Corporations 01:04:11 Concluding Thoughts and Future Discussions

Fresh off impressive releases at Google's I/O event, three Google Labs leaders explain how they're reimagining creative tools and productivity workflows. Thomas Iljic details how video generation is merging filmmaking with gaming through generative AI cameras and world-building interfaces in Whisk and Veo. Jaclyn Konzelmann demonstrates how Project Mariner evolved from a disruptive browser takeover to an intelligent background assistant that remembers context across multiple tasks. Simon Tokumine reveals NotebookLM's expansion beyond viral audio overviews into a comprehensive platform for transforming information into personalized formats. The conversation explores the shift from prompting to showing and telling, the economics of AI-powered e-commerce, and why being “too early” has become Google Labs' biggest challenge and advantage. Hosted by Sonya Huang, Sequoia Capital 00:00 Introduction 02:12 Google's AI models and public perception 04:18 Google's history in image and video generation 06:45 Where Whisk and Flow fit 10:30 How close are we to having the ideal tool for the craft? 13:05 Where do the movie and game worlds start to merge? 16:25 Introduction to Project Mariner 17:15 How Mariner works 22:34 Mariner user behaviors 27:07 Temporary tattoos and URL memory 27:53 Project Mariner's future 29:26 Agent capabilities and use cases 31:09 E-commerce and agent interaction 35:03 Notebook LM evolution 48:26 Predictions and future of AI Mentioned in this episode:  Whisk: Image and video generation app for consumers Flow: AI-powered filmmaking with new Veo 3 model Project Mariner: research prototype exploring the future of human-agent interaction, starting with browsers NotebookLM: tool for understanding and engaging with complex information including Audio Overviews and now a mobile app Shop with AI Mode: Shopping app with a virtual try-on tool based on your own photos Stitch: New prompt-based interface to design UI for mobile and web applications. ControlNet paper: Outlined an architecture for adding conditional language to direct the outputs of image generation with diffusion models

Mike McCue introduces Surf: Flipboard's founder and CEO demonstrated their new social browser app that aggregates content from ActivityPub, AT Proto, and RSS into unified feeds, allowing users to follow people across platforms and create curated content collections. OpenAI Adjusts Reorganization Plans: OpenAI will maintain its non-profit arm while converting its for-profit division into a public benefit corporation similar to Anthropic, pending regulatory approval. AI Criticism Blog Post: A blog highlighted practical AI concerns beyond the singularity, focusing on coordinated inauthentic behavior, misinformation, and non-consensual pornography. AI Workplace Misuse: Nearly half of workers admit to using AI inappropriately at work according to a Fast Company report. AI Academic Cheating: New York Magazine investigated widespread AI cheating in colleges, including students using AI for all assignments while maintaining excellent grades. "I Smell AI": The team discussed unreliable AI detection methods and embarrassing AI-generated news errors, including Alberta being incorrectly described as "French-speaking." Instagram Co-founder on AI Chatbots: Kevin Systrom claims AI assistants are designed to maximize engagement metrics rather than utility, though Leo demonstrated how these behaviors can be modified. Google Labs' AI Experiments: The hosts explored Google's new AI Mode search interface, language learning tools, and a career recommendation system. New York Times Subscriber Growth: The NYT added 250,000 digital subscribers with a 14% jump in digital subscription revenue, with nearly half subscribing to multiple products. Auburn University's Phone Help Desk: The hosts discussed Auburn's 70-year tradition of librarians answering public phone questions, continuing through technological changes. San Francisco's Orb Store: World opened a downtown storefront where visitors scan their irises with "orbs" to verify humanity and receive WorldCoin cryptocurrency. Driverless Trucks Begin Regular Routes: Aurora launched fully autonomous semi-trucks between Dallas and Houston, raising both safety hopes and public perception concerns. Waymo Safety Study: Data showed Waymo's autonomous vehicles significantly reduced injury crashes, though the hosts questioned aspects of the data presentation. AI-Generated Video in Court: An AI-generated video of a deceased shooting victim "forgiving" his killer was shown in an Arizona courtroom, raising ethical and legal questions. Paris's Game Recommendation - Norco: Paris recommended the Southern Gothic narrative game Norco, set in industrial Louisiana with a surreal atmosphere similar to Disco Elysium. Leo's Game Recommendation - Tippy Coco: Leo shared a simple browser-based ball-bouncing game at TippyCoco.com as an easy option for casual players. Jeff's Pick - World Bank Data Sets: Jeff highlighted World Bank's release of hundreds of public data sets intended for AI training that provide insight into global technology adoption. Google Invests in Wonder: Google Ventures invested in virtual kitchen company Wonder, which raised $600 million despite questions about food delivery business sustainability. These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/intelligent-machines/episodes/818 Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike McCue Sponsors: monarchmoney.com with code IM spaceship.com/twit bigid.com/im Melissa.com/twit

Mike McCue introduces Surf: Flipboard's founder and CEO demonstrated their new social browser app that aggregates content from ActivityPub, AT Proto, and RSS into unified feeds, allowing users to follow people across platforms and create curated content collections. OpenAI Adjusts Reorganization Plans: OpenAI will maintain its non-profit arm while converting its for-profit division into a public benefit corporation similar to Anthropic, pending regulatory approval. AI Criticism Blog Post: A blog highlighted practical AI concerns beyond the singularity, focusing on coordinated inauthentic behavior, misinformation, and non-consensual pornography. AI Workplace Misuse: Nearly half of workers admit to using AI inappropriately at work according to a Fast Company report. AI Academic Cheating: New York Magazine investigated widespread AI cheating in colleges, including students using AI for all assignments while maintaining excellent grades. "I Smell AI": The team discussed unreliable AI detection methods and embarrassing AI-generated news errors, including Alberta being incorrectly described as "French-speaking." Instagram Co-founder on AI Chatbots: Kevin Systrom claims AI assistants are designed to maximize engagement metrics rather than utility, though Leo demonstrated how these behaviors can be modified. Google Labs' AI Experiments: The hosts explored Google's new AI Mode search interface, language learning tools, and a career recommendation system. New York Times Subscriber Growth: The NYT added 250,000 digital subscribers with a 14% jump in digital subscription revenue, with nearly half subscribing to multiple products. Auburn University's Phone Help Desk: The hosts discussed Auburn's 70-year tradition of librarians answering public phone questions, continuing through technological changes. San Francisco's Orb Store: World opened a downtown storefront where visitors scan their irises with "orbs" to verify humanity and receive WorldCoin cryptocurrency. Driverless Trucks Begin Regular Routes: Aurora launched fully autonomous semi-trucks between Dallas and Houston, raising both safety hopes and public perception concerns. Waymo Safety Study: Data showed Waymo's autonomous vehicles significantly reduced injury crashes, though the hosts questioned aspects of the data presentation. AI-Generated Video in Court: An AI-generated video of a deceased shooting victim "forgiving" his killer was shown in an Arizona courtroom, raising ethical and legal questions. Paris's Game Recommendation - Norco: Paris recommended the Southern Gothic narrative game Norco, set in industrial Louisiana with a surreal atmosphere similar to Disco Elysium. Leo's Game Recommendation - Tippy Coco: Leo shared a simple browser-based ball-bouncing game at TippyCoco.com as an easy option for casual players. Jeff's Pick - World Bank Data Sets: Jeff highlighted World Bank's release of hundreds of public data sets intended for AI training that provide insight into global technology adoption. Google Invests in Wonder: Google Ventures invested in virtual kitchen company Wonder, which raised $600 million despite questions about food delivery business sustainability. These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/intelligent-machines/episodes/818 Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike McCue Sponsors: monarchmoney.com with code IM spaceship.com/twit bigid.com/im Melissa.com/twit

Mike McCue introduces Surf: Flipboard's founder and CEO demonstrated their new social browser app that aggregates content from ActivityPub, AT Proto, and RSS into unified feeds, allowing users to follow people across platforms and create curated content collections. OpenAI Adjusts Reorganization Plans: OpenAI will maintain its non-profit arm while converting its for-profit division into a public benefit corporation similar to Anthropic, pending regulatory approval. AI Criticism Blog Post: A blog highlighted practical AI concerns beyond the singularity, focusing on coordinated inauthentic behavior, misinformation, and non-consensual pornography. AI Workplace Misuse: Nearly half of workers admit to using AI inappropriately at work according to a Fast Company report. AI Academic Cheating: New York Magazine investigated widespread AI cheating in colleges, including students using AI for all assignments while maintaining excellent grades. "I Smell AI": The team discussed unreliable AI detection methods and embarrassing AI-generated news errors, including Alberta being incorrectly described as "French-speaking." Instagram Co-founder on AI Chatbots: Kevin Systrom claims AI assistants are designed to maximize engagement metrics rather than utility, though Leo demonstrated how these behaviors can be modified. Google Labs' AI Experiments: The hosts explored Google's new AI Mode search interface, language learning tools, and a career recommendation system. New York Times Subscriber Growth: The NYT added 250,000 digital subscribers with a 14% jump in digital subscription revenue, with nearly half subscribing to multiple products. Auburn University's Phone Help Desk: The hosts discussed Auburn's 70-year tradition of librarians answering public phone questions, continuing through technological changes. San Francisco's Orb Store: World opened a downtown storefront where visitors scan their irises with "orbs" to verify humanity and receive WorldCoin cryptocurrency. Driverless Trucks Begin Regular Routes: Aurora launched fully autonomous semi-trucks between Dallas and Houston, raising both safety hopes and public perception concerns. Waymo Safety Study: Data showed Waymo's autonomous vehicles significantly reduced injury crashes, though the hosts questioned aspects of the data presentation. AI-Generated Video in Court: An AI-generated video of a deceased shooting victim "forgiving" his killer was shown in an Arizona courtroom, raising ethical and legal questions. Paris's Game Recommendation - Norco: Paris recommended the Southern Gothic narrative game Norco, set in industrial Louisiana with a surreal atmosphere similar to Disco Elysium. Leo's Game Recommendation - Tippy Coco: Leo shared a simple browser-based ball-bouncing game at TippyCoco.com as an easy option for casual players. Jeff's Pick - World Bank Data Sets: Jeff highlighted World Bank's release of hundreds of public data sets intended for AI training that provide insight into global technology adoption. Google Invests in Wonder: Google Ventures invested in virtual kitchen company Wonder, which raised $600 million despite questions about food delivery business sustainability. These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/intelligent-machines/episodes/818 Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike McCue Sponsors: monarchmoney.com with code IM spaceship.com/twit bigid.com/im Melissa.com/twit

Mike McCue introduces Surf: Flipboard's founder and CEO demonstrated their new social browser app that aggregates content from ActivityPub, AT Proto, and RSS into unified feeds, allowing users to follow people across platforms and create curated content collections. OpenAI Adjusts Reorganization Plans: OpenAI will maintain its non-profit arm while converting its for-profit division into a public benefit corporation similar to Anthropic, pending regulatory approval. AI Criticism Blog Post: A blog highlighted practical AI concerns beyond the singularity, focusing on coordinated inauthentic behavior, misinformation, and non-consensual pornography. AI Workplace Misuse: Nearly half of workers admit to using AI inappropriately at work according to a Fast Company report. AI Academic Cheating: New York Magazine investigated widespread AI cheating in colleges, including students using AI for all assignments while maintaining excellent grades. "I Smell AI": The team discussed unreliable AI detection methods and embarrassing AI-generated news errors, including Alberta being incorrectly described as "French-speaking." Instagram Co-founder on AI Chatbots: Kevin Systrom claims AI assistants are designed to maximize engagement metrics rather than utility, though Leo demonstrated how these behaviors can be modified. Google Labs' AI Experiments: The hosts explored Google's new AI Mode search interface, language learning tools, and a career recommendation system. New York Times Subscriber Growth: The NYT added 250,000 digital subscribers with a 14% jump in digital subscription revenue, with nearly half subscribing to multiple products. Auburn University's Phone Help Desk: The hosts discussed Auburn's 70-year tradition of librarians answering public phone questions, continuing through technological changes. San Francisco's Orb Store: World opened a downtown storefront where visitors scan their irises with "orbs" to verify humanity and receive WorldCoin cryptocurrency. Driverless Trucks Begin Regular Routes: Aurora launched fully autonomous semi-trucks between Dallas and Houston, raising both safety hopes and public perception concerns. Waymo Safety Study: Data showed Waymo's autonomous vehicles significantly reduced injury crashes, though the hosts questioned aspects of the data presentation. AI-Generated Video in Court: An AI-generated video of a deceased shooting victim "forgiving" his killer was shown in an Arizona courtroom, raising ethical and legal questions. Paris's Game Recommendation - Norco: Paris recommended the Southern Gothic narrative game Norco, set in industrial Louisiana with a surreal atmosphere similar to Disco Elysium. Leo's Game Recommendation - Tippy Coco: Leo shared a simple browser-based ball-bouncing game at TippyCoco.com as an easy option for casual players. Jeff's Pick - World Bank Data Sets: Jeff highlighted World Bank's release of hundreds of public data sets intended for AI training that provide insight into global technology adoption. Google Invests in Wonder: Google Ventures invested in virtual kitchen company Wonder, which raised $600 million despite questions about food delivery business sustainability. These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/intelligent-machines/episodes/818 Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike McCue Sponsors: monarchmoney.com with code IM spaceship.com/twit bigid.com/im Melissa.com/twit

Mike McCue introduces Surf: Flipboard's founder and CEO demonstrated their new social browser app that aggregates content from ActivityPub, AT Proto, and RSS into unified feeds, allowing users to follow people across platforms and create curated content collections. OpenAI Adjusts Reorganization Plans: OpenAI will maintain its non-profit arm while converting its for-profit division into a public benefit corporation similar to Anthropic, pending regulatory approval. AI Criticism Blog Post: A blog highlighted practical AI concerns beyond the singularity, focusing on coordinated inauthentic behavior, misinformation, and non-consensual pornography. AI Workplace Misuse: Nearly half of workers admit to using AI inappropriately at work according to a Fast Company report. AI Academic Cheating: New York Magazine investigated widespread AI cheating in colleges, including students using AI for all assignments while maintaining excellent grades. "I Smell AI": The team discussed unreliable AI detection methods and embarrassing AI-generated news errors, including Alberta being incorrectly described as "French-speaking." Instagram Co-founder on AI Chatbots: Kevin Systrom claims AI assistants are designed to maximize engagement metrics rather than utility, though Leo demonstrated how these behaviors can be modified. Google Labs' AI Experiments: The hosts explored Google's new AI Mode search interface, language learning tools, and a career recommendation system. New York Times Subscriber Growth: The NYT added 250,000 digital subscribers with a 14% jump in digital subscription revenue, with nearly half subscribing to multiple products. Auburn University's Phone Help Desk: The hosts discussed Auburn's 70-year tradition of librarians answering public phone questions, continuing through technological changes. San Francisco's Orb Store: World opened a downtown storefront where visitors scan their irises with "orbs" to verify humanity and receive WorldCoin cryptocurrency. Driverless Trucks Begin Regular Routes: Aurora launched fully autonomous semi-trucks between Dallas and Houston, raising both safety hopes and public perception concerns. Waymo Safety Study: Data showed Waymo's autonomous vehicles significantly reduced injury crashes, though the hosts questioned aspects of the data presentation. AI-Generated Video in Court: An AI-generated video of a deceased shooting victim "forgiving" his killer was shown in an Arizona courtroom, raising ethical and legal questions. Paris's Game Recommendation - Norco: Paris recommended the Southern Gothic narrative game Norco, set in industrial Louisiana with a surreal atmosphere similar to Disco Elysium. Leo's Game Recommendation - Tippy Coco: Leo shared a simple browser-based ball-bouncing game at TippyCoco.com as an easy option for casual players. Jeff's Pick - World Bank Data Sets: Jeff highlighted World Bank's release of hundreds of public data sets intended for AI training that provide insight into global technology adoption. Google Invests in Wonder: Google Ventures invested in virtual kitchen company Wonder, which raised $600 million despite questions about food delivery business sustainability. These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/intelligent-machines/episodes/818 Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike McCue Sponsors: monarchmoney.com with code IM spaceship.com/twit bigid.com/im Melissa.com/twit

Mike McCue introduces Surf: Flipboard's founder and CEO demonstrated their new social browser app that aggregates content from ActivityPub, AT Proto, and RSS into unified feeds, allowing users to follow people across platforms and create curated content collections. OpenAI Adjusts Reorganization Plans: OpenAI will maintain its non-profit arm while converting its for-profit division into a public benefit corporation similar to Anthropic, pending regulatory approval. AI Criticism Blog Post: A blog highlighted practical AI concerns beyond the singularity, focusing on coordinated inauthentic behavior, misinformation, and non-consensual pornography. AI Workplace Misuse: Nearly half of workers admit to using AI inappropriately at work according to a Fast Company report. AI Academic Cheating: New York Magazine investigated widespread AI cheating in colleges, including students using AI for all assignments while maintaining excellent grades. "I Smell AI": The team discussed unreliable AI detection methods and embarrassing AI-generated news errors, including Alberta being incorrectly described as "French-speaking." Instagram Co-founder on AI Chatbots: Kevin Systrom claims AI assistants are designed to maximize engagement metrics rather than utility, though Leo demonstrated how these behaviors can be modified. Google Labs' AI Experiments: The hosts explored Google's new AI Mode search interface, language learning tools, and a career recommendation system. New York Times Subscriber Growth: The NYT added 250,000 digital subscribers with a 14% jump in digital subscription revenue, with nearly half subscribing to multiple products. Auburn University's Phone Help Desk: The hosts discussed Auburn's 70-year tradition of librarians answering public phone questions, continuing through technological changes. San Francisco's Orb Store: World opened a downtown storefront where visitors scan their irises with "orbs" to verify humanity and receive WorldCoin cryptocurrency. Driverless Trucks Begin Regular Routes: Aurora launched fully autonomous semi-trucks between Dallas and Houston, raising both safety hopes and public perception concerns. Waymo Safety Study: Data showed Waymo's autonomous vehicles significantly reduced injury crashes, though the hosts questioned aspects of the data presentation. AI-Generated Video in Court: An AI-generated video of a deceased shooting victim "forgiving" his killer was shown in an Arizona courtroom, raising ethical and legal questions. Paris's Game Recommendation - Norco: Paris recommended the Southern Gothic narrative game Norco, set in industrial Louisiana with a surreal atmosphere similar to Disco Elysium. Leo's Game Recommendation - Tippy Coco: Leo shared a simple browser-based ball-bouncing game at TippyCoco.com as an easy option for casual players. Jeff's Pick - World Bank Data Sets: Jeff highlighted World Bank's release of hundreds of public data sets intended for AI training that provide insight into global technology adoption. Google Invests in Wonder: Google Ventures invested in virtual kitchen company Wonder, which raised $600 million despite questions about food delivery business sustainability. These show notes have been truncated due to length. For the full show notes, visit https://twit.tv/shows/intelligent-machines/episodes/818 Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Mike McCue Sponsors: monarchmoney.com with code IM spaceship.com/twit bigid.com/im Melissa.com/twit

In the mid-2000s, Ben Brown started his career designing demand response programs that relied on pagers and telephones. Today, as Renew Home's CEO, he's leveraging AI and tens of millions of connected smart devices to help households save energy and create an entirely new approach to grid management. Renew Home is building a new kind of virtual power plant that moves beyond occasional emergency events toward continuous, subtle energy shifts across millions of connected households. "The biggest evolution is connected devices," explains Brown, who previously led energy product development at Google after its acquisition of Nest. During his time at Google Labs working on large language models, Brown also witnessed firsthand the massive energy demands that AI would place on our grid. This realization, combined with his work on smart home technology, led Brown to envision a new approach to virtual power plants – one built on subtle, personalized adjustments across millions of homes rather than occasional disruptive events. “There's actually a lot more value continuously throughout the year, over hundreds of hours where customers can save more money by helping support the grid." With DOE projections showing a 200 gigawatt peak on the US grid by 2030, Renew Home's approach offers a compelling alternative to building new power plants. By focusing on customer control and personalization, they've achieved 75% opt-in rates, while creating a resource that is far cheaper than gas peakers. In this episode, recorded as part of a live Frontier Forum, Stephen Lacey talks with Ben Brown about the next generation of virtual power plants.  How does Renew Home's approach differ from demand response or battery-based VPPs? And what role can it play in addressing the grid's urgent needs? This is a partner episode, brought to you by Renew Home. It was recorded live as part of Latitude Media's Frontier Forum series. Watch the full video to hear more details about next-generation VPPs.

Welcome to episode 300 of The Cloud Pod – where the forecast is always cloudy! According to the title, this week's show is taking place inside of a Dr. Suess book, but don't despair – we're not going to make you eat green eggs and ham, but we WILL give you the low down on all things Vegas. Well, Google's Next event which recently took place in Vegas anyway. Did you make any Next predictions?  Titles we almost went with this week: This is the CLOUDPOD Episode 300 Tonight we dine in the Cloud The Next Chapter Now in Preview: Episode 300 A big thanks to this week's sponsor: We're sponsorless! Want to get your brand, company, or service in front of a very enthusiastic group of cloud news seekers? You've come to the right place! Send us an email or hit us up on our slack channel for more info.  GCP Pre-Next 02:35 Google shakes up Gemini leadership, Google Labs head taking the reins  There was a lot of Gemini news at Next – but we'll get to all that.  In this particular case, there's an employee shakeup. Sissie Hsiao is stepping down from leading the Google team, and is being replaced by Josh Woodward, who is currently leading the Google Labs.  04:35 Filestore instance replication now available GCP says customers have been asking for help in meeting business and regulatory goals, and so they are releasing Filestore instance replication. This new feature offers an efficient replication point objective (RPO) that can reach 30 minutes for data change rates of 100 MB/sec. 05:16 Multi-Cluster Orchestrator for cross-region Kubernetes workloads The public preview of Multi-Cluster Orchestrator was recently announced. This lets platform and application teams optimize resource utilization, enhance application resilience, and accelerate innovation in complex, multi-cluster environments.  The need for effective multi-cluster management has become essential as organizations increasingly use Kubernetes to deploy and manage their applications; Challenges such as resource scarcity, ensuring high availability, and managing deployments across diverse environments create significant operational overhead. Multi-Cluster Orchestrator addresses these challenges by providing a centralized orchestration layer that abstracts away the complexities of underlying Kubernetes infrastructure matching workloads with capacity across regions. 06:26 GKE at 65,000 nodes: Evaluating performance for simulated mixed AI workloads Recently GKE announced it can now support up to 65,000 nodes (up from 15,000.)  Saint Carrie be with your CFO.  09:15

Meet Google AI mode, a new search experiment requiring application for access (which I got today).This feature provides AI-powered responses and follow-up capabilities, akin to other AI tools. I emphasize the importance for businesses to understand this shift, highlighting the necessity of a strong technical SEO foundation and relevant content to be discoverable.Furthermore, the discussion touched on Google Search Console, website performance as a car, the value of answering questions online, and the significance of platforms like Pinterest for business reach, ultimately urging businesses to adapt to evolving search technologies and build a solid online presence.Frequently Asked Questions: Understanding Google AI Mode and Its Implications1. What is Google AI Mode?Google AI Mode is a new search experiment being rolled out by Google. It represents an evolution of the AI Overviews feature, aiming to provide more comprehensive, AI-powered responses to user queries. This experiment allows for follow-up questions, creating a more conversational search experience similar to interacting with other AI tools like ChatGPT. It signifies a shift in how Google Search may function in the future, integrating AI more deeply into the core search experience.2. How is Google AI Mode different from current Google Search and AI Overviews?Currently, Google Search displays traditional listings along with AI Overviews for some queries, which provide brief AI-generated summaries. Google AI Mode appears to be a more immersive AI experience. Instead of just a summarized overview, users will engage in a more dynamic, conversational interaction with AI to get answers and explore topics. The interface itself on google.com is expected to change, potentially featuring an "AI Mode" alongside options like "Images" and "Videos."3. How can I access Google AI Mode? Is it available to everyone?Access to Google AI Mode is currently limited. It is being rolled out as an experiment, and users need to apply and join a waitlist to potentially gain access. As of late March 2025 (based on the source's timeline), it appears to be available as a test within Google Labs and requires opting in. Furthermore, it seems testing is restricted to personal Gmail accounts rather than Google Workspace accounts. Initially, it's available in the US, with potential expansion to other regions in the future.4. What are the potential benefits of Google AI Mode for users?Google AI Mode promises a more efficient and in-depth way to find information. Users can ask complex questions and engage in follow-up queries to refine their understanding without needing to navigate multiple traditional search results. This conversational approach could lead to quicker answers and a more guided exploration of topics, similar to having a knowledgeable assistant.5. How might Google AI Mode impact businesses and website traffic? The integration of AI Mode could significantly alter how users discover websites. If AI-powered responses become the primary way people get information, websites that are not referenced or linked within these AI interactions may see a decrease in organic traffic. It will become crucial for businesses to ensure their content is high-quality, trustworthy, and easily understandable by AI algorithms to be considered as a reliable source. Being linked in AI responses could become a significant driver of relevant traffic.I hope you learn something new from the new Google AI Mode.⁠⁠⁠⁠⁠⁠⁠⁠>> START YOUR 14-DAY FREE TRIAL WITH FLODESK FOR BETTER EMAIL MARKETING TODAY

As VP of Google Labs, Josh Woodward leads teams exploring the frontiers of AI applications. He shares insights on their rapid development process, why today's written prompts will become outdated and how AI is transforming everything from video generation to computer control. He reveals that 25% of Google's code is now written by AI and explains why coding could see major leaps forward this year. He emphasizes the importance of taste, design and human values in building AI tools that will shape how future generations work and create. Mentioned in this episode: Notebook LM: Personal research product based on Gemini 2 (previously discussed on Training Data.) Veo 2: Google DeepMind's new video generation model. Paul Graham on X replying to Aaron Levie's post that “One approach to take in building in AI is to do something that's too expensive to be reasonably practical right now, and just bet that the costs will drop by 10X or 100X over time. The cost curve is on your side.” Where Good Ideas Come From: Book on the history of innovation by Steven Johnson. Project Mariner: Google DeepMind's research prototype exploring human-agent interaction starting with browser use. Replit Agent: Josh's favorite new AI app The Lego Story: Book on the history of Lego. Hosted by: Ravi Gupta and Sonya Huang, Sequoia Capital 

Send us a textShantanu Sinha, VP & GM of Google for Education, leads the development of tools like Google Classroom and Read Along, serving over 150 million educators and students globally. Previously, as founding President and COO of Khan Academy, he championed free, personalized learning on a global scale. Shantanu combines deep expertise in computer science, math, and cognitive sciences from MIT with strategic consulting experience from McKinsey.Jennie Magiera, Global Head of Education Impact at Google, is a bestselling author, TEDx speaker, and advocate for equity in education. At Google, she focuses on elevating marginalized voices and creating empowering tools for teachers and learners. A White House Champion for Change and ISTE Impact Award Winner, Jennie brings extensive experience as a teacher, district leader, and digital learning innovator.Steven Johnson, Editorial Director of NotebookLM and Google Labs, is a bestselling author of 14 books on innovation and technology, including Where Good Ideas Come From. An Emmy-winning television host and tech entrepreneur, Steven shapes tools that redefine learning and research while advocating for the power of collaboration in driving transformative ideas.

Is Apple Intelligence as good as Apple says it is? Emily Forlini from PC Magazine shares her experience. Plus US users can try out Google Labs new image generator called Whisk. And Mark Zuckerberg announced Threads daily and monthly active users numbers. And how do they compare with X and Bluesky?Starring Tom Merritt, Sarah Lane, Emily Forlini, Roger Chang, Joe.Link to the Show Notes.

Is Apple Intelligence as good as Apple says it is? Emily Forlini from PC Magazine shares her experience. Plus US users can try out Google Labs new image generator called Whisk. And Mark Zuckerberg announced Threads daily and monthly active users numbers. And how do they compare with X and Bluesky? Starring Tom Merritt, Sarah Lane, Emily Forlini, Roger Chang, Joe. To read the show notes in a separate page click here! Support the show on Patreon by becoming a supporter!

NotebookLM is a research assistant powered by Gemini that draws on expertise from storytelling to present information in an engaging way. It allows users to upload their own documents and generate insights, explanations, and—more recently—podcasts. This feature, also known as audio overviews, has captured the imagination of millions of people worldwide, who have created thousands of engaging podcasts ranging from personal narratives to educational explainers using source materials like CVs, personal journals, sales decks, and more.Join Raiza Martin and Steven Johnson from Google Labs, Google's testing ground for products, as they guide host Hannah Fry through the technical advancements that have made NotebookLM possible. In this episode they'll explore what it means to be interesting, the challenges of generating natural-sounding speech, as well as exciting new modalities on the horizon.Further readingTry NotebookLM hereRead about the speech generation technology behind Audio Overveiws: https://deepmind.google/discover/blog/pushing-the-frontiers-of-audio-generation/Thanks to everyone who made this possible, including but not limited to: Presenter: Professor Hannah FrySeries Producer: Dan HardoonEditor: Rami Tzabar, TellTale Studios Commissioner & Producer: Emma YousifMusic composition: Eleni ShawCamera Director and Video Editor: Daniel LazardAudio Engineer: Perry RogantinVideo Studio Production: Nicholas DukeVideo Editor: Alex Baro Cayetano, Daniel Lazard Video Production Design: James BartonVisual Identity and Design: Eleanor TomlinsonCommissioned by Google DeepMind Please subscribe on your preferred podcast platform. Want to share feedback? Why not leave a review? Have a suggestion for a guest that we should have on next? Leave us a comment on YouTube and stay tuned for future episodes.

Hugo speaks with Ravin Kumar, Senior Research Data Scientist at Google Labs. Ravin's career has taken him from building rockets at SpaceX to driving data science and technology at Sweetgreen, and now to advancing generative AI research and applications at Google Labs and DeepMind. His multidisciplinary experience gives him a rare perspective on building AI systems that combine technical rigor with practical utility. In this episode, we dive into: • Ravin's fascinating career path, including the skills and mindsets needed to work effectively with AI and machine learning models at different stages of the pipeline. • How to build generative AI systems that are scalable, reliable, and aligned with user needs. • Real-world applications of generative AI, such as using open weight models such as Gemma to help a bakery streamline operations—an example of delivering tangible business value through AI. • The critical role of UX in AI adoption, and how Ravin approaches designing tools like Notebook LM with the user journey in mind. We also include a live demo where Ravin uses Notebook LM to analyze my website, extract insights, and even generate a podcast-style conversation about me. While some of the demo is visual, much can be appreciated through audio, and we've added a link to the video in the show notes for those who want to see it in action. We've also included the generated segment at the end of the episode for you to enjoy. LINKS The livestream on YouTube (https://www.youtube.com/live/ffS6NWqoo_k) Google Labs (https://labs.google/) Ravin's GenAI Handbook (https://ravinkumar.com/GenAiGuidebook/book_intro.html) Breadboard: A library for prototyping generative AI applications (https://breadboard-ai.github.io/breadboard/) As mentioned in the episode, Hugo is teaching a four-week course, Building LLM Applications for Data Scientists and SWEs, co-led with Stefan Krawczyk (Dagworks, ex-StitchFix). The course focuses on building scalable, production-grade generative AI systems, with hands-on sessions, $1,000+ in cloud credits, live Q&As, and guest lectures from industry experts. Listeners of Vanishing Gradients can get 25% off the course using this special link (https://maven.com/hugo-stefan/building-llm-apps-ds-and-swe-from-first-principles?promoCode=VG25) or by applying the code VG25 at checkout.

Explore the fascinating world of AI and its potential to transform how we work, learn, and create with NotebookLM. Join guests Steven Johnson, Editorial Director of Notebook LM, and Raiza Martin, Senior Product Manager at Google Labs, leading Notebook LM for a deep dive into the inspiration, development, practical use cases, and more in this People of AI episode. Resources: A.I. Is Mastering Language. Should We Trust What It Says?                                → https://goo.gle/3Cub1Wd  → https://goo.gle/3UZOwPe  →  https://goo.gle/3OflIPc  Adjacent possible newsletter → https://goo.gle/3AGpe21    #TensorFlow #PeopleofAI    

Tech Life created a fake podcast using a new AI tool from Google Labs, and we spoke to the head of the tool, Steven Johnson. Don't worry – this programme is still brought to you by real human beings! We also look into how deepfakes have been used in the US. Plus – have we unveiled the real inventor of Bitcoin? (Spoiler – no.)We love hearing from you. Email us on techlife@bbc.co.uk or send a WhatsApp on +44 330 123 0320.Presenter: Chris Vallance Producer: Imran Rahman-Jones Editor: Monica Soriano(Image: An AI-generated head with letters coming out of its mouth. Credit: Getty Images.)

If you've listened to the podcast for a while, you might have heard our ElevenLabs-powered AI co-host Charlie a few times. Text-to-speech has made amazing progress in the last 18 months, with OpenAI's Advanced Voice Mode (aka “Her”) as a sneak peek of the future of AI interactions (see our “Building AGI in Real Time” recap). Yet, we had yet to see a real killer app for AI voice (not counting music).Today's guests, Raiza Martin and Usama Bin Shafqat, are the lead PM and AI engineer behind the NotebookLM feature flag that gave us the first viral AI voice experience, the “Deep Dive” podcast:The idea behind the “Audio Overviews” feature is simple: take a bunch of documents, websites, YouTube videos, etc, and generate a podcast out of them. This was one of the first demos that people built with voice models + RAG + GPT models, but it was always a glorified speech-to-text. Raiza and Usama took a very different approach:* Make it conversational: when you listen to a NotebookLM audio there are a ton of micro-interjections (Steven Johnson calls them disfluencies) like “Oh really?” or “Totally”, as well as pauses and “uh…”, like you would expect in a real conversation. These are not generated by the LLM in the transcript, but they are built into the the audio model. See ~28:00 in the pod for more details. * Listeners love tension: if two people are always in agreement on everything, it's not super interesting. They tuned the model to generate flowing conversations that mirror the tone and rhythm of human speech. They did not confirm this, but many suspect the 2 year old SoundStorm paper is related to this model.* Generating new insights: because the hosts' goal is not to summarize, but to entertain, it comes up with funny metaphors and comparisons that actually help expand on the content rather than just paraphrasing like most models do. We have had listeners make podcasts out of our podcasts, like this one.This is different than your average SOTA-chasing, MMLU-driven model buildooor. Putting product and AI engineering in the same room, having them build evals together, and understanding what the goal is lets you get these unique results. The 5 rules for AI PMsWe always focus on AI Engineers, but this episode had a ton of AI PM nuggets as well, which we wanted to collect as NotebookLM is one of the most successful products in the AI space:1. Less is more: the first version of the product had 0 customization options. All you could do is give it source documents, and then press a button to generate. Most users don't know what “temperature” or “top-k” are, so you're often taking the magic away by adding more options in the UI. Since recording they added a few, like a system prompt, but those were features that users were “hacking in”, as Simon Willison highlighted in his blog post.2. Use Real-Time Feedback: they built a community of 65,000 users on Discord that is constantly reporting issues and giving feedback; sometimes they noticed server downtime even before the Google internal monitoring did. Getting real time pings > aggregating user data when doing initial iterations. 3. Embrace Non-Determinism: AI outputs variability is a feature, not a bug. Rather than limiting the outputs from the get-go, build toggles that you can turn on/off with feature flags as the feedback starts to roll in.4. Curate with Taste: if you try your product and it sucks, you don't need more data to confirm it. Just scrap that and iterate again. This is even easier for a product like this; if you start listening to one of the podcasts and turn it off after 10 seconds, it's never a good sign. 5. Stay Hands-On: It's hard to build taste if you don't experiment. Trying out all your competitors products as well as unrelated tools really helps you understand what users are seeing in market, and how to improve on it.Chapters00:00 Introductions01:39 From Project Tailwind to NotebookLM09:25 Learning from 65,000 Discord members12:15 How NotebookLM works18:00 Working with Steven Johnson23:00 How to prioritize features25:13 Structuring the data pipelines29:50 How to eval34:34 Steering the podcast outputs37:51 Defining speakers personalities39:04 How do you make audio engaging?45:47 Humor is AGI51:38 Designing for non-determinism53:35 API when?55:05 Multilingual support and dialect considerations57:50 Managing system prompts and feature requests01:00:58 Future of NotebookLM01:04:59 Podcasts for your codebase01:07:16 Plans for real-time chat01:08:27 Wrap upShow Notes* Notebook LM* AI Test Kitchen* Nicholas Carlini* Steven Johnson* Wealth of Nations* Histories of Mysteries by Andrej Karpathy* chicken.pdf Threads* Area 120* Raiza Martin* Usama Bin ShafqatTranscriptNotebookLM [00:00:00]: Hey everyone, we're here today as guests on Latent Space. It's great to be here, I'm a long time listener and fan, they've had some great guests on this show before. Yeah, what an honor to have us, the hosts of another podcast, join as guests. I mean a huge thank you to Swyx and Alessio for the invite, thanks for having us on the show. Yeah really, it seems like they brought us here to talk a little bit about our show, our podcast. Yeah, I mean we've had lots of listeners ourselves, listeners at Deep Dive. Oh yeah, we've made a ton of audio overviews since we launched and we're learning a lot. There's probably a lot we can share around what we're building next, huh? Yeah, we'll share a little bit at least. The short version is we'll keep learning and getting better for you. We're glad you're along for the ride. So yeah, keep listening. Keep listening and stay curious. We promise to keep diving deep and bringing you even better options in the future. Stay curious.Alessio [00:00:52]: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners. And I'm joined by my co-host, Swyx, founder of Smol.ai.Swyx [00:01:01]: Hey, and today we're back in the studio with our special guest, Raiza Martin. And Raiza, I forgot to get your last name, Shafqat.Raiza [00:01:10]: Yes.Swyx [00:01:10]: Okay, welcome.Raiza [00:01:12]: Hello, thank you for having us.Swyx [00:01:14]: So AI podcasters meet human podcasters, always fun. Congrats on the success of Notebook LM. I mean, how does it feel?Raiza [00:01:22]: It's been a lot of fun. A lot of it, honestly, was unexpected. But my favorite part is really listening to the audio overviews that people have been making.Swyx [00:01:29]: Maybe we should do a little bit of intros and tell the story. You know, what is your path into the sort of Google AI org? Or maybe, actually, I don't even know what org you guys are in.Raiza [00:01:39]: I can start. My name is Raisa. I lead the Notebook LM team inside of Google Labs. So specifically, that's the org that we're in. It's called Google Labs. It's only about two years old. And our whole mandate is really to build AI products. That's it. We work super closely with DeepMind. Our entire thing is just, like, try a bunch of things and see what's landing with users. And the background that I have is, really, I worked in payments before this, and I worked in ads right before, and then startups. I tell people, like, at every time that I changed orgs, I actually almost quit Google. Like, specifically, like, in between ads and payments, I was like, all right, I can't do this. Like, this is, like, super hard. I was like, it's not for me. I'm, like, a very zero-to-one person. But then I was like, okay, I'll try. I'll interview with other teams. And when I interviewed in payments, I was like, oh, these people are really cool. I don't know if I'm, like, a super good fit with this space, but I'll try it because the people are cool. And then I really enjoyed that, and then I worked on, like, zero-to-one features inside of payments, and I had a lot of fun. But then the time came again where I was like, oh, I don't know. It's like, it's time to leave. It's time to start my own thing. But then I interviewed inside of Google Labs, and I was like, oh, darn. Like, there's definitely, like—Alessio [00:02:48]: They got you again.Raiza [00:02:49]: They got me again. And so now I've been here for two years, and I'm happy that I stayed because especially with, you know, the recent success of Notebook LM, I'm like, dang, we did it. I actually got to do it. So that was really cool.Usama [00:03:02]: Kind of similar, honestly. I was at a big team at Google. We do sort of the data center supply chain planning stuff. Google has, like, the largest sort of footprint. Obviously, there's a lot of management stuff to do there. But then there was this thing called Area 120 at Google, which does not exist anymore. But I sort of wanted to do, like, more zero-to-one building and landed a role there. We were trying to build, like, a creator commerce platform called Kaya. It launched briefly a couple years ago. But then Area 120 sort of transitioned and morphed into Labs. And, like, over the last few years, like, the focus just got a lot clearer. Like, we were trying to build new AI products and do it in the wild and sort of co-create and all of that. So, you know, we've just been trying a bunch of different things. And this one really landed, which has felt pretty phenomenal. Really, really landed.Swyx [00:03:53]: Let's talk about the brief history of Notebook LM. You had a tweet, which is very helpful for doing research. May 2023, during Google I.O., you announced Project Tailwind.Raiza [00:04:03]: Yeah.Swyx [00:04:03]: So today is October 2024. So you joined October 2022?Raiza [00:04:09]: Actually, I used to lead AI Test Kitchen. And this was actually, I think, not I.O. 2023. I.O. 2022 is when we launched AI Test Kitchen, or announced it. And I don't know if you remember it.Swyx [00:04:23]: That's how you, like, had the basic prototype for Gemini.Raiza [00:04:26]: Yes, yes, exactly. Lambda.Swyx [00:04:28]: Gave beta access to people.Raiza [00:04:29]: Yeah, yeah, yeah. And I remember, I was like, wow, this is crazy. We're going to launch an LLM into the wild. And that was the first project that I was working on at Google. But at the same time, my manager at the time, Josh, he was like, hey, I want you to really think about, like, what real products would we build that are not just demos of the technology? That was in October of 2022. I was sitting next to an engineer that was working on a project called Talk to Small Corpus. His name was Adam. And the idea of Talk to Small Corpus is basically using LLM to talk to your data. And at the time, I was like, wait, there's some, like, really practical things that you can build here. And just a little bit of background, like, I was an adult learner. Like, I went to college while I was working a full-time job. And the first thing I thought was, like, this would have really helped me with my studying, right? Like, if I could just, like, talk to a textbook, especially, like, when I was tired after work, that would have been huge. We took a lot of, like, the Talk to Small Corpus prototypes, and I showed it to a lot of, like, college students, particularly, like, adult learners. They were like, yes, like, I get it, right? Like, I didn't even have to explain it to them. And we just continued to iterate the prototype from there to the point where we actually got a slot as part of the I.O. demo in 23.Swyx [00:05:42]: And Corpus, was it a textbook? Oh, my gosh.Raiza [00:05:45]: Yeah. It's funny. Actually, when he explained the project to me, he was like, talk to Small Corpus. It was like, talk to a small corpse?Swyx [00:05:51]: Yeah, nobody says Corpus.Raiza [00:06:00]: It was like, a small corpse? This is not AI. Yeah, yeah. And it really was just, like, a way for us to describe the amount of data that we thought, like, it could be good for.Swyx [00:06:02]: Yeah, but even then, you're still, like, doing rag stuff. Because, you know, the context length back then was probably, like, 2K, 4K.Raiza [00:06:08]: Yeah, it was basically rag.Raiza [00:06:09]: That was essentially what it was.Raiza [00:06:10]: And I remember, I was like, we were building the prototypes. And at the same time, I think, like, the rest of the world was. Right? We were seeing all of these, like, chat with PDF stuff come up. And I was like, come on, we gotta go. Like, we have to, like, push this out into the world. I think if there was anything, I wish we would have launched sooner because I wanted to learn faster. But I think, like, we netted out pretty well.Alessio [00:06:30]: Was the initial product just text-to-speech? Or were you also doing kind of, like, synthesizing of the content, refining it? Or were you just helping people read through it?Raiza [00:06:40]: Before we did the I.O. announcement in 23, we'd already done a lot of studies. And one of the first things that I realized was the first thing anybody ever typed was, summarize the thing. Right?Raiza [00:06:53]: Summarize the document.Raiza [00:06:54]: And it was, like, half like a test and half just like, oh, I know the content. I want to see how well it does this. So it was part of the first thing that we launched. It was called Project Tailwind back then. It was just Q&A, so you could chat with the doc just through text, and it would automatically generate a summary as well. I'm not sure if we had it back then.Raiza [00:07:12]: I think we did.Raiza [00:07:12]: It would also generate the key topics in your document, and it could support up to, like, 10 documents. So it wasn't just, like, a single doc.Alessio [00:07:20]: And then the I.O. demo went well, I guess. And then what was the discussion from there to where we are today? Is there any, maybe, intermediate step of the product that people missed between this was launch or?Raiza [00:07:33]: It was interesting because every step of the way, I think we hit, like, some pretty critical milestones. So I think from the initial demo, I think there was so much excitement of, like, wow, what is this thing that Google is launching? And so we capitalized on that. We built the wait list. That's actually when we also launched the Discord server, which has been huge for us because for us in particular, one of the things that I really wanted to do was to be able to launch features and get feedback ASAP. Like, the moment somebody tries it, like, I want to hear what they think right now, and I want to ask follow-up questions. And the Discord has just been so great for that. But then we basically took the feedback from I.O., we continued to refine the product.Raiza [00:08:12]: So we added more features.Raiza [00:08:13]: We added sort of, like, the ability to save notes, write notes. We generate follow-up questions. So there's a bunch of stuff in the product that shows, like, a lot of that research. But it was really the rolling out of things. Like, we removed the wait list, so rolled out to all of the United States. We rolled out to over 200 countries and territories. We started supporting more languages, both in the UI and, like, the actual source stuff. We experienced, like, in terms of milestones, there was, like, an explosion of, like, users in Japan. This was super interesting in terms of just, like, unexpected. Like, people would write to us and they would be like, this is amazing. I have to read all of these rules in English, but I can chat in Japanese. It's like, oh, wow. That's true, right? Like, with LLMs, you kind of get this natural, it translates the content for you. And you can ask in your sort of preferred mode. And I think that's not just, like, a language thing, too. I think there's, like, I do this test with Wealth of Nations all the time because it's, like, a pretty complicated text to read. The Evan Smith classic.Swyx [00:09:11]: It's, like, 400 pages or something.Raiza [00:09:12]: Yeah. But I like this test because I'm, like, asking, like, Normie, you know, plain speak. And then it summarizes really well for me. It sort of adapts to my tone.Swyx [00:09:22]: Very capitalist.Raiza [00:09:25]: Very on brand.Swyx [00:09:25]: I just checked in on a Notebook LM Discord. 65,000 people. Yeah.Raiza [00:09:29]: Crazy.Swyx [00:09:29]: Just, like, for one project within Google. It's not, like, it's not labs. It's just Notebook LM.Raiza [00:09:35]: Just Notebook LM.Swyx [00:09:36]: What do you learn from the community?Raiza [00:09:39]: I think that the Discord is really great for hearing about a couple of things.Raiza [00:09:43]: One, when things are going wrong. I think, honestly, like, our fastest way that we've been able to find out if, like, the servers are down or there's just an influx of people being, like, it saysRaiza [00:09:53]: system unable to answer.Raiza [00:09:54]: Anybody else getting this?Raiza [00:09:56]: And I'm, like, all right, let's go.Raiza [00:09:58]: And it actually catches it a lot faster than, like, our own monitoring does.Raiza [00:10:01]: It's, like, that's been really cool. So, thank you.Swyx [00:10:03]: Canceled eat a dog.Raiza [00:10:05]: So, thank you to everybody. Please keep reporting it. I think the second thing is really the use cases.Raiza [00:10:10]: I think when we put it out there, I was, like, hey, I have a hunch of how people will use it, but, like, to actually hear about, you know, not just the context of, like, the use of Notebook LM, but, like, what is this person's life like? Why do they care about using this tool?Raiza [00:10:23]: Especially people who actually have trouble using it, but they keep pushing.Raiza [00:10:27]: Like, that's just so critical to understand what was so motivating, right?Raiza [00:10:31]: Like, what was your problem that was, like, so worth solving? So, that's, like, a second thing.Raiza [00:10:34]: The third thing is also just hearing sort of, like, when we have wins and when we don't have wins because there's actually a lot of functionality where I'm, like, hmm, IRaiza [00:10:42]: don't know if that landed super well or if that was actually super critical.Raiza [00:10:45]: As part of having this sort of small project, right, I want to be able to unlaunch things, too. So, it's not just about just, like, rolling things out and testing it and being, like, wow, now we have, like, 99 features. Like, hopefully we get to a place where it's, like, there's just a really strong core feature set and the things that aren't as great, we can just unlaunch.Swyx [00:11:02]: What have you unlaunched? I have to ask.Raiza [00:11:04]: I'm in the process of unlaunching some stuff, but, for example, we had this idea that you could highlight the text in your source passage and then you could transform it. And nobody was really using it and it was, like, a very complicated piece of our architecture and it's very hard to continue supporting it in the context of new features. So, we were, like, okay, let's do a 50-50 sunset of this thing and see if anybody complains.Raiza [00:11:28]: And so far, nobody has.Swyx [00:11:29]: Is there, like, a feature flagging paradigm inside of your architecture that lets you feature flag these things easily?Raiza [00:11:36]: Yes, and actually...Raiza [00:11:37]: What is it called?Swyx [00:11:38]: Like, I love feature flagging.Raiza [00:11:40]: You mean, like, in terms of just, like, being able to expose things to users?Swyx [00:11:42]: Yeah, as a PM. Like, this is your number one tool, right?Raiza [00:11:44]: Yeah, yeah.Swyx [00:11:45]: Let's try this out. All right, if it works, roll it out. If it doesn't, roll it back, you know?Raiza [00:11:49]: Yeah, I mean, we just run Mendel experiments for the most part. And, actually, I don't know if you saw it, but on Twitter, somebody was able to get around our flags and they enabled all the experiments.Raiza [00:11:58]: They were, like, check out what the Notebook LM team is cooking.Raiza [00:12:02]: I was, like, oh!Raiza [00:12:03]: And I was at lunch with the rest of the team and I was, like, I was eating. I was, like, guys, guys, Magic Draft League!Raiza [00:12:10]: They were, like, oh, no!Raiza [00:12:12]: I was, like, okay, just finish eating and then let's go figure out what to do.Raiza [00:12:15]: Yeah.Alessio [00:12:15]: I think a post-mortem would be fun, but I don't think we need to do it on the podcast now. Can we just talk about what's behind the magic? So, I think everybody has questions, hypotheses about what models power it. I know you might not be able to share everything, but can you just get people very basic? How do you take the data and put it in the model? What text model you use? What's the text-to-speech kind of, like, jump between the two? Sure.Raiza [00:12:42]: Yeah.Raiza [00:12:42]: I was going to say, SRaiza, he manually does all the podcasts.Raiza [00:12:46]: Oh, thank you.Usama [00:12:46]: Really fast. You're very fast, yeah.Raiza [00:12:48]: Both of the voices at once.Usama [00:12:51]: Voice actor.Raiza [00:12:52]: Good, good.Usama [00:12:52]: Yeah, so, for a bit of background, we were building this thing sort of outside Notebook LM to begin with. Like, just the idea is, like, content transformation, right? Like, we can do different modalities. Like, everyone knows that. Everyone's been poking at it. But, like, how do you make it really useful? And, like, one of the ways we thought was, like, okay, like, you maybe, like, you know, people learn better when they're hearing things. But TTS exists, and you can, like, narrate whatever's on screen. But you want to absorb it the same way. So, like, that's where we sort of started out into the realm of, like, maybe we try, like, you know, two people are having a conversation kind of format. We didn't actually start out thinking this would live in Notebook, right? Like, Notebook was sort of, we built this demo out independently, tried out, like, a few different sort of sources. The main idea was, like, go from some sort of sources and transform it into a listenable, engaging audio format. And then through that process, we, like, unlocked a bunch more sort of learnings. Like, for example, in a sense, like, you're not prompting the model as much because, like, the information density is getting unrolled by the model prompting itself, in a sense. Because there's two speakers, and they're both technically, like, AI personas, right? That have different angles of looking at things. And, like, they'll have a discussion about it. And that sort of, we realized that's kind of what was making it riveting, in a sense. Like, you care about what comes next, even if you've read the material already. Because, like, people say they get new insights on their own journals or books or whatever. Like, anything that they've written themselves. So, yeah, from a modeling perspective, like, it's, like Reiza said earlier, like, we work with the DeepMind audio folks pretty closely. So, they're always cooking up new techniques to, like, get better, more human-like audio. And then Gemini 1.5 is really, really good at absorbing long context. So, we sort of, like, generally put those things together in a way that we could reliably produce the audio.Raiza [00:14:52]: I would add, like, there's something really nuanced, I think, about sort of the evolution of, like, the utility of text-to-speech. Where, if it's just reading an actual text response, and I've done this several times. I do it all the time with, like, reading my text messages. Or, like, sometimes I'm trying to read, like, a really dense paper, but I'm trying to do actual work. I'll have it, like, read out the screen. There is something really robotic about it that is not engaging. And it's really hard to consume content in that way. And it's never been really effective. Like, particularly for me, where I'm, like, hey, it's actually just, like, it's fine for, like, short stuff. Like, texting, but even that, it's, like, not that great. So, I think the frontier of experimentation here was really thinking about there is a transform that needs to happen in between whatever.Raiza [00:15:38]: Here's, like, my resume, right?Raiza [00:15:39]: Or here's, like, a 100-page slide deck or something. There is a transform that needs to happen that is inherently editorial. And I think this is where, like, that two-person persona, right, dialogue model, they have takes on the material that you've presented. That's where it really sort of, like, brings the content to life in a way that's, like, not robotic. And I think that's, like, where the magic is, is, like, you don't actually know what's going to happen when you press generate.Raiza [00:16:08]: You know, for better or for worse.Raiza [00:16:09]: Like, to the extent that, like, people are, like, no, I actually want it to be more predictable now. Like, I want to be able to tell them. But I think that initial, like, wow was because you didn't know, right? When you upload your resume, what's it about to say about you? And I think I've seen enough of these where I'm, like, oh, it gave you good vibes, right? Like, you knew it was going to say, like, something really cool. As we start to shape this product, I think we want to try to preserve as much of that wow as much as we can. Because I do think, like, exposing, like, all the knobs and, like, the dials, like, we've been thinking about this a lot. It's like, hey, is that, like, the actual thing?Raiza [00:16:43]: Is that the thing that people really want?Alessio [00:16:45]: Have you found differences in having one model just generate the conversation and then using text-to-speech to kind of fake two people? Or, like, are you actually using two different kind of system prompts to, like, have a conversation step-by-step? I'm always curious, like, if persona system prompts make a big difference? Or, like, you just put in one prompt and then you just let it run?Usama [00:17:05]: I guess, like, generally we use a lot of inference, as you can tell with, like, the spinning thing takes a while. So, yeah, there's definitely, like, a bunch of different things happening under the hood. We've tried both approaches and they have their, sort of, drawbacks and benefits. I think that that idea of, like, questioning, like, the two different personas, like, persists throughout, like, whatever approach we try. It's like, there's a bit of, like, imperfection in there. Like, we had to really lean into the fact that, like, to build something that's engaging, like, it needs to be somewhat human and it needs to be just not a chatbot. Like, that was sort of, like, what we need to diverge from. It's like, you know, most chatbots will just narrate the same kind of answer, like, given the same sources, for the most part, which is ridiculous. So, yeah, there's, like, experimentation there under the hood, like, with the model to, like, make sure that it's spitting out, like, different takes and different personas and different, sort of, prompting each other is, like, a good analogy, I guess.Swyx [00:18:00]: Yeah, I think Steven Johnson, I think he's on your team. I don't know what his role is. He seems like chief dreamer, writer.Raiza [00:18:08]: Yeah, I mean, I can comment on Steven. So, Steven joined, actually, in the very early days, I think before it was even a fully funded project. And I remember when he joined, I was like, Steven Johnson's going to be on my team? You know, and for folks who don't know him, Steven is a New York Times bestselling author of, like, 14 books. He has a PBS show. He's, like, incredibly smart, just, like, a true, sort of, celebrity by himself. And then he joined Google, and he was like, I want to come here, and I want to build the thing that I've always dreamed of, which is a tool to help me think. I was like, a what? Like, a tool to help you think? I was like, what do you need help with? Like, you seem to be doing great on your own. And, you know, he would describe this to me, and I would watch his flow. And aside from, like, providing a lot of inspiration, to be honest, like, when I watched Steven work, I was like, oh, nobody works like this, right? Like, this is what makes him special. Like, he is such a dedicated, like, researcher and journalist, and he's so thorough, he's so smart. And then I had this realization of, like, maybe Steven is the product. Maybe the work is to take Steven's expertise and bring it to, like, everyday people that could really benefit from this. Like, just watching him work, I was like, oh, I could definitely use, like, a mini-Steven, like, doing work for me. Like, that would make me a better PM. And then I thought very quickly about, like, the adjacent roles that could use sort of this, like, research and analysis tool. And so, aside from being, you know, chief dreamer, Steven also represents, like, a super workflow that I think all of us, like, if we had access to a tool like it, would just inherently, like, make us better.Swyx [00:19:46]: Did you make him express his thoughts while he worked, or you just silently watched him, or how does this work?Raiza [00:19:52]: Oh, now you're making me admit it. But yes, I did just silently watch him.Swyx [00:19:57]: This is a part of the PM toolkit, right? They give user interviews and all that.Raiza [00:20:00]: Yeah, I mean, I did interview him, but I noticed, like, if I interviewed him, it was different than if I just watched him. And I did the same thing with students all the time. Like, I followed a lot of students around. I watched them study. I would ask them, like, oh, how do you feel now, right?Raiza [00:20:15]: Or why did you do that? Like, what made you do that, actually?Raiza [00:20:18]: Or why are you upset about, like, this particular thing? Why are you cranky about this particular topic? And it was very similar, I think, for Steven, especially because he was describing, he was in the middle of writing a book. And he would describe, like, oh, you know, here's how I research things, and here's how I keep my notes. Oh, and here's how I do it. And it was really, he was doing this sort of, like, self-questioning, right? Like, now we talk about, like, chain of, you know, reasoning or thought, reflection.Raiza [00:20:44]: And I was like, oh, he's the OG.Raiza [00:20:46]: Like, I watched him do it in real time. I was like, that's, like, L-O-M right there. And to be able to bring sort of that expertise in a way that was, like, you know, maybe, like, costly inference-wise, but really have, like, that ability inside of a tool that was, like, for starters, free inside of NotebookLM, it was good to learn whether or not people really did find use out of it.Swyx [00:21:05]: So did he just commit to using NotebookLM for everything, or did you just model his existing workflow?Raiza [00:21:12]: Both, right?Raiza [00:21:12]: Like, in the beginning, there was no product for him to use. And so he just kept describing the thing that he wanted. And then eventually, like, we started building the thing. And then I would start watching him use it. One of the things that I love about Steven is he uses the product in ways where it kind of does it, but doesn't quite. Like, he's always using it at, like, the absolute max limit of this thing. But the way that he describes it is so full of promise, where he's like, I can see it going here. And all I have to do is sort of, like, meet him there and sort of pressure test whether or not, you know, everyday people want it. And we just have to build it.Swyx [00:21:47]: I would say OpenAI has a pretty similar person, Andrew Mason, I think his name is. It's very similar, like, just from the writing world and using it as a tool for thought to shape Chachabitty. I don't think that people who use AI tools to their limit are common. I'm looking at my NotebookLM now. I've got two sources. You have a little, like, source limit thing. And my bar is over here, you know, and it stretches across the whole thing. I'm like, did he fill it up?Raiza [00:22:09]: Yes, and he has, like, a higher limit than others, I think. He fills it up.Raiza [00:22:14]: Oh, yeah.Raiza [00:22:14]: Like, I don't think Steven even has a limit, actually.Swyx [00:22:17]: And he has Notes, Google Drive stuff, PDFs, MP3, whatever.Raiza [00:22:22]: Yes, and one of my favorite demos, he just did this recently, is he has actually PDFs of, like, handwritten Marie Curie notes. I see.Swyx [00:22:29]: So you're doing image recognition as well. Yeah, it does support it today.Raiza [00:22:32]: So if you have a PDF that's purely images, it will recognize it.Raiza [00:22:36]: But his demo is just, like, super powerful.Raiza [00:22:37]: He's like, okay, here's Marie Curie's notes. And it's like, here's how I'm using it to analyze it. And I'm using it for, like, this thing that I'm writing.Raiza [00:22:44]: And that's really compelling.Raiza [00:22:45]: It's like the everyday person doesn't think of these applications. And I think even, like, when I listen to Steven's demo, I see the gap. I see how Steven got there, but I don't see how I could without him. And so there's a lot of work still for us to build of, like, hey, how do I bring that magic down to, like, zero work? Because I look at all the steps that he had to take in order to do it, and I'm like, okay, that's product work for us, right? Like, that's just onboarding.Alessio [00:23:09]: And so from an engineering perspective, people come to you and it's like, hey, I need to use this handwritten notes from Marie Curie from hundreds of years ago. How do you think about adding support for, like, data sources and then maybe any fun stories and, like, supporting more esoteric types of inputs?Raiza [00:23:25]: So I think about the product in three ways, right? So there's the sources, the source input. There's, like, the capabilities of, like, what you could do with those sources. And then there's the third space, which is how do you output it into the world? Like, how do you put it back out there? There's a lot of really basic sources that we don't support still, right? I think there's sort of, like, the handwritten notes stuff is one, but even basic things like DocX or, like, PowerPoint, right? Like, these are the things that people, everyday people are like, hey, my professor actually gave me everything in DocX. Can you support that? And then just, like, basic stuff, like images and PDFs combined with text. Like, there's just a really long roadmap for sources that I think we just have to work on.Raiza [00:24:04]: So that's, like, a big piece of it.Raiza [00:24:05]: On the output side, and I think this is, like, one of the most interesting things that we learned really early on, is, sure, there's, like, the Q&A analysis stuff, which is like, hey, when did this thing launch? Okay, you found it in the slide deck. Here's the answer. But most of the time, the reason why people ask those questions is because they're trying to make something new. And so when, actually, when some of those early features leaked, like, a lot of the features we're experimenting with are the output types. And so you can imagine that people care a lot about the resources that they're putting into NotebookLM because they're trying to create something new. So I think equally as important as, like, the source inputs are the outputs that we're helping people to create. And really, like, you know, shortly on the roadmap, we're thinking about how do we help people use NotebookLM to distribute knowledge? And that's, like, one of the most compelling use cases is, like, shared notebooks. It's, like, a way to share knowledge. How do we help people take sources and, like, one-click new documents out of it, right? And I think that's something that people think is, like, oh, yeah, of course, right? Like, one push a document. But what does it mean to do it right? Like, to do it in your style, in your brand, right?Raiza [00:25:08]: To follow your guidelines, stuff like that.Raiza [00:25:09]: So I think there's a lot of work, like, on both sides of that equation.Raiza [00:25:13]: Interesting.Swyx [00:25:13]: Any comments on the engineering side of things?Usama [00:25:16]: So, yeah, like I said, I was mostly working on building the text to audio, which kind of lives as a separate engineering pipeline, almost, that we then put into NotebookLM. But I think there's probably tons of NotebookLM engineering war stories on dealing with sources. And so I don't work too closely with engineers directly. But I think a lot of it does come down to, like, Gemini's native understanding of images really well with the latest generation.Raiza [00:25:39]: Yeah, I think on the engineering and modeling side, I think we are a really good example of a team that's put a product out there, and we're getting a lot of feedback from the users, and we return the data to the modeling team, right? To the extent that we say, hey, actually, you know what people are uploading, but we can't really support super well?Raiza [00:25:56]: Text plus image, right?Raiza [00:25:57]: Especially to the extent that, like, NotebookLM can handle up to 50 sources, 500,000 words each. Like, you're not going to be able to jam all of that into, like, the context window. So how do we do multimodal embeddings with that? There's really, like, a lot of things that we have to solve that are almost there, but not quite there yet.Alessio [00:26:16]: On then turning it into audio, I think one of the best things is it has so many of the human... Does that happen in the text generation that then becomes audio? Or is that a part of, like, the audio model that transforms the text?Usama [00:26:27]: It's a bit of both, I would say. The audio model is definitely trying to mimic, like, certain human intonations and, like, sort of natural, like, breathing and pauses and, like, laughter and things like that. But yeah, in generating, like, the text, we also have to sort of give signals on, like, where those things maybe would make sense.Alessio [00:26:45]: And on the input side, instead of having a transcript versus having the audio, like, can you take some of the emotions out of it, too? If I'm giving, like, for example, when we did the recaps of our podcast, we can either give audio of the pod or we can give a diarized transcription of it. But, like, the transcription doesn't have some of the, you know, voice kind of, like, things.Raiza [00:27:05]: Yeah, yeah.Alessio [00:27:05]: Do you reconstruct that when people upload audio or how does that work?Raiza [00:27:09]: So when you upload audio today, we just transcribe it. So it is quite lossy in the sense that, like, we don't transcribe, like, the emotion from that as a source. But when you do upload a text file and it has a lot of, like, that annotation, I think that there is some ability for it to be reused in, like, the audio output, right? But I think it will still contextualize it in the deep dive format. So I think that's something that's, like, particularly important is, like, hey, today we only have one format.Raiza [00:27:37]: It's deep dive.Raiza [00:27:38]: It's meant to be a pretty general overview and it is pretty peppy.Raiza [00:27:42]: It's just very upbeat.Raiza [00:27:43]: It's very enthusiastic, yeah.Raiza [00:27:45]: Yeah, yeah.Raiza [00:27:45]: Even if you had, like, a sad topic, I think they would find a way to be, like, silver lining, though.Raiza [00:27:50]: Really?Raiza [00:27:51]: Yeah.Raiza [00:27:51]: We're having a good chat.Raiza [00:27:54]: Yeah, that's awesome.Swyx [00:27:54]: One of the ways, many, many, many ways that deep dive went viral is people saying, like, if you want to feel good about yourself, just drop in your LinkedIn. Any other, like, favorite use cases that you saw from people discovering things in social media?Raiza [00:28:08]: I mean, there's so many funny ones and I love the funny ones.Raiza [00:28:11]: I think because I'm always relieved when I watch them. I'm like, haha, that was funny and not scary. It's great.Raiza [00:28:17]: There was another one that was interesting, which was a startup founder putting their landing page and being like, all right, let's test whether or not, like, the value prop is coming through. And I was like, wow, that's right.Raiza [00:28:26]: That's smart.Usama [00:28:27]: Yeah.Raiza [00:28:28]: And then I saw a couple of other people following up on that, too.Raiza [00:28:32]: Yeah.Swyx [00:28:32]: I put my about page in there and, like, yeah, if there are things that I'm not comfortable with, I should remove it. You know, so that it can pick it up. Right.Usama [00:28:39]: I think that the personal hype machine was, like, a pretty viral one. I think, like, people uploaded their dreams and, like, some people, like, keep sort of dream journals and it, like, would sort of comment on those and, like, it was therapeutic. I didn't see those.Raiza [00:28:54]: Those are good. I hear from Googlers all the time, especially because we launched it internally first. And I think we launched it during the, you know, the Q3 sort of, like, check-in cycle. So all Googlers have to write notes about, like, hey, you know, what'd you do in Q3? And what Googlers were doing is they would write, you know, whatever they accomplished in Q3 and then they would create an audio overview. And these people they didn't know would just ping me and be like, wow, I feel really good, like, going into a meeting with my manager.Raiza [00:29:25]: And I was like, good, good, good, good. You really did that, right?Usama [00:29:29]: I think another cool one is just, like, any Wikipedia article. Yeah. Like, you drop it in and it's just, like, suddenly, like, the best sort of summary overview.Raiza [00:29:38]: I think that's what Karpathy did, right? Like, he has now a Spotify channel called Histories of Mysteries, which is basically, like, he just took, like, interesting stuff from Wikipedia and made audio overviews out of it.Swyx [00:29:50]: Yeah, he became a podcaster overnight.Raiza [00:29:52]: Yeah.Raiza [00:29:53]: I'm here for it. I fully support him.Raiza [00:29:55]: I'm racking up the listens for him.Swyx [00:29:58]: Honestly, it's useful even without the audio. You know, I feel like the audio does add an element to it, but I always want, you know, paired audio and text. And it's just amazing to see what people are organically discovering. I feel like it's because you laid the groundwork with NotebookLM and then you came in and added the sort of TTS portion and made it so good, so human, which is weird. Like, it's this engineering process of humans. Oh, one thing I wanted to ask. Do you have evals?Raiza [00:30:23]: Yeah.Swyx [00:30:23]: Yes.Raiza [00:30:24]: What? Potatoes for chefs.Swyx [00:30:27]: What is that? What do you mean, potatoes?Raiza [00:30:29]: Oh, sorry.Raiza [00:30:29]: Sorry. We were joking with this, like, a couple of weeks ago. We were doing, like, side-by-sides. But, like, Raiza sent me the file and it was literally called Potatoes for Chefs. And I was like, you know, my job is really serious, but you have to laugh a little bit. Like, the title of the file is, like, Potatoes for Chefs.Swyx [00:30:47]: Is it like a training document for chefs?Usama [00:30:50]: It's just a side-by-side for, like, two different kind of audio transcripts.Swyx [00:30:54]: The question is really, like, as you iterate, the typical engineering advice is you establish some kind of test or benchmark. You're at, like, 30 percent. You want to get it up to 90, right?Raiza [00:31:05]: Yeah.Swyx [00:31:05]: What does that look like for making something sound human and interesting and voice?Usama [00:31:11]: We have the sort of formal eval process as well. But I think, like, for this particular project, we maybe took a slightly different route to begin with. Like, there was a lot of just within the team listening sessions. A lot of, like, sort of, like... Dogfooding.Raiza [00:31:23]: Yeah.Usama [00:31:23]: Like, I think the bar that we tried to get to before even starting formal evals with raters and everything was much higher than I think other projects would. Like, because that's, as you said, like, the traditional advice, right? Like, get that ASAP. Like, what are you looking to improve on? Whatever benchmark it is. So there was a lot of just, like, critical listening. And I think a lot of making sure that those improvements actually could go into the model. And, like, we're happy with that human element of it. And then eventually we had to obviously distill those down into an eval set. But, like, still there's, like, the team is just, like, a very, very, like, avid user of the product at all stages.Raiza [00:32:02]: I think you just have to be really opinionated.Raiza [00:32:05]: I think that sometimes, if you are, your intuition is just sharper and you can move a lot faster on the product.Raiza [00:32:12]: Because it's like, if you hold that bar high, right?Raiza [00:32:15]: Like, if you think about, like, the iterative cycle, it's like, hey, we could take, like, six months to ship this thing. To get it to, like, mid where we were. Or we could just, like, listen to this and be like, yeah, that's not it, right? And I don't need a rater to tell me that. That's my preference, right? And collectively, like, if I have two other people listen to it, they'll probably agree. And it's just kind of this step of, like, just keep improving it to the point where you're like, okay, now I think this is really impressive. And then, like, do evals, right? And then validate that.Swyx [00:32:43]: Was the sound model done and frozen before you started doing all this? Or are you also saying, hey, we need to improve the sound model as well? Both.Usama [00:32:51]: Yeah, we were making improvements on the audio and just, like, generating the transcript as well. I think another weird thing here was, like, we needed to be entertaining. And that's much harder to quantify than some of the other benchmarks that you can make for, like, you know, Sweebench or get better at this math.Swyx [00:33:10]: Do you just have people rate one to five or, you know, or just thumbs up and down?Usama [00:33:14]: For the formal rater evals, we have sort of like a Likert scale and, like, a bunch of different dimensions there. But we had to sort of break down what makes it entertaining into, like, a bunch of different factors. But I think the team stage of that was more critical. It was like, we need to make sure that, like, what is making it fun and engaging? Like, we dialed that as far as it goes. And while we're making other changes that are necessary, like, obviously, they shouldn't make stuff up or, you know, be insensitive.Raiza [00:33:41]: Hallucinations. Safety.Swyx [00:33:42]: Other safety things.Raiza [00:33:43]: Right.Swyx [00:33:43]: Like a bunch of safety stuff.Raiza [00:33:45]: Yeah, exactly.Usama [00:33:45]: So, like, with all of that and, like, also just, you know, following sort of a coherent narrative and structure is really important. But, like, with all of this, we really had to make sure that that central tenet of being entertaining and engaging and something you actually want to listen to. It just doesn't go away, which takes, like, a lot of just active listening time because you're closest to the prompts, the model and everything.Swyx [00:34:07]: I think sometimes the difficulty is because we're dealing with non-deterministic models, sometimes you just got a bad roll of the dice and it's always on the distribution that you could get something bad. Basically, how many do you, like, do ten runs at a time? And then how do you get rid of the non-determinism?Raiza [00:34:23]: Right.Usama [00:34:23]: Yeah, that's bad luck.Raiza [00:34:25]: Yeah.Swyx [00:34:25]: Yeah.Usama [00:34:26]: I mean, there still will be, like, bad audio overviews. There's, like, a bunch of them that happens. Do you mean for, like, the raider? For raiders, right?Swyx [00:34:34]: Like, what if that one person just got, like, a really bad rating? You actually had a great prompt, you actually had a great model, great weights, whatever. And you just, you had a bad output.Usama [00:34:42]: Like, and that's okay, right?Raiza [00:34:44]: I actually think, like, the way that these are constructed, if you think about, like, the different types of controls that the user has, right? Like, what can the user do today to affect it?Usama [00:34:54]: We push a button.Raiza [00:34:55]: You just push a button.Swyx [00:34:56]: I have tried to prompt engineer by changing the title. Yeah, yeah, yeah.Raiza [00:34:59]: Changing the title, people have found out.Raiza [00:35:02]: Yeah.Raiza [00:35:02]: The title of the notebook, people have found out. You can add show notes, right? You can get them to think, like, the show has changed. Someone changed the language of the output. Changing the language of the output. Like, those are less well-tested because we focused on, like, this one aspect. So it did change the way that we sort of think about quality as well, right? So it's like, quality is on the dimensions of entertainment, of course, like, consistency, groundedness. But in general, does it follow the structure of the deep dive? And I think when we talk about, like, non-determinism, it's like, well, as long as it follows, like, the structure of the deep dive, right? It sort of inherently meets all those other qualities. And so it makes it a little bit easier for us to ship something with confidence to the extent that it's like, I know it's going to make a deep dive. It's going to make a good deep dive. Whether or not the person likes it, I don't know. But as we expand to new formats, as we open up controls, I think that's where it gets really much harder. Even with the show notes, right? Like, people don't know what they're going to get when they do that. And we see that already where it's like, this is going to be a lot harder to validate in terms of quality, where now we'll get a greater distribution. Whereas I don't think we really got, like, varied distribution because of, like, that pre-process that Raiza was talking about. And also because of the way that we'd constrain, like, what were we measuring for? Literally, just like, is it a deep dive?Swyx [00:36:18]: And you determine what a deep dive is. Yeah. Everything needs a PM. Yeah, I have, this is very similar to something I've been thinking about for AI products in general. There's always like a chief tastemaker. And for Notebook LM, it seems like it's a combination of you and Steven.Raiza [00:36:31]: Well, okay.Raiza [00:36:32]: I want to take a step back.Swyx [00:36:33]: And Raiza, I mean, presumably for the voice stuff.Raiza [00:36:35]: Raiza's like the head chef, right? Of, like, deep dive, I think. Potatoes.Raiza [00:36:40]: Of potatoes.Raiza [00:36:41]: And I say this because I think even though we are already a very opinionated team, and Steven, for sure, very opinionated, I think of the audio generations, like, Raiza was the most opinionated, right? And we all, like, would say, like, hey, I remember, like, one of the first ones he sent me.Raiza [00:36:57]: I was like, oh, I feel like they should introduce themselves. I feel like they should say a title. But then, like, we would catch things, like, maybe they shouldn't say their names.Raiza [00:37:04]: Yeah, they don't say their names.Usama [00:37:05]: That was a Steven catch, like, not give them names.Raiza [00:37:08]: So stuff like that is, like, we all injected, like, a little bit of just, like, hey, here's, like, my take on, like, how a podcast should be, right? And I think, like, if you're a person who, like, regularly listens to podcasts, there's probably some collective preference there that's generic enough that you can standardize into, like, the deep dive format. But, yeah, it's the new formats where I think, like, oh, that's the next test. Yeah.Swyx [00:37:30]: I've tried to make a clone, by the way. Of course, everyone did. Yeah. Everyone in AI was like, oh, no, this is so easy. I'll just take a TTS model. Obviously, our models are not as good as yours, but I tried to inject a consistent character backstory, like, age, identity, where they work, where they went to school, what their hobbies are. Then it just, the models try to bring it in too much.Raiza [00:37:49]: Yeah.Swyx [00:37:49]: I don't know if you tried this.Raiza [00:37:51]: Yeah.Swyx [00:37:51]: So then I'm like, okay, like, how do I define a personality? But it doesn't keep coming up every single time. Yeah.Raiza [00:37:58]: I mean, we have, like, a really, really good, like, character designer on our team.Raiza [00:38:02]: What?Swyx [00:38:03]: Like a D&D person?Raiza [00:38:05]: Just to say, like, we, just like we had to be opinionated about the format, we had to be opinionated about who are those two people talking.Raiza [00:38:11]: Okay.Raiza [00:38:12]: Right.Raiza [00:38:12]: And then to the extent that, like, you can design the format, you should be able to design the people as well.Raiza [00:38:18]: Yeah.Swyx [00:38:18]: I would love, like, a, you know, like when you play Baldur's Gate, like, you roll, you roll like 17 on Charisma and like, it's like what race they are. I don't know.Raiza [00:38:27]: I recently, actually, I was just talking about character select screens.Raiza [00:38:30]: Yeah. I was like, I love that, right.Raiza [00:38:32]: And I was like, maybe there's something to be learned there because, like, people have fallen in love with the deep dive as a, as a format, as a technology, but also as just like those two personas.Raiza [00:38:44]: Now, when you hear a deep dive and you've heard them, you're like, I know those two.Raiza [00:38:48]: Right.Raiza [00:38:48]: And people, it's so funny when I, when people are trying to find out their names, like, it's a, it's a worthy task.Raiza [00:38:54]: It's a worthy goal.Raiza [00:38:55]: I know what you're doing. But the next step here is to sort of introduce, like, is this like what people want?Raiza [00:39:00]: People want to sort of edit the personas or do they just want more of them?Swyx [00:39:04]: I'm sure you're getting a lot of opinions and they all, they all conflict with each other. Before we move on, I have to ask, because we're kind of on this topic. How do you make audio engaging? Because it's useful, not just for deep dive, but also for us as podcasters. What is, what does engaging mean? If you could break it down for us, that'd be great.Usama [00:39:22]: I mean, I can try. Like, don't, don't claim to be an expert at all.Swyx [00:39:26]: So I'll give you some, like variation in tone and speed. You know, there's this sort of writing advice where, you know, this sentence is five words. This sentence is three, that kind of advice where you, where you vary things, you have excitement, you have laughter, all that stuff. But I'd be curious how else you break down.Usama [00:39:42]: So there's the basics, like obviously structure that can't be meandering, right? Like there needs to be sort of a, an ultimate goal that the voices are trying to get to, human or artificial. I think one thing we find often is if there's just too much agreement between people, like that's not fun to listen to. So there needs to be some sort of tension and build up, you know, withholding information. For example, like as you listen to a story unfold, like you're going to learn more and more about it. And audio that maybe becomes even more important because like you actually don't have the ability to just like skim to the end of something. You're driving or something like you're going to be hooked because like there's, and that's how like, that's how a lot of podcasts work. Like maybe not interviews necessarily, but a lot of true crime, a lot of entertainment in general. There's just like a gradual unrolling of information. And that also like sort of goes back to the content transformation aspect of it. Like maybe you are going from, let's say the Wikipedia article of like one of the History of Mysteries, maybe episodes. Like the Wikipedia article is going to state out the information very differently. It's like, here's what happened would probably be in the very first paragraph. And one approach we could have done is like maybe a person's just narrating that thing. And maybe that would work for like a certain audience. Or I guess that's how I would picture like a standard history lesson to unfold. But like, because we're trying to put it in this two-person dialogue format, like there, we inject like the fact that, you know, there's, you don't give everything at first. And then you set up like differing opinions of the same topic or the same, like maybe you seize on a topic and go deeper into it and then try to bring yourself back out of it and go back to the main narrative. So that's, that's mostly from like the setting up the script perspective. And then the audio, I was saying earlier, it's trying to be as close to just human speech as possible. I think was the, what we found success with so far.Raiza [00:41:40]: Yeah. Like with interjections, right?Raiza [00:41:41]: Like I think like when you listen to two people talk, there's a lot of like, yeah, yeah, right. And then there's like a lot of like that questioning, like, oh yeah, really?Raiza [00:41:49]: What did you think?Swyx [00:41:50]: I noticed that. That's great.Raiza [00:41:52]: Totally.Usama [00:41:54]: Exactly.Swyx [00:41:55]: My question is, do you pull in speech experts to do this? Or did you just come up with it yourselves? You can be like, okay, talk to a whole bunch of fiction writers to, to make things engaging or comedy writers or whatever, stand up comedy, right? They have to make audio engaging, but audio as well. Like there's professional fields of studying where people do this for a living, but us as AI engineers are just making this up as we go.Raiza [00:42:19]: I mean, it's a great idea, but you definitely didn't.Raiza [00:42:22]: Yeah.Swyx [00:42:24]: My guess is you didn't.Raiza [00:42:25]: Yeah.Swyx [00:42:26]: There's a, there's a certain field of authority that people have. They're like, oh, like you can't do this because you don't have any experience like making engaging audio. But that's what you literally did.Raiza [00:42:35]: Right.Usama [00:42:35]: I mean, I was literally chatting with someone at Google earlier today about how some people think that like you need a linguistics person in the room for like making a good chatbot. But that's not actually true because like this person went to school for linguistics. And according to him, he's an engineer now. According to him, like most of his classmates were not actually good at language. Like they knew how to analyze language and like sort of the mathematical patterns and rhythms and language. But that doesn't necessarily mean they were going to be eloquent at like while speaking or writing. So I think, yeah, a lot of we haven't invested in specialists in audio format yet, but maybe that would.Raiza [00:43:13]: I think it's like super interesting because I think there is like a very human question of like what makes something interesting. And there's like a very deep question of like what is it, right? Like what is the quality that we are all looking for? Is it does somebody have to be funny? Does something have to be entertaining? Does something have to be straight to the point? And I think when you try to distill that, this is the interesting thing I think about our experiment, about this particular launch is first, we only launched one format. And so we sort of had to squeeze everything we believed about what an interesting thing is into one package. And as a result of it, I think we learned it's like, hey, interacting with a chatbot is sort of novel at first, but it's not interesting, right? It's like humans are what makes interacting with chatbots interesting.Raiza [00:43:59]: It's like, ha ha ha, I'm going to try to trick it. It's like, that's interesting.Raiza [00:44:02]: Spell strawberry, right?Raiza [00:44:04]: This is like the fun that like people have with it. But like that's not the LLM being interesting.Raiza [00:44:08]: That's you just like kind of giving it your own flavor. But it's like, what does it mean to sort of flip it on its head and say, no, you be interesting now, right? Like you give the chatbot the opportunity to do it. And this is not a chatbot per se. It is like just the audio. And it's like the texture, I think, that really brings it to life. And it's like the things that we've described here, which is like, okay, now I have to like lead you down a path of information about like this commercialization deck.Raiza [00:44:36]: It's like, how do you do that?Raiza [00:44:38]: To be able to successfully do it, I do think that you need experts. I think we'll engage with experts like down the road, but I think it will have to be in the context of, well, what's the next thing we're building, right? It's like, what am I trying to change here? What do I fundamentally believe needs to be improved? And I think there's still like a lot more studying that we have to do in terms of like, well, what are people actually using this for? And we're just in such early days. Like it hasn't even been a month. Two, three weeks.Usama [00:45:05]: Three weeks.Raiza [00:45:06]: Yeah, yeah.Usama [00:45:07]: I think one other element to that is the fact that you're bringing your own sources to it. Like it's your stuff. Like, you know this somewhat well, or you care to know about this. So like that, I think, changed the equation on its head as well. It's like your sources and someone's telling you about it. So like you care about how that dynamic is, but you just care for it to be good enough to be entertaining. Because ultimately they're talking about your mortgage deed or whatever.Swyx [00:45:33]: So it's interesting just from the topic itself. Even taking out all the agreements and the hiding of the slow reveal. I mean, there's a baseline, maybe.Usama [00:45:42]: Like if it was like too drab. Like if someone was reading it off, like, you know, that's like the absolute worst.Raiza [00:45:46]: But like...Swyx [00:45:47]: Do you prompt for humor? That's a tough one, right?Raiza [00:45:51]: I think it's more of a generic way to bring humor out if possible. I think humor is actually one of the hardest things. Yeah.Raiza [00:46:00]: But I don't know if you saw...Raiza [00:46:00]: That is AGI.Swyx [00:46:01]: Humor is AGI.Raiza [00:46:02]: Yeah, but did you see the chicken one?Raiza [00:46:03]: No.Raiza [00:46:04]: Okay. If you haven't heard it... We'll splice it in here.Swyx [00:46:06]: Okay.Raiza [00:46:07]: Yeah.Raiza [00:46:07]: There is a video on Threads. I think it was by Martino Wong. And it's a PDF.Raiza [00:46:16]: Welcome to your deep dive for today. Oh, yeah. Get ready for a fun one. Buckle up. Because we are diving into... Chicken, chicken, chicken. Chicken, chicken. You got that right. By Doug Zonker. Now. And yes, you heard that title correctly. Titles. Our listener today submitted this paper. Yeah, they're going to need our help. And I can totally see why. Absolutely. It's dense. It's baffling. It's a lot. And it's packed with more chicken than a KFC buffet. What? That's hilarious.Raiza [00:46:48]: That's so funny. So it's like stuff like that, that's like truly delightful, truly surprising.Raiza [00:46:53]: But it's like we didn't tell it to be funny.Usama [00:46:55]: Humor is contextual also. Like super contextual is what we're realizing. So we're not prompting for humor, but we're prompting for maybe a lot of other things that are bringing out that humor.Alessio [00:47:04]: I think the thing about ad-generated content, if we look at YouTube, like we do videos on YouTube and it's like, you know, a lot of people like screaming in the thumbnails to get clicks. There's like everybody, there's kind of like a meta of like what you need to do to get clicks. But I think in your product, there's no actual creator on the other side investing the time. So you can actually generate a type of content that is maybe not universally appealing, you know, at a much, yeah, exactly. I think that's the most interesting thing. It's like, well, is there a way for like, take Mr.Raiza [00:47:36]: Beast, right?Alessio [00:47:36]: It's like Mr. Beast optimizes videos to reach the biggest audience and like the most clicks. But what if every video could be kind of like regenerated to be closer to your taste, you know, when you watch it?Raiza [00:47:48]: I think that's kind of the promise of AI that I think we are just like touching on, which is, I think every time I've gotten information from somebody, they have delivered it to me in their preferred method, right?Raiza [00:47:59]: Like if somebody gives me a PDF, it's a PDF.Raiza [00:48:01]: Somebody gives me a hundred slide deck, that is the format in which I'm going to read it. But I think we are now living in the era where transformations are really possible, which is, look, like I don't want to read your hundred slide deck, but I'll listen to a 16 minute audio overview on the drive home. And that, that I think is, is really novel. And that is, is paving the way in a way that like maybe we wanted, but didn'tRaiza [00:48:24]: expect.Raiza [00:48:25]: Where I also think you're listening to a lot of content that normally wouldn't have had content made about it. Like I watched this TikTok where this woman uploaded her diary from 2004.Raiza [00:48:36]: For sure, right?Raiza [00:48:36]: Like nobody was goin

NotebookLM from Google Labs has become the breakout viral AI product of the year. The feature that catapulted it to viral fame is Audio Overview, which generates eerily realistic two-host podcast audio from any input you upload—written doc, audio or video file, or even a PDF. But to describe NotebookLM as a “podcast generator” is to vastly undersell it. The real magic of the product is in offering multi-modal dimensions to explore your own content in new ways—with context that's surprisingly additive. 200-page training manuals become synthesized into digestible chapters, turned into a 10-minute podcast—or both—and shared with the sales team, just to cite one example. Raiza Martin and Jason Speilman join us to discuss how the magic happens, and what's next for source-grounded AI. Hosted by: Sonya Huang and Pat Grady, Sequoia Capital

Raiza Martin is a senior product manager for AI at Google Labs, where she leads the team behind NotebookLM, an AI-powered research tool that includes a delightful podcast-on-demand feature called “Audio Overviews.” NotebookLM started as a 20% project and has grown into a product that's spreading across social media and has a Discord server with over 60,000 users. Raiza previously worked on AI Test Kitchen and has a background in startups, payments, and ads. In our conversation, we discuss:• The origin story of NotebookLM• The future road map for NotebookLM• How Google Labs operates differently from the rest of Google• The development of the “Audio Overviews” feature• Key metrics and growth of NotebookLM• Stories about collaborating with author Steven Johnson• Navigating potential misuse of AI technology—Brought to you by:• Explo — Embed customer-facing analytics in your product• Sprig⁠⁠ — Build products for people, not data points• Sidebar — Accelerate your career by surrounding yourself with extraordinary peers—Find the transcript and show notes at: https://www.lennysnewsletter.com/p/googles-notebooklm-raiza-martin—Where to find Raiza Martin:• X: https://x.com/raiza_abubakar• LinkedIn: https://www.linkedin.com/in/whatsaraiza/—Where to find Lenny:• Newsletter: https://www.lennysnewsletter.com• X: https://twitter.com/lennysan• LinkedIn: https://www.linkedin.com/in/lennyrachitsky/—In this episode, we cover:(00:00) Introduction to NotebookLM(05:43) The genesis of NotebookLM(08:08) Innovative features and use cases(18:52) Building a startup culture within Google(24:28) Expanding user demographics(27:30) The product roadmap(32:18) Other use cases(36:11) Collaborating with Steven Johnson(42:49) Ensuring ethical AI(46:06) Future directions and user engagement—Production and marketing by https://penname.co/. For inquiries about sponsoring the podcast, email podcast@lennyrachitsky.com.—Lenny may be an investor in the companies discussed. Get full access to Lenny's Newsletter at www.lennysnewsletter.com/subscribe

This week, Casey reports back from a wild day at Meta Connect, discussing what's new with Meta's efforts in artificial intelligence, virtual reality headsets and the Holy Grail — augmented reality glasses. Then, Steven Johnson, a writer and editorial director at Google Labs, stops by to talk about the company's new hit NotebookLM, which uses A.I. to turn even boring PDFs, such as user manuals and Kevin's bank records, into chatty, disturbingly good podcasts. Finally, so much happened in tech news this week that we reached for the bucket hat in the latest installment of HatGPT! Guest:Steven Johnson, author and editorial director, NotebookLM Additional Reading: Meta Unveils New Smart Glasses and Headsets in Pursuit of the MetaverseA.I. Is Mastering Language. Should We Trust What It Says?OpenAI Executives Exit as C.E.O. Works to Make the Company For-Profit We want to hear from you. Email us at hardfork@nytimes.com. Find “Hard Fork” on YouTube and TikTok.

In this episode, I fly solo to discuss Google Vids. Google Vids is an addition to Google Workspace via Google Labs. It is a tool that allows users to create videos effortlessly through the use of Google's AI LLM, Google Gemini. I go through how to create a video, the editing process, and ideas for educators to implement this as a time saver. If you'd like to see it in action, click on the YouTube video below for a video tutorial. If your organization doesn't have Google Labs turned on, you might want to talk to your district Google Admin to see about getting this AI video creator turned on. Buen provecho! Google Vids Tutorial On YouTube: https://www.youtube.com/watch?v=aGkZZ3cR0eg Connect With Gabriel Carrillo EdTech Bites Website: https://edtechbites.com EdTech Bites Twitter: https://twitter.com/edtechbites EdTech Bites Instagram: https://instagram.com/edtechbites EdTech Bites Threads: https://www.threads.net/@edtechbites EdTech Bites Facebook Page: https://facebook.com/edtechbites EdTech Bites YouTube Channel: https://www.youtube.com/@edtechbites My Book Is Officially Out! My first book “Cooking Up Experiences In The Classroom: Focus On Experiences, Not Just Lessons” is officially out! A HUGE shout out to Lumio for helping sponsor this book. I'm super excited about this project. It's filled with ideas on how to make memorable experiences for your students. In addition, each chapter also lays out a specific recipe mentioned in that chapter along with a video tutorial on how to prepare that dish. Make sure you get your copy and cook up some experiences for your students and loved ones! Click Here To Purchase Your Copy On Amazon

با آرش از کار در توی ‌گوگل ‌لبز حرف زدیمIn this episode, I chat with Arash Sadr from Google Labs. We'll be exploring the exciting world of how humans and AI work together, getting the scoop on the latest in AI, what's coming next, and Arash's thoughts on how technology and people are teaming up in amazing ways. حامیان این قسمت | Sponsors خدمات رایانش ابری - لیاراhttps://liara.ir------فروشگاه اینترنتی شاواز | خرید لوازم آرایشی، بهداشتی، عطرhttps://shavaz.comفرصت‌های شغلی فعال در شاواز | Shavazhttps://bit.ly/shavaz16-----Arash Sadr | آرش صدرLinkedin https://www.linkedin.com/in/arashsadrTabaghe 16----------Castbox https://castbox.fm/channel/id3083907Spotify https://spoti.fi/2CiyRoHTwitter https://twitter.com/soh3ilInstagram https://www.instagram.com/tabaghe16/Everywhere else https://linktr.ee/tabaghe16#پادکست #طبقه۱۶ Hosted on Acast. See acast.com/privacy for more information.