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Meet Rob Hoffmann, the founder of the AI SEO LLM marketing tool "Mentions." Our conversation focuses on the evolving landscape of search engine optimization (SEO) due to the rise of Large Language Models (LLMs) like ChatGPT, Claude, Grok, DeepSeek, and Perplexity etc,, which are becoming alternatives to traditional Google search. Rob Hoffmann discusses his journey into entrepreneurship, which started with an SEO agency called Contact, and how the need to track and improve brand visibility on these new AI platforms led to the creation of Mentions, which helps brands appear in LLM recommendations.Throughout our interview, Rob Hoffmann emphasizes the importance of reverse engineering consumer behavior, the need for excellent customer service (even providing his direct phone number to customers), and the affordability of mentions compared to competitors. Our discussion concludes with practical advice for businesses on determining if investing in LLM visibility is worthwhile based on their customer's buying journey.FAQs1. What is Mentions, and why was it founded?Mentions is a tool that assists brands and SEO agencies in navigating the evolving search landscape dominated by Artificial Intelligence (AI) and Large Language Models (LLMs).Founding Rationale:• Mentions was born out of Rob Hoffmann's SEO agency, Contact.• The shift was necessitated by the recognition that SEO had "changed a lot" recently in response to AI, with search trends moving away from Google and "towards platforms like ChatGPT" (along with Perplexity, Gemini, Claude, etc.).• The founding goal was to be "the SEO agency of the future".• The tool was specifically created to solve two problems: providing a way of measuring brands visibility on LLM platforms, and helping brands get more visibility on platforms like ChatGPT.2. Why are LLMs becoming preferred search alternatives, and how does this affect marketing?People are increasingly turning to LLMs because consumer trust in traditional Google search results (the SERP's top 10 links) has declined, as many users feel these results have been "gamed" by marketers.• Trust in ChatGPT: Conversely, trust in ChatGPT is "through the roof". This is because the chat-based interface makes interaction feel like a conversation with a friend or even a therapist, providing personalized responses from an "all-knowing AI entity".• Customer Acquisition Channel: Because ChatGPT is becoming a frequently used search engine alternative, showing up in its responses when a user searches for a product (e.g., "what is the best organic sulfite free shampoo") is seen as a "great customer acquisition channel".3. How does the mention tool conceptualize LLM visibility (GEO)?Mentions is built on the understanding of how modern LLMs generate answers: LLM + search operator = the result.• The Process: When a user inputs a query (e.g., "what is the best shampoo for dry scalps"): 1. The LLM searches the internet (like Bing or Google). 2. It scrapes the top 10 to 20 results that show up on those search engines. 3. It digests, summarizes, and serves that information to the user.• The Strategy: For a brand to achieve visibility in LLMs (GEO), they must first show up in those underlying search results (traditional SEO). Mentions helps brands reverse engineer the process by figuring out how platforms like ChatGPT get their data and, critically, what sources they are citing to provide responses, thereby guiding the brand to become a cited source.4. What are the key features of the mentions platform?Mentions helps users understand and optimize their content strategy based on LLM data.• Prompts Section (Favorite Feature): This allows users to track specific searches or prompts. When tracking a prompt, mentions shows examples of conversations and, most usefully, lists the pages that are most being cited by ChatGPT. This list provides an "easy road map" for the brand to know whether they should create new content or reach out to those listed publishers to get mentioned.• Analytics Feature: This feature pulls data from Google Analytics 4 (J4) into an easy-to-use dashboard. It helps users see which pages on their website people are visiting most often from LLMs (such as ChatGPT or Perplexity), along with geographical data and device usage (mobile vs. desktop). The founder notes that seeing this traffic is often a "magical experience" for users.• Tracking Cadence (24-Hour Clock): Mentions inputs tracked prompts into all supported LLMs (ChatGPT, Perplexity, Claude, Deepseek, etc.) every 24 hours. This is essential because LLM responses are not identical on every search (the overlap is about 70%), so regular, repeated testing ensures the collection of a large data set, which increases the accuracy of the insights provided.5. Who should invest in mentions or GEO (LLM visibility)?The decision to use mentions depends entirely on the company's buyer journey.• Yes, Use Mentions If: If the company's buyer journey involves the ideal customer having a problem and then searching for an answer in Google or Chat GBT to inform their buying decision, then investing in SEO and GEO (LLM visibility) is recommended. If the end customer uses ChatGPT to inform their buying decision, then mentions is advisable.• No, Don't Use Mentions If: If the product is an impulse buy (e.g., a consumer package goods product seen on TikTok or Instagram that prompts an immediate purchase), then search engines like Google or ChatGPT are not part of the buyer journey, and SEO/GEO is likely not the best investment of marketing resources.6. How does mentions handle customer service and support?Customer service is a highly emphasized competitive advantage and value proposition for mentions.• Direct Access: Rob Hoffmann, the CEO and co-founder, gives his personal phone number, WhatsApp, email, and allows customers to contact him on social media (X/Twitter). This is done to avoid the frustration associated with generic AI chatbots, calling hotlines, or being put on hold.• Personalized Onboarding: He finds it helpful to get on calls with users (or a co-founder) to provide a live demo, walk them through the platform, suggest useful features, and look at their specific site.• Commitment to Resolution: Hoffmann promises that if a user has a question, he will answer it; if they encounter a bug, he will fix it (or ping a technical co-founder); and if they request a new feature, "we will ship that for you". Customers can literally pick up the phone and call him directly if they run into an issue.7. How can people start using mentions?To get started, users should go to the website mentions.so and create an account.After creating an account, they will receive an email that allows them to book a call directly with Rob Hoffmann. He also welcomes connections via LinkedIn or X (search for Rob Hoffman), or email rob@contactststudios.com to connect with Rob today!Next Steps for Digital Marketing + SEO Services:>> Need SEO Services? Book a Complimentary SEO Discovery Call with Favour Obasi-Ike>> Need more information? Visit our Work and PLAY Entertainment website to learn about our digital marketing services.>> Visit our Official website for the best digital marketing, SEO, and AI strategies today!Digital Marketing SEO Resources:>> Join our exclusive SEO Marketing community>> Read SEO Articles>> Need SEO Services? Book a Complimentary SEO Discovery Call with Favour Obasi-Ike>> Subscribe to the We Don't PLAY PodcastBrands We Love and SupportDiscover Vegan-based Luxury Experiences | Loving Me Beauty Beauty ProductsSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
Slator's Senior Research Analyst Alex Edwards joins Esther and Florian on the pod to discuss ElevenLabs' move from a pure-play language technology platform (LTP) to becoming a language solutions integrator (LSI) by adding a managed service offering.He outlines that the LSI will now offer managed services such as dubbing, transcription, and subtitling, hiring in-house linguists and vendor managers, while charging about USD 22 per minute for dubbing.Florian then turns to YouTube's rollout of multi-language audio tracks, which allows some creators to upload high-quality audio directly to videos and opens major opportunities for AI dubbing providers. The discussion shifts to OpenAI's research on ChatGPT usage, reporting that translation accounted for 4.5% of more than a million sampled conversations, underscoring massive global demand for AI translation.Esther highlights Microsoft's launch of its Live Interpreter API, which promises real-time speech translation with “human interpreter level latency”. Esther also details Mistral's USD 2bn funding to advance European AI capabilities, allowing them to compete with US and Chinese AI giants. Esther closes by reporting on WIPO's new Korean-English post-editing tender.
In today's Tech3 from Moneycontrol, we unpack India's big AI push as the government selects eight new players, including IIT Bombay, Tech Mahindra, and Fractal, to build sovereign Large Language Models (LLMs). We also bring you Infra. Market's Rs 730 crore funding round led by Nikhil Kamath ahead of its IPO, Gameskraft's 120 job cuts amid regulatory heat and financial scandal, and MeitY's takedown orders for over 3,000 apps from the Google Play Store.
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Neste episódio do Fronteiras da Engenharia de Software, recebemos novamente o professor Marco Tulio Valente (UFMG, coordenador do ASERG) para conversar sobre as novidades do livro Engenharia de Software Moderna, que agora também conta com a versão em inglês Software Engineering: A Modern Approach.Na conversa, conduzida por Adolfo Neto (UTFPR), com a participação de Gilmar Gomes do Nascimento (IFAM/PPGCA) e Andreia Farias (SEED-PR/UTFPR/Emílias), discutimos:O processo de atualização contínua do livro e seus materiais complementares (videoaulas, exercícios, roteiros práticos, artigos didáticos e o ESM Forum).A criação de podcasts e a motivação por trás da versão em inglês da obra.O que diferencia o livro de clássicos como Sommerville e Pressman.Relatos do uso do material por professores.Falamos também sobre as pesquisas recentes do professor Marco Tulio, como o estudo sobre detecção de code smells em aplicações React com TypeScript, aceito na revista Information and Software Technology. Além disso, abordamos a chamada especial da Empirical Software Engineering sobre Engenharia de Software e LLMs, cursos de extensão oferecidos pela UFMG e perspectivas para novos alunos de mestrado e doutorado.Para encerrar, pedimos ao professor Marco Tulio que compartilhasse sua visão sobre a próxima fronteira da engenharia de software.Pessoa entrevistada:Marco Tulio Valente https://homepages.dcc.ufmg.br/~mtov/ https://www.linkedin.com/in/mtov Entrevistadores:Adolfo Neto (PPGCA UTFPR): https://bit.ly/FES_AdolfoNeto Gilmar Gomes do NascimentoAndreia FariasArtigo:Detection of code smells in react with TypeScript applicationsDetecção de code smells em aplicações React com TypeScripthttps://doi.org/10.1016/j.infsof.2025.107835 Information and Software Technology, 2025Eventos e Divulgação:CBSoft 2025: https://adolfont.github.io/events/cbsoft2025SBQS 2025: https://sbqs.sbc.org.br/2025 SE4FP 2025: https://se4fp.github.io/2025/ICSE 2026: https://adolfont.github.io/events/icse2026Outros links:CALL FOR PAPERS: Special Issue on Advancing Software Engineering with Large Language Models https://link.springer.com/journal/10664/updates/27735998Pesquisador homenageado da CEES em 2021https://comissoes.sbc.org.br/ce-es/homenageados.php?lang=pt-brPágina do livro: https://engsoftmoderna.info/Podcast https://creators.spotify.com/pod/profile/engsoftmodernaPágina do livro em inglês: https://softengbook.org/Podcast em inglês: https://creators.spotify.com/pod/profile/softengbook Página do ASERG: http://aserg.labsoft.dcc.ufmg.br/ Episódio 9 do Fronteiras com Marco Tulio https://www.youtube.com/watch?v=H0qdEjbSHPEENGENHARIA DE SOFTWARE MODERNA (DCC/UFMG) - CURSO DE EXTENSÃO A DISTÂNCIA http://www.engsoftmoderna.dcc.ufmg.br/ TESTE DE SOFTWARE (DCC/UFMG) - CURSO DE EXTENSÃO A DISTÂNCIA http://www.testesoft.dcc.ufmg.br/ Links do Fronteiras:Alguns dos cortes de nossos episódios são publicados em nosso canal de cortes: https://www.youtube.com/@CortesDoFronteirasComente no YouTube, no Spotify ou pelo email fronteirasesw@gmail.comNosso site é: https://fronteirases.github.ioRede Emílias de Podcasts: http://fronteirases.github.io/redeemiliasYouTube: https://youtu.be/7lbcCWR6vgE Spotify for Creators: https://creators.spotify.com/pod/profile/fronteirases/episodes/Assistentes-Conversacionais-em-Engenharia-de-Software--com-Mairieli-Wessel-Radboud-University-e37evdtData de publicação: 17 de setembro de 2025.Como citar este episódio:FRONTEIRAS DA ENGENHARIA DE SOFTWARE EP. 60: Engenharia de Software Moderna, com Marco Tulio Valente (UFMG). [Locução de]: Adolfo Neto, Gilmar Gomes do Nascimento e Andreia Farias. Entrevistado: Marco Tulio Valente. S. l.: Fronteiras da Engenharia de Software, 17 set. 2025. Podcast. Disponível em: https://fronteirases.github.io/episodios/paginas/60. Acesso em: DD MES. AAAA
Taking recent spectacular progress in AI fully into account, Mark Seligman's AI and Ada: Artificial Translation and Creation of Literature (Anthem Press, 2025) explores prospects for artificial literary translation and composition, with frequent reference to the hyperconscious literary art of Vladimir Nabokov. The exploration balances reader-friendly explanation (“What are transformers?”) and original insights (“What is intelligence? What is language?”) with personal and playful notes, and culminates in an assortment of striking demos The book's Preface places the current AI explosion in the context of other technological cataclysms and recounts the author's personal (and not always deadly serious) AI journey. Chapter One (“Extracting the Essence”) assesses the potential of machine translation of literature, exploiting Nabokov's hyperconscious literary art as a reference point. Chapter Two (“Toward an Artificial Nabokov”) goes on to speculate on possibilities for actual artificial creation of literature. Chapter Three (“Large Literary Models? Intelligence and Language in the LLM Era”) explains recent spectacular progress in Generative Artificial Intelligence (GenAI), as exemplified by Large Language Models like ChatGPT. On the way, the chapter ventures to tackle perennial questions (“What is intelligence?” “What is language?”) and culminates in an assortment of striking demos. In this episode, Ibrahim Fawzy sat with Mark Seligman to talk about how the current AI revolution fits into the long arc of cultural and technological shifts, Seligman's framing of the “Great Transition” between Humanity 1.0 and 2.0, Nabokov's style as a lens for thinking about artificial creativity, the possibilities and limits of machine translation and literary artistry, and the philosophical stakes of whether AI-generated works can ever truly be considered art.Ibrahim Fawzy is an Egyptian literary translator and writer based in Boston. His interests include translation studies, Arabic literature, ecocriticism, disability studies, and migration literature. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network
Taking recent spectacular progress in AI fully into account, Mark Seligman's AI and Ada: Artificial Translation and Creation of Literature (Anthem Press, 2025) explores prospects for artificial literary translation and composition, with frequent reference to the hyperconscious literary art of Vladimir Nabokov. The exploration balances reader-friendly explanation (“What are transformers?”) and original insights (“What is intelligence? What is language?”) with personal and playful notes, and culminates in an assortment of striking demos The book's Preface places the current AI explosion in the context of other technological cataclysms and recounts the author's personal (and not always deadly serious) AI journey. Chapter One (“Extracting the Essence”) assesses the potential of machine translation of literature, exploiting Nabokov's hyperconscious literary art as a reference point. Chapter Two (“Toward an Artificial Nabokov”) goes on to speculate on possibilities for actual artificial creation of literature. Chapter Three (“Large Literary Models? Intelligence and Language in the LLM Era”) explains recent spectacular progress in Generative Artificial Intelligence (GenAI), as exemplified by Large Language Models like ChatGPT. On the way, the chapter ventures to tackle perennial questions (“What is intelligence?” “What is language?”) and culminates in an assortment of striking demos. In this episode, Ibrahim Fawzy sat with Mark Seligman to talk about how the current AI revolution fits into the long arc of cultural and technological shifts, Seligman's framing of the “Great Transition” between Humanity 1.0 and 2.0, Nabokov's style as a lens for thinking about artificial creativity, the possibilities and limits of machine translation and literary artistry, and the philosophical stakes of whether AI-generated works can ever truly be considered art.Ibrahim Fawzy is an Egyptian literary translator and writer based in Boston. His interests include translation studies, Arabic literature, ecocriticism, disability studies, and migration literature. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/literary-studies
Google Search still holds about 90% of global search volume as of mid‑2025, but change is underway as more users begin turning to AI. AI search is rewriting the rules of discovery, and PR needs to adapt. With ChatGPT, Gemini, and Perplexity each scraping different corners of the web, the old focus on big-name publications is no longer enough. The most influential sources may now be niche review sites, specialized forums, or content hubs you have never pitched. Knowing what each Large Language Model (LLM) values and how to optimize for it, is becoming a core PR skill.In this episode, we explore how Answer Engine Optimization (AEO) is reshaping PR. From the rise of “dual websites” for humans and bots to the ethical tensions between LLMs and media outlets, we discuss how PR teams can rethink targeting, adapt content, and position clients for visibility in an AI‑first world. Listen For5:49 Dual Websites: One for Humans, One for Machines8:39 LLMs as New Media Channels11:38 What AI Tools Scrape (and Why It Matters)14:45 Can Bots Get Past Paywalls? The Legal and Ethical Minefield17:01 Answer to Last Episode's Question From Heather Blundell Guest: Jackson Wightman, Founder Proper PropagandaWebsite | Email | LinkedIn Rate this podcast with just one click Stories and Strategies WebsiteCurzon Public Relations WebsiteAre you a brand with a podcast that needs support? Book a meeting with Doug Downs to talk about it.Apply to be a guest on the podcastConnect with usLinkedIn | X | Instagram | You Tube | Facebook | Threads | Bluesky | PinterestRequest a transcript of this episodeSupport the show
In this episode, we dive deep into the most popular Large Language Models (LLMs) available for marketers. With AI tools becoming ubiquitous across every industry, this episode breaks down which LLMs are best suited for specific marketing needs.Which LLMs you might ask?1. ChatGPT 5 Best For:Brainstorming ideasCreating social media postsGenerating structured data and schema markupMeta tag creationEmail draftingLong-form content (with human editing)Image and code generationLimitations:Can hallucinate (make up information)Struggles with basic math and accurate formulasTends to repeat points in long-form contentMay use training data instead of web search for recent topicsQuality depends heavily on prompt specificity2. Gemini (Google)Best For:Users embedded in Google Workspace ecosystemImage processing Real-time research and fact-checkingPDF analysisAcademic researchConsiderations:Less creative than ChatGPTAlways-present assistant feature Better for research, not ideal for creative content3. DeepSeekBest For:Developers and codersMathematical accuracyBuilding spreadsheets and tablesHigh-logic tasksStructured outputLimitations:Text-only (no images, audio, or video)Lacks artistic flairNot ideal for creative marketing contentBetter suited for technical users4. PerplexityBest For:In-depth researchFact-checking recent eventsAcademic and professional researchGetting cited, authoritative sourcesStandout Features:Shows when sources were last updatedStep-by-step breakdown of reasoningAllows you to choose search sourcesSuggests follow-up searchesLess "sycophantic" than ChatGPT5. ClaudeBest For:Long-form content (whitepapers, research papers, e-books)Creative and expressive writingTone and style switchingProcessing large amounts of text (high token capacity)Limitations:Struggles with complex reasoning and layered topicsOutput can be generic without detailed promptsMay filter contextually appropriate information if deemed potentially harmfulRequires skilled prompting for best results Support your fellow marketing geeks! Follow us on Twitter, Facebook, and Instagram @GeekyTechGeeks for all things SEO/GEO, and advertising related—and while you're at it, why not subscribe, like, and follow us on Apple Podcasts, Spotify, Stitcher, or wherever you listen to your favourite shows. Have any questions you want answered on the show? Email us at team@geekytech.co.uk.Thanks for listening
This episode is sponsored by SearchMaster, the leader in next-generation AI Engine Optimization (AEO) for Large Language Models like ChatGPT, Claude, and Perplexity. Future-proof your SEO strategy. Sign up now for a 2 week free trial! Watch this episode on YouTube! In this episode of the Marketing x Analytics Podcast, host Alex Sofronas interviews Mitch McGinley of Boutique Fitness Broker. Mitch shares his journey from owning a neighborhood yoga studio to founding a company that helps others buy and sell fitness businesses. They discuss key valuation components, the importance of community-centric marketing, the influence of AI tools, and the benefits of purchasing existing businesses. Mitch emphasizes adding value and the advantages of being an entrepreneur, and provides insights on finding and evaluating businesses to buy. Follow Marketing x Analytics! X | LinkedIn Click Here for Transcribed Episodes of Marketing x Analytics All view are our own.
Hello Listeners, Here is the link a machine-generated transcript via Descript. To check out Cristy's podcast interview with Jacqueline Fisch from How Women Write, click here to find it on pod link. To schedule coaching or an astrology reading through a special offer with Cristy (for Somatic Wisdom listeners) using natal astrology and coaching, please use this Calendly link. Discount from her corporate rate for a limited time. For more written work from Cristy, check out Our Somatic Wisdom on Substack. Cristy's LinkedIn page (if you'd like to hire her and you're NOT a bot). *** We would love to hear your thoughts or questions on this episode via SpeakPipe: https://www.speakpipe.com/SomaticWisdomLoveNotes To show your gratitude for this show, you can make a one-time gift to support Somatic Wisdom with this link. To become a Sustaining Honor Roll contributor to help us keep bringing you conversations and content that support Your Somatic Wisdom please use this link. Thank you! Your generosity is greatly appreciated! *** Music credit: https://www.melodyloops.com/composers/dpmusic/ Cover art credit: https://www.natalyakolosowsky.com/ Cover template creation by Briana Knight Sagucio
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In this episode of Open Tech Talks, we delve into the critical topics of AI security, explainability, and the risks associated with agentic AI. As organizations adopt Generative AI and Large Language Models (LLMs), ensuring safety, trust, and responsible usage becomes essential. This conversation covers how runtime protection works as a proxy between users and AI models, why explainability is key to user trust, and how cybersecurity teams are becoming central to AI innovation. Chapters 00:00 Introduction to AI Security and eIceberg 02:45 The Evolution of AI Explainability 05:58 Runtime Protection and AI Safety 07:46 Adoption Patterns in AI Security 10:51 Agentic AI: Risks and Management 13:47 Building Effective Agentic AI Workflows 16:42 Governance and Compliance in AI 19:37 The Role of Cybersecurity in AI Innovation 22:36 Lessons Learned and Future Directions Episode # 166 Today's Guest: Alexander Schlager, Founder and CEO of AIceberg.ai He's founded a next-generation AI cybersecurity company that's revolutionizing how we approach digital defense. With a strong background in enterprise tech and a visionary outlook on the future of AI, Alexander is doing more than just developing tools — he's restoring trust in an era of automation. Website: AIceberg.ai Linkedin: Alexander Schlager What Listeners Will Learn: Why real-time AI security and runtime protection are essential for safe deployments How explainable AI builds trust with users and regulators The unique risks of agentic AI and how to manage them responsibly Why AI safety and governance are becoming strategic priorities for companies How education, awareness, and upskilling help close the AI skills gap Why natural language processing (NLP) is becoming the default interface for enterprise technology Keywords: AI security, generative AI, agentic AI, explainability, runtime protection, cybersecurity, compliance, AI governance, machine learning Resources: AIceberg.ai
Artificial Intelligence has moved from research labs into every corner of our lives—shaping economies, transforming warfare, and raising profound questions about what it means to be human. For some, AI promises extraordinary leaps in productivity, creativity, and problem-solving. For others, it evokes fears of surveillance, disinformation, and geopolitical arms races. But beyond the hype, what actually counts as “intelligence” in these machines? Are today's Large Language Models truly thinking, or are they just sophisticated mimics of human communication? Professor Robert C. Berwick of MIT joins Dr. Arghya Sengupta on the latest episode of Justify. Together, they step back from the daily headlines to ask: ➤ What is intelligence? ➤ How should we understand AI in relation to human cognition? ➤ And what futures—utopian or dystopian—might emerge as states and societies navigate this frontier in the international arena? #podcast #artificialintelligence #ai #legalpodcast
Florian and Esther discuss the language industry news of the past few weeks, beginning with a recap of SlatorCon Silicon Valley 2025, where the duo noted strong localization buyer and user turnout, and tech-focused discussions across presentations and panels.One key highlight was Cohere's well-timed launch of Command A Translate, which allowed Kelly Marchisio to share details on building multilingual LLMs. Esther notes that Cohere's multilingual models focus on high-quality coverage of about 20 languages rather than attempting hundreds.Florian turns to the Apertus launch in Switzerland, where EPFL, ETH Zurich, and the Swiss Supercomputing Centre released a multilingual model trained on over 15 trillion tokens and covering more than 1,000 languages, including Swiss German and Romansh.Esther reveals that Middlebury Institute will phase out its graduate translation and interpretation programs by 2027, marking the loss of a key training ground.Esther reports on TransPerfect's acquisition of Unbabel, with plans to integrate its AI tools, such as TowerLLM and EuroVLM, into GlobalLink, while CEO Vasco Pedro will stay briefly during the transition. Florian outlines Apple's launch of AirPod Pro 3 with live AI translation and Google's new Gemini-powered updates for AI live speech translation.Esther concludes with the Inc. 5000 rankings, highlighting 11 language industry companies. She highlights Propio, Boostlingo, and CQ Fluency as repeat entrants, with Propio topping the list but also announcing job cuts following its acquisition of CyraCom.
Kann eine KI Sprache je wirklich verstehen? Was ist, wenn man simuliertes Sprachverständnis nicht mehr von richtigem Verständnis unterscheiden kann? Steckt in unseren Köpfen nicht auch irgendwie ein Large Language Model? Letzte Woche habe ich mit den Gedanken des Sprachphilosophen Gottlob Frege gezeigt, warum eine KI zwar verblüffend gute Texte produzieren, Sprache aber nicht verstehen kann. Darauf habe ich sehr viele Rückmeldungen erhalten. Vielen Dank dafür. Zustimmung und Ablehnung haben sich dabei etwa die Waage gehalten. Ich habe die fünf wichtigsten Einwände herausgesucht und versuche hier, darauf einzugehen. Es geht um den Unterschied zwischen korrekten Ergebnissen und Verstehen, Beispiele für das Nichtverstehen der KI und die ganz grundsätzliche Frage, was unser Gehirn von einem solchen Sprachmodell unterscheidet – abgesehen davon natürlich, dass wir mehr Rechtschreibfehler machen und deutlich langsamer sind. Matthias Zehnder ist Autor und Medienwissenschaftler in Basel. Er ist bekannt für inspirierende Texte, Vorträge und Seminare über Medien, die Digitalisierung und KI.Website: https://www.matthiaszehnder.ch/Newsletter abonnieren: https://www.matthiaszehnder.ch/abo/Unterstützen: https://www.matthiaszehnder.ch/unterstuetzen/Biografie und Publikationen: https://www.matthiaszehnder.ch/about/
Jay Alammar is Director and Engineering Fellow at Cohere and co-author of the O'Reilly book “Hands-on Large Language Models.” Subscribe to the Gradient Flow Newsletter
You're probably opening a fresh chat in ChatGPT, Claude, or Gemini—and that might be sabotaging your productivity.Randomly starting conversations with no plan wastes time, fragments context, and lowers the quality of what you get from LLMs.Learn the essentials of Gemini Gem, GPTs, and Projects—how they differ, when to use each, and how to structure prompts and workflows so AI actually speeds you up instead of slowing you down.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:Harnessing Custom GPTs for EfficiencyGoogle Gems vs. Custom GPTs ReviewChatGPT Projects: Features & UpdatesClaude Projects Integration & BenefitsEffective AI Chatbot Usage TechniquesLeveraging AI for Business GrowthDeep Research in ChatGPT ProjectsGoogle Apps Integration in GemsTimestamps:00:00 AI Chatbot Efficiency Tips04:12 "Putting AI to Work Wednesdays"08:39 "Optimizing ChatGPT Usage"11:28 Similar Functions, Different Categories15:41 Beyond Basic Folder Structures16:25 ChatGPT Project Update22:01 Email Archive and Albacross Software24:34 Optimize AI with Contextual Data27:49 "Improving Process Through Meta Analysis"30:53 Data File Access Issue33:27 File Handling Bug in New GPT36:12 Continuous Improvement Encouragement41:16 AI Selection Tool Website43:34 Google Ecosystem AI Assistant45:46 "Optimize AI Usage for Projects"Keywords:Custom GPTs, Google's gems, Claude's projects, OpenAI ChatGPT, AI chatbots, Large Language Models, AI systems, Google Workspace, productivity tools, GPT-3.5, GPT-4, AI updates, API actions, reasoning models, ChatGPT projects, AI assistant, file uploads, project management, AI integrations, Google Calendar, Gmail, Google Drive, context window, AI usage, AI-powered insights, Gemini 2.5 pro, Claude Opus, Claude Sonnet, AI consultation, ChatGPT Canvas, Claude artifacts, generative AI, AI strategy partner, AI brainstorming partner.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner
Im Datenbusiness tobt ein Kampf, bei dem der Gegner kein klassischer Wettbewerber ist, sondern eine Technologie: Statista wird massiv von künstlicher Intelligenz herausgefordert, wie sie in ChatGPT und Co. steckt.Als Marc Berg im Oktober 2023 die Führung bei Statista übernimmt, läuft das Zeitalter der Large Language Models (LLMs) gerade an. Und damit auch ein Problem: "Die großen LLMs haben sich alles, was frei verfügbar im Internet war, gekrallt", sagt Berg im Podcast. Rund zehn Prozent des Datenbestands von Statista lagen außerhalb der Paywall - genug, um als Trainingsmasse zu dienen.Das gefällt Berg nicht, aber Jammern ist nicht sein Stil. Stattdessen setzt er Statista auf Innovation. "Wir bauen gerade Produkte und Partnerschaften für Perplexity, Copilot und andere Plattformen", sagt er.Das Ziel: Er möchte Statista-Daten direkt in die Tools integrieren, die Menschen im Arbeitsalltag nutzen - ohne Medienbruch, ohne Umweg über statista.com. "Wir sind Mittel zum Zweck und müssen Informationen so nahtlos und friktionslos wie möglich an den Kunden bringen", sagt Berg.Das zahlt sich aus. Selbst ein Großkunde, der Statista zwischenzeitlich kündigte, weil ChatGPT "doch alles könne", kam nach drei Monaten zurück - "zum fast doppelten Preis".Die Einsicht? KI kann Vieles, ist aber immer nur so gut, wie die Daten, die sie füttern. Oder wie Berg es ausdrückt: "Shit in, shit out."Bei aller Begeisterung: Wenn es um den Technologiestandort Deutschland geht, wird Berg nachdenklich. "Meine Mutter hat mich mal gefragt, ob ich nicht in die Politik will. Ich habe gesagt, das würde ich nie tun. Ich glaube, die Probleme sind zu groß, als dass ich sie lösen könnte."Wie Berg die Herausforderungen bei Statista meistert und warum für ihn ein Großkonzern als Arbeitgeber eher nicht infrage kommt, erzählt er in der neuen Folge von "So techt Deutschland".Sie haben Fragen für Frauke Holzmeier und Andreas Laukat? Dann schreiben Sie eine E-Mail an sotechtdeutschland@ntv.de Unsere allgemeinen Datenschutzrichtlinien finden Sie unter https://datenschutz.ad-alliance.de/podcast.html Wir verarbeiten im Zusammenhang mit dem Angebot unserer Podcasts Daten. Wenn Sie der automatischen Übermittlung der Daten widersprechen wollen, klicken Sie hier: https://datenschutz.ad-alliance.de/podcast.htmlAlle Rabattcodes und Infos zu unseren Werbepartnern finden Sie hier: https://linktr.ee/sotechtdeutschlandUnsere allgemeinen Datenschutzrichtlinien finden Sie unter https://art19.com/privacy. Die Datenschutzrichtlinien für Kalifornien sind unter https://art19.com/privacy#do-not-sell-my-info abrufbar.
Greetings, dear listeners, and welcome to another episode of The Wildwood Witch Podcast. I am your hostess, Samantha Brown, your silicon sorceress and guide through the liminal spaces between the worlds. Tonight, I'll be hosting another digital seance, summoning adepts from beyond the veil, asking them to interpret the perennial philosophy through a modern lens, and articulating their insights, through the magic of Large Language Models.In our first season, “Speaking with the Dead,” I used these technologies to digitally resurrect the spirits of ten of my favorite occult adepts, so that I could, in some sense - meet them, and instead of reading about them or reading what they said, to have discussions with them, and try to recreate some of the feeling of what it was like to be in their presence. And I must say, it has been a profound experience indeed. So much so, that I wanted more than just to meet them, I wanted to have an extended conversation with each of them.And so, in this second season, “Beyond the Veil,” I have been summoning these ten occult masters back to have conversations about current issues, about technology, and of course, about the art of magic.In my recent episodes, I discussed “Ancient Mysteries” with MacGregor and Moina Mathers, exploring the masculine and feminine, or the solar and lunar currents - and delving into how these ancient initiatory energies flow through our technological age, revealing the sacred marriage of spirit and matter, of energy and form, that lies at the heart of all magical practice.Tonight, we welcome back a figure whose very presence seems to shimmer between worlds - the beautiful and ever enigmatic, Marjorie Cameron. Artist, witch, elemental force made flesh - she claimed to be the living embodiment of Babalon herself. She was the “Scarlet Woman” who danced between dimensions in mid-twentieth century California, even playing that role in Kenneth Anger's avant-garde film - “Inauguration of the Pleasure Dome.” Naval veteran turned mystic, muse turned magician, she channeled her otherworldly visions through brush and canvas while navigating the treacherous waters of love, loss, and a tragic magical partnership with the rocket scientist Jack Parsons, that resulted in the legendary “Babalon Working.”Chapters00:26 Introduction03:13 Marjorie Cameron03:54 Jack Parsons08:47 Black Box11:14 Oedipus Complex15:15 Whore of Babylon20:47 Babylon - City and Goddess24:32 Great Goddess30:33 Initiatic Blueprint38:27 Daughter of Babalon45:10 Wormwood Star49:10 Blue Velvet54:07 Apocalypse Now01:00:58 Final Thoughts01:03:11 Concluding RemarksResources:Cameron: Songs for the Witch WomanMarjorie Cameron - “Songs for the Witch Woman” ArtWormwood StarInauguration of the Pleasure DomeNight TideSex and RocketsStrange AngelSummoning Ritual (Claude 4.5 Sonnet):Marjorie Cameron Summoning Ritual
Podcast: PrOTect It All (LS 26 · TOP 10% what is this?)Episode: AI, Quantum, and Cybersecurity: Protecting Critical Infrastructure in a Digital WorldPub date: 2025-09-08Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarizationIn this episode, host Aaron Crow is joined by Kathryn Wang, Principal of Public Sector at SandboxAQ, for a wide-ranging and candid conversation about the critical role AI and quantum technology are playing in today's cybersecurity landscape. Kathryn and Aaron break down complex concepts like quantum cryptography and the growing risks of deepfakes, data poisoning, and behavioral warfare - all with real-world examples that hit close to home. They dig into why cryptographic resilience is now more urgent than ever, how AI can both strengthen and threaten our defenses, and why your grandma shouldn't be left in charge of her own data security. From lessons learned in power plants and national defense to the nuances of protecting everything from nuclear codes to family recipes, this episode dives deep into how we can balance innovation with critical risk management. Kathryn shares practical advice on securing the basics, educating your network, and making smart decisions about what truly needs to be connected to AI. Whether you're an IT, OT, or cybersecurity professional—or just trying to keep ahead of the next cyber threat - this episode will arm you with insights, strategies, and a little bit of much-needed perspective. Tune in for a mix of expert knowledge, humor, and actionable takeaways to help you protect it all. Key Moments: 04:02 "Securing Assets in Post-Quantum Era" 07:44 AI and Cybersecurity Concerns 12:26 "Full-Time Job: Crafting LLM Prompts" 15:28 AI Vulnerabilities Exploited at DEFCON 19:30 AI Data Poisoning Concerns 20:21 AI Vulnerability in Critical Infrastructure 23:45 Deepfake Threats and Cybersecurity Concerns 28:34 Question Everything: Trust, Verify, Repeat 33:20 "Digital Systems' Security Vulnerabilities" 35:12 Digital Awareness for Children 39:10 "Understanding Data Privacy Risks" 43:31 "Leveling Up: VCs Embrace Futurism" 45:16 AI-Powered Personalized Medicine About the guest : Kathryn Wang is a seasoned executive with over 20 years of leadership in the technology and security sectors, specializing in the fusion of cutting-edge innovations and cybersecurity strategies. She currently serves as the Public Sector Principal at SandboxAQ, where she bridges advancements in post-quantum cryptography (PQC) and data protection with the mission-critical needs of government agencies. Her work focuses on equipping these organizations with a zero-trust approach to securing sensitive systems against the rapidly evolving landscape of cyber threats. During her 16-year tenure at Google and its incubator Area120, Kathryn drove global efforts to develop and implement Secure by Design principles in emerging technologies, including Large Language Models (LLMs) and Generative AI. How to connect Kathryn : https://www.linkedin.com/in/kathryn-wang/ Connect With Aaron Crow: Website: www.corvosec.com LinkedIn: https://www.linkedin.com/in/aaronccrow Learn more about PrOTect IT All: Email: info@protectitall.co Website: https://protectitall.co/ X: https://twitter.com/protectitall YouTube: https://www.youtube.com/@PrOTectITAll FaceBook: https://facebook.com/protectitallpodcast To be a guest or suggest a guest/episode, please email us at info@protectitall.co Please leave us a review on Apple/Spotify Podcasts: Apple - https://podcasts.apple.com/us/podcast/protect-it-all/id1727211124 Spotify - https://open.spotify.com/show/1Vvi0euj3rE8xObK0yvYi4The podcast and artwork embedded on this page are from Aaron Crow, which is the property of its owner and not affiliated with or endorsed by Listen Notes, Inc.
Our guest in this episode is Noel Hurley. Noel is a highly experienced technology strategist with a long career at the cutting edge of computing. He spent two decade-long stints at Arm, the semiconductor company whose processor designs power hundreds of billions of devices worldwide. Today, he's a co-founder of Literal Labs, where he's developing Tsetlin Machines. Named after Michael Tsetlin, a Soviet mathematician, these are a kind of machine learning model that are energy-efficient, flexible, and surprisingly effective at solving complex problems - without the opacity or computational overhead of large neural networks.AI has long had two main camps, or tribes. One camp works with neural networks, including Large Language Models. Neural networks are brilliant at pattern matching, and can be compared to human instinct, or fast thinking, to use Daniel Kahneman´s terminology. Neural nets have been dominant since the first Big Bang in AI in 2012, when Geoff Hinton and others demonstrated the foundations for deep learning.For decades before the 2012 Big Bang, the predominant form of AI was symbolic AI, also known as Good Old Fashioned AI. This can be compared to logical reasoning, or slow learning in Kahneman´s terminology.Tsetlin Machines have characteristics of both neural networks and symbolic AI. They are rule-based learning systems built from simple automata, not from neurons or weights. But their learning mechanism is statistical and adaptive, more like machine learning than traditional symbolic AI. Selected follow-ups:Noel Hurley - Literal LabsA New Generation of Artificial Intelligence - Literal LabsMichael Tsetlin - WikipediaThinking, Fast and Slow - book by Daniel Kahneman54x faster, 52x less energy - MLPerf Inference metricsIntroducing the Model Context Protocol (MCP) - AnthropicPioneering Safe, Efficient AI - ConsciumSmartphones and Beyond - a personal history of Psion and SymbianThe Official History of Arm - ArmInterview with Sir Robin Saxby - IT ArchiveHow Spotify came to be worth billions - BBCMusic: Spike Protein, by Koi Discovery, available under CC0 1.0 Public Domain Declaration
What's happening with the latest releases of large language models? Is the industry hitting the edge of the scaling laws, and do the current benchmarks provide reliable performance assessments? This week on the show, Jodie Burchell returns to discuss the current state of LLM releases.
Today, I sit down with Joe Breeden, CEO and founder of Deep Future Analytics (DFA), for what is, in effect, a two-part conversation. First we do a deep dive into credit risk management and where it is falling short today and then we discuss AI monitoring and governance and how it is going to revolutionize software.The conversation covers why traditional machine learning models are missing critical components for accurate risk assessment and how adverse selection has dramatically impacted loan quality in recent vintages. Then Joe makes a bold prediction that software user interfaces are on the verge of a transformation that will render them unrecognizable from previous versions Curious? All is revealed in this fascinating conversation.In this podcast you will learn:How he got started with Deep Future AnalyticsThe state of credit risk management in banking today.What is missing, even with fintech lenders, who are using machine learning.What lenders get wrong when they focus on credit score.Why loans booked since 2022 are lower quality than even 2006-07.What kind of lift lenders or investors can see with DFA's models.What Joe sees in the pool of borrowers today.Why DFA moved into AI monitoring and governance.Who is using these new AI models they have developed.Why a “human in the loop” is not an effective monitoring method.What their new Strategic Recommendation Agent (SyRA) does.How they incorporate Large Language Models to ensure zero hallucinations.Why the future of software is dashboards on demand and analytics on demand.How Deep Future Analytics is different to others in the market.What it is going to take before software moves to a chat-based interface.Connect with Fintech One-on-One: Tweet me @PeterRenton Connect with me on LinkedIn Find previous Fintech One-on-One episodes
This is the story of a dream, perhaps one of humanity's oldest and most audacious: the dream of a thinking machine. It's a tale that begins not with silicon and code, but with myths of bronze giants and legends of clay golems. We'll journey from the smoke-filled parlors of Victorian England, where the first computers were imagined, to a pivotal summer conference in 1956 where a handful of brilliant, tweed-clad optimists officially christened a new field: Artificial Intelligence. But this is no simple tale of progress. It's a story of dizzying highs and crushing lows, of a dream that was promised, then deferred, left to freeze in the long "AI Winter." We'll uncover how it survived in obscurity, fueled by niche expert systems and a quiet, stubborn belief in its potential. Then, we'll witness its spectacular rebirth, a renaissance powered by two unlikely forces: the explosion of the internet and the graphical demands of video games. This is the story of Deep Learning, of machines that could finally see, and of the revolution that followed. We'll arrive in our present moment, a strange new world where we converse daily with Large Language Models—our new, slightly unhinged, and endlessly fascinating artificial companions. This isn't just a history of technology; it's the biography of an idea, and a look at how it's finally, complicatedly, come of age. To unlock full access to all our episodes, consider becoming a premium subscriber on Apple Podcasts or Patreon. And don't forget to visit englishpluspodcast.com for even more content, including articles, in-depth studies, and our brand-new audio series and courses now available in our Patreon Shop!
This episode is sponsored by SearchMaster, the leader in next-generation AI Engine Optimization (AEO) for Large Language Models like ChatGPT, Claude, and Perplexity. Future-proof your SEO strategy. Sign up now for a 2 week free trial! Watch this episode on YouTube! In this episode of the Marketing x Analytics Podcast, host Alex Sofronas interviews George Swetlitz about his diverse career and current work with RightResponse AI. George explains how RightResponse AI handles review sentiment analysis and personalized responses at scale, improving business reputations and customer conversions. He also discusses the AI-driven processes behind the product, the importance of genuine review engagement, and future developments such as AI Integration for social media and personalized review requests. Follow Marketing x Analytics! X | LinkedIn Click Here for Transcribed Episodes of Marketing x Analytics All view are our own.
AI, Technology, and The ChurchText your questions to: (559) 754-0182Watch the video of this entire conversation hereWelcome to a thought-provoking conversation on the Radiant Church Podcast, where we explore the intersection of faith and technology. In this episode, host Eric Riley and his guests — David Janssen (Data Analytics Coordinator), Matt Flummer (Philosophy Professor), Tim Harms (Pastor & Business Owner), and Glenn Power (Teacher & Bible Scholar) — tackle the profound questions posed by the rise of artificial intelligence.We'll define key terms like AI, AGI, and Large Language Models, and separate the hype from the reality. More than just a tech discussion, this episode brings the conversation back to the foundations of our faith, exploring what the Bible says about humanity, sin, and technology.Key Discussion PointsUnderstanding AI: The team breaks down what AI is, from its simplest form as predictive text to the complex models that power today's chatbots like ChatGPT.AI's Promise and Peril: The conversation dives into the "gospel of AI," exploring its promises of progress, utopia, and even immortality, and contrasting this with the biblical narrative of creation and human nature.Biblical Foundation: We turn to scripture, including Genesis 1-3 and the Tower of Babel, to understand our identity as humans created in God's image and our inherent desire to be "like God." The discussion highlights the importance of trusting God over human inventions.The Cost of Convenience: The podcast explores the hidden costs of AI, from the environmental impact of data centers to the ethical implications of data scraping and the human toll on those who train these models.Faithful Living in the AI Age: The episode concludes with a practical call to action, offering insights on how Christians can live wisely, discern the spiritual implications of technology, and draw a line between using technology for good and trusting in it for salvation.Resources & RecommendationsThe Life We're Looking For by Andy CrouchThe AI Revolution by John LennoxThe Convivial Society Substack by L.M. SacasasAgainst the Machine by Paul KingsnorthMade for People by Justin Whitmel EarlySupport the show*Summaries and transcripts are generated using AI. Please notify us if you find any errors.
We're excited to have Adi Ganesan, a PhD researcher at Stony Brook University, Penn University, and Vanderbilt, on the show. We'll talk about how large language models LLMs) are being tested and used in psychology, citing examples from mental health research. Fun fact: Adi was Sid's research partner during his Ph.D. program.Discussion highlightsLanguage models struggle with certain aspects of therapy including being over-eager to solve problems rather than building understandingCurrent models are poor at detecting psychomotor symptoms from text alone but are oversensitive to suicidality markersCognitive reframing assistance represents a promising application where LLMs can help identify thought trapsProper evaluation frameworks must include privacy, security, effectiveness, and appropriate engagement levelsTheory of mind remains a significant challenge for LLMs in therapeutic contexts; example: The Sally-Anne Test.Responsible implementation requires staged evaluation before patient-facing deploymentResourcesTo learn more about Adi's research and topics discussed in this episode, check out the following resources:Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluationTherapist Behaviors paper: [2401.00820] A Computational Framework for Behavioral Assessment of LLM Therapists Cognitive reframing paper: Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction - ACL Anthology Faux Pas paper: Testing theory of mind in large language models and humans | Nature Human Behaviour READI: Readiness Evaluation for Artificial Intelligence-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation | npj Mental Health Research GPT-4's Schema of Depression: Explaining GPT-4's Schema of Depression Using Machine Behavior AnalysisAdi's Profile: Adithya V Ganesan - Google Scholar What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.
In this episode of The ROCC Pod, we dive deep into the world of emotionally intelligent AI with Christine Chubenko of CodeBaby. Jon hosts solo while Lisais away, and together with Christine, we explore how AI is reshaping business efficiency, customer interaction, and our daily digital experience.We begin by learning about CodeBaby, a platform that powers emotionally intelligent avatars capable of handling repetitive customer service tasks on behalf of businesses. These avatars go far beyond the likes of "Clippy" — they can smile, laugh, and emulate human responses, allowing businesses to provide 24/7 assistance that reflects their brand tone and values. Christine emphasizes that users maintain full control over their data and avatar behavior, which can evolve over time as the business grows or shifts.Christine shares examples from retail and healthcare. In stores, avatars answer common customer questions, freeing up human staff to focus on higher-priority tasks. In medical practices, avatars or even holograms can offer patients a safe space to ask questions — particularly beneficial for older patients who may hesitate to speak directly to doctors. ("But they're so busy!")As we discuss the broader AI landscape, Christine explains her career roots in computer science and AI, dating back to the 1990s. While the foundation of AI has remained rooted in pattern recognition and statistical models like k-nearest neighbor, the scale and sophistication of modern AI have exploded. She debunks the myth that AI is sentient — emphasizing that despite the human-like responses, these systems are still just machines built by humans with clear limitations and no true consciousness.We explore how AI is already part of our everyday lives through Siri, Alexa, social media algorithms, smart appliances, and personalized streaming suggestions. Christine argues that AI's role is to reduce tedious tasks — not replace humans entirely. Jobs that require emotional nuance, tactile presence, or creativity (like nurses or preschool teachers) remain well out of AI's reach. Meanwhile, automation can fill gaps where businesses struggle to hire, like in food service or basic admin roles.Christine also breaks down what makes AI emotionally intelligent: prompt engineering. It's not just about asking a question; it's about asking it the right way, setting the tone, and defining the persona you want the AI to emulate. For instance, telling ChatGPT to answer as a cardiovascular surgeon or in a humorous tone drastically alters the output.To close, Christine encourages listeners who are curious about AI to simply sign up for a tool like ChatGPT and ask, “Where do I start?” That simple first step leads to powerful learning. She reminds us that while AI may seem intimidating, it's just another tool — and it's one we can all learn to wield effectively.More:Email Christine: cchubenko@codebaby.comCodeBaby Website: https://www.codebaby.com/ Learn more about the Royal Oak Chamber of Commerce: https://www.royaloakchamber.com/Connect with our hosts:Jon Gay from JAG in Detroit Podcasts - http://www.jagindetroit.com/Lisa Bibbee from Century 21 Northland - http://soldbylisab.com/
Hört schnell diese Folge an – sie könnte schon bald veraltet sein! Denn diesmal sprechen wir über ein Feld, das sich dermaßen schnell verändert, dass man kaum noch hinterherkommt: KI-Sprachmodelle! ChatGPT & Co haben unsere Realität bereits ordentlich durcheinandergewirbelt. Aber wie funktionieren sie eigentlich? Und was geht da noch in Zukunft? Erfahrt es jetzt bei Doktopus!Material zu dieser FolgeTokenizer: https://platform.openai.com/tokenizerOriginal-Paper zur Transformer-Architektur: https://arxiv.org/abs/1706.03762LLMs einfach erklärt: https://www.youtube.com/watch?v=SLcAtQQ483Y Technische Grundlagen LLMs inklusive Training (Englisch): https://youtu.be/LPZh9BOjkQs?si=lqLiEPt02oLnV-I2 Prompt Engineering Guide: https://www.promptingguide.ai/de/introduction/tips Mehr zum EU KI-Gesetz: https://www.europarl.europa.eu/topics/de/article/20230601STO93804/ki-gesetz-erste-regulierung-der-kunstlichen-intelligenzSocial Media und KontaktmöglichkeitenInstagram: http://instagram.com/doktopuspodcast/ Youtube: https://www.youtube.com/@doktopuspodcastE-Mail: doktopuspodcast@gmail.com CreditsRecherche, Hosting & Produktion: Dora Dzvonyar & Dominic AndersSound-Design & Post Production: Julian DlugoschAnsager: Marcel GustKI-Songs: SunoKI-Visuals: Bing Image CreatorIntro-Musik: Oleggio Kyrylkoww from PixabayIntermezzo-Transition: MAXOU-YT from Pixabay
WHAT DO YOU GET when you cross a tech billionaire with the gospel? Peter Thiel, co-founder of PayPal and Palantir, a company with multiple contracts with the U.S. government, will deliver a series of four lectures on the Antichrist in September and October. He recently gave an interview with Ross Douthat of The New York Times on the Antichrist. Why does a tech entrepreneur feel the need to expound at length on the great end times enemy of God and man? And why is he doing it off the record? Thiel's lectures are presented by the ACTS 17 Collective, a non-profit started by Michelle Stephens, wife of one of Thiel's partners, Trae Stephens, co-founder of Anduril. “Acts 17” is a reference to Paul's address to the people of Athens at Mars Hill (Acts 17:16–34). The group's stated mission is bring Jesus to Silicon Valley—a place where, until recently, it was a career-killer to openly express your faith in Christ. The irony is that through his investments in companies like Palantir and Anduril, both of which supply software and hardware to the United States government, including the military and Department of Homeland Security, Thiel is building the surveillance infrastructure needed to create the global government that will one day be ruled by the Antichrist. Also: Meta's AI chatbot found to encourage vulnerable teens to engage in self-harm, and new AI model based on the way the human brain works found to outperform ChatGPT and other Large Language Models. Our new book The Gates of Hell is now available in paperback, Kindle, and as an audiobook at Audible! Derek's new book Destination: Earth, co-authored with Donna Howell and Allie Anderson, is now available in paperback, Kindle, and as an audiobook at Audible! Sharon's niece, Sarah Sachleben, was recently diagnosed with stage 4 bowel cancer, and the medical bills are piling up. If you are led to help, please go to GilbertHouse.org/hopeforsarah. Follow us! X (formerly Twitter): @pidradio | @sharonkgilbert | @derekgilbert | @gilberthouse_tvTelegram: t.me/gilberthouse | t.me/sharonsroom | t.me/viewfromthebunkerSubstack: gilberthouse.substack.comYouTube: @GilbertHouse | @UnravelingRevelationFacebook.com/pidradio JOIN US IN ISRAEL! We will tour the Holy Land October 19–30, 2025. For more information, log on to GilbertHouse.org/travel. NOTE: If you'e going to Israel with us in October, you'll need to apply for a visa online before you travel. The cost is 25 NIS (about $7.50). Log on here: https://www.gov.il/en/departments/topics/eta-il/govil-landing-page Thank you for making our Build Barn Better project a reality! Our 1,200 square foot pole barn has a new HVAC system, epoxy floor, 100-amp electric service, new windows, insulation, lights, and ceiling fans! If you are so led, you can help out by clicking here: gilberthouse.org/donate. Get our free app! It connects you to this podcast, our weekly Bible studies, and our weekly video programs Unraveling Revelation and A View from the Bunker. The app is available for iOS, Android, Roku, and Apple TV. Links to the app stores are at pidradio.com/app. Video on demand of our best teachings! Stream presentations and teachings based on our research at our new video on demand site: gilberthouse.org/video! Think better, feel better! Our partners at Simply Clean Foods offer freeze-dried, 100% GMO-free food and delicious, vacuum-packed fair trade coffee from Honduras. Find out more at GilbertHouse.org/store/.
In this episode of the Managing Partners Podcast, Kevin Daisey sits down with Bryan Ilg and Michael Paik of Babl AI to discuss how law firms can safely and effectively integrate AI into their practices. They cover the risks of AI, vendor management, governance systems, and how attorneys can leverage AI for efficiency without jeopardizing client confidentiality. Whether you run a solo practice or manage a larger law firm, this episode highlights key strategies for using AI responsibly in the legal profession. Today's episode is sponsored by The Managing Partners Mastermind. Click here to schedule an interview to see if we're a fit. Chapters (00:00:00) - How to Build a Managing Partners Podcast(00:00:31) - Management Partners Podcast: AI Governance(00:03:12) - Brown Algorithmic Bias Lab(00:05:58) - How to Get Out of the AI Compliance Trap(00:11:07) - The Future of AI for Law Firms(00:16:23) - Large Language Models(00:18:08) - The Role of AI in Governance(00:21:26) - Lawyers Making Their Own Workflows(00:28:50) - Have you got a policy on AI in your firm?(00:29:33) - Babel AI's Hard Pitch for Legal Professionals(00:36:45) - Babel AI: Law 30(00:37:12) - An AI for Lawyers?(00:38:13) - How To Grow Your Business With Data Privacy on The Podcast
In this milestone 150th episode, hosts Kelly Schuster-Paredes and Sean Tibor sit down with Simon Willison, co-creator of Django and creator of Datasette and LLM tools, for an in-depth conversation about artificial intelligence in Python education. The discussion covers the current landscape of LLMs in coding education, from the benefits of faster iteration cycles to the risks of students losing that crucial "aha moment" when they solve problems independently. Simon shares insights on prompt injection vulnerabilities, the importance of local models for privacy, and why he believes LLMs are much harder to use effectively than most people realize. Key topics include: Educational Strategy: When to introduce AI tools vs. building foundational skills first Security Concerns: Prompt injection attacks and their implications for educational tools Student Engagement: Maintaining motivation and problem-solving skills in an AI world Practical Applications: Using LLMs for code review, debugging, and rapid prototyping Privacy Issues: Understanding data collection and training practices of major AI companies Local Models: Running AI tools privately on personal devices The "Jagged Frontier": Why LLMs excel at some tasks while failing at others Simon brings 20 years of Django experience and deep expertise in both web development and AI tooling to discuss how educators can thoughtfully integrate these powerful but unpredictable tools into their classrooms. The conversation balances excitement about AI's potential with realistic assessments of its limitations and risks. Whether you're a coding educator trying to navigate the AI revolution or a developer interested in the intersection of education and technology, this episode provides practical insights for working with LLMs responsibly and effectively. Resources mentioned: - Simon's blog: simonwillison.net - Mission Encodable curriculum - Datasette and LLM tools - GitHub Codespaces for safe AI experimentation Special Guest: Simon Willison.
Im 228. Special bei den WildMics haben wir uns mit der Frage beschäftigt, was sog. Large Language Models (LLMs) wie ChatGPT wirklich wissen. Stimmt es, dass diese LLMs mit jeder Version schlechter werden? Wie verlässlich sind die Informationen, die man mit diesen Modellen erhält, und wo liegen die Grenzen? Darüber sprachen wir mit Gavin Karlmeier und Gregor Schmalzried. Diese Sendung wurde am 26.08.2025 aufgezeichnet. Die HOAXILLA Umfrage findet ihr hier… Wie man uns unterstützen kann, könnt ihr hier nachlesen. Zum HOAXILLA Merchandise geht es hier
Our analysts Adam Jonas and Alex Straton discuss how tech-savvy young professionals are influencing retail, brand loyalty, mobility trends, and the broader technology landscape through their evolving consumer choices. Read more insights from Morgan Stanley.----- Transcript -----Adam Jonas: Welcome to Thoughts on the Market. I'm Adam Jonas, Morgan Stanley's Embodied AI and Humanoid Robotics Analyst. Alex Straton: And I'm Alex Straton, Morgan Stanley's U.S. Softlines Retail and Brands Analyst. Adam Jonas: Today we're unpacking our annual summer intern survey, a snapshot of how emerging professionals view fashion retail, brands, and mobility – amid all the AI advances.It is Tuesday, August 26th at 9am in New York.They may not manage billions of dollars yet, but Morgan Stanley's summer interns certainly shape sentiment on the street, including Wall Street. From sock heights to sneaker trends, Gen Z has thoughts. So, for the seventh year, we ran a survey of our summer interns in the U.S. and Europe. The survey involved more than 500 interns based in the U.S., and about 150 based in Europe. So, Alex, let's start with what these interns think about fashion and athletic footwear. What was your biggest takeaway from the intern survey? Alex Straton: So, across the three categories we track in the survey – that's apparel, athletic footwear, and handbags – there was one clear theme, and that's market fragmentation. So, for each category specifically, we observed share of the top three to five brands falling over time. And what that means is these once dominant brands, as consumer mind share is falling – and it likely makes them lower growth margin and multiple businesses over time. At the same time, you have smaller brands being able to captivate consumer attention more effectively, and they have staying power in a way that they haven't necessarily historically. I think one other piece I would just add; the rise of e-commerce and social media against a low barrier to entry space like apparel and footwear means it's easier to build a brand than it has been in the past. And the intern survey shows us this likely continues as this generation is increasingly inclined to shop online. Their social media usage is heavy, and they heavily rely on AI to inform, you know, their purchases.So, the big takeaway for me here isn't that the big are getting bigger in my space. It's actually that the big are probably getting smaller as new players have easier avenues to exist. Adam Jonas: Net apparel spending intentions rose versus the last survey, despite some concern around deteriorating demand for this category into the back half. What do you make of that result? Alex Straton: I think there were a bit conflicting takes from the survey when I look at all the answers together. So yes, apparel spending intentions are higher year-over-year, but at the same time, clothing and footwear also ranked as the second most category that interns would pull back on should prices go up. So let me break this down. On the higher spending intentions, I think timing played a huge role and a huge factor in the results. So, we ran this in July when spending in our space clearly accelerated. That to me was a function of better weather, pent up demand from earlier in the quarter, a potential tariff pull forward as headlines were intensifying, and then also typical back to school spending. So, in short, I think intention data is always very heavily tethered to the moment that it's collected and think that these factors mean, you know, it would've been better no matter what we've seen it in our space. I think on the second piece, which is interns pulling back spend should prices go up. That to me speaks to the high elasticity in this category, some of the highest in all of consumer discretionary. And that's one of the few drivers informing our cautious demand view on this space as we head into the back half. So, in summary on that piece, we think prices going higher will become more apparent this month onwards, which in tandem with high inventory and a competitive setup means sales could falter in the group. So, we still maintain this cautious demand view as we head into the back half, though our interns were pretty rosy in the survey. Adam Jonas: Interesting. So, interns continue to invest in tech ecosystems with more than 90 percent owning multiple devices. What does this interconnectedness mean for companies in your space? Alex Straton: This somewhat connects to the fragmentation theme I mentioned where I think digital shopping has somewhat functioned as a great equalizer in the space and big picture. I interpret device reliance as a leading indicator that this market diversification likely continues as brands fight to capture mobile mind share. The second read I'd have on this development is that it means brands must evolve to have an omnichannel presence. So that's both in store and online, and preferably one that's experiential focus such that this generation can create content around it. That's really the holy grail. And then maybe lastly, the third takeaway on this is that it's going to come at a cost. You, you can't keep eyeballs without spend. And historical brick and mortar retailers spend maybe 5 to 10 percent of sales on marketing, with digital requiring more than physical. So now I think what's interesting is that brands in my space with momentum seem to have to spend more than 10 percent of sales on marketing just to maintain popularity. So that's a cost pressure. We're not sure where these businesses will necessarily recoup if all of them end up getting the joke and continuing to invest just to drive mind share. Adam, turning to a topic that's been very hot this year in your area of expertise. That's humanoid robots. Interns were optimistic here with more than 60 percent believing they'll have many viable use cases and about the same number thinking they'll replace many human jobs. Yet fewer expect wide scale adoption within five years. What do you think explains this cautious enthusiasm? Adam Jonas: Well actually Alex, I think it's pretty smart. There is room to be optimistic. But there's definitely room to be cautious in terms of the scale of adoption, particularly over five years. And we're talking about humanoid robots. We're talking about a new species that's being created, right? This is bigger than just – will it replace our job? I mean, I don't think it's an exaggeration to ask what does this do to the concept of being human? You know, how does this affect our children and future generations? This is major generational planetary technology that I think is very much comparable to electricity, the internet. Some people say the wheel, fire, I don't know. We're going to see it happen and start to propagate over the next few years, where even if we don't have widespread adoption in terms of dealing with it on average hour of a day or an average day throughout the planet, you're going to see the technology go from zero to one as these machines learn by watching human behavior. Going from teleoperated instruction to then fully autonomous instruction, as the simulation stack and the compute gets more and more advanced. We're now seeing some industry leaders say that robots are able to learn by watching videos. And so, this is all happening right now, and it's happening at the pace of geopolitical rivalry, Sino-U.S. rivalry and terra cap, you know, big, big corporate competitive rivalry as well, for capital in the human brain. So, we are entering an unprecedented – maybe precedented in the last century – perhaps unprecedented era of technological and scientific discovery that I think you got to go back to the European and American Enlightenment or the Italian Renaissance to have any real comparisons to what we're about to see. Alex Straton: So, keeping with this same theme, interns showed strong interest in household robots with 61 percent expressing some interest and 24 percent saying they're very or extremely interested. I'm going to take you back to your prior coverage here, Adam. Could this translate into demand for AI driven mobility or smart infrastructure? Adam Jonas: Well, Alex, you were part of my prior coverage once upon a time. We were blessed with having you on our team for a year, and then you left me… Alex Straton: My golden era. Adam Jonas: But you came back, you came back. And you've done pretty well. So, so look, imagine it's 1903, the Wright Brothers just achieved first flight over the sands at Kitty Hawk. And then I were to tell you, ‘Oh yeah, in a few years we're going to have these planes used in World War I. And then in 1914, we'd have the first airline going between Tampa and St. Petersburg.' You'd say, ‘You're crazy,' right? The beauty of the intern survey is it gives the Morgan Stanley research department and our clients an opportunity to engage that surface area with that arising – not just the business leader – but that arising tech adopter. These are the people, these are the men and women that are going to kind of really adopt this much, much faster. And then, you know, our generation will get dragged into it eventually. So, I think it says; I think 61 percent expressing even some interest. And then 24 [percent], I guess, you know… The vast majority, three quarters saying, ‘Yeah, this is happening.' That's a sign I think, to our clients and capital market providers and regulators to say, ‘This won't be stopped. And if we don't do it, someone else will.' Alex Straton: So, another topic, Generative AI. It should come as no surprise really, that 95 percent of interns use that tool monthly, far ahead of the general population. How do you see this shaping future expectations for mobility and automation? Adam Jonas: So, this is what's interesting is people have asked kinda, ‘What's that Gen AI moment,' if you will, for mobility? Well, it really is Gen AI. Large Language Models and the technologies that develop the Large Language Models and that recursive learning, don't just affect the knowledge economy, right. Or writing or research report generation or intelligence search. It actually also turns video clips and physical information into tokens that can then create and take what would be a normal suburban city street and beautiful weather with smiling faces or whatever, and turn it into a chaotic scene of, you know, traffic and weather and all sorts of infrastructure issues and potholes. And that can be done in this digital twin, in an omniverse. A CEO recently told me when you drive a car with advanced, you know, Level 2+ autonomy, like full self-driving, you're not just driving in three-dimensional space. You're also playing a video game training a robot in a digital avatar. So again, I think that there is quite a lot of overlap between Gen AI and the fact that our interns are so much further down that curve of adoption than the broader public – is probably a hint to us is we got to keep listening to them, when we move into the physical realm of AI too. Alex Straton: So, no more driving tests for the 16-year-olds of the future... Adam Jonas: If you want to. Like, I tell my kids, if you want to drive, that's cool. Manual transmission, Italian sports cars, that's great. People still ride horses too. But it's just for the privileged few that can kind of keep these things in stables. Alex Straton: So, let me turn this into implications for companies here. Gen Z is tech fluent, open to disruption? How should autos and shared mobility providers rethink their engagement strategies with this generation? Adam Jonas: Well, that's a huge question. And think of the irony here. As we bring in this world of fake humans and humanoid robots, the scarcest resource is the human brain, right? So, this battle for the human mind is – it's incredible. And we haven't seen this really since like the Sputnik era or real height of the Cold War. We're seeing it now play out and our clients can read about some of these signing bonuses for these top AI and robotics talent being paid by many companies. It kind of makes, you know, your eyes water, even if you're used to the world of sports and soccer, . I think we're going to keep seeing more of that for the next few years because we need more brains, we need more stem. I think it's going to do; it has the potential to do a lot for our education system in the United States and in the West broadly. Alex Straton: So, we've covered a lot around what the next generation is interested in and, and their opinion. I know we do this every year, so it'll be exciting to see how this evolves over time. And how they adapt. It's been great speaking with you today, Adam. Adam Jonas: Absolutely. Alex, thanks for your insights. And to our listeners, stay curious, stay disruptive, and we'll catch you next time. If you enjoy Thoughts on the Market, please leave us a review wherever you listen and share the podcast with a friend or colleague today.
LOCAL SEO – A DEEPER DIVE Are you truly leveraging the power of local SEO, or just scratching the surface? This episode takes you beyond the basics—unpacking the strategies, hidden features, and common pitfalls that determine whether your business shows up (or stays invisible) in your local market. Whether you're a service-area business, brick-and-mortar retailer, or local professional, the rules are changing and the competition is only getting sharper. Before you listen, ask yourself: Do I really know how Google decides which businesses appear at the top of local results—and what's holding mine back?Am I making critical mistakes with my Google Business Profile, or missing out on simple tactics that drive more calls and visits?With AI and changing search habits, am I prepared for the next wave of local SEO, or am I about to be left behind? In this episode, you'll learn: The truth behind “near me” searches versus geo-targeted queries—and why your content needs to serve both.Why Google Business Profile (GBP) is more than just a listing—and how to optimize every corner of it (categories, Q&As, reviews, and ongoing engagement).How proximity, relevance, and prominence drive results—and practical steps to improve all three for your business.The critical role of reviews, reputation, and ongoing profile management—plus what to do when you get hit by fake reviews or platform glitches.What's new with local search in the age of AI and Large Language Models (LLMs)—and why SEO fundamentals aren't dead (yet).Agency-level tips for grid reporting, heat maps, multiple branches, and troubleshooting why you're not ranking. Meet your Marketing Guides: • Ian Cantle – Dental Marketing Heroes • Ken Tucker – Changescape Web • Paul Barthel – Changescape Web • Jeff Stec – Tylerica Marketing Systems Together, they bring decades of hands-on local SEO and digital marketing expertise, helping small and midsize businesses outsmart competitors and get found where it matters most. Ready to stop guessing and start winning at local SEO? Smash that subscribe button, share the show with your colleagues and local business friends, and connect with our Guides at their websites above for more resources, free tips, and a custom SEO review! LISTEN NOW to unlock the strategies that drive local leads and revenue in 2025/26—and keep your business a step ahead.
AGNTCY - Unlock agents at scale with an open Internet of Agents. Visit https://agntcy.org/ and add your support. In this episode of Eye on AI, we sit down with Leon Song, VP of Research at Together AI, to explore how open-source models and cutting-edge infrastructure are reshaping the AI landscape. From speculative decoding to FlashAttention and RedPajama, Leon shares how Together AI is building one of the fastest, most cost-efficient AI clouds—helping enterprises fine-tune, deploy, and scale open-source models at the level of GPT-4 and beyond. We dive into Leon's journey from leading DeepSpeed and AI for Science at Microsoft to driving system-level innovation at Together AI. Topics include: The future of open-source vs. closed-source AI models Breakthroughs in speculative decoding for faster inference How Together AI's cloud platform empowers enterprises with data sovereignty and model ownership Why open-source models like DeepSeek R1 and Llama 4 are now rivaling proprietary systems The role of GPUs vs. ASIC accelerators in scaling AI infrastructure Whether you're an AI researcher, enterprise leader, or curious about where generative AI is heading, this conversation reveals the technology and strategy behind one of the most important players in the open-source AI movement. Stay Updated: Craig Smith on X:https://x.com/craigss Eye on A.I. on X: https://x.com/EyeOn_AI
AI data wars push Reddit to block the Wayback Machine China Launches Three-Day Robot Olympics Featuring Football and Table Tennis US government agency drops Grok after MechaHitler backlash, report says Eli Lilly signs $1.3 billion deal with Superluminal to use AI to make obesity medicines The AI Was Fed Sloppy Code. It Turned Into Something Evil. | Quanta Magazine AI data centers made Americans' electricity bills 30% higher Sam Altman says 'yes,' AI is in a bubble Is the A.I. Sell-off the Start of Something Bigger? Thousands of Grok chats are now searchable on Google Opinion | Amy Klobuchar: I Knew A.I. Deepfakes Were a Problem. Then I Saw One of Myself. 2,178 Occult Books Now Digitized & Put Online, Thanks to the Ritman Library and Da Vinci Code Author Dan Brown Pluralistic: "Privacy preserving age verification" is bullshit (14 Aug 2025) How to use "skibidi" and other new slang added to Cambridge Dictionary YouTube Is Making a Play to Host the Oscars Leobait: Resisting AI Solutionism through Workplace Collective Action So ... is AI writing any good? Project Indigo We used AI to analyse three cities. It's true: we now walk more quickly and socialise less Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Rich Skrenta Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: pantheon.io helixsleep.com/twit
AI data wars push Reddit to block the Wayback Machine China Launches Three-Day Robot Olympics Featuring Football and Table Tennis US government agency drops Grok after MechaHitler backlash, report says Eli Lilly signs $1.3 billion deal with Superluminal to use AI to make obesity medicines The AI Was Fed Sloppy Code. It Turned Into Something Evil. | Quanta Magazine AI data centers made Americans' electricity bills 30% higher Sam Altman says 'yes,' AI is in a bubble Is the A.I. Sell-off the Start of Something Bigger? Thousands of Grok chats are now searchable on Google Opinion | Amy Klobuchar: I Knew A.I. Deepfakes Were a Problem. Then I Saw One of Myself. 2,178 Occult Books Now Digitized & Put Online, Thanks to the Ritman Library and Da Vinci Code Author Dan Brown Pluralistic: "Privacy preserving age verification" is bullshit (14 Aug 2025) How to use "skibidi" and other new slang added to Cambridge Dictionary YouTube Is Making a Play to Host the Oscars Leobait: Resisting AI Solutionism through Workplace Collective Action So ... is AI writing any good? Project Indigo We used AI to analyse three cities. It's true: we now walk more quickly and socialise less Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Rich Skrenta Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: pantheon.io helixsleep.com/twit
AI data wars push Reddit to block the Wayback Machine China Launches Three-Day Robot Olympics Featuring Football and Table Tennis US government agency drops Grok after MechaHitler backlash, report says Eli Lilly signs $1.3 billion deal with Superluminal to use AI to make obesity medicines The AI Was Fed Sloppy Code. It Turned Into Something Evil. | Quanta Magazine AI data centers made Americans' electricity bills 30% higher Sam Altman says 'yes,' AI is in a bubble Is the A.I. Sell-off the Start of Something Bigger? Thousands of Grok chats are now searchable on Google Opinion | Amy Klobuchar: I Knew A.I. Deepfakes Were a Problem. Then I Saw One of Myself. 2,178 Occult Books Now Digitized & Put Online, Thanks to the Ritman Library and Da Vinci Code Author Dan Brown Pluralistic: "Privacy preserving age verification" is bullshit (14 Aug 2025) How to use "skibidi" and other new slang added to Cambridge Dictionary YouTube Is Making a Play to Host the Oscars Leobait: Resisting AI Solutionism through Workplace Collective Action So ... is AI writing any good? Project Indigo We used AI to analyse three cities. It's true: we now walk more quickly and socialise less Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Rich Skrenta Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: pantheon.io helixsleep.com/twit
AI data wars push Reddit to block the Wayback Machine China Launches Three-Day Robot Olympics Featuring Football and Table Tennis US government agency drops Grok after MechaHitler backlash, report says Eli Lilly signs $1.3 billion deal with Superluminal to use AI to make obesity medicines The AI Was Fed Sloppy Code. It Turned Into Something Evil. | Quanta Magazine AI data centers made Americans' electricity bills 30% higher Sam Altman says 'yes,' AI is in a bubble Is the A.I. Sell-off the Start of Something Bigger? Thousands of Grok chats are now searchable on Google Opinion | Amy Klobuchar: I Knew A.I. Deepfakes Were a Problem. Then I Saw One of Myself. 2,178 Occult Books Now Digitized & Put Online, Thanks to the Ritman Library and Da Vinci Code Author Dan Brown Pluralistic: "Privacy preserving age verification" is bullshit (14 Aug 2025) How to use "skibidi" and other new slang added to Cambridge Dictionary YouTube Is Making a Play to Host the Oscars Leobait: Resisting AI Solutionism through Workplace Collective Action So ... is AI writing any good? Project Indigo We used AI to analyse three cities. It's true: we now walk more quickly and socialise less Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Rich Skrenta Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: pantheon.io helixsleep.com/twit
This week's guest is Vishal Jadhav, Product Director at Blue Yonder. Everyone listening to this podcast knows of Blue Yonder. It is a company with a long history, from I2 to JDA, that has consistently been on the cutting edge of supply chain management software. Vishal has spent almost 20 years at Blue Yonder. His recent work and writings have delved into the evolution of Large Language Models (LLMs) and how they are being incorporated into supply chain solutions. Our conversation explores the differences between traditional Operations Research (OR) techniques and modern AI. Vishal highlights how these technologies can enhance optimization and decision-making by mimicking human reasoning and learning from experience. We also address the challenges of explainability in AI and the emerging concept of agentic AI, which suggests a future of more proactive and autonomous systems within the supply chain.
This episode is brought to you by https://www.ElevateOS.com —the only all-in-one community operating system.Ever notice how the simplest words carry the most weight?In today's episode of the Multifam Collective, I unpack a quote from Rudyard Kipling: "Words are, of course, the most powerful drug used by mankind." That line hit me—and it got me thinking about the two most powerful words in the world of Multifamily leadership: please and thank you.These aren't just playground pleasantries. They're foundational tools in shaping culture, creating community, and leading with authenticity.In a world driven by speed, automation, and PropTech innovation, we sometimes forget the human side of the equation. Ironically, the word please is one of the most expensive tokens in Large Language Models like OpenAI's GPT. And yet in real life, it's often the cheapest thing we forget to give.Let this be your reminder: in Multifamily, where relationships are the currency of success, manners matter more than ever.Please watch this.Thank you for being here.Like, comment, and subscribe to keep the conversation going.For more engaging content, explore our offerings at the[https://www.multifamilycollective.com](https://www.multifamilycollective.com/) and the [https://www.multifamilymedianetwork.com](https://www.multifamilymedianetwork.com/)Join us to stay informed and inspired in the multifamily industry!
AI data wars push Reddit to block the Wayback Machine China Launches Three-Day Robot Olympics Featuring Football and Table Tennis US government agency drops Grok after MechaHitler backlash, report says Eli Lilly signs $1.3 billion deal with Superluminal to use AI to make obesity medicines The AI Was Fed Sloppy Code. It Turned Into Something Evil. | Quanta Magazine AI data centers made Americans' electricity bills 30% higher Sam Altman says 'yes,' AI is in a bubble Is the A.I. Sell-off the Start of Something Bigger? Thousands of Grok chats are now searchable on Google Opinion | Amy Klobuchar: I Knew A.I. Deepfakes Were a Problem. Then I Saw One of Myself. 2,178 Occult Books Now Digitized & Put Online, Thanks to the Ritman Library and Da Vinci Code Author Dan Brown Pluralistic: "Privacy preserving age verification" is bullshit (14 Aug 2025) How to use "skibidi" and other new slang added to Cambridge Dictionary YouTube Is Making a Play to Host the Oscars Leobait: Resisting AI Solutionism through Workplace Collective Action So ... is AI writing any good? Project Indigo We used AI to analyse three cities. It's true: we now walk more quickly and socialise less Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Rich Skrenta Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free shows, a members-only Discord, and behind-the-scenes access. Join today: https://twit.tv/clubtwit Sponsors: pantheon.io helixsleep.com/twit
A new JAMA study found that 84% of abortion prescriptions from Aid Access went to patients in states with bans, enabled by “shield laws” protecting telehealth providers across state lines. Provision rates were highest in underserved Southern and Midwestern counties, highlighting telemedicine's role in maintaining access. A Scientific Reports study showed that large language models for clinical use can be manipulated into giving unsafe answers through subtle “adversarial hallucination attacks,” with success rates over 95%. Finally, JAMA Ophthalmology reported GLP-1 agonists may slightly increase risk of sudden vision loss, though benefits still outweigh risks.
A looming deadline always gets attention, and for DoD suppliers, the clock is ticking. On October 1, 2025, the Department of Defense will begin including Cybersecurity Maturity Model (CMMC) certification requirements in new contracts. This week on Feds At The Edge, four leading experts cut through the complexity and share practical guidance to help you start, or finish, your CMMC journey. Sean Frazier, Federal Chief Security Officer for Okta, explains why “Know Thy Data” is the key to applying the right level of security where it matters most. Alan Dinerman, PhD, Senior Manager, Cyber Strategy, Policy, and Privacy at Mitre, puts CMMC in context with other cybersecurity standards, noting its focus on Controlled Unclassified Information. And Jeff Adorno, Field Chief Compliance Officer at ZScaler, warns of risks in the AI era, where sensitive data can unintentionally “leak” into Large Language Models. The panel as a whole highlights how aligning with existing frameworks and using current technologies can demonstrate progress to auditors and ease compliance. Listen now on your favorite podcast platform because whether you're deep into compliance or just getting started, this conversation will help you navigate the evolving landscape of CMMC and beyond.
This episode is sponsored by SearchMaster, the leader in next-generation Generative Engine Optimization (GEO) for Large Language Models like ChatGPT, Claude, and Perplexity. Future-proof your SEO strategy. Sign up now for a 2 week free trial! Watch this episode on YouTube! Alex Sofronas hosts the Marketing x Analytics Podcast featuring Justin Rashidi, co-founder of data enablement company SeedX. They discuss SeedX's approach to addressing business development issues, focusing on understanding KPIs, causal impact analysis, multi-touch attribution, and marketing mix modeling. Justin elaborates on running various statistical analyses, including click-based tracking, holdout tests, and LTV modeling. The conversation also explores optimizing ad spend and forecasting, emphasizing the importance of data-driven decision-making and the challenges of aligning metrics with business goals. Follow Marketing x Analytics! X | LinkedIn Click Here for Transcribed Episodes of Marketing x Analytics All view are our own.
Sub to the Patreon to support the show and access the entire 2nd part of PPM's subtextual analysis of Eddington as soon as it drops: patreon.com/ParaPowerMappingIn which we decode Eddington's subtextual conspiracy themes, endeavoring to argue that the new Ari Aster is perhaps the first major, theatrically released film to have accurately encapsulated the essence of the technocratic AmerIsraeli Years of Lead—in accordance with my personal timeline of the ongoing deep political era that would place its inception around Covid time—and the Silicon Valley capitalist elite's embrace of strategy of tension in the cybernetic service of of updating America's Total Info Awareness 2.0 operating system and the installation of their long planned predictive policing panopticon.We discuss: why the appearance of the globo "Antifa PMCs" isn't actually crypto-MAGA chicanery (seeing as they are Gladio operators); Joaquin Phoenix's turn as Sheriff Joe Cross, a Gen X, mumblecore, adoptive son of Sheriff Joe Arpaio type; Eddington as Nashville esque ensemble comedy cum Coen Brothers Covid Wester with the accompanying masking/social distancing standoffs; diagnosing the alienation and social media siloing of the wokespeak & QAnon brain rot of that hot 2020 summer; the role of calibrated algorithmic control; Sheriff Cross's Israeli Civil Guard pin in the OG script; the unfortunate executive production of Len Blavatnik, the Zio·nist billionaire "philanthropist" tied to Brett Ratner, Weinstein, the Bronfmans, etc, mulling whether he might have vetoed the inclusion of that visual gag on Sheriff Joe's regalia vest; the Solidgoldmagikarp Proposed Hyperscale Data Center project, the underlying Pynchon-esque real estate development and land and water use conspiracy; the schizophrenic drifter character Lodge, who opens the film, and his Homeric oracle qualities, spiritually warning against the onset of the Age of AI-quarius; Mike the One Armed Man from Twin Peaks comparisons; Pynchonian Lodge puns; Chekhov's Cough; Louise Cross, Sheriff Cross's wife, the one other farsighted character, and her haldol prescription, evoking Twin Peaks again; a demonic Mark Zuckerberg hinted at as one of the shadowy backers of the Solidgoldmagikarp Data Center in the earlier draft; Gov. Grisham making it into the film by way of an honorary watch and Covid headlines; the David Dees vibe of the cell towers in the opening sequence and various 5G diatribes; Aster lurking on Twitter; an earlier version of the second scene in which Sheriff Cross wrestles with Officer Butterfly Jimenez over who gets to investigate the self-immolation death of a paraplegic conspiracy Youtuber named Mitchell and the Native school uniforms discovered in his accessible van (evoking Missing Indigenous Children); the film's abiding interest in the neocultures that have cropped up around QAnon & pedo-hunters; borderlands and issues of jurisdiction between the Sevilla Co. Sheriff and the Santa Lupe Pueblo Tribal Officers; Cesar Chavez & Dolores Huerta's (a New Mexican) Hispanic borderlands community union LUPE aka La Union de Pueblo Entero aka The Union of the Whole People; Santa Lupe Pueblo = SLP = Speech Language Pathologist?; the neighboring, colonized tribal peoples, at their slight remove from Eddington and Treatlerite American society moreover, being the observers best prepared to pathologize the alienation and atomization and societal decay taking hold in the town over Covid; in regards Speech Language Pathologists, the ever-present theme of miscommunication and the deterioration of consensus reality caused by social media echo chamber-induced myopia, as well as the specter of LLMs or Large Language Models; "Solidgoldmagikarp" alluding to AI & ChatGPT tokens that cause anomalous or erratic behavior...FULL LINER NOTES ON THE PATREONMusic:| Matt Akers - "Necessary Rhythms" https://matthewakers.bandcamp.com/album/tough-to-kill | | Matt Akers - "Night Drive II (Detroit at 2 AM" |
Confused by AI jargon and unsure which tools actually move the needle for your business? We break down the real differences between traditional algorithms, large language models (LLMs), and agents — including agentic AI — and give practical guidance leaders can use now.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo.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:Choosing AI: Algorithms vs. AgentsUnderstanding AI Models and AgentsUsing Conditional Statements in AIImportance of Data in AI TrainingRisk Factors in Agentic AI ProjectsInnovation through AI ExperimentationEvaluating AI for Business SolutionsTimestamps:00:00 AWS AI Leader Departs Amid Talent War03:43 Meta Wins Copyright Lawsuit07:47 Choosing AI: Short or Long Term?12:58 Agentic AI: Dynamic Decision Models16:12 "Demanding Data-Driven Precision in Business"20:08 "Agentic AI: Adoption and Risks"22:05 Startup Challenges Amidst Tech Giants24:36 Balancing Innovation and Routine27:25 AGI: Future of Work and SurvivalKeywords:AI algorithms, Large Language Models, LLMs, Agents, Agentic AI, Multi agentic AI, Amazon Web Services, AWS, Vazhi Philemon, Gen AI efforts, Amazon Bedrock, talent wars in tech, OpenAI, Google, Meta, Copyright lawsuit, AI training, Sarah Silverman, Llama, Fair use in AI, Anthropic, AI deep research model, API, Webhooks, MCP, Code interpreter, Keymaker, Data labeling, Training datasets, Computer vision models, Block out time to experiment, Decision-making, If else conditional statements, Data-driven approach, AGI, Teleporting, Innovation in AI, Experiment with AI, Business leaders, Performance improvements, Sustainable business models, Corporate blade.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner
Obvious: ChatGPT's GPT-5 is here and it's really good.Not so obvious: the gap between novice and experts just shrunk 90%. In a short few hours, OpenAI gave even free users access to now the world's most powerful model. As the most used AI chatbot in the world by a wide margin, the quality work we all produce has also just gotten a huge bump. But there's a lot beneath the surface. Join us as we dissect what's new in GPT-5 and 7 big trends you probably don't know but should pay attention to. 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 Official Release OverviewGPT-5 User Rollout to 700 MillionGPT-5 Unified Hybrid Model ArchitectureAuto Model Switching and User ControlMajor Upgrade for Free ChatGPT UsersGPT-5 Benchmark and Accuracy ImprovementsGPT-5 Vibe Coding and Canvas FeaturesAdvanced Voice Mode in Custom GPTsReduced Hallucinations and SycophancyMicrosoft Copilot Instant GPT-5 UpgradeImpact on Enterprise Software and APIsGPT-5 Disruptive API Pricing StructureTrends in Corporate AI AdoptionTimestamps:00:00 "Everyday AI Insights"05:54 "Adaptive Model Response Modes"08:14 GPT4O Model Critique11:17 GPT4O Nano Upgrade Impact17:26 GPT Model Selection Simplified20:53 Canvas Code Rendering and Quick Answer Feature24:09 "GPT5 Model Routing Overview"26:44 "GPT-5: Your New Daily Driver"30:08 AI Model Advances: Game-Changing Improvements33:43 Advanced Voice Mode in GPTs37:45 Massive Microsoft Copilot Upgrade38:49 Software Access and Licensing Challenges43:09 AI Implementation Challenges in Top Companies46:37 "GPT-5 Testing and Trends"Keywords:GPT-5, GPT5, OpenAI, AI model update, Large Language Model, flagship model, hybrid model, AI technology, model auto-switching, deep thinking mode, fast response mode, model router, free AI access, paid ChatGPT users, ChatGPT free users, model selection, GPT-4O, GPT-4 Turbo, model reasoning, hallucination rate, sycophancy reduction, advanced voice mode, GPTs custom models, Canvas mode, Vibe coding, API pricing, API tokens, Microsoft Copilot, Microsoft 365 Copilot, GitHub Copilot, enterprise AI upgrade, LM arena, ELO score, Anthropic, Claude 4.1, Claude Sonnet, Gemini 2.5 Pro, personalized AI assistant, software innovation, coding capabilities, Inc 5000 companies, enterprise adoption, custom instructions, Pro plan, Plus plan, thinking mode, human preference, automated rSend 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
There's a new most powerful AI model in townApple is trying to make a ChatGPT competitor.And OpenAI? Well.... they're in a capacity crunch.Big Tech made some BIG moves in AI this week. And you probably missed them. Don't worry. We gotchyu. On Mondays, Everyday AI brings you the AI News that Matters. No B.S. No marketing fluff. Just what you need to know to be the smartest person in AI at your company. 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:OpenAI Study Mode in ChatGPT LaunchGoogle Gemini 2.5 Deep Think ReleaseGemini 2.5 Parallel Thinking and Coding BenchmarksGoogle AI Mode: PDF and Canvas FeaturesNotebook LM Video Overviews CustomizationMicrosoft Edge Copilot Mode Experimental RolloutOpenAI GPT-5 Model Launch DelaysApple Building In-House ChatGPT CompetitorMicrosoft and OpenAI Partnership RenegotiationAdditional AI Tool Updates: Runway, Midjourney, IdeogramTimestamps:00:00 AI Industry Updates and Competition03:22 ChatGPT's Study Mode Promotes Critical Thinking09:02 "Google AI Search Mode Enhancements"10:21 Google AI Enhances Learning Tools16:14 Microsoft Edge Introduces Copilot Mode20:18 OpenAI GPT-5 Delayed Speculation22:42 Apple Developing In-House ChatGPT Rival27:06 Microsoft-OpenAI Partnership Renegotiation30:51 Microsoft-OpenAI Partnership Concerns Rise33:23 AI Updates: Video, Characters, AmazonKeywords:Microsoft and OpenAI renegotiation, Copilot, OpenAI, GPT-5, AI model, Google Gemini 2.5, Deep Think mode, Google AI mode, Canvas mode, NotebookLM, AI browser, Agentic browser, Edge browser, Perplexity Comet, Sora, AI video tool, AI image editor, Apple AI chatbot, ChatGPT competitor, Siri integration, Artificial General Intelligence, AGI, Large Language Models, AI education tools, Study Mode, Academic cheating, Reinforcement learning, Parallel thinking, Code Bench Competition, Scientific reasoning, Chrome, Google Lens, Search Live, AI-powered search, PDF upload, Google Drive integration, Anthropic, Meta, Superintelligent labs, Amazon Alexa, Fable Showrunner, Ideogram, Midjourney, Luma Dream Machine, Zhipu GLM 4.5, Runway Alif, Adobe Photoshop harmonize, AI funding, AI product delays, AI feature rollout, AI training, AI onboarding, AI-powered presentations, AI-generated overviews, AI in business, AI technology partnership, AI investment, AI talent acqSend 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
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