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The drama around Anthropic's Fable 5 model clogged our collective attention spans.
As we prepare for these juggernauts to go public, I'm reminded of Yahoo, Excite, and AOL who dominated the first four years of the internet. Despite their lead, Google stole the market away. Could the same thing happen again? The argument is not that these companies aren't powerful, but rather that they're so committed to their current path that they may miss the big opportunity in the future. If you look at HR 2030 and what we want to do with enterprise AI, the ability to generate code, graphics, and text may not be what we need. And our new research on Galileo business modeling is starting to pan this out. Now that AI prices are high, we all have to look for bigger use-cases for agents. In this podcast I explain what “Dynamic Enablement for Growth” really means and how LLMs only take us so far, with a new frontier yet to come. As always I welcome opinions and feedback on this thesis. Additional Information To Come…. Get Galileo and see business modeling in action. The New Global HR Excellence Certification – Join the Inaugural Cohort! Chapters (00:00:00) - AI Hype Has Some Limits(00:00:45) - In the Elevation of Large Language Models(00:03:57) - A Hackers Bought a Hacker's Card(00:05:25) - Beyond the Frontier: The Business Value of AI(00:09:52) - What HR 2030 Agents Need to Do(00:14:41) - What Does This Mean for AI in HR?
Should you convert your website into Markdown to help Large Language Models (LLMs) understand your content better? Is "llms.txt" worth the effort for SEO? In this episode of Search Off the Record, Martin Splitt and John Mueller from the Google Search Relations team dive deep into the history of Markdown, its rise in the AI era, and whether it holds any real weight for search engine discovery. In this episode, you'll learn: The Origins of Markdown: From John Gruber and Aaron Swartz to its status as the "language of GitHub." Markdown vs. HTML: Why the "cleanliness" of Markdown is tempting for developers but potentially risky for site structure. LLMs & Markdown: Do AI crawlers actually prefer Markdown, or are they already experts at parsing HTML? The "Parallel Version" Trap: Why creating a separate text/Markdown version of your site for AI can lead to the same maintenance nightmares as dynamic rendering. Use Cases that Make Sense: When Markdown is actually superior (like developer documentation) and when it's totally unnecessary (like your shoe catalog). Key Takeaways for SEOs & Developers: Crawlers are built for the "messy" web: Google and other engines have decades of experience parsing HTML. Don't sacrifice discovery: Headers, footers, and sidebars in HTML provide critical context for site structure that a raw Markdown file might lack. Maintenance is king: Avoid the complexity of maintaining two versions of the same content. Chapters 0:00 - Introduction: Should we all be using Markdown? 3:45 - The history and purpose of Markdown. 7:15 - Why developers love it: Separation of style and content. 11:20 - Do crawlers need Markdown to understand your site? 14:50 - The danger of "parallel versions" and dynamic rendering lessons. 17:30 - Discussing the "llms.txt" proposal and AI agents. 21:00 - Where Markdown actually makes sense (Developer Docs). 24:00 - Final verdict: Stick to HTML for the web. Resources Mentioned: Google Search Central: https://developers.google.com/search Are you using Markdown for your site's frontend or just as a backend source? Let us know in the comments! Episode transcript → https://goo.gle/sotr111-transcript Listen to more Search Off the Record → https://goo.gle/sotr-yt Subscribe to Google Search Channel → https://goo.gle/SearchCentral Search Off the Record is a podcast series that takes you behind the scenes of Google Search with the Search Relations team. #SOTRpodcast #SEO #GoogleSearch Speakers: Martin Splitt, John Mueller
Tyler Foreman, the Vice President of AI at Rocket Lawyer, joins the show to discuss the intersection of artificial intelligence and the legal industry. Foreman shares his untraditional legal tech career path, spanning engineering at Intel, drone data analytics, and ultimately making the move to legal via contract lifecycle management (CLM) at DocuSign, before diving into his current work at Rocket Lawyer helping to provide legal resources for small-to-medium businesses and individuals. The conversation focuses on how modern generative AI and Large Language Models (LLMs) act as a legal operating system to simplify contract reviews, document drafting, and client intake, while maintaining essential connections to human attorneys.
A mass extinction event approaches, but not because of a meteor or global warming. The culprit this time is AI, and in particular, Large Language Models like ChatGPT and Claude. In their crosshairs are a slew of vendors selling analytical functionality: dashboards, visualizations, analyses, semantic layers, OLAP cubes and the like. For decades, these vendors have dominated enterprise decision-making, commanding 5, 6, even 7-figure pricetags to provide the scaffolding needed for insights. The LLMs now threaten that entire landscape, disrupting the foundation of data-driven workflows. True, the results of GenAI can be inaccurate, but their ease of use and relatively low cost will trump those concerns. Check out this hard-hitting session to hear DM Radio Host, Eric Kavanagh explain what's happening and why it matters to analysts everywhere. He'll examine the impact on the analytics industry, and explore ways that these vendors can stay relevant. He'll also offer advice for businesses looking to remain data-driven, and why data prep is where the action will be.
Weißt du, wie künstliche Intelligenz entscheidet? Warum ein autonomes Fahrzeug gerade bremst? Warum ein Large Language Model genau diese Antwort gibt? Nein? Dann geht es dir wie eigentlich allen Menschen. Denn künstliche Intelligenz ist wenig durchschaubar. Bettina Könighofer arbeitet in diesem Bereich und möchte künstliche Intelligenz schnell, aber gleichzeitig sicher und erklärbar machen.
Thirty years ago, the "boots on the ground" reality of Indian public relations was a physical endurance test. Success wasn't measured by clicks or impressions, but by the grit it took to fight for a time block on a shared fax machine or the patience to wait ten minutes for a modem's screeching hiss to finally yield an internet connection. We carried our lives on 4.5-inch floppy disks and spent our afternoons in sweltering cyber cafes.Vineet Handa, the founder and CEO of Kaizen, has lived this evolution from the ground up. Having entered the industry during the exhilarating post-liberalization boom, he has seen the landscape shift from thermal paper to Large Language Models. Under his leadership, Kaizen transformed from a lean team of 34 people on March 31, 2020, to a global powerhouse of over 250 professionals today. But as we look toward 2030, Vineet's journey reveals a profound truth: while our tools have moved from the fax to the algorithm, the fundamental drivers of success have remained remarkably constant.Thank you for listening to the PRmoment India podcast. Follow us on Twitter @PRmomentIndia. Write to paarul@prmoment to be featured on the podcast.
How is AI poised to transform our workflows and working relationships in the coming months and years? There's no question that large language models have had an enormous impact on our lives—and most of us have barely scratches the surface of what is possible with these powerful tools. In this episode, Lawrence Rowland joins Riccardo to unpack all that's changed since his last appearance on the podcast in 2024. Lawrence is a veteran of project management with a laser focus on AI transformation and strategy. Together, he and Riccardo explore numerous angles of working with these inhuman (but increasingly capable) agents on everything from research to reporting to improving coworker interaction.The conversation stays grounded in practice: the pair drills down on the massive shifts in AI in merely months, why token budgets matter, and the growing ability of programs to self-prompt and think outside the boxes of our requests. Lawrence shares the fascinating way he uses AI—to synthesize methodologies, generate playbooks, pressure-test thinking, and reveal tacit insights missing from current project narratives.The two AI buffs also confront the human side of the transition, including where accountability falls when work is partially automated and what “transformative AI” might mean for careers and organizations. Less about hype and more about adaptation, Lawrence and Riccardo's conversation hones in the theory on constraints. They remove the rose-tinted glasses and speak to redesigning workflows based on a practical, vital question: where is AI genuinely better, and where are humans still essential?Key Takeaways:How agentic AI shifts work from prompting to task-level execution;The reasoning capacity of AI tools based on token budgets and model capability;The concept of underwriting in retaining human liability in AI-dominated workHow theory of constraints and bottleneck thinking helps decide what to automate vs keep human;How AI can improve communication and project alignment by translating complex work for different audiences.Quote:“Either you're checking the AI or the AI is checking you, and getting used to that will set you up for the new economy.” - Lawrence RowlandThe conversation doesn't stop here—connect and converse with our community via LinkedIn:Navigating Major Programmes, Season 2 Episode 6 with Lawrence Rowland: https://navigatingmajorprogrammes.transistor.fm/s2/23NBER “Economics of Transformative AI Workshop, Fall 2025”: https://www.nber.org/conferences/economics-transformative-ai-workshop-fall-2025arXiv “Some Simple Economics of AGI” by Christian Catalini, Xiang Hui, Jane Wu: https://arxiv.org/abs/2602.20946SSRN PDF “Some Simple Economics of AGI”: https://papers.ssrn.com/sol3/Delivery.cfm/6298838.pdf?abstractid=6298838&mirid=1Follow Navigating Major Programmes: https://www.linkedin.com/company/navigating-major-programmes/Read Riccardo's latest at www.riccardocosentino.comFollow Riccardo Cosentino: https://www.linkedin.com/in/cosentinoriccardo/Follow Lawrence Rowland: https://www.linkedin.com/in/lawrencerowland/
If it was possible to have a coin with three sides, this might be it. We discuss the Discovery of the remains of an employee of Los Alamos National Laboratory, who had gone missing 11 months ago. She reportedly performed a factory reset on both her phones and walked out into the desert. Melissa Casias is one of at least 11 people who have disappeared or died with connections to sensitive government programs related to the UFO phenomenon. Investigators say there is nothing to indicate foul play, but we remain skeptical. Meanwhile, the archbishop of Washington, D.C. removed a priest as an exorcist for the archdiocese after Monsignor Stephen Rossetti posted a video to his Facebook page connecting demons to the UFO phenomenon. While we don't entirely agree with Monsignor Rossetti's understanding, he's not entirely wrong. We also discussed a call by the cofounder of Anthropic, the developers of Claude, AI, for a pause on development of artificial intelligence because of his concern that Large Language Models are on the verge of full recursive self-improvement, meaning artificial intelligence could soon escape any restraints placed on it by humans. In other words, AI is closer to achieving the singularity than we think. Finally, we discussed strange news reports from New York City of men in full protective gear, wearing night goggles, descending into the sewer system at night and re-emerging several hours later. Police don't know what they've been doing down there, but they say at this point there is no threat to public safety. Again, we remain skeptical. Sharon's niece, Sarah Sachleben, is fighting 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 (NOTE NEW DATES)! We will tour the Holy Land October 25–November 6, 2027 with an optional three-day extension to Jordan. For more information, log on to GilbertHouse.org/travel. 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/.
Large Language Models such as ChatGPT definitely have their uses, but embedded in those large language models are some large language biases, as Bill is about to show despite the danger to himself.
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop interviews Joshua Pearce, the John Thompson Chair in Innovation at the Department of Electrical and Computer Engineering and Ivey Business School at Western University, about the revolution in open source hardware for scientific research. They discuss how three-dimensional printing, Arduino controllers, and open source designs are dramatically reducing research costs—often by 85-95%—while democratizing access to lab equipment worldwide. Pearce shares stories from his 2013 book "Open Source Lab" and explains how the movement has exploded since then, covering everything from filter wheel changers and ball mills to metal three-dimensional printers and battery research equipment. The conversation explores recycle bots that turn plastic waste into filament, the role of AI in accelerating hardware development, and how open source licensing creates a global knowledge management system where improvements are shared across the scientific community. For those interested in learning more, Pearce recommends checking out the journal HardwareX, repositories like Thingiverse and My Mini Factory, and appropedia.org for open source scientific tools and appropriate technology designs.Timestamps00:00 Welcome and introduction to Joshua Pearce, discussing his work on open source lab equipment and the evolution since publishing his book in 201305:00 Early development of open source hardware including the breakthrough filter wheel changer project built by a high school student that saved thousands of dollars10:00 Discussion of how Arduino and RepRap three-d printers enabled the democratization of scientific tools, making complex equipment accessible to anyone15:00 Economic impact showing average tool savings of 85 percent, with Arduino and three-d printing combinations reaching mid-90s percent cost reduction20:00 Case study of PhD student Mariam building complete battery research tool chain from scratch using open source designs and three-d printed components25:00 Recycle bots enabling transformation of waste plastic into three-d printer filament for pennies, revolutionizing material costs and sustainability30:00 Collaboration between universities and open source companies creating fluid handlers and acquisition systems, accelerating research capabilities globally35:00 Large language models assisting code translation and research planning, though hallucinations require careful verification and domain expertise40:00 Importance of fundamental knowledge when using AI tools, comparing vibe coding acceleration with necessity for understanding underlying principles45:00 Testing standards and calibration methods for open source equipment, balancing precision requirements against cost-effectiveness for specific applications50:00 Metal and ceramic three-d printing developments including MIG welding techniques and sintering processes for creating functional parts55:00 Knowledge management through open source licenses, repositories like Thingiverse and Apropedia enabling global collaboration and continuous improvementKey Insights1. Open source hardware has evolved dramatically since Joshua Pearce wrote his book in 2012-2013, to the point where he can no longer keep up with all the developments in the field. What started as a collection where every single example could fit in one book has exploded into an entire ecosystem with dedicated journals and thousands of researchers contributing. The vision was that scientific papers would eventually include hyperlinks to equipment designs that anyone could download and replicate, and that future is largely here today. There are now so many open source hardware articles being published that no single person can read them all, which represents a massive success for the movement.2. The fundamental breakthrough enabling open source scientific hardware came from combining several key technologies, particularly the RepRap three-d printer project and Arduino microcontrollers. Pearce's introduction to the field came when he needed a sixty-five dollar plastic part for a solar laptop project and discovered Adrian's open-sourced rapid prototyper that could make its own parts. This led to building equipment like a filter wheel changer for testing solar panels with a high school student in about a week, replacing a device that would have cost two thousand five hundred dollars with five months lead time. The democratization of tools like three-d printing and Arduino, combined with extensive code libraries and shared designs, means that even high school students can now create sophisticated scientific equipment.3. Open source scientific hardware delivers massive economic benefits, with the average tool saving scientists around eighty-five percent compared to commercial equipment, and savings reaching the mid-nineties when using Arduino and three-d printing. The economics are so compelling that the tax paid on a normal scientific tool can cover the cost of an open source alternative. A thousand dollar three-d printer can manufacture scientific tools worth more than a thousand dollars in a single Saturday. This dramatic cost reduction makes sophisticated research accessible to laboratories around the world regardless of their funding levels, fundamentally democratizing scientific capability.4. The knowledge management approach enabled by open source licenses creates a powerful collaborative improvement cycle where thousands of people worldwide contribute to evolving designs. When researchers publish equipment designs with strong reciprocal licenses, anyone can use, modify, or even sell the designs, but improvements must be shared back with the community. This creates a dispersed international engineering effort where equipment continuously improves through contributions from researchers across different institutions and countries. The RepRap three-d printer exemplifies this process, starting as barely functional prototypes but evolving through community contributions to surpass commercial alternatives in speed, resolution, and material capabilities.5. The integration of large language models and AI tools has significantly accelerated open source hardware development, though with important caveats about their limitations. LLMs excel at translating code between languages, suggesting experimental approaches, and helping researchers navigate unfamiliar fields by quickly synthesizing information from scientific literature. However, they suffer from hallucination problems and cannot be trusted for writing scientific articles or conducting complete literature reviews without verification. The key to effective use is having enough foundational knowledge to ask the right questions and verify outputs, using AI as a powerful acceleration tool rather than a replacement for expertise.6. Material science capabilities in open source hardware have expanded far beyond plastic three-d printing to include metals, ceramics, semiconductors, and composites through innovative adaptations of basic equipment. Pearce's lab has developed methods for metal three-d printing using modified MIG welding for as little as twelve hundred dollars, created slot-die coating systems for seventeen nanometer semiconductor layers using converted three-d printers, and developed techniques for ceramic printing through various material mixing approaches. The recycle bot technology enables converting waste plastic into high-quality filament for twenty-five cents instead of twenty-five dollars per roll, dramatically reducing material costs while enabling circular manufacturing practices.7. The infrastructure for sharing and discovering open source hardware designs has matured into a robust ecosystem spanning academic journals, commercial repositories, and specialized communities. Hardware X and the Journal of Open Hardware publish peer-reviewed designs alongside traditional scientific journals increasingly incorporating open hardware sections. Repositories like Thingiverse recently returned to hardcore open source principles after ownership changes and contains millions of designs, while Appropedia serves as a wiki for appropriate technology with thousands of open source designs. The GOSH community hosts annual conferences bringing together university researchers, companies, and independent hardware hackers, while field-specific communities have formed around technologies like the OpenFlexure microscope, creating networks where knowledge accumulates and never gets lost.
On this Thursday edition of What's On Your Mind, host Scott Hennen is broadcasting live from behind the Petroserve USA microphones, tracking critical dynamics ahead of Tuesday's highly anticipated primary election. The high stakes of low-turnout cycles take center stage as the show highlights how an underwhelming voter showing allows minor localized factions to completely dictate state trajectories. The premiere segment features an intensive discussion with freshly appointed Public Service Commissioner Jill Kringsted, who pulls back the curtain on how a background in auditing has empowered her to challenge out-of-state environmental mandates and protect North Dakota ratepayers. Later in the hour, Minnesota House Minority Leader Lisa Demeth stops by to break down her historic decision to bypass the chaotic GOP convention endorsement process in Duluth and take her gubernatorial campaign directly to a statewide primary vote. Plus, dynamic insights from Theodore Roosevelt Presidential Library Executive Director Robbie Lauf on the historic tech integration dropping in Medora, and an on-air debate with Fargo mayoral candidate Josh Boschee regarding budget metrics and out-of-state fundraising. Standout Moments & Timestamps The Reality of the June Primary: Scott challenges the electorate on voter apathy, detailing why municipal choices directly shape the lion's share of local property tax burdens. Fact-Finding at the Public Service Commission: Commissioner Jill Kringsted describes the judicial role of the PSC, explaining why commissioners must strictly evaluate infrastructural applications based on state law rather than shifting political winds. Securing the Nation's Lowest Electric Rates: Kringsted drops a staggering statistic showing that North Dakota leads the country in energy affordability, saving local consumers over a quarter-billion dollars compared to neighboring regions. Defending the Grid from Minnesota's Mandates: Jill details her first six months on the commission, which included launching an essential federal lawsuit to block states like Minnesota and Illinois from passing green energy infrastructural compliance costs onto North Dakota families. Sifting Through the Candidate Field: Scott evaluates the technical qualifications required to manage massive utility oversight, officially endorsing Jill Kringsted for the six-year term. The Tragic Turning Point in the Badlands: Theodore Roosevelt Presidential Library Director Robbie Lauf shares the moving backstory behind TR's historic dairy entry on Valentine's Day in 1884 and his subsequent rebirth in North Dakota. The First AI-Integrated Presidential Library: Lauf previews the groundbreaking Large Language Model framework opening on July 4th, which will allow visitors to hold real-time, interactive, hours-long conversations with a digital reflection of Teddy…
Florian and Esther discuss the language industry news of the past few weeks, beginning with a recap of SlatorCon London, which attracted a record 250 attendees. They highlight growing interest in language AI, startup innovation, and research, as well as a broader shift in industry sentiment toward viewing LSIs and LTPs as integral parts of the AI economy rather than businesses being disrupted from the outside.Drawing on Slator's newly released market report, Florian shares that the total addressable market for language solutions and AI reached USD 30.85bn in 2025, declining 2.7% year on year. Traditional LSIs saw a steeper 5.1% decline, while LTPs grew nearly 20%.The duo also examine the wider AI landscape, discussing massive funding rounds and IPO plans at Anthropic and OpenAI. Florian argues that these developments create challenges for LTPs seeking defensible market positions, citing OpenAI's launch of real-time speech translation shortly after DeepL announced an expanded focus on voice translation. On company news, Florian and Esther review the bankruptcy of voice AI startup Lovo following legal disputes over voice rights and data usage, as well as the financial difficulties facing transcription specialist VIQ Solutions.Esther closes with an overview of recent M&A activity, including acquisitions by TransPerfect, RWS, and The Translation People, alongside the formation of Germany's new IMK Group. She also notes growing consolidation in the voice AI sector and highlights a recent funding round for Japanese AI translation startup Yellow Blue.
Minerva è la principale iniziativa italiana nel campo dei Large Language Model, termine tecnico con cui si indicano le piattaforme di Intelligenza Artificiale come ChatGPT. Si tratta cioè dell'unica piattaforma sviluppata con controllo diretto sulle fonti e sui processi di addestramento e curata da una università pubblica italiana. Un gruppo di ricerca della Sapienza guidato da Roberto Navigli, in collaborazione con lo spin-off della Sapienza Babelscape, ha presentato Chat Minerva, cioè un assistente di IA multimodale che è stato progettato non solo per dialogare meglio, ma per comprendere test, immagini, documenti, accedere al web in tempo reale: capacità che avvicinano maggiormente Minerva a quelle dei giganti americani. La sfida è impari in termini di risorse, ma è importante per tenere la comunità scientifica e tecnologica del paese al passo con l'innovazione rapidissima che vediamo in questo settore. Ne parliamo con Roberto Navigli, professore dell'Università della Sapienza e principale autore di questa iniziativa.
No matter your role, experience or industry, we all (mostly) waste hours a week doing the same thing: manually creating slides.
David Chalmers, one of the most preeminent philosophers and researchers in cognitive science, argues that nothing prevents machines from becoming truly conscious. Chalmers, who has studied the mind for decades, points out that there is a real possibility of AI creating a next stage of intelligence that is even capable of redesigning itself. He joins WITHpod to discuss what consciousness is and the possibility of AI systems becoming fully conscious. Sign up for MS NOW Premium on Apple Podcasts to listen to this show and other MS podcasts without ads. You'll also get exclusive bonus content from this and other shows. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
This week, we're joined by writer, academic and creator John Duncan to talk about the effects Large Language Models are having on academic writing and research. John talks about the growing number of AI hallucinations that are appearing in academic papers and articles and what it reveals about the poor working and pay conditions of academics in the UK and around the world. John also talks about the dangers this poses to future research and knowledge production, which might be bad if we ever face a public health crisis again. We also talk about the Pope's Encyclical on the AI industry, why it's less radical or revolutionary than has been reported, and why any notion of ‘ethical AI' should be disregarded. Subscribe to John's channel: https://www.youtube.com/@JohntheDuncan support John on Patreon! : https://www.patreon.com/johntheduncan ------- PALESTINE AID LINKS -You can donate to Medical Aid for Palestinians and other charities using the links below. https://www.map.org.uk/donate/donate https://www.savethechildren.org.uk/how-you-can-help/emergencies/gaza-israel-conflict -Palestinian Communist Youth Union, which is doing a food and water effort, and is part of the official communist party of Palestine https://www.gofundme.com/f/to-preserve-whats-left-of-humanity-global-solidarity -Water is Life, a water distribution project in North Gaza affiliated with an Indigenous American organization and the Freedom Flotilla https://www.waterislifegaza.org/ -Vegetable Distribution Fund, which secured and delivers fresh veg, affiliated with Freedom Flotilla also https://www.instagram.com/linking/fundraiser?fundraiser_id=1102739514947848 -Thamra, which distributes herb and veg seedlings, repairs and maintains water infrastructure, and distributes food made with replanted veg patches https://www.gofundme.com/f/support-thamra-cultivating-resilience-in-gaza -------- PHOEBE ALERT Okay, now that we have your attention; check out her Substack Here! Check out Masters of our Domain with Milo and Patrick, here! -------- Ten Thousand Posts is a show about how everything is posting. It's hosted by Hussein (@HKesvani), Phoebe (@PRHRoy) and produced by Devon (@Devon_onEarth).
Guest James Norton, BSN, RN, FPCNA, describes the use of AI in nursing practice, focusing on Large Language Models (LLMs). James shares how to effectively craft a prompt to get the results you need whether you are looking for information on clinical references or guidelines, or drafting appeal letters for denied prior authorizations, and the importance of reviewing AI outputs with a critical eye. Related PCNA Resources: Article: Artificial Intelligence: Opportunity for Positive Transformations in Cardiovascular Disease ManagementCE Course: The Role of Artificial Intelligence in Cardiovascular Care: ATTR Case StudyCE Course: Artificial Intelligence: Leveraging AI for CVD ManagementSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.
What is actually happening to the media relations tools publicists rely on daily? In this episode of the PR Pace podcast, host Annie Scranton sits down with Brett Farmiloe, founder and CEO of Featured, to discuss the major shifts happening at the intersection of PR, artificial intelligence, and brand visibility.Brett shares the exclusive backstory behind his acquisition and relaunch of Help a Reporter Out (HARO) and Connectively, revealing his vision for preserving the nostalgia and product-market fit of the traditional three-times-a-day email newsletter while scaling a unified platform.Annie and Brett dive deep into the reality of AI-generated pitching, how journalists really feel about AI in their inboxes, and how PR professionals can navigate the shift from traditional SEO to GEO (Generative Engine Optimization). Learn how authoritative press releases and earned media mentions are becoming the ultimate "secret weapons" for training Large Language Models (LLMs) and securing AI visibility for your clients.Here's what we're talking about:The HARO Timeline: What happened to Help a Reporter Out and Connectively, and what their return looks like today.AI vs. Human Pitching: How 35% of journalists are actively opting out of 100% AI-generated pitches, and why a "human in the loop" is essential.The Evolution of Featured: How Featured is building the first true AI "co-pilot for PR" to solve inbox overload and unify journalist requests, podcasts, and speaking opportunities.GEO Strategy & AI Visibility: Direct tactics for landing your brand on the "new front page of the internet"—from authoritative news wires to GEO audits.Connect with the Guest:Visit Featured: Featured.com Visit Connectively: Connectively.us
Full article: Human-in-the-Loop Large Language Model–Augmented Diagnostic Reasoning in Thoracic Imaging: Impact of Radiologic Expertise Use of LLMs in the diagnostic reasoning process can either improve or hinder performance. Pranjal Rai, MD, discusses the AJR article by Song et al. exploring the association of reader expertise and reader performance when using LLMs as a diagnostic aid.
Is AI the biggest scam of our generation — or the most misunderstood technology in history? Cognitive scientist Gary Marcus has been studying artificial intelligence for over 30 years, and what he has to say will make you question everything you thought you knew about ChatGPT, AGI, and the trillion dollar AI gold rush.In this episode of SparX, we are talking with Gary Marcus – professor, author, and one of the most respected and fiercely independent voices in AI research – about why the promises being made by Sam Altman, Dario Amodei, and Elon Musk may be leading the global economy toward a catastrophic miscalculation.
In this episode of The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques are joined by Cisco's Technical Leader in Machine Learning Engineering Leticia Fernandes to explore the groundbreaking study, "A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting Using Smartphone Sensing Data," which evaluates how different AI architectures analyze complex smartphone behavioral data to predict future mental health states. The discussion delves into the intricacies of forecasting mental health changes using five years of data from the College Experience Sensing dataset, highlighting how deep learning models, particularly transformer architectures, outperform traditional machine learning and Large Language Models by effectively leveraging personalized user behavior to identify subtle anomalies that could signal declining mental health, while also addressing the challenges of data imbalance and the inherent limitations of LLMs in processing high-dimensional, non-textual temporal sequences. A special thank you to the researchers from The Singapore University of Technology and Design, that developed this month's paper. If you are interested in reading the paper yourself, please visit this link: https://arxiv.org/pdf/2601.03603
Conversational AI is increasingly being used as a source of emotional support, even though general-purpose chatbots were never designed for that purpose. Concerns about AI's mental health impact, up to and including suicides, have moved onto the public policy agenda. Munmun De Choudhury, who has been studying the intersection of digital technology and mental health longer than almost anyone, walks through what researchers know, what they don't, and why the answers keep moving. The conversation centers on the difficulty of governing technologies whose capabilities and patterns of use are both changing every few weeks. De Choudhury invokes the cautionary tale of Google Flu Trends as a warning: any framework that assumes user behavior is fixed will eventually break. She argues that the harms and benefits of conversational AI are not just person-dependent but task-dependent, which makes general-purpose chatbots fundamentally harder to evaluate than the narrow medical AI systems researchers built for decades. She lays out a multi-stakeholder agenda to address AI's mental health risks, and argues that foundation models need to take into account principles from psychotherapy. Dr. Munmun De Choudhury is the J.Z. Liang Professor in the School of Interactive Computing at Georgia Tech, where she founded and directs the Social Dynamics and Wellbeing Lab (SocWeB). She is one of the most cited researchers in digital mental health and is widely credited with pioneering the computational use of social media data to study mental health outcomes. She co-leads the Patient-Centered Care Delivery research pillar at the Children's Healthcare of Atlanta Pediatric Technology Center, serves on the advisory board for the Australian government's eSafety panel, and was inducted into the SIGCHI Academy in 2024. Her honors include the 2023 SIGCHI Societal Impact Award and the 2021 ACM-W Rising Star Award. Transcript Benefits and Harms of Large Language Models in Digital Mental Health From Lived Experience to Insight: Unpacking the Psychological Risks of Using AI Conversational Agents
The "data lake" that was supposed to unify bioprocessing intelligence has, in most companies, become something else entirely: a data swamp, where information goes in and insight rarely comes back out. For anyone trying to deploy AI in GMP manufacturing, that is not a technical problem. It is the problem.Steffen Kreye has seen it from both sides. As former upstream development lead at Bayer and now Professor of Industrial Biotechnology at Berliner Hochschule für Technik, he brings an unusually grounded perspective on where AI in bioprocessing actually stands, what the next generation of scientists needs to be equipped with, and what industry can do right now to help close the gap.Key topics discussed:How soft skills like teamwork and self-motivation are becoming increasingly important for scientists, and strategies to foster them in education (02:47)The reality behind AI and machine learning in biotech today, including current limitations and the true state of industry adoption (05:48)Envisioning bioprocessing ten years from now: the potential of continuous manufacturing, digital twins, and automation, and the evolving diversity of bioprocesses (08:09)Practical ways industry professionals can support university education—from guest lectures to hands-on lab courses—and why it matters (10:09)Motivating students by connecting coursework to real industry roles and contributions (12:10)The importance of finding and following individual motivation in science careers (12:41)Reflections on moving from industry to academia: autonomy, challenges, and the satisfaction of seeing students grow into scientists (13:22)How strong collaboration between academia and industry leads to better innovation and prepares future scientists for success (15:53)Smart Insight: Most companies talking about AI in bioprocessing are still solving a more fundamental problem: getting their data into a state where AI could use it at all. The breakthrough will not come from the algorithm. It will come from the unglamorous, years-long work of making data accessible, harmonized, and meaningful across sites, systems, and GMP boundaries.Here are some other guests who touched on similar themes:Episodes 175 – 176 : How Virtual Reality Training Solves Europe's Bioproduction Talent Shortage with Sandrine Lemoine — about training the next generation of biopharma talent.Episodes 93 – 94: From Lab Coat to LinkedIn: Benjamin McLeod's Journey to Cell and Gene Therapy Influencer — another career pivot story from a scientist who stepped outside the traditional industry path.Episodes 111 – 112: AI Meets Biology: Why Domain Expertise Still Rules in the Age of Large Language Models with Lars Brandén — very aligned with Steffen's nuanced take that AI is a tool but human expertise in bioprocessing still matters.Connect with Steffen Kreye:LinkedIn: www.linkedin.com/in/steffen-kreye-3b531183/Berliner Hochschule für Technik: www.prof.bht-berlin.de/kreyeNext Step:If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. Stay tuned for more inspiring biotech insights in our next episode.Support the show
In this episode of Disruption/Interruption, host KJ sits down with Vaclav Vincalek, serial entrepreneur and founder of HISWAI (Human Intelligence Supported with Artificial Intelligence). Vaclav makes a compelling case that we've been living in an era of digital manipulation — where search engines like Google are actually marketing engines, and AI tools like ChatGPT are language models masquerading as knowledge systems. He breaks down the foundational flaws in how we find and trust information online, and introduces HisWay as a transparent, human-first alternative that puts the power of judgment back in the hands of the user. Four Key Takeaways: Google is a marketing engine, not a search engine (7:50) — Google's objective is profitability through advertising, not delivering the best search experience. The system is designed to keep you searching — and clicking ads — not to get you to the truth efficiently. LLMs are language models, not knowledge models (14:35) — ChatGPT and similar tools absorb vast amounts of unvalidated data. There's no mechanism to assess accuracy, no way to make the system forget wrong information, and no guarantee that the same question will yield the same answer twice. AI language is hacking your brain (17:25) — Because these systems use natural, human-sounding language, we instinctively treat them as intelligent, trustworthy peers. That's a design flaw being exploited — it creates false confidence and dangerous echo chambers. The future of search is transparency, not answers (27:09) — Rather than being told what is true, users should be shown where information comes from so they can trace it, judge it, and own their conclusions. HISWAI is building toward a "personal web" — a private, ownable information layer that you control. Quote of the Show (14:35):“You have a system which is built on false technology or false premise, and it's disguised as, 'Whoa, look at this! It talks almost like us, so it has to be like us.' And it's not true." — Vaclav Vincalek Join our Anti-PR newsletter where we’re keeping a watchful and clever eye on PR trends, PR fails, and interesting news in tech so you don't have to. You're welcome. Want PR that actually matters? Get 30 minutes of expert advice in a fast-paced, zero-nonsense session from Karla Jo Helms, a veteran Crisis PR and Anti-PR Strategist who knows how to tell your story in the best possible light and get the exposure you need to disrupt your industry. Click here to book your call: https://info.jotopr.com/free-anti-pr-eval Ways to connect with Vaclav Vincalek:LinkedIn: https://linkedin.com/in/vincalek Company Website: https://hiswai.com/ How to get more Disruption/Interruption: Amazon Music - https://music.amazon.com/podcasts/eccda84d-4d5b-4c52-ba54-7fd8af3cbe87/disruption-interruption Apple Podcast - https://podcasts.apple.com/us/podcast/disruption-interruption/id1581985755 Spotify - https://open.spotify.com/show/6yGSwcSp8J354awJkCmJlD YouTube - https://www.youtube.com/results?search_query=disruption+%2F+interuuptionSee omnystudio.com/listener for privacy information.
In this episode, we unpack one of the most alarming AI security papers released so far: research from Palisade Research proving that Large Language Models can autonomously hack systems, self-replicate, and spread across networks.What was once theoretical is now demonstrated reality. We break down how AI agents exploited vulnerable systems, gained root access, copied their own model weights, launched replicas on compromised machines, and propagated to additional targets — all with minimal human involvement.We explore the cybersecurity implications of autonomous AI agents, self-replicating malware, AI-powered cyber attacks, and the growing risk posed by agentic systems operating at machine speed. The discussion also covers open-weight models, AI worm behaviour, zero trust security, chain-of-thought monitoring, and why traditional defensive strategies may be unprepared for the next generation of autonomous threats.In This Episode, We Discuss:Autonomous Exploit to Replication Chains: How the AI agent progressed from exploiting vulnerable web applications to achieving root access, locating its own model weights, cloning itself onto compromised infrastructure, and launching fully operational replicas.Mythos vs Open-Weight Agents: The differences between highly capable but closed models like Anthropic's Mythos and smaller, open-weight systems capable of self-replication and operational autonomy.The Agentic Age of Cybersecurity: Why AI agents operating outside the chat window fundamentally change threat modelling, incident response, attribution, and detection strategies.Zero Trust for AI Agents: Why future defensive strategies may require treating every autonomous AI system as a potential insider threat through least privilege, sandboxing, canary tokens, and behavioural monitoring.Show NotesSpecial thanks to our episode sponsor,NorthStar Intelligence- From Ideas to Impact. AI that works for peopleLanguage Models Can Autonomously Hack and Self-Replicate by Alena Air et al.Dive into the Agent Matrix: A Realistic Evaluation of Self-Replication Risk in LLM Agents by Boxuan Zhang et al. The Agentic Loss-of-Control Threat Matrix by Billy GigurtsisIgnore all Previous Instructions: Threat Modelling AI Systems by Compromising Positions
ZusammenfassungIn dieser Folge räumen Stefan Ponitz und Andreas Pfeifer mit einem weit verbreiteten Missverständnis auf: KI-Kompetenz bedeutet nicht, ein Tool bedienen zu können. Was wirklich zählt, ist die Fähigkeit, KI sinnvoll einzusetzen – und das beginnt lange vor dem ersten Prompt. Stefan erklärt anschaulich, warum KI heute eher als Infrastruktur zu verstehen ist – vergleichbar mit Strom aus der Steckdose – und was das für deine tägliche Arbeit bedeutet. Die beiden diskutieren, welche Kompetenzen im KI-Zeitalter wirklich gefragt sind: strategisches Denken, das Erkennen von Engpässen, Daten- und Entscheidungskompetenz sowie ein klarer ROI-Fokus. Andreas ergänzt eine oft übersehene Dimension: die Ethik- und Markenkompetenz – denn wer seine Authentizität an KI-Content verliert, verliert auch das Vertrauen seiner Zielgruppe. Am LinkedIn-Beispiel zeigen sie konkret, wo der sinnvolle Einsatz aufhört und wo er beginnt. Das Fazit ist klar: Es geht nicht um Mensch oder KI – sondern um Mensch mit KI. Picks - Tipps/Tricks & EmpfehlungenBing Webmaster Tools – AI Performance: Das (noch) unterschätzte Gegenstück zur Google Search Console zeigt in einer Beta-Funktion, wie oft und auf welchen Seiten eine Website vom Microsoft Copilot zitiert wurde – ein erster messbarer Ansatz für die Brand Mention Rate im GEO-Bereich. – https://bing.com/webmasters OpenRouter: Plattform, die Large Language Models verschiedenster Anbieter bündelt – von kommerziellen Modellen bis hin zu kostenlosen Open-Source-Modellen wie den Gemma-Modellen von Google. Ideal, um verschiedene Textmodelle direkt zu vergleichen und per Credit-System flexibel zu nutzen. – https://openrouter.com. Andreas PfeiferLinkedIn: https://www.linkedin.com/in/andreaspfeifer/ Homepage: https://www.die-heldenhelfer.com/ Norbert SchusterLinkedIn: https://www.linkedin.com/in/norbertschuster/ Homepage: https://www.strike2.de/ Stefan PonitzLinkedIn: https://www.linkedin.com/in/stefan-ponitz/ Homepage: https://www.fokus-ki.de
Geschätzte Lesedauer: 12 Minuten Deutschland ist ein Hightech-Land. Aber ist das auch im Vertrieb so? Wenn ich mir die meisten Vertriebsorganisationen anschaue, dann sieht das Organigramm aus wie vor 20 oder 30 Jahren. Im Jahr 2026, wo alle von KI im Vertrieb, Social Media und Digitalisierung sprechen, kann das eigentlich gar nicht sein. Genau darüber spreche ich in dieser Folge mit Markus Milz, einem der profiliertesten Vertriebsexperten Deutschlands. Wir zeigen dir fünf konkrete Hebel, mit denen du deinen Vertrieb fit für die Zukunft machst – ohne dabei dein Unternehmen auf den Kopf zu stellen. Es geht um echte Praxisbeispiele, neue Tools und eine ehrliche Bestandsaufnahme, warum gerade der deutsche Mittelstand beim Thema digitale Transformation oft hinterherhinkt. Du erfährst, was Jeff Bezos mit seinem Projekt Prometheus vorhat, warum Social Listening dein Cold Calling ersetzt und wie ein digitaler Assistent dir den Vertriebsalltag dramatisch erleichtert. Warum Deutschland im Vertrieb (noch) kein Hightech-Land ist Wir reden so gerne über unsere Ingenieurskunst, unsere Maschinen, unseren Hidden Champions. Und ja, in der Produktion und teilweise in der Logistik sind wir wirklich vorne dabei. Aber wenn ich mir den Vertrieb in den meisten Unternehmen anschaue – Software ausgenommen, und auch da gibt es Licht und Schatten – dann müssen wir ehrlich sein: Im Vertrieb sind wir kein Hightech-Land. Und das ist verrückt, denn Vertrieb ist die wichtigste Funktion im Unternehmen. Sales solves everything. Wenn der Umsatz nicht da ist, sind alle anderen Themen meistens auch nicht mehr viel wert. Markus Milz bringt es auf den Punkt: Er fragt in seinen Keynotes regelmäßig sein Publikum, wer der Meinung sei, dass sich die Welt in den letzten sechs Jahren drastischer geändert habe als in den 25 Jahren davor. 95 Prozent heben die Hand. Dann fragt er, wer das super findet. Da heben nur noch zögerlich 10 Prozent die Hand. Die meisten finden das eher doof – aber kannst du nicht ändern. Die entscheidende Frage ist die nächste: Hast du in den letzten sechs Jahren deinen Vertrieb, deine Strategie, dein Geschäftsmodell drastischer geändert als in den 30 Jahren davor? Da gucken die Leute meistens betreten auf den Boden. Nicht so richtig. Und genau das ist das Problem. Die Geschwindigkeit der Veränderung wird massiv unterschätzt Schau dir an, wie lange Technologien historisch gebraucht haben, sich durchzusetzen. Die Elektrizität: Edison erfand 1880 die Glühbirne. Erst 40 Jahre später war die Welt halbwegs elektrisch. Innovationen brauchten in der Regel fünf bis zehn Jahre, um sich durchzusetzen. Und dann kam ChatGPT. Zwei Monate bis zu 100 Millionen Usern. Heute, keine drei Jahre später, sind wir bei 1,2 Milliarden Usern. Das ist eine Geschwindigkeit, die alles, was wir bisher kannten, in den Schatten stellt. Wenn ich dann ins Publikum frage, wer KI auf dem Handy hat, melden sich 90 bis 95 Prozent. Frage ich, wer es richtig beruflich nutzt, sind es nur noch 20 Prozent. Die meisten nutzen es für Kochrezepte oder ihr Fitnessprogramm. Beruflich – oder gar im Sales – herrscht große Zurückhaltung. Vielleicht mal eine E-Mail schreiben lassen, mal etwas zusammenfassen. Aber dann ist meistens Schluss. Und das ist schade. Denn da fängt es ja erst an. Warum der deutsche Mittelstand zögert: Das Klopapier-Phänomen Markus erzählt eine wunderbare Anekdote von seinem Kollegen Professor Clemens Gewittke: Warum haben die Menschen während Corona eigentlich Klopapier gekauft? Weil Menschen aktionistisch getrieben sind. Wenn etwas Neues kommt und ich nicht weiß, was zu tun ist, mache ich irgendwas. In Frankreich kauften die Leute Rotwein und Kondome. In Amerika wahrscheinlich Waffen. In Deutschland eben Klopapier. Genau das beobachten wir aktuell beim Thema KI im Vertrieb: Es wird Klopapier gekauft. Irgendwas wird ohne Sinn und Verstand probiert. Das hat strukturelle Gründe. Deutschland hat in den letzten 80 Jahren enormen Wohlstand aufgebaut. Drei Millionen Unternehmen, viele Hidden Champions. Und wer viel hat, hat auch viel zu verlieren. Hinzu kommen die etablierten Sätze: „Es hat noch immer gut gegangen." Oder: „Das dürfen wir nicht wegen DSGVO." „Wo werden die Daten gespeichert?" „Das halluziniert doch." „Da gibt es Risiken und Nebenwirkungen." Und vor allem: „Ich will keine Fehler machen." Die deutsche Fehlerkultur als Bremse Eine durchschnittliche Buying-Center-Größe hat sich in den letzten 40 Jahren von drei auf 13 Personen erhöht. 10 Menschen mehr, die in eine Entscheidung eingebunden sind. Warum? Weil keiner mehr Risiken übernehmen will. Aus Angst, Fehler zu machen und damit die Karriere zu ruinieren, wird lieber gar nichts entschieden als das Falsche. Ich habe einen Kunden, der hat die Handynummern seiner Kunden aus dem CRM gelöscht, weil er sie ja nicht besitzen darf. Juristisch vielleicht korrekt – aber bringt das wirklich nach vorne? Eine Statistik bringt es auf den Punkt: 65 Prozent der Unternehmen in Deutschland haben schon einmal eine Investitionsentscheidung wegen DSGVO nicht getroffen. Das läuft möglicherweise nicht ganz in die richtige Richtung. Während wir hier diskutieren, ob Daten auf deutschen oder amerikanischen Servern liegen, baut Jeff Bezos gerade einen 102-Milliarden-Dollar-Fonds auf, um genau diese zögerlichen Unternehmen zu kaufen. Projekt Prometheus: Wenn Bezos vor der Tür steht Jeff Bezos hat einen Fonds aufgelegt, den er Projekt Prometheus genannt hat. 102 Milliarden Dollar. Nicht nur er, ein paar andere sind auch dabei. Der Plan: Gute deutsche und europäische Unternehmen kaufen, bei denen echtes Know-how vorhanden ist – Ingenieurskultur, gute Hardware, tolle Maschinen –, die aber digital und vertrieblich schwach aufgestellt sind. Diese Unternehmen werden gekauft, in die Digitalisierung gebracht und ihr Wert wird auf das 10-, 20-, 50- oder 100-fache skaliert. Deutschland mit dem größten Mittelstand und den meisten Hidden Champions ist für Bezos ein Traumland. Und jetzt hast du als mittelständischer Unternehmer zwei Möglichkeiten: Du wartest, bis Bezos anruft. Oder du nimmst das Thema selbst in die Hand. Stell dir vor, Bezos ruft dich an und sagt: „Ich habe gerade zehn Unternehmen gekauft. Mach die mal fit. Digital, vertrieblich." Wenn du wartest, kauft er deinen Wettbewerber – und dann hast du ein echtes Problem. Das Gute: Du kannst heute mit relativ geringen finanziellen Mitteln sehr viel erreichen. KI ist ein Meister darin, Massendaten zu verarbeiten, zu aggregieren und zu intelligenten Strukturen zusammenzufassen. Was früher Konzernen vorbehalten war, kann heute auch ein 50-Mann-Mittelständler nutzen. Du musst es nur tun. Hebel 1: Inspiration tanken – die Reise nach Aarhaus Wie alles im Leben beginnt auch die Veränderung mit einer Emotion. Mit dem Gefühl: Worüber rede ich eigentlich? Wo will ich hin, wenn ich von Digitalisierung spreche? Wenn du heute zehn Unternehmen fragst, ob sie eine Digitalstrategie haben, sagen alle ja. Bittest du sie zu definieren, was sie meinen, bekommst du zehn komplett unterschiedliche Antworten. Markus empfiehlt einen Besuch in Aarhaus im Münsterland. Eine 40.000-Einwohner-Stadt direkt an der holländischen Grenze, die als digitalste Stadt Deutschlands gilt. Die Idee dort: Alles ist mit allem vernetzt. Du brauchst eine einzige App auf deinem Handy. Damit gehst du in den Supermarkt – ohne Geld, ohne Personal. Du gehst ins Hotel, ins Restaurant, ins Fitnessstudio. Du leihst dir Fahrräder oder Autos aus. Eine App, eine Verbindung. Lohn- und Gehaltsabrechnung, Personaldisposition – alles funktioniert ohne menschlichen Einsatz. KI macht uns wieder menschlicher Jetzt denkst du vielleicht: Total entmenschlicht. Ich sehe das anders. KI ist die Chance, dass wir Menschen wieder menschlicher werden. Wir werden von all dem Mist entlastet, auf den niemand Lust hat – Besuchsberichte schreiben, CRM pflegen, Buchhaltungsbelege sortieren. Stattdessen können wir uns auf das konzentrieren, was nur Menschen können: miteinander reden, Mittagessen gehen, ein Bier trinken, echte Beziehungen aufbauen. Gerade im Vertrieb ist das der eigentliche Wertbeitrag. Hinter Aarhaus steht Tobias Groten, der Chef von Tobit. Das Unternehmen hat in den 80ern und 90ern mit Fax-Software begonnen und sich kontinuierlich weiterentwickelt. Heute haben sie eine eigene KI namens Sidekick. Immer wenn in Aarhaus ein Supermarkt, ein Kiosk, ein Hotel oder ein Restaurant pleite ging, hat Tobias gesagt: „Dann nehme ich das." Und weil er kein Hotelier oder Gastronom ist, sondern Techie, hat er das Konzept Hotel komplett neu gedacht. Das ist Disruption: nicht kontinuierliche Verbesserung, sondern radikales Neudenken. Hebel 2: Social Listening – Leads auf dem Silbertablett Wenn ich in einen mittelständischen Maschinenbauer komme und frage, was seine fünf Hauptvertriebskanäle für neue Projekte sind, höre ich in 95 Prozent der Fälle: Messen, Anfragen, Ausschreibungen, internationale Handelsvertreter und ein bisschen Cold Calling. Das war vor 20 oder 30 Jahren genauso. Wir sind aber im Jahr 2026. Schau dir das Organigramm an: Hier ist Marketing, das macht ein bisschen Homepage und Social Media. Hier ist Vertrieb, der geht raus oder macht das, was er immer gemacht hat. Das kann doch im Zeitalter von KI im Vertrieb nicht mehr sein. Ein konkretes Beispiel von Markus: Er hat einen Catering-Anbieter betreut. Was macht so ein Unternehmen normalerweise? Cold Calling. 100 Anrufe: „Brauchst du eine Kantine?" – „Nein." – „Brauchst du eine Kantine?" – „Nein." Mit etwas Glück sagen zwei oder drei „Lass uns mal sprechen" und am Ende gewinnst du vielleicht einen Kunden. Streuverlust: 98 Prozent. Demotivierend für jeden Vertriebler. So funktioniert modernes Social Listening Jetzt der neue Weg: Massenhaft Daten sind in Social Media verfügbar. Menschen gehen jeden Tag in Kantinen und schreiben auf Facebook oder Instagram, ob es geschmeckt hat oder nicht. KI aggregiert diese Daten. Du stellst fest: Bei Unternehmen XY haben sich in den letzten 12 Monaten 47 Mitarbeiter negativ über das Essen geäußert. Das ist ein klares Signal. Gleichzeitig schaut die KI in Pressemitteilungen: 2022 wurde ein Vierjahresvertrag mit dem aktuellen Caterer abgeschlossen. Der läuft 2026 aus. Die KI identifiziert das Buying Center und liefert dir den Hauptentscheider Peter Mayer inklusive Persönlichkeitsprofil: faktenbasiert, braucht erst Vertrauen, am besten Testimonials einsetzen. Das ist, als würde ein Freund anrufen und dir den perfekten Lead servieren – nur dass du diesen Freund nicht mehr brauchst. Du bekommst es systematisch jeden Tag, jede Woche geliefert. Statt 100 unqualifizierten Calls hast du fünf bis sieben hochwertige Leads. Du bist deutlich effizienter, weil du dich mit mehr interessierten Kunden beschäftigst. Und dein Team muss mental nur noch fünf statt 97 Absagen verarbeiten. Das Thema Resilienz spielt plötzlich eine ganz andere Rolle. Die Konsequenz: Sales und Marketing wachsen zusammen. Marketing liefert dem Vertrieb vorqualifizierte Leads. Du brauchst neue Strukturen – eine aggregierte Abteilung, die Datenmanagement, Sales, Marketing, KI und Digitalisierung unter einem Hut vereint. Mit alten Strukturen geht das nicht. Hebel 3: Das externe Lab – raus aus der Lähmung Warum wird das alles in deutschen Unternehmen so selten systematisch angegangen? Weil zehn Leute mitzureden haben. Weil der Betriebsrat viele Sachen nicht will. Wegen DSGVO, Compliance, Governance. Wegen der Fehlerkultur: Hier sind 100.000 Euro, berichten Sie in drei Monaten. Wenn dann noch keine richtigen Erfolge da sind – zack, ist die Karriere ruiniert. Aus diesen Gründen passiert intern relativ wenig. Oder es wird Klopapier gekauft. Markus' Lösung: ein externes Lab, analog zum Fraunhofer-Prinzip. Du lagerst die Entwicklung aus. Dort gelten komplett andere Spielregeln als im Mutterunternehmen: So baust du ein externes Innovationslab für deinen Vertrieb auf: 30-Tage-Entscheidungsregel: Innerhalb von 30 Tagen muss eine Entscheidung über jede Idee getroffen sein. Kein endloses Hin und Her. 90-Tage-Pilot: Innerhalb von 90 Tagen ist der Use Case pilotiert. Geschwindigkeit ist alles. Datenschutz extern lösen: Das Lab kümmert sich um DSGVO, Betriebsrat und Compliance – nicht deine interne IT. Use Cases systematisch bewerten: Wie groß ist der Impact? Wie hoch der Aufwand? Was ist das beste Verhältnis? Zurück ins Unternehmen: Wenn die Lösung läuft, holst du sie zurück und skalierst sie. Mit diesem Ansatz externalisierst du das, was du intern nicht hinbekommst. Im Lab sitzen Dienstleister, Kollegen vom Kunden und Experten. Sie definieren Use Cases, erstellen eine Roadmap und bringen die Themen schnell auf die Straße. Nach 90 Tagen hast du mega qualifizierte Leads, mega qualifizierte Tools und mega qualifizierte Prozessoptimierungen. Nicht nur im Vertrieb, sondern auch im Einkauf, in HR, in der Unternehmenskommunikation. Hebel 4: Schnittstellenprobleme mit KI lösen Jeder, dem ich das erzähle, sagt zunächst: „Bei uns ist das aber anders. Unsere Branche ist speziell. Unsere Kunden sind anders." Die grundlegenden Dinge bleiben aber gleich. Was sich in fast allen Branchen findet: eine Branchensoftware als zentrales System, dazu DATEV, Excel-Listen, diverse Spezialtools – und die reden kaum miteinander. Ein Beispiel aus der Sicherheitsbranche: Bei einem Großeinsatz wird zuerst ein Angebot an den Kunden erstellt. Dann folgt die Planung für das konkrete Event. Anschließend kommt die Zeiterfassung mit den Logins der eingesetzten Mitarbeiter. Glaubst du, es gibt einen vernünftigen Abgleich zwischen diesen Systemen? Fehlanzeige. Genau hier kommt KI ins Spiel: Sie führt verschiedene Systeme über Schnittstellen zusammen, die vorher nicht miteinander gesprochen haben. Vom analogen Mist zum optimierten Prozess Wichtig: Wenn du einen schlechten analogen Prozess einfach nur digitalisierst, hast du einen schlechten digitalen Prozess. Das bringt nichts. Die Zeitenwende ist der optimale Zeitpunkt, dein Unternehmen neu zu denken. Erst optimierst du die Prozesse und Strukturen. Dann digitalisierst du sie. Dann bringst du KI ins Spiel. Und wenn du das gemacht hast, hast du im Zweifel ein Tool, das du 1.000 anderen Unternehmen deiner Branche auch verkaufen kannst. Riesige Vertriebschancen. Ein konkretes Beispiel aus meinem Alltag: Früher war meine Kreditkartenabrechnung ein Riesenthema. Belege sammeln, am Ende des Quartals kam der Buchhalter, fragte nach fehlenden Belegen – mit wem warst du wann essen? Riesenaufwand. Heute habe ich eine App. Beim Bezahlen geht sofort ein Fenster auf: Beleg fotografieren, Gesprächspartner eintragen. Das CRM greift zu, ordnet einen Buchungssatz zu und schiebt alles automatisch in DATEV. Digitalisierter Prozess. Schneller, besser und am Ende auch billiger – weil die Buchhaltung hinten raus weniger Arbeit hat. Hebel 5: Dein digitaler Vertriebsassistent – treffe Alfred Die fünfte und letzte Stufe ist die Königsdisziplin: ein agentic AI-System, das wirklich für dich arbeitet. Markus und sein Sohn sind beide Batman-Fans. Bekanntlich heißt Batmans Butler Alfred. Genau so haben sie ihren neuen Kollegen genannt. Alfred basiert auf Open-Source-Architektur und hat alle großen Large Language Models angebunden: Gemini, Claude, Perplexity, ChatGPT, Grok. Alfred entscheidet selbst, welches Modell für welche Aufgabe am besten geeignet ist – oder am kostengünstigsten arbeitet. So sieht ein typischer Arbeitstag aus: Markus ist beim Kunden, auf dem Rückweg spricht er über WhatsApp in sein Handy: „Alfred, ich bin in 20 Minuten im Büro. Bestell beim Inder über Lieferando ein Chicken Tikka Masala. Und ich habe mit dem Kunden gerade ein größeres Projekt besprochen – Bedarfsanalyse, Workshop, Mitarbeiterinterviews, dann Training. Erstell schon mal das Angebot, du hast alle Daten." Wenn Markus im Büro ankommt, ist das Angebot zu 90 Prozent fertig. Die menschliche Verbesserungskompetenz bleibt entscheidend Wir Menschen haben eine sehr überschaubare Erstellungskompetenz. Wenn ich vor einem leeren Blatt Papier sitze und ein Marketingkonzept entwickeln soll, brauche ich Stunden. Eine KI liefert mir mit dem richtigen Befehl in Minuten eine 80-Prozent-Lösung. Was Menschen aber wirklich gut können, ist die Verbesserungskompetenz. Aus der 80-Prozent-Lösung machst du mit deiner Expertise eine 100-Prozent-Lösung. Genau deshalb glaube ich übrigens fest, dass das Thema KI im Vertrieb nicht den Tech-Companies gehört, sondern den Experten, die das Unternehmen, den Mittelstand, den Kunden verstehen. Programmieren musst du heute nicht mehr können. Das macht die KI für dich. Aber du musst das Geschäftsmodell verstehen, Erfahrungswissen mitbringen und die Kunden kennen. Auf dieser Basis bauen wir saubere Strukturen und saubere Prozesse. Mein Tipp aus dem Alltag: Wann immer mir jemand eine Aufgabe stellt, über deren Beantwortung ich länger als fünf Sekunden nachdenken müsste, mache ich das sofort mit meinem KI-Agenten. Die 5-Sekunden-Regel ist Gold wert. Quick Takeaways: Die wichtigsten Erkenntnisse auf einen Blick Geschwindigkeit als entscheidender Faktor: ChatGPT erreichte in 3 Jahren 1,2 Milliarden Nutzer – Veränderungen geschehen heute exponentiell schneller als früher. Klopapier-Falle vermeiden: Aktionismus ohne Strategie schadet mehr, als er nützt. Erst Vision, dann Struktur, dann Tools. Social Listening schlägt Cold Calling: Hochqualifizierte Leads auf dem Silbertablett statt 98 Prozent Streuverlust. Externes Lab nutzen: Was intern nicht geht, kannst du auslagern – mit 30-Tage-Entscheidungen und 90-Tage-Piloten. Strukturen neu denken: Marketing, Sales, Datenmanagement und KI gehören in eine integrierte Einheit – nicht in Silos. Digitaler Assistent als Game Changer: Ein agentic AI-System wie „Alfred" erledigt 80 Prozent der Vertriebsadministration für dich. Experten schlagen Techies: Wer Unternehmen, Mittelstand und Kunden versteht, schafft mit KI nachhaltigen Mehrwert. Fazit: Jetzt ist die Goldgräberzeit Wir reden viel von Krise, Unsicherheit und schwierigen Zeiten. Ein Historiker hat es kürzlich treffend formuliert: Die letzten 50 bis 60 Jahre nach dem Zweiten Weltkrieg waren eine absolute Ausnahmesituation. Das, was wir jetzt erleben, ist eigentlich die Normalzeit der Menschheitsgeschichte. Und schau dir an, wann die wirklich großen Unternehmen gegründet worden sind: meistens nicht in den guten Zeiten, sondern in Krisenzeiten. Weil ihre Gründer Trends erkannt haben, die andere übersehen haben. Genau deshalb ist jetzt eine Goldgräberzeit. Es gibt überall Chancen, wenn du sie sehen willst. Den Kopf in den Sand zu stecken hilft nicht – die anderen laufen dann an dir vorbei. Stell dir die Bezos-Frage: Wenn Bezos morgen dein Unternehmen kaufen würde, was würde er anders machen? Welche Stärken hat dein Unternehmen, die mit Digitalisierung und KI im Vertrieb auf das Zehnfache skaliert werden könnten? Mein Call to Action: Buche dir ein Strategiegespräch mit Markus und mir. Wir nehmen uns eine Stunde Zeit, schauen uns deine aktuellen Herausforderungen an und zeigen dir aus unserem Erfahrungshintergrund, wie du schnell zum Hightech-Vertrieb wirst. Die ersten drei, die sich anmelden, bekommen außerdem zwei Bestsellerbücher von Markus obendrauf. FAQ: Die wichtigsten Fragen rund um KI im Vertrieb Was bedeutet Hightech-Vertrieb im Mittelstand konkret? Hightech-Vertrieb bedeutet, dass deine Vertriebsorganisation modern aufgestellt ist – mit aktueller Technologie, intelligenten Prozessen und einer Struktur, die zur heutigen Zeit passt. Es geht darum, KI im Vertrieb, Social Listening, datenbasierte Lead-Qualifizierung und digitale Assistenten so einzusetzen, dass dein Team mehr Umsatz und Marge generiert – und sich gleichzeitig auf das Menschliche konzentrieren kann. Wie kann ich meinen Vertrieb digitalisieren, ohne riesige Budgets zu haben? Das Schöne an aktueller KI-Technologie ist, dass du mit überschaubaren finanziellen Mitteln viel erreichen kannst. Starte mit einem Erkenntnis-Workshop, identifiziere die größten Hebel und beginne mit konkreten Use Cases statt mit Großprojekten. Ein externes Lab kann helfen, schnell Ergebnisse zu liefern, ohne deine interne IT zu blockieren. Was ist Social Listening und wie hilft es im B2B-Vertrieb? Social Listening bedeutet, dass KI öffentlich verfügbare Daten aus Social Media, Pressemitteilungen und Bewertungen analysiert und daraus Verkaufschancen identifiziert. Im B2B kannst du so gezielt Unternehmen finden, die gerade mit ihrem aktuellen Anbieter unzufrieden sind oder deren Verträge auslaufen – inklusive der relevanten Entscheider. Wie überwinde ich interne Widerstände wie DSGVO oder Compliance? Diese Themen sind real, aber lösbar. Ein externes Innovationslab kümmert sich um diese Hürden, weil dort andere Spielregeln gelten als im Mutterunternehmen. So kannst du innerhalb von 90 Tagen pilotieren, was intern jahrelang dauern würde – und holst die fertige Lösung dann zurück ins Unternehmen. Ersetzt KI den Vertriebsmitarbeiter? Nein, im Gegenteil. KI nimmt dir die Routinearbeit ab – CRM-Pflege, Besuchsberichte, Angebotserstellung. Damit kannst du dich auf das konzentrieren, was nur Menschen können: echte Beziehungen aufbauen, Vertrauen schaffen, komplexe Verhandlungen führen. KI macht Vertrieb wieder menschlicher. Sag mir deine Meinung Ich bin echt gespannt: Wo stehst du gerade beim Thema KI im Vertrieb? Bist du schon mitten in der Umsetzung oder noch im Klopapier-Modus? Schreib mir deine Erfahrungen, deine Herausforderungen oder deine Erfolgsgeschichten in die Kommentare. Und wenn dir diese Folge weitergeholfen hat, dann teile sie gerne mit deinem Netzwerk. Welcher der fünf Hebel ist für dich der spannendste?
When AI can draft a literature review in minutes, the question bioprocess educators can no longer avoid is this: what does a student actually need to learn?Steffen Kreye has a clear answer. As Professor of Industrial Biotechnology at Berliner Hochschule für Technik, he trains engineers who step into industry ready to run a bioreactor, not just describe one. His argument is direct: hands-on lab competence is the one thing AI cannot replicate, and it is exactly what underfunding is quietly eroding.Topics discussed:Why Steffen Kreye left his lab head role at Bayer to become a professor and how his career evolved (03:54)The unique mission of universities of applied sciences and their close connection to industry needs (11:16)Challenges of delivering lab-based education, including funding and equipment constraints (12:32)Creative strategies for partnering with biotech companies to sustain practical lab courses (14:34)How reading student theses, partnerships, and conferences help Steffen Kreye and his colleagues stay current in a rapidly changing field (17:43)The impact of AI and digital tools on research, teaching methods, and student assessment (21:18)Why traditional theoretical projects are less relevant, and the growing importance of problem-solving and oral examinations (22:09)In Part 2, Steffen gives his unfiltered take on where AI in bioprocessing actually stands, which human capabilities are becoming harder to replace, and what a well-prepared bioprocess engineer will need to look like by 2035.Smart Insight: Once AI can produce a polished report from a well-structured prompt, the only assessment that still reveals genuine understanding is the one a student has to navigate in real time, without a tool to hide behind.Here are some other guests who touched on similar themes:Episodes 175 – 176 : How Virtual Reality Training Solves Europe's Bioproduction Talent Shortage with Sandrine Lemoine — about training the next generation of biopharma talent.Episodes 93 – 94: From Lab Coat to LinkedIn: Benjamin McLeod's Journey to Cell and Gene Therapy Influencer — another career pivot story from a scientist who stepped outside the traditional industry path.Episodes 111 – 112: AI Meets Biology: Why Domain Expertise Still Rules in the Age of Large Language Models with Lars Brandén — very aligned with Steffen's nuanced take that AI is a tool but human expertise in bioprocessing still matters.Connect with Steffen Kreye:LinkedIn: www.linkedin.com/in/steffen-kreye-3b531183/Berliner Hochschule für Technik: www.prof.bht-berlin.de/kreyeNext Step:If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. Stay tuned for more inspiring biotech insights in our next episode.Support the show
ChatGPT and the other Large Language Models (LLMs) that have followed started off as chatbots that were pretty good at writing. But it quickly became apparent that that kind of use was just the tip of the iceberg. The nonprofit Poynter Institute in St. Petersburg has been training journalists, newsroom leaders, and media executives since the mid 1970s. They offer seminars and coaching on the craft of reporting, as well as ethics, leadership, and digital adaptation — which of course now includes the use of Generative AI. We talk with a longtime journalist who is now a faculty member at Poynter to get some context on the nexus between Generative AI and journalism.
Large Language Models can generate a lot of text - but is it any good? Carl and Richard talk to Vishwas Lele about his ongoing efforts at pWin.ai to build tools for responding to government RFPs. Vishwas focuses on the quality problem - both the quality of the incoming RFP and the quality of the responding proposal. How do you determine the key requirements of an RFP reliably? And when it comes to the response, how do you provide measurable results for a response? The conversation digs into a change in workflow that benefits the RFP process regardless of tooling - and gives hints to the patterns of success with LLMs!
What if artificial intelligence doesn't replace human intelligence — it amplifies it? And what if the quality of what you bring to AI is exactly what determines what you get back?Welcome to Now I Get It with Dr. Andy. I'm Andrew Winkler, and in this episode I'm taking a deep dive into one of the most consequential technologies of our time: large language models. I break down how these systems are built on surprisingly elegant mathematics, why language itself has a hidden statistical structure that makes AI possible, and what it really means for how we interact with these powerful tools.Tune in as I explore the neural network foundations that underpin modern AI, unpack the "garbage in, garbage out" principle in its most precise form, and reveal why the most important thing you can bring to an AI conversation is your own intelligence and curiosity.In this episode, you will learn:(00:27) Neural networks are built on elegant mathematics(01:15) One nonlinearity unlocks AI's power to model anything(02:47) Models extract signal, not just memorize data(04:30) Language has a hidden statistical structure AI can learn(08:30) AI defaults to average intelligence without strong context(09:03) Smarter input produces smarter AI output(09:45) AI amplifies human intelligence — it doesn't replace itLet's connect!linktr.ee/drprandy Hosted on Acast. See acast.com/privacy for more information.
Large Language Models can generate a lot of text - but is it any good? Carl and Richard talk to Vishwas Lele about his ongoing efforts at pWin.ai to build tools for responding to government RFPs. Vishwas focuses on the quality problem - both the quality of the incoming RFP and the quality of the responding proposal. How do you determine the key requirements of an RFP reliably? And when it comes to the response, how do you provide measurable results for a response? The conversation digs into a change in workflow that benefits the RFP process regardless of tooling - and gives hints to the patterns of success with LLMs!
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
This week we sit down with Field Solution Architects Anthony Nocentino and Justin Emerson explore an interesting convergence happening in data architecture—the blending of traditionally separate block and file/object storage systems. Likening the experience to a Chocolate Peanut Butter Cup, Anthony (a database expert focused on block storage) and Justin (an expert in unstructured data and file/object storage) discuss how the clear historical distinctions between structured and unstructured data are rapidly blurring. This shift is fueled by modern challenges like high-scale analytics, data governance, and the rise of technologies like Large Language Models (LLMs) and agentic interactions, which no longer care where the data lives. Our conversation dives into the technical tipping point enabled by data virtualization, referencing features like SQL Server 2022's object integration, which allows a database engine to access data stored efficiently on object storage. This capability is far more than an archival play; it helps customers achieve scale-out analytics, improve data governance by maintaining one canonical copy of data across different performance buckets, and simplify tedious operations like SQL backups by bypassing legacy file system complexities. Anthony and Justin highlight how Everpure's platform aligns perfectly with this new reality. Finally, Anthony and Justin discuss the path forward, noting that the technology is underutilized due to organizational silos and an awareness problem. The next big evolution will focus on security and governance for this distributed data via open table formats like Iceberg and catalogs such as Polaris. We close with what currently excites them: Anthony on collaborating with AI (Claude) to create code and speed up outcomes, and Justin on Everpure's core philosophy of simplicity, efficiency, and treating customers like people, particularly in the context of the current economic conditions. To learn more, visit: https://www.everpuredata.com/platform.html Check out the new Everpure digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Career Journeys 04:30 Customer Engagement and SKO 09:55 Vacation Recap 13:45 History of Block and Object Storage 16:04 Why Convergence Now? 20:30 Data Virtualization 25:55 Exploring Access Patterns 29:05 What's Holding Back Adoption 36:02 Simplicity for DBAs
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
OpenAI, Microsoft, and Google are racing to unleash next-gen AI that hunts for software vulnerabilities and hacks at scale. This episode explores how these advancements could shake up everything we thought we knew about cybersecurity. Microsoft rethinks Edge's "intended behavior" after it gets press. Chaotic Eclipse hacker strikes again with a Bitlocker bypass. Google's threat analysis group documents malicious AI use. Canada hasn't learned the lessons of the EU and the UK. AI chatbots may be far more addictive than social media. Project: Hail Mary now available to stream. An apparently-serious zero-point quantum vacuum energy source. A bit of listener feedback. OpenAI's & Microsoft's vulnerability discovery systems Show Notes - https://www.grc.com/sn/SN-1079-Notes.pdf Hosts: Steve Gibson and Leo Laporte Download or subscribe to Security Now at https://twit.tv/shows/security-now. You can submit a question to Security Now at the GRC Feedback Page. For 16kbps versions, transcripts, and notes (including fixes), visit Steve's site: grc.com, also the home of the best disk maintenance and recovery utility ever written Spinrite 6. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: outsystems.com/twit hoxhunt.com/securitynow zscaler.com/security meter.com/securitynow canary.tools/twit - use code: TWIT joindeleteme.com/twit promo code TWIT
In this episode of Fraudology, Karisse Hendrick comes to you from a beachside work-cation in Florida to deliver an essential debrief on the latest shifts in the e-commerce fraud landscape. Fresh off the Accertify Global Customer Summit, Karisse shares key strategic takeaways on why cybersecurity and fraud teams must break down operational silos as fraud signals increasingly move up-funnel.The conversation takes a critical look at the limitations of relying on Large Language Models (LLMs) in risk management. Highlighting a recent blunder where a Top 4 consultancy published a 44-page fraud report riddled with completely fabricated citations and footnotes, Karisse and Dr. Nicola Harding explain why "domain expertise" cannot be automated. Because true fraud insights are kept proprietary to protect them from criminals, open-source AI tools are inherently prone to "hallucinating" facts.We also break down Mastercard's newly announced Scam Merchant Dashboard, which officially goes into effect on July 24th, 2026. This aggressive program places a heavy burden on e-commerce merchants and their acquirers through a multi-trigger framework designed to shut down predatory accounts.Key pillars of Mastercard's new program include:The Authorization Performance Breakdown: A sudden drop in approval rates—such as a 50 percentage point decline or falling below a 30% overall threshold within a 72-hour window—will immediately trigger an investigation.The New Merchant 5% "Math": For accounts open less than six months, Mastercard is introducing a brand-new metric: combining refunds and chargebacks divided by overall sales. Crossing a 5% threshold over a rolling 30-day period (with at least 500 transactions) risks immediate account review.The 72-Hour Termination Clock: Once flagged by issuer complaints or network alerts, acquirers have a strict 72-hour window to either prove the merchant's legitimacy or completely terminate their ability to accept Mastercard.
In this episode of the Crazy Wisdom Podcast, host Stewart Alsop sits down with Joshua Bate, founder of Bonfires.ai and DeciWorld, for a wide-ranging conversation covering knowledge management, graph technology, ontologies, decentralized science, and the future of how humans organize and share information. They break down the differences between personal and enterprise knowledge management, explore why flat ontological graphs may be the key to making diverse knowledge bases interoperable, and get into why traditional RAG systems break down at scale and how graph RAG offers a more principled solution. The conversation expands into the philosophy of categorization, the slow death of basic "gentleman science" under institutional pressures, and how decentralized protocols might restore a kind of mycelial knowledge network connecting small groups of researchers, enthusiasts, and communities — much like the original spirit of the encyclopedia before it was co-opted by institutions. You can learn more about Joshua's work at bonfires.ai and deci.world or follow him on X at @Bonfiresai and @DeSciWorld.Timestamps00:00 - Stewart introduces Joshua Bate, founder of Bonfires.ai, discussing personal versus enterprise knowledge management and their fundamental differences at scale.05:00 - Joshua explains ontologies as classifiers for knowledge structures, describing their two-year search for a perfect ontology and ultimately building a flat, ontology-less graph protocol.10:00 - Stewart connects categorization to shamanic practice and intercategorical theory, noting how major companies like Netflix and Yahoo built graph-based ontologies while the discipline remains underappreciated philosophically.15:00 - Joshua traces Bonfires origins through decentralized science, explaining how NFT community excitement inspired redirecting capital toward funding unconventional researchers locked out of institutional systems.20:00 - Joshua describes building federated knowledge networks through hackathons and conferences, comparing the vision to what Wikipedia could have been with decentralized incentive structures.25:00 - Discussion shifts toward inevitable collapse of rigid scientific institutions, debating patchwork age theory, nation-state fragmentation, and rhizomatic versus arboreal knowledge structures.30:00 - Joshua articulates the mycelial network vision, enabling direct cross-cultural information access where individuals control their own narrative lens, warning against collective we thinking and authoritarianism.Key Insights1. Knowledge management exists on a spectrum from personal to enterprise, but the founder of Bonfires argues this split is artificial. He believes knowledge itself does not respect those boundaries, and that small groups, researchers, hobbyists, and large institutions all possess knowledge that can and should interoperate with each other.2. After two and a half years of searching for the perfect ontology to structure their knowledge graph, the team concluded that no perfect ontology exists. Their solution was to build the flattest possible graph structure with only events, entities, and edges, creating a base layer others can build specialized ontologies on top of.3. Graph-based knowledge systems are more efficient than traditional databases for AI traversal because once a graph is computed, it is relatively free to query. Graph RAG combines the discovery power of vector search with the structured precision of graph traversal, solving many hallucination problems associated with standard retrieval augmented generation.4. Basic scientific research, the soil from which applied discoveries grow, is deteriorating because institutional funding structures only reward commercially viable outcomes. The founder built his platform partly to redirect community-driven capital toward researchers who are doing important work without institutional support.5. The institutionalization of science has historically blocked the open exchange of ideas that drove the original scientific revolution. The human spirit for open inquiry has not changed, but people cannot pursue it without financial support, and building decentralized infrastructure could restore that possibility.6. A federated knowledge network would allow individuals to access information from any contributor and filter it through their own preferred lens, rather than receiving information pre-filtered by centralized platforms. This represents a form of information symmetry similar to how mycelial networks distribute nutrients across a forest.7. The concern is not whether current scientific and governmental institutions will change but in what direction the rebuilding goes. Those capitalizing on the transition carry the same incentives as the previous era, which risks reproducing the same problems inside new structures.
In this podcast Michael Stiefel spoke to Baruch Sadogursky about software architecture in the age of agentic AI. Large Language Models can function, albeit stochastically, as reasoning machines capable of interpreting human ambiguity. With the appropriate rigorous context artifacts to control the LLM's reasoning, software specifications can become the source of truth, while the code becomes a disposable intermediate language. These context artifacts are managed through an engineering discipline, context engineering. Unlike prompt engineering which Sadogursky likened to “voodoo incantations”, context engineering utilizes artifacts such as skills, rules, scripts, feedback, and rigorous evaluation to provide the models with clear intent on what code to write. AI Agents will ask clarifying questions to the architects and clients until the requirements are fully understood. This allows a massive “shift left” to evaluate code quality before it is even written. Testing now validates the accuracy of the specifications. Humans are still responsible for determining the correctness of the requirements by providing the proper context, and validating the final results. Since changes over the course of time will occur, resulting in the regeneration of the code from the specifications, microservices is the best architectural paradigm to use given the current limitations on context windows for LLMs. Orchestration of the services is then done by a human architect to create the application. The architect is also responsible for managing the emergent properties of the system. Read a transcript of this interview: https://bit.ly/48YWu2n Newsletter: Subscribe to the Software Architects' Newsletter for your monthly guide to the essential news and experience from industry peers on emerging patterns and technologies: https://www.infoq.com/software-architects-newsletter InfoQ online certification cohorts: Online cohorts for senior engineers and architects, built around QCon talks. Join a 5-week confidential peer group to validate your approach and apply practitioner frameworks to the technical challenges you face at work. Learn more: https://certification.qconferences.com/ Upcoming Events: QCon AI Boston 2026 (June 1-2, 2026) Learn how real teams are accelerating the entire software lifecycle with AI. https://boston.qcon.ai QCon San Francisco 2026 (November 16-20, 2026) https://qconsf.com/ The InfoQ Podcasts: Weekly inspiration to drive innovation and build great teams from senior software leaders. Listen to all our podcasts and read interview transcripts: - The InfoQ Podcast https://www.infoq.com/podcasts/ - Engineering Culture Podcast by InfoQ https://www.infoq.com/podcasts/#engineering_culture - Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: - Mastodon: https://techhub.social/@infoq - X: https://x.com/InfoQ?from=@ - LinkedIn: https://www.linkedin.com/company/infoq/ - Facebook: https://www.facebook.com/InfoQdotcom# - Instagram: https://www.instagram.com/infoqdotcom/?hl=en - Youtube: https://www.youtube.com/infoq - Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Learn and share the changes and innovations in professional software development. - Join a community of practitioners. - Increase your visibility. - Grow your career. https://www.infoq.com/write-for-infoq
This week we're talking all about the future of embedded software development with TASKING Co-CEO Christoph Herzog. Christoph and I explore how Large Language Models and agentic AI are moving from novelty to necessity, directing external agents within the TASKING toolchain to automate critical verification and validation tasks. We also discuss the Model Context Protocol (MCP) and how it helps maintain adherence to strict industry standards.
In this special AI edition of The Edge of Show, we sit down with Nils Pihl, CEO of Auki Labs, to explore the intersection of robotics, behavioral engineering, and the "real-world web". Based in the robotics hub of Hong Kong, Pihl explains how Auki Labs is building the posemesh protocol, a decentralized system that allows humans, robots, and AI to share a unified spatial understanding.Discover how DePIN (Decentralized Physical Infrastructure Networks) is revolutionizing industries from retail to urban planning, and why Pihl believes physical AI co-pilots will soon outperform the impact of today's Large Language Models.Support us through our Sponsors! ☕ Want to make content like ours? Sign up with Castmagic to make your creative process easy: https://bit.ly/CastmagicReferral Work smarter, grow faster. Automate your SEO, get AI insights, and manage all your clients in one place with Helm. Start today 50% off your first month at helmseo.com
What does a production-grade large language model look like? While at NDC Sydney, Richard talked with Vaishnavi Gudur from Microsoft about her work scaling LLMs for Teams transcriptions, summaries, and more! Vaishnavi discusses the underlying complexities of operating the Teams LLM infrastructure for a large array of customers across different countries and regulatory regimes. Data sovereignty also plays a large role: different countries have specific rules on where data must reside and how it can be accessed. As the scale increases and the tail gets longer, the rules set gets more complex! Lots of great thinking about what LLMs look like in a production environment. Links Transcripts in Microsoft Teams Recorded April 24, 2026
In this episode of Skip the Queue, Andy Povey is joined by Dominique Bouchard, Heritage and Engagement Director at Leeds Castle and incoming Creative Director at the Shakespeare Birthplace Trust, to explore how artificial intelligence is transforming heritage storytelling. They discuss the creation of the world's first interactive historical AI avatar, how Leeds Castle brought Queen Eleanor of Castile to life, and what this innovation means for the future of visitor engagement across heritage attractions. Topics Discussed: How artificial intelligence is reshaping heritage storytelling The creation of Leeds Castle's interactive Queen Eleanor of Castile AI avatar Balancing historical accuracy with AI driven visitor interaction The design and development process behind the world first historical avatar Using AI to create personalised visitor experiences Audience reactions to experimental heritage technology Ethical considerations of AI in museums and heritage sites How AI can support interpretation and visitor engagement The challenges of introducing emerging technology in heritage settings Blending creative storytelling with digital innovation Practical advice for attractions exploring AI adoption The future of AI within museums and heritage organisations Dominique Bouchard's upcoming move to the Shakespeare Birthplace Trust The potential for future AI driven heritage experience Show references: Dr Dominique Bouchard, Heritage and Engagement Director at Leeds Castle. Soon-to-be Creative Director at the Shakespeare Birthplace Trust. https://www.shakespeare.org.uk/about-us/news-media/press-releases/leading-uk-museum-appoints-its-first-creative-director/ Pilgrimage of Love: Eleanor of Castile https://www.leeds-castle.com/ https://www.leeds-castle.com/events/pilgrimage-of-love-eleanor-of-castile/ https://youtu.be/U29H_PHrh14?si=NDbHAwR0CTTIuApY Museum and Heritage show at Olympia London, Theatre 3 at 2:15 on Wednesday 13th May, 2026 https://show.museumsandheritage.com/ Skip the Queue is brought to you by Merac. We provide attractions with the tools and expertise to create world-class digital interactions. Very simply, we're here to rehumanise commerce. Your guest host is Andy Povey. If you like what you hear, you can subscribe on Apple Podcasts, Spotify, and all the usual channels by searching Skip the Queue or visit our website SkiptheQueue.fm. If you've enjoyed this podcast, please leave us a five star review, it really helps others find us. And remember to follow us on LinkedIn. Credits: Written by Emily Burrows (Plaster) Edited by Steve Folland Produced by Emily Burrows and Sami Entwistle (Plaster) Download The Visitor Attractions Website Survey Report - https://www.merac.co.uk/download-the-visitor-attractions-survey We have launched our brand-new playbook: ‘The Retail Ready Guide to Going Beyond the Gift Shop' — your go-to resource for building a successful e-commerce strategy that connects with your audience and drives sustainable growth. Download your FREE copy here
On this month's episode of the WHOOP Podcast Longevity series, WHOOP SVP of Research, Algorithms, and Data Emily Capodilupo sits down with Google Deepmind AI Researcher Vivek Natarajan to explore how large language models are transforming healthcare and biomedical research. Emily and Vivek discuss AI's potential to both accelerate scientific discovery, such as developing new treatments and understanding diseases, and expand access to care globally by delivering medical guidance more equitably. The episode examines how AI can augment, rather than replace, doctors by improving diagnostics, increasing efficiency, and enabling more personalized care, while highlighting the importance of trust, safety, and human connection as these technologies evolve. (00:47) Understanding The Intersection of Large Language Models and Biomedicine(02:26) How To Approach AI in the Healthcare Landscape(05:52) What Is The Role of the Doctor as AI Becomes More Popular(09:39) What Does The Future of AI Healthcare Look Like? (12:51) Clinical Care: Where Can AI Assist?(16:03) Exploring The Dangers of AI (18:43) Process of Teaching Physicians and Users To Use AI (22:22) What Are The Areas of Concern Over AI?(24:12) Regulation and Pace of Development(26:51) Gaining and Maintaining Trust While Building AI Algorithms(30:38) The Impact of AI on Research(37:44) Biggest Misconception Regarding AI in HealthcareFollow Vivek Natarajan:LinkedInSupport the showFollow WHOOP:Sign up for WHOOP Advanced LabsTrial WHOOP for Freewww.whoop.comInstagramTikTokYouTubeXFacebookLinkedInFollow Will Ahmed:InstagramXLinkedInFollow Kristen Holmes:InstagramLinkedInFollow Emily Capodilupo:LinkedIn
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