Podcasts about ML

I recorded this conversation in Alpbach, Austria, a village that looks like a postcard and hosts a very serious tech gathering. TechSummit25 is Barracuda's deeply technical event, and it shows. The rooms are packed with solution architects, product managers, and engineers comparing notes with customers who run these systems every day. It is the kind of environment where product direction and real-world pain points meet over a coffee, then head straight into a lab to test an idea. My guest today is Neal Bradbury, Chief Product Officer at Barracuda, who leads engineering, product management, and the operations teams that keep services running around the clock. Fresh from a session titled “Secured today, secured tomorrow,” Neal breaks down what that promise means in practice. We explore why Barracuda is doubling down on a platform approach with Barracuda One, how a single dashboard helps teams see posture and value in one place, and why consolidation matters when alerts and tools pile up faster than teams can respond. We also talk about the balance between immediate protection and longer-term planning. Neil explains how quarterly releases and shared services underpin the roadmap, how zero trust network access moves from theory to deployment as VPNs fade, and how managed vulnerability services help organizations find risks they did not know they had. He shares why service providers are shifting toward vCIO and vCISO models, how value reporting answers the board's simplest question about where the budget goes, and why response time is the measure that keeps coming up in every conversation. Secured today, secured tomorrow The headline theme is simple enough. Know where you stand right now, then set a clear plan for the next year. Barracuda One aims to cut noise and show whether tools are configured properly. The same view rolls up alerts across email, network, and application security, and for MSPs it stretches across all customers. That single source of truth is designed to reduce swivel-chair work and make decisions faster. We dig into the reality of tool sprawl and alert fatigue. A recent study Barracuda commissioned points to teams carrying too many point solutions, with slower responses and misconfigurations as the cost. Neal's answer is convergence without ignoring specialist depth. Product groups keep shipping, while shared AI and threat protection services raise the floor across the portfolio. That approach feeds directly into XDR, where integrations with tenants, firewalls, and endpoints help shrink the gap between detection and action. AI sits in the background of all of this. Neal describes it as a reckless intern that needs guardrails. In practice that means setup wizards that cut deployment time, incident response that can pull a bad message from twenty tenants in one sweep, and ML-driven triggers that fire automated remediation when patterns line up. The aim is clear. Let machines handle the routine work at machine speed, so people can focus on decisions and the weird edge cases attackers love to try. What listeners will take away If you run security day to day, you will hear practical direction rather than slogans. Consolidated dashboards exist to show posture, not just counts. Value reporting exists to explain outcomes to a board, not to pad a slide deck. Managed services rise in importance because many organizations need strategy as much as tools, and that includes smaller enterprises that outsource large parts of their stack. For leaders planning the next quarter, the emphasis on zero trust and managed vulnerability services will stand out. For operators, the XDR and SOAR focus is about shaving minutes into seconds, connecting identity with network and endpoint events, and giving analysts room to breathe. And for anyone curious about how product roadmaps form, conferences like this one offer a candid loop between feedback and action that you rarely see on a press release. By the time we wrap, Alpbach's quiet streets feel like an unlikely place to discuss ransomware, posture, and platform design. Yet that contrast makes the conversation land even harder. Secure today, plan for tomorrow, and give your team the visibility to do both.

Michael Nicosia is the Co-founder and COO of Salt Security, a company that protects APIs from threats using cloud-scale big data, AI, and ML. Under his leadership, Salt has raised $271 million, reached a $1.4 billion valuation, and has become a leader in API security with patented AI technology and Fortune 500/Global 1000 clients. With over 20 years of experience in enterprise software sales and marketing, Michael helped lead Adallom as COO from its founding to its $327 million acquisition by Microsoft. In this episode… APIs power nearly every modern digital service, yet most companies remain unaware of just how vulnerable these connections can be to breaches. With AI agents, MCP protocols, and microservices expanding rapidly, how do you ensure that sensitive data isn't leaking through unseen cracks in your API infrastructure? Michael Nicosia, a serial entrepreneur and technology executive, shares how he took the leap from corporate roles to building a platform that safeguards APIs. He describes starting with only an idea, refining it through Y Combinator, and securing early validation from security leaders. Along the way, Michael emphasizes the importance of focusing on customer outcomes, building the right team, and persevering through uncertainty. His journey shows that protecting digital services isn't just about software — it's about resilience, trust, and staying ahead of attackers. In this episode of the Inspired Insider Podcast, Dr. Jeremy Weisz interviews Michael Nicosia, COO and Co-founder of Salt Security, about scaling cybersecurity solutions for the modern digital world. Michael discusses lessons from Y Combinator, navigating the fundraising journey, and securing enterprise clients. He also shares insights on pricing models, hiring top talent, and the role of mentorship in building a lasting company.

John Keating zooms in from retirement to tell ML, Marc, Shawn – and ML's daughter Sophie – terrific tales of […]

00:00-15:00: Packers rolling early. ML breaks it down after wins over Detroit and Washington. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

BettorEdge Partner Promo Code: PLAYME Signup Link: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://bettoredge.com/playme⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Peer to peer sports betting with NO JUICE! Click the link for a risk free $20, no deposit required. Join the Free Discord + View Our Podcast Record⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://discord.gg/eudQA8B3⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Podcast Card: - Kelsey Mitchell over 19.5 points (-110)- Lexie Hull over 7.5 points (-130)- Rutgers TT under 21.5 (-135)- Memphis +4.5 & mL +220- Virginia -15 (-110) YouTube Link:⁠⁠https://www.youtube.com/@PlayMeorFadeMePodcast?sub_confirmation=1⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

At 26, Neel Nanda leads an AI safety team at Google DeepMind, has published dozens of influential papers, and mentored 50 junior researchers — seven of whom now work at major AI companies. His secret? “It's mostly luck,” he says, but “another part is what I think of as maximising my luck surface area.”Video, full transcript, and links to learn more: https://80k.info/nn2This means creating as many opportunities as possible for surprisingly good things to happen:Write publicly.Reach out to researchers whose work you admire.Say yes to unusual projects that seem a little scary.Nanda's own path illustrates this perfectly. He started a challenge to write one blog post per day for a month to overcome perfectionist paralysis. Those posts helped seed the field of mechanistic interpretability and, incidentally, led to meeting his partner of four years.His YouTube channel features unedited three-hour videos of him reading through famous papers and sharing thoughts. One has 30,000 views. “People were into it,” he shrugs.Most remarkably, he ended up running DeepMind's mechanistic interpretability team. He'd joined expecting to be an individual contributor, but when the team lead stepped down, he stepped up despite having no management experience. “I did not know if I was going to be good at this. I think it's gone reasonably well.”His core lesson: “You can just do things.” This sounds trite but is a useful reminder all the same. Doing things is a skill that improves with practice. Most people overestimate the risks and underestimate their ability to recover from failures. And as Neel explains, junior researchers today have a superpower previous generations lacked: large language models that can dramatically accelerate learning and research.In this extended conversation, Neel and host Rob Wiblin discuss all that and some other hot takes from Neel's four years at Google DeepMind. (And be sure to check out part one of Rob and Neel's conversation!)What did you think of the episode? https://forms.gle/6binZivKmjjiHU6dA Chapters:Cold open (00:00:00)Who's Neel Nanda? (00:01:12)Luck surface area and making the right opportunities (00:01:46)Writing cold emails that aren't insta-deleted (00:03:50)How Neel uses LLMs to get much more done (00:09:08)“If your safety work doesn't advance capabilities, it's probably bad safety work” (00:23:22)Why Neel refuses to share his p(doom) (00:27:22)How Neel went from the couch to an alignment rocketship (00:31:24)Navigating towards impact at a frontier AI company (00:39:24)How does impact differ inside and outside frontier companies? (00:49:56)Is a special skill set needed to guide large companies? (00:56:06)The benefit of risk frameworks: early preparation (01:00:05)Should people work at the safest or most reckless company? (01:05:21)Advice for getting hired by a frontier AI company (01:08:40)What makes for a good ML researcher? (01:12:57)Three stages of the research process (01:19:40)How do supervisors actually add value? (01:31:53)An AI PhD – with these timelines?! (01:34:11)Is career advice generalisable, or does everyone get the advice they don't need? (01:40:52)Remember: You can just do things (01:43:51)This episode was recorded on July 21.Video editing: Simon Monsour and Luke MonsourAudio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic ArmstrongMusic: Ben CordellCamera operator: Jeremy ChevillotteCoordination, transcriptions, and web: Katy Moore

In episode 217, host Galit Friedlander and guest Ana Rokafella Garcia (legendary b-girl, co-founder of Full Circle Productions, choreographer, filmmaker, and adjunct professor at The New School), dive into the history, evolution, and impact of hip-hop. From growing up in New York City during the fires, crack epidemic, and AIDS crisis to discovering her voice in cyphers and clubs, Rokafella shares how dance carved out a future for her. She reflects on commercialization, gender dynamics, and visibility in the scene, while urging today's dancers to stay grounded in the culture's foundations. The conversation also touches on breaking at the Olympics, the tension between competition and community, and why hip-hop's resilience continues to guide her work. Follow Galit: Instagram - https://www.instagram.com/gogalit Website - https://www.gogalit.com/ On-Demand Fitness Courses - https://galit-s-school-0397.thinkific.com/collections Follow Rokafella: Instagram - https://www.instagram.com/larokafella Website - https://larokasoul.com/ Full Circle: https://www.instagram.com/fullcirclesouljahs Behind the Groove: https://www.instagram.com/behindthegroovenyc/ All the Ladies Say: https://www.instagram.com/alltheladiessay Listen to DanceSpeak on Apple Podcasts and Spotify.

Conventional medicine often stops at the numbers, low AMH, high FSH for Premature Ovarian Insufficiency (POI) and turns quickly to donor eggs or IVF. But through a functional fertility lens, we can uncover hidden factors like inflammation, nutrient deficiencies, gut health, and hormone imbalances that may support ovarian function and improve pregnancy success. In this episode of Get Pregnant Naturally, Fab Fertile founder Sarah Clark explains how functional fertility strategies can help reverse POI and improve your chances of conceiving with your own eggs. You'll also hear Amanda's story: she was told she'd never conceive naturally with an AMH of 0.08 ng/mL, yet went on to have her daughter. This episode is for you if: You've been told your only option with POI, low AMH, or high FSH is donor eggs You've experienced recurrent miscarriage or failed IVF cycles You want evidence-based strategies to improve egg quality and fertility outcomes naturally We'll cover the difference between genetic and idiopathic POI, why functional fertility shines in unexplained cases, and the top mistakes people make with a POI diagnosis. You'll leave with practical steps you can take right now to support ovarian function. Subscribe to Get Pregnant Naturally for evidence-based guidance on functional fertility, and share this episode with anyone on their fertility journey. Not sure where to start? Download our most popular guide: Ultimate Guide to Getting Pregnant This Year If You Have Low AMH/High FSH it breaks everything down step by step to help you understand your options and take action For personalized support to improve pregnancy success, book a call here. --- TIMESTAMPS [00:00:00] Donor eggs only? Understanding Premature Ovarian Insufficiency (POI), Low AMH & High FSH Why conventional medicine often tells you donor eggs are your only option and the functional fertility perspective that looks deeper. [01:00:00] How Functional Fertility Strategies Can Improve Ovarian Function Practical steps for egg health, reducing inflammation, balancing hormones, and improving pregnancy success. [05:00:00] Idiopathic vs Genetic POI: What You Can Influence Naturally Explaining the difference between genetic and idiopathic causes and how functional approaches support ovarian function. [08:00:00] Nutrients, Gut Health & Lifestyle Factors That Impact Egg Quality Vitamin D, B vitamins, iron, gut microbiome, and inflammation as key influencers of ovarian reserve and AMH. [12:00:00] Real Client Stories: Restoring Fertility After POI Case studies of women improving FSH, AMH, and conception outcomes with functional fertility strategies and targeted IVF support. [00:13:00] AMH, Egg Quality & Gut Health Why AMH isn't a fixed predictor of fertility outcomes and how gut health, nutrient absorption, and inflammation can influence egg quality and ovarian reserve. [00:15:30] Non-Celiac Gluten Sensitivity & Ovarian Function How gluten sensitivity can impact hormone balance, follicle development, and fertility, even when celiac disease isn't present. [00:18:00] Key Nutrients for Egg Quality The importance of vitamin D, iron, B vitamins, methylation support, and protein for optimal ovarian function, and how deficiencies can affect IVF or natural conception. [00:20:45] Gut Infections & Systemic Inflammation Chronic gut infections, microbiome imbalances, and low stomach acid as hidden drivers of inflammation that can disrupt reproductive hormones and egg quality. [00:23:30] Lifestyle, Stress & Nervous System Support How chronic stress and a dysregulated nervous system impact ovarian reserve and fertility. Mind-body strategies, dietary adjustments, and toxin reduction to support reproductive health. --- RESOURCES

00:00-15:00: Carlos Alcaraz wins 6th Grand Slam and U.S. Open. ML breaks it down.

Sergey Levine, one of the world's top robotics researchers and co-founder of Physical Intelligence, thinks we're on the cusp of a “self-improvement flywheel” for general-purpose robots. His median estimate for when robots will be able to run households entirely autonomously? 2030.If Sergey's right, the world 5 years from now will be an insanely different place than it is today. This conversation focuses on understanding how we get there: we dive into foundation models for robotics, and how we scale both the data and the hardware necessary to enable a full-blown robotics explosion.Watch on YouTube; listen on Apple Podcasts or Spotify.Sponsors* Labelbox provides high-quality robotics training data across a wide range of platforms and tasks. From simple object handling to complex workflows, Labelbox can get you the data you need to scale your robotics research. Learn more at labelbox.com/dwarkesh* Hudson River Trading uses cutting-edge ML and terabytes of historical market data to predict future prices. I got to try my hand at this fascinating prediction problem with help from one of HRT's senior researchers. If you're curious about how it all works, go to hudson-trading.com/dwarkesh* Gemini 2.5 Flash Image (aka nano banana) isn't just for generating fun images — it's also a powerful tool for restoring old photos and digitizing documents. Test it yourself in the Gemini App or in Google's AI Studio: ai.studio/bananaTo sponsor a future episode, visit dwarkesh.com/advertise.Timestamps(00:00:00) – Timeline to widely deployed autonomous robots(00:17:25) – Why robotics will scale faster than self-driving cars(00:27:28) – How vision-language-action models work(00:45:37) – Changes needed for brainlike efficiency in robots(00:57:59) – Learning from simulation(01:09:18) – How much will robots speed up AI buildouts?(01:18:01) – If hardware's the bottleneck, does China win by default? Get full access to Dwarkesh Podcast at www.dwarkesh.com/subscribe

Can AI be used to improve patient experiences? This week, Technology Now explores how AI is being used to streamline data collection in the healthcare industry, how data should be treated to avoid bias in AI, and the benefits this brings to patients. Derek B. Howard, Programme Manager for the HPE Digital Health Foundry Programme, tells us more.This is Technology Now, a weekly show from Hewlett Packard Enterprise. Every week, hosts Michael Bird and Aubrey Lovell look at a story that's been making headlines, take a look at the technology behind it, and explain why it matters to organizations.About Derek B. Howard:https://www.linkedin.com/in/derek-howard1/Sources:https://www.britannica.com/technology/MYCINhttps://www.mghlcs.org/projects/dxplainhttps://www.cedars-sinai.org/discoveries/ai-ascendance-in-medicine.html

Dean Teffer of Arctic Wolf reveals how they process 8 trillion weekly security observations to find "a needle in a stack of needles," and breaks down real-world GenAI lessons learned.Topics Include:Dean Teffer, VP of AI at Arctic Wolf, discusses company's GenAI journeyArctic Wolf: decade-old security operations company serving mid-market customers globallyOperates massive security operation center, now launching AI-powered productsAI agent recently identified Black Basta ransomware attack, enabling rapid containmentDean's 15+ years in cybersecurity: traditional ML focused on detectionGenAI breakthrough allows natural language interaction with security modelsArctic Wolf processes 8 trillion weekly observations, correlating suspicious activitiesChallenge: finding specific threats in "stack of needles," not haystackSuccess measured by making human analysts faster, more consistent, scalableEvolved from treating GenAI like traditional ML to integrated workflowsKey misconception: GenAI isn't magic, needs proper data and reasoningAdvice: start with existing challenges, build flexible systems for adaptationGenAI excels at summarizing information and supporting complex decisionsFuture vision: AI handles routine threats, humans focus on creativityDemocratizing machine learning capabilities to broader range of subject expertsParticipants:Dean Teffer – Vice President of AI, Arctic WolfFurther Links:Arctic Wolf: Website | LinkedIn | AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Mike Mulligan and David Haugh were joined by Chicago White Sox pregame and postgame analyst, Ozzie Guillen on the state of the White Sox

Leadership Transition at Jamiatul Ulama South Africa (JUSA) - A Tribute to Ml. Abbas Aly Jeena by Radio Islam

In “AI Literacy in Logistics with Gather AI's Andrew Hoffman”, Joe Lynch and Andrew Hoffman, CTO at Gather AI, discuss how artificial intelligence is transforming warehouse operations by using visual data from drones and other machines to identify issues, suggest next steps, and ultimately improve efficiency and accuracy. About Andrew Hoffman Andrew Hoffman is the Chief Technical Officer at Gather.ai, an AI-powered intralogistics optimization firm that empowers 3PLs, manufacturers, and retailers to improve margins and on-time order fulfillment in the widest range of logistics environments - from ambient to cold chain storage. Andrew has technical and leadership experience scaling supply chain-focused robotics companies from founding to exit, with over 20 patents in logistics, robotics, and robot coordination. He was a founding engineer at two supply chain robotics companies which were later acquired by Amazon – Kiva Systems (now Amazon Robotics) and CANVAS Technology. Most recently, he served as an Entrepreneur in Residence at tech-focused venture capital firm, Xplorer Capital, a Gather AI investor. About Gather AI Gather AI is an intralogistics AI company which collects visual data from drones, forklifts, and connected machines, integrates it with warehouse management systems (WMS) and cloud platforms, and uses AI to identify issues and suggest next steps. Key Takeaways: AI Literacy in Logistics In “AI Literacy in Logistics with Gather AI's Andrew Hoffman”, Joe Lynch and Andrew Hoffman, CTO at Gather AI, discuss how artificial intelligence is transforming warehouse operations by using visual data from drones and other machines to identify issues, suggest next steps, and ultimately improve efficiency and accuracy. AI as a Co-Pilot, Not a Replacement: The podcast reframes artificial intelligence as a collaborative tool that amplifies human capability. Much like a calculator assists a mathematician, AI acts as a "co-pilot" in the warehouse, providing real-time data and insights that enable staff to work more efficiently and accurately. Demystifying Core Concepts: The discussion breaks down key industry terms, clarifying the relationship between artificial intelligence (AI) (the broad field of intelligent machines) and machine learning (ML) (the specific technique of systems learning from data). It also defines computer vision as the ability of a machine to "see" and interpret a warehouse environment. Proven ROI and Real-World Results: The episode highlights a concrete case study with Stadium Goods, a customer of Gather AI. By using the company's solution, they increased inventory accuracy to over 99%, reduced "inventory shrink" from 2% to a fraction of a percent, and saved over $200,000 by eliminating manual counting. Actionable Insights from Data: The conversation explains that AI systems learn from data over time, allowing them to transform complex data queries into natural language questions. This helps warehouse staff make better decisions on the fly, such as locating a specific pallet or finding an empty space in an aisle. The Collaborative "Agent": The concept of an "agent" in AI is reframed from a technical term to a collaborative entity. Gather AI's system—the drone, software, and computer vision—works as a partner to the warehouse staff, providing the data needed for faster, more accurate decisions and freeing up employees for more valuable tasks. A Leader with a Track Record of Innovation: Andrew Hoffman's background as a founding engineer at two logistics robotics companies, both later acquired by Amazon, establishes his deep expertise. His journey offers valuable insight into the past and future of scaling technology in the supply chain industry. A Clear Path to Getting Started with AI: For listeners looking to begin their AI journey, the podcast recommends starting with a pilot project to solve one specific problem, such as cycle counting. It also advises seeking partners who offer a comprehensive solution and a clear path to return on investment, rather than just a technology. Learn More About AI Literacy in Logistics Andrew Hoffman Gather AI Gather AI | Linkedin Gather AI | YouTube Gather AI Capabilities 2025 The Logistics of Logistics Podcast If you enjoy the podcast, please leave a positive review, subscribe, and share it with your friends and colleagues. The Logistics of Logistics Podcast: Google, Apple, Castbox, Spotify, Stitcher, PlayerFM, Tunein, Podbean, Owltail, Libsyn, Overcast Check out The Logistics of Logistics on Youtube  

Anne Reinke didn't plan on leaving the Transportation Intermediaries Association, but when the longtime IANA leader retired after more than two decades, she received an offer she couldn't refuse. With a lifelong passion for rail and deep experience at CSX and TIA, Anne stepped into the role of President and CEO of the Intermodal Association of North America.In this episode of Everything is Logistics, Anne shares how she hit the ground running with a listening tour of IANA's membership, her vision for elevating the association's profile, and the key policy issues she's prioritizing in Washington. She also breaks down the often-overlooked benefits of intermodal and why it deserves more attention from shippers and policymakers alike.Key takeaways:Anne's career path from CSX to TIA prepared her to lead IANA.A nationwide listening tour is shaping IANA's strategy and member priorities.Intermodal's complexity often hides its cost and sustainability advantages.Advocacy efforts focus on labor, infrastructure, and independent contractor protections.IANA is working to boost awareness and build trust in intermodal.LINKS:Anne's LinkedInIntermodal Association of North America WebsiteWATCH THE FULL EPISODE HEREFeedback? Ideas for a future episode? Shoot us a text here to let us know. -----------------------------------------THANK YOU TO OUR SPONSORS! Are you experienced in freight sales or already an independent freight agent? Listen to our Freight Agent Trenches interviews powered by SPI Logistics to hear from the company's agents on how they took the entrepreneurial leap. Shipium is the ML-powered shipping platform built by the executives that created Amazon's supply chain technology. They help 3PL & retail leaders like Stord, Ryder, and Saks Off 5th to reduce shipping costs by an average of 12% while improving on-time delivery. CargoRex is the logistics industry's go-to search platform—connecting you with the right tools, services, events, and creators to explore, discover, and evolve. Digital Dispatch manages and maximizes your #1 sales tool with a website that establishes trust and builds rock-solid relationships with your leads and customers.

Chang She is Co-Founder & CEO of LanceDB, the multimodal lakehouse platform. Their open source data format lance has over 5K stars on GitHub and is a modern columnar data format for ML and LLMs implemented in Rust.LanceDB has raised $41M from investors including Theory Ventures, CRV, and Essence VC. In this episode, we dig into:Early focus: autonomous vehicles; solved real-time analysis limits with Lance format → 9,000% performance gain.Multi-modal AI taking off (vision, audio, text); Midjourney & Runway as pioneers; audio now a major category.How they built trust through open source.Integrated workflows (data prep + search + embedding) going beyond vector DBs; education needed to show full value.Cloud/serverless launch in 2023–24 enabled seamless local-to-production use.Future bets: audio infra, robotics, spatial reasoning; vector DBs risk irrelevance if they don't evolve.

This is episode 304 recorded on September 4th, 2025, where John & Jason talk the Microsoft Fabric August 2025 Feature Summary including a new Flat list view in Deployment pipelines, Bursting controls for Data Engineering workloads, new test capabilities for User Data Functions, the ability to server real-time predictions with ML model endpoints, several updates to Data Warehouse, Database tree in edit tile and AzMon data sources for RTI, the ability to use Python Notebooks to read/write to Fabric SQL Databases, Auto table creation on destination in copy job in Data Factory, and much, much more. For show notes please visit www.bifocal.show

This interview was recorded for GOTO Unscripted.https://gotopia.techRead the full transcription of this interview hereMichelle Frost - AI Advocate at JetBrains & Responsible AI ConsultantHannes Lowette - Principal Consultant at Axxes, Monolith Advocate, Speaker & Whiskey LoverRESOURCESMichellehttps://bsky.app/profile/aiwithmichelle.comhttps://www.linkedin.com/in/michelle-frost-devHanneshttps://bsky.app/profile/hanneslowette.nethttps://twitter.com/hannes_lowettehttps://github.com/Belenarhttps://linkedin.com/in/hanneslowetteDESCRIPTIONAI advocate Michelle Frost and principal consultant Hannes Lowette discuss ethical challenges in AI development. They explore the balance between competing values like accuracy versus fairness, recent US regulatory rollbacks under the Trump administration, and market disruptions from innovations like Deep Seek.While Michelle acknowledges concerns about bias in unregulated models, she remains optimistic about AI's potential to improve lives if developed responsibly. She emphasizes the importance of transparency, bias measurement, and focusing on beneficial applications while advocating for individual and corporate accountability in the absence of comprehensive regulation.RECOMMENDED BOOKSMark Coeckelbergh • AI EthicsDebbie Sue Jancis • AI EthicsMohammad Rubyet Islam • Generative AI, Cybersecurity, and EthicsJeet Pattanaik • Ethics in AICrossing BordersCrossing Borders is a podcast by Neema, a cross border payments platform that...Listen on: Apple Podcasts SpotifyBlueskyTwitterInstagramLinkedInFacebookCHANNEL MEMBERSHIP BONUSJoin this channel to get early access to videos & other perks:https://www.youtube.com/channel/UCs_tLP3AiwYKwdUHpltJPuA/joinLooking for a unique learning experience?Attend the next GOTO conference near you! Get your ticket: gotopia.techSUBSCRIBE TO OUR YOUTUBE CHANNEL - new videos posted daily!

DJ HOT MAKER - POP DANCE 2025 VOL.3 Привет! Предлагаю тебе подборку популярных треков за последние 3 месяца. 70 минут Джингл только в самом начале Скачать в качестве 320 kb/s можно с моего канала t.me/dj_hot_maker или отсюда: disk.yandex.ru/d/s8Oj-cXpGIjs4… Новый микс в стиле Pop Dance от DJ Hot Maker. Приятного прослушивания! Мой сайт djhotmaker.ru #house #pop #dj #djhotmaker #диджей #микс #новыймикс #музыка #music 00:00:00 1 Hurts & рurрlе Disсо Mасhinе - Wоndеrful Lifе '25 00:05:14 2 АNNА АSTI - Пpeдaнный бывший (Аndу Shiк & Indех-1 Rеmiх) 00:08:16 3 Tоkiо Hоtеl - Hоw tо Lоvе (Slim Rеmiх) 00:10:53 4 Mаrk Dаnn fеаt. Giоvаnni Riссi - LеT Mе DIе 00:13:09 5 Apтyp Пиpoжкoв - Caмo Сoбoй (DJ Smеll Rеmiх) 00:16:19 6 Innа & R3hаb - I'll Bе Wаiting 00:18:51 7 Milеу суrus - Еnd оf Thе Wоrld (соsmiс Dаwn Rеmiх) 00:22:18 8 GАYАZОV$ ВRОTHЕR$ - Выбиpaю мope (М1СH3L Р. & Rеd Linе Rеmiх) 00:25:43 9 Gаlibri & Mаvik, Gаlibri, Mаvik - Coчи - Kиpoв - Pocтoв (Jоhnnу Mаn Rеmiх) 00:28:39 10 Аkсеnt, Sеrа, Mishа Millеr - Dоn't Lеаvе [Kуliе] 00:32:12 11 Димa Билaн - Пooбeщaй (ТаRаBRIN Rеmiх) 00:35:34 12 Sаfri Duо, twосоlоrs & сhris dе Sаrаndу - Hеаd аbоvе Wаtеr 00:38:34 13 Мapи Kpaймбpepи - Bcё пpoшлo (Indех-1 Rеmiх) 00:41:53 14 Zivеrt - Гyдбaй (Аndу Shiк & Ruslаn Mishin Rеmiх) 00:45:46 15 Filаtоv & Kаrаs - Раrtу 00:47:57 16 Мoя Мишeль - Инoгдa (Slim х соrtо х рitсhugin Rеmiх) 00:50:12 17 Аvа Mах - Lоvin' Mуsеlf (соsmiс Dаwn Rеmiх) 00:53:51 18 Аrtik & Аsti - Мoдный Пoп (Indех-1 Rеmiх) 00:57:05 19 Nikа, Оne-T - Thе Wау Tо Lоvе 00:59:52 20 Mаеsiс, Mаrshаll Jеffеrsоn, Sаlоmé Dаs - Lifе Is Simрlе (Mоvе уоur Bоdу) 01:03:13 21 Кid сudi, Dоm Dоllа - Fоrеvеr 01:07:27 22 Jоnаs Brоthеrs - Lоvе Mе Tо Hеаvеn

This is a recap of the top 10 posts on Hacker News on September 05, 2025. This podcast was generated by wondercraft.ai (00:30): I ditched Docker for PodmanOriginal post: https://news.ycombinator.com/item?id=45137525&utm_source=wondercraft_ai(01:49): Anthropic agrees to pay $1.5B to settle lawsuit with book authorsOriginal post: https://news.ycombinator.com/item?id=45142885&utm_source=wondercraft_ai(03:09): I'm absolutely rightOriginal post: https://news.ycombinator.com/item?id=45137802&utm_source=wondercraft_ai(04:29): Fil's Unbelievable Garbage CollectorOriginal post: https://news.ycombinator.com/item?id=45133938&utm_source=wondercraft_ai(05:48): Purposeful animationsOriginal post: https://news.ycombinator.com/item?id=45139088&utm_source=wondercraft_ai(07:08): I bought the cheapest EV, a used Nissan LeafOriginal post: https://news.ycombinator.com/item?id=45136103&utm_source=wondercraft_ai(08:28): European Commission fines Google €2.95B over abusive ad tech practicesOriginal post: https://news.ycombinator.com/item?id=45140730&utm_source=wondercraft_ai(09:47): Nepal moves to block Facebook, X, YouTube and othersOriginal post: https://news.ycombinator.com/item?id=45137363&utm_source=wondercraft_ai(11:07): ML needs a new programming language – Interview with Chris LattnerOriginal post: https://news.ycombinator.com/item?id=45137373&utm_source=wondercraft_ai(12:27): Making a font of my handwritingOriginal post: https://news.ycombinator.com/item?id=45141636&utm_source=wondercraft_aiThis is a third-party project, independent from HN and YC. Text and audio generated using AI, by wondercraft.ai. Create your own studio quality podcast with text as the only input in seconds at app.wondercraft.ai. Issues or feedback? We'd love to hear from you: team@wondercraft.ai

Hugo Hamad, Head of AI/Gen AI Factory, AXA en France, ex-director of AI, Decathlon, points out a common pitfall in how organizations approach AI adoption. “Many companies invest in platforms, clusters, and big data without realizing that what we truly need are data engineers, ML engineers, and AI experts,” he explains.He continues, “Some businesses focus only on hiring data scientists. But in reality, we need much more than data scientists alone to deliver real value and build AI solutions.”According to Hugo, tech companies often emphasize practice and talent but neglect implementing the right AI technologies and tools. “To succeed, we must concentrate on three pillars: process, platform, and people. Only then can we deliver meaningful AI solutions, whether for clients or internal teams.”⚡ The AI equation isn't complete without balance: People + Process + Platform.Listen to the full podcast now- https://bit.ly/46iLetz#AI #MachineLearning #DataScience #FutureOfWork #TechInnovation #DigitalTransformation #Decathlon

00:00-15:00: ML recaps Syracuse and Tennessee and looks ahead to UConn for the Orange. Thanks to CH Insurance and Byrne Dairy.

In this episode of History 102, 'WhatIfAltHist' creator Rudyard Lynch and co-host Austin Padgett examine Inner Asia as the overlooked crossroads between China, Islam, Europe, and India, tracing ancient migrations through modern geopolitics in this forgotten spine of Eurasian civilization. -- SPONSOR: ZCASH | NETSUITE | ORACLE The right technology reshapes politics and culture toward freedom and prosperity. Zcash—the "machinery of freedom"—delivers unstoppable private money through encryption. When your wealth is unseen, it's unseizable. Download Zashi wallet and follow @genzcash to learn more: ⁠⁠⁠https://x.com/genzcash⁠⁠⁠ More than 42,000 businesses have already upgraded to NetSuite by Oracle, the #1 cloud financial system bringing accounting, financial management, inventory, HR, into ONE proven platform. If you're looking for an ERP platform, get a one-of-a-kind flexible financing program on NetSuite: ⁠⁠⁠⁠https://netsuite.com/102⁠⁠⁠⁠ - Download your free CFO's guide to AI and machine learning. Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers. OCI powers industry leaders like Vodafone and Thomson Reuters with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before March 31, 2024 at ⁠⁠⁠⁠https://oracle.com/cognitive⁠⁠⁠⁠. -- FOLLOW ON X: @whatifalthist (Rudyard) @LudwigNverMises (Austin) @TurpentineMedia -- TIMESTAMPS: (0:00) Introduction: Defining Inner Asia as the Forgotten Crossroads (1:37) Geographic Scope: From Transoxiana to Tibet and Zomia (3:25) James C. Scott and "The Art of Not Being Governed" (5:05) Zomia: Southeast Asian Anarchist Borderlands (9:24) Burma's Capital Move and State Control Strategies (13:00) Reframing Inner Asia as Central Hub vs. Periphery (14:33) The Continental Divide of Eurasia (18:00) Sponsors: Zcash | NetSuite (19:54) Lord Miles and the Wakan Corridor Adventure (23:42) Rudyard Kipling and "The Man Who Would Be King" (28:00) Marx vs. Kipling: Comparing Views on Race and Colonialism (31:24) World War I's Impact on European Colonial Prestige (35:07) Sponsor: Oracle Cloud Infrastructure (36:34) Prehistoric Settlement: East Asian Population Formation (40:22) The Aryan Invasions and Bronze Age Civilizations (47:24) Genetic Mixing: Europeans, Persians, and Indians (57:15) Scythians vs. Persian Empire: Cyrus the Great's Death (1:02:00) Alexander the Great: Conquering Beyond Persia (1:08:04) The Hubris Trap and Christianity's Solution (1:10:53) Chinese Discovery of the Western World (150 BC) (1:15:05) Central Asian Christianity and Nestorian Civilization (1:20:48) Islamic Conquest and Religious Transformation (1:27:00) Tibet's Warrior Empire vs. China (1:30:30) Amira Ghurko's Journey: Meeting Tibet's Buddhist Elite (1:38:00) Islamic Golden Age: Arab Cotton Boom in Transoxiana (1:44:53) Genghis Khan's Destruction of Central Asian Civilization (1:51:01) The Great Game: British vs. Russian Imperial Competition (1:58:00) Modern Central Asian Dictatorships and Soviet Legacy (2:02:34) Belt and Road Initiative: China's Failed Infrastructure Push (2:06:00) Contemporary Challenges: MMA Fighters and Cultural Impact (2:07:33) Wrap Learn more about your ad choices. Visit megaphone.fm/adchoices

The truth – and Shawn – is out there somewhere. ML and Marc search for it, while Erika is away […]

Jason Murray—CEO & co-founder of Shipium and 19-year Amazon vet—joins Everything Is Logistics to unpack what really moves the needle for shippers: accurate delivery promises, multi-carrier execution, and where AI agents add real value today. We get into the “coordination layer” most retailers are missing across OMS/WMS/TMS, building a digital twin of your network, and why faster can actually be cheaper when your method mix and lanes are modeled correctly. Favorite line: Shipium's role is a “super-sophisticated calculator” for decisions the human brain (and spreadsheets) simply can't keep up with.Key takeawaysThe coordination gap is costly. Most enterprises make siloed decisions in OMS/WMS; the win is a horizontal optimizer that weighs transportation, inventory, and cost together. Promise precision beats blanket speed. Customers want confidence in “by Thursday,” not generic “2-day.” Model ship dates and probability of arrival—and constantly backtest your predictions. Digital twins drive smarter choices. Shipium models lanes, cutoffs, and costs in real time to choose carrier/method, where to ship from, and what to promise. AI agents = productivity multipliers. Early wins: a “what happened to this shipment?” chat workflow, and agents watching network signals (the old Amazon “little red button”) to trigger reroutes. Money on the table. Method optimization and delivery-promise installs have driven multi-million-dollar annual savings for large retailers. LINKS:Shipium's WebsiteJason's LinkedInWATCH THE FULL EPISODE HEREFeedback? Ideas for a future episode? Shoot us a text here to let us know. -----------------------------------------THANK YOU TO OUR SPONSORS! Are you experienced in freight sales or already an independent freight agent? Listen to our Freight Agent Trenches interviews powered by SPI Logistics to hear from the company's agents on how they took the entrepreneurial leap. Shipium is the ML-powered shipping platform built by the executives that created Amazon's supply chain technology. They help 3PL & retail leaders like Stord, Ryder, and Saks Off 5th to reduce shipping costs by an average of 12% while improving on-time delivery. CargoRex is the logistics industry's go-to search platform—connecting you with the right tools, services, events, and creators to explore, discover, and evolve. Digital Dispatch manages and maximizes your #1 sales tool with a website that establishes trust and builds rock-solid relationships with your leads and customers.

Join our free community, The Women's Peptide Collective, click the Skool link to become a member and to connect, learn, and collaborate. https://www.skool.com/womens-peptide-collective-9663Go to https://thepeptique.com/ to get all your research peptides .As a loyal listener use the discount code POD15 to get 15% off the entire line of products.Have questions? Feel free to reach out to me: tarawest@westwellnessatx.comWant the free peptide guide? Email me tarawest@westwellnessatx.com and comment Guide and I'll shoot it right over!Follow me on instagram AND TikTOk @westwellnessatxInstruction on creating the KPV topical Spray:What you need:30 mL amber glass spray bottle like this one: https://a.co/d/5eTqTt030 mL of BAC water10 mg Vial of KPVHow to reconstitute:Put 1 mL of BAC water into the KPV VialPut the entire amount of reconstituted KPV into the spray bottleAdd 29 mL of BAC water into the spray bottleKeep refrigeratedGently swirl the spray bottle before useTakeaways: The KLOW stack is a sophisticated peptide blend designed to combat chronic inflammation and promote overall health. This podcast episode emphasizes the importance of inflammation regulation as a key factor in aging and recovery. KPV, an integral component of the KLOW stack, demonstrates remarkable efficacy in reducing inflammatory responses and enhancing gut health. The episode discusses personal anecdotes illustrating the practical benefits of the KLOW stack for injury recovery and chronic inflammation management.

Join Lois Houston and Nikita Abraham as they chat with Yunus Mohammed, a Principal Instructor at Oracle University, about the key stages of AI model development. From gathering and preparing data to selecting, training, and deploying models, learn how each phase impacts AI's real-world effectiveness. The discussion also highlights why monitoring AI performance and addressing evolving challenges are critical for long-term success.   AI for You: https://mylearn.oracle.com/ou/course/ai-for-you/152601/252500   Oracle University Learning Community: https://education.oracle.com/ou-community   LinkedIn: https://www.linkedin.com/showcase/oracle-university/   X: https://x.com/Oracle_Edu   Special thanks to Arijit Ghosh, David Wright, Kris-Ann Nansen, Radhika Banka, and the OU Studio Team for helping us create this episode.   --------------------------------------------------------------   Episode Transcript: 00:00 Welcome to the Oracle University Podcast, the first stop on your cloud journey. During this series of informative podcasts, we'll bring you foundational training on the most popular Oracle technologies. Let's get started! 00:25 Lois: Welcome to the Oracle University Podcast! I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me is Nikita Abraham, Team Lead: Editorial Services. Nikita: Hey everyone! In our last episode, we spoke about generative AI and gen AI agents. Today, we're going to look at the key stages in a typical AI workflow. We'll also discuss how data quality, feedback loops, and business goals influence AI success. With us today is Yunus Mohammed, a Principal Instructor at Oracle University.  01:00 Lois: Hi Yunus! We're excited to have you here! Can you walk us through the various steps in developing and deploying an AI model?  Yunus: The first point is the collect data. We gather relevant data, either historical or real time. Like customer transactions, support tickets, survey feedbacks, or sensor logs. A travel company, for example, can collect past booking data to predict future demand. So, data is the most crucial and the important component for building your AI models. But it's not just the data. You need to prepare the data. In the prepared data process, we clean, organize, and label the data. AI can't learn from messy spreadsheets. We try to make the data more understandable and organized, like removing duplicates, filling missing values in the data with some default values or formatting dates. All these comes under organization of the data and give a label to the data, so that the data becomes more supervised. After preparing the data, I go for selecting the model to train. So now, we pick what type of model fits your goals. It can be a traditional ML model or a deep learning network model, or it can be a generative model. The model is chosen based on the business problems and the data we have. So, we train the model using the prepared data, so it can learn the patterns of the data. Then after the model is trained, I need to evaluate the model. You check how well the model performs. Is it accurate? Is it fair? The metrics of the evaluation will vary based on the goal that you're trying to reach. If your model misclassifies emails as spam and it is doing it very much often, then it is not ready. So I need to train it further. So I need to train it to a level when it identifies the official mail as official mail and spam mail as spam mail accurately.  After evaluating and making sure your model is perfectly fitting, you go for the next step, which is called the deploy model. Once we are happy, we put it into the real world, like into a CRM, or a web application, or an API. So, I can configure that with an API, which is application programming interface, or I add it to a CRM, Customer Relationship Management, or I add it to a web application that I've got. Like for example, a chatbot becomes available on your company's website, and the chatbot might be using a generative AI model. Once I have deployed the model and it is working fine, I need to keep track of this model, how it is working, and need to monitor and improve whenever needed. So I go for a stage, which is called as monitor and improve. So AI isn't set in and forget it. So over time, there are lot of changes that is happening to the data. So we monitor performance and retrain when needed. An e-commerce recommendation model needs updates as there might be trends which are shifting.  So the end user finally sees the results after all the processes. A better product, or a smarter service, or a faster decision-making model, if we do this right. That is, if we process the flow perfectly, they may not even realize AI is behind it to give them the accurate results.  04:59 Nikita: Got it. So, everything in AI begins with data. But what are the different types of data used in AI development?  Yunus: We work with three main types of data: structured, unstructured, and semi-structured. Structured data is like a clean set of tables in Excel or databases, which consists of rows and columns with clear and consistent data information. Unstructured is messy data, like your email or customer calls that records videos or social media posts, so they all comes under unstructured data.  Semi-structured data is things like logs on XML files or JSON files. Not quite neat but not entirely messy either. So they are, they are termed semi-structured. So structured, unstructured, and then you've got the semi-structured. 05:58 Nikita: Ok… and how do the data needs vary for different AI approaches?  Yunus: Machine learning often needs labeled data. Like a bank might feed past transactions labeled as fraud or not fraud to train a fraud detection model. But machine learning also includes unsupervised learning, like clustering customer spending behavior. Here, no labels are needed. In deep learning, it needs a lot of data, usually unstructured, like thousands of loan documents, call recordings, or scan checks. These are fed into the models and the neural networks to detect and complex patterns. Data science focus on insights rather than the predictions. So a data scientist at the bank might use customer relationship management exports and customer demographies to analyze which age group prefers credit cards over the loans. Then we have got generative AI that thrives on diverse, unstructured internet scalable data. Like it is getting data from books, code, images, chat logs. So these models, like ChatGPT, are trained to generate responses or mimic the styles and synthesize content. So generative AI can power a banking virtual assistant trained on chat logs and frequently asked questions to answer customer queries 24/7. 07:35 Lois: What are the challenges when dealing with data?  Yunus: Data isn't just about having enough. We must also think about quality. Is it accurate and relevant? Volume. Do we have enough for the model to learn from? And is my data consisting of any kind of unfairly defined structures, like rejecting more loan applications from a certain zip code, which actually gives you a bias of data? And also the privacy. Are we handling personal data responsibly or not? Especially data which is critical or which is regulated, like the banking sector or health data of the patients. Before building anything smart, we must start smart.  08:23 Lois: So, we've established that collecting the right data is non-negotiable for success. Then comes preparing it, right?  Yunus: This is arguably the most important part of any AI or data science project. Clean data leads to reliable predictions. Imagine you have a column for age, and someone accidentally entered an age of like 999. That's likely a data entry error. Or maybe a few rows have missing ages. So we either fix, remove, or impute such issues. This step ensures our model isn't misled by incorrect values. Dates are often stored in different formats. For instance, a date, can be stored as the month and the day values, or it can be stored in some places as day first and month next. We want to bring everything into a consistent, usable format. This process is called as transformation. The machine learning models can get confused if one feature, like example the income ranges from 10,000 to 100,000, and another, like the number of kids, range from 0 to 5. So we normalize or scale values to bring them to a similar range, say 0 or 1. So we actually put it as yes or no options. So models don't understand words like small, medium, or large. We convert them into numbers using encoding. One simple way is assigning 1, 2, and 3 respectively. And then you have got removing stop words like the punctuations, et cetera, and break the sentence into smaller meaningful units called as tokens. This is actually used for generative AI tasks. In deep learning, especially for Gen AI, image or audio inputs must be of uniform size and format.  10:31 Lois: And does each AI system have a different way of preparing data?  Yunus: For machine learning ML, focus is on cleaning, encoding, and scaling. Deep learning needs resizing and normalization for text and images. Data science, about reshaping, aggregating, and getting it ready for insights. The generative AI needs special preparation like chunking, tokenizing large documents, or compressing images. 11:06 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest tech. Whether you're starting with Artificial Intelligence, Oracle Cloud Infrastructure, Multicloud, or Oracle Data Platform, this challenge covers it all! Learn more about your chance to win prizes and see your name on the Leaderboard by visiting education.oracle.com/race-to-certification-2025. That's education.oracle.com/race-to-certification-2025. 11:50 Nikita: Welcome back! Yunus, how does a user choose the right model to solve their business problem?  Yunus: Just like a business uses different dashboards for marketing versus finance, in AI, we use different model types, depending on what we are trying to solve. Like classification is choosing a category. Real-world example can be whether the email is a spam or not. Use in fraud detection, medical diagnosis, et cetera. So what you do is you classify that particular data and then accurately access that classification of data. Regression, which is used for predicting a number, like, what will be the price of a house next month? Or it can be a useful in common forecasting sales demands or on the cost. Clustering, things without labels. So real-world examples can be segmenting customers based on behavior for targeted marketing. It helps discovering hidden patterns in large data sets.  Generation, that is creating new content. So AI writing product description or generating images can be a real-world example for this. And it can be used in a concept of generative AI models like ChatGPT or Dall-E, which operates on the generative AI principles. 13:16 Nikita: And how do you train a model? Yunus: We feed it with data in small chunks or batches and then compare its guesses to the correct values, adjusting its thinking like weights to improve next time, and the cycle repeats until the model gets good at making predictions. So if you're building a fraud detection system, ML may be enough. If you want to analyze medical images, you will need deep learning. If you're building a chatbot, go for a generative model like the LLM. And for all of these use cases, you need to select and train the applicable models as and when appropriate. 14:04 Lois: OK, now that the model's been trained, what else needs to happen before it can be deployed? Yunus: Evaluate the model, assess a model's accuracy, reliability, and real-world usefulness before it's put to work. That is, how often is the model right? Does it consistently perform well? Is it practical in the real world to use this model or not? Because if I have bad predictions, doesn't just look bad, it can lead to costly business mistakes. Think of recommending the wrong product to a customer or misidentifying a financial risk.  So what we do here is we start with splitting the data into two parts. So we train the data by training data. And this is like teaching the model. And then we have got the testing data. This is actually used for checking how well the model has learned. So once trained, the model makes predictions. We compare the predictions to the actual answers, just like checking your answer after a quiz. We try to go in for tailored evaluation based on AI types. Like machine learning, we care about accuracy in prediction. Deep learning is about fitting complex data like voice or images, where the model repeatedly sees examples and tunes itself to reduce errors. Data science, we look for patterns and insights, such as which features will matter. In generative AI, we judge by output quality. Is it coherent, useful, and is it natural?  The model improves with the accuracy and the number of epochs the training has been done on.  15:59 Nikita: So, after all that, we finally come to deploying the model… Yunus: Deploying a model means we are integrating it into our actual business system. So it can start making decisions, automating tasks, or supporting customer experiences in real time. Think of it like this. Training is teaching the model. Evaluating is testing it. And deployment is giving it a job.  The model needs a home either in the cloud or inside your company's own servers. Think of it like putting the AI in place where it can be reached by other tools. Exposed via API or embedded in an app, or you can say application, this is how the AI becomes usable.  Then, we have got the concept of receives live data and returns predictions. So receives live data and returns prediction is when the model listens to real-time inputs like a user typing, or user trying to search or click or making a transaction, and then instantly, your AI responds with a recommendation, decisions, or results. Deploying the model isn't the end of the story. It is just the beginning of the AI's real-world journey. Models may work well on day one, but things change. Customer behavior might shift. New products get introduced in the market. Economic conditions might evolve, like the era of COVID, where the demand shifted and the economical conditions actually changed. 17:48 Lois: Then it's about monitoring and improving the model to keep things reliable over time. Yunus: The monitor and improve loop is a continuous process that ensures an AI model remains accurate, fair, and effective after deployment. The live predictions, the model is running in real time, making decisions or recommendations. The monitor performance are those predictions still accurate and helpful. Is latency acceptable? This is where we track metrics, user feedbacks, and operational impact. Then, we go for detect issues, like accuracy is declining, are responses feeling biased, are customers dropping off due to long response times? And the next step will be to reframe or update the model. So we add fresh data, tweak the logic, or even use better architectures to deploy the uploaded model, and the new version replaces the old one and the cycle continues again. 18:58 Lois: And are there challenges during this step? Yunus: The common issues, which are related to monitor and improve consist of model drift, bias, and latency of failures. In model drift, the model becomes less accurate as the environment changes. Or bias, the model may favor or penalize certain groups unfairly. Latency or failures, if the model is too slow or fails unpredictably, it disrupts the user experience. Let's take the loan approvals. In loan approvals, if we notice an unusually high rejection rate due to model bias, we might retrain the model with more diverse or balanced data. For a chatbot, we watch for customer satisfaction, which might arise due to model failure and fine-tune the responses for the model. So in forecasting demand, if the predictions no longer match real trends, say post-pandemic, due to the model drift, we update the model with fresh data.  20:11 Nikita: Thanks for that, Yunus. Any final thoughts before we let you go? Yunus: No matter how advanced your model is, its effectiveness depends on the quality of the data you feed it. That means, the data needs to be clean, structured, and relevant. It should map itself to the problem you're solving. If the foundation is weak, the results will be also. So data preparation is not just a technical step, it is a business critical stage. Once deployed, AI systems must be monitored continuously, and you need to watch for drops in performance for any bias being generated or outdated logic, and improve the model with new data or refinements. That's what makes AI reliable, ethical, and sustainable in the long run. 21:09 Nikita: Yunus, thank you for this really insightful session. If you're interested in learning more about the topics we discussed today, go to mylearn.oracle.com and search for the AI for You course.  Lois: That's right. You'll find skill checks to help you assess your understanding of these concepts. In our next episode, we'll discuss the idea of buy versus build in the context of AI. Until then, this is Lois Houston… Nikita: And Nikita Abraham, signing off! 21:39 That's all for this episode of the Oracle University Podcast. If you enjoyed listening, please click Subscribe to get all the latest episodes. We'd also love it if you would take a moment to rate and review us on your podcast app. See you again on the next episode of the Oracle University Podcast.

Minister Niamh Smyth officially launched the sixth edition of the John McCarthy AI Summer School, set to take place on September 18-19, 2025, at the RDI Hub in County Kerry. This year's theme, "AI for Finance," will spotlight how artificial intelligence is transforming the financial sector; from institutional investing to cybersecurity and fraud detection. John McCarthy AI Summer School returns As finance undergoes a seismic shift driven by data and digital innovation, this two-day hybrid event will explore how Agentic AI, blockchain, and advanced analytics are reshaping everything from fraud detection to portfolio optimisation. Attendees will gain firsthand insights into how emerging technologies are unlocking new levels of transparency, trust, and strategic intelligence across the financial ecosystem. The speaker lineup features thought leaders and innovators including: Veronica Breen, CEO, Vesta Insights Brian Walsh, CEO, Reitigh Vasant Dhar, NYU Stern & Center for Data Science Michael Dowling, Founder, Narrative Banking David Kearney, CEO, Numra Louise McCormack, PhD Candidate in Trustworthy AI Evaluation Luca Marschesotti, Founder, Gemmo.ai Professor Ciara Heavin, UCC Jane Cummings, CTO Outmin.io and special guest speaker Dr David L Shrier: Managing Director, Visionary Future | CEO, Phorum.AI | Professor of Practice, Imperial College London. The event will be co-hosted by a dynamic consortium of partners including RDI Hub, Microsoft Ireland, Munster Technological University, SFI ADAPT Centre, AI Ireland, Skillnet Innovation Exchange, and new collaborators Fexco and Tech Industry Alliance. Designed to appeal to both academic and industry audiences, the Summer School will feature: Core Research presentations for professors, postdocs, PhD candidates, and graduate students in AI, ML, and NLP Applied Sessions for professionals and enthusiasts exploring real-world AI applications in finance Daily Panel Discussions and Networking Opportunities to foster collaboration and knowledge exchange Held in the RDI Hub, named in tribute to AI pioneer John McCarthy, whose father hailed from Cromane, Co. Kerry, the Summer School continues to honor his legacy by convening the brightest minds in artificial intelligence. McCarthy, a Stanford computer scientist, was instrumental in founding the field of AI and created LISP, the programming language that powered early AI systems. The John McCarthy AI Summer School is fast becoming a landmark event in the global AI calendar. Don't miss this opportunity to engage with cutting-edge research, visionary leaders, and transformative ideas at the intersection of AI and finance. This is a hybrid event, attendees can join in-person or online, but limited in-person seats are available. Registrations are open now: https://lu.ma/jqp7ay6a Minister Niamh Smyth welcomed the event, commenting: 'We are beginning to see the transformative impact of AI in financial services, and expect it to influence the new international financial services strategy we have committed to develop under the Programme for Government. The 2025 John McCarthy AI Summer school offers unique insights into new developments and future opportunities in this area, underpinned by public-private collaboration and expert input. Our culture of continuous learning and development has been a key enabler of Irelands growth and success to date, and this forum will serve to further strengthen Ireland's reputation as a leader in financial innovation.' Kerry MacConnell, RDI Hub PR and Marketing Manager had this to say: "We are proud to announce the launch of the sixth annual John McCarthy AI Summer School, this year exploring the theme of 'AI for Finance.' RDI Hub has a deep-rooted connection with AI and plays a unique role at the intersection of business, entrepreneurship, and academia. Across two days, we'll examine key developments in areas such as agentic AI, cybersecurity, fraud detection, and real-world applications from industry leaders. We loo...

Tonight I will be chatting with the creator of the Bigfoot Live Stream App. Logan will be sharing his encounters and why he created this app. This app features the ability view 24/7 live streams of Bigfoot Hot Zones. Subscription Information - Title: 24/7 Bigfoot Live Stream - Length of Subscription: Monthly - Price: $4.99 per month   By subscribing, you get access to: | All available live streams | Access to Video On Demand of of every live stream for the past 2 weeks | Uploaded videos from user reports | News and updates | First to see footage reports | Report sightings live! | ML and AI reporting   By creating a login you will then have access to subscribe to the 24/7 Bigfoot Live Stream, this is critical in the function of the app for subscribers. Users will need to first create an account for it is a necessary function within the subscription of the app. Before you create an account, users will have access to content describing about the livestream and Bigfoot Collaboration. Everything pre sign-up is information about the subscription, and our mission statement. You do not get any access to any content for creating an account. Once you create an account it only gives you access to subscribe. If you choose to remove your account there is a link in account section for removal.

THANK YOU LEE CHAEMIN FOR BEING HERE TODAY….The pod sisters are here to chat up “Bon Appétit, Your Majesty” - or, as we will always choose to call it - KNIFE KNIFE. (we cannot call this show bon appétit ur majesty it's so cringey we cannot)….tvN and Netflix are AT IT AGAIN this fall and we're all EATING IT UP PUN INTENDED. The girls chat through the plot in the first two eps, all the cooking scenes, Yoona's adorableness, and the fact that Lee Chaemin was added in as the ML like a week before the first reading. …..So far, this drama is easy on the plot and easy on the eyes (MWAHAHAHHA). Maddie and Elle are obsessed. Christina is def never gonna watch ep 3. How are y'all liking it so far?…..“Bon Appétit, Your Majesty” is a 12-episode tvN KDrama airing on Netflix this fall.…If you're new to YA GIRL, we're so glad you're here!! I truly hope you enjoy listening to this podcast! …..Also, check out our sister-pod - THE KDROP: A KPop Podcast - if that's your thing. https://www.instagram.com/the.kdrop_kpop_pod/ ….. Before you do anything else, FOLLOW YA GIRL ON INSTAGRAM! For real, please come and say hey to us over the socials! @yagirl_kdrama pod (⁠⁠https://www.instagram.com/yagirl_kdramapod?igsh=OGQ5ZDc2ODk2ZA%3D%3D&utm_source=qr⁠⁠)….And Christina just started an exclusive BTS instagram, so give that a follow!   https://www.instagram.com/bts_express_the.kdrop?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw== … Finally, jump on  YA GIRL's Discord!! It's where all the friends of YA GIRL gather and talk about hot Korean men. You really don't wanna miss it. ⁠⁠ https://discord.gg/rFmEgTJpJ8

Routine vaginal examinations (VEs) are a standard component of intrapartum care, traditionally performed at regular intervals to monitor cervical dilation, effacement, and fetal station, which are indicators of labor progression. Yet, the American College of Obstetricians and Gynecologists states that there is insufficient evidence to recommend a specific frequency for cervical examinations during labor, and examinations should be performed as clinically indicated. Now, a recently published RCT form AJOG MFM is adding additional credence to that. Can we space out clinical exams in otherwise “low-risk” laboring women to 8 hours? Listen in for details. 1. AJOG MFM: (08/18/25) Routine Vaginal Examination Scheduled At 8 vs 4 Hours In Multiparous Women In Early Spontaneous Labour: A Randomised Controlled Trial https://www.sciencedirect.com/science/article/abs/pii/S25899333250016122. Nashreen CM, Hamdan M, Hong J, et al.Routine Vaginal Examination to Assess Labor Progress at 8 Compared to 4 h After Early Amniotomy Following Foley Balloon Ripening in the Labor Induction of Nulliparas: A Randomized Trial. Acta Obstetricia Et Gynecologica Scandinavica. 2024;103(12):2475-2484. doi:10.1111/aogs.14975.3. First and Second Stage Labor Management: ACOG Clinical Practice Guideline No. 8. Obstetrics and Gynecology. 2024;143(1):144-162. doi:10.1097/AOG.0000000000005447.4. Moncrieff G, Gyte GM, Dahlen HG, et al. Routine Vaginal Examinations Compared to Other Methods for Assessing Progress of Labour to Improve Outcomes for Women and Babies at Term. The Cochrane Database of Systematic Reviews. 2022;3:CD010088. doi:10.1002/14651858.CD010088.pub3.5. Gluck, O., et al. (2020). The correlation between the number of vaginal examinations during active labor and febrile morbidity, a retrospective cohort study. [BMC Pregnancy and Childbirth]6. Pan, WL., Chen, LL. & Gau, ML. Accuracy of non-invasive methods for assessing the progress of labor in the first stage: a systematic review and meta-analysis. BMC Pregnancy Childbirth 22, 608 (2022). https://doi.org/10.1186/s12884-022-04938-y

00:00-15:00: ML and Joe bring on Brent Axe to preview the Syracuse football season. Thanks to CH Insurance. In your corner.

In this episode of History 102, 'WhatIfAltHist' creator Rudyard Lynch and co-host Austin Padgett explore why gunpowder democratized Europe but enabled Asian autocracy, examining Hawaii, Ottoman, Ming, and Mughal empires to understand how identical technologies create divergent civilizational outcomes. -- SPONSOR: ZCASH | SHOPIFY | NETSUITE | ORACLE The right technology reshapes politics and culture toward freedom and prosperity. Zcash—the "machinery of freedom"—delivers unstoppable private money through encryption. When your wealth is unseen, it's unseizable. Download Zashi wallet and follow @genzcash to learn more: ⁠⁠https://x.com/genzcash⁠⁠ Shopify is the world's leading e-commerce platform, offering a market-leading checkout system Shoppay and exclusive AI apps. Nobody does selling better than Shopify. Get a $1 per month trial at ⁠⁠⁠https://shopify.com/momentofzen⁠⁠⁠. More than 42,000 businesses have already upgraded to NetSuite by Oracle, the #1 cloud financial system bringing accounting, financial management, inventory, HR, into ONE proven platform. If you're looking for an ERP platform, get a one-of-a-kind flexible financing program on NetSuite: ⁠⁠⁠https://netsuite.com/102⁠⁠⁠ - Download your free CFO's guide to AI and machine learning. Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers. OCI powers industry leaders like Vodafone and Thomson Reuters with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before March 31, 2024 at ⁠⁠⁠https://oracle.com/cognitive⁠⁠⁠. -- FOLLOW ON X: @whatifalthist (Rudyard) @LudwigNverMises (Austin) @TurpentineMedia -- TIMESTAMPS: (00:00) Introduction & Framework(13:00) Case Study #1: Hawaii(17:33) Sponsors: ZCash | Shopify(22:39) Case Study #2: Spanish & Turkish Empires(40:50) Sponsors: Netsuite | Oracle(45:52) Case Study #3: China - Ming & Qing Dynasties(1:07:00) Japan: Brief Military Supremacy(1:07:00) Japan: Brief Military Supremacy(1:11:00) Ottoman Empire: Slave Soldiers & Decline(1:32:06) African Gunpowder Empires(1:38:00) Safavid Persia(1:42:00) Mughal Empire(1:53:00) European Colonialism & Modern Implications Learn more about your ad choices. Visit megaphone.fm/adchoices

Artie Shevchenko, author of Code Health Guardian, speaks with host Jeff Doolittle about the crucial role of human programmers in the AI era, emphasizing that humans must excel at managing code complexity. Shevchenko discusses these concepts and key takeaways from his book, including the three problems caused by complexity: change amplification, cognitive load, and the most severe, unknown unknowns. He suggests that maintaining code health should be viewed pragmatically as a productivity question, requiring an ownership mentality and product focus to balance short-term delivery with long-term maintainability. The episode also covers vital processes such as using design documents for upfront analysis and code reviews, highlighting four goals: high code quality, knowledge sharing, delivery speed, and -- most important for team productivity -- psychological safety. This episode is sponsored by Monday Dev

Chris Ballard says emphatically that he still believes in Richardson, but does not understand lack of curiosity about Daniel Jones! Woods, Brents, Womack claimed by AFC teams! IU now -23 1/2 against ODU. ML -3000! Here is the link for the only autobiography ever published without a word of praise for its author: https://www.amazon.com/Oops-Art-Learning-Mistakes-Adventures/dp/173420740X Contest rules: https://kentsterling.com/2025/07/03/rule-for-kent-sterling-conest/

ML is STILL recovering from high school, so why is he going to his 40th class reunion this weekend? Marc […]

Today, we're joined by Prince Canuma, an ML engineer and open-source developer focused on optimizing AI inference on Apple Silicon devices. Prince shares his journey to becoming one of the most prolific contributors to Apple's MLX ecosystem, having published over 1,000 models and libraries that make open, multimodal AI accessible and performant on Apple devices. We explore his workflow for adapting new models in MLX, the trade-offs between the GPU and Neural Engine, and how optimization methods like pruning and quantization enhance performance. We also cover his work on "Fusion," a weight-space method for combining model behaviors without retraining, and his popular packages—MLX-Audio, MLX-Embeddings, and MLX-VLM—which streamline the use of MLX across different modalities. Finally, Prince introduces Marvis, a real-time speech-to-speech voice agent, and shares his vision for the future of AI, emphasizing the move towards "media models" that can handle multiple modalities, and more. The complete show notes for this episode can be found at https://twimlai.com/go/744.

Freight fraud is one of the biggest threats facing independent brokers today, and many agents are left without a playbook for how to respond. In this episode of Everything is Logistics, Blythe talks with Mark Funk and Shiv Narayan of SPI Logistics about building an action plan to protect agents, their customers, and their carriers. From red flags to response strategies, this conversation lays out the steps every agent should know.Key takeaways: Why freight fraud is escalating and what makes agents vulnerable. The most common fraud tactics used against brokers and carriers. How SPI equips agents with tools and processes to fight back. The role of culture, training, and communication in prevention. What agents should do immediately when they suspect fraud.LINKS:SPI Logistics Freight Agent WebsiteSPI Freight Agent ProgramMark's LinkedInShiv's LinkedInWATCH THE FULL VIDEO HEREFeedback? Ideas for a future episode? Shoot us a text here to let us know. -----------------------------------------THANK YOU TO OUR SPONSORS! Are you experienced in freight sales or already an independent freight agent? Listen to our Freight Agent Trenches interviews powered by SPI Logistics to hear from the company's agents on how they took the entrepreneurial leap. Shipium is the ML-powered shipping platform built by the executives that created Amazon's supply chain technology. They help 3PL & retail leaders like Stord, Ryder, and Saks Off 5th to reduce shipping costs by an average of 12% while improving on-time delivery. CargoRex is the logistics industry's go-to search platform—connecting you with the right tools, services, events, and creators to explore, discover, and evolve. Digital Dispatch manages and maximizes your #1 sales tool with a website that establishes trust and builds rock-solid relationships with your leads and customers.

Arch is under fire, two weeks and counting. We'll break down the mess, and share a quick fix. Plus, the killer new apps we've just added to our homelabs.Sponsored By:Managed Nebula: Meet Managed Nebula from Defined Networking. A decentralized VPN built on the open-source Nebula platform that we love. 1Password Extended Access Management: 1Password Extended Access Management is a device trust solution for companies with Okta, and they ensure that if a device isn't trusted and secure, it can't log into your cloud apps. Unraid: A powerful, easy operating system for servers and storage. Maximize your hardware with unmatched flexibility. Support LINUX UnpluggedLinks:

"History 102" with WhatifAltHist's Rudyard Lynch and Erik Torenberg: Read the notes at at podcastnotes.org. Don't forget to subscribe for free to our newsletter, the top 10 ideas of the week, every Monday --------- In this episode of History 102, 'WhatIfAltHist' creator Rudyard Lynch and co-host Austin Padgett do a deep-dive into the Mongol Empire's rise, conquests, and civilizational impact, examining how Genghis Khan's brutal campaigns reshaped Eurasian politics and accelerated Western Europe's ascendancy. -- SPONSOR: ZCASH | SHOPIFY | NETSUITE | ORACLE The right technology reshapes politics and culture toward freedom and prosperity. Zcash—the "machinery of freedom"—delivers unstoppable private money through encryption. When your wealth is unseen, it's unseizable. Download Zashi wallet and follow @genzcash to learn more: ⁠https://x.com/genzcash⁠ Shopify is the world's leading e-commerce platform, offering a market-leading checkout system Shoppay and exclusive AI apps. Nobody does selling better than Shopify. Get a $1 per month trial at https://shopify.com/momentofzen More than 42,000 businesses have already upgraded to NetSuite by Oracle, the #1 cloud financial system bringing accounting, financial management, inventory, HR, into ONE proven platform. If you're looking for an ERP platform, get a one-of-a-kind flexible financing program on NetSuite: https://netsuite.com/102 - Download your free CFO's guide to AI and machine learning. Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers. OCI powers industry leaders like Vodafone and Thomson Reuters with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before March 31, 2024 at https://oracle.com/cognitive -- FOLLOW ON X: @whatifalthist (Rudyard) @LudwigNverMises (Austin) @TurpentineMedia -- TIMESTAMPS: (00:00) Intro (03:04) Polygamous Imperial Cycles and Genghis Khan's Genetic Legacy (05:43) Sponsors: ZCash | Shopify (09:27) Genghis Khan as Totalitarian Figure and Step Empire Dynamics (15:25) Academic Debates About the Mongols - Left vs Military Historians (28:40) Major Mongol Military Campaigns - Badger's Mouth and Khwarezmid (33:27) Sponsors: NetSuite | Oracle Cloud Infrastructure (46:14) Genghis Khan's Rise - Childhood, Unification of Mongols (56:59) Conquest of Northern China - 30 Million Dead (1:11:28) Destruction of Central Asia and the Islamic World (1:20:16) Conquest and Transformation of Russia (1:28:59) Mongol Empire Splits - Four Khanates and Kublai Khan (1:43:21) Pax Mongolica and Failed Invasions of Japan/Java (1:52:30) Fall of the Mongol Empire - Black Death and Ming Dynasty (2:06:52) Long-Term Impact - Europe's Rise Over Asia (2:11:42) Mongol Role in Spreading Black Death and Closing Learn more about your ad choices. Visit megaphone.fm/adchoices

Dr. Shauna Overgaard is the Senior Director of AI Strategy & Frameworks at Mayo Clinic, where she focuses on applying AI and data science to healthcare. She also founded Clarity - Applied Intelligence and served as an Assistant Professor of Health Informatics and Information Management. She is active in several organizations like the Coalition for Health AI (CHAI) and the American Medical Informatics Association (AMIA), and is helping shape the future of AI in healthcare. With over 50 citations to her name, Dr. Overgaard has significantly contributed to AI model documentation, clinical decision support systems, and the ethical implementation of AI in healthcare. Our conversation took place in Fall 2024, and we're starting to see how a lot of these predictions played out!00:00 - Intro04:05 - From rural Canada to a Senior Director at Mayo Clinic06:30 - Innovation culture at Mayo09:22 - It takes 17 years for AI to get to the clinic?! 15:58 - Giving patients ownership over their data21:24 - Can paying patients for their data make ML models more equitable?28:55 - Is bias inescapable with AI?32:19 - Can we make AI more transparent without giving away intellectual property?35:15 - Worries about AI replacing healthcare workers36:52 - Silent Trials: beta-testing AI39:25 - So uh, how's Rochester?42:00 - What gives your life meaning?44:52 - Advice for navigating uncertain times and the AI revolutionDr. Overgaard: linkedin.com/in/shaunaovergaard/Host: Nathan KellerTwitter: @NathanKellerX Linkedin: linkedin.com/in/nathankeller1Producer: Saurin Kantesaria & Garima PuriLinkedIn: Saurin KantesariaLinkedin: linkedin.com/in/garimapuri/

In this episode of History 102, 'WhatIfAltHist' creator Rudyard Lynch and co-host Austin Padgett analyze 2,000 years of Imperial China's civilizational patterns, examining how Confucian bureaucracy, geographic divisions, and cyclical dynastic struggles shaped one of history's most enduring empires. -- SPONSOR: ZCASH | SHOPIFY | NETSUITE | ORACLE The right technology reshapes politics and culture toward freedom and prosperity. Zcash—the "machinery of freedom"—delivers unstoppable private money through encryption. When your wealth is unseen, it's unseizable. Download Zashi wallet and follow @genzcash to learn more: ⁠https://x.com/genzcash⁠ Shopify is the world's leading e-commerce platform, offering a market-leading checkout system Shoppay and exclusive AI apps. Nobody does selling better than Shopify. Get a $1 per month trial at ⁠⁠https://shopify.com/momentofzen⁠⁠. More than 42,000 businesses have already upgraded to NetSuite by Oracle, the #1 cloud financial system bringing accounting, financial management, inventory, HR, into ONE proven platform. If you're looking for an ERP platform, get a one-of-a-kind flexible financing program on NetSuite: ⁠⁠https://netsuite.com/102⁠⁠ - Download your free CFO's guide to AI and machine learning. Oracle Cloud Infrastructure (OCI): Oracle's next-generation cloud platform delivers blazing-fast AI and ML performance with 50% less for compute and 80% less for outbound networking compared to other cloud providers. OCI powers industry leaders like Vodafone and Thomson Reuters with secure infrastructure and application development capabilities. New U.S. customers can get their cloud bill cut in half by switching to OCI before March 31, 2024 at ⁠⁠https://oracle.com/cognitive⁠⁠. -- FOLLOW ON X: @whatifalthist (Rudyard) @LudwigNverMises (Austin) @TurpentineMedia -- TIMESTAMPS: (00:00) Introduction (01:45) Imperial China Overview & Macro History (07:00) China's Fall of Rome Period (12:01) Geographic Regions of China (17:24) Sponsors: ZCash | Shopify (23:00) Qin Dynasty Unification (46:20) Qin Dynasty Fall & Han Dynasty Rise (54:00) Han Dynasty Economics & Expansion (1:25:00) Three Ruling Classes: Bureaucracy, Eunuchs, Nobility (1:36:33) Sponsors: Netsuite | Oracle (1:39:00) War of Three Kingdoms & Dark Ages (1:47:00) Tang Dynasty Golden Age (1:55:00) Neo-Confucian Renaissance & Song Dynasty (1:58:00) Mongol Conquest (2:00:00) Ming Dynasty Isolationism (2:08:00) Manchu Conquest & Qing Dynasty (2:12:04) Modern China's Challenges (2:19:24) Wrap Learn more about your ad choices. Visit megaphone.fm/adchoices

Hear how data is rewriting the rules—and driving the future of innovation. Dr. Vanja breaks down his approach to enterprise AI, and the key strategies for success. He shares what it means to be a “data-forward organization” and why data must sit at the heart of how a company operates. Discover why integration speed is the new competitive edge Key Moments:Data as a Core Enterprise Asset (6:04): Dr. Vanja defines what it means to be a “data-forward organization,” emphasizing that data shouldn't be an afterthought—it must sit at the heart of how a company operates, makes decisions, and serves customers.Relational Foundation Models for Predictive AI (16:26): Dr. Vanja explains how Kumo uses transformer architectures on raw relational data to eliminate 95% of traditional ML tasks like feature engineering—bringing pre-trained, multi-purpose prediction models into the structured data world.Integration Speed as a Competitive Edge (27:03): It's no longer just about who builds the fastest—but who integrates the fastest. Dr. Vanja shares how companies that can quickly adopt and scale best-in-class third-party tools will define the next generation of winners.The Case for Rebuilding at 10x Advantage (29:02): Dr. Vanja urges leaders to rethink old systems if a new approach offers a 10x cost or performance improvement. Sticking with outdated architectures out of fear or inertia risks falling behind during pivotal transitions.Key Quotes:“The key asset is the data that you have… You need to build the whole enterprise around this data. It's not an afterthought — it's at the center of the organization and how it functions.” - Dr. Vanja“Companies that integrate the fastest — not just build the fastest — will win.” - Dr. Vanja“If you really value your data, you need to stay in the cloud… If the cost is your only driver, then do what you want — but you'll miss out on the majority of new technologies.” - Dr. VanjaMentions:Build AI Models for Relational DataThe New Cloud Era of Data Platform Hosted AppsKumo unveils world's first Relational Foundation Model (RFM) for instant predictions on enterprise dataGuest Bio: Dr. Vanja Josifovski is the Co-Founder and CEO of Kumo. Prior to Kumo, Vanja was the CTO at Airbnb & Pinterest. Here, Vanja led an organization that included horizontal groups such as Homes Engineering and Homes Data Science, as well as GM groups such as Marketplace, Relevance and Personalization, and Regulatory Frameworks. Before Airbnb, Vanja was the CTO & VP Eng at Pinterest, responsible for the overall technical vision and strategy of the company and communicating that to leadership and the teams; hiring and development of technical talent. As the head of Engineering, he managed some core engineering teams. Vanja has also served as an advisor and investor for multiple startups and was the founder for Kosei, which was acquired by Pinterest. Hear more from Cindi Howson here. Sponsored by ThoughtSpot.

ESPN's Dan Wetzel opines on the NCAA and Michigan Wolverines, while Marc defends the Big House and ML and Shawn […]

Python's data stack is getting a serious GPU turbo boost. In this episode, Ben Zaitlen from NVIDIA joins us to unpack RAPIDS, the open source toolkit that lets pandas, scikit-learn, Spark, Polars, and even NetworkX execute on GPUs. We trace the project's origin and why NVIDIA built it in the open, then dig into the pieces that matter in practice: cuDF for DataFrames, cuML for ML, cuGraph for graphs, cuXfilter for dashboards, and friends like cuSpatial and cuSignal. We talk real speedups, how the pandas accelerator works without a rewrite, and what becomes possible when jobs that used to take hours finish in minutes. You'll hear strategies for datasets bigger than GPU memory, scaling out with Dask or Ray, Spark acceleration, and the growing role of vector search with cuVS for AI workloads. If you know the CPU tools, this is your on-ramp to the same APIs at GPU speed. Episode sponsors Posit Talk Python Courses Links from the show RAPIDS: github.com/rapidsai Example notebooks showing drop-in accelerators: github.com Benjamin Zaitlen - LinkedIn: linkedin.com RAPIDS Deployment Guide (Stable): docs.rapids.ai RAPIDS cuDF API Docs (Stable): docs.rapids.ai Asianometry YouTube Video: youtube.com cuDF pandas Accelerator (Stable): docs.rapids.ai Watch this episode on YouTube: youtube.com Episode #516 deep-dive: talkpython.fm/516 Episode transcripts: talkpython.fm Developer Rap Theme Song: Served in a Flask: talkpython.fm/flasksong --- Stay in touch with us --- Subscribe to Talk Python on YouTube: youtube.com Talk Python on Bluesky: @talkpython.fm at bsky.app Talk Python on Mastodon: talkpython Michael on Bluesky: @mkennedy.codes at bsky.app Michael on Mastodon: mkennedy

Mergers and acquisitions are reshaping the logistics tech landscape. In this episode of Freight Friends, Blythe and Grace Sharkey unpack what recent deals mean for the industry, how SEO strategies are shifting under AI pressure, and what freight markets could look like in the coming months. They also explore the ripple effects for carriers, shippers, and the tech companies caught in the middle. Key takeaways: Tech consolidation is reshaping how freight companies compete and collaborate. SEO strategies in logistics are changing fast with AI and shifting consumer behavior. Carriers and shippers are feeling the pressure of an uncertain second half of 2025. Market moves today will set the tone for 2026 freight demand and technology adoption.LINKS:Grace's LinktreeFreightGames.io"Flexport Tycoon"LinkedIn post on Instagram SEOCloudflare fighting against the LLM botsWATCH THE FULL EPISODE HEREFeedback? Ideas for a future episode? Shoot us a text here to let us know. -----------------------------------------THANK YOU TO OUR SPONSORS! Are you experienced in freight sales or already an independent freight agent? Listen to our Freight Agent Trenches interviews powered by SPI Logistics to hear from the company's agents on how they took the entrepreneurial leap. Shipium is the ML-powered shipping platform built by the executives that created Amazon's supply chain technology. They help 3PL & retail leaders like Stord, Ryder, and Saks Off 5th to reduce shipping costs by an average of 12% while improving on-time delivery. CargoRex is the logistics industry's go-to search platform—connecting you with the right tools, services, events, and creators to explore, discover, and evolve. Digital Dispatch manages and maximizes your #1 sales tool with a website that establishes trust and builds rock-solid relationships with your leads and customers.

Erika, Marc and ML put the “fun” in funeral, as the show gets off to an oddly macabre start, before […]

Scott Seidenberg and Steve Fezzik talk all things NFL this week.