Podcasts about ML

🧭 REBEL Rundown 🗝️ Key Points 💨 Peak vs. Plateau Pressures: PIP reflects total airway resistance and compliance, while Pplat isolates alveolar compliance—elevations in both suggest decreased lung compliance (e.g., ARDS, pulmonary edema, pneumothorax).🧱 PEEP Protects Alveoli: Maintains alveolar recruitment and prevents collapse; typical range 5–8 cmH₂O, but higher levels may benefit moderate–severe ARDS.️ Driving Pressure (ΔP = Pplat − PEEP): Lower ΔP reduces atelectrauma and improves outcomes; optimize by adjusting PEEP thoughtfully.💥 Prevent VILI: Keep Pplat < 30 cmH₂O, use low tidal volumes (6 mL/kg IBW), and monitor for barotrauma, volutrauma, atelectrauma, and biotrauma.📚 Evidence-Based Practice: ARDSNet and subsequent trials confirm that lung-protective ventilation—low Vt, limited pressures, and individualized PEEP—improves survival in ARDS. Click here for Direct Download of the Podcast. 📝 Introduction This episode reviews essential ventilator pressures and how to interpret them during ICU rounds. 🚀 Under Pressure Peak Inspiratory Pressure (PIP)Definition: Total pressure required to deliver a breath.Reflects: Airway resistance + lung/chest wall compliance.Common Causes of ↑ PIP:Mucus pluggingBiting the endotracheal tubeKinked tubing or bronchospasmPlateau Pressure (Pplat)Definition: Alveolar pressure measured after an inspiratory hold.Reflects: Lung compliance (stiffness of lung tissue).When Both PIP & Pplat Are Elevated:→ Indicates poor compliance (e.g., ARDS, pulmonary edema, pneumothorax).Positive End-Expiratory Pressure (PEEP)Definition: Pressure remaining in airways at end-expiration to prevent alveolar collapse.Typical Range: 5–8 cmH₂O but needs to titrated to meet patient requirements Notes:Provides physiologic “glottic” PEEP in intubated patients.Using high PEEP strategy shows mortality benefit only in moderate–severe ARDS in meta-analysis.Driving Pressure (ΔP)Definition: ΔP = Pplat − PEEP.Reflects: Pressure needed to keep alveoli open during the respiratory cycle.Goal: Lower ΔP → less atelectrauma & improved outcomes.Optimize: Increase PEEP to reduce ΔP and alveolar cycling. 📖 Interpreting High PIP/High Pplat ↑ PIP & ↑ PplatInterpretation: ↓ ComplianceCommon Causes: ARDS, pulmonary edema, pleural effusion, pneumothorax↑ PIP & Normal/Low PplatInterpretation: ↑ Airway ResistanceCommon Causes: Mucus plug, bronchospasm, tube obstruction or biting 🤕 Ventilator-Associated Lung Injury (VILI) Barotrauma:Mechanism: Excessive airway pressure damages alveoli.Prevention: Keep Pplat < 30 cmH₂O.Volutrauma:Mechanism: Overdistension from excessive tidal volumes.Prevention: Use low tidal volume ventilation (6 mL/kg ideal body weight).ARDSNet trial: 6 mL/kg → lower mortality compared to 12 mL/kg.Ideal Body Weight: Based on height and sex, not actual weight.Typical patient: Tidal Volume: 6–8 mL/kg IBWARDS: Tidal Volume: 4–6 mL/kg IBWAtelectrauma:Mechanism: Repeated opening/collapse of unstable alveoli.Prevention: Optimize PEEP to keep alveoli open and reduce driving pressure.Biotrauma:Mechanism: Inflammatory cascade (↑ IL-6, TNF-α) from mechanical injury.Effect: Can trigger systemic inflammation & multiorgan dysfunction.Prevention: Minimize all other forms of VILI. Post Peer Reviewed By: Marco Propersi, DO (Twitter/X: @Marco_propersi), and Mark Ramzy, DO (X: @MRamzyDO) 👤 Show Notes Joel Rios Rodriguez, MD PGY 3 Internal Medicine Resident Cape Fear Valley Internal Medicine Residency Program Fayetteville NC Aspiring Pulmonary Critical Care Fellow 🔎 Your Deep-Dive Starts Here REBEL Core Cast – Pediatric Respiratory Emergencies: Beyond Viral Season Welcome to the Rebel Core Content Blog, where we delve ... Pediatrics Read More REBEL Core Cast 143.0–Ventilators Part 3: Oxygenation & Ventilation — Mastering the Balance on the Ventilator When you take the airway, you take the wheel and ... Thoracic and Respiratory Read More REBEL Core Cast 142.0–Ventilators Part 2: Simplifying Mechanical Ventilation – Most Common Ventilator Modes Mechanical ventilation can feel overwhelming, especially when faced with a ... Thoracic and Respiratory Read More REBEL Core Cast 141.0–Ventilators Part 1: Simplifying Mechanical Ventilation — Types of Breathes For many medical residents, the ICU can feel like stepping ... Thoracic and Respiratory Read More REBEL Core Cast 140.0: The Power and Limitations of Intraosseous Lines in Emergency Medicine The sicker the patient, the more likely an IO line ... Procedures and Skills Read More REBEL Core Cast 139.0: Pneumothorax Decompression On this episode of the Rebel Core Cast, Swami takes ... Procedures and Skills Read More The post REBEL Core Cast 147.0–Ventilators Part 5: Key Mechanical Ventilator Pressures & Definitions Made Simple appeared first on REBEL EM - Emergency Medicine Blog.

00:00-15:00: Dallas Stars. Fun to watch. ML says they are well run and will be here the whole season. Thanks to Rosie's Corner and Ken's Auto Detailing. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

In the era of massive data lakes, the ability to extract instant security insights without managing complex infrastructure is a strategic game-changer. This episode explores Amazon Athena, a serverless interactive query service that enables you to analyze S3 data directly using standard ANSI SQL. Discover how to transform raw logs into actionable intelligence, optimize your cloud costs with pay-per-query pricing, and significantly streamline your compliance audits across the entire AWS ecosystem.

With cybercrime costs projected to reach $10.5 trillion this year, legacy security perimeters are no longer enough to protect modern enterprises. This episode breaks down the pivotal architecture trends of 2025, from the transition to identity-first Zero Trust models to the rise of quantum-resistant cryptography. Listeners will discover how to build a decentralized, AI-powered defense strategy that scales across multi-cloud environments while ensuring long-term data privacy and compliance.

00:00-15:00: Bills-Patriots recap. ML breaks it down. These are simply your 2025 Buffalo Bills. Thanks to CH Insurance and Rosie's Corner. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

It's a little more ML, A LOT more unfiltered. In this episode, we talk about "Momterns" last day with the show, plus she brought us gifts we opened during the show! Find out why we had to end this episode abruptly!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Join Chief Technologist, John Janek, and Data Scientist, Mariana Ritchie, as they discuss various aspects of artificial intelligence from its influence on daily work environments, the use and advancements in technology, and its role in shaping future developments.Mariana discusses her journey at the company, touching on her early projects building ML pipelines to diving into the rapid progress and expansion of AI, following the release of ChatGPT. John and Mariana continue the conversation discussing the evolution of AI from natural language processing to multi-agent systems, highlighting the integration of large language models (LLMs) and the transition towards more specialized and efficient use cases.The conversation also shares insights into the future of AI, emphasizing the relevance of LLMs, the rise of smaller, specialized models, and the role of robust data practices. The episode concludes with Mariana sharing advice for aspiring technologists, urging them to build strong foundational knowledge and highlighting the criticality of being an active, engaged practitioner in the rapidly evolving AI landscape.

00:00-15:00: ML breaks down Quinn Hughes getting traded to the Wild. Thanks to Byrne Dairy and Ken's Auto Detailing. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Datawizz is pioneering continuous reinforcement learning infrastructure for AI systems that need to evolve in production, not ossify after deployment. After building and exiting RapidAPI—which served 10 million developers and had at least one team at 75% of Fortune 500 companies using and paying for the platform—Founder and CEO Iddo Gino returned to building when he noticed a pattern: nearly every AI agent pitch he reviewed as an angel investor assumed models would simultaneously get orders of magnitude better and cheaper. In a recent episode of BUILDERS, we sat down with Iddo to explore why that dual assumption breaks most AI economics, how traditional ML training approaches fail in the LLM era, and why specialized models will capture 50-60% of AI inference by 2030. Topics Discussed Why running two distinct businesses under one roof—RapidAPI's developer marketplace and enterprise API hub—ultimately capped scale despite compelling synergy narratives The "Big Short moment" reviewing AI pitches: every business model assumed simultaneous 1-2 order of magnitude improvements in accuracy and cost Why companies spending 2-3 months on fine-tuning repeatedly saw frontier models (GPT-4, Claude 3) obsolete their custom work The continuous learning flywheel: online evaluation → suspect inference queuing → human validation → daily/weekly RL batches → deployment How human evaluation companies like Scale AI shift from offline batch labeling to real-time inference correction queues Early GTM through LinkedIn DMs to founders running serious agent production volume, working backward through less mature adopters ICP discovery: qualifying on whether 20% accuracy gains or 10x cost reductions would be transformational versus incremental The integration layer approach: orchestrating the continuous learning loop across observability, evaluation, training, and inference tools Why the first $10M is about selling to believers in continuous learning, not evangelizing the category GTM Lessons For B2B Founders Recognize when distribution narratives mask structural incompatibility: RapidAPI had 10 million developers and teams at 75% of Fortune 500 paying for the platform—massive distribution that theoretically fed enterprise sales. The problem: Iddo could always find anecdotes where POC teams had used RapidAPI, creating a compelling story about grassroots adoption. The critical question he should have asked earlier: "Is self-service really the driver for why we're winning deals, or is it a nice-to-have contributor?" When two businesses have fundamentally different product roadmaps, cultures, and buying journeys, distribution overlap doesn't create a sustainable single company. Stop asking if synergies exist—ask if they're causal. Qualify on whether improvements cross phase-transition thresholds: Datawizz disqualifies prospects who acknowledge value but lack acute pain. The diagnostic questions: "If we improved model accuracy by 20%, how impactful is that?" and "If we cut your costs 10x, what does that mean?" Companies already automating human labor often respond that inference costs are rounding errors compared to savings. The ideal customers hit differently: "We need accuracy at X% to fully automate this process and remove humans from the loop. Until then, it's just AI-assisted. Getting over that line is a step-function change in how we deploy this agent." Qualify on whether your improvement crosses a threshold that changes what's possible, not just what's better. Use discovery to map market structure, not just validate hypotheses: Iddo validated that the most mature companies run specialized, fine-tuned models in production. The surprise: "The chasm between them and everybody else was a lot wider than I thought." This insight reshaped their entire strategy—the tooling gap, approaches to model development, and timeline to maturity differed dramatically across segments. Most founders use discovery to confirm their assumptions. Better founders use it to understand where different cohorts sit on the maturity curve, what bridges or blocks their progression, and which segments can buy versus which need multi-year evangelism. Target spend thresholds that indicate real commitment: Datawizz focuses on companies spending "at a minimum five to six figures a month on AI and specifically on LLM inference, using the APIs directly"—meaning they're building on top of OpenAI/Anthropic/etc., not just using ChatGPT. This filters for companies with skin in the game. Below that threshold, AI is an experiment. Above it, unit economics and quality bars matter operationally. For infrastructure plays, find the spend level that indicates your problem is a daily operational reality, not a future consideration. Structure discovery to extract insight, not close deals: Iddo's framework: "If I could run [a call where] 29 of 30 minutes could be us just asking questions and learning, that would be the perfect call in my mind." He compared it to "the dentist with the probe trying to touch everything and see where it hurts." The most valuable calls weren't those that converted to POCs—they came from people who approached the problem differently or had conflicting considerations. In hot markets with abundant budgets, founders easily collect false positives by selling when they should be learning. The discipline: exhaust your question list before explaining what you build. If they don't eventually ask "What do you do?" you're not surfacing real pain. Avoid the false-positive trap in well-funded categories: Iddo identified a specific risk in AI: "You can very easily run these calls, you think you're doing discovery, really you're doing sales, you end up getting a bunch of POCs and maybe some paying customers. So you get really good initial signs but you've never done any actual discovery. You have all the wrong indications—you're getting a lot of false positive feedback while building the completely wrong thing." When capital is abundant and your space is hot, early revenue can mask product-market misalignment. Good initial signs aren't validation if you skipped the work to understand why people bought. // Sponsors: Front Lines — We help B2B tech companies launch, manage, and grow podcasts that drive demand, awareness, and thought leadership. www.FrontLines.io The Global Talent Co. — We help tech startups find, vet, hire, pay, and retain amazing marketing talent that costs 50-70% less than the US & Europe. www.GlobalTalent.co // Don't Miss: New Podcast Series — How I Hire Senior GTM leaders share the tactical hiring frameworks they use to build winning revenue teams. Hosted by Andy Mowat, who scaled 4 unicorns from $10M to $100M+ ARR and launched Whispered to help executives find their next role. Subscribe here: https://open.spotify.com/show/53yCHlPfLSMFimtv0riPyM

Richard Bonneau, Vice President of Machine Learning for Drug Discovery at Genentech and Roche, provides Pitt's HexAI podcast host, Jordan Gass-Pooré, with an insider view on how his team is fundamentally changing and accelerating how new drug candidate molecules are designed, predicted, and optimized.Geared for students in computational sciences and hybrid STEM fields, the episode introduces listeners to uses of AI and ML in molecular design, the biomolecular structure and structure-function relationships that underpin drug discovery, and how distinct teams at Genentech work together through an integrated computational system.Richard and Jordan use the opportunity to touch on how advances in the molecule design domain can inspire and inform advances in computational pathology and laboratory medicine. Richard also delves into the critical role of Explainable AI (XAI), interpretability, and error estimation in the drug design-prototype-test cycle, and provides advice on domain knowledge and skills needed today by students interested in joining teams like his at Genentech and Roche.

Together, Leon and Oscar share how applied EDA practices remain the backbone of trustworthy analytics pipelines in both academic and industry settings. Their discussion highlights the challenges and lessons learned from building the EDA Toolkit, and why reproducible workflows are more important than ever in the age of AI and ML.Key Highlights:Reproducible EDA: How to standardize exploratory data analysis workflows for consistent and trustworthy insights.Open-Source Innovation: The design and impact of the EDA Toolkit, bridging research, healthcare, and education.Best Practices for Analytics: Lessons learned from creating tools that make EDA more intuitive and scalable across projects.The Future of Data Science Workflows: Why reproducibility and standardization matter in modern AI/ML pipelines.

The cybersecurity landscape is shifting as AI evolves from a "nice-to-have" tool to the core engine of both cyber attacks and enterprise defense. By 2026, simply knowing security fundamentals won't be enough—professionals must become AI-Powered Generalists capable of managing autonomous security agents and securing complex ML pipelines. This episode explores the critical AI skills required to lead in 2026, ensuring you move beyond manual tasks and into high-value strategic roles.

It's all about Data Pipelines. Join Pure Storage Field Solution Architect Chad Hendron and Solutions Director Andrew Silifant for a deep dive into the evolution of data management, focusing on the Data Lakehouse architecture and its role in the age of AI and ML. Our discussion looks at the Data Lakehouse as a powerful combination of a data lake and a data warehouse, solving problems like "data swamps” and proprietary formats of older systems. Viewers will learn about technological advancements, such as object storage and open table formats, that have made this new architecture possible, allowing for greater standardization and multiple tooling functions to access the same data. Our guests also explore current industry trends, including a look at Dremio's 2025 report showing the rapid adoption of Data Lakehouses, particularly as a replacement for older, inefficient systems like cloud data warehouses and traditional data lakes. Gain insight into the drivers behind this migration, including the exponential growth of unstructured data and the need to control cloud expenditure by being more prescriptive about what data is stored in the cloud versus on-premises. Andrew provides a detailed breakdown of processing architectures and the critical importance of meeting SLAs to avoid costly and frustrating pipeline breaks in regulated industries like banking. Finally, we provide practical takeaways and a real-world case study. Chad shares a customer success story about replacing a large, complex Hadoop cluster with a streamlined Dremio and Pure Storage solution, highlighting the massive reduction in physical space, power consumption, and management complexity. Both guests emphasize the need for better governance practices to manage cloud spend and risk. Andrew underscores the essential, full-circle role of databases—from the "alpha" of data creation to the "omega" of feature stores and vector databases for modern AI use cases like Retrieval-Augmented Generation (RAG). Tune in to understand how a holistic data strategy, including Pure's Enterprise Data Cloud, can simplify infrastructure and future-proof your organization for the next wave of data-intensive workloads. To learn more, visit https://www.purestorage.com/solutions/ai/data-warehouse-streaming-analytics.html Check out the new Pure Storage digital customer community to join the conversation with peers and Pure experts: https://purecommunity.purestorage.com/ 00:00 Intro and Welcome 03:15 Data Lakehouse Primer 08:31 Stat of the Episode on Lakehouse Usage 10:50 Challenges with Data Pipeline access 13:58 Assessing Organization Success with Data Cleaning 16:07 Use Cases for the Data Lakehouse 20:41 Case Study on Data Lakehouse Use Case 24:11 Hot Takes Segment

Онбординг часто воспринимают как набор экранов в начале продукта, хотя на самом деле это один из самых сильных продуктовых инструментов, который напрямую влияет на активацию, удержание и LTV. В этом выпуске разбираем, зачем нужен онбординг, как он работает в B2C и B2B, почему интерактив почти всегда выигрывает у статичных экранов, как персонализация и локализация меняют конверсию, и почему хороший онбординг не спасёт плохой продукт, но плохой способен испортить даже отличный. Говорим про реальные кейсы, метрики, A/B-тесты, friction, empty states и ошибки, которые команды продолжают повторять. Партнёр команды Podlodka — наши давние друзья @AvitoTech. Это команда с крутыми процессами, культурой здравого смысла и эксперимента. Узнать про их технологии, подходы и прокачку компетенций в командах можно по ссылкам: – Статья "Решаем задачи ML эффективнее: платформа, которая экономит ресурсы, время и нервы" clc.to/7EaWdQ – Статья "Всегда ли сегментация данных при анализе увеличивает эффективность экспериментов?" clc.to/clok5w – Статья "Как продакту выжить в мире ИИ-фичей" clc.to/gFffpQ Реклама. ООО "Авито Тех”, ИНН 9710089440, erid:2SDnjcAfMuH Также ждем вас, ваши лайки, репосты и комменты в мессенджерах и соцсетях!
 Telegram-чат: t.me/podlodka Telegram-канал: t.me/podlodkanews Страница в Facebook: www.facebook.com/podlodkacast/ Twitter-аккаунт: twitter.com/PodcastPodlodka Ведущие в выпуске: Андрей Смирнов, Аня Симонова Полезные ссылки: Вакансии Adapty adapty.io/careers/

Simba Khadder is the founder and CEO of Featureform, now at Redis, working on real-time feature orchestration and building a context engine for AI and agents.Context Engineering 2.0, Simba Khadder // MLOps Podcast #352Join the Community: https://go.mlops.community/YTJoinInGet the newsletter: https://go.mlops.community/YTNewsletter// AbstractFeature stores aren't dead — they were just misunderstood. Simba Khadder argues the real bottleneck in agents isn't models, it's context, and why Redis is quietly turning into an AI data platform. Context engineering matters more than clever prompt hacks.// BioSimba Khadder leads Redis Context Engine and Redis Featureform, building both the feature and context layer for production AI agents and ML models. He joined Redis via the acquisition of Featureform, where he was Founder & CEO. At Redis, he continues to lead the feature store product as well as spearhead Context Engine to deliver a unified, navigable interface connecting documents, databases, events, and live APIs for real-time, reliable agent workflows. He also loves to surf, go sailing with his wife, and hang out with his dog Chupacabra.// Related LinksWebsite: featureform.comhttps://marketing.redis.io/blog/real-time-structured-data-for-ai-agents-featureform-is-joining-redis/~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreJoin our Slack community [https://go.mlops.community/slack]Follow us on X/Twitter [@mlopscommunity](https://x.com/mlopscommunity) or [LinkedIn](https://go.mlops.community/linkedin)] Sign up for the next meetup: [https://go.mlops.community/register]MLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Simba on LinkedIn: /simba-k/Timestamps:[00:00] Context engineering explanation[00:25] MLOps and feature stores[03:36] Selling a company experience[06:34] Redis feature store evolution[12:42] Embedding hub[20:42] Human vs agent semantics[26:41] Enrich MCP data flow[29:55] Data understanding and embeddings[35:18] Search and context tools[39:45] MCP explained without hype[45:15] Wrap up

This interview was recorded for the GOTO Book Club.http://gotopia.tech/bookclubRead the full transcription of the interview here:https://gotopia.tech/episodes/399Matt Housley - Co-Author of "Fundamentals of Data Engineering", Keynote Speaker & PodcasterJoe Reis - Co-Author of "Fundamentals of Data Engineering", Keynote Speaker, Professor & PodcasterRESOURCESMatthttps://www.linkedin.com/in/housleymatthewJoehttps://www.linkedin.com/in/josephreishttps://github.com/JoeReishttps://joereis.substack.comLinkhttps://mathstodon.xyz/@tao/114915604830689046DESCRIPTIONJoe Reis and Matt Housley, co-authors of "Fundamentals of Data Engineering," discuss the evolution of their field three years after their book's publication. They explore how the rise of AI tools has transformed data engineering practices, the ongoing importance of foundational knowledge, and the challenges facing junior engineers in an AI-dominated landscape. The conversation covers the balance between leveraging AI assistance and maintaining core expertise, the resurgence of classical techniques, and why fundamental principles remain more relevant than ever.RECOMMENDED BOOKSJoe Reis & Matt Housley • Fundamentals of Data Engineering • https://amzn.to/4n85049Karen Hao • Empire of AI • https://amzn.to/46qeL6BKeach Hagey • The Optimist • https://amzn.to/4nlcS20Parmy Olson • Supremacy • https://amzn.to/3IpHdgIPeter Norvig & Stuart Russel • Artificial Intelligence • https://amzn.to/420ZgR8David Foster • Generative Deep Learning • https://amzn.to/48ZgP4xSol Rashidi • Your AI Survival Guide • https://amzn.to/3UFYnKCHow Hacks HappenHacks, scams, cyber crimes, and other shenanigans explored and explained. Presented...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!

AI models feel smarter than their real-world impact. They ace benchmarks, yet still struggle with reliability, strange bugs, and shallow generalization. Why is there such a gap between what they can do on paper and in practiceIn this episode from The Dwarkesh Podcast, Dwarkesh talks with Ilya Sutskever, cofounder of SSI and former OpenAI chief scientist, about what is actually blocking progress toward AGI. They explore why RL and pretraining scale so differently, why models outperform on evals but underperform in real use, and why human style generalization remains far ahead.Ilya also discusses value functions, emotions as a built-in reward system, the limits of pretraining, continual learning, superintelligence, and what an AI driven economy could look like. Resources:Transcript: https://www.dwarkesh.com/p/ilya-sutsk...Apple Podcasts: https://podcasts.apple.com/us/podcast...Spotify: https://open.spotify.com/episode/7naO... Stay Updated:If you enjoyed this episode, be sure to like, subscribe, and share with your friends!Find a16z on X: https://x.com/a16zFind a16z on LinkedIn: https://www.linkedin.com/company/a16zListen to the a16z Podcast on Spotify: https://open.spotify.com/show/5bC65RDvs3oxnLyqqvkUYXListen to the a16z Podcast on Apple Podcasts: https://podcasts.apple.com/us/podcast/a16z-podcast/id842818711Follow our host: https://x.com/eriktorenbergPlease note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures](http://a16z.com/disclosures.  Stay Updated:Find a16z on XFind a16z on LinkedInListen to the a16z Show on SpotifyListen to the a16z Show on Apple PodcastsFollow our host: https://twitter.com/eriktorenberg Please note that the content here is for informational purposes only; should NOT be taken as legal, business, tax, or investment advice or be used to evaluate any investment or security; and is not directed at any investors or potential investors in any a16z fund. a16z and its affiliates may maintain investments in the companies discussed. For more details please see a16z.com/disclosures. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In episode 220, host Galit Friedlander and guest Chad Geiger (dance agent at The Movement Talent Agency) pull back the curtain on what representation really looks like from the agency side and what dancers often misunderstand about it. We talk about essential pieces of a sustainable dance career: communication, contracts, headshots and resumes that actually serve you, and how your choices off the floor impact your opportunities just as much as your training on it. Chad shares insight on navigating direct bookings, building trust with your team, and why “doing the basics well” is still one of the biggest differentiators in today's industry. Follow Galit: Instagram – https://www.instagram.com/gogalit Website – https://www.gogalit.com/ Fit From Home – https://galit-s-school-0397.thinkific.com/courses/fit-from-home You can connect with Chad Geiger on https://www.instagram.com/chad_geiger Listen to DanceSpeak on Apple Podcasts and Spotify.

00:00-20:00: ML breaks down ND getting left out of the CFP. Thanks to Byrne Dairy and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Being told you have Primary Ovarian Insufficiency (POI) or premature menopause can feel like the door has closed on your fertility. But these terms don't mean the same thing and understanding the distinction is essential, especially if you're still hoping to conceive. In this episode, we break down what's actually happening hormonally in each condition, why they're often confused, and how a functional fertility approach can help you understand what may still be possible. You'll learn: The key differences between POI, premature menopause, and early menopause What your labs are really telling you about ovarian function Signs that your ovaries may still be active even if your cycle has stopped Which functional tests give deeper insight into thyroid, immune, gut, and adrenal factors that influence ovarian health How inflammation, autoimmune activity, stress physiology, and nutrient imbalances can drive ovarian shutdown Supportive nutritional, lifestyle, and mind-body strategies that may improve hormone communication and egg health When to combine functional and conventional care to optimize your chances of conception This episode is especially for you if: You've been told you have POI, premature menopause, or early menopause and want clarity about whether your ovaries have truly stopped functioning You're under 45 with irregular or missing cycles, hot flashes, or elevated FSH, and want to understand your next steps from a functional-fertility lens You've felt dismissed or told "it's over," yet you want to explore supportive strategies that may help your hormones and ovaries regain activity, naturally or alongside medical care Next Steps in Your Fertility Journey 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 Understanding POI and early menopause and why the distinction changes your fertility options when cycles are irregular or absent. 01:45 What POI before age 40 means and how irregular periods and fluctuating FSH can still indicate remaining ovarian activity. 03:00 Real examples of women with AMH at 0.04 ng/mL and 0.08 ng/mL who conceived by addressing inflammation, gut health, thyroid, and stress patterns. 04:00 How disrupted communication between the brain and ovaries drives POI and the role of autoimmunity, nutrient status, and the nervous system. 05:00 What premature menopause looks like on labs and why confirming ovarian shutdown matters when planning next steps. 06:10 How some women in their forties regain cycles and conceive naturally and what this reveals about hormonal resilience. 08:00 Factors that accelerate ovarian aging, including elevated hsCRP, gut infections, thyroid imbalance, environmental toxins, and nutrient gaps. 09:50 Why the gut and vaginal microbiome influence egg quality and implantation and how hidden infections affect fertility outcomes. 10:50 How functional thyroid ranges guide fertility decisions and why a TSH below 2 mIU/L supports better ovarian signaling and hormone balance. 14:40 Nutrition, mitochondrial support, mineral balance, and mind body work that help improve egg health and ovulation signaling. --- Resources

In this episode of Data in Biotech, host Ross Katz sits down with Eshani Galermo, Staff Scientist at SCIEX, to explore the next generation of mass spectrometry in pharma and biopharma. Eshani explains how innovations like the ZenoTOF 8600 are redefining sensitivity, selectivity, and workflow efficiency in bioanalytical chemistry. Discover how high-resolution accurate mass (HRAM) systems are unlocking new capabilities in drug discovery, clinical studies, and regulatory science. What you'll learn in this episode: >> Why traditional mass spectrometry falls short in modern bioanalysis >> How the ZenoTOF 8600 enhances sensitivity and reduces sample volume needs >> The role of high-resolution systems in detecting complex drug metabolites >> How automation tools are streamlining workflows for bioanalytical scientists >> The complementary role of AI and ML in mass spec data analysis Meet our guest Eshani Galermo is a Staff Scientist at SCIEX, where she leads global strategic marketing initiatives for pharma and biopharma quant applications. With deep expertise in bioanalytical chemistry and mass spectrometry, she has held multiple scientific roles across SCIEX, Emery Pharma, and Genentech.  About the host Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Our Guest: Sponsor: CorrDyn, a data consultancyConnect with Eshani Galermo on LinkedIn  Connect with Us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode!Connect with Ross Katz on LinkedIn Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

What does it actually take to move machine learning from experiments into production reliably, responsibly, and at scale?In this episode of Alexa's Input (AI), Alexa talks with Maria Vechtomova, co-founder of Marvelous MLOps and an O'Reilly author-in-progress on MLOps with Databricks. Maria shares how her background in data science led her into MLOps, and why most teams struggle not because of tools, but because of missing processes, traceability, and shared understanding across teams.Alexa and Maria dive into what separates good MLOps from fragile deployments, why shipping notebooks as “production” creates long-term pain, and how traceability across code, data, and environment forms the foundation for reliable ML systems. They also explore how LLM applications are reshaping MLOps tooling, and where the biggest skill gaps still exist between platform, data, and AI engineers.A must-listen for anyone building, operating, or scaling machine learning systems and for teams trying to make MLOps less magical and more marvelous.Learn more about Marvelous MLOps and Maria's work below.LinksWatch: ⁠⁠https://www.youtube.com/@alexa_griffith⁠⁠Read: ⁠⁠⁠⁠https://alexasinput.substack.com/⁠⁠⁠⁠Listen: https://creators.spotify.com/pod/profile/alexagriffith/More: ⁠⁠https://linktr.ee/alexagriffith⁠⁠Website: ⁠⁠https://alexagriffith.com/⁠⁠LinkedIn: ⁠⁠https://www.linkedin.com/in/alexa-griffith/⁠⁠Find out more about the guest at:LinkedIn: https://www.linkedin.com/in/maria-vechtomova/TakeawaysMaria started as a data analyst and transitioned into MLOps.She emphasizes the importance of tracking data, code, and environment in MLOps.MLOps is a practice to bring machine learning models to production reliably.Good deployment processes require modular code and proper tracking.MLOps differs from DevOps due to the complexities of data and model drift.Education is crucial for bridging gaps between teams in AI.Small steps can lead to better MLOps practices.Scaling MLOps requires understanding the unique data of different brands.The rise of LLMs is changing the MLOps landscape.Effective teaching methods involve step-by-step guidance.Chapters00:00 Introduction to MLOps and Maria's Journey02:11 Maria's Path to MLOps and Knowledge Sharing04:41 The Importance of MLOps in AI Deployments10:12 Defining MLOps and Its Challenges11:38 MLOps vs. DevOps: Key Differences13:00 Overcoming Stagnation in MLOps16:04 Small Steps Towards Better MLOps Practices19:29 Scaling MLOps in Large Organizations21:58 The Impact of LLMs on MLOps23:58 The Shift from Traditional ML to AI Applications26:51 Evolving Roles in AI Engineering28:33 Databricks: A Comprehensive AI Platform31:45 Future of AI Platforms and Regulations34:26 Bridging Skill Gaps in AI Teams38:42 The Importance of Context in AI Development40:40 Foundational Skills for MLOps Professionals45:43 Integrating Personal Passions with Professional Growth47:30 Building Impactful AI Communities

00:00-15:00: Colorado Avalanche. Unstoppable. ML breaks it down so far. Thanks to Rosie's Corner and Marz Motors. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Zooooheeeeeee, we maakten een aflevering over borstvoeding en jullie lieten van je horen! Onze mailbox stroomde vol met de meest uiteenlopende reacties. Van ‘ik voel me gezien’ tot ‘ik heb de aflevering afgezet want wat een teleurstelling’. We lezen een paar van jullie reacties voor in podcast en pruttelen nog een beetje na over het voeden der baby’s. Verder brak ML’s klomp toen ze lucht kreeg van een NO van jewelste: dakloze moeders met kinderen die bij de gemeente aankloppen voor opvang krijgen te horen ‘je regelt het zelf maar, anders bellen we Veilig Thuis’. Het systeem dat is opgezet voor de kwetsbaarste groep, faalt. En Nydia doet haar best om Anja Meulenbelt geen icoon te noemen. Maar ja, ze is het. Sue us! Ga voor de shownotes en het transcript naar damnhoney.nl/aflevering-260DAMN, HONEY wordt gemaakt door Marie Lotte Hagen en Nydia van VoorthuizenIn deze aflevering hoor je advertenties voor Theater Rotterdam en Scapino Ballet Rotterdam- Heartbeats, een documentaire performance van Nastaran Razawi Khorasani, is werk dat raakt, troost biedt en je meeneemt in een wereld die zowel persoonlijk als groter is dan jezelf. T/m 15 februari 2026 op tournee door Nederland en België. Meer informatie tr.nl/heartbeats- Vanaf januari is Origin; the future is Femine te zien in de theaters! Tickets & info via: scapinoballet.nl/origin editwerk: Daniël van de Poppe jingles: Lucas de Gier website: Liesbeth Smit DAMN, HONEY is onderdeel van Dag & Nacht Media. Heb je interesse om te adverteren in deze podcast? Neem dan contact op met Dag en Nacht Media via adverteren@dagennacht.nlSee omnystudio.com/listener for privacy information.

In the race to train and deploy generative AI models, companies have poured hundreds of billions of dollars into GPUs, chips that have become essential for the parallel processing needs of large language models.Nvidia alone has forecast $500 billion in sales across 2025 and 2026, driven largely by Jensen Huang, founder and CEO at Nvidia, recently stated that “inference has become the most compute-intensive phase of AI — demanding real-time reasoning at planetary scale”. Google is meeting these demands in its own way. Unlike other firms reliant on chips by Nvidia, AMD, and others, Google has long used its in-house ‘tensor processing units' (TPUs) for AI training and inference.What are the benefits and drawbacks of Google's reliance on TPUs? And how do its chips stack up against the competition?In this episode, Jane and Rory discuss TPUs – Google's specialized processors for AI and ML – and how they could help the hyperscaler outcompete its rivals.Read more:

00:00-15:00: ML breaks down what's next for the Bengals now that the playoffs are out of the picture. How do they get back in contention for 2026? Thanks to CH Insurance and Byrne Dairy. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Tento víkend vás zveme třeba na vánoční trh do Jízdárny Světce, na hrad Rabí nebo na zámek Mlázovy. Podívejte se na naše tipy a pozvánky.

Mlčení značné části předních evropských politiků na příval urážek a pomluv valících se v posledních dnech z druhé strany Atlantiku je na první dobrou nepochopitelné. Vypadá jako slabost, jako důkaz, že se většina lídrů bude držet taktiky, kterou spustil generální tajemník NATO Mark Rutte, když v létě nazval Donalda Trumpa trošičku ironicky, ale především vážně „naším taťkou“. Tedy čekat, až se bouře přežene, a do té doby obměkčovat pána Bílého domu pochlebováním a podlézáním.Všechny díly podcastu Názory a argumenty můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

00:00-20:00: Bills beat Bengals 39-34. Full recap from ML. Plus, can this jump start Buffalo the rest of the way? Thanks to Rosie's Corner and CH Insurance. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this episode, I sit down with Alvitta Ottley and Paul Parsons to recap everything that happened at the 2025 IEEE VIS Conference in Vienna. We talk about our experiences co-organizing the VisCom workshop, the surprising attendance, and the standout keynote from Moritz Stefaner. Alvitta shares insights on accessibility research and the surge of LLM-focused visualization papers, while Paul walks us through his award-winning work on design cognition and how practitioners develop ideas. We also reflect on the evolving identity of the visualization field, from methodological rigor to the role of practitioners, interdisciplinarity, and ethical tensions. It's a wide-ranging, candid conversation about where visualization research is headed — and what we hope to see next year in Boston.Subscribe to the PolicyViz Podcast wherever you get your podcasts.Become a patron of the PolicyViz Podcast for as little as a buck a monthCheck out the VIS website.Follow me on Instagram, LinkedIn, Substack, Twitter, Website, YouTubeEmail: jon@policyviz.com

Connect to John Gilroy on LinkedIn   https://www.linkedin.com/in/john-gilroy/ Want to listen to other episodes? www.Federaltechpodcast.com In the world of federal technology we are being deluged with so much information about Artificial Intelligence that we may not see what some of other technologies that may have as great an impact as AI.  The White House, the OMB (M-23-02), the Office of the National Cyber Director have made it clear that the time to prepare for post-quantum cryptography is now.  Agencies are required to inventory cryptographic systems, prioritize high-value assets, and build migration plans in line with NIST standards. Today, we sit down with Eric Hay from Quantum Xchange to look at making this transition.  During the interview, Hay handles issues like technology, operations and appropriate strategy. He highlights the role of NIST in developing and approving new algorithms like NIST PQC Post Quantum Encryption, ML, and CHEM. Eric explains the five-step process for transitioning to these new standards: discovery, prioritization, deployment, monitoring, and management. Rather than spending time evaluating algorithms, Eric Hay stresses the importance of a network-centric approach, suggesting that agencies focus on securing data transport first. Eric predicts Q day, when current encryption methods could be compromised, within 3-5 years, with some European partners aiming for 2029.    

A secret marriage in Detroit and a secretive engagement party in Lansing start this week's show, before ML and Marc […]

00:00-20:00: ML says the Rams have it all and are peaking at the right time. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us a textDr. Shalini Ojha, Professor of Neonatal Medicine at University of Nottingham, presents the Feed One trial examining full enteral feeding (60 mL/kg/day) from day one versus gradual advancement in 30-32 week infants. While the primary outcome of hospital length of stay showed no difference (median 32 days), full feeding significantly reduced parenteral nutrition use, IV cannulations, and associated interventions without increasing necrotizing enterocolitis risk (4 versus 6 cases). This pragmatic trial challenges the unfounded fear that early full feeding causes NEC—demonstrating that moderate preterm infants can safely receive complete enteral nutrition from birth, simplifying care particularly in resource-limited settings while avoiding complications from IV access and parenteral nutrition. Support the showAs always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below. Enjoy!

The worlds of IT security and operations are being pulled together and AI is a catalyst that's making it happen. The focus on observability that's been part of the DevOps movement, is playing an important role in improving security effectiveness and Scott Crawford, Mark Ehr and Mike Fratto return to look at how this is happening with host Eric Hanselman. Security teams have always wrestled with making effective use of telemetry data from the infrastructure and applications they are securing. Correlating data from just the security tooling is hard enough, let alone adding operational data to the mix. Security Information and Event Management (SIEM) systems came into existence many years ago specifically to address this problem, but they were complex to configure and operate and needed tending to stay accurate. The volumes of data coming from observability initiatives was promising, but new approaches were required and AI and ML have been key to unlocking that value. Once again, we've hit an opportunity where it's all about the data and getting it to where it can be put to work. The Open Telemetry project simplified data interchange, but the question remained as to where all of this data had to live. It's not practical to get all of the data in one place, but data fabrics and federation can manage access effectively. Better correlation opens the door to many possibilities, including building a single source of truth for IT assets. There's a lot of benefit to bringing security and operations together.   More S&P Global Content: AI for security: Agentic AI will be a focus for security operations in 2025 AI in action: unleashing agentic potential   For S&P Global subscribers: 2026 Trends in Information Security Deal Analysis: Palo Alto Acquires Chronosphere Big Picture Report: 2026 AI Outlook – Unleashing agentic potential   Credits: Host/Author: Eric Hanselman Guests: Scott Crawford, Mark Ehr, Mike Fratto Producer/Editor: Feranmi Adeoshun Published With Assistance From: Sophie Carr, Kyra Smith

Major health organizations, including the CDC and ACOG, recommend universal Hepatitis C Virus (HCV) screening for all pregnant women during each pregnancy and at time of delivery. Ideally, pregnant women should be screened for hepatitis C virus infection at the first prenatal visit of each pregnancy. If the antibody screen result is positive, hepatitis C virus RNA polymerase chain reaction testing is done to confirm the diagnosis. The risk of perinatal transmission of HCV is up to 9%, with at least one-third of transmissions occurring antenatally. While antiviral therapy is recommended for Hepatitis B in pregnancy with a viral load greater than 200,000 international units/mL to decrease the risk of vertical transmission, the same is not the case for Hep C. According to the ACOG CPG #6 from September 2023, there are no standard treatment protocols for Hep C in pregnancy but a new publication from the PINK journal (7 Dec 2025) is calling for a change. That new publication is, “Hepatitis C Treatment During Pregnancy: Time for a Practice Change”. Listen in for details. 1. ACOG CPG #6; Sept 20262. Bhattacharya D, Aronsohn A, Price J, Lo Re V. Hepatitis C Guidance 2023 Update: AASLD-IDSA Recommendations for Testing, Managing, and Treating Hepatitis C Virus Infection. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America. 2023;:ciad319. doi:10.1093/cid/ciad319.3. Chappell CA, Kiser JJ, Brooks KM, et al. Sofosbuvir/¬Velpatasvir Pharmacokinetics, Safety, and Efficacy in Pregnant People With Hepatitis C Virus. Clinical Infectious Diseases : An Official Publication of the Infectious Diseases Society of America. 2025;80(4):744-751. doi:10.1093/cid/ciae595.4. Reau N, Munoz SJ, Schiano T. Liver Disease During Pregnancy. The American Journal of Gastroenterology. 2022;117(10S):44-52. doi:10.14309/ajg.0000000000001960.5. Dutra, Karley et al. Hepatitis C Treatment During Pregnancy: Time for a Practice Change. American Journal of Obstetrics & Gynecology MFM, Volume 0, Issue 0, 1018656. Society for Maternal-Fetal Medicine Consult Series #56: Hepatitis C in Pregnancy-Updated Guidelines: Replaces Consult Number 43, November 2017. Society for Maternal-Fetal Medicine (SMFM). Electronic address: pubs@smfm.org, Dotters-Katz SK, Kuller JA, Hughes BL. American Journal of Obstetrics and Gynecology. 2021;225(3):B8-B18. doi:10.1016/j.ajog.2021.06.008

🧭 REBEL Rundown 🗝️ Key Points ❌ Don’t chase perfect numbers: Adequate and safe is often better than “perfect but harmful.”💨 Oxygenation levers: Start with FiO₂ and PEEP, but remember MAP is the true driver.🫁 Ventilation levers: Adjust RR and TV, tailored to underlying physiology.🚫 Watch your obstructive patients: Sometimes less RR is more. Click here for Direct Download of the Podcast. 📝 Introduction Ventilator management can feel overwhelming—there are so many knobs to turn, numbers to watch, and changes to make. But before adjusting any settings, it’s crucial to understand why the patient is in distress in the first place, because the right strategy depends on the underlying cause. In this episode, we’ll walk through three different cases to see how the approach changes depending on the problem at hand. ️ The 4 Main Ventilator Settings Tidal Volume (Vt) 🌬️ Amount of air delivered with each breath Typically set based on ideal body weight (6–8 mL/kg for lung protection) Respiratory Rate (RR) ⏱️ Number of breaths delivered per minute Adjusted to control minute ventilation and manage CO₂ FiO₂ (Fraction of Inspired Oxygen) ⛽ Percentage of oxygen delivered Adjusted to maintain adequate oxygenation (goal SpO₂ 92–96%, PaO₂ 55–80 mmHg). PEEP (Positive End-Expiratory Pressure) 🎈 Pressure maintained in the lungs at the end of exhalation to prevent alveolar collapse and improve oxygenation 🧮 Modes of Ventilation AC/VC (Assist Control – Volume Control)How it Works: Delivers a set tidal volume with each breath (whether patient- or machine-triggered).When It’s Used / Pros: Most common initial mode; guarantees minute ventilation; good for patients with variable effort.Limitations / Cons: May cause patient–ventilator dyssynchrony if set volumes don’t match patient’s demand.AC/PC (Assist Control – Pressure Control)How it Works: Delivers a set inspiratory pressure for each breath; tidal volume varies depending on lung compliance/resistance.When It’s Used / Pros: Useful in ARDS (lung-protective strategy), limits peak airway pressures.Limitations / Cons: Tidal volume not guaranteed; must closely monitor volumes and minute ventilation.PRVC (Pressure-Regulated Volume Control)How it Works: Hybrid: set target tidal volume, ventilator adjusts inspiratory pressure breath-to-breath to achieve it (within limits).When It’s Used / Pros: Common default mode on newer vents; combines benefits of VC (guaranteed volume) + PC (pressure limitation).Limitations / Cons: Can increase pressures if compliance worsens.SIMV (Synchronized Intermittent Mandatory Ventilation)How it Works: Delivers set breaths, but allows spontaneous patient breaths in between (without guaranteed volume).When It’s Used / Pros: Used for weaning; allows patient effort.Limitations / Cons: Risk of increased work of breathing if spontaneous breaths are inadequate.PSV (Pressure Support Ventilation)How it Works: Every breath is patient-initiated; ventilator provides preset pressure support to overcome airway resistance.When It’s Used / Pros: Weaning trials; patients with intact drive who just need assistance.Limitations / Cons: Not a full-support mode; not for unstable patients without spontaneous drive. ♟️ Ventilation Strategies Airway ProtectionLow GCS, seizure, strokeLoss of gag/cough reflexHigh aspiration risk (vomiting, GI bleed, poor mental status)Hypoxemic Respiratory FailureSevere pneumoniaARDSPulmonary edemaInhalation injuryVentilatory (Hypercapnic) Failure / Increased Ventilation DemandSevere metabolic acidosis (DKA, sepsis, renal failure) → need high minute ventilationCOPD, asthma (if decompensating)Neuromuscular weakness (myasthenia, Guillain–Barré, spinal cord injury)Airway Obstruction / Anticipated Loss of AirwayTumor, anaphylaxis, angioedemaFacial or airway traumaPre-op / anticipated deterioration Post Peer Reviewed By: Marco Propersi, DO (Twitter/X: @Marco_propersi), and Mark Ramzy, DO (X: @MRamzyDO) 👤 Show Notes Priyanka Ramesh, MD PGY 1 Internal Medicine Resident Cape Fear Valley Internal Medicine Residency Program Fayetteville NC Aspiring Pulmonary Critical Care Fellow 🔎 Your Deep-Dive Starts Here REBEL Core Cast – Pediatric Respiratory Emergencies: Beyond Viral Season Welcome to the Rebel Core Content Blog, where we delve ... Pediatrics Read More REBEL Core Cast 143.0–Ventilators Part 3: Oxygenation & Ventilation — Mastering the Balance on the Ventilator When you take the airway, you take the wheel and ... Thoracic and Respiratory Read More REBEL Core Cast 142.0–Ventilators Part 2: Simplifying Mechanical Ventilation – Most Common Ventilator Modes Mechanical ventilation can feel overwhelming, especially when faced with a ... Thoracic and Respiratory Read More REBEL Core Cast 141.0–Ventilators Part 1: Simplifying Mechanical Ventilation — Types of Breathes For many medical residents, the ICU can feel like stepping ... Thoracic and Respiratory Read More REBEL Core Cast 140.0: The Power and Limitations of Intraosseous Lines in Emergency Medicine The sicker the patient, the more likely an IO line ... Procedures and Skills Read More REBEL Core Cast 139.0: Pneumothorax Decompression On this episode of the Rebel Core Cast, Swami takes ... Procedures and Skills Read More The post REBEL Core Cast 146.0–Ventilators Part 4: Setting up the Ventilator appeared first on REBEL EM - Emergency Medicine Blog.

00:00-15:00: ML breaks down what's next for the Lions after topping the Cowboys on TNF. Playoff possibilities and more. Thanks to CH Insurance and Rosie's Corner. Hosted by Simplecast, an AdsWizz company. See https://pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us a textDr. Ariel Salas, recent R01 recipient, discusses challenging traditional feeding volume targets in preterm infants. His multi-center trial compares 180-200 versus 140-160 mL/kg/day volumes, examining body composition changes rather than weight alone. Salas emphasizes targeting fat-free mass gains over simple weight gain, as this component associates with better long-term neurodevelopmental outcomes. Body composition analysis reveals compartmental changes invisible to daily weights—distinguishing extracellular versus intracellular water shifts. This outcome provides reasonable compromise between immediate intervention effects and long-term results. Salas advocates acknowledging practice variation as opportunity for equipoise and fair testing, challenging arbitrary standards that persist despite limited evidence supporting them. Support the showAs always, feel free to send us questions, comments, or suggestions to our email: nicupodcast@gmail.com. You can also contact the show through Instagram or Twitter, @nicupodcast. Or contact Ben and Daphna directly via their Twitter profiles: @drnicu and @doctordaphnamd. The papers discussed in today's episode are listed and timestamped on the webpage linked below. Enjoy!

This podcast is brought to you by Outcomes Rocket, your exclusive healthcare marketing agency. Learn how to accelerate your growth by going to⁠ outcomesrocket.com Thoughtful, problem-first innovation drives real clinical impact in healthcare. In this episode, Saul Marquez and Ed Gaudet from Censinet host Dr. Jason Hill, Innovation Officer at Ochsner Health, and David Leingang, Director of Innovation Data Science at Ochsner Health, to discuss how their team uses machine learning, workflow redesign, and data-driven insights to reduce physician message burden and improve patient routing. They share how analyzing 2.4 million inbox messages revealed that 4% were tied to weight-loss drugs, prompting the creation of a new weight-management digital medicine program instead of an AI tool. They explain how reorganizing message flows, adding e-visits, and using ML to uncover hidden system strain has improved efficiency, while predictive deterioration models saved lives but had to be retrained as outcomes changed. The conversation closes with an exploration of value-based care, problem-solving in AI, and the AHEAD Network's role in advancing healthcare innovation. Tune in and learn how practical AI, smarter workflows, and cross-industry collaboration are reshaping modern healthcare! Resources Connect with and follow Jason Hill on LinkedIn. Follow and connect with David Leingang on LinkedIn. Follow Ochsner Health on LinkedIn and explore their website!

Maney came across an article where a man in India found a snake unconscious and gave it mouth-to-mouth CPR. He brought it back to life! We asked each other what animal we'd give CPR to. Plus, Maney wants to know why LauRen is not in the holiday spirit yet, and our "Momtern" tells us which age is harder to parent, toddler or teenager? Listen every week to the Wrap Party for an unscendored ML podcast! See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Our guest on this week's episode is Marc Schaffer, principal economist at Breakthrough Fuel. Sometime in the new year we will likely find out whether the huge mega-merger between two historic railroads will go through. Union Pacific and Norfolk Southern aim to join forces to create a true transcontinental railroad. But it's not without controversy. What will the effects be on our nation's supply chains? This week's guest offers some insights. Warehouses are getting smarter every day. A study released just after Thanksgiving shows that artificial intelligence (AI) and machine learning (ML) are no longer just experimental tools but are becoming core drivers of productivity, accuracy, and workforce evolution in the warehouse. This is according to a study from Mecalux and the MIT Intelligent Logistics Systems Lab at MIT's Center for Transportation and Logistics. This week we reported on a story about the people behind AI and other leading edge technologies. It wasn't about the software coders who write the instructions for AI and large language models and other platforms. But rather it was about the electricians and manufacturing experts who keep all this stuff running. The reports came from Siemens USA, the American arm of the German industrial technology provider, and they announced a plan to train 200,000 electricians and manufacturing experts by 2030. Supply Chain Xchange  also offers a podcast series called Supply Chain in the Fast Lane.  It is co-produced with the Council of Supply Chain Management Professionals. A new series is now available on Top Threats to our Supply Chains. It covers topics including Geopolitical Risks, Economic Instability, Cybersecurity Risks, Threats to energy and electric grids; Supplier Risks, and Transportation Disruptions  Go to your favorite podcast platform to subscribe and to listen to past and future episodes. The podcast is also available at www.thescxchange.com.Articles and resources mentioned in this episode:Breakthrough FuelStudy: AI now imbedded in 60% of warehousesSiemens USA plans to hire 200,000 electricians and manufacturing experts by 2030Visit Supply Chain XchangeListen to CSCMP and Supply Chain Xchange's Supply Chain in the Fast Lane podcastSend feedback about this podcast to podcast@agilebme.comThis podcast episode is sponsored by: ID LabelOther linksAbout DC VELOCITYSubscribe to DC VELOCITYSign up for our FREE newslettersAdvertise with DC VELOCITY

Before StatQuest became the go-to learning companion for millions of AI and ML practitioners…Before the “BAM! Double BAM! Triple BAM!” became a teaching tool that many learners adore...There was just one guy in a genetics lab, trying desperately to explain his data analysis to coworkers so they didn't think he was working magic.In this deeply personal and inspiring episode, Joshua Starmer (CEO & Founder | StatQuest) shares the real story behind his rise — a journey shaped by strategy, struggle, blunt feedback, and a relentless desire to make complicated ideas simple.What you'll discover:

This episode examines a new CNA model that can help government officials optimally deploy unmanned systems, and how it overlaps with our existing tools. Guest Biographies Arpita Verma is an expert in optimization, modeling, and simulation in CNA's Data Science for Production Program. John Crissman is an expert in artificial intelligence (AI), machine learning (ML) and natural language processing in CNA's Center for Data Management Analytics. Rebekah Yang is an expert in artificial intelligence and machine learning (AI/ML) for FAA NextGen and a specialist in data visualization and modeling in CNA's Center for Data Management Analytics. Further Reading UAS Cooperative Airspace Traffic Simulation (UCATS) First Responder Awareness Monitoring during Emergencies (FRAME) 

As the Women in Machine Learning Workshop (WiML) marks its 20th annual gathering, cofounders, friends, and collaborators Jenn Wortman Vaughan and Hanna Wallach reflect on WiML's evolution, navigating the field of ML, and their work in responsible AI.Show notes

In this episode of The Identity at the Center Podcast, hosts Jim McDonald and Jeff Steadman catch up with John Tolbert, Director of Cybersecurity Research at KuppingerCole Analysts, to talk about the rapidly evolving world of Fraud Reduction Intelligence Platforms (FRIP).They explore:The six capabilities of modern fraud reduction systemsHow AI and machine learning are both helping and hurting fraud preventionWhy shared signals and orchestration are critical for financial and e-commerce use casesHow identity verification, device intelligence, and behavioral biometrics work togetherThe role of usability and integration in FRI adoptionPlus, stick around for a fun discussion about concerts, classic rock, and which legendary bands they wish they'd seen live.Listen now to learn how identity, fraud, and AI are colliding — and what's next for fraud intelligence.Connect with John: https://www.linkedin.com/in/john-tolbert/Fraud Reduction Intelligence Platforms - Finance (KuppingerCole Report): https://www.kuppingercole.com/research/lc80841/fraud-reduction-intelligence-platforms-financeFraud Reduction Intelligence Platforms - eCommerce (KuppingerCole Report): https://www.kuppingercole.com/research/bc81030/fraud-reduction-intelligence-platforms-ecommerceConnect with us on LinkedIn:Jim McDonald: https://www.linkedin.com/in/jimmcdonaldpmp/Jeff Steadman: https://www.linkedin.com/in/jeffsteadman/Visit the show on the web at http://idacpodcast.comChapter Timestamps:00:00 – Jim's passwordless rant and setup woes05:00 – Introducing guest John Tolbert06:30 – Catching up: four years since John's last appearance07:30 – What is CIAM and how has it evolved?09:30 – Understanding Fraud Reduction Intelligence Platforms (FRIP)10:00 – The six core capabilities of FRI solutions13:00 – Are most vendors point solutions or full platforms?14:00 – How identity verification is improving16:00 – SaaS and API-driven fraud detection models18:00 – What kinds of fraud can (and can't) FRI prevent?21:00 – The growing problem of bots and automation22:00 – Fraud trends in finance: scams, account takeovers, and synthetic identities25:00 – Information sharing and the role of shared signals28:00 – Collaboration vs. competition in fraud prevention31:00 – Fraud in e-commerce: bots, loyalty points, and returns abuse34:00 – Streaming and citizen fraud use cases36:00 – Where do FRI capabilities fit within IAM platforms?43:00 – The importance of orchestration and integration44:30 – The role of AI and ML in fraud prevention47:30 – Smart questions for evaluating FRI vendors50:30 – Concert talk: Pink Floyd, Metallica, and the ones that got away58:00 – Wrap-up and where to find John Tolbert's reportsKeywords:Fraud Reduction Intelligence, FRI Platforms, John Tolbert, KuppingerCole, Identity at the Center, IDAC, IAM, CIAM, Cybersecurity Research, Fraud Prevention, Machine Learning, Artificial Intelligence, Behavioral Biometrics, Device Intelligence, Identity Verification, Risk Orchestration, API Security, Financial Fraud, E-Commerce Fraud, Shared Signals, Jim McDonald, Jeff Steadman, IDAC Podcast

Artificial Intelligence and Machine Learning in Human Resources: A Concise Guide by Dr. C. Rasmussen https://www.amazon.com/Artificial-Intelligence-Machine-Learning-Resources/dp/B0FWZQXHMG Curtisrasmussen.focalpointcoaching.com What if a computer could help find the perfect employee or predict who might leave a job? This exciting idea opens the door to a new way of working. Overview This guide explains how artificial intelligence (AI) and machine learning (ML) are transforming human resources (HR). Smart computer programs can quickly review thousands of job applications to find the best candidates, suggest training tailored to employees’ needs, and predict which workers might quit, helping managers take action to keep them. The book includes real-world examples, like how large companies use AI to save time, and covers benefits, such as improved hiring, as well as key concerns, like protecting personal information. At just 61 pages, it's concise by design, following Richard Feynman's wisdom: “If you can’t explain something simply, you don’t understand it well enough.” More pages don't equal more value; in fact, lengthy texts can bury useful insights. Since every organization is unique, this book equips HR professionals and managers with the right questions to ask rather than a rigid roadmap, making it a practical tool for anyone curious about the future of work. About the author Dr. Curtis “Curt” Rasmussen is a leading expert in industrial-organizational psychology with a Ph.D. from Walden University. He specializes in blending human skills with artificial intelligence (AI) and machine learning (ML) to make workplaces better and more efficient. With years of experience in research, consulting, and government roles, he helps businesses use data and tech wisely. His career highlights include owning Cyber-Human Performance Tech, LLC, where he advises small and mid-sized companies on adding AI to hiring and daily tasks while keeping things ethical. He also guides students in George Mason University’s Data Engineering program, focusing on AI tools like natural language processing and computer vision. At the Cybersecurity and Infrastructure Security Agency (CISA), he led workforce planning as a senior I/O psychologist, creating surveys and frameworks that improved employee satisfaction by 45% and helped with smarter hiring. Earlier, he reviewed AI and data science proposals for the Department of Commerce, National Academy of Medicine, and the Office of the Director of National Intelligence, making sure projects were strong and fair. Dr. Rasmussen has invented patent-pending tools like the Multidimensional Algorithm Structure (MAS), which picks the best AI methods by checking data and company needs, and the eXplainable Artificial Intelligence Construct (XAIC), which makes AI easy to understand and trust by involving people in decisions. These ideas help fix common AI problems, like failures or hidden biases.

Send Bidemi a Text Message!In this episode, host Bidemi Ologunde spoke with data scientist and AI/machine learning (ML) enthusiast Daria Dubovskaia in a wide-ranging conversation about cybersecurity, data analytics, and building robust ML systems in the real world. Daria shares her journey from studying rocket propulsion in Russia to moving to the United States, completing a Master's degree in Data Science at CUNY, and working at a healthcare startup in Tampa, Florida. Along the way, she talks about cleaning messy data, deploying production models in the cloud, protecting sensitive information, and communicating complex insights to non technical stakeholders. This episode is full of practical lessons for anyone interested in data-driven decision-making, career pivots into tech, and the growing intersection of machine learning and cybersecurity.Support the show

No soup for Erika Erickson, whose big scoop about a whistleblower at Campbell's is making national news … and ML, […]