Podcasts about predictive

Hey y'all, today I'm sitting down with Timothy Frie and honestly, I could talk about his brain all day. He's a neuro-nutritionist, researcher and writer who takes the big stuff - grief, trauma, burnout, stress - and shows us how it all connects to the way we eat. We get into why our bodies do what they do around food, how the brain makes patterns we don't even realise, and what it actually takes to create real, lasting change. This conversation is one of those that sticks with you.Highlights:(08:31) Predictive maps: why our brains link food to stress(16:13) The power of safety in healing our body(24:23) Nutrition isn't diet rules, it's cell-level survival(32:12) Supplements as support when life feels too much(40:58) Food freedom starts with trusting your own bodyFind out more:Website's HERE and HEREInstagramThreadsBrain Fried NewsletterQualia Mind - click hereCoupon Code: SHOCKANDYALL (15% off any purchase)Visit Nicole's on demand fitness platform for live weekly classes and a recorded library of yoga, strength training, guided audio meditations and mobility (Kinstretch) classes, as well: https://www.sweatandstillness.comGrab Nicole's bestselling children's book and enter your email for A FREE GIFT: https://www.yolkedbook.comFind Nicole on Instagram:https://www.instagram.com/nicolesciacca/Tik Tok: https://www.tiktok.com/@thenicolesciaccaFacebook: https://www.facebook.com/nicolesciaccayoga/Youtube:https://www.youtube.com/channel/UC1X8PPWCQa2werd4unex1eAPractice yoga with Nicole in person in Santa Monica, CA at Aviator Nation Ride. Get the App to book in: https://apps.apple.com/us/app/aviator-nation-ride/id1610561929Book a discovery call or virtual assessment with Nicole here: https://www.calendly.com/nicolesciaccaThis Podcast is proudly produced by Wavemakers Audio

This is a story with a lad in it called Aladdin.   Support the show and get extra episodes www.patreon.com/worldaroundewe 

Game B's theosophical utopia vs. Dark Enlightenment's technocracy, esoteric roots, surveillance tech, fantasy delusions, and resisting AI-driven control During our podcast break, enjoy this replay of Courtenay's appearance on Out Of This World with host Jamie Hanshaw from June 2025. Key topics: Dialectic of Game B (decentralized, theosophical utopia) vs. Dark Enlightenment (authoritarian technocracy), both pushing a technological singularity/noosphere. Game B details: 2011 Stanton meetings (Jim Rutt, Jordan Hall, Brett Weinstein), transpolitical movement, civium projects, collective intelligence, and UN/climate narrative ties. Dark Enlightenment: Curtis Yarvin (Mencius Moldbug), Nick Land's accelerationism, Peter Thiel's network states, and technocracy roots (e.g., Elon Musk's grandfather). Esoteric influences: Theosophy, Gnosticism, neoplatonism, occult symbolism (e.g., Saturn, X logo), and subversion of Christianity (Jordan Hall, liturgy for communitarianism). Cultural impact: Predictive programming (e.g., Mickey 17, Dollhouse), surveillance tech (internet of nanobio things, Palantir), and fantasy delusions shaping technocratic agendas. Courtenay's insights: Decoding technocrat lingo, resisting utopian traps, and preserving free will against AI-driven control. Follow and Connect with Jamie Hanshaw:

The FF Dynasty, Season 9, Episode 82: Casey and Joseph @KoalatyStats dive into his Predictive Weighted Opportunity Score (PWOPR, pronounced Pee Whopper) to find Dynasty trade targets before they explode in value. -Seriously if you can produce a metric with a better R^2 score than our guy Joseph please leave a comment below we'd love to hear from you • SIGN UP TODAY FOR up to $200 in Prophet Cash BACK -- USE CODE: FFD WHEN SIGNING UP AT https://prophetx.onelink.me/E5Yi/FFD or DOWNLOAD THE PROPHETX APP #ProphetX ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Fantasy Football Survivor⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ (NEW GAME!) Free ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ + ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Discord⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ 5 Star Contest ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠here⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠! ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Twitter⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Insta⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠TikTok⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ --

In this episode of the En Factor, we are joined by serial entrepreneur Xavier Serrano, who is now the founder and CEO of Marqi Medical. After spending over two decades in the insurance world, Xavier transitioned out of his previous venture to revolutionize preventive healthcare with Marqi. Using resources like artificial intelligence, advanced testing and diagnostics, and personalized coaching, Xavier and his team have developed a personalized predictive healthcare platform that works to identify and prevent significant illnesses and diseases before they truly become significant.  In this conversation, Xavier and Dr. Rebecca White take a deep dive into a variety of topics including Xavier's journey from insurance to starting his own holistic healthcare platform, navigating and managing the many relationships and moving parts involved in leading Marqi Medical, and the importance of prioritizing your own personal health as an entrepreneur. Tune in as you won't want to miss a second of Xavier's incredible journey, insights and advice! Key Words - Healthcare Innovation, Technology Entrepreneurship

A cautionary tale about a life-threatening reaction to the double flu/Covid booster; Are migraines predictive of heart attacks? Where does all our recycling go? (The answer will shock you!); Ultra-processed foods damage male reproductive health.

Artificial intelligence is radically transforming the military—an institution grappling with an eligibility crisis at home. According to the Department of Defense, a mere 23% of Americans aged 17 to 24 qualify for military force without needing a waiver, meaning that 77% are currently ineligible due to factors like obesity, drug use, and mental or physical health issues. This stark reality intensifies the urgency for smarter, data-driven solutions: AI-driven analytics that can refine recruiting strategies, predict readiness, and help optimize both talent acquisition and human capital management.But how exactly can AI help the U.S. military win the “war for talent” while also modernizing its massive infrastructure? What lessons can private industry learn from the defense sector's adoption of AI-driven workforce strategies?In this episode of DisruptED, host Ron Stefanski sits down with Tyler Zagurski, Vice President of Talent at r4 Technologies and a retired U.S. Marine Corps colonel with 31 years of service. Together, they explore how artificial intelligence is being leveraged to transform both the workforce and the military force of the future. Their conversation spans from deeply personal origin stories to the broader technological and organizational revolutions happening today.Key Discussion Points:The shrinking pool of military-eligible talent: Fewer than one in four young Americans qualify to serve, making AI tools essential in identifying, attracting, and retaining candidates.AI-powered logistics and sustainment: Predictive analytics are being used to forecast asset readiness, prevent costly breakdowns, and optimize supply chains across global military operations.Human-centered decision intelligence: AI is not replacing military leaders but equipping them with better insights to retain talent, deploy personnel effectively, and maintain mission readiness.Tyler Zagurski is the Vice President of Talent at r4 Technologies, where he applies AI-driven solutions to defense and national security challenges. Before entering private industry, Zagurski served 31 years in the U.S. Marine Corps as an infantry officer, commanding units in combat and spearheading service-level transformation initiatives such as Talent Management 2030. A graduate of the U.S. Army War College, he has deep expertise in workforce modernization, logistics, and education strategy, making him a leading voice at the intersection of AI and military readiness.

In the late 2010s, a group of sheriffs out of Pasco County, Florida, believed they could predict crime. The Sheriff's Department there had piloted a program called “Intelligence-Led Policing” and the program would allegedly analyze disparate points of data to identify would-be criminals.But in reality, the program didn't so much predict crime, as it did make criminals out of everyday people, including children. High schoolers' grades were fed into the Florida program, along with their attendance records and their history with “office discipline.” And after the “Intelligence-Led Policing” service analyzed the data, it instructed law enforcement officers on who they should pay visit to, who they should check in on, and who they should pester.As reported by The Tampa Bay Times in 2020:“They swarm homes in the middle of the night, waking families and embarrassing people in front of their neighbors. They write tickets for missing mailbox numbers and overgrown grass, saddling residents with court dates and fines. They come again and again, making arrests for any reason they can.One former deputy described the directive like this: ‘Make their lives miserable until they move or sue.'”Predictive policing can sound like science fiction, but it is neither scientific nor is it confined to fiction.Police and sheriff's departments across the US have used these systems to plug broad varieties of data into algorithmic models to try and predict not just who may be a criminal, but where crime may take place. Historical crime data, traffic information, and even weather patterns are sometimes offered up to tech platforms to suggest where, when, and how forcefully police units should be deployed.And when the police go to those areas, they often find and document minor infractions that, when reported, reinforce the algorithmic analysis that an area is crime-ridden, even if those crimes are, as the Tampa Bay Times investigation found, a teenager smoking a cigarette, or stray trash bags outside a home.Today, on the Lock and Code podcast with host David Ruiz, we speak with Emily Galvin-Almanza, cofounder of Partners for Justice and author of the upcoming book “The Price of Mercy,” about predictive policing, its impact on communities, and the dangerous outcomes that might arise when police offload their decision-making to data.“ I am worried about anything that a data broker can sell, they can sell to a police department, who can then feed that into an algorithmic or AI predictive policing system, who can then use that system—based on the purchases of people in ‘Neighborhood A'—to decide whether to hyper-police ‘Neighborhood A.'”Tune in today.You can also find us on Apple Podcasts, Spotify, and whatever preferred podcast platform you use.For all our cybersecurity coverage, visit Malwarebytes Labs at malwarebytes.com/blog.Show notes and credits:Intro Music: “Spellbound” by Kevin MacLeod (incompetech.com)Licensed under Creative Commons: By Attribution 4.0 Licensehttp://creativecommons.org/licenses/by/4.0/Outro Music: “Good God” by Wowa...

Vanese McNeill, co-producer of the Magical Egypt series, returns to the show. In this episode, we explore the hows and possible whys of spirit involvement in history, from the super-elite level down to the everyday. And also across to some of the sillier claims about AI floating in the air like so many plasma entities. And all of this was inspired by a post Vanese wrote on the Magical Egypt substack about ontozoology back in June. So that's where we begin, and this is where we go after: 1. The Ontozoology Framework What invisible beings actually exist in the ecosystem Moving beyond Christian binary (demons vs. angels) to animist complexity The difference between malevolent, benevolent, and simply non-human agendas 2. Catherine Austin Fitts & Demonic Capital The exact quote about interdimensional intelligences controlling capital Why this is being discussed now by credible people The problem with oversimplifying complex entities as "Satan" 3. Conscious vs. Unconscious Elite Collaboration How much is deliberate ritual worship vs. unconscious possession The decline of magical literacy among modern elites Why materialist worldviews make people more vulnerable to influence 4. Historical Magic & Modern Mind Control Ancient Egyptian spirit relationships as information exchange How modern "operations" are rituals without the practitioners knowing it Predictive programming as contemporary magic 5. The AI & Slop Demon Problem Why "The Architect" and similar AIs aren't sentient but are dangerous How AI creates mental conditions ripe for spiritual colonisation The difference between AI hosting demons vs. AI making minds available for possession 6. UFOs, Aliens, and Interdimensional Beings The reframing of UFO phenomena as spiritual rather than extraterrestrial Catherine's invitation to meet "aliens in human bodies" Distinguishing between visiting organisms and native invisible ecology

"You don't get paid for the hour. You get paid for the value you bring to the hour." – Jim Rohn Check Out These Highlights: In the midst of all the chaos, how do you stand out from the crowd and become the only logical choice in your field of expertise? In this episode, my guest will share proven principles that, when applied, have empowered people to monetize their life's mission and create a lasting legacy for their family. So I ask you, “Are you ready to break free from your limiting beliefs, so you can design your destiny and monetize your expertise like never before?” About Joel Bauer: Joel has trained some of the world's top income-producing speakers, presenters, and online marketers to monetize their life experience, both online and offline. For decades, Joel's principles have set the bar for speaking, teaching, and closing at the highest level. By applying Joel's strategies, you can break free from limitations, design your destiny, and monetize your expertise like never before. How to Get in Touch with Joel Bauer: Website:   https://joelbauer.com/ Email:  support@joelbauer.com Gift - 5Moves2Authority on Instagram:  https://www.instagram.com/joelbauermentor/  direct message the phrase, “5Moves”  Stalk me online! LinkTree: https://linktr.ee/conniewhitman   Subscribe to the Changing the Sales Game Podcast on your favorite podcast streaming service or YouTube. New episodes are posted every week - listen as Connie delves into new sales and business topics, or addresses problems you may have in your business.

We made it to Episode 6 - the final in our 15 Architectural Decisions series and saved some of the best for last - Predictive and Proactive support (Pure1) combined with our Evergreen architecture and non-disruptive everything. In this episode, JD, Andrew and I dive into Pure Storage's foundational principles that ensure continuous operation and future-proof your storage. We explore the concept of "Non-Disruptive Everything," a core tenet that allows for seamless upgrades and expansions without any downtime. This ties directly into the innovative Evergreen Storage Model, which redefines the ownership experience by eliminating forklift upgrades and enabling independent evolution of performance, capacity, and features over a decade-long array lifecycle. We also discuss how Pure's proactive and predictive support, powered by Pure1, plays a critical role in maintaining this non-disruptive environment. Pure1's advanced analytics, anomaly detection, and data protection assessments provide unparalleled visibility and ensure that potential issues are identified and addressed before they impact operations. This integrated approach, from architecture to support, highlights how Pure Storage consistently delivers a cloud-like experience on-premises, setting a new standard for reliability and longevity in the storage industry. Thanks for watching our series. Series Overview: Pure Storage's foundational approach to product engineering is guided by 15 architectural decisions that were established at the company's inception and have shaped both the technical and user experience across its product lines. These architectural choices were not made arbitrarily—they stem from a deliberate focus on simplicity, efficiency, and scalability, ensuring Pure could deliver storage solutions that break away from legacy complexity and enable continuous innovation without compromise. This series will guide viewers through all of the 15 principles, helping you understand why certain choices were made, how they impact your operations, and how they compare to other industry features and products. Join Pure Report podcast hosts Rob Ludeman, Andrew Miller, and J.D Wallace for this fun technical retrospective on Pure Storage.

What if the clean energy transition is built on one of the dirtiest secrets in industry—rare earth metals? They power EVs, AI, and defense systems, yet most are processed overseas with toxic, outdated methods. In this episode, Nicholas Myers, CEO of Phoenix Tailings, shares how his company is transforming mining waste into a clean, domestic source of critical materials. From backyard prototypes to pioneering world-first predictive complexation, Nick shares the grit, innovation, and vision it takes to scale deep tech in one of the world's most entrenched industries—and why zero-waste, zero-emission metals are vital for both energy security and the environment. Tune in to hear how backyard experiments grew into a global metals revolution.--- Hey Climate Tech enthusiasts! Check out Leaders on a Mission, where I interview climate tech leaders shaking up the industry and bringing us the next wave of sustainable solutions. Join me for a deep dive into the future of green innovation—exploring the highs, lows, and everything in between of pioneering new technologies.Tune in on…YouTube: https://www.youtube.com/@leadersonamissionNet0Spotify: https://open.spotify.com/show/7o41ubdkzChAzD9C53xH82Apple Podcasts: https://podcasts.apple.com/us/podcast/leaders-on-a-mission/id1532211726…to listen to the latest episodes!Timestamps:00:58 – Nick's unconventional journey04:17 – Backyard experiments begin07:22 – Tackling mining waste11:23 – Predictive complexation breakthrough13:40 – AI in metallurgy17:04 – US-China supply chain exposure19:15 – Scaling defense capacity22:56 – Financing beyond VC25:05 – Rare earths in production28:27 – Lessons from fundraising31:43 – The three-year IPO vision33:35 – Building high-performance teams38:31 – Values that drive Phoenix TailingsUseful links: Phoenix Tailings website: https://phoenixtailings.com/Phoenix Tailings LinkedIn: https://www.linkedin.com/company/phoenixtailings/Nicholas Myers  LinkedIn: https://www.linkedin.com/in/nicholaswilliammyers/Leaders on a Mission website: https://cs-partners.net/podcasts/Simon Leich's LinkedIn: https://www.linkedin.com/in/executive-talent-headhunter-agtech-foodtech-agrifoodtech-agritech/

September 2024 Chris Bygraves blasted onto Unexplained Inc. from down under for the very first time for an epic encounter. This time to close out August the chat doesn't go nearly as long but it is just as epic. The episode title touches on a lot of what gets discussed but here are some additional points.- Phantom Phil's recent reading material and how it connects to the Orion Wars.- Spiritual stereotyping and professional ethics.- Judgment and shaming within truth seeking and spirituality- Events concerning Earth's conflicts reflecting what's going on in other parts of the galaxy.- Predictive programming from 1984....ie: The Dark Crystal & DreamscapePlus much much more....Connect with Unexplained Inc. here:https://www.unexplainedinc.com/Watch the episode, like and subscribe on Rumble...here:https://rumble.com/user/Unexplainedinc

Today we cover more of John Coleman's forgotten predictive text from the 90s as well as films that accidentally predicted where we are in culture, and more secrets of Klaus and the popes. https://www.eventbrite.com/e/athens-jerusalem-orthodox-art-philosophy-life-tickets-1598008298839?aff=oddtdtcreator Send Superchats at any time here: https://streamlabs.com/jaydyer/tip Join this channel to get access to perks: https://www.youtube.com/channel/UCnt7Iy8GlmdPwy_Tzyx93bA/join PRE-Order New Book Available in Sept here: https://jaysanalysis.com/product/esoteric-hollywood-3-sex-cults-apocalypse-in-films/ Get started with Bitcoin here: https://www.swanbitcoin.com/jaydyer/ The New Philosophy Course is here: https://marketplace.autonomyagora.com/philosophy101 Set up recurring Choq subscription with the discount code JAY44LIFE for 44% off now https://choq.com Lore coffee is here: https://www.patristicfaith.com/coffee/ Subscribe to my site here: https://jaysanalysis.com/membership-account/membership-levels/ Follow me on R0kfin here: https://rokfin.com/jaydyer Music by Amid the Ruins 1453 https://www.youtube.com/@amidtheruinsOVERHAUL Join this channel to get access to perks: https://www.youtube.com/channel/UCnt7Iy8GlmdPwy_Tzyx93bA/join Join this channel to get access to perks: https://www.youtube.com/channel/UCnt7Iy8GlmdPwy_Tzyx93bA/join #comedy #religion #podacstBecome a supporter of this podcast: https://www.spreaker.com/podcast/jay-sanalysis--1423846/support.

Darshan H. Brahmbhatt, Podcast Editor of JACC: Advances, discusses a recently published original research paper on Evaluating the Performance and Potential Bias of Predictive Models for Detection of Transthyretin Cardiac Amyloidosis.

Tony Shipley, a seasoned entrepreneur and founder of Queen City Angels, joins Jeff Bloomfield to share his journey from a Tennessee farm to building one of America's fastest-growing tech companies and mentoring over 125 startups. Tony reveals the timeless lessons from his upbringing, the importance of intellectual curiosity and critical thinking in entrepreneurship, and why being coachable is the most underrated trait for founders. This episode is a roadmap for anyone seeking to build a meaningful business while leaving a lasting legacy. This isn't just a conversation about startups—it's a masterclass on building something that matters. Whether you're a founder, executive, or aspiring leader, Tony's wisdom will challenge how you think about success, decision-making, and leadership. If you've ever wondered how to turn ideas into impact—or how to avoid the mistakes that sink early-stage companies—this episode is a must-hear. Entrepreneurship is simple, not easy: Businesses succeed when they focus on customer problems, not just clever ideas. Coachability trumps confidence: Founders who “know it all” rarely get funded or scale successfully. Fail fast, learn faster: Quick iteration saves time, money, and credibility. Every founder is first selling themselves, not just their product. Networking is a superpower: Mentors and advisors are everywhere if you're willing to ask. Intellectual curiosity and critical thinking are the twin engines of progress. Predictive programming drives unconscious decision-making—be aware of yours. Fractional expertise beats lone hero syndrome—hire where you lack skills. Legacy is built on people and memories, not exits or net worth. Ideas aren't fragile—if you're afraid to share them, they're not strong enough. 00:00 – Opening & Tony's $1B startup impact 03:45 – Growing up on a Tennessee farm: lessons for life and business 12:30 – The stages of entrepreneurship (idea → MVP → scale) 20:30 – Networking and the power of asking the right questions 23:30 – Fear, pride, and the failure coin in entrepreneurship 28:40 – Intellectual curiosity and critical thinking as superpowers 31:00 – Predictive programming and founder patterns 35:30 – Building a lasting personal and professional legacy Why You Should Listen Top 10 Takeaways Chapter Markings 39:35 – Resources and how to get started in entrepreneurship

Wasabi Technologies (a Boston-based cloud storage company with over $530M in funding, a decade of hypergrowth, 450+ team members, and multiple exabytes of storage deployed) sets high standards for its finance and analytics teams.  Both teams are under pressure to deliver fast, accurate, and actionable insights. Marcos Bento, Director Business Intelligence at Wasabi Technologies and David Suter, Director, Financial Planning And Analysis at Wasabi Technologies join us for a special two-parter. They delve deep into how their teams work together, across functions to turn raw data into real-time financial intelligence. In this episode:  Building and gaining trust between BI and FP&A  Getting from “10% out” to within a couple percentage points for forecasting  KPIs such as dollars per terabyte  BI and FP&A as investigative reporters  Predictive analytics: forecast in the 6 or 12 months working with sales  The manifesto for AI at Wasabi 

Join me for a podcast summary looking at Ai in orthodonticsand its clinical application. A growing topic in orthodontics, and one of themost featured topics at this years AAO. This summary is based on 3 lectures fromthis year's summer meeting by Juan Francisco Gonzalez & Jean Marc Retrouvey,Tarek ElShebiny , Jonas Bianchi and Lucia Cevidanes. We will look whatAi is, the way it works and its clinical application, as well as a criticalview on this young field.  What is Ai: 1.       Technology that enables computers and machinesto simulate human intelligence, perform 1 task very well, e.g. voice command, Youtuberecommendations2.       Predictive modelling, makes calculations,  convert information into numbers or categoriesand recognise patterns  Levels of Ai: Machine learning, Neural Networks and Deep Learning1.       Machine learninga.       The ability for a machine to learn from data andpast experience to identify patterns and make predictions  2.       Neural Networks  a.       Specific model which relies on interconnectednodes, which perform a mathematical calculation of associations , patterns, andprobabilities 3.       Deep learninga.       Is a complex version of neural networks Virtual patient·     CBCT segment + STL file – segmentation of theteeth and roots, with labelling of different stuctureso  Can print model, visualise ideal vector andcalculate ideal vectoro  However clinician still required to establish biomechanics ·     CBCT integration for aligner cases, Unpublishedthesis Khalid Alotaibi:o  Treatment planning confidence increased 50%, leastchange was treatment planning modification  Diagnostic data:·     Ai cephalometric tracingo  46% of 24 landmarks 2.0mm withino  4 different programmes  Iortho, Webceph, Orthodc, cephxo  All landmarks had good overall agreement butvariation in identification  ·     Facial Analysis·     Automated 3D facial asymmetry analysis usingmachine learning  Adel 2025o  Study – 7 landmarks o  Identified manually and with deep learning o  5 accurate, 2 significant difference but notclinically relevant Diagnostic accuracy of photos·     Clinical photos assessment by Ai, and comparedto clinical examination·     Sensitivity 72%, specificity 54% Vaughan & Ahmed2025  Growth prediction·     Poor agreement age 9  Comparison between direct, virtual and AI bonding·     DIBs – uses Ai for bonding·     Compare Ai Vs user modified indirect bonding Vsdirect bonding (gold standard), 0.5mm significant ·     Incisors accurate·     Premolars and lower laterals inaccurate  Monitoring Previous podcast exploring the accuracy of remote monitoringo   with Ferlito 2022 80%repeatability from 2 scans 44.7% repeatability and reproducibility  Bracket removal from scan and retainer fitTarek Assessment of virtual bracket removal by artificialintelligence and thermoplastic retainer fit AJODO 2024o  Retainers for both – clinically acceptable    FDA approval of Ai in dentistry·     FDA - Software of Medical Diagnosis § 4  dental:·     Dental Monitoring·     Ray Co ·     X-Nav technologies·     Densply Sirona    What's next·     More data learning to train AI model·     Robotics customising appliances per patient    

In this episode of the Engineering Influence Podcast, we're joined by Eileen Canady, CMO of BST Global, to discuss the groundbreaking findings from the 2025 Global AEC AI & Data Insights Report. From AI adoption trends to workforce transformation, we explore how engineering firms are embracing AI, overcoming challenges, and reshaping the future of the industry.  

This is a clip from The After Dark Show with Don Rogers! Get access to the full episode and all thier content on all podcast platforms or click the link belowFull episode here! https://www.spreaker.com/episode/s2-e39-aurora-diamondheart-bg-cast-rick-morty-predictive-programing-disclosure--67401951Get access to every episode of The After Dark Show with Don Rogers https://www.spreaker.com/podcast/the-after-dark-show-with-don-rogers--6191691Forbidden Knowledge Network https://forbiddenknowledge.news/Become a supporter of this podcast: https://www.spreaker.com/podcast/forbidden-knowledge-news--3589233/support.

In this episode, Lois Houston and Nikita Abraham continue their discussion on AI fundamentals, diving into Data Science with Principal AI/ML Instructor Himanshu Raj. They explore key concepts like data collection, cleaning, and analysis, and talk about how quality data drives impactful insights.   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: Hello and welcome to the Oracle University Podcast. I'm Lois Houston, Director of Innovation Programs with Oracle University, and with me today is Nikita Abraham, Team Lead: Editorial Services.  Nikita: Hi everyone! Last week, we began our exploration of core AI concepts, specifically machine learning and deep learning. I'd really encourage you to go back and listen to the episode if you missed it.   00:52 Lois: Yeah, today we're continuing that discussion, focusing on data science, with our Principal AI/ML Instructor Himanshu Raj.  Nikita: Hi Himanshu! Thanks for joining us again. So, let's get cracking! What is data science?  01:06 Himanshu: It's about collecting, organizing, analyzing, and interpreting data to uncover valuable insights that help us make better business decisions. Think of data science as the engine that transforms raw information into strategic action.  You can think of a data scientist as a detective. They gather clues, which is our data. Connect the dots between those clues and ultimately solve mysteries, meaning they find hidden patterns that can drive value.  01:33 Nikita: Ok, and how does this happen exactly?  Himanshu: Just like a detective relies on both instincts and evidence, data science blends domain expertise and analytical techniques. First, we collect raw data. Then we prepare and clean it because messy data leads to messy conclusions. Next, we analyze to find meaningful patterns in that data. And finally, we turn those patterns into actionable insights that businesses can trust.  02:00 Lois: So what you're saying is, data science is not just about technology; it's about turning information into intelligence that organizations can act on. Can you walk us through the typical steps a data scientist follows in a real-world project?  Himanshu: So it all begins with business understanding. Identifying the real problem we are trying to solve. It's not about collecting data blindly. It's about asking the right business questions first. And once we know the problem, we move to data collection, which is gathering the relevant data from available sources, whether internal or external.  Next one is data cleaning. Probably the least glamorous but one of the most important steps. And this is where we fix missing values, remove errors, and ensure that the data is usable. Then we perform data analysis or what we call exploratory data analysis.  Here we look for patterns, prints, and initial signals hidden inside the data. After that comes the modeling and evaluation, where we apply machine learning or deep learning techniques to predict, classify, or forecast outcomes. Machine learning, deep learning are like specialized equipment in a data science detective's toolkit. Powerful but not the whole investigation.  We also check how good the models are in terms of accuracy, relevance, and business usefulness. Finally, if the model meets expectations, we move to deployment and monitoring, putting the model into real world use and continuously watching how it performs over time.  03:34 Nikita: So, it's a linear process?  Himanshu: It's not linear. That's because in real world data science projects, the process does not stop after deployment. Once the model is live, business needs may evolve, new data may become available, or unexpected patterns may emerge.  And that's why we come back to business understanding again, defining the questions, the strategy, and sometimes even the goals based on what we have learned. In a way, a good data science project behaves like living in a system which grows, adapts, and improves over time. Continuous improvement keeps it aligned with business value.   Now, think of it like adjusting your GPS while driving. The route you plan initially might change as new traffic data comes in. Similarly, in data science, new information constantly help refine our course. The quality of our data determines the quality of our results.   If the data we feed into our models is messy, inaccurate, or incomplete, the outputs, no matter how sophisticated the technology, will be also unreliable. And this concept is often called garbage in, garbage out. Bad input leads to bad output.  Now, think of it like cooking. Even the world's best Michelin star chef can't create a masterpiece with spoiled or poor-quality ingredients. In the same way, even the most advanced AI models can't perform well if the data they are trained on is flawed.  05:05 Lois: Yeah, that's why high-quality data is not just nice to have, it's absolutely essential. But Himanshu, what makes data good?   Himanshu: Good data has a few essential qualities. The first one is complete. Make sure we aren't missing any critical field. For example, every customer record must have a phone number and an email. It should be accurate. The data should reflect reality. If a customer's address has changed, it must be updated, not outdated. Third, it should be consistent. Similar data must follow the same format. Imagine if the dates are written differently, like 2024/04/28 versus April 28, 2024. We must standardize them.   Fourth one. Good data should be relevant. We collect only the data that actually helps solve our business question, not unnecessary noise. And last one, it should be timely. So data should be up to date. Using last year's purchase data for a real time recommendation engine wouldn't be helpful.  06:13 Nikita: Ok, so ideally, we should use good data. But that's a bit difficult in reality, right? Because what comes to us is often pretty messy. So, how do we convert bad data into good data? I'm sure there are processes we use to do this.  Himanshu: First one is cleaning. So this is about correcting simple mistakes, like fixing typos in city names or standardizing dates.  The second one is imputation. So if some values are missing, we fill them intelligently, for instance, using the average income for a missing salary field. Third one is filtering. In this, we remove irrelevant or noisy records, like discarding fake email signups from marketing data. The fourth one is enriching. We can even enhance our data by adding trusted external sources, like appending credit scores from a verified bureau.  And the last one is transformation. Here, we finally reshape data formats to be consistent, for example, converting all units to the same currency. So even messy data can become usable, but it takes deliberate effort, structured process, and attention to quality at every step.  07:26 Oracle University's Race to Certification 2025 is your ticket to free training and certification in today's hottest technology. 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. 08:10 Nikita: Welcome back! Himanshu, we spoke about how to clean data. Now, once we get high-quality data, how do we analyze it?  Himanshu: In data science, there are four primary types of analysis we typically apply depending on the business goal we are trying to achieve.  The first one is descriptive analysis. It helps summarize and report what has happened. So often using averages, totals, or percentages. For example, retailers use descriptive analysis to understand things like what was the average customer spend last quarter? How did store foot traffic trend across months?  The second one is diagnostic analysis. Diagnostic analysis digs deeper into why something happened. For example, hospitals use this type of analysis to find out, for example, why a certain department has higher patient readmission rates. Was it due to staffing, post-treatment care, or patient demographics?  The third one is predictive analysis. Predictive analysis looks forward, trying to forecast future outcomes based on historical patterns. For example, energy companies predict future electricity demand, so they can better manage resources and avoid shortages. And the last one is prescriptive analysis. So it does not just predict. It recommends specific actions to take.  So logistics and supply chain companies use prescriptive analytics to suggest the most efficient delivery routes or warehouse stocking strategies based on traffic patterns, order volume, and delivery deadlines.   09:42 Lois: So really, we're using data science to solve everyday problems. Can you walk us through some practical examples of how it's being applied?  Himanshu: The first one is predictive maintenance. It is done in manufacturing a lot. A factory collects real time sensor data from machines. Data scientists first clean and organize this massive data stream, explore patterns of past failures, and design predictive models.  The goal is not just to predict breakdowns but to optimize maintenance schedules, reducing downtime and saving millions. The second one is a recommendation system. It's prevalent in retail and entertainment industries. Companies like Netflix or Amazon gather massive user interaction data such as views, purchases, likes.  Data scientists structure and analyze this behavioral data to find meaningful patterns of preferences and build models that suggest relevant content, eventually driving more engagement and loyalty. The third one is fraud detection. It's applied in finance and banking sector.  Banks store vast amounts of transaction record records. Data scientists clean and prepare this data, understand typical spending behaviors, and then use statistical techniques and machine learning to spot unusual patterns, catching fraud faster than manual checks could ever achieve.  The last one is customer segmentation, which is often applied in marketing. Businesses collect demographics and behavioral data about their customers. Instead of treating all the customers same, data scientists use clustering techniques to find natural groupings, and this insight helps businesses tailor their marketing efforts, offers, and communication for each of those individual groups, making them far more effective.  Across all these examples, notice that data science isn't just building a model. Again, it's understanding the business need, reviewing the data, analyzing it thoughtfully, and building the right solution while helping the business act smarter.  11:44 Lois: Thank you, Himanshu, for joining us on this episode of the Oracle University Podcast. We can't wait to have you back next week for part 3 of this conversation on core AI concepts, where we'll talk about generative AI and gen AI agents.     Nikita: And if you want to learn more about data science, visit mylearn.oracle.com and search for the AI for You course. Until next time, this is Nikita Abraham…  Lois: And Lois Houston signing off!  12:13 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.

The future of Disease Care medicine is described as Predictive, Preventive, Personalized, Participatory, and Precision.  But as we first started to discuss on last week's show, Dr. Prather says all of those elements are already available in Structure-Function Health Care.  In this episode, we talk about:—How two patients diagnosed with the same Cancer can have different underlying causes for the same Disease. —The importance of getting to the root cause of an issue in Structure-Function Health Care, while Disease Care focuses on symptoms. —Why Dr. Prather actually cautions his patients against doing parasite cleanses or a Liver flush on their own.  And the gentle approach he utilizes instead. —The critical aspect of patient participation for Structure-Function Care to work, as the doctor and patient have to "work together."—How patients who understand their health condition actually report less pain and discomfort.  And why Dr. Prather always makes sure a patient receives a copy their lab tests with his own notes showing the numbers they are trying to achieve.—The features of Homeopathic Medicine that were actually copied by the pharmaceutical industry and even "led the way" in many of changes made by pharmaceutical companies. —Why both the diagnostics and treatments in Dr. Prather's office all focus on precision to target exactly what the patient needs.  And why the "normal" ranges listed on lab tests are not the "ideal" ranges you actually need for good health. —How Dr. Prather describes Disease Care like a football team's offense, while Structure-Function Care is a team's defense.  And how "defense wins championships".—Why using Structure-Function Health Care as primary care can improve outcomes by 85% and can also solve America's National debt.—The number one thing Dr. Prather would change in how medicine is practiced today. http://www.TheVoiceOfHealthRadio.com

In this episode of the TestGuild DevOps Toolchain Podcast, host Joe Colantonio sits down with Jennifer Rahmani, Co-founder and COO of Thoras.ai, a company redefining how infrastructure scales with AI-driven predictive technology. Drawing from her years as a DevOps engineer in the defense tech sector, Jennifer shares how she and her twin sister turned real-world frustrations into a reliability-first platform that eliminates the guesswork from scaling. We discuss how Thoras.ai integrates with Kubernetes to predict workload demand minutes—or even hours—in advance, allowing teams to maintain high availability without overspending. Jennifer explains why they use the right AI for the right use case, how their predictive autoscaling works in multi-cloud and hybrid environments, and how it helps SREs avoid downtime during unpredictable events like Black Friday or major product launches. Whether you're dealing with noisy data, high cloud bills, or sleepless nights worrying about reliability, this episode delivers practical insights for making smarter scaling decisions.

Demetrios chats with Arpita Vats about how LLMs are shaking up recommender systems. Instead of relying on hand-crafted features and rigid user clusters, LLMs can read between the lines—spotting patterns in user behavior and content like a human would. They cover the perks (less manual setup, smarter insights) and the pain points (latency, high costs), plus how mixing models might be the sweet spot. From timing content perfectly to knowing when traditional methods still win, this episode pulls back the curtain on the future of recommendations.// BioArpita Vats is a passionate and accomplished researcher in the field of Artificial Intelligence, with a focus on Natural Language Processing, Recommender Systems, and Multimodal AI. With a strong academic foundation and hands-on experience at leading tech companies such as LinkedIn, Meta, and Staples, Arpita has contributed to cutting-edge projects spanning large language models (LLMs), privacy-aware AI, and video content understanding.She has published impactful research at premier venues and actively serves as a reviewer for top-tier conferences like CVPR, ICLR, and KDD. Arpita's work bridges academic innovation with industry-scale deployment, making her a sought-after collaborator in the AI research community.Currently, she is engaged in exploring the alignment and safety of language models, developing robust metrics like the Alignment Quality Index (AQI), and optimizing model behavior across diverse input domains. Her dedication to advancing ethical and scalable AI reflects both in her academic pursuits and professional contributions.// Related Links#recommendersystems #LLMs #linkedin ~~~~~~~~ ✌️Connect With Us ✌️ ~~~~~~~Catch all episodes, blogs, newsletters, and more: https://go.mlops.community/TYExploreMLOps Swag/Merch: [https://shop.mlops.community/]Connect with Demetrios on LinkedIn: /dpbrinkmConnect with Arpita on LinkedIn: /arpita-v-0a14a422/Timestamps:[00:00] Smarter Content Recommendations[05:19] LLMs: Next-Gen Recommendations[09:37] Judging LLM Suggestions[11:38] Old vs New Recommenders[14:11] Why LLMs Get Stuck[16:52] When Old Models Win[22:39] After-Booking Rec Magic[23:26] One LLM to Rule Models[29:14] Personalization That Evolves[32:39] SIM Beats Transformers in QA[35:35] Agents Writing Research Papers[37:12] Big-Company Agent Failures[41:47] LinkedIn Posts Fade Faster[46:04] Clustering Shifts Social Feeds[47:01] Vanishing Posts, Replay Mode

NOLA Con Review Wildkat Open House and Match Announcement Mutant Rabbits WWESPN Review of The Accountant 2 The Champ Suge Whyte and a single call from Multiple Dave's more ......... live channel {Every Sunday Night ∙ 8-10P CT} https://nrr.mixlr.com/ all past shows available at Spotify: https://tinyurl.com/4bwkzfw7 Apple Podcasts​: https://goo.gl/SvRBJB SoundCloud​: https://soundcloud.com/not-real-radio Or Your Podcast App Store: https://www.zazzle.com/store/not_real_store

The future of Disease Care medicine is described as Predictive, Preventive, Personalized, Participatory, and Precision.  But Dr. Prather says all of those elements are already available in the Structure-Function Health Care that he practices.  In this episode, find out:—The FDA distinction between Disease Care Medicine and Structure-Function Health Care.—Why Dr. Prather says that "Structure-Function has always had the 5 P's" which are "integral" to this philosophy of care.—How Structure-Function Health Care is best known for its preventative benefits, but should also be better known for its necessity after a disease has already developed. —How the functional lab tests at Holistic Integration can "pick up on problems way before they come", which can then be prevented.—Why Dr. Prather cautions against drastic action because of genetic tests that show a tendency of a patient to develop a disease in the future. —The amazing story of a patient during the AIDS crisis who developed all the symptoms of AIDS after receiving what turned out to be a false HIV-positive test, but the symptoms disappeared after the second (and correct) negative test came back. —How Dr. Prather encouragingly says that "everything can be changed, everything can be helped, and everything can be prevented".  And why his experience with patients causes him to conclude that "having the right attitude makes all the difference in the world". —The importance of keeping the body structurally-aligned, as well as functionally-aligned, for the prevention of future issues.  And how Dr. Prather sees that 90% of people no longer fit the "normal" pattern of structural alignment due to changes that he never witnessed at the start of his career 40 years ago.—The reason Dr. Prather says anti-Cholesterol statin drugs have done "more harm than good".  And why using Cholesterol drugs as a preventative for future Cardiovascular Disease has failed. —Why Dr. Prather says that his three foundational lab tests (The Prather Profile Bloodwork, a Hair Analysis, and a stool kit) are usually all he needs to get a patient healthy before the need for "fancy" testing. http://www.TheVoiceOfHealthRadio.com

Recorded live at the Ascend Conference in Salt Lake City, this episode of ShiftShapers features John Troutman, Vice President of Business Development at Mind Club America. Host David A. Saltzman and John discuss the “mutual mystification” surrounding mental health solutions—and how employers and advisors can start cutting through the noise with real access, real engagement, and real outcomes.From personal burnout to a professional mission, John shares how his experience as a former pastor helped shape his drive to help a million people get the mental health support they need. The conversation unpacks access issues, poor utilization reporting, stigma, and the vital importance of employer-led engagement.

How do we move from reactive recruiting to predictive talent acquisition? In an era where performance metrics drive every business decision, recruitment often remains stubbornly intuition-based, especially when evaluating soft skills like empathy, communication, and critical thinking. What if AI could help us predict performance before a candidate even starts? And what happens when we connect hiring decisions to actual performance outcomes, creating a feedback loop that makes recruiters smarter over time? Can technology help us understand which risks are worth taking, and which gut feelings we should trust?  My guests this week are Veronique Lacasse, Senior Manager at Bell Canada, and Stephane Rivard, Co-founder and CEO of HiringBranch. In our conversation, Veronique and Stephane walk us through Bell's integrated approach to talent acquisition, which utilizes AI assessment data on soft skills to create a predictive, performance-driven hiring system that is still very much driven by human recruiters. In the interview, we discuss: Soft skills and business performance Measuring soft skills with AI driven assessments Challenges in high-volume hiring Building a feedback loop in recruiting, onboarding, and training Showing recruiters the performance outcomes of their decisions The candidate experience Why human recruiters aren't being replaced What does the future look like? Recruiting Future helps Talent Acquisition teams drive measurable impact by developing their strategic capability in Foresight, Influence, Talent, and Technology. If you're interested in finding out how your TA function measures up in these four critical areas, I've created the free FITT for the Future Assessment. It'll give you personalised insights to help you build strategic clarity and drive greater impact immediately. Just head to mattalder.me/podcast to complete the assessment; it takes less than 5 minutes. Follow this podcast on Apple Podcasts. Follow this podcast on Spotify.

My guest today is S Parthasarathy, better known as Spart who is the founder at CuedIn Technologies.In this episode, Spart shares his extensive journey in the software engineering field. Spart's story begins with his initial projects in COBOL programming at Tata Burroughs and the World Bank, working on critical systems in retail, logistics, and financial sectors. He recounts his impactful stint at SWIFT, contributing to the foundation of what has become modern-day financial messaging systems. Spart reflects on his decision to pivot from electrical engineering to computer science, driven by his interest in the engineering of large systems. He details his tenure at Ramco Systems, implementing document-based transactions, model-based code generation, and achieving several tech milestones, including internet integration and 32-bit upgrades. After Ramco, Spart's continued passion for software engineering led him to co-found a SaaS-based ERP solution company, targeting SMEs. Despite early challenges and market readiness issues, he gained crucial insights into cloud-native architectures.Spart's career path took another turn towards consulting and exploring AI, specifically focusing on probabilistic graph learning and the challenges of natural language processing in software engineering. He emphasizes the importance of non-functional requirements, application architecture, and the potential of tools like Generative AI (GenAI) to enhance software development processes. Spart concludes by reflecting on the ongoing evolution of software engineering and his current projects aimed at making software engineering more accessible and efficient with modern tools.Spart has 40+ years of experience in various aspects of software services area covering consultancy, business systems management, product development and Engineering management. Worked with large North American Organizations handling complex projects to implement transaction processing business solutions and data communication networks. Key interests: • Gen AI based solutioning for key business activities • Gen AI enabled SW engineering • Contextual social network driven approach for building business systems • Predictive analytics over operational databases using Bayesian causal networks • Implementing innovative platform based techno-commercial models for software solutions/services delivery. • Cloud computing and SOA based multi-tenant solution Architecturehttps://www.linkedin.com/in/spartp/

The Grizzlies announced that they will play their first ever regular season game in Europe, as they will face the Orlando Magic in Berlin and London as part of the NBA's Europe Games. This decision means that the Grizzlies will not host a nationally televised MLK Day game, which breaks from the longstanding tradition since the team moved to Memphis.On this episode, Keith goes over that decision (0:50), and then he speaks with former NBA front office worker Owen Phillips about the most (and least) predictive stats from summer league, the new rule change about heaves, and some other general NBA topics (7:45).You can sign up for Owen's newsletter at theF5.substack.comJoin the listener slack and get a bonus episode at Patreon.com/FastbreakBreakfastUse promo code FBBF and get up to $1000 in bonus cash at Underdog Fantasy: play.underdogfantasy.com/p-fastbreak-breakfastCheck out the NBA and Grizzlies merchandise at teepublic.com/stores/fastbreak-breakfastGet $20 off your first purchase at SeatGeek with promo code: FASTBREAKBREAK

What if your infrastructure could predict demand before it happens? In this episode, Nilo Rahmani, CEO and co-founder of Thoras AI, breaks down how predictive scaling is transforming the Kubernetes landscape. With over a decade of experience in site reliability engineering, Nilo shares why the observability market is slower to adopt AI—and why that might finally be changing. If you're navigating the pressures of DevOps or building AI tools for technical teams, this conversation is a must-listen.Key TakeawaysAI adoption in reliability engineering isn't about replacing humans—it's about reducing fire drills and enabling better decision-making.Predictive scaling using ML can dramatically cut cloud costs and reduce latency—without compromising reliability.DevOps teams remain cautious with AI due to the high stakes of downtime and the need for human-in-the-loop decision-making.The best tools won't just optimize infrastructure—they'll increase engineer confidence and operational readiness.Nilo's founder journey started with a thesis and became unstoppable once she “couldn't unsee the better way.”Timestamped Highlights[01:02] What Thoras AI actually does—and how it tackles the double challenge of utilization and cost[03:12] Why reliability engineering is a high-stakes, thankless job and how AI can change that[08:54] Can AI fully handle outages at 2 a.m.? Why human-in-the-loop still matters[13:22] The low-hanging fruit: where ML delivers value fast in infrastructure planning[17:56] Increasing confidence, not replacing engineers—rethinking developer experience with AI[24:38] Nilo's founder story: from SRE to CEO, driven by a problem too obvious to ignoreQuote of the Episode“I couldn't unsee that there's a better way. Using machine learning to make decisions in reliability engineering is the obvious next step.”Resources MentionedThoras AI: thoras.aiConnect with Nilo on LinkedIn: linkedin.com/in/nilo-devopsCall to ActionEnjoyed this episode? Share it with someone who lives on-call or is building for DevOps teams. Subscribe on your favorite platform and leave a review—it helps more tech professionals discover the show.

In this episode of Onc Now, host Jonathan Sackier is joined by Stephen Freedland, Surgical Oncologist at Cedars-Sinai Medical Center, Los Angeles, California, USA, to discuss the intersections of biology, behaviour, and bias in prostate cancer. From diet and exercise to disparities in outcomes and promising data on enzalutamide, Freedland challenges assumptions and advocates for a more holistic, equitable approach to care.  Timestamps   01:20 – Background and clinical journey 02:39 – Cancer myths: what needs busting 03:22 – Lifestyle's role in prostate cancer 05:18 – One habit all cancer patients should adopt 06:39 – Yes or No round 10:30 – Diet, obesity, and exercise: the data 13:25 – Mediterranean diet and outcomes 15:30 – Predictive lifestyle factors 16:19 – Racial disparities and root causes 18:35 – Gender-affirming treatment and prostate cancer in trans women 23:20 – Enzalutamide: latest data and developments 25:51 – Broader therapeutic landscape 28:40 – Risk of recurrence 30:19 – Advice for young oncologists 34:00 – Three wishes for the future of prostate cancer care 

In this episode, I dive into a real, timely conversation with Scott Brighton, CEO of Bonterra — a social good software company helping nonprofits raise and give more through data-driven innovation. We talk candidly about what AI and data can actually do for nonprofit leaders looking to grow their impact, especially when resources are tight.If you've ever felt overwhelmed by the idea of “using data” or confused about what AI can realistically do for your small-but-mighty team, this one's for you.We talk about the why and the how — and Scott shares some real-world tools, examples, and strategies that can help organizations like yours scale smarter (not harder). This is about doing more of what works, with less burnout.

Mike Noble of Noble Predictive Insights joins Arizona's Morning News to talk about the shift to GOP registrations and other political statistics recently released

Heat is the leading weather-related cause of death in the United States. A team from the National Weather Service spent more than a decade, mostly on their own time, to develop a tool that could link weather data about heat with heat-related injury data to predict heat risk. When that tool went nation-wide, it transformed emergency management, public health, and education. It also earned this team a 2025 Service to America Medal. Here to tell us more about this life-saving technology are Michael Staudenmaier, Chad Kahler, and Mark Loeffelbein of the National Weather Service.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In this episode of Breaking Math, Autumn explores the complex world of AI ethics, focusing on its implications in education, the accuracy of AI systems, the biases inherent in algorithms, and the challenges of data privacy. The discussion emphasizes the importance of ethical considerations in mathematics and computer science, advocating for transparency and accountability in AI systems. Autumn also highlights the role of mathematicians in addressing these ethical dilemmas and the need for society to engage critically with AI technologies.Takeaways AI systems can misinterpret student behavior, leading to false accusations. Bias in AI reflects historical prejudices encoded in data. Predictive analytics can help identify at-risk students but may alter their outcomes. Anonymization of data is often ineffective in protecting privacy. Differential privacy offers a way to share data while safeguarding individual identities. Ethics should be a core component of algorithm design. The impact of biased algorithms can accumulate over time. Mathematicians must understand both technical and human aspects of AI. Society must question the values embedded in AI systems. Small changes in initial conditions can lead to vastly different outcomes.Chapters 00:00 Introduction to AI Ethics 02:14 The Accuracy and Implications of AI in Education 04:14 Bias in AI and Its Consequences 05:45 Data Privacy Challenges in AI 06:37 Mathematical Solutions for Ethical AI 08:04 The Role of Mathematicians in AI Ethics 09:42 The Future of AI and Ethical ConsiderationsSubscribe to Breaking Math wherever you get your podcasts.Become a patron of Breaking Math for as little as a buck a monthFollow Breaking Math on Twitter, Instagram, LinkedIn, Website, YouTube, TikTokFollow Autumn on Twitter and InstagramBecome a guest hereemail: breakingmathpodcast@gmail.com

In this episode of ShiftShapers, host David A. Saltzman talks with Ramesh Kumar, CEO and co-founder of zakipoint health. Ramesh shares how a personal mission to help his father navigate healthcare led to a professional pursuit: transforming overwhelming claims data into actionable insights for employers, advisors, and members.The conversation explores the evolution of predictive modeling, the growing importance of fiduciary responsibility in plan design, and how AI-powered platforms are helping advisors simplify benefit decisions and improve outcomes. Ramesh offers real-world examples of how data can shape plan strategy, change behavior, and improve care—before costs spiral out of control.

Dutch & Tena discuss Black Mirror Season 7 Episode 1 entitled Common People.This episode depicts the struggles of the everyday "common people" while intermingling the transhumance agenda. In order for protagonist Mike to save his wife Amanda's life, he authorizes an experimental surgery that ties her living consciousness to a subscription plan. Tune in to hear our discussion.https://www.therealistthevisionary.comBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-realist-the-visionary--3304218/support.

In this episode of Onc Now, host Jonathan Sackier is joined by Jean Abraham, Professor of Precision Breast Cancer Medicine at the University of Cambridge, to delve into the future of integrated cancer medicine, the evolving role of clinical trials, and how precision oncology is redefining outcomes for patients with breast cancer across the UK.  Timestamps  00:00 – Introduction  01:28 – Quickfire round  09:50 – Cambridge institutes for precision medicine  12:53 – Predictive/prognostic tools in breast cancer  15:33 – The future of breast cancer clinical trials  18:34 – Integrating genomic data  22:31 – Addressing cost barriers  24:22 – How AI is transforming breast cancer care  28:08 – Why oncology?  30:04 – A message to non-oncologists  31:54 – Abraham's three wishes for healthcare  

Episode 251: In 2018, Dell set out to do something big: turn customer feedback into a system that could not only provide insights, but help set priorities and run the business. They had the data. They had the intent. But they made a compromise that many organizations settle on: Rather than enforce one unified approach to customer feedback, they allowed each team to build its own. While this helped with initial adoption and change management, it also led to fragmentation—multiple tools, different methods, no shared truth. And it got worse over time. Real progress ultimately would require centralizing what had become scattered. When Dell's Marc Stein appeared on this podcast in 2018 (episode 129), the company had just completed its EMC merger and launched a chief customer office. The ambition: one integrated Net Promoter System to tie sentiment to economics and put the customer at the center of every decision. But good intentions ran into a harsh reality: Every function was listening to customers, but no one was hearing the same thing.  In this episode, Mike Valanzola, Dell's Senior Director of Voice of Customer and NPS Operations, picks up the story. He explains how misaligned tools, siloed ownership, and governance gaps made customer feedback hard to act on. His team didn't want to tear down what existed. Instead, they brought it together. Through consolidation, centralization, shared standards, and stronger governance, they transformed scattered signals into an enterprise-wide system of action. The turning point came with the Customer Experience Roadmap and Accreditation. The CXRA gave Dell a framework to drive internal accountability and rebuild trust in the system. As Mike describes, cross-functional teams now meet weekly to act on shared signals. Tomorrow's goal? Make every employee a promoter and make every signal actionable. Guest: Mike Valanzola, Senior Director, Voice of Customer and NPS Operations, Dell Technologies Host: Rob Markey, Partner, Bain & Company Give us feedback: Customer Confidential Podcast Feedback Send us a note: Contact Rob Time-Stamped Topics 00:01 - Marc Stein's 2018 ambition: a unified CX system 03:50 - Why integration faltered: fragmentation, politics, data overload 06:20 - Mike's mission: centralize tools, enforce governance 10:00 - Transforming custom systems to create shared accountability 13:30 - Early delivery surprises and sentiment gaps 17:10 - Predictive models and operational fixes 21:00 - How Dell built trust in the new NPS engine 27:45 - Weekly action meetings: turning listening into doing 35:30 - Why CXRA certification mattered, internally and externally 40:00 - Reflections on past company decisions Notable Quotes “ We have a robust  partner community that allows us to  expand our scale in terms of the customers that we can  touch. Each and every one of those folks has some things that are important for us to hear.” [8:00]  ”We do—and did—as a company, listen regularly, but we didn't always hear. The reason for that came down to every function across the company, ultimately doing their own listening programs, using their own application, governing how they listened, controlling what they got back, and not sharing it.” [18:00]  ”We had been really in a run-the-business function, really focused on  NPS management, really focused on owning  that measurement for the company. And now, I was proposing a large-scale, end-to-end corporate transformation that was going to require my own team  to think about how we operate, and effectively operate differently.” [28:00] Additional Resources Hear our 2018 podcast with Marc Stein on Dell's original CX ambition, Bringing Net Promoter to Scale Learn more about Bain's MyCX Roadmap & Accreditation

In this episode of the HealthSpan Podcast, Dr. Robert Todd Hurst, MD, FACC, FASE sits down with Dr. Dr. Hamed Abbaszadegan, MD, MBA, FACP, FAMIA,to explore how artificial intelligence is reshaping the future of healthcare. They discuss everything from AI-powered clinical decision-making and ambient medical notetaking to the promise (and pitfalls) of predictive health modeling and personalized care.  Dr. Hamed Abbaszadegan is a double board-certified internist and clinical informaticist. He currently serves as a physician executive at Stanson Health and is a recognized leader in clinical informatics. Formerly the director of the Clinical Informatics Fellowship at the University of Arizona College of Medicine – Phoenix, he combines real-world hospital experience with cutting-edge technology insight.   Key Time Stamps:  00:40 – Introduction to Dr. Hamed  01:45 - AI's role in healthcare  05:00 – The evolution from paper charts to electronic records and AI support  07:05 – Ambient tech and how it eases physician burden  08:30 – AI-generated patient summaries and real-world applications   10:00 – Potential future of AI support in clinical evaluation   13:10 – Dr. Hamed's vision of healthcare: Predictive modeling   16:20 – Communicating risk: How AI may help patients make better decisions   20:18 – Precision medicine and interoperability challenges  22:15 – Natural language processing   24:35 – AI, the future of medical assistants   26:50 – Standards in healthcare AI and the USB-C analogy  29:20 – Can healthcare get to AI more efficiently than previous technologies  31:00 – AI's hidden presence in modern imaging and clinical tools  33:25 – Reviving the doctor-patient relationship through technology  35:35 – What is clinical informatics and how Dr. Hamed found his path to it  39:25 – A peek into his side project: The Secret Job Society podcast  This information is for educational purposes only and is not medical advice. Don't make any decisions about your medical treatment without first talking to your doctor.    Connect with Dr. Hamed here:   https://www.linkedin.com/in/hamedabbaszadegan/   abbaszadeganmdmba@gmail.com  Connect with HealthspanMD:  https://www.linkedin.com/company/healthspanmd/   https://www.facebook.com/healthspanmd   https://www.instagram.com/healthspanmd/  https://quiz.healthspanmd.com/    

Join us for bonus stuff every week at patreon.com/timbutterly  Support the show and get 20% off your Chubbies order with code TBS at https://www.chubbiesshorts.com UPCOMING SHOWS AT https://timbutterly.com Catch new eps of Metal Girl Solid live - https://www.twitch.tv/timbutterly  

What does it take to run 500 events across 16 weeks and still make it home for bedtime stories? In this powerhouse episode, Dwayne Kerrigan sits down with Vaneli Martinov, Executive Vice President of Operations for the world-renowned Wellington International.Vaneli opens up about the reality behind high-performance leadership—juggling relentless operations, motherhood, and building a career from the ground up. From her early days as an intern at the most expensive golf club in the world to project-managing $200M developments, her story is a masterclass in grit, grace, and grounded leadership.You'll learn how she balances chaos with calm, leads massive teams with empathy, and why being the "swan on the surface, but paddling like hell underneath" is one of her greatest strengths.Timestamps:00:00 – The Swan Mentality: Grace above, chaos below01:10 – Meet Vaneli Martinov03:00 – Managing 500 events in 16 weeks05:45 – Operational teardown + seasonal rebuilds07:00 – Balancing career, marriage & motherhood08:45 – Living in fight-or-flight: Thriving in chaos10:00 – Hustling her way from intern to executive14:45 – Saying yes before you're ready17:00 – Early career lessons in elite service & proactivity21:00 – Building trust through “nothing is beneath me” leadership23:00 – Predictive thinking vs reactive firefighting26:00 – Scaling up from operations grunt to strategic executive29:00 – 80–100 hour weeks: Was it worth it?31:00 – Redefining hustle with purpose and family in mind35:00 – The truth about toxic relationships and healthy ambition38:00 – The hidden challenges of being a woman in male-dominated leadership44:30 – How Vaneli leads with feminine strength and authenticity48:00 – Modeling the right leadership style through lived experience52:00 – Networking with purpose vs performative connectionKey Takeaways & Resources:1. Say Yes Before You're ReadyVaneli's career is built on courageous action—even when she didn't feel ready. She leaned into the unknown and learned through doing.2. Leadership Is Earned Through ActionShe models respect by showing up in the trenches. From hauling trash to handling investors, no job is beneath her—and that credibility compounds.3. Early Career: Hustle + Humility WinsWhat set her apart wasn't grades—it was drive. Her advice to young professionals? Show up curious, proactive, and willing to do anything.4. Learn to Anticipate, Not Just ReactWhether managing elite clientele or massive operations, success lies in predicting needs and solving problems before they arise.5. Redefining Feminine LeadershipVaneli rejected the “bulldog boss” stereotype and leaned into emotional intelligence, empathy, and warmth—without sacrificing strength.6. Balance Isn't Static—It's IntentionalAs a wife, mother, and exec, she's constantly pivoting. Her north star? Show up with positivity, and choose grace over perfection.Connect with Dwayne KerriganFacebook: https://www.facebook.com/thedwaynekerriganpodcastInstagram: https://www.instagram.com/dwaynekerriganpodcast/Linked In: https://www.linkedin.com/in/dwayne-kerrigan-998113281/Website: http://www.dwaynekerrigan.comDisclaimer...

Scientific Sense ® by Gill Eapen: Prof. Trey Ideker is Professor of Medicine, Bioengineering and Computer Science, and former Chief of Genetics, at the University of California San Diego (UCSD). He is also the Director of the Bridge2AI Functional Genomics Data Generation Program and Co-Director of the Cancer Cell Map Initiative. Please subscribe to this channel:https://www.youtube.com/c/ScientificSense?sub_confirmation=1

Drs. Thomas Hemmerling and Arman Valadkhani discuss the article “Diastolic Versus Systolic or Mean Intraoperative Hypotension as Predictive of Perioperative Myocardial Injury in a White-Box Machine-Learning Model” published in the July 2025 issue of Anesthesia & Analgesia.

About Hugh Massie:Hugh Massie is the Executive Chairman and Founder of DNA Behavior International – the Behavior and Money Insights Company. As a Titan 100 CEO and renowned Behavioral Solutions Architect, he approaches every opportunity with the proven perspective that “behavior makes money.” Through his analytical approach and AI-driven technologies, Hugh helps growth-minded leaders build “category king” people-centric businesses that enhance decision-making, culture, and performance. His purpose is to empower people to align their natural, hard-wired talents and financial behaviors to create an Exponential Future with reduced stress. Hugh's moonshot is to scale DNA Behavior's AI-powered BeSci Tech Platform to inform over 1 billion people annually by 2030, increasing life, financial, and business longevity by 30+ years. In this episode, Dean Newlund and Hugh Massie discuss:Predictive insights from human behavior in leadership and business strategySelf-awareness and hardwired traits as drivers of leadership effectivenessGovernance dynamics in enabling or constraining powerful executivesOrganizational resilience in the face of AI disruption and rapid innovationBehavioral economics as a tool for sustainable financial performance Key Takeaways:Behavior serves as a powerful leading indicator of future financial and cultural performance, allowing organizations to anticipate outcomes before they materialize in traditional reports.Digitally scanning leadership behavior using publicly available data enables predictive profiling at scale, offering strategic insights into executive decision-making styles without requiring formal assessments.Sustained business success is often linked to a balanced presence of six core behavioral traits, including results-driven, innovation-focused, fiscal control, financial prudence, and relationship engagement.When leadership strengths such as vision, influence, or risk-taking are left unchecked—particularly in environments with weak governance—they can backfire and lead to long-term brand erosion and internal dysfunction. "Your strengths, overplayed, can be a struggle and, if not attended to, become a weakness.” — Hugh Massie Connect with Hugh Massie:  Website: https://dnabehavior.com/YouTube: https://www.youtube.com/@dnabehaviorLinkedIn: https://www.linkedin.com/in/hughmassie/X (Twitter): https://x.com/HughMassieFacebook: https://www.facebook.com/hugh.massie/Instagram: https://www.instagram.com/hughmassie/  See Dean's TedTalk “Why Business Needs Intuition” here: https://www.youtube.com/watch?v=EEq9IYvgV7I Connect with Dean:YouTube: https://www.youtube.com/channel/UCgqRK8GC8jBIFYPmECUCMkwWebsite: https://www.mfileadership.com/The Mission Statement E-Newsletter: https://www.mfileadership.com/blog/LinkedIn: https://www.linkedin.com/in/deannewlund/X (Twitter): https://twitter.com/deannewlundFacebook: https://www.facebook.com/MissionFacilitators/Email: dean.newlund@mfileadership.comPhone: 1-800-926-7370 Show notes by Podcastologist: Hanz Jimuel AlvarezAudio production by Turnkey Podcast Productions. You're the expert. Your podcast will prove it.

The Legion Of Doom head out to Montauk, NY for our 13th year in a row. This time we bring the Podcast with us, we discuss:- US strikes on Iranian nuclear facilities-Religion and what happens in the afterlife-Predictive programming in movies-Artificial Intelligence and it's consequencesText us comments or questions we can answer on the showPatriot Cigar Company Premium Cigars from Nicaragua, use our Promo Code: DOOMED for 15% off your purchase. https://www.mypatriotcigars.com/usa/DOOMED Support our show by subscribing using the link: https://www.buzzsprout.com/796727/support Support the showGo to Linktree.com/TwoDoomedMen for all our socials where we continue the conversation in between episodes.

Join us to hear Jennifer's incredible unexpected journey from mysterious, itchy skin blotches leading to a full psoriasis diagnosis and how she found the right treatment for her with dermatologist Dr. Christina Feser and Mind.Px. Listen as Jennifer tells her diagnostic odyssey from itchy, skin blotches to psoriasis with host Max Blitstein as together they explore her journey from topical treatments to biologics, and the emotional toll this disease can take. Learn how this psoriasis patient found relief with Dr. Christina Feser through the use of new precision medicine technology to find the right treatment for her. This episode highlights the importance of proactive care and the life-changing impact of finding the right medical support. Timestamps: ·       (0:00)  Intro to Psound Bytes™ and guest welcome dermatologist Dr. Christina Feser and her patient Jennifer Kirsch. ·       (1:26)  Jennifer's life prior to her diagnosis journey with psoriasis. ·       (2:29)  Symptoms, diagnosis, and initial treatment course. ·       (4:26)  The path to taking control of Jennifer's psoriasis begins with a Mind.Px test. ·       (9:15)  Timeline for the Mind.Px test results and how it felt to get the test. ·       (12:14) Implementation and results of the treatment recommended by Dr. Feser and the Mind.Px test. ·       (17:22) The emotional impact of living with psoriasis.   ·       (19:24) Be proactive when something is not right. Listen to your body. ·       (19:57) Getting on the right medicine initially has value.  ·       (21:16) While the journey is not easy, taking advantage of available technology offers better management of patients with psoriasis. 3 Key Takeaways: ·       Diagnosis of psoriasis can be challenging but it all starts with finding the right health care provider who listens to you as a patient.   ·       Technology now exists through Mind.Px to help providers and patients select a therapy that is more biologically appropriate to manage their psoriasis. ·       Be proactive in taking steps to ensure you receive the right treatment for you and your health. Guest Bios:   Dr. Christina Feser is a board-certified dermatologist with Nashville Skin, a comprehensive dermatology center for adults and children where she specializes in the latest medical treatments for skin diseases such as psoriasis that impact the skin, scalp, hair, and nails. She has a passion for research, serving as Principal Investigator on numerous clinical trials with a focus on psoriasis, eczema, alopecia, vitiligo, and other skin diseases. She is a fellow of the American Academy of Dermatology, the American Osteopathic College of Dermatology, and is a member of the Nashville Dermatology Society and the Tennessee Dermatology Society. Jennifer Kirsch, a patient of Dr. Feser's, was diagnosed with psoriasis in November 2024 after initially being diagnosed with eczema. Taking matters into her own hands, Jennifer not only found the right care with Dr. Feser, but also the right treatment to help her live her life without the itch associated with psoriasis.  Resources: Ø  Current Biologics on the Market https://www.psoriasis.org/current-biologics-on-the-market/ Ø  Mindera Health – Shaping the Era of Digital Predictive Skin Analytics       https://minderahealth.com

Summary In this episode, Ali Damron delves into the concept of neuroplastic symptoms, emphasizing the significant role fear plays in chronic symptoms. She explains how fear can manifest in various forms and how it can perpetuate a cycle of symptoms.  The discussion covers the importance of understanding the symptom-fear cycle, breaking the fear loop, and reconditioning the brain to respond differently to symptoms. Ali also introduces the concept of somatic tracking as a tool for healing and encourages listeners to approach their symptoms with curiosity and gentleness.   Takeaways Many chronic symptoms may stem from brain patterns rather than physical damage. Fear can manifest in various forms, affecting our health. Changing our response to symptoms can help alleviate them. The symptom-fear cycle can create a feedback loop of anxiety and symptoms. Self-compassion and reducing pressure are crucial for healing. Predictive coding can lead to anticipating symptoms based on fear. It's essential to break the fear loop to regain control over symptoms. Reconditioning the brain involves teaching it that sensations are safe. Avoiding triggers can reinforce fear; exposure is necessary for healing. Somatic tracking is a powerful tool for changing the brain's relationship to symptoms. Chapters 00:00 Introduction to Neuroplastic Symptoms 09:10 Understanding the Symptom-Fear Cycle 15:04 Breaking the Fear Loop 23:50 Reconditioning the Brain   Ali's Resources:  Calm the Chaos: Practical Tips and Tools for Stopping Anxiety in It's Tracks Course! Consults with Ali  BIOptimizers Magnesium Breakthrough 10% off using code ALIDAMRON10 www.alidamron.com/magnesium Master Your Perimenopause Course + Toolkit "Am I in Perimenopause?" Checklist.  What Hormone is Imbalanced? Quiz! Fullscript (Get 10% off all supplements) "How To Balance Your Hormones For Better Sleep, Mood, Periods and Energy" Free, On Demand Training Website  Ali's Instagram Ali's Facebook Group: Holistic Health with Ali Damron