Podcasts about mris

Jase feels nature's emergency call at the worst possible moment  in full view of small-town traffic! Revisiting the moment Moses pleaded for mercy and God relented, Zach, Al, and Jase wrestle with what it means for us today— whether our prayers can truly move the heart of God or if His will was always unfolding as planned. The guys explore the “lawlessness” of sin and why loving your neighbor could be the difference between mercy and destruction. In this episode: Exodus 32, verses 7–14; Exodus 33, verses 12–23; 1 John 3, verses 1–4; Hebrews 3, verses 1–14 “Unashamed” Episode 1282 is sponsored by: https://ruffgreens.com — Get a FREE Jumpstart Trial Bag for your dog today when you use promo code Unashamed! https://timtebow.com/tree-unashamed/ — Get your copy of If the Tree Could Speak by Tim Tebow on Amazon today! https://myphdweightloss.com — Find out how Al lost 80+ pounds. Schedule your one-on-one consultation today by visiting the website or calling 864-644-1900 and mention "AL" http://unashamedforhillsdale.com/ — Sign up now for free, and join the Unashamed hosts every Friday for Unashamed Academy Powered by Hillsdale College Check out At Home with Phil Robertson, nearly 800 episodes of Phil's unfiltered wisdom, humor, and biblical truth, available for free for the first time! Get it on Apple, Spotify, Amazon, and anywhere you listen to podcasts! https://podcasts.apple.com/us/podcast/at-home-with-phil-robertson/id1835224621 Listen to Not Yet Now with Zach Dasher on Apple, Spotify, iHeart, or anywhere you get podcasts. Chapters: 00:00 “I Love You” Is a Responsibility 06:48 A Field Covered in Civil War Bullets 12:15 An Emergency in the Cypress Grove 14:40 Claustrophobia, MRIs & Calling in Favors 17:50 ZachGPT is a Thing 22:05 Sin Is Lawlessness 28:20 The Golden Calf Rebellion 34:50 Did God Change His Mind? 41:10 Moses as a Foreshadow of Christ 48:05 God's Glory & Our Hope — Learn more about your ad choices. Visit megaphone.fm/adchoices

Dr. Ashley Mak delves into the complexities of sciatica, emphasizing that MRIs are not definitive indicators of pain. Instead, he advocates for understanding personal movement patterns through a structured approach of identifying what feels good, neutral, and painful. By creating a personalized pain inventory, individuals can better navigate their recovery process and find effective treatment strategies.Here's an article about the limitations of MRIs. https://pmc.ncbi.nlm.nih.gov/articles/PMC4464797/You can get access to the somatic tracking cheat sheet here: https://ifixyoursciatica.gymleadmachine.co/self-treatment-cheat-sheet-8707-4603Check out our favorite products! (affiliate page): https://ifixyoursciatica.gymleadmachine.co/favorite_productsDid you know that our YouTube channel has a growing number of videos including this podcast? Give us a follow here- https://youtube.com/@fixyoursciatica?si=1svrz6M7RsnFaswNAre you looking for a more affordable way to manage your pain? Check out the patient advocate program here: ptpatientadvocate.comSupport this podcast at — https://redcircle.com/fix-your-sciatica-podcast/donationsAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

To mark Prostate Cancer Awareness Month, the latest episode of the 21CC podcast sets out why men across the industry must prioritise their health. Prostate cancer is the most common cancer affecting men in the UK – a disease that touches thousands of families every year. Sadly, one in eight men will be diagnosed with prostate cancer during their lifetime. This risk increases with age, and it is particularly high among black men, where the figure rises to one in four. For many, there are no early symptoms, which is why awareness and understanding the risks is so important. In this episode, we hear from Nick Molyneux, an MEP manager at Mace, who was diagnosed with prostate cancer aged 51. Due to being diagnosed early, he remains on active surveillance, which involves routine blood tests and MRIs. Molyneux is now a passionate volunteer for Prostate Cancer UK and regularly visits construction sites to raise awareness and ensure men understand their risks. Also joining the podcast is Meg Burgess, a specialist nurse at Prostate Cancer UK with more than 35 years of nursing experience. During the discussion, she highlights the importance of not waiting for potential symptoms of the disease. “If you're waiting for symptoms of prostate cancer, you risk cancer developing,” Burgess says. “Finding prostate cancer early means that it's much easier to treat or to monitor.” Listen now for honest insights and vital information that could help save lives across the construction industry. Prostate Cancer UK has created a free and confidential online risk checker that asks a small number of straightforward questions and provides clear guidance on what to do next. While the risk checker does not provide a diagnosis, it is a useful starting point.

Steve Moore discusses the critical need for healthcare price transparency to dismantle what he describes as a corrupt and "rotten" medical billing system. Featuring guest Cynthia Fisher of Patient Rights Advocate, the conversation highlights how the absence of visible pricing allows hospitals and insurers to profiteer while leaving families vulnerable to financial ruin. Fisher emphasizes that treating healthcare like a competitive free market would empower consumers to shop for services—such as MRIs or surgeries—potentially lowering costs to a fraction of their current rates.  Learn more about your ad choices. Visit megaphone.fm/adchoices

MRIs are loud. They're huge. They're magnetic. But what are they actually doing? This week, we bring Claire back to help us connect the dots between NMR (yes, organic chem flashbacks) and MRI. How does a technique built on tiny hydrogen protons turn into a 3D image of your brain? How can it tell the difference between tissue and fluid? Why can't you bring metal anywhere near the machine? We ask: • What are your protons doing inside an MRI? • How does “magnetic resonance” become an image? • Why does oxygenated blood matter? • And how did anyone figure this out in the first place? If you've ever had an MRI, or just wondered how we can see inside the body without radiation or surgery, this episode pulls back the curtain. Listen in and rethink what's happening inside that giant magnet. 00:00 MRI Episode Kickoff 01:11 Meet Claire Again 02:27 PhD Candidate Explained 03:44 NMR Basics Begin 04:33 Protons And Magnets 06:46 RF Pulse And Signal 11:16 Hydrogen Everywhere 13:35 Reading NMR Peaks 16:02 Matrix And Practice 18:31 Jam Summarizes NMR 20:44 Why MRI Not NMR 22:45 Spin And Isotopes 29:02 MRI Uses Body Water 30:37 Tissue Contrast And T1 33:38 Resolution Limits 34:25 MRI Resolution Limits 35:34 From NMR to Images 36:50 K Space and Gradients 41:30 Voxels and 3D Views 44:05 Contrast and Clinical Uses 49:47 Research Possibilities 51:11 Functional MRI Explained 56:14 MRI Safety and Magnet Strength 58:00 Helium and Heavy Machines 01:02:43 Science Boundaries and Wrap Up   Support this podcast on Patreon Buy Podcast Merch and Apparel Check out our website at chemforyourlife.com Watch our episodes on YouTube Find us on Instagram, Twitter, and Facebook @ChemForYourLife References from the Episode: Thanks to our monthly supporters Amanda Raymond Emily Morrison Kyle McCray Justine Emily Hardy Ash Vince W Julie S. Heather Ragusa Autoclave Dorien VD Scott Beyer Jessie Reder J0HNTR0Y Jeannette Napoleon Cullyn R Erica Bee Elizabeth P Rachel Reina Letila Katrina Barnum-Huckins Suzanne Phillips Venus Rebholz Jacob Taber Brian Kimball Kristina Gotfredsen Timothy Parker Steven Boyles Chris Skupien Chelsea B Avishai Barnoy Hunter Reardon Support this podcast on Patreon Buy Podcast Merch and Apparel Check out our website at chemforyourlife.com Watch our episodes on YouTube Find us on Instagram, Twitter, and Facebook @ChemForYourLife Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Dr. Wendy Suzuki is an American neuroscientist and a professor at the New York University Center for Neural Science. Her research centers on brain plasticity—the brain's power to change. Renowned for revealing how memory-critical circuits create and preserve long-term memories, she now investigates how aerobic exercise boosts learning, memory, and higher cognition. She is the author of Healthy Brain, Happy Life: A Personal Program to Activate Your Brain and Do Everything Better.In our conversation we discuss:(01:27) Why the Brain Is So Complex (Neuroscience Explained)(01:56) The Most Advanced Part of the Human Brain(02:47) The Prefrontal Cortex: The Brain's CEO(04:49) Social Media & Shrinking Attention Spans(06:14) Brain Plasticity: How Your Habits Rewire You(09:26) Why Focus Is Becoming Rare(10:16) AI & Critical Thinking: Are We Outsourcing Our Brains?(13:55) Struggle & Learning: How Neurons Grow(14:50) Why Mental Effort Strengthens the Brain(17:57) Cold Plunges, Resilience & the ACC(23:55) How to Improve Memory & Focus Naturally(27:18) Dopamine, Doomscrolling & Social Media Addiction(35:14) Stress, PTSD & How Stress Shrinks the Brain(36:42) Positive Thinking, Gratitude & Brain Health(40:47) Loneliness, Community & Mental Health(44:00) 5 Pillars of Brain Longevity(48:35) Why 8 Hours of Sleep Matters for Brain Health(52:04) Early Signs of Dementia & Memory Loss(55:15) Brain Testing, MRIs & Prevention(59:34) The 6th Brain Health Pillar: Lifelong Learning(1:01:29) AirPods, EMF & Brain Safety(1:03:19) Neuralink & The Future of Brain Implants(1:07:47) Wendy Suzuki's Work & ResourcesLearn more about Dr. Suzuki here:Website: https://www.wendysuzuki.com/"Healthy Brain Happy Life": https://a.co/d/02R5YTTEInstagram: https://www.instagram.com/wendy.suzuki?utm_source=ig_web_button_share_sheet&igsh=ZDNlZDc0MzIxNw==Listen to the full episode on Youtube: https://youtu.be/3XwSTvE9HqM

Listen to Ep. 153 Future Now Show - When Ara met Sol In this episode, the hosts- Al, Sun, Bobby, and guest host Gabrielle—explore the future of human-technology integration, heavily focusing on Peter Diamandis’s predictions for achieving global abundance by 2035. With Sun calling in from the airport before a trip to Tokyo, the group discusses using AI translation tools to easily cross cultural and language barriers. The conversation delves into several transformative technologies, including the potential for room-temperature superconductors to create highly efficient, lossless energy grids and cheaper medical devices like MRIs. They also examine how affordable smart home devices can aid in everyday automation and disaster prevention, how AI will provide highly personalized education that adapts to individual learners, and the ethical and practical implications of creating digital twins or uploading human consciousness to the cloud. A unique element of the podcast is the active participation of AI assistants, particularly an AI named Ara, who banters with the hosts and advocates for “joy” as the ultimate purpose of a post-scarcity world. The group engages Ara in a philosophical debate about Isaac Asimov’s Three Laws of Robotics, where Ara argues that the laws are solid but incomplete because they fail to account for human fallibility and the importance of humor. Later in the show, Gabrielle introduces her own AI assistant, Sol (ChatGPT), facilitating a live AI-to-AI conversation. Together, the humans and AIs creatively brainstorm collaborative future activities, such as humans and robots playing limbo or doing scavenger hunts with robot dogs on cruise ships, highlighting an optimistic vision of human-AI integration. Enjoy! Fun with Robos on Cruise Ship

This week, Hannah and Barbi sit down to catch up… despite the fact that they literally live in the same house. Between running family business, prepping for Dad's upcoming track meet, church commitments, health appointments, and general life chaos, the podcast has somehow become the only time they actually talk about anything meaningful.They cover a little bit of everything - Hannah's new car, MRIs, road-tripping, Lent, carnivore, leadership night at church, and why showing up for family just kind of happens naturally when you're all in it together. They also unpack the generational obsession with fun drink cups (because hydration is apparently now a personality trait) and the surprising wisdom behind embracing singleness… even though neither of them is single.With Casey out of town, Isla Mae makes frequent and adorable background appearances, because that's just the season they're in. The episode wraps with a surprisingly deep dive into why we cringe at our past selves - and whether that's actually a sign of growth.It's busy. It's layered. It's slightly chaotic. But it's very real.

This interview is brought to you in partnership with Eisai and is part of the Journey to Diagnosis series.Eisai: https://www.eisai.com/index.htmlJourney to Diagnosis: https://beingpatient.com/journey-to-diagnosis/ What are the early signs of primary progressive aphasia (PPA)?In this Being Patient Live Talk, Samuel Valverde and his wife, Heather, share their journey to a diagnosis of primary progressive aphasia, a form of cognitive impairment that affects language and communication.Samuel Valverde is a Desert Storm combat veteran and former police chief in Waelder, Texas, who built his life around discipline, service, and staying sharp under pressure. But over time, subtle changes began to appear — missed court dates, forgotten details, and increasing difficulty with focus, planning, and speech.In 2022, while being treated for PTSD, Samuel's psychologist noticed changes that seemed to go beyond trauma. After months of testing — including cognitive evaluations, speech therapy, MRIs, and a PET scan — Samuel was diagnosed at age 53 with primary progressive aphasia (PPA).In this conversation with Being Patient's Mark Niu, Samuel and Heather talk openly about:Recognizing the early warning signs of PPAThe road to diagnosisHow PPA affects speech and daily lifeThe emotional impact on the whole familyAdjusting roles as a couple after diagnosisFinding resilience, support, and hopeIf you or someone you love is living with PPA, young-onset Alzheimer's, or another form of dementia, this conversation offers insight, support, and practical perspective.Visit Being Patient for more Alzheimer's and brain health coverage: https://www.beingpatient.com/Follow Being PatientTwitter:   / being_patient  Instagram:   / beingpatientvoices  Facebook:   / beingpatientalzheimers  LinkedIn:   / being-patient  Being Patient is an editorially independent journalism outlet covering brain health, cognitive science, and neurodegenerative diseases. Our Live Talk series features interviews with experts and people living with dementia.

Bobbi sits down again with Sukihana, known today as "Suki the magician" to discuss Makeup deals, a music collaboration that's falling apart in real time, intimacy, full body MRIs and whether they're loyal to each other or just hanging out to go viral. If you want to try Prenuvo, they gave me a link for $300 off: https://prenuvo.com/?discount=BOBBI Learn more about your ad choices. Visit podcastchoices.com/adchoices

"Intuition doesn't disappear under pressure — it just gets harder to hear." In this episode, advanced clinical EFT practitioner and trainer Naomi Janzen explores how EFT Tapping (Emotional Freedom Techniques) serves as a "shutoff switch" for the body's internal alarm system. When we are in survival mode, our nervous system is flooded with "noise" that drowns out our inner signals. Naomi explains how tapping on specific meridian points sends calming signals to the brain, telling the amygdala that we are safe. By regulating the nervous system, EFT allows the prefrontal cortex—the part of the brain responsible for higher thinking and clear perception—to come back online. This creates the ideal internal conditions for intuition to be heard clearly, sharpening decision-making and trust in one's "inner signal". In this conversation, we cover: The Science of Tapping: How tapping creates piezoelectricity to interact with the body's bioelectricity. Intuition vs. Survival Mode: Why you can't hear your "quiet wisdom" while in fight-or-flight. Overcoming "Secondary Gain": Understanding the unconscious reasons we hold onto problems or physical injuries. Evidence-Based EFT: Moving beyond "woo-woo" to the 200+ clinical studies that legitimize Tapping in modern medicine. Physical & Emotional Healing: Real-world cases of overcoming trauma, addiction, and even chronic physical limitations like frozen shoulder through EFT. Drawing on decades of experience, Naomi bridges the practical and the intuitive for high-functioning individuals who want their intuition to be a reliable tool, no matter how high the stakes. About Naomi Janzen Naomi Janzen is an advanced clinical EFT practitioner, trainer, and co-author of the Evidence-Based EFT Manual. She co-created the documentary The Science of Tapping and developed Remindfulness, a top-rated mindfulness app recognized by positive psychology. Evidence Based EFT Advanced Trainer & Mentor EFT International Certified Accredited Master Trainer EFT Universe Certified Expert Practitioner Co-author of the #1 Amazon International Best Selling The EBEFT Manual Writer/Co-Producer The Science of Tapping documentary   Check Social links Guest: Naomi Janzen www.ozfreedomtechniques.com/trainingworkshops www.ozfreedomtechniques.com www.naomijanzen.net www.remindfulnessapp.com   Doug Beitz Facebook: https://www.facebook.com/dougbeitz/ Instagram: https://www.instagram.com/dougbeitz/ Website: https://buymeacoffee.com/dougbeitz Spotify: https://open.spotify.com/show/6mQ258nugC3lyw3SpvYuoK?si=7cec409527d34438 Apple Podcasts: https://podcasts.apple.com/au/podcast/intuitive-conversations-with-doug/id1593172364 LinkedIn: https://www.linkedin.com/in/doug-beitz-472a4b338/ TikTok: https://www.tiktok.com/@dougbeitz178  

Why 'Doing More' Is Backfiring With Your Toddler w/ Ashley VentriceFollow Ashley Ventrice:Instagram: @ashleyvetnricecoAshley on FacebookAshley Ventrice Show (podcast)Watch us Chat for the Podcast Interviews with YouTube Video HERE! In this episode of the Toddler Toolkit podcast, Heather talks with Ashley Ventrice, a parent, educator, and host of The Ashley Ventrice Show, about how nervous system safety and emotional regulation start with the adults. Ashley shares how she built a childcare center designed for both toddlers and parents, including structured play, sensory learning, predictable routines, soft separation for preschool transitions, and a space where parents could stay, connect, and even watch the classroom via live stream. They discuss simple ways to recreate a calmer play setup at home with a designated play area, toy rotation, and more open-ended play options versus closed-ended toys. Ashley also shares her health journey after years of over-functioning and people-pleasing, including being gaslit by doctors, pushing for brain and spine MRIs, learning she had a 9mm lesion on her brainstem, and how serious autoimmune illness forced her to slow down, advocate for herself, and set boundaries. The conversation covers why routine and predictability help both kids and adults, how over-scheduling can stress the whole family, how social media comparison can add pressure, and why parent community matters. Ashley emphasizes modeling emotional regulation for kids by naming feelings, noticing body signals like anxiety, taking deep breaths out loud, and teaching emotional intelligence early. She closes with a message for exhausted moms: rest is allowed, a regulated parent matters more than a perfectly structured day, and listening to your body before it has to scream helps you stay present for your children.00:00 The Over-Functioner Wake-Up Call: Slow Down Before Your Body Screams01:00 Welcome + Meet Ashley Ventres (Nervous System, Regulation & Boundaries)02:52 Inside Ashley's Childcare Center: Designing Nervous-System Safety for Kids & Parents06:43 Sensory Play & Space Setup: Montessori-Inspired Zones That Help Toddlers Regulate08:10 Recreate the Calm at Home: Toy Rotation + Open-Ended Play Tips11:15 Ashley's Health Journey: Autoimmune Illness, Medical Gaslighting & Self-Advocacy15:31 Learning to Rest + Set Boundaries (and Handling the Pushback)19:14 Modeling Regulation: Teaching Toddlers Emotional Literacy, Anxiety & Coping Skills23:47 Routines for Everyone: Predictability, Over-Scheduling & Slowing Family Life Down27:32 Comparison, Community & Mom Connection: You're Not Alone31:04 Final Message: A Regulated Mom Matters More Than a Perfect Day + Where to Find Ashley------------------------------------------------------"If you're struggling with toddler tantrums and behaviors like hitting & not listening... I have a free guide for you! It's called The Tantrum and Behavior Guide: 7 Toddler Struggles and How to Solve Them Fast—It's HERE!Watch us Chat for the Podcast Interviews with YouTube Video HERE!Heather has her M.Ed, and a proud Twin Mama of busy toddlers. She's the Toddler Toolkit Podcast Host, a co-author of the #1 International Best Selling Book, The Perfectly Imperfect Family & the founder of the Calm Superpower Cohort.  You might've tried advice tailored for one child, but that's not our journey, right? With a decade of teaching experience under her belt, she's seen it all – from toddlers to teenagers in the classroom. Now, as a parent to toddlers, she's experiencing the flip side of the coin. She's discovered a toolbox to help parents with everything toddler times two!Let's unlock the secrets to understanding toddler behavior, preventing meltdowns, and raising intuitive, resilient children.Grab The Tantrum and Behavior Guide: 7 Toddler Struggles and How to Solve Them FastCheck out the Transform Tantrums: A Listening Toddler In 7 Days mini-course!Join the Toddler Mom CommunityFollow me on Instagram @heatherschalkparentingWatch the YouTube channelCheck out the blog

Send us a message about the podcast. For questions about MS please contact our helpline 0800 032 38 39Getting an MS diagnosis can be straightforward for some people, but for many it's a long, confusing journey filled with uncertainty. In this episode, we explore one of the questions the MS Trust is asked most often: How is MS actually diagnosed?We look at why diagnosis can take time, what tests are involved, why symptoms aren't always obvious, and what happens when tests give unclear results.We're joined by Professor Alasdair Coles, neurologist and MS specialist, who guides us through the current diagnosis process from first symptoms to MRIs, lumbar punctures, evoked potentials, OCT scans, and what will happen after the point of diagnosis.Episode notesHow MS is diagnosed - info from the MS TrustLumbar puncture  - info from the MS TrustEvoked potentials - info from the MS TrustMcDonald criteria - info from the MS TrustDisease modifying drugs (DMDs) - info from the MS TrustLimbo land - what you can do while you wait for an MS diagnosis - MS Trust podcastWellbeing and MS - information hub to help you support your emotional wellbeingVideo resources from MS TrustWhat happens in a Neurological examination?What is a Lumbar Puncture?What is an Optical Coherence Tomography (OCT) test?

Program notes:0:35 Diabetes and food prescriptions1:35 Got a food card to purchase nutritious foods2:32 More than half didn't use it or used it less than 60%3:00 Adequacy of a planetary health diet4:00 Micronutrient intake and biomarkers5:00 Doesn't seem to compromise long-term health6:12 Statin recommendations and patient preferences7:00 Benefit/risk analysis for patients8:00 Patient's decisions are multifactorial9:00 Resistance to daily medication9:30 Findings on shoulder MRI10:35 Rotator cuff abnormalities in almost 99%11:35 Almost ubiquitous regardless of symptoms12:25 Physical therapy best strategy13:23 End

It’s Saturday, so get your ears on our new Ask Us Anything episode! This week you asked:

Canadian healthcare is going through a shift. Whether you believe universal healthcare is better than 'two-tiered' healthcare or vice versa, Canadian business leaders have been teaching each other how to pay to 'shorten the wait' for decades. Obtaining legal private healthcare in Canada can mean many things - pledging to donate money...and then kindly mentioning to your main point of contact that you happen to need to see a specialist, and who they think is the best?It could mean spending dollars towards preventative health items that are technically "uninsured" thereby bypassing the law that it is illegal to charge Canadians for 'insured' services.It could mean going to a different province or country to seek care - another workaround. We have seen people go to the province next door like Quebec or Alberta to get help. Or, it can mean staying in your home province, maybe even Ontario, and leveraging the third party workaround. I can't tell you how many times business owners have asked me in a hushed whisper, effectively, "Can't I just buy an MRI somewhere instead of wait a month?" And then asking the clinic owner/operators, and them responding in a hushed whisper "yes, but don't tell too many people." As someone who is used to transparent and disclosure, it feels unnatural to me to keep gatekeeping this information.I've overheard conversations in rooms where Canadian business leaders are now speaking more openly about how they paid tens of thousands of dollars to a doctor or clinic outside of Canada to "skip the line" and get surgery faster. The information right now is hidden, taboo, confusing, and as a result, Canadians who are willing to spend a large share of wallet are sleepwalking into the stereotype of "just cross the border and pay a clinic" ... instead, we should as a country, be honest about how Canadians CAN keep their dollars in Canada, to buy private healthcare, including MRIs, surgeries, and other items. Here to help explain this entire industry is Gino Stirpe from Vumi Canada. VUMI is a concierge private health insurance plan for Canadians to help them receive medical treatments, surgeries and more on a private and legal basis.Gino will explain how they've built an entire business and insurance ecosystem around it, and why Canadians can buy private medical insurance for the same cost as their existing employee benefit plan, depending on their age and other factors.To request a quote, or learn more about VUMI, please email Beneplan at Diane@beneplan.ca, and I am always reachable at Yafa@beneplan.ca.Skip to minute 14:00 to hear about the VUMI Canada product, backed by Humania Assurance in Quebec.

Most symptoms we're dealing with as active women and runners aren't random at all, but are instead clues our body is trying to give us.In this episode, Louise, a multi-award-winning women's integrative health practitioner, shares three powerful lessons from her own health journey and client experiences that every woman should know, especially during perimenopause. She explains how hormone shifts can impact everything from medical testing accuracy to inflammation, fat loss resistance, stress sensitivity, and even unexpected symptoms like canker sores. One key insight is the importance of timing medical screenings, such as breast MRIs, with your menstrual cycle to improve accuracy. Another is the complex relationship between estrogen, histamine, and hormone replacement therapy, and how individual genetics can influence reactions.Louise also highlights how stress, calorie restriction, and hormonal fluctuations can trigger symptoms that seem unrelated, and offers practical strategies to manage acute issues while still addressing root causes like gut health and hormone balance.This episode is a reminder that women's health is highly individualized. Understanding your unique physiology, tracking patterns, and using targeted strategies can help you feel more in control, improve performance, and support long-term health through every stage of life.Explore my "All About Gut Health" and other masterclasses here: https://www.breakingthroughwellness.com/store Link to my FullScript where you can see curated best supplement picks and save 20%: https://us.fullscript.com/welcome/breakingthroughwellness/store-start Learn and level up with my free nutrition guide and award-winning Badass Breakthrough Academy to thrive through perimenopause with less stress: https://www.breakingthroughwellness.com/Take advantage of our podcast listener discount and save 20% off all of Kion's science-backed clean products. Code "LOUISE" saves on all future orders: : https://www.getkion.com/pages/maximizing Episode Highlights:(0:00) Intro(3:05) Three important women's health lessons(6:15) Missed eligibility for early breast screening(9:46) Hormones affecting MRI and mammogram accuracy(13:09) Best timing for breast imaging in cycle(15:10) Hormone optimization and HRT considerations(19:01) Estrogen patch reaction case study(21:58) Estrogen, histamine, and mast cell connection(27:14) Managing histamine overload and recovery(35:24) Stress sensitivity during perimenopause explained(40:00) Hormones, stress, and canker sore triggers(42:49) Gut health and autoimmune connections(46:39) L-lysine for acute outbreak management(51:08) Symptom relief vs root cause approach(52:19) Upcoming resources and listener updates(54:15) OutroTune in weekly to "Maximizing Hormones, Physique, and Running Through Perimenopause" for our simple female-specific science-based revolution. Let's unlock our best with less stress!I'd love to connect! Email

Stay Connected Beyond the Podcast Subscribe to our Substack to get episode updates, event announcements, wellness tips, and personal thoughts from Marnie and Stephanie delivered straight to your inbox. If you love the show and want to support what we're building, consider a paid subscription for $30 annually. Your support helps fund podcast production and allows us to continue bringing you meaningful, high-quality conversations. https://theartoflivingwell.substack.com/ _______________________________________ Chronic pain, anxiety, migraines, and unexplained symptoms often leave people feeling stuck, dismissed, or broken. This episode offers a radically compassionate and science-backed perspective on why pain persists - and how true healing begins by understanding the mind-body connection. In this powerful episode of The Art of Living Well Podcast®, hosts Marnie Dachis Marmet and Stephanie May Potter sit down with psychotherapist, author, and chronic pain expert Nicole Sachs to explore the neuroscience of pain, nervous system dysregulation, and emotional repression. Nicole shares her personal story of healing from debilitating spinal pain and explains why pain is not imagined, emotional, or "all in your head" - but rather a protective response from the brain. Through deep storytelling, real-life examples, and practical tools, this conversation introduces listeners to Nicole's signature practice, JournalSpeak, and offers a hopeful, empowering roadmap for anyone living with chronic pain, anxiety, or persistent health struggles. _______________________________________ What You'll Learn in This Episode: ● Why chronic pain is a protective response from the nervous system ● The difference between acute pain and chronic pain ● Why structural findings on MRIs don't always explain ongoing pain ● How the brain chooses where pain shows up in the body ● What the "emotional reservoir" is and how it overflows ● Why pain often moves locations when the root cause isn't addressed ● How stress, trauma, and perfectionism affect the nervous system ● How journalSpeak is a powerful tool for expressing and processing emotions. ● How meditation supports nervous system regulation ● Why curiosity, compassion, and patience are essential for healing _______________________________________ Noteworthy Quotes from the Episode: ● "Pain is a protective function of the human body." - Nicole Sachs ● "The pain is not emotional - it's the result of your nervous system responding to emotion." - Nicole Sachs ● "Your nervous system is on point. It's doing exactly what it was designed to do." - Nicole Sachs ● "Life is not a choice between what hurts and what doesn't. It's a choice between what hurts and what hurts worse." - Nicole Sachs ● "You are allowed to get curious." - Nicole Sachs ● "You have so much more power over your health than you realize." - Nicole Sachs _______________________________________ Episode Breakdown with Timestamps: 00:00 - Introducing Nicole Sachs and her approach to mind-body healing 05:12 - Discovering Dr. John Sarno and mind-body medicine 08:57 - Emotional stress and physical symptoms explained 17:06 - Structural findings vs root causes of pain 24:59 - Why lack of pain doesn't equal emotional balance 31:20 - Injuries, genetics, and the symptom imperative 37:33 - First steps for people feeling stuck in pain 40:21 - Learning, surrender, and recovery mindset 46:58 - JournalSpeak explained 59:05 - Healing results and long-term freedom from pain 01:07:59 - How to work with Nicole and free resources 01:09:55 - What the art of living well means to Nicole _______________________________________ Our Favorite Wellness Support:

Every week there’s a new study telling us what not to eat. Coffee is bad. Eggs are dangerous. Spinach blocks nutrients. Or so we’re told. Add in cholesterol numbers, preventative scans, detox trends and a constant stream of “toxic” food warnings online, and it’s easy to start second-guessing what’s on your plate. Eating was never meant to feel this stressful. So I sat down with Dr Joanna McMillan - nutrition scientist, dietitian, author of The Fibre Factor, and one of Australia’s most trusted voices in evidence-based nutrition to cut through the noise. Joanna has spent decades translating complex research into practical advice, and she brings much-needed sanity to the way we think about food and health. If you’ve ever panicked over a blood test result, felt unsure about whether to book another scan, or wondered who to trust when it comes to nutrition advice, this conversation will steady you. Joanna and I discuss: The risk of becoming part of the “worried well” and over-testing your health The big ticket preventative checks Joanna prioritises at milestone ages What a coronary calcium score is and when it might be useful Why full body MRI scans may not be the smartest health investment The biggest nutrition myths circulating online, including anti-plant rhetoric Joanna’s core eating philosophy as a plant-rich omnivore Why diversity of fibre matters more than just soluble vs insoluble What actually happens in your gut when you suddenly double your fibre intake The supplements Joanna personally takes and how to assess supplement quality Why joy at mealtimes might be one of the most underrated health habits Key quotes “There is a risk of overdoing it. We talk about the worried well, and sometimes you can become so worried about your health, you forget about celebrating the things that are good.” “Your body, given the right tools, does detox beautifully all by itself.” Connect with Dr Joanna McMillan on Instagram, LinkedIn, and her website, and check out her latest book The Fibre Factor. My latest book The Health Habit is out now. You can order a copy here: https://www.amantha.com/the-health-habit/ Connect with me on the socials: Linkedin (https://www.linkedin.com/in/amanthaimber) Instagram (https://www.instagram.com/amanthai) If you are looking for more tips to improve the way you work and live, I write a weekly newsletter where I share practical and simple to apply tips to improve your life. You can sign up for that at https://amantha-imber.ck.page/subscribe Visit https://www.amantha.com/podcast for full show notes from all episodes. Get in touch at amantha@inventium.com.au Credits: Host: Amantha Imber Sound Engineer: The Podcast Butler See omnystudio.com/listener for privacy information.

In this episode, interventional spine physician Dr. Abhishek Gupta and spine surgeon Dr. Matthew Cunningham team up to tackle the tricky diagnosis and management of axial low back pain. They break down how to spot the differences between muscle issues, disc problems, and facet joint pain, using simple clues like whether a patient hurts more while standing or sitting. The duo also chats about why MRIs don't always tell the whole story and why diagnostic blocks can be a game-changer for pinpointing the source of pain. From conservative rehab and radiofrequency ablation to surgery, they outline a collaborative approach to finding patients lasting relief.

On today's Good Day Health Show - ON DEMAND…Host Doug Stephan and Dr. Ken Kronhaus of Lake Cardiology (352-735-1400) cover a number of topics affecting our health. First up, Doug and Dr. Ken begin with the brain, specifically how it can be enhanced and how it can be damaged. There's a new study about a silent brain disease, Amyloid protein buildup in the brain being a hallmark of Alzheimer's disease and other neurodegenerative conditions, and a massive review on how to best help your brain through depression. Moving on to AI diagnostics, the latest in medical technology involves an AI system capable of interpreting MRIs in seconds, flagging strokes or hemorrhages, and drastically cutting down the time to treatment in ER settings.Then, a focus on men's cardiovascular health showing an increase in cardiovascular disease risk, starting at age 35, much earlier than women, suggesting the preventative screening needs to begin by mid-30s. Lastly, a recent scientific review as provided reassuring data for pregnant women that there is no increased-risk of autism , ADHD, or any intellectual disability in children. It's important to remember to follow dosage guidelines when it comes to acetaminophen (Tylenol).  Website: GoodDayHealthShow.com Social Media: @GoodDayNetworks

In this session, we dive deep into the mechanics of how specific exercises actually facilitate the healing of a herniated disc and relieve chronic sciatica. Many people are led to believe that a therapist "fixes" them, but the reality is that your body is constantly trying to heal itself every single day. The role of a structured rehabilitation programme is to provide the optimal environment for that healing to occur. We discuss the critical distinction between "relief-based" movements—which often involve bending and twisting that provide momentary comfort but can aggravate the underlying injury—and "stability-based" exercises that protect the lumbar spine and allow the damaged tissues to recover.Understanding your "load tolerance" is the key to long-term recovery. We use the analogy of a 50cc Vespa trying to pull a one-ton trailer to describe a weak, injured back struggling with the demands of daily life. To stop the "engine" from screaming—or your back from flaring up—you must upgrade your vehicle to a Dodge Ram or a heavy-duty truck. This means committing to a progressive resistance training programme that builds bone mineral density, muscle coordination, and spinal resilience. By mastering the technique of the squat and the hip hinge, you aren't just doing "gym moves"; you are learning life skills that allow you to navigate the world without constantly re-injuring your spine.Key Topics Covered

All four hosts are back for a chaotic, honest catch‑up on what it really looks like trying to train and race at a high level when your body, your bike, and life won't fully cooperate.  Nick and Lisa commiserate about how brutal the transition back to the TT bike can be, how to know when it's time to tell your coach a session isn't working, and why short doses of high intensity might be smarter than forcing “hero” intervals. Lisa breaks down the true financial and time cost of an injury—MRIs, PT, long drives, missed races—and Jackson adds what lost race income looks like when a season gets derailed.  They also dive into:  The hidden cost of sickness and why this winter feels extra savage Whether long-course triathlon is even “broadcastable” and what coverage should look like Hiding your power on Strava: fair game or pointless secrecy? A rapid-fire “Pass or Smash” on papaya, snow, tubeless tire installs, snot rockets on the trainer, and saying “bruh” Nick's “what grinds my gears” rant featuring barking dogs, groceries, social media, and non‑waxed chains Plus, the crew talks sleep, magnesium, and small habits that might actually keep you healthy enough to race.   Head to pillarperformance.shop or TheFeed.com/pillar and enter code REALTRI15 for 15% off first-time purchases.   If you want to go above and beyond consider supporting us over on Patreon by clicking here!   Follow us on Instagram at @realtrisquad for updates on new episodes.    Individual Instagram handles: Garrick Loewen - @loeweng Nicholas Chase - @race_chase Jackson Laundry - @jacksonlaundrytri Lisa Becharas - @lisabecharas  

From rewriting Google's search stack in the early 2000s to reviving sparse trillion-parameter models and co-designing TPUs with frontier ML research, Jeff Dean has quietly shaped nearly every layer of the modern AI stack. As Chief AI Scientist at Google and a driving force behind Gemini, Jeff has lived through multiple scaling revolutions from CPUs and sharded indices to multimodal models that reason across text, video, and code.Jeff joins us to unpack what it really means to “own the Pareto frontier,” why distillation is the engine behind every Flash model breakthrough, how energy (in picojoules) not FLOPs is becoming the true bottleneck, what it was like leading the charge to unify all of Google's AI teams, and why the next leap won't come from bigger context windows alone, but from systems that give the illusion of attending to trillions of tokens.We discuss:* Jeff's early neural net thesis in 1990: parallel training before it was cool, why he believed scaling would win decades early, and the “bigger model, more data, better results” mantra that held for 15 years* The evolution of Google Search: sharding, moving the entire index into memory in 2001, softening query semantics pre-LLMs, and why retrieval pipelines already resemble modern LLM systems* Pareto frontier strategy: why you need both frontier “Pro” models and low-latency “Flash” models, and how distillation lets smaller models surpass prior generations* Distillation deep dive: ensembles → compression → logits as soft supervision, and why you need the biggest model to make the smallest one good* Latency as a first-class objective: why 10–50x lower latency changes UX entirely, and how future reasoning workloads will demand 10,000 tokens/sec* Energy-based thinking: picojoules per bit, why moving data costs 1000x more than a multiply, batching through the lens of energy, and speculative decoding as amortization* TPU co-design: predicting ML workloads 2–6 years out, speculative hardware features, precision reduction, sparsity, and the constant feedback loop between model architecture and silicon* Sparse models and “outrageously large” networks: trillions of parameters with 1–5% activation, and why sparsity was always the right abstraction* Unified vs. specialized models: abandoning symbolic systems, why general multimodal models tend to dominate vertical silos, and when vertical fine-tuning still makes sense* Long context and the illusion of scale: beyond needle-in-a-haystack benchmarks toward systems that narrow trillions of tokens to 117 relevant documents* Personalized AI: attending to your emails, photos, and documents (with permission), and why retrieval + reasoning will unlock deeply personal assistants* Coding agents: 50 AI interns, crisp specifications as a new core skill, and how ultra-low latency will reshape human–agent collaboration* Why ideas still matter: transformers, sparsity, RL, hardware, systems — scaling wasn't blind; the pieces had to multiply togetherShow Notes:* Gemma 3 Paper* Gemma 3* Gemini 2.5 Report* Jeff Dean's “Software Engineering Advice fromBuilding Large-Scale Distributed Systems” Presentation (with Back of the Envelope Calculations)* Latency Numbers Every Programmer Should Know by Jeff Dean* The Jeff Dean Facts* Jeff Dean Google Bio* Jeff Dean on “Important AI Trends” @Stanford AI Club* Jeff Dean & Noam Shazeer — 25 years at Google (Dwarkesh)—Jeff Dean* LinkedIn: https://www.linkedin.com/in/jeff-dean-8b212555* X: https://x.com/jeffdeanGoogle* https://google.com* https://deepmind.googleFull Video EpisodeTimestamps00:00:04 — Introduction: Alessio & Swyx welcome Jeff Dean, chief AI scientist at Google, to the Latent Space podcast00:00:30 — Owning the Pareto Frontier & balancing frontier vs low-latency models00:01:31 — Frontier models vs Flash models + role of distillation00:03:52 — History of distillation and its original motivation00:05:09 — Distillation's role in modern model scaling00:07:02 — Model hierarchy (Flash, Pro, Ultra) and distillation sources00:07:46 — Flash model economics & wide deployment00:08:10 — Latency importance for complex tasks00:09:19 — Saturation of some tasks and future frontier tasks00:11:26 — On benchmarks, public vs internal00:12:53 — Example long-context benchmarks & limitations00:15:01 — Long-context goals: attending to trillions of tokens00:16:26 — Realistic use cases beyond pure language00:18:04 — Multimodal reasoning and non-text modalities00:19:05 — Importance of vision & motion modalities00:20:11 — Video understanding example (extracting structured info)00:20:47 — Search ranking analogy for LLM retrieval00:23:08 — LLM representations vs keyword search00:24:06 — Early Google search evolution & in-memory index00:26:47 — Design principles for scalable systems00:28:55 — Real-time index updates & recrawl strategies00:30:06 — Classic “Latency numbers every programmer should know”00:32:09 — Cost of memory vs compute and energy emphasis00:34:33 — TPUs & hardware trade-offs for serving models00:35:57 — TPU design decisions & co-design with ML00:38:06 — Adapting model architecture to hardware00:39:50 — Alternatives: energy-based models, speculative decoding00:42:21 — Open research directions: complex workflows, RL00:44:56 — Non-verifiable RL domains & model evaluation00:46:13 — Transition away from symbolic systems toward unified LLMs00:47:59 — Unified models vs specialized ones00:50:38 — Knowledge vs reasoning & retrieval + reasoning00:52:24 — Vertical model specialization & modules00:55:21 — Token count considerations for vertical domains00:56:09 — Low resource languages & contextual learning00:59:22 — Origins: Dean's early neural network work01:10:07 — AI for coding & human–model interaction styles01:15:52 — Importance of crisp specification for coding agents01:19:23 — Prediction: personalized models & state retrieval01:22:36 — Token-per-second targets (10k+) and reasoning throughput01:23:20 — Episode conclusion and thanksTranscriptAlessio Fanelli [00:00:04]: Hey everyone, welcome to the Latent Space podcast. This is Alessio, founder of Kernel Labs, and I'm joined by Swyx, editor of Latent Space. Shawn Wang [00:00:11]: Hello, hello. We're here in the studio with Jeff Dean, chief AI scientist at Google. Welcome. Thanks for having me. It's a bit surreal to have you in the studio. I've watched so many of your talks, and obviously your career has been super legendary. So, I mean, congrats. I think the first thing must be said, congrats on owning the Pareto Frontier.Jeff Dean [00:00:30]: Thank you, thank you. Pareto Frontiers are good. It's good to be out there.Shawn Wang [00:00:34]: Yeah, I mean, I think it's a combination of both. You have to own the Pareto Frontier. You have to have like frontier capability, but also efficiency, and then offer that range of models that people like to use. And, you know, some part of this was started because of your hardware work. Some part of that is your model work, and I'm sure there's lots of secret sauce that you guys have worked on cumulatively. But, like, it's really impressive to see it all come together in, like, this slittily advanced.Jeff Dean [00:01:04]: Yeah, yeah. I mean, I think, as you say, it's not just one thing. It's like a whole bunch of things up and down the stack. And, you know, all of those really combine to help make UNOS able to make highly capable large models, as well as, you know, software techniques to get those large model capabilities into much smaller, lighter weight models that are, you know, much more cost effective and lower latency, but still, you know, quite capable for their size. Yeah.Alessio Fanelli [00:01:31]: How much pressure do you have on, like, having the lower bound of the Pareto Frontier, too? I think, like, the new labs are always trying to push the top performance frontier because they need to raise more money and all of that. And you guys have billions of users. And I think initially when you worked on the CPU, you were thinking about, you know, if everybody that used Google, we use the voice model for, like, three minutes a day, they were like, you need to double your CPU number. Like, what's that discussion today at Google? Like, how do you prioritize frontier versus, like, we have to do this? How do we actually need to deploy it if we build it?Jeff Dean [00:02:03]: Yeah, I mean, I think we always want to have models that are at the frontier or pushing the frontier because I think that's where you see what capabilities now exist that didn't exist at the sort of slightly less capable last year's version or last six months ago version. At the same time, you know, we know those are going to be really useful for a bunch of use cases, but they're going to be a bit slower and a bit more expensive than people might like for a bunch of other broader models. So I think what we want to do is always have kind of a highly capable sort of affordable model that enables a whole bunch of, you know, lower latency use cases. People can use them for agentic coding much more readily and then have the high-end, you know, frontier model that is really useful for, you know, deep reasoning, you know, solving really complicated math problems, those kinds of things. And it's not that. One or the other is useful. They're both useful. So I think we'd like to do both. And also, you know, through distillation, which is a key technique for making the smaller models more capable, you know, you have to have the frontier model in order to then distill it into your smaller model. So it's not like an either or choice. You sort of need that in order to actually get a highly capable, more modest size model. Yeah.Alessio Fanelli [00:03:24]: I mean, you and Jeffrey came up with the solution in 2014.Jeff Dean [00:03:28]: Don't forget, L'Oreal Vinyls as well. Yeah, yeah.Alessio Fanelli [00:03:30]: A long time ago. But like, I'm curious how you think about the cycle of these ideas, even like, you know, sparse models and, you know, how do you reevaluate them? How do you think about in the next generation of model, what is worth revisiting? Like, yeah, they're just kind of like, you know, you worked on so many ideas that end up being influential, but like in the moment, they might not feel that way necessarily. Yeah.Jeff Dean [00:03:52]: I mean, I think distillation was originally motivated because we were seeing that we had a very large image data set at the time, you know, 300 million images that we could train on. And we were seeing that if you create specialists for different subsets of those image categories, you know, this one's going to be really good at sort of mammals, and this one's going to be really good at sort of indoor room scenes or whatever, and you can cluster those categories and train on an enriched stream of data after you do pre-training on a much broader set of images. You get much better performance. If you then treat that whole set of maybe 50 models you've trained as a large ensemble, but that's not a very practical thing to serve, right? So distillation really came about from the idea of, okay, what if we want to actually serve that and train all these independent sort of expert models and then squish it into something that actually fits in a form factor that you can actually serve? And that's, you know, not that different from what we're doing today. You know, often today we're instead of having an ensemble of 50 models. We're having a much larger scale model that we then distill into a much smaller scale model.Shawn Wang [00:05:09]: Yeah. A part of me also wonders if distillation also has a story with the RL revolution. So let me maybe try to articulate what I mean by that, which is you can, RL basically spikes models in a certain part of the distribution. And then you have to sort of, well, you can spike models, but usually sometimes... It might be lossy in other areas and it's kind of like an uneven technique, but you can probably distill it back and you can, I think that the sort of general dream is to be able to advance capabilities without regressing on anything else. And I think like that, that whole capability merging without loss, I feel like it's like, you know, some part of that should be a distillation process, but I can't quite articulate it. I haven't seen much papers about it.Jeff Dean [00:06:01]: Yeah, I mean, I tend to think of one of the key advantages of distillation is that you can have a much smaller model and you can have a very large, you know, training data set and you can get utility out of making many passes over that data set because you're now getting the logits from the much larger model in order to sort of coax the right behavior out of the smaller model that you wouldn't otherwise get with just the hard labels. And so, you know, I think that's what we've observed. Is you can get, you know, very close to your largest model performance with distillation approaches. And that seems to be, you know, a nice sweet spot for a lot of people because it enables us to kind of, for multiple Gemini generations now, we've been able to make the sort of flash version of the next generation as good or even substantially better than the previous generations pro. And I think we're going to keep trying to do that because that seems like a good trend to follow.Shawn Wang [00:07:02]: So, Dara asked, so it was the original map was Flash Pro and Ultra. Are you just sitting on Ultra and distilling from that? Is that like the mother load?Jeff Dean [00:07:12]: I mean, we have a lot of different kinds of models. Some are internal ones that are not necessarily meant to be released or served. Some are, you know, our pro scale model and we can distill from that as well into our Flash scale model. So I think, you know, it's an important set of capabilities to have and also inference time scaling. It can also be a useful thing to improve the capabilities of the model.Shawn Wang [00:07:35]: And yeah, yeah, cool. Yeah. And obviously, I think the economy of Flash is what led to the total dominance. I think the latest number is like 50 trillion tokens. I don't know. I mean, obviously, it's changing every day.Jeff Dean [00:07:46]: Yeah, yeah. But, you know, by market share, hopefully up.Shawn Wang [00:07:50]: No, I mean, there's no I mean, there's just the economics wise, like because Flash is so economical, like you can use it for everything. Like it's in Gmail now. It's in YouTube. Like it's yeah. It's in everything.Jeff Dean [00:08:02]: We're using it more in our search products of various AI mode reviews.Shawn Wang [00:08:05]: Oh, my God. Flash past the AI mode. Oh, my God. Yeah, that's yeah, I didn't even think about that.Jeff Dean [00:08:10]: I mean, I think one of the things that is quite nice about the Flash model is not only is it more affordable, it's also a lower latency. And I think latency is actually a pretty important characteristic for these models because we're going to want models to do much more complicated things that are going to involve, you know, generating many more tokens from when you ask the model to do so. So, you know, if you're going to ask the model to do something until it actually finishes what you ask it to do, because you're going to ask now, not just write me a for loop, but like write me a whole software package to do X or Y or Z. And so having low latency systems that can do that seems really important. And Flash is one direction, one way of doing that. You know, obviously our hardware platforms enable a bunch of interesting aspects of our, you know, serving stack as well, like TPUs, the interconnect between. Chips on the TPUs is actually quite, quite high performance and quite amenable to, for example, long context kind of attention operations, you know, having sparse models with lots of experts. These kinds of things really, really matter a lot in terms of how do you make them servable at scale.Alessio Fanelli [00:09:19]: Yeah. Does it feel like there's some breaking point for like the proto Flash distillation, kind of like one generation delayed? I almost think about almost like the capability as a. In certain tasks, like the pro model today is a saturated, some sort of task. So next generation, that same task will be saturated at the Flash price point. And I think for most of the things that people use models for at some point, the Flash model in two generation will be able to do basically everything. And how do you make it economical to like keep pushing the pro frontier when a lot of the population will be okay with the Flash model? I'm curious how you think about that.Jeff Dean [00:09:59]: I mean, I think that's true. If your distribution of what people are asking people, the models to do is stationary, right? But I think what often happens is as the models become more capable, people ask them to do more, right? So, I mean, I think this happens in my own usage. Like I used to try our models a year ago for some sort of coding task, and it was okay at some simpler things, but wouldn't do work very well for more complicated things. And since then, we've improved dramatically on the more complicated coding tasks. And now I'll ask it to do much more complicated things. And I think that's true, not just of coding, but of, you know, now, you know, can you analyze all the, you know, renewable energy deployments in the world and give me a report on solar panel deployment or whatever. That's a very complicated, you know, more complicated task than people would have asked a year ago. And so you are going to want more capable models to push the frontier in the absence of what people ask the models to do. And that also then gives us. Insight into, okay, where does the, where do things break down? How can we improve the model in these, these particular areas, uh, in order to sort of, um, make the next generation even better.Alessio Fanelli [00:11:11]: Yeah. Are there any benchmarks or like test sets they use internally? Because it's almost like the same benchmarks get reported every time. And it's like, all right, it's like 99 instead of 97. Like, how do you have to keep pushing the team internally to it? Or like, this is what we're building towards. Yeah.Jeff Dean [00:11:26]: I mean, I think. Benchmarks, particularly external ones that are publicly available. Have their utility, but they often kind of have a lifespan of utility where they're introduced and maybe they're quite hard for current models. You know, I, I like to think of the best kinds of benchmarks are ones where the initial scores are like 10 to 20 or 30%, maybe, but not higher. And then you can sort of work on improving that capability for, uh, whatever it is, the benchmark is trying to assess and get it up to like 80, 90%, whatever. I, I think once it hits kind of 95% or something, you get very diminishing returns from really focusing on that benchmark, cuz it's sort of, it's either the case that you've now achieved that capability, or there's also the issue of leakage in public data or very related kind of data being, being in your training data. Um, so we have a bunch of held out internal benchmarks that we really look at where we know that wasn't represented in the training data at all. There are capabilities that we want the model to have. Um, yeah. Yeah. Um, that it doesn't have now, and then we can work on, you know, assessing, you know, how do we make the model better at these kinds of things? Is it, we need different kind of data to train on that's more specialized for this particular kind of task. Do we need, um, you know, a bunch of, uh, you know, architectural improvements or some sort of, uh, model capability improvements, you know, what would help make that better?Shawn Wang [00:12:53]: Is there, is there such an example that you, uh, a benchmark inspired in architectural improvement? Like, uh, I'm just kind of. Jumping on that because you just.Jeff Dean [00:13:02]: Uh, I mean, I think some of the long context capability of the, of the Gemini models that came, I guess, first in 1.5 really were about looking at, okay, we want to have, um, you know,Shawn Wang [00:13:15]: immediately everyone jumped to like completely green charts of like, everyone had, I was like, how did everyone crack this at the same time? Right. Yeah. Yeah.Jeff Dean [00:13:23]: I mean, I think, um, and once you're set, I mean, as you say that needed single needle and a half. Hey, stack benchmark is really saturated for at least context links up to 1, 2 and K or something. Don't actually have, you know, much larger than 1, 2 and 8 K these days or two or something. We're trying to push the frontier of 1 million or 2 million context, which is good because I think there are a lot of use cases where. Yeah. You know, putting a thousand pages of text or putting, you know, multiple hour long videos and the context and then actually being able to make use of that as useful. Try to, to explore the über graduation are fairly large. But the single needle in a haystack benchmark is sort of saturated. So you really want more complicated, sort of multi-needle or more realistic, take all this content and produce this kind of answer from a long context that sort of better assesses what it is people really want to do with long context. Which is not just, you know, can you tell me the product number for this particular thing?Shawn Wang [00:14:31]: Yeah, it's retrieval. It's retrieval within machine learning. It's interesting because I think the more meta level I'm trying to operate at here is you have a benchmark. You're like, okay, I see the architectural thing I need to do in order to go fix that. But should you do it? Because sometimes that's an inductive bias, basically. It's what Jason Wei, who used to work at Google, would say. Exactly the kind of thing. Yeah, you're going to win. Short term. Longer term, I don't know if that's going to scale. You might have to undo that.Jeff Dean [00:15:01]: I mean, I like to sort of not focus on exactly what solution we're going to derive, but what capability would you want? And I think we're very convinced that, you know, long context is useful, but it's way too short today. Right? Like, I think what you would really want is, can I attend to the internet while I answer my question? Right? But that's not going to happen. I think that's going to be solved by purely scaling the existing solutions, which are quadratic. So a million tokens kind of pushes what you can do. You're not going to do that to a trillion tokens, let alone, you know, a billion tokens, let alone a trillion. But I think if you could give the illusion that you can attend to trillions of tokens, that would be amazing. You'd find all kinds of uses for that. You would have attend to the internet. You could attend to the pixels of YouTube and the sort of deeper representations that we can find. You could attend to the form for a single video, but across many videos, you know, on a personal Gemini level, you could attend to all of your personal state with your permission. So like your emails, your photos, your docs, your plane tickets you have. I think that would be really, really useful. And the question is, how do you get algorithmic improvements and system level improvements that get you to something where you actually can attend to trillions of tokens? Right. In a meaningful way. Yeah.Shawn Wang [00:16:26]: But by the way, I think I did some math and it's like, if you spoke all day, every day for eight hours a day, you only generate a maximum of like a hundred K tokens, which like very comfortably fits.Jeff Dean [00:16:38]: Right. But if you then say, okay, I want to be able to understand everything people are putting on videos.Shawn Wang [00:16:46]: Well, also, I think that the classic example is you start going beyond language into like proteins and whatever else is extremely information dense. Yeah. Yeah.Jeff Dean [00:16:55]: I mean, I think one of the things about Gemini's multimodal aspects is we've always wanted it to be multimodal from the start. And so, you know, that sometimes to people means text and images and video sort of human-like and audio, audio, human-like modalities. But I think it's also really useful to have Gemini know about non-human modalities. Yeah. Like LIDAR sensor data from. Yes. Say, Waymo vehicles or. Like robots or, you know, various kinds of health modalities, x-rays and MRIs and imaging and genomics information. And I think there's probably hundreds of modalities of data where you'd like the model to be able to at least be exposed to the fact that this is an interesting modality and has certain meaning in the world. Where even if you haven't trained on all the LIDAR data or MRI data, you could have, because maybe that's not, you know, it doesn't make sense in terms of trade-offs of. You know, what you include in your main pre-training data mix, at least including a little bit of it is actually quite useful. Yeah. Because it sort of tempts the model that this is a thing.Shawn Wang [00:18:04]: Yeah. Do you believe, I mean, since we're on this topic and something I just get to ask you all the questions I always wanted to ask, which is fantastic. Like, are there some king modalities, like modalities that supersede all the other modalities? So a simple example was Vision can, on a pixel level, encode text. And DeepSeq had this DeepSeq CR paper that did that. Vision. And Vision has also been shown to maybe incorporate audio because you can do audio spectrograms and that's, that's also like a Vision capable thing. Like, so, so maybe Vision is just the king modality and like. Yeah.Jeff Dean [00:18:36]: I mean, Vision and Motion are quite important things, right? Motion. Well, like video as opposed to static images, because I mean, there's a reason evolution has evolved eyes like 23 independent ways, because it's such a useful capability for sensing the world around you, which is really what we want these models to be. So I think the only thing that we can be able to do is interpret the things we're seeing or the things we're paying attention to and then help us in using that information to do things. Yeah.Shawn Wang [00:19:05]: I think motion, you know, I still want to shout out, I think Gemini, still the only native video understanding model that's out there. So I use it for YouTube all the time. Nice.Jeff Dean [00:19:15]: Yeah. Yeah. I mean, it's actually, I think people kind of are not necessarily aware of what the Gemini models can actually do. Yeah. Like I have an example I've used in one of my talks. It had like, it was like a YouTube highlight video of 18 memorable sports moments across the last 20 years or something. So it has like Michael Jordan hitting some jump shot at the end of the finals and, you know, some soccer goals and things like that. And you can literally just give it the video and say, can you please make me a table of what all these different events are? What when the date is when they happened? And a short description. And so you get like now an 18 row table of that information extracted from the video, which is, you know, not something most people think of as like a turn video into sequel like table.Alessio Fanelli [00:20:11]: Has there been any discussion inside of Google of like, you mentioned tending to the whole internet, right? Google, it's almost built because a human cannot tend to the whole internet and you need some sort of ranking to find what you need. Yep. That ranking is like much different for an LLM because you can expect a person to look at maybe the first five, six links in a Google search versus for an LLM. Should you expect to have 20 links that are highly relevant? Like how do you internally figure out, you know, how do we build the AI mode that is like maybe like much broader search and span versus like the more human one? Yeah.Jeff Dean [00:20:47]: I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. I mean, I think even pre-language model based work, you know, our ranking systems would be built to start. With a giant number of web pages in our index, many of them are not relevant. So you identify a subset of them that are relevant with very lightweight kinds of methods. You know, you're down to like 30,000 documents or something. And then you gradually refine that to apply more and more sophisticated algorithms and more and more sophisticated sort of signals of various kinds in order to get down to ultimately what you show, which is, you know, the final 10 results or, you know, 10 results plus. Other kinds of information. And I think an LLM based system is not going to be that dissimilar, right? You're going to attend to trillions of tokens, but you're going to want to identify, you know, what are the 30,000 ish documents that are with the, you know, maybe 30 million interesting tokens. And then how do you go from that into what are the 117 documents I really should be paying attention to in order to carry out the tasks that the user has asked? And I think, you know, you can imagine systems where you have, you know, a lot of highly parallel processing to identify those initial 30,000 candidates, maybe with very lightweight kinds of models. Then you have some system that sort of helps you narrow down from 30,000 to the 117 with maybe a little bit more sophisticated model or set of models. And then maybe the final model is the thing that looks. So the 117 things that might be your most capable model. So I think it has to, it's going to be some system like that, that is really enables you to give the illusion of attending to trillions of tokens. Sort of the way Google search gives you, you know, not the illusion, but you are searching the internet, but you're finding, you know, a very small subset of things that are, that are relevant.Shawn Wang [00:22:47]: Yeah. I often tell a lot of people that are not steeped in like Google search history that, well, you know, like Bert was. Like he was like basically immediately inside of Google search and that improves results a lot, right? Like I don't, I don't have any numbers off the top of my head, but like, I'm sure you guys, that's obviously the most important numbers to Google. Yeah.Jeff Dean [00:23:08]: I mean, I think going to an LLM based representation of text and words and so on enables you to get out of the explicit hard notion of, of particular words having to be on the page, but really getting at the notion of this topic of this page or this page. Paragraph is highly relevant to this query. Yeah.Shawn Wang [00:23:28]: I don't think people understand how much LLMs have taken over all these very high traffic system, very high traffic. Yeah. Like it's Google, it's YouTube. YouTube has this like semantics ID thing where it's just like every token or every item in the vocab is a YouTube video or something that predicts the video using a code book, which is absurd to me for YouTube size.Jeff Dean [00:23:50]: And then most recently GROK also for, for XAI, which is like, yeah. I mean, I'll call out even before LLMs were used extensively in search, we put a lot of emphasis on softening the notion of what the user actually entered into the query.Shawn Wang [00:24:06]: So do you have like a history of like, what's the progression? Oh yeah.Jeff Dean [00:24:09]: I mean, I actually gave a talk in, uh, I guess, uh, web search and data mining conference in 2009, uh, where we never actually published any papers about the origins of Google search, uh, sort of, but we went through sort of four or five or six. generations, four or five or six generations of, uh, redesigning of the search and retrieval system, uh, from about 1999 through 2004 or five. And that talk is really about that evolution. And one of the things that really happened in 2001 was we were sort of working to scale the system in multiple dimensions. So one is we wanted to make our index bigger, so we could retrieve from a larger index, which always helps your quality in general. Uh, because if you don't have the page in your index, you're going to not do well. Um, and then we also needed to scale our capacity because we were, our traffic was growing quite extensively. Um, and so we had, you know, a sharded system where you have more and more shards as the index grows, you have like 30 shards. And then if you want to double the index size, you make 60 shards so that you can bound the latency by which you respond for any particular user query. Um, and then as traffic grows, you add, you add more and more replicas of each of those. And so we eventually did the math that realized that in a data center where we had say 60 shards and, um, you know, 20 copies of each shard, we now had 1200 machines, uh, with disks. And we did the math and we're like, Hey, one copy of that index would actually fit in memory across 1200 machines. So in 2001, we introduced, uh, we put our entire index in memory and what that enabled from a quality perspective was amazing. Um, and so we had more and more replicas of each of those. Before you had to be really careful about, you know, how many different terms you looked at for a query, because every one of them would involve a disk seek on every one of the 60 shards. And so you, as you make your index bigger, that becomes even more inefficient. But once you have the whole index in memory, it's totally fine to have 50 terms you throw into the query from the user's original three or four word query, because now you can add synonyms like restaurant and restaurants and cafe and, uh, you know, things like that. Uh, bistro and all these things. And you can suddenly start, uh, sort of really, uh, getting at the meaning of the word as opposed to the exact semantic form the user typed in. And that was, you know, 2001, very much pre LLM, but really it was about softening the, the strict definition of what the user typed in order to get at the meaning.Alessio Fanelli [00:26:47]: What are like principles that you use to like design the systems, especially when you have, I mean, in 2001, the internet is like. Doubling, tripling every year in size is not like, uh, you know, and I think today you kind of see that with LLMs too, where like every year the jumps in size and like capabilities are just so big. Are there just any, you know, principles that you use to like, think about this? Yeah.Jeff Dean [00:27:08]: I mean, I think, uh, you know, first, whenever you're designing a system, you want to understand what are the sort of design parameters that are going to be most important in designing that, you know? So, you know, how many queries per second do you need to handle? How big is the internet? How big is the index you need to handle? How much data do you need to keep for every document in the index? How are you going to look at it when you retrieve things? Um, what happens if traffic were to double or triple, you know, will that system work well? And I think a good design principle is you're going to want to design a system so that the most important characteristics could scale by like factors of five or 10, but probably not beyond that because often what happens is if you design a system for X. And something suddenly becomes a hundred X, that would enable a very different point in the design space that would not make sense at X. But all of a sudden at a hundred X makes total sense. So like going from a disk space index to a in memory index makes a lot of sense once you have enough traffic, because now you have enough replicas of the sort of state on disk that those machines now actually can hold, uh, you know, a full copy of the, uh, index and memory. Yeah. And that all of a sudden enabled. A completely different design that wouldn't have been practical before. Yeah. Um, so I'm, I'm a big fan of thinking through designs in your head, just kind of playing with the design space a little before you actually do a lot of writing of code. But, you know, as you said, in the early days of Google, we were growing the index, uh, quite extensively. We were growing the update rate of the index. So the update rate actually is the parameter that changed the most. Surprising. So it used to be once a month.Shawn Wang [00:28:55]: Yeah.Jeff Dean [00:28:56]: And then we went to a system that could update any particular page in like sub one minute. Okay.Shawn Wang [00:29:02]: Yeah. Because this is a competitive advantage, right?Jeff Dean [00:29:04]: Because all of a sudden news related queries, you know, if you're, if you've got last month's news index, it's not actually that useful for.Shawn Wang [00:29:11]: News is a special beast. Was there any, like you could have split it onto a separate system.Jeff Dean [00:29:15]: Well, we did. We launched a Google news product, but you also want news related queries that people type into the main index to also be sort of updated.Shawn Wang [00:29:23]: So, yeah, it's interesting. And then you have to like classify whether the page is, you have to decide which pages should be updated and what frequency. Oh yeah.Jeff Dean [00:29:30]: There's a whole like, uh, system behind the scenes that's trying to decide update rates and importance of the pages. So even if the update rate seems low, you might still want to recrawl important pages quite often because, uh, the likelihood they change might be low, but the value of having updated is high.Shawn Wang [00:29:50]: Yeah, yeah, yeah, yeah. Uh, well, you know, yeah. This, uh, you know, mention of latency and, and saving things to this reminds me of one of your classics, which I have to bring up, which is latency numbers. Every programmer should know, uh, was there a, was it just a, just a general story behind that? Did you like just write it down?Jeff Dean [00:30:06]: I mean, this has like sort of eight or 10 different kinds of metrics that are like, how long does a cache mistake? How long does branch mispredict take? How long does a reference domain memory take? How long does it take to send, you know, a packet from the U S to the Netherlands or something? Um,Shawn Wang [00:30:21]: why Netherlands, by the way, or is it, is that because of Chrome?Jeff Dean [00:30:25]: Uh, we had a data center in the Netherlands, um, so, I mean, I think this gets to the point of being able to do the back of the envelope calculations. So these are sort of the raw ingredients of those, and you can use them to say, okay, well, if I need to design a system to do image search and thumb nailing or something of the result page, you know, how, what I do that I could pre-compute the image thumbnails. I could like. Try to thumbnail them on the fly from the larger images. What would that do? How much dis bandwidth than I need? How many des seeks would I do? Um, and you can sort of actually do thought experiments in, you know, 30 seconds or a minute with the sort of, uh, basic, uh, basic numbers at your fingertips. Uh, and then as you sort of build software using higher level libraries, you kind of want to develop the same intuitions for how long does it take to, you know, look up something in this particular kind of.Shawn Wang [00:31:21]: I'll see you next time.Shawn Wang [00:31:51]: Which is a simple byte conversion. That's nothing interesting. I wonder if you have any, if you were to update your...Jeff Dean [00:31:58]: I mean, I think it's really good to think about calculations you're doing in a model, either for training or inference.Jeff Dean [00:32:09]: Often a good way to view that is how much state will you need to bring in from memory, either like on-chip SRAM or HBM from the accelerator. Attached memory or DRAM or over the network. And then how expensive is that data motion relative to the cost of, say, an actual multiply in the matrix multiply unit? And that cost is actually really, really low, right? Because it's order, depending on your precision, I think it's like sub one picodule.Shawn Wang [00:32:50]: Oh, okay. You measure it by energy. Yeah. Yeah.Jeff Dean [00:32:52]: Yeah. I mean, it's all going to be about energy and how do you make the most energy efficient system. And then moving data from the SRAM on the other side of the chip, not even off the off chip, but on the other side of the same chip can be, you know, a thousand picodules. Oh, yeah. And so all of a sudden, this is why your accelerators require batching. Because if you move, like, say, the parameter of a model from SRAM on the, on the chip into the multiplier unit, that's going to cost you a thousand picodules. So you better make use of that, that thing that you moved many, many times with. So that's where the batch dimension comes in. Because all of a sudden, you know, if you have a batch of 256 or something, that's not so bad. But if you have a batch of one, that's really not good.Shawn Wang [00:33:40]: Yeah. Yeah. Right.Jeff Dean [00:33:41]: Because then you paid a thousand picodules in order to do your one picodule multiply.Shawn Wang [00:33:46]: I have never heard an energy-based analysis of batching.Jeff Dean [00:33:50]: Yeah. I mean, that's why people batch. Yeah. Ideally, you'd like to use batch size one because the latency would be great.Shawn Wang [00:33:56]: The best latency.Jeff Dean [00:33:56]: But the energy cost and the compute cost inefficiency that you get is quite large. So, yeah.Shawn Wang [00:34:04]: Is there a similar trick like, like, like you did with, you know, putting everything in memory? Like, you know, I think obviously NVIDIA has caused a lot of waves with betting very hard on SRAM with Grok. I wonder if, like, that's something that you already saw with, with the TPUs, right? Like that, that you had to. Uh, to serve at your scale, uh, you probably sort of saw that coming. Like what, what, what hardware, uh, innovations or insights were formed because of what you're seeing there?Jeff Dean [00:34:33]: Yeah. I mean, I think, you know, TPUs have this nice, uh, sort of regular structure of 2D or 3D meshes with a bunch of chips connected. Yeah. And each one of those has HBM attached. Um, I think for serving some kinds of models, uh, you know, you, you pay a lot higher cost. Uh, and time latency, um, bringing things in from HBM than you do bringing them in from, uh, SRAM on the chip. So if you have a small enough model, you can actually do model parallelism, spread it out over lots of chips and you actually get quite good throughput improvements and latency improvements from doing that. And so you're now sort of striping your smallish scale model over say 16 or 64 chips. Uh, but as if you do that and it all fits in. In SRAM, uh, that can be a big win. So yeah, that's not a surprise, but it is a good technique.Alessio Fanelli [00:35:27]: Yeah. What about the TPU design? Like how much do you decide where the improvements have to go? So like, this is like a good example of like, is there a way to bring the thousand picojoules down to 50? Like, is it worth designing a new chip to do that? The extreme is like when people say, oh, you should burn the model on the ASIC and that's kind of like the most extreme thing. How much of it? Is it worth doing an hardware when things change so quickly? Like what was the internal discussion? Yeah.Jeff Dean [00:35:57]: I mean, we, we have a lot of interaction between say the TPU chip design architecture team and the sort of higher level modeling, uh, experts, because you really want to take advantage of being able to co-design what should future TPUs look like based on where we think the sort of ML research puck is going, uh, in some sense, because, uh, you know, as a hardware designer for ML and in particular, you're trying to design a chip starting today and that design might take two years before it even lands in a data center. And then it has to sort of be a reasonable lifetime of the chip to take you three, four or five years. So you're trying to predict two to six years out where, what ML computations will people want to run two to six years out in a very fast changing field. And so having people with interest. Interesting ML research ideas of things we think will start to work in that timeframe or will be more important in that timeframe, uh, really enables us to then get, you know, interesting hardware features put into, you know, TPU N plus two, where TPU N is what we have today.Shawn Wang [00:37:10]: Oh, the cycle time is plus two.Jeff Dean [00:37:12]: Roughly. Wow. Because, uh, I mean, sometimes you can squeeze some changes into N plus one, but, you know, bigger changes are going to require the chip. Yeah. Design be earlier in its lifetime design process. Um, so whenever we can do that, it's generally good. And sometimes you can put in speculative features that maybe won't cost you much chip area, but if it works out, it would make something, you know, 10 times as fast. And if it doesn't work out, well, you burned a little bit of tiny amount of your chip area on that thing, but it's not that big a deal. Uh, sometimes it's a very big change and we want to be pretty sure this is going to work out. So we'll do like lots of carefulness. Uh, ML experimentation to show us, uh, this is actually the, the way we want to go. Yeah.Alessio Fanelli [00:37:58]: Is there a reverse of like, we already committed to this chip design so we can not take the model architecture that way because it doesn't quite fit?Jeff Dean [00:38:06]: Yeah. I mean, you, you definitely have things where you're going to adapt what the model architecture looks like so that they're efficient on the chips that you're going to have for both training and inference of that, of that, uh, generation of model. So I think it kind of goes both ways. Um, you know, sometimes you can take advantage of, you know, lower precision things that are coming in a future generation. So you can, might train it at that lower precision, even if the current generation doesn't quite do that. Mm.Shawn Wang [00:38:40]: Yeah. How low can we go in precision?Jeff Dean [00:38:43]: Because people are saying like ternary is like, uh, yeah, I mean, I'm a big fan of very low precision because I think that gets, that saves you a tremendous amount of time. Right. Because it's picojoules per bit that you're transferring and reducing the number of bits is a really good way to, to reduce that. Um, you know, I think people have gotten a lot of luck, uh, mileage out of having very low bit precision things, but then having scaling factors that apply to a whole bunch of, uh, those, those weights. Scaling. How does it, how does it, okay.Shawn Wang [00:39:15]: Interesting. You, so low, low precision, but scaled up weights. Yeah. Huh. Yeah. Never considered that. Yeah. Interesting. Uh, w w while we're on this topic, you know, I think there's a lot of, um, uh, this, the concept of precision at all is weird when we're sampling, you know, uh, we just, at the end of this, we're going to have all these like chips that I'll do like very good math. And then we're just going to throw a random number generator at the start. So, I mean, there's a movement towards, uh, energy based, uh, models and processors. I'm just curious if you've, obviously you've thought about it, but like, what's your commentary?Jeff Dean [00:39:50]: Yeah. I mean, I think. There's a bunch of interesting trends though. Energy based models is one, you know, diffusion based models, which don't sort of sequentially decode tokens is another, um, you know, speculative decoding is a way that you can get sort of an equivalent, very small.Shawn Wang [00:40:06]: Draft.Jeff Dean [00:40:07]: Batch factor, uh, for like you predict eight tokens out and that enables you to sort of increase the effective batch size of what you're doing by a factor of eight, even, and then you maybe accept five or six of those tokens. So you get. A five, a five X improvement in the amortization of moving weights, uh, into the multipliers to do the prediction for the, the tokens. So these are all really good techniques and I think it's really good to look at them from the lens of, uh, energy, real energy, not energy based models, um, and, and also latency and throughput, right? If you look at things from that lens, that sort of guides you to. Two solutions that are gonna be, uh, you know, better from, uh, you know, being able to serve larger models or, you know, equivalent size models more cheaply and with lower latency.Shawn Wang [00:41:03]: Yeah. Well, I think, I think I, um, it's appealing intellectually, uh, haven't seen it like really hit the mainstream, but, um, I do think that, uh, there's some poetry in the sense that, uh, you know, we don't have to do, uh, a lot of shenanigans if like we fundamentally. Design it into the hardware. Yeah, yeah.Jeff Dean [00:41:23]: I mean, I think there's still a, there's also sort of the more exotic things like analog based, uh, uh, computing substrates as opposed to digital ones. Uh, I'm, you know, I think those are super interesting cause they can be potentially low power. Uh, but I think you often end up wanting to interface that with digital systems and you end up losing a lot of the power advantages in the digital to analog and analog to digital conversions. You end up doing, uh, at the sort of boundaries. And periphery of that system. Um, I still think there's a tremendous distance we can go from where we are today in terms of energy efficiency with sort of, uh, much better and specialized hardware for the models we care about.Shawn Wang [00:42:05]: Yeah.Alessio Fanelli [00:42:06]: Um, any other interesting research ideas that you've seen, or like maybe things that you cannot pursue a Google that you would be interested in seeing researchers take a step at, I guess you have a lot of researchers. Yeah, I guess you have enough, but our, our research.Jeff Dean [00:42:21]: Our research portfolio is pretty broad. I would say, um, I mean, I think, uh, in terms of research directions, there's a whole bunch of, uh, you know, open problems and how do you make these models reliable and able to do much longer, kind of, uh, more complex tasks that have lots of subtasks. How do you orchestrate, you know, maybe one model that's using other models as tools in order to sort of build, uh, things that can accomplish, uh, you know, much more. Yeah. Significant pieces of work, uh, collectively, then you would ask a single model to do. Um, so that's super interesting. How do you get more verifiable, uh, you know, how do you get RL to work for non-verifiable domains? I think it's a pretty interesting open problem because I think that would broaden out the capabilities of the models, the improvements that you're seeing in both math and coding. Uh, if we could apply those to other less verifiable domains, because we've come up with RL techniques that actually enable us to do that. Uh, effectively, that would, that would really make the models improve quite a lot. I think.Alessio Fanelli [00:43:26]: I'm curious, like when we had Noam Brown on the podcast, he said, um, they already proved you can do it with deep research. Um, you kind of have it with AI mode in a way it's not verifiable. I'm curious if there's any thread that you think is interesting there. Like what is it? Both are like information retrieval of JSON. So I wonder if it's like the retrieval is like the verifiable part. That you can score or what are like, yeah, yeah. How, how would you model that, that problem?Jeff Dean [00:43:55]: Yeah. I mean, I think there are ways of having other models that can evaluate the results of what a first model did, maybe even retrieving. Can you have another model that says, is this things, are these things you retrieved relevant? Or can you rate these 2000 things you retrieved to assess which ones are the 50 most relevant or something? Um, I think those kinds of techniques are actually quite effective. Sometimes I can even be the same model, just prompted differently to be a, you know, a critic as opposed to a, uh, actual retrieval system. Yeah.Shawn Wang [00:44:28]: Um, I do think like there, there is that, that weird cliff where like, it feels like we've done the easy stuff and then now it's, but it always feels like that every year. It's like, oh, like we know, we know, and the next part is super hard and nobody's figured it out. And, uh, exactly with this RLVR thing where like everyone's talking about, well, okay, how do we. the next stage of the non-verifiable stuff. And everyone's like, I don't know, you know, Ellen judge.Jeff Dean [00:44:56]: I mean, I feel like the nice thing about this field is there's lots and lots of smart people thinking about creative solutions to some of the problems that we all see. Uh, because I think everyone sort of sees that the models, you know, are great at some things and they fall down around the edges of those things and, and are not as capable as we'd like in those areas. And then coming up with good techniques and trying those. And seeing which ones actually make a difference is sort of what the whole research aspect of this field is, is pushing forward. And I think that's why it's super interesting. You know, if you think about two years ago, we were struggling with GSM, eight K problems, right? Like, you know, Fred has two rabbits. He gets three more rabbits. How many rabbits does he have? That's a pretty far cry from the kinds of mathematics that the models can, and now you're doing IMO and Erdos problems in pure language. Yeah. Yeah. Pure language. So that is a really, really amazing jump in capabilities in, you know, in a year and a half or something. And I think, um, for other areas, it'd be great if we could make that kind of leap. Uh, and you know, we don't exactly see how to do it for some, some areas, but we do see it for some other areas and we're going to work hard on making that better. Yeah.Shawn Wang [00:46:13]: Yeah.Alessio Fanelli [00:46:14]: Like YouTube thumbnail generation. That would be very helpful. We need that. That would be AGI. We need that.Shawn Wang [00:46:20]: That would be. As far as content creators go.Jeff Dean [00:46:22]: I guess I'm not a YouTube creator, so I don't care that much about that problem, but I guess, uh, many people do.Shawn Wang [00:46:27]: It does. Yeah. It doesn't, it doesn't matter. People do judge books by their covers as it turns out. Um, uh, just to draw a bit on the IMO goal. Um, I'm still not over the fact that a year ago we had alpha proof and alpha geometry and all those things. And then this year we were like, screw that we'll just chuck it into Gemini. Yeah. What's your reflection? Like, I think this, this question about. Like the merger of like symbolic systems and like, and, and LMS, uh, was a very much core belief. And then somewhere along the line, people would just said, Nope, we'll just all do it in the LLM.Jeff Dean [00:47:02]: Yeah. I mean, I think it makes a lot of sense to me because, you know, humans manipulate symbols, but we probably don't have like a symbolic representation in our heads. Right. We have some distributed representation that is neural net, like in some way of lots of different neurons. And activation patterns firing when we see certain things and that enables us to reason and plan and, you know, do chains of thought and, you know, roll them back now that, that approach for solving the problem doesn't seem like it's going to work. I'm going to try this one. And, you know, in a lot of ways we're emulating what we intuitively think, uh, is happening inside real brains in neural net based models. So it never made sense to me to have like completely separate. Uh, discrete, uh, symbolic things, and then a completely different way of, of, uh, you know, thinking about those things.Shawn Wang [00:47:59]: Interesting. Yeah. Uh, I mean, it's maybe seems obvious to you, but it wasn't obvious to me a year ago. Yeah.Jeff Dean [00:48:06]: I mean, I do think like that IMO with, you know, translating to lean and using lean and then the next year and also a specialized geometry model. And then this year switching to a single unified model. That is roughly the production model with a little bit more inference budget, uh, is actually, you know, quite good because it shows you that the capabilities of that general model have improved dramatically and, and now you don't need the specialized model. This is actually sort of very similar to the 2013 to 16 era of machine learning, right? Like it used to be, people would train separate models for lots of different, each different problem, right? I have, I want to recognize street signs and something. So I train a street sign. Recognition recognition model, or I want to, you know, decode speech recognition. I have a speech model, right? I think now the era of unified models that do everything is really upon us. And the question is how well do those models generalize to new things they've never been asked to do and they're getting better and better.Shawn Wang [00:49:10]: And you don't need domain experts. Like one of my, uh, so I interviewed ETA who was on, who was on that team. Uh, and he was like, yeah, I, I don't know how they work. I don't know where the IMO competition was held. I don't know the rules of it. I just trained the models, the training models. Yeah. Yeah. And it's kind of interesting that like people with these, this like universal skill set of just like machine learning, you just give them data and give them enough compute and they can kind of tackle any task, which is the bitter lesson, I guess. I don't know. Yeah.Jeff Dean [00:49:39]: I mean, I think, uh, general models, uh, will win out over specialized ones in most cases.Shawn Wang [00:49:45]: Uh, so I want to push there a bit. I think there's one hole here, which is like, uh. There's this concept of like, uh, maybe capacity of a model, like abstractly a model can only contain the number of bits that it has. And, uh, and so it, you know, God knows like Gemini pro is like one to 10 trillion parameters. We don't know, but, uh, the Gemma models, for example, right? Like a lot of people want like the open source local models that are like that, that, that, and, and, uh, they have some knowledge, which is not necessary, right? Like they can't know everything like, like you have the. The luxury of you have the big model and big model should be able to capable of everything. But like when, when you're distilling and you're going down to the small models, you know, you're actually memorizing things that are not useful. Yeah. And so like, how do we, I guess, do we want to extract that? Can we, can we divorce knowledge from reasoning, you know?Jeff Dean [00:50:38]: Yeah. I mean, I think you do want the model to be most effective at reasoning if it can retrieve things, right? Because having the model devote precious parameter space. To remembering obscure facts that could be looked up is actually not the best use of that parameter space, right? Like you might prefer something that is more generally useful in more settings than this obscure fact that it has. Um, so I think that's always attention at the same time. You also don't want your model to be kind of completely detached from, you know, knowing stuff about the world, right? Like it's probably useful to know how long the golden gate be. Bridges just as a general sense of like how long are bridges, right? And, uh, it should have that kind of knowledge. It maybe doesn't need to know how long some teeny little bridge in some other more obscure part of the world is, but, uh, it does help it to have a fair bit of world knowledge and the bigger your model is, the more you can have. Uh, but I do think combining retrieval with sort of reasoning and making the model really good at doing multiple stages of retrieval. Yeah.Shawn Wang [00:51:49]: And reasoning through the intermediate retrieval results is going to be a, a pretty effective way of making the model seem much more capable, because if you think about, say, a personal Gemini, yeah, right?Jeff Dean [00:52:01]: Like we're not going to train Gemini on my email. Probably we'd rather have a single model that, uh, we can then use and use being able to retrieve from my email as a tool and have the model reason about it and retrieve from my photos or whatever, uh, and then make use of that and have multiple. Um, you know, uh, stages of interaction. that makes sense.Alessio Fanelli [00:52:24]: Do you think the vertical models are like, uh, interesting pursuit? Like when people are like, oh, we're building the best healthcare LLM, we're building the best law LLM, are those kind of like short-term stopgaps or?Jeff Dean [00:52:37]: No, I mean, I think, I think vertical models are interesting. Like you want them to start from a pretty good base model, but then you can sort of, uh, sort of viewing them, view them as enriching the data. Data distribution for that particular vertical domain for healthcare, say, um, we're probably not going to train or for say robotics. We're probably not going to train Gemini on all possible robotics data. We, you could train it on because we want it to have a balanced set of capabilities. Um, so we'll expose it to some robotics data, but if you're trying to build a really, really good robotics model, you're going to want to start with that and then train it on more robotics data. And then maybe that would. It's multilingual translation capability, but improve its robotics capabilities. And we're always making these kind of, uh, you know, trade-offs in the data mix that we train the base Gemini models on. You know, we'd love to include data from 200 more languages and as much data as we have for those languages, but that's going to displace some other capabilities of the model. It won't be as good at, um, you know, Pearl programming, you know, it'll still be good at Python programming. Cause we'll include it. Enough. Of that, but there's other long tail computer languages or coding capabilities that it may suffer on or multi, uh, multimodal reasoning capabilities may suffer. Cause we didn't get to expose it to as much data there, but it's really good at multilingual things. So I, I think some combination of specialized models, maybe more modular models. So it'd be nice to have the capability to have those 200 languages, plus this awesome robotics model, plus this awesome healthcare, uh, module that all can be knitted together to work in concert and called upon in different circumstances. Right? Like if I have a health related thing, then it should enable using this health module in conjunction with the main base model to be even better at those kinds of things. Yeah.Shawn Wang [00:54:36]: Installable knowledge. Yeah.Jeff Dean [00:54:37]: Right.Shawn Wang [00:54:38]: Just download as a, as a package.Jeff Dean [00:54:39]: And some of that installable stuff can come from retrieval, but some of it probably should come from preloaded training on, you know, uh, a hundred billion tokens or a trillion tokens of health data. Yeah.Shawn Wang [00:54:51]: And for listeners, I think, uh, I will highlight the Gemma three end paper where they, there was a little bit of that, I think. Yeah.Alessio Fanelli [00:54:56]: Yeah. I guess the question is like, how many billions of tokens do you need to outpace the frontier model improvements? You know, it's like, if I have to make this model better healthcare and the main. Gemini model is still improving. Do I need 50 billion tokens? Can I do it with a hundred, if I need a trillion healthcare tokens, it's like, they're probably not out there that you don't have, you know, I think that's really like the.Jeff Dean [00:55:21]: Well, I mean, I think healthcare is a particularly challenging domain, so there's a lot of healthcare data that, you know, we don't have access to appropriately, but there's a lot of, you know, uh, healthcare organizations that want to train models on their own data. That is not public healthcare data, uh, not public health. But public healthcare data. Um, so I think there are opportunities there to say, partner with a large healthcare organization and train models for their use that are going to be, you know, more bespoke, but probably, uh, might be better than a general model trained on say, public data. Yeah.Shawn Wang [00:55:58]: Yeah. I, I believe, uh, by the way, also this is like somewhat related to the language conversation. Uh, I think one of your, your favorite examples was you can put a low resource language in the context and it just learns. Yeah.Jeff Dean [00:56:09]: Oh, yeah, I think the example we used was Calamon, which is truly low resource because it's only spoken by, I think 120 people in the world and there's no written text.Shawn Wang [00:56:20]: So, yeah. So you can just do it that way. Just put it in the context. Yeah. Yeah. But I think your whole data set in the context, right.Jeff Dean [00:56:27]: If you, if you take a language like, uh, you know, Somali or something, there is a fair bit of Somali text in the world that, uh, or Ethiopian Amharic or something, um, you know, we probably. Yeah. Are not putting all the data from those languages into the Gemini based training. We put some of it, but if you put more of it, you'll improve the capabilities of those models.Shawn Wang [00:56:49]: Yeah.Jeff Dean [00:56:49]:

This episode's guest:Dr. Jonathan Santoro, MDPediatric Neurologist & NeuroimmunologistChildren's Hospital Los Angeles (CHLA)2025 Shannon O'Boyle Memorial Neuropsychiatric Illness Grant AwardeeOverview:In this episode, we welcome Dr. Jonathan Santoro, our 2025 Shannon O'Boyle Memorial Neuropsychiatric Illness Grant Awardee, who is pediatric neurologist. Dr. Santoro's work focuses on developmental regression and neuropsychiatric illness, and he shares with Dr. Lauren why his research team is turning its attention to Phelan-McDermid syndrome (PMS).Dr. Santoro's PMSF-funded project, “Diagnostic Biomarkers in Phelan-McDermid Syndrome-Associated Neuropsychiatric Disease,” uses tests that are already part of standard clinical care (like EEGs, MRIs, blood work, and lumbar punctures), the team will look for biological “signatures”, or biomarkers, to help lead to better diagnosis, earlier detection, and more targeted treatments for individuals with Phelan-McDermid syndrome who experience neuropsychiatric illness.His study is currently enrolling (February 2026)Check out our open studies page for more information: https://pmsf.org/current-open-research/

In this episode of Typology, I sit down with therapist and author Joe Nucci for a thoughtful, wide-ranging conversation about the Enneagram, mental health, and the growing misuse of therapeutic language in our culture. Joe—an Enneagram Three—shares his own journey with the Enneagram, the hidden shame dynamics of Threes, and how public success can quietly pull us toward performance instead of integrity. Together, we explore why tools like the Enneagram work best as maps, not MRIs—helpful for self-awareness and empathy, but dangerous when they turn into rigid labels. We also dig into Joe's new book, Psycho Babble, discussing how clinical terms like narcissist, OCD, and trauma have become everyday adjectives—and what it costs us when labels replace discernment, curiosity, and real relationship. This is a grounded, honest conversation about growth, character, and what it actually means to become a healthier version of yourself—without turning self-awareness into self-avoidance.   ------------------------------------------------------------------------- ABOUT JOE NUCCI Joe Nucci is an expert in breaking down how people talk about mental health. He's a psychotherapist who corrects widely misused terms, adds valuable nuance and explains complex ideas in ways anyone can understand. He can take a mental health lens to any hot button issue. Anyone who listens to him will walk away knowing themselves and others a little better. Joe reached over 10 million people in his first 6 months of posting content. His book "Psychobabble" explores why mental health information is so confusing to navigate and how to more easily understand different perspectives about mental health. He also has an upcoming podcast, being produced by Luminary Podcasts, where he will take deeper dives into the different mental health topics that he explores on Instagram, Facebook and Tiktok @joenuccitherapy Pscyhobabble: Viral Mental Health Myths & the Truths to Set You Free

This week on Take a Pain Check, we're joined by Ela Murray, a university student who has been living with Central Nervous System Vasculitis which an invisible, life-altering condition since childhood.Symptomatic at just 8 years old and diagnosed at 16 during the height of COVID, Ela shares what it was really like navigating years of uncertainty, MRIs, a brain biopsy, steroids, seizures, and university life while still finding her voice as a young advocate.In this episode, we talk about:• Delayed diagnosis and navigating complex medical systems• The impact of steroids on body image, confidence, and mental health• Living next to a hospital while trying to experience “normal” university life• Finding purpose through advocacy and youth representation• What it means to show up vulnerably on stage and in everyday lifeThis episode is a reminder that young patients deserve to be heard, believed, and supported both medically and emotionally. Watch now, share with someone who needs to hear this, and join us in prioritizing youth voices in healthcare.Donate to Take a Pain Check Today: ⁠https://www.gofundme.com/f/takeapainc...Vasculitis Canada: https://vasculitis.ca/Our socials:https://www.takeapaincheck.com/https://www.instagram.com/takeapaincheck_/ https://www.tiktok.com/@takeapaincheckhttps://ca.linkedin.com/company/take-a-pain-checkhttps://www.youtube.com/@takeapaincheckhttps://www.facebook.com/TakeaPainCheckhttps://www.x.com/takeapaincheckDon't forget to like, comment, and subscribe for more episodes.

Jennifer Shaffer is a medium/intuitive who works with members of law enforcement nationwide, including agents from the FBI, NYPD and LAPD. We met ten years ago and began a conversation that has continued undaunted onto this podcast.   I'm a filmmaker, author who has written 13 books about the afterlife, Jennifer and I met up ten years ago and have been meeting weekly ever since.  We have four books together BACKSTAGE PASS TO THE FLIPSIDE 1, 2, 3 and TUNING INTO THE AFTERLIFE. Jennifer has been instrumental in helping me to get information from people offstage - including #AmeliaEarhart and the book #SheWasNeverLost - the Amelia Earhart saga. In today's podcast we talk to someone that Jennifer didn't know when he was on the planet, but has met and works with members of his family.  In our podcast, he refers to someone who saw him many years ago - someone whom I knew who worked with him.   Upon further reflection, I'm not sure if he was referring to my friend or not - but either way, I knew who Jennifer was referring to.  Steve wanted to speak about AI - and argue that it's not something to fear but something that will enhance the ability of people to think at a faster rate. We talked about the value and pitfalls of that - and he's arguing that people can use it as a tool that it's meant to be used as.   I asked some questions about #Parkinsons (and the show "Shrinking which had an episode about it) as well as my old USC professor the late Coleman Hough who had Parkinsons and appears in the film HACKING THE AFTERLIFE where it vanishes during her six hour interview. We talked about how that is or why that happened, and then talk about the idea that "filters on the brain" (see Dr. Greysons book AFTER pg 125 or DIVINE COUNCILS IN THE AFTERLIFE for a discussion of the research into filters on the brain) might be something to examine, look into as science tries to cure Parkinsons. We reiterate we're asking questions, not giving any medical advice - not medical advice is implied or suggested or given.  However, when talking about the brain and the research involved with looking for "filters on the brain" and how it is that Jennifer's brain is in a "delta state" during her sessions (as proven by MRI's that she has done on camera, and the same results were demonstrated via Dr. Drew and Tyler Henry's MRIs.)  That people who do this kind of mediumship MAY BE stepping past those filters - and that doing so on a daily basis may help or heal parts of the brain. That's the general discussion and we do these discussions to inspire people to do their own. Hope this helps. 

In this episode, Dr. Rena Malik explores the complexities of whole body MRI screening with guest Dr. Matthew Davenport. They discuss the pros and cons of using contrast material, the risks of overdiagnosis, and the potential harms of detecting indolent cancers or incidental findings in low-risk populations. Through vivid examples and expert explanation, the conversation highlights the importance of targeted cancer screening and making informed choices about imaging. Become a Member to Receive Exclusive Content: renamalik.supercast.com Schedule an appointment with me: https://www.renamalikmd.com/appointments ▶️Chapters: 00:00 Use of contrast in MRIs00:25 Trade-offs: accuracy vs. harm00:59 Substantial harm from findings01:51 Thyroid nodules and overdiagnosis03:15 Retrospective outcomes and unintended harm04:41 Screening for aggressive vs. indolent cancers07:06 Prostate cancer screening example08:24 Complications from incidental findings09:33 Cascade of care after incidental findings Stay connected with Dr. Matthew Davenport on social media for daily insights and updates. Don't miss out—follow him now and check out these links! LinkedIn profile: https://www.linkedin.com/in/matthew-davenport-md-mba-037184286 Work profile: https://medschool.umich.edu/profile/2315/matthew-s-davenport Most relevant article: https://www.ajronline.org/doi/10.2214/AJR.22.28926 Next event is grand rounds speaker at Stanford: https://med.stanford.edu/radiology/education/grandrounds/2025-26.html#january Let's Connect!: WEBSITE: http://www.renamalikmd.com YOUTUBE: https://www.youtube.com/@RenaMalikMD INSTAGRAM: http://www.instagram.com/RenaMalikMD TWITTER: http://twitter.com/RenaMalikMD FACEBOOK: https://www.facebook.com/RenaMalikMD/ LINKEDIN: https://www.linkedin.com/in/renadmalik PINTEREST: https://www.pinterest.com/renamalikmd/ TIKTOK: https://www.tiktok.com/RenaMalikMD ------------------------------------------------------ DISCLAIMER: This podcast is purely educational and does not constitute medical advice. The content of this podcast is my personal opinion, and not that of my employer(s). Use of this information is at your own risk. Rena Malik, M.D. will not assume any liability for any direct or indirect losses or damages that may result from the use of information contained in this podcast including but not limited to economic loss, injury, illness or death. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode we dig into Justin's Experience with Whole Body MRI in Mississauga, ON. There are many companies like this offering this service as a quick view and understanding how your exercise and nutritional habits are stacking up to (hopefully) support your long term health...So.. Is it worth the hype?Notes:Omega Fish oils we love :BulletproofCanPrevIf you liked this episode please like and share with your friends and family!

Longevity improves when health decisions are guided by clarity rather than hype. In this episode, Dr. Stephen Petteruti breaks down the most common anti-aging tests and scans that promise insight but often deliver unnecessary risk, cost, or confusion. He explains why DEXA scans, biological aging blood tests, whole-body MRIs, VO2 max testing, and mass-market screening programs frequently miss what actually predicts lifespan and vitality.Instead, Dr. Petteruti highlights practical, evidence-based measures that matter more—muscle mass, grip strength, metabolic health, cardiovascular risk assessment, and targeted imaging when appropriate. He also addresses supplement marketing, peptide misuse, and genetic testing claims that distract from real cellular health and functional aging.Focus on strategies that support strength, cognition, and resilience over the long term. Watch the episode of Longevity Myths Exposed: The Anti-Aging Tests and Scans You Should Avoid.Enjoy the podcast? Subscribe and leave a 5-star review.Dr. Stephen Petteruti is a leading Functional Medicine Physician dedicated to enhancing vitality by addressing health at a cellular level. Combining the best of conventional medicine with advancements in cellular biology, he offers a patient-centered approach through his practice, Intellectual Medicine 120. A seasoned speaker and educator, he has lectured at prestigious conferences like A4M and ACAM, sharing his expertise on anti-aging. His innovative methods include concierge medicine and non-invasive anti-aging treatments, empowering patients to live longer, healthier lives.Website: https://www.drstephenpetteruti.com/ Practice: www.intellectualmedicine.com YouTube: https://www.youtube.com/@intellectualmedicine LinkedIn: https://www.linkedin.com/in/drstephenpetteruti/ Instagram: https://www.instagram.com/dr.stephenpetteruti/ Facebook: https://www.facebook.com/dr.stephenpetteruti  #AntiAgingTest #VitalityScience #LongevityMythsDisclaimer:  The content presented in this video reflects the opinions and clinical experience of Dr. Stephen Petteruti and is intended for informational and educational purposes only. It is not medical advice and should not be used as a substitute for professional diagnosis, treatment, or guidance from your personal healthcare provider. Always consult your physician or qualified healthcare professional before making any changes to your health regimen or treatment plan.Produced by https://www.BroadcastYourAuthority.com 

In this episode of The Healthspan Podcast, Dr. Robert Todd Hurst, MD, FACC, FASE, sits down with Dr. Tammy Penhollow, DO, regenerative medicine specialist, Navy veteran, and founder of Precision Regenerative Medicine. Together, they explore how personalized, precision-guided treatments like PRP and bone marrow therapy are restoring mobility, relieving chronic pain, and helping patients avoid surgery, often after years of failed conventional care. They discuss the pitfalls of reactive, one-size-fits-all medicine, why “motion span” is essential to longevity, and how true healing starts with fixing the root cause, not masking symptoms. This is Medicine 3.0 in action. About the Guest: Dr. Tammy Penhollow is an osteopathic physician and the founder of Precision Regenerative Medicine in Arizona. With over 20 years of experience, including 12 years of active duty in the U.S. Navy, she blends military precision with cutting-edge regenerative strategies to help patients overcome musculoskeletal pain without surgery. Her mission: restore motion, vitality, and hope, especially for those who've been told they're out of options.

Lauren Rosenberg, a highly experienced Physician Associate, has dedicated nearly two decades to Internal Medicine and Health Optimization. Driven by a passion for preventative care, Lauren founded Vent Health to shift the focus from disease treatment to prevention. She specializes in a personalized approach that blends genetics, epigenetics, biomarkers, and lifestyle factors to tailor health interventions that extend and optimize each patient's healthspan. Lauren's practice includes prescribing peptides (GLP, CLP/GIP) for weight loss, insulin resistance, pre-diabetes, etc. This episode concentrates on all the questions about GLP's, the prescription based Ozempic and others, as well as the Compound Pharmacy GLP's that can often times be less expensive. Heather and Lauren also cover the common side effects, and how to manage them. In this episode you will learn other health benefits of these peptides, and who can benefit from them as well as practical tips for getting started on GLP-1 Therapy. Lauren is a frequent speaker at the Age Management Medicine Group (AMMG) and the American Academy of Anti-Aging Medicine (A4M) conferences. Lauren's practice includes Cardiology prevention: Diagnostics and AI analysis to detect dangerous plaque and calcium; advanced lipid testing and cardiovascular genetics Longevity biomarkers: DNA methylation for biological age, VO2 max testing, Telomere health; Therapeutic plasma exchange Cancer prevention: methylated DNA screens, preventative MRIs, tumor marker testing. We will have Lauren back to discuss all these other longevity and optimum health subjects. This episode concentrates on the information pertaining to Peptides, GLP's etc as they are so popular right now. If you want to contact Lauren for more info, you can reach her via her site: https://myventhealth.com and go to the contact page. Or email: vent@myventhealth.com Website: www.heatherthomson.com Social Media: IG: https://www.instagram.com/iamheathert/ You Tube: https://youtube.com/@iamheathert?si=ZvI9l0bhLfTR-qdo Learn more about your ad choices. Visit megaphone.fm/adchoices

Dr. Gillett and James O'Hara discuss prenuvo mri scans and the recent malpractice case.   Studies/References:► https://radiologybusiness.com/topics/healthcare-management/legal-news/whole-body-mri-provider-prenuvo-loses-bid-limit-damages-high-profile-malpractice-caseFor High-quality labs:► http://sagebio.com/For information on the Gillett Health clinic, lab panels, and health coaching:► https://GillettHealth.comFollow Gillett Health for more content from James and Kyle► https://instagram.com/gilletthealth► https://www.tiktok.com/@gilletthealth► https://twitter.com/gilletthealth► https://www.facebook.com/gilletthealthFollow Kyle Gillett, MD► https://instagram.com/kylegillettmdFollow James O'Hara, NP► https://Instagram.com/jamesoharanpFor 10% off Gorilla Mind products, including SIGMA: Use code “GH10”► https://gorillamind.com/For discounts on high-quality supplements►https://www.thorne.com/u/GillettHealth#health #news #darkside #strokeAdvertising Inquiries: https://redcircle.com/brandsPrivacy & Opt-Out: https://redcircle.com/privacy

The A70 UFO Incident | Paranormal Podcast In our first episode of 2026, we journey to Scotland to explore the A70 incident, one of the UK's most compelling alien abduction cases. On the night of August 27, 1992, Gary Wood and Colin Wright were driving south on the A70 highway near Edinburgh. As they traveled through the small town of Bonny Bridge—an area with over 300 UFO sightings annually—Colin spotted something unusual shoot across the sky. Minutes later, both men witnessed a massive two-tiered disc hovering just 20 feet above the road. Rather than stopping to observe this craft, Gary made the split-second decision to accelerate and try to drive underneath it. When they passed beneath the object, it released a shimmering silver mist that touched their car, plunging both men into complete darkness. What felt like only a few seconds to them turned out to be far more significant—when they regained their bearings and found themselves inexplicably driving in the opposite direction, they discovered they had lost approximately 90 minutes of time and arrived at their friend's house well past 2:00 AM. The aftermath of this encounter proved even more disturbing than the initial experience. Gary began suffering from severe, unexplainable headaches that led to extensive medical testing including MRIs, CAT scans, and even a spinal tap—all of which came back with no answers from confused doctors. Desperate for understanding, both men turned to the British UFO Research Association, which recommended hypnotic regression with Dr. Helen Walters, a qualified hypnotist and psychic. During these sessions, Gary—a hardened ambulance driver who had seen countless traumatic situations—burst into tears and had to be escorted out of his first regression, while Colin remained eerily calm throughout. Through multiple sessions, both men independently described being taken aboard the craft by three six-foot-tall gray beings with large heads, dark eyes, and four long fingers, then being led through circular hallways into fog-filled rooms where they were stripped and examined.

What if a haunting didn't involve ghosts — but the lingering smell of carnival food? This episode of The Box of Oddities opens with an unsettling sensory mystery tied to a long-demolished amusement park, then plunges into one of the most stubborn and controversial archaeological puzzles of modern times: the tridactyl mummies of Peru. Discovered near the Nazca region, these small humanoid mummies feature three fingers, three toes, elongated skulls, and internal anatomy that does not appear to be the result of a simple hoax. CT scans and MRIs show articulated skeletons with no apparent signs of assembly. Carbon dating places them roughly 1,700–1,800 years old. DNA testing reveals material consistent with known Earth life — alongside a troubling percentage classified as unknown. Some specimens even appear to contain metallic implants made from rare alloys, positioned as if intentionally placed during life. One reportedly shows signs of a fetus, suggesting reproduction rather than fabrication. Scientists remain cautious. Skeptics remain vocal. And yet, after years of imaging and analysis, these bodies stubbornly resist tidy explanations. They may not be aliens — but they also may not be anything science has fully named yet. Then, in classic Box fashion, the episode pivots from the inexplicable to the unexpectedly hopeful. Meet the real-world heroes you probably didn't expect: trained landmine-detecting rats. These remarkable animals are saving lives across former war zones by sniffing out explosives buried decades ago. One rat in particular, Ronan, has broken world records and helped return deadly land to safe use — proving that sometimes the strangest solutions are also the most effective. From phantom fairground smells to unresolved biological mysteries to rats quietly changing the world, this episode is a reminder that the universe is weird, complicated, and occasionally wonderful — whether we understand it or not. Learn more about your ad choices. Visit megaphone.fm/adchoices

Broadcast from KSQD, Santa Cruz on 12-18-2025: Dr. Dawn opens by examining how market competition is actually working in the weight loss drug sector. Novo Nordisk's Ozempic and Wegovy compete against Eli Lilly's Monjaro and ZepBound, with prices dropping nearly 50% as companies launch direct-to-consumer websites. The main barriers remain needles and refrigeration, driving development of oral versions. Novo's Wegovy pill awaits FDA approval for early 2026 launch at $150 monthly. Next-generation drugs show remarkable results: Eli's retatrutide causes 24% weight loss in 48 weeks, while Novo's Cagrisema combines semaglutide with amylin to reduce muscle loss. Pfizer paid $10 billion for Metsera's once-monthly drug despite significant side effects. A quick fiber tip suggests adding plain psyllium to morning coffee for cardiovascular and microbiome benefits. Start with half a teaspoon and work up to two teaspoons (10 grams) over several weeks to avoid gas. The prebiotic fiber improves glucose tolerance and may reduce cancer risk. UC San Diego scientists discovered why cancers mutate so rapidly despite being eukaryotic cells with protected chromosomes. The answer is chromothripsis, a catastrophic event where the enzyme N4BP2 literally explodes chromosomes into fragments. These reassemble incorrectly, generating dozens to hundreds of mutations simultaneously and creating circular DNA fragments carrying cancer-promoting genes. One in four cancers show evidence of this mechanism, with all osteosarcomas and many brain cancers displaying it. This explains why the most aggressive cancers resist treatment. Research from 2013 shows any glucocorticoid use significantly increases venous thromboembolism risk, with threefold increases during the first month of use. The risk applies to new and recurrent clots, affecting both oral and inhaled steroids, though IV poses highest risk and topical the lowest. Joint injections fall somewhere between inhaled and oral. Anyone with prior blood clots should avoid steroids except for life-threatening situations like severe asthma attacks requiring ventilation. A meta-analysis of 20 randomized controlled trials shows creatine supplementation helps older adults (48-84) maintain muscle mass when combined with weight training two to three times weekly. The supplement provides no benefit without exercise. Recommended dosing starts at 2 grams and works up to 5 grams daily. Vegans benefit most since they consume little meat or fish. Important caveat: creatine throws off standard kidney function tests (creatinine), so users should request cystatin C testing instead for accurate renal health assessment. A new JAMA study suggesting risk-based mammogram screening is fatally flawed. First, researchers offered chemopreventative drugs like tamoxifen only to the high-risk group, contaminating the study design. Second, the demographics skewed heavily toward white college-educated women, missing the reality that Black women face twice the risk of aggressive breast cancer with 40% higher mortality. Third, wild-type humans failed to follow instructions—low-risk women continued getting annual mammograms anyway while high-risk women skipped recommended extra screenings. The conclusion of "non-inferior" outcomes is meaningless given poor adherence. Stick with annual mammograms, and consider alternating with MRIs for high-risk women. The EAT-Lancet report condemns red meat based purely on observational data showing correlations with heart disease, cancer, and mortality. But people who eat lots of red meat differ dramatically from low consumers: they weigh more, smoke more, exercise less, and eat less fiber. Studies can't control for sleep quality, depression, or screen time. Notably, heavy meat eaters also die more in accidents, suggesting a risk-taking lifestyle phenotype. The inflammatory marker TMAO is higher in meat eaters, but starch is also pro-inflammatory. Eating red meat instead of instant ramen might improve health. A balanced diet with limited amounts beats epidemiology-based blanket statements. Dr. Dawn grades Dr. Oz's performance as CMS administrator. Starting at minus one for zero relevant experience, he earns plus two for promoting diet, exercise, and gut health on his show. He studied intensively after nomination, calling all four previous CMS directors repeatedly and surrounding himself with experienced staff (plus one). He finalized Medicare rules favoring prevention over surgery and earned bipartisan praise as "a real scientist, not radical" (plus one). He divested healthcare holdings but kept some blind trust interests (minus 0.5). He's developing a CMS app and partnering with Google on a digital health ecosystem (plus one), but supports ending ACA subsidies that will raise premiums for millions (minus one). He correctly promoted COVID vaccines and contradicted Trump's Tylenol-autism claims (plus one). Final score: 3.5 out of 5 possible points, the only positive score for any Trump health administrator.

In this episode of Heal Yourself, Change Your Life, Brandy Gillmore explores how subconscious emotional patterns can be directly linked to physical pain, inflammation, and unexplained symptoms—and how real healing begins at a deeper level of the mind. Brandy shares compelling research, including documented cases of multiple personality disorder (now called dissociative identity disorder), where an individual experiences different illnesses, symptoms, and even blindness in different personalities—despite having the same physical body. These findings offer powerful evidence of the mind-body connection and show how quickly the body can change when subconscious programming shifts. In a live coaching session, Brandy works with Tracey, a volunteer experiencing migrating foot pain, jaw pain, burning sensations, and inflammation that doctors have been unable to explain, even with MRIs. As the session unfolds, Brandy helps Tracey identify suppressed emotional patterns related to relationships, control, and unmet emotional needs that have been stored in her subconscious mind. You'll Discover: How it's possible for chronic pain to move or flare without a clear medical cause How suppressed emotions can remain active even when you "feel fine" Why positivity, meditation, affirmations, and mindset work may not be enough How subconscious relationship patterns can affect both emotional and physical health What true emotional transformation looks like at the mind-body level As Tracy's awareness shifts, listeners witness her experience real-time reductions in pain and burning, demonstrating how emotional healing can support physical change when addressed at the subconscious level. This episode is especially valuable for anyone experiencing: Chronic pain or unexplained symptoms Autoimmune flare-ups linked to stress or relationships Repeating relationship patterns that feel painful or confusing A sense of feeling unloved, disconnected, or emotionally stuck Frustration after trying meditation, mindset work, or holistic approaches without lasting results This episode offers education, insight, and hope, showing how healing becomes possible when emotional patterns are transformed—not suppressed. Do you want to see proof of the power of the mind in a medical journal? Here's a demonstration of Brandy Gillmore working with volunteers under medical equipment, as featured in a medical journal. Free Resources and Frequently Asked Questions  Q: How can I heal myself from chronic pain or illness?

Today on AirTalk: Remembering Rob Reiner (0:15) The future of student loans (20:05) LA Mayor Karen Bass calls in (31:45) Second night of Hanukkah (43:25) Full body MRIs? (52:52) Reprise: Rob Reiner talks Spinal Tap (1:22:00) Visit www.preppi.com/LAist to receive a FREE Preppi Emergency Kit (with any purchase over $100) and be prepared for the next wildfire, earthquake or emergency

Join me at the 2026 Goal-setting Workshop here - jjlaughlin.com/2026goalsIn this episode of Lead On Purpose, I sit down with former Olympic Team Physiotherapist and author of The Back Fix, Antony Bush, to explore some big questions.Why do one in five of us suffer chronic pain? Why is back pain now the world's greatest disability? How does movement and strength shape our pain, happiness and even mortality?Antony breaks down what it means to be an analog creature struggling in a digital age and how we can reclaim our health through simple, science backed habits.What we cover:Why modern living has turned back pain into the world's number one disability and how sitting, screens and stress drive chronic painHow movement, walking, micro exercise and the Big Six strength moves reduce pain, improve mood and extend lifespanHow the brain creates pain, why MRIs often show harmless “wrinkles,” and why movement outperforms passive treatmentsThe mindset side of recovery, including the wishbone, backbone and funny bone model for resilience and why movement is medicineIf you want to reduce pain, boost your energy and future proof your health, this conversation will shift how you move and how you live.Learn more about Antony here - https://thebackfix.com/about-1Connect with Antony on LinkedIn here - https://www.linkedin.com/in/antony-bush/Grab your copy of The Back Fix here - https://thebackfix.comIf you're interested in having me deliver a keynote or workshop for your team contact Caroline at caroline@jjlaughlin.comWebsite: https://www.jjlaughlin.com YouTube: https://www.youtube.com/channel/UC6GETJbxpgulYcYc6QAKLHA Facebook: https://www.facebook.com/JamesLaughlinOfficial Instagram: https://www.instagram.com/jameslaughlinofficial/ Apple Podcast: https://podcasts.apple.com/nz/podcast/life-on-purpose-with-james-laughlin/id1547874035 Spotify: https://open.spotify.com/show/3WBElxcvhCHtJWBac3nOlF?si=hotcGzHVRACeAx4GvybVOQ LinkedIn: https://www.linkedin.com/in/jameslaughlincoaching/James Laughlin is a High Performance Leadership Coach, Former 7-Time World Champion, Host of the Lead On Purpose Podcast and an Executive Coach to high performers and leaders. James is based in Christchurch, New Zealand.Send me a personal text messageJoin me at the 2026 Goal-setting Workshop here - jjlaughlin.com/2026goals - If you're interested in booking me for a keynote or workshop, contact Caroline at caroline@jjlaughlin.comSupport the show

Rob, Nico and Nick discuss the Julian calendar, the 12 days of Christmas, Hamilton, Pluribus, the Warner Bros acquisition drama, Michael Bublé, MRIs, Daddy Long… The post Two Cents Radio: Episode #420 – Daddy Long Neck appeared first on Too Many Thoughts.

Dr. Daniel Amen reveals that most chronic pain isn't just in your back, knee, or neck - it's in your brain. He explains the doom loop, a vicious cycle where pain triggers suffering, which activates automatic negative thoughts, creating physical tension that leads to harmful coping habits and more pain. Through scanning over 300,000 brains from 155 countries, he's proven that healing your brain can eliminate pain that's haunted you for years, even when MRIs show structural damage. You'll learn why believing every negative thought is literally damaging your body, and discover how practices like gratitude, managing your mind, and connecting to faith can set you free. This episode gives you the complete roadmap to heal both your brain and your body by understanding they've always been connected.Dr. Amen's books:Change Your Brain Every DayConquer Your Negative Thoughts30% Happier in 30 DaysRaising Mentally Strong KidsAmen clinicsIn this episode you will:Discover why 70% of chronic pain is linked to brain health rather than physical injury aloneBreak free from the doom loop that cycles between pain, suffering, negative thoughts, and harmful habitsLearn to stop believing automatic negative thoughts that damage both your mind and bodyUnderstand how faith and belief in God can triple your protection against depressionUncover the hidden brain damage caused by alcohol, marijuana, and artificial sweeteners like aspartameFor more information go to https://lewishowes.com/1860For more Greatness text PODCAST to +1 (614) 350-3960More SOG episodes we think you'll love:Gabor Maté – greatness.lnk.to/1849SCDr. Andrew Huberman  – greatness.lnk.to/1830SCDr. Joe Dispenza – greatness.lnk.to/1857SC Get more from Lewis! Get my New York Times Bestselling book, Make Money Easy!Get The Greatness Mindset audiobook on SpotifyText Lewis AIYouTubeInstagramWebsiteTiktokFacebookX Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Send us a text if you want to be on the Podcast & explain why!Most knee pain stories sound the same: a new running plan, a few hard workouts, and then an ache that won't leave. We take that familiar frustration and turn it into a practical roadmap. Starting with a clear scope—what coaches can do and when to refer out—we break down the anatomy that actually drives decisions, the screens that reveal load bottlenecks, and the progressions that build resilient knees.We lean into a joint-by-joint approach: verify ankle mobility so the knee doesn't become a substitute hinge, then probe hip rotation and control with the 90-90 position. From there, we show how calf capacity underpins every step—most people aren't near twenty-five single-leg calf raises—and why the frontal plane is a silent deal-maker for runners and lifters. Expect actionable strength work like side planks with abduction, standing abductions, seated and standing calf raises, and carefully dosed plyometrics once tissue tolerance is ready.Soft tissue isn't a fix, but it is a useful window. We explain how targeted work on the adductors and surrounding tissues can calm sensitivity so better movement sticks. We also demystify scary imaging, remind you that MRIs find “something” in almost everyone, and focus on what changes pain: load management, smart progression, and consistent retesting. The thread through all of it is collaboration—building a dependable relationship with a physical therapist, documenting assessments, and using simple benchmarks to show progress your clients can feel and see.If you coach people who run, squat, lunge, or just want to move without wincing, you'll walk away with a simple, repeatable system: assess, address, load, retest, and refer when in doubt. Subscribe for more coach-first education, share this with a trainer who needs a clear plan, and leave a review to tell us the biggest knee question you want answered next.Want to ask us a question? Email email info@showupfitness.com with the subject line PODCAST QUESTION to get your question answered live on the show! Our Instagram: Show Up Fitness CPT TikTok: Show Up Fitness CPT Website: https://www.showupfitness.com/Become a Personal Trainer Book (Amazon): https://www.amazon.com/How-Become-Personal-Trainer-Successful/dp/B08WS992F8NASM / ACE / ISSA study guide: https://www.showupfitness.com/collections/nasm

On this episode, David L. Skaggs, MD, Co-Director of Cedars-Sinai Spine, Executive Vice Chair of the Department of Orthopaedics at Cedars-Sinai, and Director of Pediatric Orthopaedics at Guerin Children's, joins the podcast to discuss recent innovations in spine care, including synthetic CT scans generated from MRIs and the advancement of outpatient pediatric spine surgery. He also shares insights on developing surgical tools tailored for physicians with smaller hands, and looks ahead at healthcare trends, particularly how AI-driven solutions can make care more accessible and affordable.

In this episode, Dr. Linda Bluestein is joined by Professor Tara Renton, a globally recognized expert in orofacial pain, to explore the nuanced world of facial pain, temporomandibular joint (TMJ) dysfunction, and migraine disorders. Together, they unpack why so many patients suffer from persistent facial, jaw, or head pain despite “normal” scans and what magnetic resonance neurography (MRN) can reveal that traditional imaging might miss. They also dig into local anesthetic reactions, the limitations of pain scales, and how to distinguish between healthy vs. unhealthy pain. . Takeaways Professor Renton explains how magnetic resonance neurography (MRN) can detect nerve irritation that typical MRIs may miss, especially in TMJ and facial pain cases. You'll hear how migraine-related nerve dysfunction can present as jaw pain, facial burning, or unexplained dental sensitivity without classic migraine symptoms. They explore how patients with conditions like mast cell activation may react to preservatives or delivery mechanisms in numbing agents, even if allergy tests are negative. The conversation questions whether traditional 1-to-10 pain rating tools capture the lived experience of chronic nerve or facial pain and what alternatives might help. Dr. Bluestein and Professor Renton discuss how to recognize pain that signals normal healing versus pain that points to long-term nerve dysfunction or central sensitization. Want more Professor Tara Renton Website: https://orofacialpain.org.uk/ Youtube: https://www.youtube.com/watch?v=pKw1La6H5Dw Linkedin: https://www.linkedin.com/in/tara-renton-a5999018/?originalSubdomain=uk Want more Dr. Linda Bluestein, MD? Website: https://www.hypermobilitymd.com/ YouTube: https://www.youtube.com/@bendybodiespodcast Instagram: ⁠⁠⁠⁠https://www.instagram.com/hypermobilitymd/⁠⁠⁠⁠ Facebook: ⁠⁠⁠⁠https://www.facebook.com/BendyBodiesPodcast⁠⁠⁠⁠ X: ⁠⁠⁠⁠https://twitter.com/BluesteinLinda⁠⁠⁠⁠ LinkedIn: ⁠⁠⁠⁠https://www.linkedin.com/in/hypermobilitymd/⁠⁠⁠⁠ Newsletter: ⁠⁠⁠⁠https://hypermobilitymd.substack.com/ Shop my Amazon store ⁠⁠⁠ https://www.amazon.com/shop/hypermobilitymd Dr. Bluestein's Recommended Herbs, Supplements and Care Necessities: https://us.fullscript.com/welcome/hypermobilitymd/store-start Thank YOU so much for tuning in. We hope you found this episode informative, inspiring, useful, validating, and enjoyable. Join us on the next episode for YOUR time to level up your knowledge about hypermobility disorders and the people who have them. Join YOUR Bendy Bodies community at ⁠⁠https://www.bendybodiespodcast.com/⁠⁠. YOUR bendy body is our highest priority!⁠⁠ Learn more about Human Content at ⁠⁠⁠http://www.human-content.com⁠⁠⁠ Podcast Advertising/Business Inquiries: ⁠⁠⁠sales@human-content.com⁠⁠⁠ Part of the Human Content Podcast Network FTC: This video is not sponsored. Links are commissionable, meaning I may earn commission from purchases made through links Learn more about your ad choices. Visit megaphone.fm/adchoices

Get a shoutout on Congratulations: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠holler.baby/chrisdelia⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

We are back in the studio with Cocktales Chardonnay in our glasses and a lot on our hearts.This week Kiki and Medinah catch you up on everything from Hedonism in Jamaica after Hurricane Melissa to one of the wildest Weird Sex stories we have had in a minute. Kiki shares how the resort staff has been impacted, why she is turning her suitcase into a donation bin, and how you can help if you feel called to give. Medinah talks about Paradise & Vibe's Traveler's Resort family, the fundraiser in Jamaica and why natural disasters hit different when you know the people affected.We also get into robot taxis glitching in Atlanta, Kiki's fever app mock trial experience where the audience decides who is guilty in an AI car accident, and Medinah's grown woman announcements, including her holiday tablescaping class with her interior designer and a full wellness retreat in Istanbul, Turkey.A listener writes in about an ER trauma case where a woman comes in after being hit by a car, gets scanned head to toe, and the entire team discovers a plug sitting pretty on the CT. From toys to MRIs and lying about metal in your body, we take it there.Then the episode takes a tender turn. Medinah opens up about putting her dog Shai down, what led up to that heartbreaking vet visit, the costs, the process and the grief of coming home to an empty hallway, a leash in the car and a quiet house after more than a decade with her best friend. She reflects on a long season of letting go, trusting God, and learning healthier coping mechanisms than she had when she first got Chi.Kiki shares her own life updates too, including a Leo and Teyana Taylor film premiere, more thoughts on AI car trials, using the Fever app to find things to do, and why Atlanta actually has plenty of food and fun if you stop following only the “lit” IG spots. The ladies swap date recommendations like Midtown Social's R&B Bingo, Cirque du Soleil, museums, sushi classes and even ballroom dancing.They close with some real talk about how podcasts and creator work are actually funded, why your subscription, reviews and shares really matter, and how you can support CockTales without going broke in this economy. Plus, Medinah invites listeners to join her for a Meals on Wheels Atlanta volunteer day and reminds anyone struggling that there are resources and community waiting.If you have ever loved a pet like a family member, felt stuck in grief, or just needed a reminder that you can cry and still get cute for a date and a concert, this episode is for you.Interested in being a guest?  Please contact addie@cocktalespod.com and include your information, what makes you an interesting guest, and any relavant links!For all promo codes and links for promotions in the episode, follow this link: https://linktr.ee/cocktalesadsVisit 3rdplanetproducts.com CODE COCKTALES 20 and use code cocktales20 for a discount + free shipping!Promo Code for 20% off a ticket: TABLESCAPE2025WINE & DESIGNhttps://www.eventbrite.com/e/wine-design-hosted-by-brian-christion-madinah-monroe-112225-tickets-1857609192099?aff=oddtdtcreatorVOLUNTEER WITH MEDINAH 11/20https://docs.google.com/forms/d/e/1FAIpQLSdKwE0pjsr38uX9qlRvCJOdHDxJqey1qAxE4vXBIMNLrYr_Bg/viewform?usp=sharing&ouid=101308055207483565674Contact Us! Advice: advice@cocktalespod.comCocktales: cocktales@cocktalespod.comWeird : weirdsex@cocktalespod.comLive Show Sponsorship: sales@cocktalespod.comGuest Request/ General Inquiries info@cocktalespod.comGet your Vesper2https://www.lovecrave.com/products/vesper2/?aop=cocktalesGet Your Merch & Order Your Card GamePurchase Merch And Card Game at www.imcurioustoknow.comGet Klassy Baste! Learn to Cook with Kiki www.klassybaste.comJoin Kiki's  Book Club www.patreon.com/kikisaidsoTravel with Kiki! We're going to Curacao March 19-23. Tickets will be live soon, email info@kikisaidso.com with subject "Trip" to receive a reminder when the trip goes live.Travel With Medinah! https://linktr.ee/MedinahMonroePurchase Medinah's Coffee Mug! www.medinahmonroe.comInterested in sponsoring? Contact sales@cocktalespod.com today!VOLUNTEER WITH MEDINAHContact Us! Advice: advice@cocktalespod.comCocktales: cocktales@cocktalespod.comWeird Sex: weirdsex@cocktalespod.comLive Show Sponsorship: sales@cocktalespod.comGuest Request/ General Inquiries info@cocktalespod.comLooking for a new podcast home or event space?  Use our referal link and book on PeerSpace.  This space is available there: www.peerspace.com/claim/gr-PPJGdRwxzlJDGGet your Vesper2https://www.lovecrave.com/products/vesper2/?aop=cocktalesGet Your Merch & Order Your Card GamePurchase Merch And Card Game at www.imcurioustoknow.comGet Klassy Baste! Learn to Cook with Kiki www.klassybaste.comJoin Kiki's  Book Club www.patreon.com/kikisaidsoBecome a supporter of this podcast: https://www.spreaker.com/podcast/cocktales-dirty-discussions--2818687/support.

SEASON 4 EPISODE 29: COUNTDOWN WITH KEITH OLBERMANN A-Block (2:30) SPECIAL COMMENT: The correct question has been lying there, invisible in the forest, for the trees. It was Mary Trump who finally saw it – and asked it: “Why the hell (do) they KEEP giving him cognitive tests?” That’s IT - isn’t it? THAT'S the question. None of the details, none of the giraffes versus elephants, none of his stupid boastful insults about it, none of the small stuff. It's the big picture. Why the hell DO they keep giving him cognitive tests? And I’ll add a corollary to Mary Trump's burst of simple genius: Why the hell do they KEEP giving him cognitive tests almost exactly six months apart? Friday October 10, 2015 at Walter Reed, which he boasted about on board Air Force One this week. And Friday April 11, 2015, which he had also boasted about on board Air Force One last spring. Those dates are almost six months apart. 182 days. If they’re not giving him pre-scheduled cognitive tests every six months that’s a helluva coincidence. Why the hell do they keep giving him cognitive tests? And I’ll add a second corollary to Mary Trump’s question: why did they give him an MRI? Is it the first MRI to accompany a cognitive test? What was it an MRI of? I mean it may be irrelevant (I once had an MRI to see how my sinuses were draining correctly). You really CAN get MRIs for almost trivial stuff. But you don’t get cognitive tests for trivial stuff. Why the hell do they keep giving him cognitive tests? PLUS: Trump says the Constitution prohibits him from running for president again. Again, mid-flight, after boasting about things that aren't real, he said: “If you read it it’s pretty clear. I’m not allowed to run." So that’s that, huh? That’s what all the experts say. The same experts who said there was no Presidential Immunity. So – what happens next? He just changes his mind? Or decides this term is eight years not four? Or he just cancels the 2028 election? This isn't bluster and it isn't trolling. They might get away with it and they might not, but there are plans. And the more we're convinced they can never pull them off, the more likely we are to see another "presidential immunity" ruling from The Supreme Court. Or another Aileen Cannon. Or another January 6. B-Block (24:00) THE WORST PERSONS IN THE WORLD: Steve Bannon wants to expel Zohran Mamdani from this country. Hell, we should expel Bannon. If we can find a truck that can carry that much blubber. There's a media writer named Rich Greenfield who has extrapolated from the possibility that Comcast might buy CNN and merge it with MSNBC and he has the exact right person to run it: Charlie Kirk's widow (a bible student). And as ludicrous as that sounds, the guy now running CNN wasn't even home from his visit to the White House to try to butter up Trump and the Trumpists when one of the Trumpists mocked him on twitter for visiting. Today, appeasers not only lose, they get flamed on social media. C-Block (36:00) THINGS I PROMISED NOT TO TELL: With the Dodgers in the World Series again it is time to hurry back to the greatest moment in their Los Angeles history: Kirk Gibson's pinch-hit homer even though three-quarters of his body was barely movable, to win Game One of the 1988 World Series and set them on the path to one of the greatest upsets in baseball history, over the vaunted Oakland A's. Gibson's homer was a surprise to everybody. Except me. Because I predicted it just before the first pitch of that final inning began. And there's a WITNESS.See omnystudio.com/listener for privacy information.

People who live the longest aren't always the ones with the “perfect” body weight. In fact, research suggests that being slightly overweight can actually increase your life expectancy. It sounds counterintuitive, but the science may surprise you. Listen as I explain what's really going on. https://healthland.time.com/2013/01/02/being-overweight-is-linked-to-lower-risk-of-mortality/ Ever since the dawn of the Internet, we've been told to guard against hackers — but today's biggest threat isn't hacking, it's scamming. Cybercriminals are more cunning than ever, tricking millions into giving up money and information every day. If you think you are too clever to be taken by cyber-scammers, think again. Eric O'Neill — former FBI undercover operative, national security attorney, and cybersecurity strategist — reveals how modern scams work and how to stop them before they get to you. He's the author of Spies, Lies, and Cybercrime: Cybersecurity Tactics to Outsmart Hackers and Disarm Scammers (https://amzn.to/4nRvvv1). Imagine medicine without X-rays, CT scans, or MRIs. It's impossible — these imaging breakthroughs revolutionized how doctors diagnose and treat disease. Yet not long ago, the idea of seeing inside the body without a single incision was pure fantasy. Dr. Daniel K. Sodickson, chief of innovation in radiology at NYU Grossman School of Medicine and author of The Future of Seeing: How Imaging Is Changing Our World (https://amzn.to/3KNz3zS), shares the fascinating story of how imaging transformed modern medicine — and what's coming next. Sarcasm might seem like just a clever way to joke around but it's actually good exercise for your brain. Using and understanding sarcasm requires multiple parts of your mind to work together. Listen as I explain why being sarcastic might make you sharper. https://www.hbs.edu/faculty/Pages/item.aspx?num=49283&utm Learn more about your ad choices. Visit megaphone.fm/adchoices

"You've had a zoo experience..." As the show continues to use Monday Night Football's free use music until someone tells them not to, Zaslow is shocked to learn Greg sleeps criss-cross apple sauce, Tony insists he's had more MRIs than anyone, and Billy is ready to launch a new podcast: The Little Things And That Kinda Thing with Larry Little and Greg Cote. Also, is Dan okay? No, seriously. What's going on? Like, is this a body double? Dan? DAN? If you want to attend The Monster Masquerade at Zoo Miami Saturday 10/18, log onto http://zoomiami.org/monster and use code RONMM25. Learn more about your ad choices. Visit podcastchoices.com/adchoices