Podcasts about definitions

Another major first time move in the most powerful group on Earth. First lady Melania Trump has taken the gavel at the UN Security Council and ended a 70 year precedent. It's strategic visibility. America doesn't act restrained or pretend. The highest expression of power is dominance with grace. Speak softly, and always with that stick. Unseen and unfunded agendas often involve children. Symbols become precedents. Then precedents become the new normal. Defining leadership is a new ball game today. Dominance without humiliation. Nobody is talking about nukes. Internal data base leaks are deliberate by federal employees. Pray for all the people losing their lives. The UFO show is coming up. The zoomies are getting very bold. The Texas elections seem spotty. Some book deal instructions. Read your Bible to learn this. Karma is not always a pleasant teacher.

Definitions are always evolving, but how will the next decade redefine the role of the Money Laundering Reporting Officer?This week, The Laundry comes to you live from the PIMFA Financial Crime Conference 2026.Our expert host, Marit Rødevand, is joined by Aaron Guilder, MLRO at Rathbones, Evgenia Giannoulopoulou, MLRO at Rothschild & Co, and Richard Bernstein, Compliance Director & MLRO at JM Finn, to ask: What is the future for MLROs? The panel discuss: The ever-evolving challenges facing today's MLROs, how the scope of the role is shifting in a digital-first landscape, and the timeless (and new) skills required to perform the role effectively.Producer: Matthew Dunne-MilesEditor: Dominic DelargyVideographer: Loïs DunfordGet your free ticket for the The Laundry Live Oslo here! ____________________________________The Laundry podcast explores the complex world of financial crime, anti-money laundering (AML), compliance, sanctions, and global financial regulation.Hosted by Marit Rødevand, Fredrik Riiser, and Robin Lycka, each episode features in-depth conversations with leading experts from banking, fintech, regulatory bodies, and investigative journalism.Tune in as we dissect headline news, unpack regulatory trends, and examine the real-world consequences of non-compliance — all through a uniquely compliance-focused lens.The Laundry is proudly produced by Strise.Get in touch at: laundry@strise.aiSubscribe to our newsletter, Fresh Laundry, here. Hosted on Acast. See acast.com/privacy for more information.

Superb stand-up, writer and podcaster Amy Matthews has a table booked this week. But does the Dream Restaurant have the perfect amount of twinkle?Amy Matthews is at the Edinburgh Fringe and on tour with her new show ‘Definitions of Toast'. Dates and tickets info here. Watch Amy's special ‘I Feel Like I'm Made of Spiders' on ITVX.Buy Amy's vinyl ‘Commute With The Foxes' on Monkey Barrel Records.Follow Amy on Instagram @amyfmatthewsWatch the video version of this episode on the Off Menu YouTube on Thu 5 Mar.Off Menu is now on YouTube: @offmenupodcastFollow Off Menu on Instagram and TikTok: @offmenuofficial.And go to our website www.offmenupodcast.co.uk for a list of restaurants recommended on the show.Off Menu is a comedy podcast hosted by Ed Gamble and James Acaster.Produced, recorded and edited by Ben Williams for Plosive.Video production by Megan McCarthy for Plosive.Artwork by Paul Gilbey (photography and design). Hosted on Acast. See acast.com/privacy for more information.

Stefan Molyneux looks at how mysticism, philosophy, and communication overlap, in response to a listener's question about higher powers, emphasizing the use of reason and precise definitions to cut through vagueness in talks about belief. The discussion covers ideas like consciousness, love, and attachment, with him arguing that genuine moral love goes beyond basic instincts. He points out the problems vague terms create in society and pushes for common definitions to improve how people communicate. On dreams, Molyneux sees them as straightforward experiences from life, not as sources of mystical insight. He wraps up by noting the role of clear thinking and rational talk in dealing with complicated aspects of life, and encourages people to express their thoughts with care.Emails:Hello Stefan,Following your most recent, as of today, FDR podcast.(6292). I wanted to hopefully offer you some perspective that may or may not be helpful. As before, I understand that your time is valuable. I do think though that my perspective, linked to IQ and seeing things very differently to you, might be of aid. The reason I have added this onto an existing email is just for familiarity because I will mildly use this backdrop for additional thoughts. I did talk to you briefly on podcast 6147. But I wanted to offer you my thought process here because it might offer you some insight into your value in a way you had not considered. Firstly, what I believe is important background as to my perspective on this entire mysticism thing. I do believe in the existence of something higher and more powerful and that has communicated with us. Certainly, a little through the bible. But mostly not through the bible. There is channeling, including the human design chart, to back this up. So I do believe the new age at its core has some good concepts. BUT, I also believe that there is a huge, and incredibly powerful toxic element of the new age. There is a mix of non complete understandings and such. For this reason, I do think that your perspective and that of many who have similar perspectives is valuable. In that keeping things to objective reality. To challenge said toxicity. There is more to this understanding. But I think that explains the core of my thoughts. People that are truly inclined to the spiritual stuff I look at will find it. But people that don't really commit and use the bare minimum of it to justify madness. It is good that that is challenged. it is similar in some ways, if you imagine a society that has innovators and Socrates following philosophers. The innovators want to do innovating and the Socrates people want nothing to exist or be real or whatever. Even though philosophy as a discipilne is extremely useful and powerful. Some of those innovators might be best served in dismissing it as the ravings of lunatics and just getting on with Innovating. So I want to describe the dream I had that stopped me talking further about mysticism. I fully acknowledge none of this makes sense since I have no following. But it still might offer an interesting perspective. It is of course not likely that if I offered a genuine challenge to your view on that that evildoers would pick it up and run with it. But apparently the dreams thought it was a suitable fear to highlight. So I went with it. My argument on mysticism would be as follows. This is not something I am committed to or care about but it was what I was thinking. It is now the story in something else I want to express. Firstly, your original statement is that mysticism is the gateway to mental illness. Firstly of course, I wrote to you on the definition of mysticism. Which I would use my own after having defined it due to the problems with yours that I highlighted. I would further refine that now by defining a primary and secondary faith. But anyway, per your argument, I would say, if mysticism is a gateway to mental illness. Then that would assume it would not in general, be used to solve mental illness. I would further refine the use of symbolic things to reach understandings. Such as tarot cards. By asking why do we dream. Why does our subconscious communicate in such a way? I would answer this by saying what is the alternative? The alternative being that without the subtlety and indirectness. The subconscious would communicate more like a dictator. Even giving the information without veil would have this effect. Since once we know the right thing to do we have more responsibility and consequences than before we know that. So what does this sound like? This sounds like schizophrenia! I would then talk about how a possible theory for it, is that if the problem gets too serious. If the subconscious mind is screaming too loudly. It busts through the conscious/ subconscious barrier too loudly, and that's where this comes from. (This is roughly what I think happened with my schizophrenic break, some of my ideas come indirectly from the psychologist Elinor Greenberg who talks about how dreams help low level schizophrenics)This would then correlate schizophrenia, and that kind of non objective, symbolic understandings. More with the symptom of other problems than with it being the cause. I would also define mainstream faith based Christianity as mysticism. As per my earlier example. And show times when this has been used to help people. Such as when the Ukraine war used to go a bit crazy women on Gab used to put loads of Christian sayings out. Women cannot biologically deal with war, but they still have fear, so a tool like mysticism to reduce the fear is perhaps highly positive. So now I get to the point. Like I said and strongly believe. It is unlikely evildoers would take such a reasoning as this and run with it to dent your power. But the dreams still responded like this was the case. The dream I had, (I do not like to tell others my dreams I prefer to interpret but I am making an exception here). I was about to make a few youtube videos on this. But I had a dream with Pearl Davis being aggressively tortured. She has mentioned a few times over the years how she has been sued and things. It was a pretty shocking dream. It felt kind of real. But what I think it could mean, is that your platform and output in this kind of social war, was significantly impacting people like Pearl by pushing back on intensely female and active toxicity we are currently witnessing (Taking us back to the point on mysticism and the Socrates philosophers analogy).I realise you might not interpret it the same way. Like, you might believe that all individuals in our dreams are parts of ourselves along a Family Systems therapy line. But I just wanted to provide that feedback in case it does provide some perspective or help in some way. Best Wishes,Joe ---It has been some years since I listened to your last podcast, 'Why animals can't love.' At that point, I quit Molyneaux. It has occurred and re occurred to me that you continued to make consciousness or choice the mandatory when it comes to capacity to love.This thinking backs exactly into a contradiction. We know that infants have neither consciousness nor choice, yet, any parent knows the infant loves. Toddlers are compelled to love, but they love nonetheless. Teenagers, etc. Not only compelled to love, but can be. Of course, Molyneaux would say, 'But that's no real love.' But some of it is. The child still wants to love the parent even when virtue (lack) seeks to negate. Some part of that child does still love. I always believed that your false philosophy on animals and love conditions backed directly into the right, even obligation, to abort children. The threadline of your 'philosophy' justified abortion. Since the infant has no choice or consciousness. He is more animal, less human. The right to kill seems elementary. That's always deeply concerned me that something is off center in your work. Mean spirited. Resentful. Death-loving. A hint of Crowley, even though 98% of your takes are good. I know you made your cash on bitcoin. Congratulations. Make an atheist like yourself proud. Your constant promise that you'd go down as a philosopher great, today and/or in 400 years from now, shows no evidence.GET FREEDOMAIN MERCH! https://shop.freedomain.com/SUBSCRIBE TO ME ON X! https://x.com/StefanMolyneuxFollow me on Youtube! https://www.youtube.com/@freedomain1GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK!https://peacefulparenting.com/Join the PREMIUM philosophy community on the web for free!Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows!You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more!See you soon!https://freedomain.locals.com/support/promo/UPB2025

Peptides are everywhere in longevity and functional medicine, but what does the law actually say? In this episode, Dr. Matt Kaeberlein breaks down what a peptide is and the complex regulatory landscape governing peptides in the United States, from FDA approval frameworks to the legal gray zones surrounding compounding pharmacies and unapproved substances.Drawing on the Federal Food, Drug, and Cosmetic Act and real-world enforcement cases, this episode offers a clear-eyed, non-judgmental examination of what is legal, what is tolerated, and what carries genuine risk for physicians, pharmacies, and patients alike. An essential listen for anyone navigating the rapidly evolving world of peptide therapeutics.Timestamps:00:00 — The Tailor Made Compounding Case: Criminal Prosecution & FDA-Flagged Peptides00:54 — Welcome & Episode Overview: The Regulatory Environment of Peptides01:36 — What Is a Peptide? Definitions and Biological Context03:26 — Are Peptides Regulated by the FDA?04:16 — Two Regulatory Frameworks: Biologics vs. Chemically Synthesized Drugs05:17 — The Food, Drug, and Cosmetic Act: Which Peptides Fall Under It?06:11 — Why Two Frameworks Exist: A Brief Legislative History07:06 — The Core Rule: FDA Approval vs. Unapproved Status07:54 — FDA-Approved Peptides: What Can Legally Be Prescribed09:11 — Compounding Pharmacies: When Is It Legal?10:01 — The Unapproved Peptide Bucket: A Long List10:55 — Legal Status of Unapproved Peptides Under U.S. Law12:34 — The Bulk Drug Substances List: What It Is and What It Isn't14:11 — FDA's "Significant Safety Risk" Flagged Peptides15:04 — Why Some Pharmacies Still Compound Unapproved Peptides16:44 — The Gray Area: Speeding Analogies and Cultural Norms17:38 — Degrees of Risk: Formulation, Approval Status, and Data Quality18:38 — Who Bears Legal Liability? Pharmacies, Physicians, and Patients19:39 — The Tailor Made Compounding Case: DOJ Action and Criminal Conviction20:34 — Risk to Physicians and Patients Summarized21:24 — Self-Sourcing Peptides Online: What Consumers Should Know22:14 — Prescription Drugs Without a Prescription: Legal Nuance23:06 — Final Thoughts: The State of Peptide Medicine Today23:58 — How Enforcement Norms Can Shift and Why It Matters Now24:48 — Questions to Ask Yourself: Risk Tolerance and Physician Choice25:21 — An Informed Consent Framework for Peptide Use26:03 — Coming Up: Individual Peptide Deep-Dives in Future Episodes

Old radio tandem for tonight as Mo and Sam man the fort for episode 15. They take two calls from girls who are looking to define what they have in a relationship. We have all been through it, perhaps? Let's check it out.Caller #1 is Aly 30yrs from Cairns, Australia. Aly is dating two guys and she wants to finally select one of them to be the exclusive -- but is he ready for that?Caller #2 is Jamie 42yrs from Toronto, Canada. Jamie and her BF have been together for 4 years and though they talk about the future a lot, hanggang promise ring lang si boy. When can she demand a better ring?GTWM and Good Times Radio are now streaming exclusively live on Discord!Join the Discord community by going to ⁠⁠www.discord.gg/goodtimesradio

Jason Chats with Emily Vincent and Jonathan Chaffer.

Welcome to Building Passive Income with CREI Collin Most passive investors never read the operating agreement—and that's a mistake. The operating agreement is the rulebook for how the syndication operates. It defines your rights, the sponsor's powers, how profits are distributed, when you get paid, and what happens if things go wrong. In this episode, CREI Collin decodes the operating agreement, breaking down the 10 key sections every investor must understand. You'll learn what rights you have as a limited partner or non-managing member, what red flags to watch for, and what questions to ask before you sign. Learn how to read an operating agreement with confidence. CREI Collin decodes the 10 key sections that define your rights as a passive investor. Key Topics Covered: What is an operating agreement (and limited partnership agreement)? The 10 key sections of an operating agreement Your rights as a limited partner or non-managing member What you can and can't do as a passive investor Red flags to watch for in an operating agreement Questions to ask sponsors about the operating agreement How to protect yourself when reviewing an operating agreement Timestamps: [00:00] Introduction: Why most investors don't read the operating agreement [02:30] What is an operating agreement and why it matters [04:45] Section 1: Definitions [05:30] Section 2: Capital Contributions [06:15] Section 3: Allocations of Profits and Losses [07:00] Section 4: Distributions [08:15] Section 5: Management and Control [09:30] Section 6: Voting Rights [10:45] Section 7: Transfer Restrictions [11:45] Section 8: Capital Calls [12:45] Section 9: Sponsor Removal [13:45] Section 10: Dissolution and Liquidation [14:45] Your rights as a limited partner or non-managing member [16:30] Red flags to watch for [18:15] Questions to ask sponsors [20:00] Recap and action steps Key Takeaways: The operating agreement (for LLCs) or limited partnership agreement (for LPs) is the governing document that defines your rights, the sponsor's powers, and the rules for how the deal operates. Focus on 10 key sections: Definitions, Capital Contributions, Allocations, Distributions, Management and Control, Voting Rights, Transfer Restrictions, Capital Calls, Sponsor Removal, and Dissolution. As a limited partner or non-managing member, you have the right to receive distributions, financial information, and a K-1, and you may have limited voting or consent rights. You generally don't have day-to-day control or the right to easily exit. Red flags include unclear governance, broad discretion without guardrails, mandatory capital calls with severe penalties (dilution, loss of rights, reduced distributions, or forfeiture), vague distribution language, difficult or impossible sponsor removal, severe transfer restrictions, and overly broad indemnification clauses. Ask detailed questions about control, distributions, capital calls, voting or consent rights, transfers, and exit strategy. Read the operating agreement carefully, consult with an attorney if investing significant capital, and evaluate calmly if something feels off. Resources Mentioned: Chapters (00:00:01) - Building Passive Income(00:01:46) - What Am I Signing?(00:02:44) - Subscription Agreement and Investor Questionnaire(00:05:21) - Representations and Warranties(00:07:43) - Accredited Investors: Final Checks and Red flags(00:12:53) - The subscription agreement and investor questionnaire are the final legal documents you sign

Have you ever felt lost in conversations where resolution seems out of reach? Have you wondered why every attempt you make to explain is met with roadblocks that leave you even more frustrated? If so, you might be encountering what Annette Oltmans of The Mend Project calls "The Maze of Confusion."  The Maze of Confusion is a strategy used by emotional abusers to derail genuine communication. By weaving a complex web of distractions and dead ends, they prevent meaningful dialogue, leaving their partners overwhelmed and disoriented. Instead of engaging in healthy conversations, these tactics create barriers to understanding and resolution, leading to increased confusion and emotional pain.   In this conversation, Annette and Karla talk about the tactics used to block communication in emotionally abusive relationships from their own experience and from their work with thousands of abuse victims and survivors. Their conversation contrasts unhealthy tactics that prevent resolution with the characteristics of healthy communication that foster understanding and resolution.   This podcast is packed with helpful information that empowers abuse victims and abuse survivors. They need labels for the tactics that are used to control them. They need to understand the motives that drive the abuser's behavior. They need validation to counter the gaslighting and invalidation.      Understanding and navigating this maze can be challenging, but you don't have to do it alone. Annette is the founder of The Mend Project, an organization that seeks to educate, equip, and restore all who are impacted by emotional abuse and train those who interface with them personally or professionally. Please take a moment to review their resources. #confusion  #emotionalabuse  #emotionalabusesurvivor #domesticviolencesurvivors    Resources and Links:    The MEND Project - https://themendproject.com/   Find Clarity and Healing Course - https://themendproject.com/find-clarity-and-healing-course/   "My Journey Through Double Abuse" - interview of Annette Oltmans' story. https://www.podbean.com/eas/pb-a4tmz-1004118   "I Was a Covert Emotional Abuser" - Interview of Bucky Oltmans' story  https://www.podbean.com/eas/pb-ebkpt-107fd04   Maze of Confusion, Terms and Definitions and Other Free Resources from The MEND Project - https://themendproject.com/resources/   Karla Downing's Classes - https://www.changemyrelationship.com/current-and-upcoming-classes/     Website: https://www.changemyrelationship.com/ Facebook: https://www.facebook.com/ChangeMyRelationship YouTube: https://www.youtube.com/@changemyrelationship Watch this video on YouTube: https://youtu.be/ewZv3bY0tcg

Defining Israel's Deep Political and Demographic Divides. Peter Berkowitz clarifies crucial definitions in Israelipolitics, explaining why a one-state solution would destroy Israel's democratic and Jewish character. He outlines how traditional left-right divisions have morphed into pro- or anti-Netanyahu factions, heavily influenced by religious demographics and the ultra-Orthodox community's contentious role in military service. #101900 SAINT LAWRENCE

Some people define leadership the wrong way. These definitions don't describe strong leadership but its opposite. In this week's episode, Chad shares three common weak definitions of leadership and how you can avoid them, overcome them, and rise above them to be a strong leader. Audio Production by Podsworth Media - https://podsworth.com 

In this episode of History 102, 'WhatIfAltHist' creator Rudyard Lynch and co-host Austin Padgett analyze the trajectory of Neoliberalism, exploring global wealth breakthroughs, the rise of technocratic bureaucracies, and recent populist shifts through a critical historical lens. -- FOLLOW ON X: @whatifalthist (Rudyard) @LudwigNverMises (Austin) @TurpentineMedia -- TIMESTAMPS: (00:00) Intro (05:58) Defining the Age of Neoliberalism (09:44) The Technocratic Compromise (16:49) The "Shared Illusion" and the Projector Screen (19:10) Definitions of Freedom: Anglo-Saxon vs. French (20:51) The "Terrarium" of Modern Consciousness (31:36) COVID-19 and the Lifting of the Veil (36:38) The Decline of Europe and the Rise of Natural Elites (43:48) The Professional Betrayal: Academia and Medicine (1:05:49) The Greek Crisis and the Fragility of the EU (1:17:08) Brexit and the Nihilism of the UK (1:28:10) Post-Soviet Russia: From Chaos to Postmodernism (1:36:58) The Rise and Threat of China (1:46:25) The "Methodist" Success of South Korea (1:56:56) 9/11 and the Failure of National Confidence (2:15:18) Wrap Learn more about your ad choices. Visit megaphone.fm/adchoices

Macht im Coaching – ein Thema, das wir gerne vermeiden. Und genau deshalb so relevant ist.   In der aktuellen Podcastfolge sprechen wir offen über ein Thema, das im Coaching immer präsent ist – auch dann, wenn wir es nicht benennen: Macht. Denn Coaching ist kein machtfreier Raum.   Allein durch Rolle, Expertise, Prozesssteuerung, Deutungen und Fragen entsteht Einfluss. Die entscheidende Frage ist nicht ob wir Macht haben – sondern wie bewusst und verantwortungsvoll wir damit umgehen.   In dieser Folge geht es u. a. um: ▪️ Definitions- und Prozessmacht im Coaching ▪️ subtile Formen von Einfluss und Zuschreibung ▪️ Machtmissbrauch vs. professionelle Verantwortung ▪️ Macht in Firmencoachings und Dreiecksverhältnissen ▪️ Diversität, Privilegien und blinde Flecken ▪️ und ganz konkret: Was Coaches tun können, um Macht reflektiert zu nutzen – statt sie unbewusst wirken zu lassen  

This week, Meghan and Jen host the Women's Quarterly Initiatives. She's joined by Evelyn Eury, Dr Claire Muselman, Kirsten Kaiser Kus and Dawn-Ann Dykes to discuss ways to empower women. 

In Part 2 of this Q&A series stemming from questions in the webinar, Managing Your AI Teammate, Eric Naiburg continues the conversation with Darrell Fernandes, diving deeper into how AI is reshaping the way Scrum Teams work.Together, they explore practical applications of AI in Scrum — from drafting and refining user stories to strengthening Definitions of Done and improving Gherkin statements. Darrell shares how AI can help teams create clearer, more consistent, and testable backlog items, while also warning against over-reliance.Eric and Darrell examine AI's impact on team dynamics, including how meeting-recording tools can summarize conversations, capture action items, and support retrospectives. They also address the human side of adoption: differing mental models, fear of change, and the critical role Scrum Masters play as enablers — not gatekeepers — of AI experimentation.Finally, they tackle a topic many teams overlook: total cost of ownership. As AI capabilities expand, Product Owners must understand infrastructure, data, and operational costs to avoid unintended financial consequences.If you're navigating how to thoughtfully integrate AI into your Scrum Team — balancing opportunity, risk, and cost — this episode offers practical insights and grounded guidance.

In this episode of Develop This!, host Dennis Fraise sits down with Phil Schneider, Project Principal at Global Location Strategies (GLS), to unpack one of the most critical challenges facing communities today: site readiness. With more than 30 years of global consulting experience and nearly 400 site selection engagements across manufacturing, headquarters, R&D, technology, and shared services, Phil brings a site selector's unfiltered perspective on how the site selection landscape has fundamentally changed—and why many communities are struggling to keep up. The conversation explores how manufacturing site selection projects now move at hyper speed, why risk aversion among companies has intensified, and how the shortage of truly competitive industrial sites is reshaping economic development strategy. Phil also dives into the persistent problem of inconsistent definitions of "ready sites" across states and programs—and how that inconsistency can derail projects before they even get started. A major focus of the episode is the work of the Site Selectors Guild to establish national standards for site readiness. Phil explains how standardized criteria, data transparency, and data integrity can dramatically improve a community's competitiveness—and save both site selectors and economic developers valuable time. This episode is essential listening for any economic development professional looking to align their site readiness efforts with real-world site selection expectations. Key Takeaways Site readiness is now a baseline requirement, not a competitive advantage. Site selection timelines have compressed dramatically, increasing pressure on communities. There is a national shortage of quality, build-ready industrial sites. Companies are increasingly risk-averse, demanding better data and fewer unknowns. Definitions of "ready sites" vary widely—and that inconsistency creates friction. Economic developers and site selectors don't always evaluate readiness the same way. Data richness, accessibility, and transparency are essential to staying competitive. The Site Selectors Guild Ready Sites program helps identify gaps and raise standards. There are no perfect sites—but knowing your site's limitations matters. National site readiness standards are becoming increasingly important, even globally.

I want to get quizzical with 10 trivia questions on the definitions of words that start with the letter 'Q!'If you'd like to choose a specific topic or dedicate an episode to a friend send a donation of your choice to our PayPal (NoChitChatTrivia@gmail.com) or our Venmo @NoChitChatTrivia and write the topic you'd like in the comments: https://account.venmo.com/NoChitChatTriviaOur official store is live! Support the show by grabbing a NCCT shirt, hat, puzzle, or more: https://www.thetop10things.com/storeSocial Media Links: TikTok, Instagram, FaceBook, YouTubeVisit our sister site thetop10things.com for travel and entertainment information!Thank you to everyone who listens! Say hello or let's collaborate: nochitchattrivia@gmail.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Washington Post Layoffs-Bud Commercial-Olympic Protests?-Racist Meme-Definitions of Terms! www.headlinesandopinions.com Conversations centered around the American Experiment and our Constitution and Bill of Rights! Our goal is to provide different perspectives - give historical context - model how to talk with those whom we may disagree with - tie foundational principals to today's headlines - PLUS, have some fun along the way. Please leave us a review and share with your friends! (A PODCAST PROVIDED AND OWNED BY DURING THE BREAK PODCASTS) Brought to you by Eric Buchanan and Associates: www.buchanandisability.com ===== THANK YOU TO OUR SPONSORS: Nutrition World: https://nutritionw.com/ Vascular Institute of Chattanooga: https://www.vascularinstituteofchattanooga.com/ The Barn Nursery: https://www.barnnursery.com/ Optimize U Chattanooga: https://optimizeunow.com/chattanooga/ Guardian Investment Advisors: https://giaplantoday.com/ Alchemy Medspa and Wellness Center: http://www.alchemychattanooga.com/ Our House Studio: https://ourhousestudiosinc.com/ Team Montieth Real Estate - Lori Montieth: https://www.findchattanoogarealestate.com/ Ballinger and Associates - Risk Management: https://ballingerandassociates.com/ AirSpace Acoustics: https://www.airspaceacoustics.com/ ALL THINGS JEFF STYLES: www.thejeffstyles.com PART OF THE NOOGA PODCAST NETWORK: www.noogapodcasts.com Please consider leaving us a review on Apple and giving us a share to your friends! This podcast is powered by ZenCast.fm

Washington Post Layoffs-Bud Commercial-Olympic Protests?-Racist Meme-Definitions of Terms! www.headlinesandopinions.com Conversations centered around the American Experiment and our Constitution and Bill of Rights! Our goal is to provide different perspectives - give historical context - model how to talk with those whom we may disagree with - tie foundational principals to today's headlines - PLUS, have some fun along the way. Please leave us a review and share with your friends! (A PODCAST PROVIDED AND OWNED BY DURING THE BREAK PODCASTS) Brought to you by Eric Buchanan and Associates: www.buchanandisability.com This podcast is hosted by ZenCast.fm

In this episode of The Writing Life Podcast, novelist and essayist Matt Greene shares the process of writing his latest novel, The Definitions – a work of dystopian fiction which interrogates and plays with the relationship between language, memory and the self.   Matt is a novelist and essayist. His first novel, Ostrich, published in 2013, won a Betty Trask Award and was a Daily Telegraph book of the year. His memoir, Jew(ish) was published in 2020. His latest novel, The Definitions, was published in October 2025. He lives in London with his partner and two sons.   The Definitions is an elegant and haunting dystopian novel about a group of individuals gathered to relearn how to navigate the world after a mysterious illness strips them of their memories.    He sat down with NCW's Steph McKenna to discuss the genesis of the novel, which began as a philosophical experiment, and how working within the dystopian genre allowed him to explore how language shapes identity. They also touch on his approach to writing characters who lack memory or a sense of self, and how their gradual understanding of the world was conveyed through a playful, vivid use of simile and metaphor.

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From Palantir and Two Sigma to building Goodfire into the poster-child for actionable mechanistic interpretability, Mark Bissell (Member of Technical Staff) and Myra Deng (Head of Product) are trying to turn “peeking inside the model” into a repeatable production workflow by shipping APIs, landing real enterprise deployments, and now scaling the bet with a recent $150M Series B funding round at a $1.25B valuation.In this episode, we go far beyond the usual “SAEs are cool” take. We talk about Goodfire's core bet: that the AI lifecycle is still fundamentally broken because the only reliable control we have is data and we post-train, RLHF, and fine-tune by “slurping supervision through a straw,” hoping the model picks up the right behaviors while quietly absorbing the wrong ones. Goodfire's answer is to build a bi-directional interface between humans and models: read what's happening inside, edit it surgically, and eventually use interpretability during training so customization isn't just brute-force guesswork.Mark and Myra walk through what that looks like when you stop treating interpretability like a lab demo and start treating it like infrastructure: lightweight probes that add near-zero latency, token-level safety filters that can run at inference time, and interpretability workflows that survive messy constraints (multilingual inputs, synthetic→real transfer, regulated domains, no access to sensitive data). We also get a live window into what “frontier-scale interp” means operationally (i.e. steering a trillion-parameter model in real time by targeting internal features) plus why the same tooling generalizes cleanly from language models to genomics, medical imaging, and “pixel-space” world models.We discuss:* Myra + Mark's path: Palantir (health systems, forward-deployed engineering) → Goodfire early team; Two Sigma → Head of Product, translating frontier interpretability research into a platform and real-world deployments* What “interpretability” actually means in practice: not just post-hoc poking, but a broader “science of deep learning” approach across the full AI lifecycle (data curation → post-training → internal representations → model design)* Why post-training is the first big wedge: “surgical edits” for unintended behaviors likereward hacking, sycophancy, noise learned during customization plus the dream of targeted unlearning and bias removal without wrecking capabilities* SAEs vs probes in the real world: why SAE feature spaces sometimes underperform classifiers trained on raw activations for downstream detection tasks (hallucination, harmful intent, PII), and what that implies about “clean concept spaces”* Rakuten in production: deploying interpretability-based token-level PII detection at inference time to prevent routing private data to downstream providers plus the gnarly constraints: no training on real customer PII, synthetic→real transfer, English + Japanese, and tokenization quirks* Why interp can be operationally cheaper than LLM-judge guardrails: probes are lightweight, low-latency, and don't require hosting a second large model in the loop* Real-time steering at frontier scale: a demo of steering Kimi K2 (~1T params) live and finding features via SAE pipelines, auto-labeling via LLMs, and toggling a “Gen-Z slang” feature across multiple layers without breaking tool use* Hallucinations as an internal signal: the case that models have latent uncertainty / “user-pleasing” circuitry you can detect and potentially mitigate more directly than black-box methods* Steering vs prompting: the emerging view that activation steering and in-context learning are more closely connected than people think, including work mapping between the two (even for jailbreak-style behaviors)* Interpretability for science: using the same tooling across domains (genomics, medical imaging, materials) to debug spurious correlations and extract new knowledge up to and including early biomarker discovery work with major partners* World models + “pixel-space” interpretability: why vision/video models make concepts easier to see, how that accelerates the feedback loop, and why robotics/world-model partners are especially interesting design partners* The north star: moving from “data in, weights out” to intentional model design where experts can impart goals and constraints directly, not just via reward signals and brute-force post-training—Goodfire AI* Website: https://goodfire.ai* LinkedIn: https://www.linkedin.com/company/goodfire-ai/* X: https://x.com/GoodfireAIMyra Deng* Website: https://myradeng.com/* LinkedIn: https://www.linkedin.com/in/myra-deng/* X: https://x.com/myra_dengMark Bissell* LinkedIn: https://www.linkedin.com/in/mark-bissell/* X: https://x.com/MarkMBissellFull Video EpisodeTimestamps00:00:00 Introduction00:00:05 Introduction to the Latent Space Podcast and Guests from Goodfire00:00:29 What is Goodfire? Mission and Focus on Interpretability00:01:01 Goodfire's Practical Approach to Interpretability00:01:37 Goodfire's Series B Fundraise Announcement00:02:04 Backgrounds of Mark and Myra from Goodfire00:02:51 Team Structure and Roles at Goodfire00:05:13 What is Interpretability? Definitions and Techniques00:05:30 Understanding Errors00:07:29 Post-training vs. Pre-training Interpretability Applications00:08:51 Using Interpretability to Remove Unwanted Behaviors00:10:09 Grokking, Double Descent, and Generalization in Models00:10:15 404 Not Found Explained00:12:06 Subliminal Learning and Hidden Biases in Models00:14:07 How Goodfire Chooses Research Directions and Projects00:15:00 Troubleshooting Errors00:16:04 Limitations of SAEs and Probes in Interpretability00:18:14 Rakuten Case Study: Production Deployment of Interpretability00:20:45 Conclusion00:21:12 Efficiency Benefits of Interpretability Techniques00:21:26 Live Demo: Real-Time Steering in a Trillion Parameter Model00:25:15 How Steering Features are Identified and Labeled00:26:51 Detecting and Mitigating Hallucinations Using Interpretability00:31:20 Equivalence of Activation Steering and Prompting00:34:06 Comparing Steering with Fine-Tuning and LoRA Techniques00:36:04 Model Design and the Future of Intentional AI Development00:38:09 Getting Started in Mechinterp: Resources, Programs, and Open Problems00:40:51 Industry Applications and the Rise of Mechinterp in Practice00:41:39 Interpretability for Code Models and Real-World Usage00:43:07 Making Steering Useful for More Than Stylistic Edits00:46:17 Applying Interpretability to Healthcare and Scientific Discovery00:49:15 Why Interpretability is Crucial in High-Stakes Domains like Healthcare00:52:03 Call for Design Partners Across Domains00:54:18 Interest in World Models and Visual Interpretability00:57:22 Sci-Fi Inspiration: Ted Chiang and Interpretability01:00:14 Interpretability, Safety, and Alignment Perspectives01:04:27 Weak-to-Strong Generalization and Future Alignment Challenges01:05:38 Final Thoughts and Hiring/Collaboration Opportunities at GoodfireTranscriptShawn Wang [00:00:05]: So welcome to the Latent Space pod. We're back in the studio with our special MechInterp co-host, Vibhu. Welcome. Mochi, Mochi's special co-host. And Mochi, the mechanistic interpretability doggo. We have with us Mark and Myra from Goodfire. Welcome. Thanks for having us on. Maybe we can sort of introduce Goodfire and then introduce you guys. How do you introduce Goodfire today?Myra Deng [00:00:29]: Yeah, it's a great question. So Goodfire, we like to say, is an AI research lab that focuses on using interpretability to understand, learn from, and design AI models. And we really believe that interpretability will unlock the new generation, next frontier of safe and powerful AI models. That's our description right now, and I'm excited to dive more into the work we're doing to make that happen.Shawn Wang [00:00:55]: Yeah. And there's always like the official description. Is there an understatement? Is there an unofficial one that sort of resonates more with a different audience?Mark Bissell [00:01:01]: Well, being an AI research lab that's focused on interpretability, there's obviously a lot of people have a lot that they think about when they think of interpretability. And I think we have a pretty broad definition of what that means and the types of places that can be applied. And in particular, applying it in production scenarios, in high stakes industries, and really taking it sort of from the research world into the real world. Which, you know. It's a new field, so that hasn't been done all that much. And we're excited about actually seeing that sort of put into practice.Shawn Wang [00:01:37]: Yeah, I would say it wasn't too long ago that Anthopic was like still putting out like toy models or superposition and that kind of stuff. And I wouldn't have pegged it to be this far along. When you and I talked at NeurIPS, you were talking a little bit about your production use cases and your customers. And then not to bury the lead, today we're also announcing the fundraise, your Series B. $150 million. $150 million at a 1.25B valuation. Congrats, Unicorn.Mark Bissell [00:02:02]: Thank you. Yeah, no, things move fast.Shawn Wang [00:02:04]: We were talking to you in December and already some big updates since then. Let's dive, I guess, into a bit of your backgrounds as well. Mark, you were at Palantir working on health stuff, which is really interesting because the Goodfire has some interesting like health use cases. I don't know how related they are in practice.Mark Bissell [00:02:22]: Yeah, not super related, but I don't know. It was helpful context to know what it's like. Just to work. Just to work with health systems and generally in that domain. Yeah.Shawn Wang [00:02:32]: And Mara, you were at Two Sigma, which actually I was also at Two Sigma back in the day. Wow, nice.Myra Deng [00:02:37]: Did we overlap at all?Shawn Wang [00:02:38]: No, this is when I was briefly a software engineer before I became a sort of developer relations person. And now you're head of product. What are your sort of respective roles, just to introduce people to like what all gets done in Goodfire?Mark Bissell [00:02:51]: Yeah, prior to Goodfire, I was at Palantir for about three years as a forward deployed engineer, now a hot term. Wasn't always that way. And as a technical lead on the health care team and at Goodfire, I'm a member of the technical staff. And honestly, that I think is about as specific as like as as I could describe myself because I've worked on a range of things. And, you know, it's it's a fun time to be at a team that's still reasonably small. I think when I joined one of the first like ten employees, now we're above 40, but still, it looks like there's always a mix of research and engineering and product and all of the above. That needs to get done. And I think everyone across the team is, you know, pretty, pretty switch hitter in the roles they do. So I think you've seen some of the stuff that I worked on related to image models, which was sort of like a research demo. More recently, I've been working on our scientific discovery team with some of our life sciences partners, but then also building out our core platform for more of like flexing some of the kind of MLE and developer skills as well.Shawn Wang [00:03:53]: Very generalist. And you also had like a very like a founding engineer type role.Myra Deng [00:03:58]: Yeah, yeah.Shawn Wang [00:03:59]: So I also started as I still am a member of technical staff, did a wide range of things from the very beginning, including like finding our office space and all of this, which is we both we both visited when you had that open house thing. It was really nice.Myra Deng [00:04:13]: Thank you. Thank you. Yeah. Plug to come visit our office.Shawn Wang [00:04:15]: It looked like it was like 200 people. It has room for 200 people. But you guys are like 10.Myra Deng [00:04:22]: For a while, it was very empty. But yeah, like like Mark, I spend. A lot of my time as as head of product, I think product is a bit of a weird role these days, but a lot of it is thinking about how do we take our frontier research and really apply it to the most important real world problems and how does that then translate into a platform that's repeatable or a product and working across, you know, the engineering and research teams to make that happen and also communicating to the world? Like, what is interpretability? What is it used for? What is it good for? Why is it so important? All of these things are part of my day-to-day as well.Shawn Wang [00:05:01]: I love like what is things because that's a very crisp like starting point for people like coming to a field. They all do a fun thing. Vibhu, why don't you want to try tackling what is interpretability and then they can correct us.Vibhu Sapra [00:05:13]: Okay, great. So I think like one, just to kick off, it's a very interesting role to be head of product, right? Because you guys, at least as a lab, you're more of an applied interp lab, right? Which is pretty different than just normal interp, like a lot of background research. But yeah. You guys actually ship an API to try these things. You have Ember, you have products around it, which not many do. Okay. What is interp? So basically you're trying to have an understanding of what's going on in model, like in the model, in the internal. So different approaches to do that. You can do probing, SAEs, transcoders, all this stuff. But basically you have an, you have a hypothesis. You have something that you want to learn about what's happening in a model internals. And then you're trying to solve that from there. You can do stuff like you can, you know, you can do activation mapping. You can try to do steering. There's a lot of stuff that you can do, but the key question is, you know, from input to output, we want to have a better understanding of what's happening and, you know, how can we, how can we adjust what's happening on the model internals? How'd I do?Mark Bissell [00:06:12]: That was really good. I think that was great. I think it's also a, it's kind of a minefield of a, if you ask 50 people who quote unquote work in interp, like what is interpretability, you'll probably get 50 different answers. And. Yeah. To some extent also like where, where good fire sits in the space. I think that we're an AI research company above all else. And interpretability is a, is a set of methods that we think are really useful and worth kind of specializing in, in order to accomplish the goals we want to accomplish. But I think we also sort of see some of the goals as even more broader as, as almost like the science of deep learning and just taking a not black box approach to kind of any part of the like AI development life cycle, whether that. That means using interp for like data curation while you're training your model or for understanding what happened during post-training or for the, you know, understanding activations and sort of internal representations, what is in there semantically. And then a lot of sort of exciting updates that were, you know, are sort of also part of the, the fundraise around bringing interpretability to training, which I don't think has been done all that much before. A lot of this stuff is sort of post-talk poking at models as opposed to. To actually using this to intentionally design them.Shawn Wang [00:07:29]: Is this post-training or pre-training or is that not a useful.Myra Deng [00:07:33]: Currently focused on post-training, but there's no reason the techniques wouldn't also work in pre-training.Shawn Wang [00:07:38]: Yeah. It seems like it would be more active, applicable post-training because basically I'm thinking like rollouts or like, you know, having different variations of a model that you can tweak with the, with your steering. Yeah.Myra Deng [00:07:50]: And I think in a lot of the news that you've seen in, in, on like Twitter or whatever, you've seen a lot of unintended. Side effects come out of post-training processes, you know, overly sycophantic models or models that exhibit strange reward hacking behavior. I think these are like extreme examples. There's also, you know, very, uh, mundane, more mundane, like enterprise use cases where, you know, they try to customize or post-train a model to do something and it learns some noise or it doesn't appropriately learn the target task. And a big question that we've always had is like, how do you use your understanding of what the model knows and what it's doing to actually guide the learning process?Shawn Wang [00:08:26]: Yeah, I mean, uh, you know, just to anchor this for people, uh, one of the biggest controversies of last year was 4.0 GlazeGate. I've never heard of GlazeGate. I didn't know that was what it was called. The other one, they called it that on the blog post and I was like, well, how did OpenAI call it? Like officially use that term. And I'm like, that's funny, but like, yeah, I guess it's the pitch that if they had worked a good fire, they wouldn't have avoided it. Like, you know what I'm saying?Myra Deng [00:08:51]: I think so. Yeah. Yeah.Mark Bissell [00:08:53]: I think that's certainly one of the use cases. I think. Yeah. Yeah. I think the reason why post-training is a place where this makes a lot of sense is a lot of what we're talking about is surgical edits. You know, you want to be able to have expert feedback, very surgically change how your model is doing, whether that is, you know, removing a certain behavior that it has. So, you know, one of the things that we've been looking at or is, is another like common area where you would want to make a somewhat surgical edit is some of the models that have say political bias. Like you look at Quen or, um, R1 and they have sort of like this CCP bias.Shawn Wang [00:09:27]: Is there a CCP vector?Mark Bissell [00:09:29]: Well, there's, there are certainly internal, yeah. Parts of the representation space where you can sort of see where that lives. Yeah. Um, and you want to kind of, you know, extract that piece out.Shawn Wang [00:09:40]: Well, I always say, you know, whenever you find a vector, a fun exercise is just like, make it very negative to see what the opposite of CCP is.Mark Bissell [00:09:47]: The super America, bald eagles flying everywhere. But yeah. So in general, like lots of post-training tasks where you'd want to be able to, to do that. Whether it's unlearning a certain behavior or, you know, some of the other kind of cases where this comes up is, are you familiar with like the, the grokking behavior? I mean, I know the machine learning term of grokking.Shawn Wang [00:10:09]: Yeah.Mark Bissell [00:10:09]: Sort of this like double descent idea of, of having a model that is able to learn a generalizing, a generalizing solution, as opposed to even if memorization of some task would suffice, you want it to learn the more general way of doing a thing. And so, you know, another. A way that you can think about having surgical access to a model's internals would be learn from this data, but learn in the right way. If there are many possible, you know, ways to, to do that. Can make interp solve the double descent problem?Shawn Wang [00:10:41]: Depends, I guess, on how you. Okay. So I, I, I viewed that double descent as a problem because then you're like, well, if the loss curves level out, then you're done, but maybe you're not done. Right. Right. But like, if you actually can interpret what is a generalizing or what you're doing. What is, what is still changing, even though the loss is not changing, then maybe you, you can actually not view it as a double descent problem. And actually you're just sort of translating the space in which you view loss and like, and then you have a smooth curve. Yeah.Mark Bissell [00:11:11]: I think that's certainly like the domain of, of problems that we're, that we're looking to get.Shawn Wang [00:11:15]: Yeah. To me, like double descent is like the biggest thing to like ML research where like, if you believe in scaling, then you don't need, you need to know where to scale. And. But if you believe in double descent, then you don't, you don't believe in anything where like anything levels off, like.Vibhu Sapra [00:11:30]: I mean, also tendentially there's like, okay, when you talk about the China vector, right. There's the subliminal learning work. It was from the anthropic fellows program where basically you can have hidden biases in a model. And as you distill down or, you know, as you train on distilled data, those biases always show up, even if like you explicitly try to not train on them. So, you know, it's just like another use case of. Okay. If we can interpret what's happening in post-training, you know, can we clear some of this? Can we even determine what's there? Because yeah, it's just like some worrying research that's out there that shows, you know, we really don't know what's going on.Mark Bissell [00:12:06]: That is. Yeah. I think that's the biggest sentiment that we're sort of hoping to tackle. Nobody knows what's going on. Right. Like subliminal learning is just an insane concept when you think about it. Right. Train a model on not even the logits, literally the output text of a bunch of random numbers. And now your model loves owls. And you see behaviors like that, that are just, they defy, they defy intuition. And, and there are mathematical explanations that you can get into, but. I mean.Shawn Wang [00:12:34]: It feels so early days. Objectively, there are a sequence of numbers that are more owl-like than others. There, there should be.Mark Bissell [00:12:40]: According to, according to certain models. Right. It's interesting. I think it only applies to models that were initialized from the same starting Z. Usually, yes.Shawn Wang [00:12:49]: But I mean, I think that's a, that's a cheat code because there's not enough compute. But like if you believe in like platonic representation, like probably it will transfer across different models as well. Oh, you think so?Mark Bissell [00:13:00]: I think of it more as a statistical artifact of models initialized from the same seed sort of. There's something that is like path dependent from that seed that might cause certain overlaps in the latent space and then sort of doing this distillation. Yeah. Like it pushes it towards having certain other tendencies.Vibhu Sapra [00:13:24]: Got it. I think there's like a bunch of these open-ended questions, right? Like you can't train in new stuff during the RL phase, right? RL only reorganizes weights and you can only do stuff that's somewhat there in your base model. You're not learning new stuff. You're just reordering chains and stuff. But okay. My broader question is when you guys work at an interp lab, how do you decide what to work on and what's kind of the thought process? Right. Because we can ramble for hours. Okay. I want to know this. I want to know that. But like, how do you concretely like, you know, what's the workflow? Okay. There's like approaches towards solving a problem, right? I can try prompting. I can look at chain of thought. I can train probes, SAEs. But how do you determine, you know, like, okay, is this going anywhere? Like, do we have set stuff? Just, you know, if you can help me with all that. Yeah.Myra Deng [00:14:07]: It's a really good question. I feel like we've always at the very beginning of the company thought about like, let's go and try to learn what isn't working in machine learning today. Whether that's talking to customers or talking to researchers at other labs, trying to understand both where the frontier is going and where things are really not falling apart today. And then developing a perspective on how we can push the frontier using interpretability methods. And so, you know, even our chief scientist, Tom, spends a lot of time talking to customers and trying to understand what real world problems are and then taking that back and trying to apply the current state of the art to those problems and then seeing where they fall down basically. And then using those failures or those shortcomings to understand what hills to climb when it comes to interpretability research. So like on the fundamental side, for instance, when we have done some work applying SAEs and probes, we've encountered, you know, some shortcomings in SAEs that we found a little bit surprising. And so have gone back to the drawing board and done work on that. And then, you know, we've done some work on better foundational interpreter models. And a lot of our team's research is focused on what is the next evolution beyond SAEs, for instance. And then when it comes to like control and design of models, you know, we tried steering with our first API and realized that it still fell short of black box techniques like prompting or fine tuning. And so went back to the drawing board and we're like, how do we make that not the case and how do we improve it beyond that? And one of our researchers, Ekdeep, who just joined is actually Ekdeep and Atticus are like steering experts and have spent a lot of time trying to figure out like, what is the research that enables us to actually do this in a much more powerful, robust way? So yeah, the answer is like, look at real world problems, try to translate that into a research agenda and then like hill climb on both of those at the same time.Shawn Wang [00:16:04]: Yeah. Mark has the steering CLI demo queued up, which we're going to go into in a sec. But I always want to double click on when you drop hints, like we found some problems with SAEs. Okay. What are they? You know, and then we can go into the demo. Yeah.Myra Deng [00:16:19]: I mean, I'm curious if you have more thoughts here as well, because you've done it in the healthcare domain. But I think like, for instance, when we do things like trying to detect behaviors within models that are harmful or like behaviors that a user might not want to have in their model. So hallucinations, for instance, harmful intent, PII, all of these things. We first tried using SAE probes for a lot of these tasks. So taking the feature activation space from SAEs and then training classifiers on top of that, and then seeing how well we can detect the properties that we might want to detect in model behavior. And we've seen in many cases that probes just trained on raw activations seem to perform better than SAE probes, which is a bit surprising if you think that SAEs are actually also capturing the concepts that you would want to capture cleanly and more surgically. And so that is an interesting observation. I don't think that is like, I'm not down on SAEs at all. I think there are many, many things they're useful for, but we have definitely run into cases where I think the concept space described by SAEs is not as clean and accurate as we would expect it to be for actual like real world downstream performance metrics.Mark Bissell [00:17:34]: Fair enough. Yeah. It's the blessing and the curse of unsupervised methods where you get to peek into the AI's mind. But sometimes you wish that you saw other things when you walked inside there. Although in the PII instance, I think weren't an SAE based approach actually did prove to be the most generalizable?Myra Deng [00:17:53]: It did work well in the case that we published with Rakuten. And I think a lot of the reasons it worked well was because we had a noisier data set. And so actually the blessing of unsupervised learning is that we actually got to get more meaningful, generalizable signal from SAEs when the data was noisy. But in other cases where we've had like good data sets, it hasn't been the case.Shawn Wang [00:18:14]: And just because you named Rakuten and I don't know if we'll get it another chance, like what is the overall, like what is Rakuten's usage or production usage? Yeah.Myra Deng [00:18:25]: So they are using us to essentially guardrail and inference time monitor their language model usage and their agent usage to detect things like PII so that they don't route private user information.Myra Deng [00:18:41]: And so that's, you know, going through all of their user queries every day. And that's something that we deployed with them a few months ago. And now we are actually exploring very early partnerships, not just with Rakuten, but with other people around how we can help with potentially training and customization use cases as well. Yeah.Shawn Wang [00:19:03]: And for those who don't know, like it's Rakuten is like, I think number one or number two e-commerce store in Japan. Yes. Yeah.Mark Bissell [00:19:10]: And I think that use case actually highlights a lot of like what it looks like to deploy things in practice that you don't always think about when you're doing sort of research tasks. So when you think about some of the stuff that came up there that's more complex than your idealized version of a problem, they were encountering things like synthetic to real transfer of methods. So they couldn't train probes, classifiers, things like that on actual customer data of PII. So what they had to do is use synthetic data sets. And then hope that that transfer is out of domain to real data sets. And so we can evaluate performance on the real data sets, but not train on customer PII. So that right off the bat is like a big challenge. You have multilingual requirements. So this needed to work for both English and Japanese text. Japanese text has all sorts of quirks, including tokenization behaviors that caused lots of bugs that caused us to be pulling our hair out. And then also a lot of tasks you'll see. You might make simplifying assumptions if you're sort of treating it as like the easiest version of the problem to just sort of get like general results where maybe you say you're classifying a sentence to say, does this contain PII? But the need that Rakuten had was token level classification so that you could precisely scrub out the PII. So as we learned more about the problem, you're sort of speaking about what that looks like in practice. Yeah. A lot of assumptions end up breaking. And that was just one instance where you. A problem that seems simple right off the bat ends up being more complex as you keep diving into it.Vibhu Sapra [00:20:41]: Excellent. One of the things that's also interesting with Interp is a lot of these methods are very efficient, right? So where you're just looking at a model's internals itself compared to a separate like guardrail, LLM as a judge, a separate model. One, you have to host it. Two, there's like a whole latency. So if you use like a big model, you have a second call. Some of the work around like self detection of hallucination, it's also deployed for efficiency, right? So if you have someone like Rakuten doing it in production live, you know, that's just another thing people should consider.Mark Bissell [00:21:12]: Yeah. And something like a probe is super lightweight. Yeah. It's no extra latency really. Excellent.Shawn Wang [00:21:17]: You have the steering demos lined up. So we were just kind of see what you got. I don't, I don't actually know if this is like the latest, latest or like alpha thing.Mark Bissell [00:21:26]: No, this is a pretty hacky demo from from a presentation that someone else on the team recently gave. So this will give a sense for, for technology. So you can see the steering and action. Honestly, I think the biggest thing that this highlights is that as we've been growing as a company and taking on kind of more and more ambitious versions of interpretability related problems, a lot of that comes to scaling up in various different forms. And so here you're going to see steering on a 1 trillion parameter model. This is Kimi K2. And so it's sort of fun that in addition to the research challenges, there are engineering challenges that we're now tackling. Cause for any of this to be sort of useful in production, you need to be thinking about what it looks like when you're using these methods on frontier models as opposed to sort of like toy kind of model organisms. So yeah, this was thrown together hastily, pretty fragile behind the scenes, but I think it's quite a fun demo. So screen sharing is on. So I've got two terminal sessions pulled up here. On the left is a forked version that we have of the Kimi CLI that we've got running to point at our custom hosted Kimi model. And then on the right is a set up that will allow us to steer on certain concepts. So I should be able to chat with Kimi over here. Tell it hello. This is running locally. So the CLI is running locally, but the Kimi server is running back to the office. Well, hopefully should be, um, that's too much to run on that Mac. Yeah. I think it's, uh, it takes a full, like each 100 node. I think it's like, you can. You can run it on eight GPUs, eight 100. So, so yeah, Kimi's running. We can ask it a prompt. It's got a forked version of our, uh, of the SG line code base that we've been working on. So I'm going to tell it, Hey, this SG line code base is slow. I think there's a bug. Can you try to figure it out? There's a big code base, so it'll, it'll spend some time doing this. And then on the right here, I'm going to initialize in real time. Some steering. Let's see here.Mark Bissell [00:23:33]: searching for any. Bugs. Feature ID 43205.Shawn Wang [00:23:38]: Yeah.Mark Bissell [00:23:38]: 20, 30, 40. So let me, uh, this is basically a feature that we found that inside Kimi seems to cause it to speak in Gen Z slang. And so on the left, it's still sort of thinking normally it might take, I don't know, 15 seconds for this to kick in, but then we're going to start hopefully seeing him do this code base is massive for real. So we're going to start. We're going to start seeing Kimi transition as the steering kicks in from normal Kimi to Gen Z Kimi and both in its chain of thought and its actual outputs.Mark Bissell [00:24:19]: And interestingly, you can see, you know, it's still able to call tools, uh, and stuff. It's um, it's purely sort of it's it's demeanor. And there are other features that we found for interesting things like concision. So that's more of a practical one. You can make it more concise. Um, the types of programs, uh, programming languages that uses, but yeah, as we're seeing it come in. Pretty good. Outputs.Shawn Wang [00:24:43]: Scheduler code is actually wild.Vibhu Sapra [00:24:46]: Yo, this code is actually insane, bro.Vibhu Sapra [00:24:53]: What's the process of training in SAE on this, or, you know, how do you label features? I know you guys put out a pretty cool blog post about, um, finding this like autonomous interp. Um, something. Something about how agents for interp is different than like coding agents. I don't know while this is spewing up, but how, how do we find feature 43, two Oh five. Yeah.Mark Bissell [00:25:15]: So in this case, um, we, our platform that we've been building out for a long time now supports all the sort of classic out of the box interp techniques that you might want to have like SAE training, probing things of that kind, I'd say the techniques for like vanilla SAEs are pretty well established now where. You take your model that you're interpreting, run a whole bunch of data through it, gather activations, and then yeah, pretty straightforward pipeline to train an SAE. There are a lot of different varieties. There's top KSAEs, batch top KSAEs, um, normal ReLU SAEs. And then once you have your sparse features to your point, assigning labels to them to actually understand that this is a gen Z feature, that's actually where a lot of the kind of magic happens. Yeah. And the most basic standard technique is look at all of your d input data set examples that cause this feature to fire most highly. And then you can usually pick out a pattern. So for this feature, If I've run a diverse enough data set through my model feature 43, two Oh five. Probably tends to fire on all the tokens that sounds like gen Z slang. You know, that's the, that's the time of year to be like, Oh, I'm in this, I'm in this Um, and, um, so, you know, you could have a human go through all 43,000 concepts andVibhu Sapra [00:26:34]: And I've got to ask the basic question, you know, can we get examples where it hallucinates, pass it through, see what feature activates for hallucinations? Can I just, you know, turn hallucination down?Myra Deng [00:26:51]: Oh, wow. You really predicted a project we're already working on right now, which is detecting hallucinations using interpretability techniques. And this is interesting because hallucinations is something that's very hard to detect. And it's like a kind of a hairy problem and something that black box methods really struggle with. Whereas like Gen Z, you could always train a simple classifier to detect that hallucinations is harder. But we've seen that models internally have some... Awareness of like uncertainty or some sort of like user pleasing behavior that leads to hallucinatory behavior. And so, yeah, we have a project that's trying to detect that accurately. And then also working on mitigating the hallucinatory behavior in the model itself as well.Shawn Wang [00:27:39]: Yeah, I would say most people are still at the level of like, oh, I would just turn temperature to zero and that turns off hallucination. And I'm like, well, that's a fundamental misunderstanding of how this works. Yeah.Mark Bissell [00:27:51]: Although, so part of what I like about that question is you, there are SAE based approaches that might like help you get at that. But oftentimes the beauty of SAEs and like we said, the curse is that they're unsupervised. So when you have a behavior that you deliberately would like to remove, and that's more of like a supervised task, often it is better to use something like probes and specifically target the thing that you're interested in reducing as opposed to sort of like hoping that when you fragment the latent space, one of the vectors that pops out.Vibhu Sapra [00:28:20]: And as much as we're training an autoencoder to be sparse, we're not like for sure certain that, you know, we will get something that just correlates to hallucination. You'll probably split that up into 20 other things and who knows what they'll be.Mark Bissell [00:28:36]: Of course. Right. Yeah. So there's no sort of problems with like feature splitting and feature absorption. And then there's the off target effects, right? Ideally, you would want to be very precise where if you reduce the hallucination feature, suddenly maybe your model can't write. Creatively anymore. And maybe you don't like that, but you want to still stop it from hallucinating facts and figures.Shawn Wang [00:28:55]: Good. So Vibhu has a paper to recommend there that we'll put in the show notes. But yeah, I mean, I guess just because your demo is done, any any other things that you want to highlight or any other interesting features you want to show?Mark Bissell [00:29:07]: I don't think so. Yeah. Like I said, this is a pretty small snippet. I think the main sort of point here that I think is exciting is that there's not a whole lot of inter being applied to models quite at this scale. You know, Anthropic certainly has some some. Research and yeah, other other teams as well. But it's it's nice to see these techniques, you know, being put into practice. I think not that long ago, the idea of real time steering of a trillion parameter model would have sounded.Shawn Wang [00:29:33]: Yeah. The fact that it's real time, like you started the thing and then you edited the steering vector.Vibhu Sapra [00:29:38]: I think it's it's an interesting one TBD of what the actual like production use case would be on that, like the real time editing. It's like that's the fun part of the demo, right? You can kind of see how this could be served behind an API, right? Like, yes, you're you only have so many knobs and you can just tweak it a bit more. And I don't know how it plays in. Like people haven't done that much with like, how does this work with or without prompting? Right. How does this work with fine tuning? Like, there's a whole hype of continual learning, right? So there's just so much to see. Like, is this another parameter? Like, is it like parameter? We just kind of leave it as a default. We don't use it. So I don't know. Maybe someone here wants to put out a guide on like how to use this with prompting when to do what?Mark Bissell [00:30:18]: Oh, well, I have a paper recommendation. I think you would love from Act Deep on our team, who is an amazing researcher, just can't say enough amazing things about Act Deep. But he actually has a paper that as well as some others from the team and elsewhere that go into the essentially equivalence of activation steering and in context learning and how those are from a he thinks of everything in a cognitive neuroscience Bayesian framework, but basically how you can precisely show how. Prompting in context, learning and steering exhibit similar behaviors and even like get quantitative about the like magnitude of steering you would need to do to induce a certain amount of behavior similar to certain prompting, even for things like jailbreaks and stuff. It's a really cool paper. Are you saying steering is less powerful than prompting? More like you can almost write a formula that tells you how to convert between the two of them.Myra Deng [00:31:20]: And so like formally equivalent actually in the in the limit. Right.Mark Bissell [00:31:24]: So like one case study of this is for jailbreaks there. I don't know. Have you seen the stuff where you can do like many shot jailbreaking? You like flood the context with examples of the behavior. And the topic put out that paper.Shawn Wang [00:31:38]: A lot of people were like, yeah, we've been doing this, guys.Mark Bissell [00:31:40]: Like, yeah, what's in this in context learning and activation steering equivalence paper is you can like predict the number. Number of examples that you will need to put in there in order to jailbreak the model. That's cool. By doing steering experiments and using this sort of like equivalence mapping. That's cool. That's really cool. It's very neat. Yeah.Shawn Wang [00:32:02]: I was going to say, like, you know, I can like back rationalize that this makes sense because, you know, what context is, is basically just, you know, it updates the KV cache kind of and like and then every next token inference is still like, you know, the sheer sum of everything all the way. It's plus all the context. It's up to date. And you could, I guess, theoretically steer that with you probably replace that with your steering. The only problem is steering typically is on one layer, maybe three layers like like you did. So it's like not exactly equivalent.Mark Bissell [00:32:33]: Right, right. There's sort of you need to get precise about, yeah, like how you sort of define steering and like what how you're modeling the setup. But yeah, I've got the paper pulled up here. Belief dynamics reveal the dual nature. Yeah. The title is Belief Dynamics Reveal the Dual Nature of Incompetence. And it's an exhibition of the practical context learning and activation steering. So Eric Bigelow, Dan Urgraft on the who are doing fellowships at Goodfire, Ekt Deep's the final author there.Myra Deng [00:32:59]: I think actually to your question of like, what is the production use case of steering? I think maybe if you just think like one level beyond steering as it is today. Like imagine if you could adapt your model to be, you know, an expert legal reasoner. Like in almost real time, like very quickly. efficiently using human feedback or using like your semantic understanding of what the model knows and where it knows that behavior. I think that while it's not clear what the product is at the end of the day, it's clearly very valuable. Thinking about like what's the next interface for model customization and adaptation is a really interesting problem for us. Like we have heard a lot of people actually interested in fine-tuning an RL for open weight models in production. And so people are using things like Tinker or kind of like open source libraries to do that, but it's still very difficult to get models fine-tuned and RL'd for exactly what you want them to do unless you're an expert at model training. And so that's like something we'reShawn Wang [00:34:06]: looking into. Yeah. I never thought so. Tinker from Thinking Machines famously uses rank one LoRa. Is that basically the same as steering? Like, you know, what's the comparison there?Mark Bissell [00:34:19]: Well, so in that case, you are still applying updates to the parameters, right?Shawn Wang [00:34:25]: Yeah. You're not touching a base model. You're touching an adapter. It's kind of, yeah.Mark Bissell [00:34:30]: Right. But I guess it still is like more in parameter space then. I guess it's maybe like, are you modifying the pipes or are you modifying the water flowing through the pipes to get what you're after? Yeah. Just maybe one way.Mark Bissell [00:34:44]: I like that analogy. That's my mental map of it at least, but it gets at this idea of model design and intentional design, which is something that we're, that we're very focused on. And just the fact that like, I hope that we look back at how we're currently training models and post-training models and just think what a primitive way of doing that right now. Like there's no intentionalityShawn Wang [00:35:06]: really in... It's just data, right? The only thing in control is what data we feed in.Mark Bissell [00:35:11]: So, so Dan from Goodfire likes to use this analogy of, you know, he has a couple of young kids and he talks about like, what if I could only teach my kids how to be good people by giving them cookies or like, you know, giving them a slap on the wrist if they do something wrong, like not telling them why it was wrong or like what they should have done differently or something like that. Just figure it out. Right. Exactly. So that's RL. Yeah. Right. And, and, you know, it's sample inefficient. There's, you know, what do they say? It's like slurping feedback. It's like, slurping supervision. Right. And so you'd like to get to the point where you can have experts giving feedback to their models that are, uh, internalized and, and, you know, steering is an inference time way of sort of getting that idea. But ideally you're moving to a world whereVibhu Sapra [00:36:04]: it is much more intentional design in perpetuity for these models. Okay. This is one of the questions we asked Emmanuel from Anthropic on the podcast a few months ago. Basically the question, was you're at a research lab that does model training, foundation models, and you're on an interp team. How does it tie back? Right? Like, does this, do ideas come from the pre-training team? Do they go back? Um, you know, so for those interested, you can, you can watch that. There wasn't too much of a connect there, but it's still something, you know, it's something they want toMark Bissell [00:36:33]: push for down the line. It can be useful for all of the above. Like there are certainly post-hocVibhu Sapra [00:36:39]: use cases where it doesn't need to touch that. I think the other thing a lot of people forget is this stuff isn't too computationally expensive, right? Like I would say, if you're interested in getting into research, MechInterp is one of the most approachable fields, right? A lot of this train an essay, train a probe, this stuff, like the budget for this one, there's already a lot done. There's a lot of open source work. You guys have done some too. Um, you know,Shawn Wang [00:37:04]: There's like notebooks from the Gemini team for Neil Nanda or like, this is how you do it. Just step through the notebook.Vibhu Sapra [00:37:09]: Even if you're like, not even technical with any of this, you can still make like progress. There, you can look at different activations, but, uh, if you do want to get into training, you know, training this stuff, correct me if I'm wrong is like in the thousands of dollars, not even like, it's not that high scale. And then same with like, you know, applying it, doing it for post-training or all this stuff is fairly cheap in scale of, okay. I want to get into like model training. I don't have compute for like, you know, pre-training stuff. So it's, it's a very nice field to get into. And also there's a lot of like open questions, right? Um, some of them have to go with, okay, I want a product. I want to solve this. Like there's also just a lot of open-ended stuff that people could work on. That's interesting. Right. I don't know if you guys have any calls for like, what's open questions, what's open work that you either open collaboration with, or like, you'd just like to see solved or just, you know, for people listening that want to get into McInturk because people always talk about it. What are, what are the things they should check out? Start, of course, you know, join you guys as well. I'm sure you're hiring.Myra Deng [00:38:09]: There's a paper, I think from, was it Lee, uh, Sharky? It's open problems and, uh, it's, it's a bit of interpretability, which I recommend everyone who's interested in the field. Read. I'm just like a really comprehensive overview of what are the things that experts in the field think are the most important problems to be solved. I also think to your point, it's been really, really inspiring to see, I think a lot of young people getting interested in interpretability, actually not just young people also like scientists to have been, you know, experts in physics for many years and in biology or things like this, um, transitioning into interp, because the barrier of, of what's now interp. So it's really cool to see a number to entry is, you know, in some ways low and there's a lot of information out there and ways to get started. There's this anecdote of like professors at universities saying that all of a sudden every incoming PhD student wants to study interpretability, which was not the case a few years ago. So it just goes to show how, I guess, like exciting the field is, how fast it's moving, how quick it is to get started and things like that.Mark Bissell [00:39:10]: And also just a very welcoming community. You know, there's an open source McInturk Slack channel. There are people are always posting questions and just folks in the space are always responsive if you ask things on various forums and stuff. But yeah, the open paper, open problems paper is a really good one.Myra Deng [00:39:28]: For other people who want to get started, I think, you know, MATS is a great program. What's the acronym for? Machine Learning and Alignment Theory Scholars? It's like the...Vibhu Sapra [00:39:40]: Normally summer internship style.Myra Deng [00:39:42]: Yeah, but they've been doing it year round now. And actually a lot of our full-time staff have come through that program or gone through that program. And it's great for anyone who is transitioning into interpretability. There's a couple other fellows programs. We do one as well as Anthropic. And so those are great places to get started if anyone is interested.Mark Bissell [00:40:03]: Also, I think been seen as a research field for a very long time. But I think engineering... I think engineers are sorely wanted for interpretability as well, especially at Goodfire, but elsewhere, as it does scale up.Shawn Wang [00:40:18]: I should mention that Lee actually works with you guys, right? And in the London office and I'm adding our first ever McInturk track at AI Europe because I see this industry applications now emerging. And I'm pretty excited to, you know, help push that along. Yeah, I was looking forward to that. It'll effectively be the first industry McInturk conference. Yeah. I'm so glad you added that. You know, it's still a little bit of a bet. It's not that widespread, but I can definitely see this is the time to really get into it. We want to be early on things.Mark Bissell [00:40:51]: For sure. And I think the field understands this, right? So at ICML, I think the title of the McInturk workshop this year was actionable interpretability. And there was a lot of discussion around bringing it to various domains. Everyone's adding pragmatic, actionable, whatever.Shawn Wang [00:41:10]: It's like, okay, well, we weren't actionable before, I guess. I don't know.Vibhu Sapra [00:41:13]: And I mean, like, just, you know, being in Europe, you see the Interp room. One, like old school conferences, like, I think they had a very tiny room till they got lucky and they got it doubled. But there's definitely a lot of interest, a lot of niche research. So you see a lot of research coming out of universities, students. We covered the paper last week. It's like two unknown authors, not many citations. But, you know, you can make a lot of meaningful work there. Yeah. Yeah. Yeah.Shawn Wang [00:41:39]: Yeah. I think people haven't really mentioned this yet. It's just Interp for code. I think it's like an abnormally important field. We haven't mentioned this yet. The conspiracy theory last two years ago was when the first SAE work came out of Anthropic was they would do like, oh, we just used SAEs to turn the bad code vector down and then turn up the good code. And I think like, isn't that the dream? Like, you know, like, but basically, I guess maybe, why is it funny? Like, it's... If it was realistic, it would not be funny. It would be like, no, actually, we should do this. But it's funny because we know there's like, we feel there's some limitations to what steering can do. And I think a lot of the public image of steering is like the Gen Z stuff. Like, oh, you can make it really love the Golden Gate Bridge, or you can make it speak like Gen Z. To like be a legal reasoner seems like a huge stretch. Yeah. And I don't know if that will get there this way. Yeah.Myra Deng [00:42:36]: I think, um, I will say we are announcing. Something very soon that I will not speak too much about. Um, but I think, yeah, this is like what we've run into again and again is like, we, we don't want to be in the world where steering is only useful for like stylistic things. That's definitely not, not what we're aiming for. But I think the types of interventions that you need to do to get to things like legal reasoning, um, are much more sophisticated and require breakthroughs in, in learning algorithms. And that's, um...Shawn Wang [00:43:07]: And is this an emergent property of scale as well?Myra Deng [00:43:10]: I think so. Yeah. I mean, I think scale definitely helps. I think scale allows you to learn a lot of information and, and reduce noise across, you know, large amounts of data. But I also think we think that there's ways to do things much more effectively, um, even, even at scale. So like actually learning exactly what you want from the data and not learning things that you do that you don't want exhibited in the data. So we're not like anti-scale, but we are also realizing that scale is not going to get us anywhere. It's not going to get us to the type of AI development that we want to be at in, in the future as these models get more powerful and get deployed in all these sorts of like mission critical contexts. Current life cycle of training and deploying and evaluations is, is to us like deeply broken and has opportunities to, to improve. So, um, more to come on that very, very soon.Mark Bissell [00:44:02]: And I think that that's a use basically, or maybe just like a proof point that these concepts do exist. Like if you can manipulate them in the precise best way, you can get the ideal combination of them that you desire. And steering is maybe the most coarse grained sort of peek at what that looks like. But I think it's evocative of what you could do if you had total surgical control over every concept, every parameter. Yeah, exactly.Myra Deng [00:44:30]: There were like bad code features. I've got it pulled up.Vibhu Sapra [00:44:33]: Yeah. Just coincidentally, as you guys are talking.Shawn Wang [00:44:35]: This is like, this is exactly.Vibhu Sapra [00:44:38]: There's like specifically a code error feature that activates and they show, you know, it's not, it's not typo detection. It's like, it's, it's typos in code. It's not typical typos. And, you know, you can, you can see it clearly activates where there's something wrong in code. And they have like malicious code, code error. They have a whole bunch of sub, you know, sub broken down little grain features. Yeah.Shawn Wang [00:45:02]: Yeah. So, so the, the rough intuition for me, the, why I talked about post-training was that, well, you just, you know, have a few different rollouts with all these things turned off and on and whatever. And then, you know, you can, that's, that's synthetic data you can kind of post-train on. Yeah.Vibhu Sapra [00:45:13]: And I think we make it sound easier than it is just saying, you know, they do the real hard work.Myra Deng [00:45:19]: I mean, you guys, you guys have the right idea. Exactly. Yeah. We replicated a lot of these features in, in our Lama models as well. I remember there was like.Vibhu Sapra [00:45:26]: And I think a lot of this stuff is open, right? Like, yeah, you guys opened yours. DeepMind has opened a lot of essays on Gemma. Even Anthropic has opened a lot of this. There's, there's a lot of resources that, you know, we can probably share of people that want to get involved.Shawn Wang [00:45:41]: Yeah. And special shout out to like Neuronpedia as well. Yes. Like, yeah, amazing piece of work to visualize those things.Myra Deng [00:45:49]: Yeah, exactly.Shawn Wang [00:45:50]: I guess I wanted to pivot a little bit on, onto the healthcare side, because I think that's a big use case for you guys. We haven't really talked about it yet. This is a bit of a crossover for me because we are, we are, we do have a separate science pod that we're starting up for AI, for AI for science, just because like, it's such a huge investment category and also I'm like less qualified to do it, but we actually have bio PhDs to cover that, which is great, but I need to just kind of recover, recap your work, maybe on the evil two stuff, but then, and then building forward.Mark Bissell [00:46:17]: Yeah, for sure. And maybe to frame up the conversation, I think another kind of interesting just lens on interpretability in general is a lot of the techniques that were described. are ways to solve the AI human interface problem. And it's sort of like bidirectional communication is the goal there. So what we've been talking about with intentional design of models and, you know, steering, but also more advanced techniques is having humans impart our desires and control into models and over models. And the reverse is also very interesting, especially as you get to superhuman models, whether that's narrow superintelligence, like these scientific models that work on genomics, data, medical imaging, things like that. But down the line, you know, superintelligence of other forms as well. What knowledge can the AIs teach us as sort of that, that the other direction in that? And so some of our life science work to date has been getting at exactly that question, which is, well, some of it does look like debugging these various life sciences models, understanding if they're actually performing well, on tasks, or if they're picking up on spurious correlations, for instance, genomics models, you would like to know whether they are sort of focusing on the biologically relevant things that you care about, or if it's using some simpler correlate, like the ancestry of the person that it's looking at. But then also in the instances where they are superhuman, and maybe they are understanding elements of the human genome that we don't have names for or specific, you know, yeah, discoveries that they've made that that we don't know about, that's, that's a big goal. And so we're already seeing that, right, we are partnered with organizations like Mayo Clinic, leading research health system in the United States, our Institute, as well as a startup called Prima Menta, which focuses on neurodegenerative disease. And in our partnership with them, we've used foundation models, they've been training and applied our interpretability techniques to find novel biomarkers for Alzheimer's disease. So I think this is just the tip of the iceberg. But it's, that's like a flavor of some of the things that we're working on.Shawn Wang [00:48:36]: Yeah, I think that's really fantastic. Obviously, we did the Chad Zuckerberg pod last year as well. And like, there's a plethora of these models coming out, because there's so much potential and research. And it's like, very interesting how it's basically the same as language models, but just with a different underlying data set. But it's like, it's the same exact techniques. Like, there's no change, basically.Mark Bissell [00:48:59]: Yeah. Well, and even in like other domains, right? Like, you know, robotics, I know, like a lot of the companies just use Gemma as like the like backbone, and then they like make it into a VLA that like takes these actions. It's, it's, it's transformers all the way down. So yeah.Vibhu Sapra [00:49:15]: Like we have Med Gemma now, right? Like this week, even there was Med Gemma 1.5. And they're training it on this stuff, like 3d scans, medical domain knowledge, and all that stuff, too. So there's a push from both sides. But I think the thing that, you know, one of the things about McInturpp is like, you're a little bit more cautious in some domains, right? So healthcare, mainly being one, like guardrails, understanding, you know, we're more risk adverse to something going wrong there. So even just from a basic understanding, like, if we're trusting these systems to make claims, we want to know why and what's going on.Myra Deng [00:49:51]: Yeah, I think there's totally a kind of like deployment bottleneck to actually using. foundation models for real patient usage or things like that. Like, say you're using a model for rare disease prediction, you probably want some explanation as to why your model predicted a certain outcome, and an interpretable explanation at that. So that's definitely a use case. But I also think like, being able to extract scientific information that no human knows to accelerate drug discovery and disease treatment and things like that actually is a really, really big unlock for science, like scientific discovery. And you've seen a lot of startups, like say that they're going to accelerate scientific discovery. And I feel like we actually are doing that through our interp techniques. And kind of like, almost by accident, like, I think we got reached out to very, very early on from these healthcare institutions. And none of us had healthcare.Shawn Wang [00:50:49]: How did they even hear of you? A podcast.Myra Deng [00:50:51]: Oh, okay. Yeah, podcast.Vibhu Sapra [00:50:53]: Okay, well, now's that time, you know.Myra Deng [00:50:55]: Everyone can call us.Shawn Wang [00:50:56]: Podcasts are the most important thing. Everyone should listen to podcasts.Myra Deng [00:50:59]: Yeah, they reached out. They were like, you know, we have these really smart models that we've trained, and we want to know what they're doing. And we were like, really early that time, like three months old, and it was a few of us. And we were like, oh, my God, we've never used these models. Let's figure it out. But it's also like, great proof that interp techniques scale pretty well across domains. We didn't really have to learn too much about.Shawn Wang [00:51:21]: Interp is a machine learning technique, machine learning skills everywhere, right? Yeah. And it's obviously, it's just like a general insight. Yeah. Probably to finance too, I think, which would be fun for our history. I don't know if you have anything to say there.Mark Bissell [00:51:34]: Yeah, well, just across the science. Like, we've also done work on material science. Yeah, it really runs the gamut.Vibhu Sapra [00:51:40]: Yeah. Awesome. And, you know, for those that should reach out, like, you're obviously experts in this, but like, is there a call out for people that you're looking to partner with, design partners, people to use your stuff outside of just, you know, the general developer that wants to. Plug and play steering stuff, like on the research side more so, like, are there ideal design partners, customers, stuff like that?Myra Deng [00:52:03]: Yeah, I can talk about maybe non-life sciences, and then I'm curious to hear from you on the life sciences side. But we're looking for design partners across many domains, language, anyone who's customizing language models or trying to push the frontier of code or reasoning models is really interesting to us. And then also interested in the frontier of modeling. There's a lot of models that work in, like, pixel space, as we call it. So if you're doing world models, video models, even robotics, where there's not a very clean natural language interface to interact with, I think we think that Interp can really help and are looking for a few partners in that space.Shawn Wang [00:52:43]: Just because you mentioned the keyword

How Racism and Systemic Oppression Mirror Domestic Violence Dynamics (and Why ‘Just Leave' Doesn't Work)If you've ever thought “why don't they just leave?”—this episode is a reframe. In a rant-style but emotionally safe way, I break down why leaving toxic environments (relationships, jobs, communities, and systems) often isn't about willpower—it's about coercive control, trauma bonds, isolation, shame, and resource barriers.I'm using a framework/parallel (not claiming every experience is identical): if we can understand why someone stays in an abusive relationship, we can understand why groups stay in abusive systems—and why shaming them is part of the control.We cover: coercive control, trauma bond patterns, toxic relationships, emotionally unavailable dynamics, boundaries, generational trauma, Black fatigue (and how it gets hijacked), racism and trauma, workplace toxicity, and toxic friendships—plus what to do instead.00:00:00 — Welcome + what I'm finally naming00:01:16 — My personal context + why I'm qualified to say this00:01:51 — The racism ↔ DV parallel (framework, not identical experiences)00:03:13 — Why “just leave” isn't simple (and why shame is control)00:06:08 — “Just create your own safe spaces” (why that's historically blocked)00:07:32 — Systemic control + shaming as a tool00:09:10 — Toxic workplaces + hazing + coercive control “lite”00:10:25 — Empathy, resources, and what people ignore about leaving00:16:26 — Definitions: coercive control, isolation, trauma bonds00:20:21 — Leaving is about safety + resources + risk (not morality)00:27:29 — Reputation damage + opportunities lost (control beyond romance)00:31:03 — Black fatigue: misunderstood, hijacked, and weaponized00:37:28 — The Clear Exit Framework (step-by-step roadmap)00:41:05 — Support options + healing resources + how to get help00:43:24 — Closing encouragement + next stepsWork with me:Generational Trauma Insight Session (Paid): https://liveandbegreat.com/generational-trauma-insight-session-storeFree consult (Coaching): https://outlook.office.com/book/LiveBeGreatLLC1@liveandbegreat.com/?ismsaljsauthenabled=trueKeywords: racism, domestic violence, coercive control, trauma bonds, systemic oppression, shame, black fatigue, isolation, empathy, generational trauma

Everyone loves a masquerade, where one gets to be someone completely different – at least until the clock strikes midnight. In this season opener, the questions of who we are, who people see us as, and who we'd like to be start early, as one magical evening kicks off a series of events that are sure to turn the Ton a-tizzy. Benedict is a rake, Sophie is a servant, and somehow Lady Whistledown is still a main character. It's season four, and we're about to go downstairs. Featuring: - So many servants - Definitions of a rake, definitions of self - Finding the right person - Introducing the boy diamond - Big villain energy - Curiosity, fascination, surprise - A Cinderella story Here are is the media we talk about in this episode: - An Offer from a Gentleman, a book by Julia Quinn - Romancing Mr Bridgerton, a book by Julia Quinn - Bridgerton, a television series - Jodi's WWDD episode on virginity - Jodi's WWDD episode on romance series - The Consummate Virgin by Jodi McAlister - The Duke and I by Julia Quinn - When He Was Wicked by Julia Quinn - It's in His Kiss by Julia Quinn - Lord Byron - Heated Rivalry, a television adaptation - Queen Charlotte, a mini-series - Queen Charlotte, a WWDD special episode featuring Maxine Beneba Clarke - Cinderella, a fairytale - Joan of Arc - A Midsummer's Night Dream, a play by William Shakespeare - The Princess Bride, a film by Rob Reiner - Cleopatra - Marc Antony - Zeus - ‘Life in Technocolour', a song by Coldplay - ‘Masquerade', a song from The Phantom of the Opera, a musical by Andrew Lloyd Webber - The Bachelor, a reality TV show - The Bachelorette, a reality TV show ‘ DJ Got Us Fallin' in Love', a song by Usher - ‘Never Let You Go', a song by Third Eye Blind - Wicked, a musical by Stephen Schwartz - Ever After, a film by Andy Tennant Some extra notes: - Both Mrs Wilson and John the Footman have been regulars since season one - The Queen is dressed as the Queen of Hearts at the masquerade Our guest host this episode is the seriously smart, seriously sassy Jodi McAlister. You can hear more from Jodi by following her on instagram and tiktok and by reading her latest novel, An Academic Affair. For your TBR, Jodi has recommended Power Moves by Leesa Ronald. Jodi says the characters ‘sprang to life' for her and called the book ‘compelling'. Don't forget you can find us on facebook @bridgertonpod and instagram and bluesky @wwddpod and join the conversation using the hashtag #WWDDpod. Please follow us on your favourite podcast provider! Leaving a 5-star rating and a review will not only help us find more listeners, but also help you find joy and beauty in your world. This episode was recorded on the traditional and unceded land of the Kaurna, Wurundjeri and Boonwurrung people. Our editor is Ben McKenzie of Splendid Chaps Productions. If you need production work completed, you can find them here: splendidchaps.com

Hard-to-treat cancers like pancreatic ductal adenocarcinoma (PDAC) have long defied conventional therapies. Radiopharmaceuticals, combining targeted therapy with diagnostic power, are creating new opportunities in precision oncology.Host David Brühlmann speaks with Bryan Miller of Crown Bioscience, who explains how Crown's strategic partnerships, rigorous quality standards, and adaptive study design are shaping radiopharmaceutical development—delivering speed, safety, and real clinical impact.In this episode, you'll learn:The promise and practical implications of theranostics—agents used for both diagnosis and treatment (02:44)Definitions and distinctions between CDX (cell line-derived xenograft) and PDX (patient-derived xenograft) models, and why PDX models better recapitulate tumor heterogeneity (05:11)Strategies for building more predictive, clinically relevant research models (06:09)Balancing rapid innovation with rigorous quality standards—why robust QC systems enable speed without compromising safety (08:01)Key advice for scientists entering radiopharmaceutical development, including how to choose the right research partners (09:53)Why effective collaboration between biotech companies and CROs is akin to a well-chosen partnership (10:50)The future outlook for radiopharmaceuticals and their impact on hard-to-treat cancers (12:21)Strategic insight:Focusing on theranostic radiopharmaceuticals—agents that combine diagnostics and therapy—offers a high-impact strategy for hard-to-treat cancers like PDAC. By enabling simultaneous patient stratification and targeted treatment, theranostics can accelerate development, improve clinical outcomes, and create a competitive advantage in areas where traditional therapies are limited.Where do you see radiopharmaceuticals and advanced preclinical models making the biggest impact in oncology or beyond?Explore the full conversation to learn how Bryan Miller and Crown Bioscience are scaling innovation for the next generation of cancer therapies.Connect with Bryan Miller:LinkedIn: www.linkedin.com/in/bryan-miller-148344aaCrown Bioscience: www.crownbio.comNext step:Need fast CMC guidance? → Get rapid CMC decision support hereSupport the show

Fr. Younan William explores the concept and practical dimensions of leadership, emphasizing its foundational role in any organization, including the church. He reviews several definitions of leadership by notable figures like John Maxwell, highlighting that leadership begins with self-leadership and extends through influence, vision, and hope. Fr. Younan explains the five levels of leadership, from positional authority to personal influence, illustrating how true leadership grows through permission, production, and people development. He stresses that effective leaders balance relationships, results, and empowerment to sustain organizational growth. Using biblical references and modern examples, he shows leadership as a call to serve, develop others, and foster trust and cohesion. The talk examines different leadership styles and the need for flexibility depending on the situation. Fr. Younan also discusses common pitfalls in leadership, such as insecurity, control, and failure to delegate. He encourages leaders to cultivate humility, vision, and a commitment to developing those they lead. Subscribe to us on YouTube https://youtube.com/stpaulhouston Like us on Facebook https://facebook.com/saintpaulhouston Follow us on SoundCloud https://soundcloud.com/stpaulhouston Follow us on Instagram https://instagram.com/stpaulhouston Visit our website for schedules and to join the mailing list https://stpaulhouston.org

Kollel Iyun Halacha. Shuirim are held Sun-Thurs at 11 Gudz Road Lakewood NJ. For more info email: kih185miller@gmail.com

This week, Thom Francis welcomes Judith Kerman to the mic. She shared her work as the featured reader at the Third Thursday Poetry Night at the Social Justice Center in Albany, NY, on August 21, 2025. +++++ For over 20 years, Dan Wilcox has hosted the Third Thursday Poetry Night, welcoming poets and writers from all over the region and beyond. This open mic with a featured reader series has seen hundreds of poets take the stage and share their work with a vibrant audience of artists, writers, and creators young and old. Whether it's your first time reading poetry in public or you have been around the local literary community for years, the Third Thursday Poetry Night always feels like home. Last August, poet Judith Kerman was the featured reader. As host Dan Wilcox noted on his blog, “She began, & continued, with poems that pretend to be, or are, definitions, from her book Definitions; her first example was “Diaspora" in nine small parts, images, obliquely, historically responding to the dictionary definition.” She continued with the philosophical “Algorhythm” and “Canned Soup,” a prose poem and meditation on soup. She wrapped up her set with “Scoliosis,” “Why I Never Married,” and one final definition poem, “Israel,” in 10 tiny parts. Judith Kerman is a poet, performer, and artist who has published ten books or chapbooks of poetry. Her most recent work, Definitions, was published by Fomite Press in 2021. She has published two translations from Spanish: A Woman in Her Garden: Selected Poems of Dulce María Loynaz (White Pine Press, 2002) and Praises and Offenses: Three Women Poets from the Dominican Republic (BOA Editions, 2009). Kerman was a Fulbright Scholar to the Dominican Republic, where she translated poetry and fiction by Dominican women. She was also awarded the Abbie M. Kopps Poetry Prize and an Honorable Mention from the Great Lakes Colleges Association New Writers Award.

Lawsuits, Famous Medicine Origins, and Definitions "This Evening"

Visit ⁠⁠http://pmpdoctor.com/⁠⁠ for more PMP practice questions.In the PMP Exam Mindset, "Removing Impediments" is the ultimate expression of Servant Leadership. Your job is to act as a "shield" for the team, clearing the path so they can focus on delivering value without distraction. Definitions to Know (The "Nuance")While the exam often uses these interchangeably, understanding the slight differences helps you prioritize: Impediments: Anything that slows down the team (e.g., a slow server or unnecessary meetings).Obstacles: Barriers that can be avoided or moved with effort (e.g., a missing stakeholder approval).Blockers: Specific issues that cause a total stop on a task or story (e.g., a critical software bug). 

Stefan Molyneux takes on objections to Universally Preferable Behavior as a moral framework. He pushes back against the idea that morality stands on its own, stressing the need for clear definitions in any philosophical talk. When it comes to tying morality to gods or divine sources, he points out that fuzzy claims don't hold up as real arguments. Molyneux questions whether morality can just be about chasing the good, the true, and the beautiful, pulling in examples from religious texts to show the inconsistencies there. He looks back at how Christian morality has fallen short over time and doubts whether theocratic setups really deliver on ethics. In the end, he calls for a straightforward grasp of morality and what UPB means in practice, urging people to check their own biases and lean on real-world evidence in these discussions.Email from listener:UPB reduces down to "Morality is being". Or "By the act of living, you prefer life". Or Universal Preference for Being. But even without beings, morality still exists. So morality is God based, and is the rational pursuit of, participation in, and defense of the Good, the True, and the Beautiful (with evil being precisely whatever actively undermines or destroys those ends). Plato would agree. Jesus said, to love God with all your mind heart soul and strength, and love others as yourself, and the whole of the law rests on these two principles. It means to fight for the Good, the True, and the Beautiful - for order. Of course, this can only be done through rationality and power. So, the Good must take the power back. This cannot be done through secular materialism which only reduces to hedonism. People that hear their conscience seek rationality and God more than anything else, because everything else is temporary.However, Christianity displays false theories. The biggest one is the idea that an innocent person needed to suffer and be sacrificed for evils committed by everyone else. God would never require this because God is 100% good. The reality is that Jesus needed to be killed and resurrected so that His story would be way bigger and spread Goodness to way more people, and last forever. So, he did die for sins in that sense alone, so that more people would hear His story and turn away from sin.There is no other practical moral framework to turn to. Philosophy alone is rational, but it does not ground morality the same way God does. Actually, rationality requires one to accept God. Without God, people literally have absolutely no reason to be moral at all. And Deism's impersonal God doesn't connect with people. Christianity was working until the Jews brainwashed society and the Church and destroyed its influence on society. Notwithstanding its misinterpretations, Christianity appears to be the only effective thing people can actually believe in and follow. And Neitzche would say the will to power is too potent for UPB to control. However, Christianity at least affords a will to power of the True, the Beautiful, and the Good. Jesus whipped the little bastards in the Temple. That needs to come back, because that is all the little bastards can understand.Someone wants steak for dinner and the other person doesn't, or go hungry forever, that does not make the steak guy forcing the other to eat the steak immoral. UPB is a logical construction that fails in the real world, and honestly, not even to be a jerk, but literally no one at all gives the slightest fuck about it. Sorry for the language.And I really do appreciate your efforts and all your good works. And sure, UPB is a true logical construction, but people are irrational and will never be rational. And that is why the real world philosophy is 100% might makes right. And this is why Christianity must be forced down their throats until the world is functional again. Irrational people only understand force, and Christianity is the valid, justified, moral, virtuous, reason and purpose of true physical force against irrational and evil people.There is an attempt at logic in UPB, and it sort of works, but not really. Morality already existed before Mankind, and UPB only points out the effects of immorality, it does not define morality. And lastly, to include with all the arguments I have made against UPB. I will just say that bottom line, UPB is merely a survival instinct desire and not the creation of morality. Every person would agree that they don't want to be attacked or stolen from, simply because they want to live and survive, so that would make that universally preferable behavior. However, because this is all survival instinct based, as soon as a person sees a chance to steal or attack, that best serves their own survival, they will immediately not care the slightest about UPB because they are about their own survival over everyone else's. UPB is matter-based biologically-based morality, and simply does not hold up, just like all the other secular ethical frameworks before it - they all failed, and all secular ethical frameworks will always fail. This is because God-based, soul-based morality is the only Truth, as proven at dynamicentity.comGET FREEDOMAIN MERCH! https://shop.freedomain.com/SUBSCRIBE TO ME ON X! https://x.com/StefanMolyneuxFollow me on Youtube! https://www.youtube.com/@freedomain1GET MY NEW BOOK 'PEACEFUL PARENTING', THE INTERACTIVE PEACEFUL PARENTING AI, AND THE FULL AUDIOBOOK!https://peacefulparenting.com/Join the PREMIUM philosophy community on the web for free!Subscribers get 12 HOURS on the "Truth About the French Revolution," multiple interactive multi-lingual philosophy AIs trained on thousands of hours of my material - as well as AIs for Real-Time Relationships, Bitcoin, Peaceful Parenting, and Call-In Shows!You also receive private livestreams, HUNDREDS of exclusive premium shows, early release podcasts, the 22 Part History of Philosophers series and much more!See you soon!https://freedomain.locals.com/support/promo/UPB2025

Scott Duffy is back for round two on the podcast! Together, they pull back the curtain on the world of AI cloning for entrepreneurs, creators, and founders. You'll discover how digital twins are transforming business operations by helping leaders scale their time, impact, and sales—freeing them from repetitive tasks while deepening engagement. The hosts break down real-world use cases, compare leading platforms, and share their proven strategies for launching a successful AI clone in just seven days. If you're curious about automating business bottlenecks without sacrificing the "human" touch, this episode is for you!Topics DiscussedWhat is AI Cloning? Definitions and practical explanations of AI clones, digital twins, and how they're used for scaling knowledge and time.Clone Shop AI Process: How Joe Fier and Scott Duffy help clients audit their business, identify bottlenecks, and launch digital clones quickly and securely.Comparing Cloning Platforms: In-depth review of ChatGPT, HeyGen, and Delphi. Discussion on the strengths, weaknesses, and security concerns related to each.Scripted vs. Unscripted Clones: The difference between clones built on pre-written scripts (Heygen) and those leveraging larger knowledge bases (Delphi).Securing Your Intellectual Property: How platforms like Delphi protect personal data, and the risks of using more “open” foundational models.Cloning Use Cases: Real-world examples: HR support, sales, coaching, customer service, pastors, religious organizations, and content creation.Launching in Seven Days: Step-by-step overview of Clone Shop AI's audit, strategy, and quick-launch process for getting clients live quickly.Automations & Actions: Built-in features on Delphi for follow-ups, recap emails, memory nudges, and integrations with other systems.Self-Improvement & Content Creation: Creative ways people use their clones—from prepping for talks to writing books and blog posts using their own IP.Language & Modality Flexibility: How Delphi clones engage users across 50+ languages via text, voice, and even phone calls, ensuring high accessibility.Resources MentionedClone Shop AI: https://thecloneshop.aiDelphi: https://hustleandflowchart.com/delphiConnect with Scott DuffyLinkedIn: https://www.linkedin.com/in/scottduffymedia/Website: https://scottduffy.comConnect with Joe Fier

If you are not committed to the truth, your witness is compromised. If truth has an agenda, how does the Bible weigh in on truth in a world where it may be twisted? We need a revelation from God, lest we spiral into despair as we sift through the conflicting worldviews of subjective truth. So what is God's truth agenda? Definitions, hot takes, and a call to truth all await you in this episode. Unpack biblical truth with us today on the After Class Podcast as we prepare to walk—and stand—firm in truth in our towns, cities, and nations.

Clark County Today Editor Ken Vance responds to remarks by WSDOT Secretary Julie Meredith before the House Transportation Committee, challenging the justification for moving forward with the Interstate Bridge Replacement project as cost estimates rise into the billions. https://www.clarkcountytoday.com/opinion/opinion-wsdot-secretary-and-i-obviously-have-very-different-definitions-for-the-term-cost-effective/ #Opinion #Transportation #I5Bridge #IBR #WSDOT

Few craft categories have experienced the meteoric rise of ‘functional beverages,' and specifically, THC-infused drinks. But despite their popularity, what goes into making these drinks remains a secret, and not all THC-beverages are created equal. So today, Eli talks with Fabric founder, Tom Eddleston, about what goes into crafting functional beverages with real intention. From cannabinoids to flavor-profile development, this conversation demystifies a category that is shrouded in misinformation. We Want to Hear from You!Have a topic, craft category, or craft company you'd like to see us cover? Email us here to share those or any other thoughts you have about CRAFTED.RELATED LINKS:Blister Craft CollectiveBecome a BLISTER+ MemberFabricFabric BlogFabric x POWFarm Bill BriefBLISTER NEWSLETTER:Get It & Our Weekly Gear GiveawaysTOPICS & TIMES:An Introduction To Colorado Sober (2:17)History of Fabric (6:43)Definitions and Offerings (11:30)Ingredients (24:53)The Importance of Education First (32:02)Mental Health Advocacy (37:48)Mental Health & the Outdoors (42:38)Market Viability and Restrictions (45:36)Lobbying Efforts for Regulation (54:31)Cultural Significance of Inebriation (58:42)Zebra Striping (1:02:49)Distribution (1:05:40)Get Involved (1:10:03)SEE OUR OTHER PODCASTS:Blister CinematicBikes & Big IdeasGEAR:30Blister Podcast Hosted on Acast. See acast.com/privacy for more information.

The McGraw Show 1-14-26: Definitions of "Generation:," Foristell Data Centers and Ben Affleck CAN take notes by

The McGraw Show 1-14-26: Definitions of "Generation:," Foristell Data Centers & Ben Affleck CAN take notes by

Matt Greene's new novel The Definitions starts with new college dormmates getting to know each other. But there's a dystopian twist: The students have survived a virus that has erased people's memories. Nameless students attend school at The Center, where they're told their memories will one day return to them. In today's episode, Greene chats with NPR's Lauren Frayer about the philosophy of language, the pandemic, and some unresolved questions from his book.To listen to Book of the Day sponsor-free and support NPR's book coverage, sign up for Book of the Day+ at plus.npr.org/bookofthedayLearn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

It's a wide-ranging day on the show, starting with "The Week That Is," where Vijay Marolia, chief investment officer at Regal Point Capital, says that while the latest Jobs Report showed that unemployment remained high, investors and observers should not worry as current levels represent nearly full employment, particularly at a time when people can hold jobs in new and different ways. That gives the Federal Reserve room to cut rates, Marolia says, especially if it is willing to settle for inflation running closer to 3 percent rather than pushing to get to its historical target of 2 percent. As a result, Marolia says investors have to prepare and invest for higher inflation, especially in an environment where tariffs are fueling economic growth, because no matter what happens with the tariff case in the Supreme Court or the inflation numbers ahead, prices will not be coming down. David Trainer, founder and president at New Constructs, digs into artificial intelligence and how it is making classic stock-picking and fund-management techniques obsolete, because he believes it eliminates much of the edge a manager can gain by trading actively. He does agree with a recent interview with David Snowball of MutualFundObserver.com who said that less is more when it comes to active management, but says that A.I. — and having the best possible A.I. — is now the big determinant of which strategies can win on Wall Street. John Yoegel, author of "Real Estate Investing in Plain English. Definitions. Examples. Uses" discusses the real estate market and the ins and outs of buying income-producing properties as an alternative to stocks, bonds and cash. And Chuck discusses the latest concerns over the Federal Reserve's independence after Fed Chair Jerome Powell pushed back on Sunday against a Justice Department's investigation into his previous congressional testimony, and discusses how the allegations could impact outcomes in ways that go well beyond rate cuts.

When it's just Jess and David, everything is fabulous. But in public, Jess feels like a secret. David doesn't post photos of them, he hasn't introduced her to his family, and she barely knows his friends. Jess is left feeling like a "side piece," good enough for him, but not for his image. In this session, a couple confronts the painful gap between their private connection and their public life. They’re facing different definitions of what constitutes cheating, a history of trust issues, and the raw feeling of being ashamed by the person you love. Listen to the second part of Jess & David's session here Resources (Australia-only) 1800RESPECT: The national domestic, family, and sexual violence counselling, information, and support service. Beyond Blue: For support with anxiety, depression, and suicide prevention. Lifeline Australia: For 24/7 crisis support and suicide prevention services. Motivated Minds: Learn more about host Sarah Bays’ practice. Relationships Australia: A leading provider of relationship support services for individuals, families, and communities. The End Bits Want To Be On This Is Why We Fight? Apply here. Host: Sarah Bays Executive Producer: Naima Brown Studio Engineer: Lu Hill Audio production: Thom Lion and Jacob Round Production support: Leah Porges and Coco Lavigne Follow This Is Why We Fight on Instagram for sneak peeks and more relationship therapy content Explore all Mamamia’s podcasts here.Become a Mamamia subscriber: https://www.mamamia.com.au/subscribeSee omnystudio.com/listener for privacy information.

Regenerative agriculture is one of those terms that can spark a debate faster than a coffee-row chat about tillage, but on this episode of Ruminating with RealAg, host Amber Bell sits down with Joel Williams of Integrated Soils to keep things grounded. Williams, an independent plant and soil health educator who works with farmers around... Read More

The guys pull some low-hanging fruit with Ironic by Alanis Morissette. Often maligned by "um, actually" language critics everywhere, it can't NOT be talked about at some point.

In this episode I sat down with Russell Van Brocklen, a New York–based, state-funded dyslexia researcher with strong opinions about writing, world-building, and why so many Hollywood films collapse under the weight of their own unfinished scripts.Russell works with highly intelligent dyslexic students who are often written off early. His methods are bluntly effective. In one year, students struggling with basic literacy were producing graduate-level analytical writing. Russell wanted to explore these two points: 1. Worldbuilding for readability — how to keep complexity while still keeping the reader with you. 2. The “logic stress-test” — a practical way to catch plot holes and broken rules before the audience does. Central to his thinking is the idea of universal themes. Not vague ones, but precise, distilled ideas uncovered by relentlessly asking “why” until the real story shows itself. It's tiring work, and that's the point.We talked through character-driven world-building, using Star Wars as a reference point, and why a world should emerge from a protagonist's needs rather than decorative lore. We also dug into antagonists, not as moustache-twirling villains, but as conceptual opposition to the hero's goal. Get that wrong and the story leaks logic everywhere.Russell also uses AI extensively. Not to write for him, but to do the heavy lifting he'd rather not. Definitions, logic checks, thematic narrowing, idea pressure-testing. He treats AI like a lab assistant, not an author. His view is simple: if you can't improve on what AI gives you, that's not an AI problem. Oops.We wrapped up talking about craft, accessibility, and why structure isn't the enemy of creativity. Russell's perspective is shaped by his own experience with dyslexia and late fluency, and it shows. This is a practical, sometimes uncomfortable conversation about writing with intent, and why most stories fail long before page one.If you care about world-building, theme, or simply finishing what you start, this one is worth your time, even if I do say so myself.Episode page: https://richardsonsrubicon.com/breaking-scripts-building-worlds/Discussion: https://richardsonsrubicon.com/community/season-5-speculative-fiction-where-worlds-meet/is-ai-a-cheat-code-for-better-worldbuilding/

In this rich and wide-ranging conversation, Ali sits down with yoga teacher, author, and anatomy educator Rachel Scott to explore what it truly means to live as a body – not just to have one.Beginning with a candid inquiry into modern yoga culture, Rachel gently peels back the layers of Western commodification to reveal yoga's deeper purpose: presence, self-regulation, and intimacy with the living intelligence beneath our habits and conditioning. From there, the dialogue opens into a profound exploration of embodiment as a spiritual practice – one that includes sensation, relationship, desire, stillness, and paradox.Drawing on decades of practice, Rachel shares how yoga, anatomy study, and contemplative stillness have shaped her understanding of consciousness, love, and human connection. Together, Ali and Rachel reflect on mindfulness versus “body-fulness,” nervous-system awareness in dating and relationships, and the wisdom of listening to the body's cues around safety, timing, and consent.The conversation also moves tenderly into themes rarely spoken aloud: fertility, choice, grief, freedom, and the many ways maternal love can be expressed beyond childbirth. Rachel speaks openly about her journey through wanting children, confronting ambivalence, and ultimately trusting the larger intelligence of life – an experience that reshaped her relationships, her work, and her sense of self.Weaving together yoga philosophy, Tantra, anatomy lab awe, and everyday relational practice, this episode is an invitation to slow down, feel more, and honor the mystery of being embodied. A heartfelt exploration of love, presence, and the courage it takes to listen deeply to the body's quiet truths.FOR MORE ALI MEZEY:ALI - WebsiteALI - LinkTreeALI BIO: Ali Mezey is a Body Therapist, Family Constellation Work Facilitator, Sexologist and Media Maker with over 40 years of experience. Ali has worked in renowned rehab centers in Los Angeles for sex, drug, and alcohol addiction. She developed her groundbreaking body-based method Personal Geometry® to address the challenges of working with sexual trauma, compulsivity, dysfunctions and discontents. She works internationally with individuals, couples, and groups. Ali is also a public speaker on the intelligence of the body, a teacher of Personal Geometry® and the creator and host of The Brilliant Body Podcast.FOR MORE RACHEL SCOTT:rachelyoga.comIG/Youtube: rachelscottyogaHead Over Heels: A Yogi's Guide to Dating by Rachel ScottAll books by RachelRACHEL BIO:Rachel combines thousands of hours of teacher training experience with her academic expertise (MSc Online Education) to help yoga teachers and studios create transformational educational experiences. She supports students, teachers, and trainers to share their passion, find their voice, and inspire others.  In addition to authoring five books, she has written for Yoga International, YogaUOnline, and the Huffington Post, and exuberantly shares her knowledge through her coaching, YouTube channel, online courses, and free online classes. Find her at rachelyoga.com or on social media at rachelscottyoga.RESOURCES, DEFINITIONS, INSPIRATIONS:Integral Anatomist (and Rachel's partner), Gil Hedley and The Nerve Tour (link is to an interview of Gil speaking about it)Do yourself a favor and get yourself an Explorer Membership - a mere pittance for the wealth you'll receiveGil's Youtube Channel of amazing videosMy fantastic conversation with Gil (my very first TBBP episode!): The Body is a Gift with Gil Hedley: A Reverential Journey into the Human BodyChristopher Hareesh WallceCarlos PomedaProfessor Alexis Sanderson/Oxford (go full yoga-nerd with this guy - wow)Cheryl Strayed: Tiny Beautiful Things: Advice on Love and Life from Dear SugarDualism: It basically says that there are two things, or substances, and they are completely separate. For example, substance dualists believe that the mind is part of the soul and the soul resides completely outside of the body.Non-dualism: Non-dualism refers to the idea that all things are interconnected and not separate. Distinctions like self and other, or good and bad, are illusions created by the mind. Essentially, it's about recognizing the unity and interdependence of all phenomena.Proprioception: also referred to as kinesthesia, is the sense of body position, movement, and force. It is the unconscious awareness without visual input and is sometimes referred to as the sixth sense.There are three primary types of proprioceptors: muscle spindles, Golgi tendon organs (GTOs), and joint receptors. Each distinct type provides different information that together shape the sensory profile of the body's positioning and motion.Interoception: Interoception is awareness of your body's internal senses or signals. It identifies how you feel. You can consciously or unconsciously respond to these signals. For example, if your stomach rumbles, you know you're hungry.YOGA DEFINITIONS:Shiva: He is the Supreme Being in Shaivism, one of the major traditions within Hinduism. Shiva. God of Destruction. God of Time, Yoga, Meditation and Arts. Lord of Yogis and Physicians.

John 14:6The dictionary defines “saviour” as one who saves from danger or destruction. Have you ever been saved?

​ @DarkHorsePod  Mental Multiverses are REAL: Here's How to Stay Sane https://youtu.be/Ywr5l-YACvg?si=napJxmoZYEs69lNA   @christianbaxter_yt  Judaism, Christianity, and the Crisis of Trust https://youtu.be/Hfua2Mb62z0?si=TdxgSVkD4ulFc2rB   @transfigured3673  David Busuttil - Messianic Judaism and the Trinity https://www.youtube.com/live/xsQSElubNZg?si=FGZukheZjclvYyMb https://x.com/BretWeinstein/status/1990498507405603207  Jonathan Pageau and Bret Weinstein Can't Find Each other on Definitions of Religion and Faith Pt. 1 https://youtu.be/WVmQd1jawMw?si=zL7K2fD4C539-RjF  https://www.livingstonescrc.com/give Register for the Estuary/Cleanup Weekend https://lscrc.elvanto.net/form/94f5e542-facc-4764-9883-442f982df447 Paul Vander Klay clips channel https://www.youtube.com/channel/UCX0jIcadtoxELSwehCh5QTg https://www.meetup.com/sacramento-estuary/ My Substack https://paulvanderklay.substack.com/ Bridges of meaning https://discord.gg/CgPYjAUF Estuary Hub Link https://www.estuaryhub.com/ There is a video version of this podcast on YouTube at http://www.youtube.com/paulvanderklay To listen to this on ITunes https://itunes.apple.com/us/podcast/paul-vanderklays-podcast/id1394314333  If you need the RSS feed for your podcast player https://paulvanderklay.podbean.com/feed/  All Amazon links here are part of the Amazon Affiliate Program. Amazon pays me a small commission at no additional cost to you if you buy through one of the product links here. This is is one (free to you) way to support my videos.  https://paypal.me/paulvanderklay Blockchain backup on Lbry https://odysee.com/@paulvanderklay https://www.patreon.com/paulvanderklay Paul's Church Content at Living Stones Channel https://www.youtube.com/channel/UCh7bdktIALZ9Nq41oVCvW-A To support Paul's work by supporting his church give here. https://tithe.ly/give?c=2160640 https://www.livingstonescrc.com/give  

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

As we count down to the end of 2025, Us & Them host Trey Kay looks back at the year's whirlwind of actions and reactions. Each week presented fresh moves in the agenda President Donald Trump outlined during his campaign. First it was a reshaping of the federal government from Elon Musk's efficiency department, which slashed budgets and agencies and workers. At the same time, additional resources for the Department of Homeland Security means a significant increase in the number of immigration arrests and detentions by federal agents. The use of National Guard troops in U.S. cities tests the limits of the president's authority while those in the Mountain State mourn the death of a soldier shot in the nation's capitol. We look at how one-time culture war talking points are reengineering America's defining institutions. 

Ranking Resilience and the Importance of Water: Colleague Eric Cline uses definitions from the Intergovernmental Panel on Climate Change—coping, adapting, and transforming—to rank ancient civilizations, attributing the survival of Egypt and Assyria partly to their access to major river systems, a resource the failed Hittite empire lacked; the Phoenicians and Cypriots are ranked highest for "transforming" and becoming antifragile, while Egypt is described as merely "coping," and the Cypriots eventually lost their independence to Assyrian expansion despite their initial post-collapse success. 1953 Retry