Podcasts about computational

AI is changing how leaders think, decide, and work with their teams. But as John Cutler points out in this conversation, the real shift is not simply about faster answers or more productivity. It is about becoming more aware of the judgment systems we already use, often without noticing.In this episode of the Unlearn Podcast, I'm joined again by John Cutler, product thinker, systems explorer, and Head of Product at Dotwork. We explore how AI can help leaders expose their thinking, pressure test decisions, and build stronger team judgment, while also making it easier to accelerate poor habits, shallow work, and false confidence.John shares practical examples from product prioritization, survey design, objection handling, and team collaboration to show where AI can genuinely improve decision quality. We also get into the tradeoffs: why AI can make work feel like “hard mode,” why downtime still matters, and why intentionality is becoming one of the most important leadership skills in this moment.Key TakeawaysAI exposes how leaders make decisions: AI tends to amplify the decision system already there. When a leader's thinking is clear, AI can help make it visible and reusable; when it is vague, AI can make that vagueness move faster.Judgment is built differently depending on the situation: John explains that some judgment comes from repetition and tacit pattern recognition, while other judgment develops through coaching, discussion, and working alongside people with more experience.AI can help turn intuition into something teams can use: John's example of documenting his product prioritization heuristic shows how AI can help make internal judgment concrete. The value comes from helping others understand why certain decisions matter, not just what the decision is.Better AI use starts with knowing what you know: John contrasts product prioritization, where he has deep experience, with survey design, where he knows there is established expertise to draw from. The skill is recognizing whether AI should extend your own judgment or help you borrow from a domain expert.Teams using AI well can raise decision quality: Barry shares how AI can help teams pressure test assumptions, run scenarios, and ask disconfirming questions without losing momentum. The real advantage comes when AI strengthens collaboration rather than replacing it.AI can also accelerate bad instincts: John warns that AI can make poor thinking look polished. A team can paste AI onto an existing process and call it transformation without changing how decisions are actually made.Intentionality matters more than productivity: AI can reduce friction, but it can also remove the pauses where judgment forms. Leaders need to design space for reflection, not just optimize for more output.Additional InsightsIndividual metacognition: This is understanding how you think and make decisions. John's examples show that leaders get more value from AI when they can first make their own judgment system visible.Social metacognition: This is understanding that other people think, perceive, and engage differently. AI becomes more useful when it supports the conversation between people instead of flattening everyone into the same process.Computational metacognition: This is understanding what LLMs are good at, where they fail, and how to work with them responsibly. John argues that leaders need this skill so they know when to trust AI, when to challenge it, and when to bring in human expertise.Objection handling as a repeatable system: John's team did not ask AI to create a generic sales guide. They role-played real objections, captured the discussion, compared their responses against best practices, and turned that into a system that could review future calls.The deeper lesson: AI becomes more useful when it is connected to real work, real context, and a team's actual judgment. Without that grounding, it risks creating more output without improving the quality of decisions.Episode Highlights00:00 – Episode RecapJohn Cutler opens with a story about how judgment often comes from repetition and tacit signals, not neat frameworks. The episode explores what happens when AI starts making those hidden decision systems visible.02:02 – Guest Introduction: John CutlerBarry welcomes back John Cutler, product thinker, systems explorer, and Head of Product at Dotwork, for a conversation about judgment, decision making, and collaboration in the age of AI.04:59 – How Judgment Gets BuiltJohn explains that judgment develops differently depending on the context: through individual practice, repeated exposure, mentorship, team discussion, and comparison against examples of quality.08:58 – Making Prioritization Thinking VisibleJohn shares how he used AI to document his own scoring heuristic for product prioritization, giving a teammate deeper insight into why certain ideas mattered more than others.12:11 – Knowing When to Borrow ExpertiseUsing survey design as an example, John explains how AI can help access existing expert knowledge when you are not the expert yourself. The key is being honest about the limits of your own judgment.13:56 – From Answers to Better QuestionsBarry reflects on the shift from using AI to get answers toward using it to challenge thinking, improve decisions, and bring stronger questions to colleagues.18:04 – Why Better Surveys Lead to Better DecisionsJohn explains how improving a survey from average to strong can materially change the quality of insight a team gets back, which then affects the quality of product decisions.23:04 – Teams, AI, and Decision AdvantageBarry shares how AI can help teams maintain momentum during ideation by quickly pressure testing scenarios, asking disconfirming questions, and bringing outside information into the room.27:48 – Turning Objection Handling into a SystemJohn describes how his team recorded a live objection-handling exercise, analyzed it against best practices, and turned the team's collective knowledge into a reusable system.31:32 – The Three Forms of MetacognitionJohn introduces individual, social, and computational metacognition as three skills leaders need to work effectively with AI and with each other.35:19 – AI Exposes Leadership SystemsBarry and John discuss why AI can feel uncomfortable for leaders: it reveals whether there is a real decision-making system underneath the confidence.37:34 – When AI Makes Every Decision Feel HardJohn raises an important limitation: AI can remove small pauses in the workday, leaving people constantly operating at high cognitive load.41:58 – Productivity Fatigue and Agent OverloadBarry and John discuss the temptation to run too many AI-assisted tasks at once, and why that can create more noise rather than better outcomes.44:23 – Designing Time to ThinkBarry shares how he intentionally creates time for walking, exercise, and reflection to avoid over-optimizing for fast, reactive decisions.46:38 – Intentionality Over Process TheaterJohn explains why intentionality is different from rigid process. The opportunity is to design better systems without flattening the richness of how teams actually work.50:11 – Closing ReflectionsBarry wraps the conversation by reflecting on the opportunity for leaders to use AI not just to move faster, but to become more aware of how they think, decide, and scale judgment across teams.Useful ResourcesDotwork – John Cutler's work focuses on helping teams and organizations better understand how work, decisions, and systems connect.Artificial Organizations – Barry references the book and the CSTA loop as part of his work on AI, decision making, and organizational performance.Daniel Kahneman's System 1 and System 2 Thinking – Referenced in the discussion on snap decisions, deeper thinking, and productivity fatigue.Pugh Analysis – John mentions this as an example of a prioritization approach originally intended to help experts independently rate options and then discuss differences in judgment.Follow the HostLinkedIn: https://www.linkedin.com/in/barryoreillyPersonal site:

Scientific Sense ® by Gill Eapen: Prof. Charles Yang is Professor of Linguistics and Computer Science and Director of the Program in Cognitive Science at the University of Pennsylvania. His research interests include Language and Communication, Numerical Cognition, Language acquisition and change; Morphology and the mental lexicon, Computational linguistics and The evolution of language and cognition.Please subscribe to this channel:https://www.youtube.com/c/ScientificSense?sub_confirmation=1

In this episode of the Edufuturists podcast, Ben and Steve sit down with Shahneila Saeed for an extended version of a conversation that began at the Brilliant Festival. They dig into why play-based learning is rigorous pedagogy, how computing can be taught without a single computer, and what the games industry can teach the classroom about preparing young people for the world as it actually is.Shahneila Saeed is Head of Education at Ukie, the trade association for the UK's video games industry, and the founder and director of the Digital Schoolhouse programme. A former IT and computing teacher, she is also the author of How to Raise a Tech Genius.We cover:Why play is serious pedagogy, not a break from real learningHow "Just Dance with the Algorithm" teaches programming concepts through danceTeaching in-game AI with nothing more than a playground and some beach ballsThe classroom with no computers that reshaped Shahneila's entire approachMotivation, failure, and the problem with the GCSE "finish line"Whether schools are really preparing children for the jobs that exist right nowWhat industry actually says about the skills graduates are missingGame IP in the classroom, and how to use it without losing the pedagogyDigital Schoolhouse as a bridge between the games industry and educationHow parents can support computing and tech learning at home, including free resourcesWhether you're a teacher, school leader, edtech professional, or a parent trying to make sense of your child's screen time, this conversation will change how you think about play, computing, and the gap between school and the real world. Expect practical, low-cost ideas you can use on Monday morning, free resources you can access today, and a sharp case for why engagement has to come before assessment.Chapters:00:00 Highlights02:01 Welcome and the new Edufuturists book03:42 From IT teacher to Ukie and Digital Schoolhouse06:02 The classroom with no computers10:36 Why play is serious pedagogy12:56 Just Dance with the Algorithm15:13 Teaching game AI with a game of dodgeball16:24 Motivation, failure and the GCSE "finish line"24:01 Computational thinking at home27:44 Are we preparing kids for jobs that exist now?28:34 What industry really says about graduate skills32:08 What is Game IP and why it works in the classroom36:14 Digital Schoolhouse as a bridge to industry41:10 Parents, tech and the home conversation46:14 Quick-fire questionsThanks so much for joining us again for another episode - we appreciate you.Ben & Steve xBook  a Digital Schoolhouse workshopDigital Schoolhouse free computing resourcesDigital Schoolhouse Playful Computing ConferenceCheck out all about EdufuturistsWant to sponsor future episodes or get involved with the Edufuturists work?Get in touchGrab your copy of the new Pick 'n' Mix Education book

Today's clip is from Episode 158 featuring Stefan Radev. In this conversation, Alex Andorra and Stefan break down a core argument from their paper: Bayesian statistics has never been more computational than it is now, and simulation is the thread that ties the whole workflow together.Stefan parcellates the Bayesian workflow into four stages, and this clip covers the first two. Stage one is model specification, where the workflow community has long recommended prior predictive checks. You can do this informally, just running simulations from your model and eyeballing whether the output meets your expectations, or formally, à la Michael Betancourt, by pushing your model's high-dimensional output through a transformation into a low-dimensional, interpretable space and checking it against reality. The punchline: a surprising number of models can be discarded before you've even seen real data, yet Stefan notes these checks remain underused in practice.Stage two is model verification, where the question shifts to whether your inferences are well calibrated. This is the territory of simulation-based calibration and parameter recovery studies, classic tools that have always carried a steep computational price. You simulate thousands of synthetic datasets and run inference on every single one, which is exactly why these checks are so often skipped in papers, even though doing one well can be a contribution in its own right.Here's where amortized simulation-based inference changes the math entirely. Checks that used to take days now take seconds, and instead of laboriously running inference dataset by dataset, you get millions of posterior samples essentially for free. The calibration checks that the field has always known it should be doing finally become cheap enough to actually do.Get the full discussion hereSupport & Resources→ Support the show on Patreon→ Bayesian Modeling Course (first 2 lessons free)Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Reh-Connect, Dosentausch, Antrag auf Aufgabe, Brot mit Schinken #hsfeedback von Jürgen: Export aus Lightroom oder anderem RAW Entwickler, wie kann ich die Größe des Jpeg beeinflussen? von Udo: Fehlermeldung bei Overcast, das Episodenbild wird nicht angezeigt. HS Workshops Workshops HS Workshop-Newsletter Aufruf: Interesse … „#945 – 2 Wochen mit der Muschel verbringen“ weiterlesen

Support the show to get full episodes, full archive, and join the Discord community. The Transmitter is an online publication that aims to deliver useful information, insights and tools to build bridges across neuroscience and advance research. Visit thetransmitter.org to explore the latest neuroscience news and perspectives, written by journalists and scientists. Read more about our partnership. Sign up for Brain Inspired email alerts to be notified every time a new Brain Inspired episode is released. To explore more neuroscience news and perspectives, visit thetransmitter.org. Cristopher Moore is a professor at the Santa Fe Institute in New Mexico, and he is a computation and computational complexity expert. He recently joined a us in my complexity discussion group, and answered a bunch of our questions, but I wasn't done with him regarding what, if anything, computational complexity has to do understanding how brains and minds work. So that's why he's here today, and we discuss a wide variety of topics related to AI, computation, computational complexity, and cognition. Cris's Homepage Book: The Nature of Computation Related papers What Is a Macrostate? Subjective Observations and Objective Dynamics 0:00 - Intro 4:24 - The Nature of Computation 9:14 - Computational complexity 28:22 - Real mathematics 35:08 - Current state of AI 39:04 - Computational complexity in the AI world 47:53 - Cognition, creation, problems 56:16 - Rugged landscapes and generalization 1:13:52 - What is computation? 1:32:31 - How would you study the brain?

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Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow We have the Drehstrom, 2-Phasen, Usernamenkollision, Postleitzahlen, Bochum, Drucker, Schneidemaschinen #hsfeedback Von Kuchenmampfer Chris Inseldefinition ist sehr Pellwörmig Definition von Festland Danke für den Tipp zum Dirty Little Zine Korrektur: Die Panasonic L10 hat ein fest verbautes Objektiv Von Martin: Vuescan als … „#944 – Alter Falter“ weiterlesen

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This and all episodes at: https://aiandyou.net/ . I have been talking with Tomaso Poggio, Eugene McDermott professor in the Department of Brain and Cognitive Sciences at MIT and the Director of the Center for Brains, Minds, and Machines, and one of the founders of the field of computational neuroscience. Tomaso is a fellow of the American Academy of Arts and Sciences and of the American Association for the Advancement of Science, and a founding fellow of the Association for the Advancement of Artificial Intelligence. He develops models of brain function that illuminate human intelligence and builds intelligent machines that can mimic human performance His new book, Brains, Minds, Machines, The Mystery of Human Intelligence, the Enigmas of the Artificial, comes out this summer. We talk about learning in the brain and synaptic mechanisms, the role of sleep, what AI scientists should pay more attention to from neuroscience, other computational mechanisms in the brain besides neurons, connectomics, robotics, and… flies and worms. All this plus our usual look at today's AI headlines! Transcript and URLs referenced at HumanCusp Blog.        

Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Rüttelsensor, Retoucher, Ferrari #hsfeedback Jürgen: Analogue Photo Festival Rüdiger: Entwickler selbst herstellen Johannes: HDR-Sucher Zu Rügen bzw Ulrich Müther Probleme im Sucher bei Hitze HS Workshops Workshops HS Workshop-Newsletter Aufruf: Interesse an Licht/Mensch Workshop? Statt Werbung DANKE an alle Spender Es gibt kein … „#943 – Nicht mitbraten!“ weiterlesen

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In this episode of Data in Biotech, host Ross Katz sits down with Arvind Rao, Professor of Computational Medicine and Bioinformatics at the University of Michigan, for a discussion on the gap between what biomedical AI can do and what it can reliably be trusted to do in clinical practice. Arvind's research sits at the intersection of computational oncology and AI governance and his lab works across H&E histopathology, multiplex immunofluorescence, spatial transcriptomics, and single-cell RNA sequencing, not just to build predictive models, but to understand the full lifecycle from data to model to inference, and to ask where that lifecycle can be trusted and where it can't.  The conversation moves through two of his recent papers on SPIFEE, a graph-based framework that replaces scalar interaction scores in the tumor microenvironment with spatially resolved functional representations, and a multimodal framework that traces a path from stained tissue slides to nominated drug targets via morphological pattern discovery and spatial transcriptomic mapping.  What you'll learn in this episode:  >> Why the field's central failure is not algorithmic but translational and the gap between a model that performs well on a benchmark and one that can be consistently trusted in a high-stakes clinical setting  >> How SPIFEE replaces the conventional scalar edge representation of cell-cell interactions in the tumor microenvironment with spatially resolved functional edges >> How Arvind's multimodal framework moves from H&E pathology slides labeled with clinical outcomes, through morphological pattern discovery via multiple instance learning, to spatial transcriptomic mapping, to the nomination of molecular mechanisms and actionable drug targets >> Why Goodhart's Law applies directly to foundation model evaluation in biology  >> What the AI literacy gap costs when it goes unaddressed in healthcare and pharma organizations  Meet our guest: Arvind Rao is a Professor of Computational Medicine and Bioinformatics, with a joint appointment in Radiation Oncology, at the University of Michigan. His research focuses on establishing trust in biomedical AI predictions across the full data-to-decision pipeline, integrating H&E histopathology, spatial transcriptomics, multiplex immunofluorescence, and single-cell RNA sequencing to build models that are predictive, interpretable, and biologically credible. Alongside his research, Arvind develops AI literacy programs for healthcare and pharma professionals, helping clinical and procurement teams evaluate and govern AI systems with the rigor those decisions demand. Connect with Arvind Rao on LinkedIn: https://www.linkedin.com/in/arvind-rao-3301301ba/ About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn: https://www.linkedin.com/in/b-ross-katz/ Connect with us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn. https://www.linkedin.com/company/corrdyn/

This and all episodes at: https://aiandyou.net/ . Studying human intelligence is a matter of neuroscience, and creating software is a matter of computing, so creating artificial intelligence would be at the intersection of those fields, called computational neuroscience, and I have with me one of the founders of that field. Tomaso Poggio is the Eugene McDermott professor in the Department of Brain and Cognitive Sciences at MIT and the Director of the Center for Brains, Minds, and Machines. He is a fellow of the American Academy of Arts and Sciences and of the American Association for the Advancement of Science, and a founding fellow of the Association for the Advancement of Artificial Intelligence. His home page says that he “develops models of brain function that illuminate human intelligence and builds intelligent machines that can mimic human performance.” Wow. His new book, Brains, Minds, Machines, The Mystery of Human Intelligence, the Enigmas of the Artificial, comes out this summer. Tomaso defines computational neuroscience, and then we talk about computation in the human brain, how large language models landed for him, holography, limitations of LLMs, and backpropagation equivalents in the human brain. All this plus our usual look at today's AI headlines! Transcript and URLs referenced at HumanCusp Blog.        

Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Meine Damen und Herren…, Brücken #hsfeedback Detlef vermisste den Podcast. Warum haben Sucher kein HDR-Display? Und Danke für den Hinweis mit dem Slider-Klick. Uwe empfiehlt eine Fuji statt einer Leica. Detlef und mech. Zooms in Smartphones Peter meldet Interesse an Mensch-Workshop Jürgen meldet … „#942 – Erfolgreicher Fail“ weiterlesen

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At Data Makers Fest, a recurring theme was the tension between GenAI hype and production reality. Speakers stressed that classical ML, MLOps, evaluation, data quality, and governance remain essential—especially in regulated sectors like fintech and healthcare. Another strong theme was inclusivity: building AI that serves smaller languages, diverse communities, and practitioners beyond the English-centric ecosystem.Ryan Chaves. Head of ML at a Dutch fintech, Ryan focused on the gap between AI demos and production systems. He argued that classical ML remains critical for fraud detection and risk scoring, while GenAI works best as an accelerator on top of existing systems. He also emphasized storytelling, stakeholder communication, and mentorship as core engineering skills.Alp Öktem. Computational linguist and researcher Alp explored the imbalance between AI progress in English and low-resource languages. Through Mozilla Data Collective, he highlighted how open datasets, speech corpora, and synthetic data can expand AI access to underrepresented communities. His broader warning: fluent AI can still fail culturally, linguistically, and ethically.Agnieszka Kamińska. Working in pharmaceutical ML engineering, Agnieszka discussed extracting scientific knowledge from research documents into knowledge graphs. Her focus was reliability: LLMs help with entity extraction and relationship discovery, but trustworthy systems still require ontologies, validation layers, and production-minded engineering. She advocated a pragmatic middle ground between AI hype and skepticism.Nemanja Radojković. An MLOps engineer in finance, Nemanja reflected on how GenAI is changing software engineering itself. He argued that coding assistants improve productivity but risk weakening engineers' understanding if overused. His central point: governance, reproducibility, and platform engineering will become even more important as organizations deploy AI agents at scale.Filipa Castro. Leading AI initiatives at Euronext, Filipa described how GenAI is integrated into regulated financial workflows. Her team uses LLMs to automate document-heavy operational processes while preserving human validation. Her broader message: successful enterprise AI depends less on flashy models and more on infrastructure foundations like CI/CD, monitoring, governance, and operational rigor.Beatriz Silva. As a student volunteer pursuing a master's in data science, Beatriz represented the conference's educational and community dimension. For her, the event was about access—networking with companies, exploring thesis opportunities, and connecting academic learning with industry practice. Her perspective highlighted how conferences like Data Makers Fest help shape the next generation of AI practitioners.Connect with speakers: Ryan Chaves. Head of Machine Learning at a Dutch fintech focused on fraud detection, risk systems, and production ML. LinkedInAlp Öktem. Computational linguist and researcher focused on low-resource languages, inclusive AI, and open language datasets. LinkedInAgnieszka Kamińska. Machine Learning Engineer working on scientific knowledge extraction, knowledge graphs, and AI systems in pharma. LinkedInNemanja Radojković. Senior MLOps Engineer specializing in regulated financial systems, AI governance, and platform engineering. LinkedInFilipa Castro. AI Lead at Euronext focused on enterprise GenAI systems, operational AI strategy, and financial services automation. LinkedInBeatriz Silva. Data science master's student and conference volunteer exploring opportunities in ML and computer vision. LinkedIn

Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Dieses Securityding, Vier Sendungen in einer #hsfeedback Florian: Fuji vs Leica HS Workshops Workshops HS Workshop-Newsletter Aufruf: Interesse an Licht/Mensch Workshop? Statt Werbung DANKE an alle Spender Es gibt kein Scheitern in der Kunst Themen Klostergeister Klostergeister-Feedbackvideo Übersicht über die Inhalte Teil … „#941 – Die Luft ist blau“ weiterlesen

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Send us Fan MailDigiPath Digest #45 asks a practical question: can AI in pathology move from correlation to real clinical use? In this episode, I review four papers that push on that question from different angles: computational pathology moving toward morphology-driven molecular inference, the current state of digital cytopathology and AI, multi-omics and precision oncology in hepatocellular carcinoma, and AI literacy in veterinary education. What ties them together is not model performance alone. It is the harder question of validation, workflow fit, quantitative use, ethics, and human oversight.In the first paper, I talk about computational pathology as more than pattern recognition. The focus is on morphology-driven molecular inference, digital biomarkers, and why spatial omics matters as biological ground truth. I also discuss why continuous quantitative scoring is more useful than forcing biology into rough scoring buckets. The second paper focuses on digital cytopathology. Cytology was early for FDA-cleared AI in cervical screening, but non-gynecologic cytology is still much harder to digitize because of specimen variability and workflow complexity. I also cover telecytology, rapid onsite evaluation, automation, and quality control. The third paper looks at hepatocellular carcinoma and AI-driven precision oncology. This part is about using AI and machine learning to integrate genomics, transcriptomics, proteomics, metabolomics, radiomics, and pathology to support biomarker discovery, tumor microenvironment analysis, and treatment stratification. The fourth paper may be the most broadly useful. It proposes an AI literacy curriculum for veterinary education that covers AI fundamentals, machine learning evaluation, LLMs, ethics, liability, and academic integrity. I think that matters far beyond veterinary medicine, because if clinicians are expected to use AI tools responsibly, AI literacy cannot stay optional. Highlights 00:01 Welcome and overview of the four papers 03:02 Computational pathology and morphology-driven molecular inference 11:01 Digital cytopathology, telecytology, and QC 20:47 AI/ML in hepatocellular carcinoma precision oncology 31:04 AI literacy in veterinary education 47:42 Final takeaways and Digital Pathology 101 update ResourcesComputational Pathology as a Mechanistic Discipline: From Morphology to Molecular Data https://pubmed.ncbi.nlm.nih.gov/42052846/Advances in Digital Cytopathology and Artificial Intelligence Applications https://pubmed.ncbi.nlm.nih.gov/42046894/Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology https://pubmed.ncbi.nlm.nih.gov/42065059/Curriculum Framework for Artificial Intelligence Literacy in Veterinary Education Front Vet Sci. 2026;13:1801756 Support the showGet the "Digital Pathology 101" FREE E-book and join us!

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Generative AI and scientific journals Science Sessions are brief conversations with cutting-edge researchers, National Academy members, and policymakers as they discuss topics relevant to today's scientific community. Learn the behind-the-scenes story of work published in the Proceedings of the National Academy of Sciences (PNAS), plus a broad range of scientific news about discoveries that affect the world around us. In this episode, Yi Bu explores how generative AI has changed academic publishing. In this episode, we cover: •[00:00] Introduction. •[00:50] Computational social scientist Yi Bu tells about the policies academic journals have introduced to address generative AI. •[02:17] Bu describes the dataset he analyzed and his findings regarding journals' policies. •[04:07] He answers the question: Did journal policies have any effect on AI usage? •[05:39] Bu talks about how the rate of AI disclosure compares with estimates of probable AI use. •[06:53] He explains the takeaway for journal editors and the scientific community at large. •[07:27] He lists the caveats and limitations of the study. •[10:11] Conclusion. About Our Guest: Yi Bu Assistant Professor Peking University View related content here: https://www.pnas.org/doi/abs/10.1073/pnas.2526734123 Follow us on Spotify, Apple Podcasts, or wherever you get your podcasts for more captivating discussions on scientific breakthroughs! Visit Science Sessions on PNAS.org: https://www.pnas.org/about/science-sessions-podcast  Follow PNAS: Twitter/X Facebook LinkedIn YouTube Sign up for the PNAS Highlights newsletter

Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow #hsfeedback HS Workshops Workshops HS Workshop-Newsletter Aufruf: Interesse an Licht/Mensch Workshop? Statt Werbung DANKE an alle Spender Es gibt kein Scheitern in der Kunst Themen Klostergeister Was ist neu im Kloster Workshopthemen Welche Kurse sind noch hier? Workshopprojekte Storytelling mit mehreren Fotos Perspektiven … „#940 – Es ist keiner erstickt“ weiterlesen

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An estimated 55 million people worldwide are living with dementia, of which Alzheimer's is the most common form. This number continues to rise as global populations age. Despite the scale of the problem and large amounts of funding, no one has been able to find a cure. Could it be that data science, rather than medicine, holds the answers to tackling this disease?In this episode of Tech Tomorrow, David Elliman speaks with Alejo Nevado-Holgado, Associate Professor of Psychiatry at the University of Oxford and member of the Big Data Institute. He leads AI research within the Computational and Molecular Neuroscience Laboratory, an interdisciplinary team spanning AI, biochemistry, and bioinformatics.The conversation explores how advanced computational methods are using vast biological and clinical datasets, including genomics, transcriptomics, proteomics, stem cell imaging, brain scans, and electronic health records. This integrated approach aims to uncover disease mechanisms, identify new drug targets, and advance more personalized treatments, all supported by high-performance computing.A key challenge in Alzheimer's research is the difficulty of accessing and studying the brain. The blood-brain barrier limits treatment delivery, while the disease develops over decades before symptoms appear. The discussion also highlights ongoing scientific uncertainty about whether hallmark features such as amyloid plaques and tau tangles are causes of the disease or downstream effects.The episode examines how AI can support early detection through blood-based biomarkers and why it is particularly effective in analysing complex, high-dimensional data such as molecular structures and genomic information. The importance of combining diverse datasets, such as population-scale biobanks and drug discovery data, is emphasised as essential for progress.However, challenges remain, including the need for explainable AI systems and more complete longitudinal health data. The conversation also touches on emerging techniques like AI-driven molecular simulations, which may help predict how drugs interact within the brain.Episode Highlights01:07 – The background of Alejo's project.02:25 – Why are Alzheimer's and dementia so hard to treat?05:50 – How can neurodegenerative brain diseases be prevented?07:05 – Drug discovery and machine learning.09:43 – David's Thoughts: Multi-modal data.10:29 – Why high-quality data is so hard to access.14:55 – Why AI explainability remains an issue.17:06 – David's Thoughts: A black box within a black box.19:23 – The UK Biobank and rich medical data.23:54 – Wrap up.About Zühlke:Zühlke is a global transformation partner, with engineering and innovation at its core. We help clients envision and build their businesses for the future – running smarter today while adapting for tomorrow's markets, customers, and communities.Our multidisciplinary teams specialise in technology strategy and business innovation, digital solutions and applications, and device and systems engineering. We thrive in complex, regulated sectors such as healthcare and finance, connecting strategy, implementation, and operations to help clients build more effective and resilient businesses.Links:Zühlke WebsiteZühlke on LinkedInDavid Elliman on LinkedInProf. Alejo Nevado-Holgado BioDementia Research Oxford WebsiteUK Biobank Website

Matt is a Principal Computational Scientist at The Jackson Laboratory. Trained as a mathematician, Matt then moved into the area of systems biology - driven by a lifelong curiosity and the opportune timing of the 2009 financial crisis. He is currently working on two main projects: studying aging biology and understanding mechanisms of cardiotoxicity for drugs. This conversation was recorded in March 2026 ~~~~~The Maine Science Podcast is a production of the Maine Discovery Museum. It is recorded at Discovery Studios, at the Maine Discovery Museum, in Bangor, ME. The Maine Science Podcast is hosted and executive produced by Kate Dickerson; edited and produced by Scott Loiselle. The Discover Maine theme was composed and performed by Nick Parker. To support our work: https://www.mainediscoverymuseum.org/donate. Find us online:Maine Discovery MuseumMaine Discovery Museum on social media: Facebook Instagram LinkedIn Bluesky YouTubeMaine Science Podcast on social media: Facebook Instagram YouTubeMaine Science Festival on social media: Facebook Instagram LinkedIn YouTube© 2026 Maine Discovery Museum

Chris, Ade and Jeremiah explore the ways new technology can help you make fantastic photos.

Commentary by Dr. Elvin Kedhi.

Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Trust me bro, Schriftsatz, altes DTP Programm, Typoherzöge #hsfeedback Von Marius: 360-Projekt von Chris Webview mit Pannellum Vom Markus: Nachtrag zu „Adobe editiert hosts Datei“ Von Arel: Problem auf der Webseite Von Samuel: Workshop Lichtgestalten unbedingt Von Mars: Alternativer Fotoladen in Köln … „#939 – Leicaalter“ weiterlesen

Chris, Ade and Jeremiah explore the ways new technology can help you make fantastic photos.

Send us Fan MailI did something I've never done before for this episode — I went live from the middle of a national park. This is DigiPath Digest #42, broadcasting from the Great Sand Dunes National Park in Colorado via Starlink from my family road trip. Yes, it actually worked. And so did the papers.This episode covers four papers that all ask the same uncomfortable question from different angles: how close is AI to being genuinely useful in real pathology practice — and what's still standing in the way? From LLMs interpreting cervical Pap smears, to AI guiding breast cancer treatment decisions from a simple H&E slide, to a practical roadmap for bringing generative AI into oncology workflows — this one covers a lot of ground.I also introduced something new: my AI-powered paper summary podcast subscription. For $7 a month, AI hosts summarize digital pathology literature in a journal-club style so you can stay current without spending hours reading abstracts. I walk through how it works and why I built it.What we cover:[00:00] Going live from the wilderness — Starlink, sand dunes, and a very cold morning[02:01] How I use AI-generated audio summaries to prep for each DigiPath Digest[03:19] Paper 1: Can LLMs like ChatGPT and Gemini interpret cervical cytology? Spoiler: ~47–48% exact concordance — promising, but not there yet[10:23] Bonus: My new AI-powered paper summary subscription — $7/month, journal-club style[14:05] Paper 2: AI in oral oncology — CNNs for early lesion detection, multimodal prognostics, and the real barriers still blocking clinical adoption[20:28] Paper 3: Generative AI in oncology — from chat tools to agentic EHR-integrated assistants, and why augmentation is the goal, not automation[25:35] Paper 4: Computational pathology in breast cancer — predicting BRCA1/2, HER2, Oncotype DX, and treatment response from standard H&E slides[31:39] Final thought: the floor just got raised for all of us — how I think about new technology in pathologyResources & Links:Paper 1 – LLMs & Cervical Cytology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41931983/Paper 2 – AI in Oral Oncology (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930554/Paper 3 – Generative AI in Oncology Practice (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930309/Paper 4 – AI & Digital Pathology in Breast Cancer (PubMed): https://pubmed.ncbi.nlm.nih.gov/41930306/Watch on YouTube: https://www.youtube.com/live/O2hOU4gM0Bk?si=oH8iJ8HiBb29USG3Digital Pathology Place: https://www.digitalpathologyplace.comSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Send us Fan MailPaper Discussed in this Episode: How artificial intelligence applied to digital pathology could guide treatment personalization in breast cancer. T. Ruelle, T. Grinda, L. Del Mastro, M. Lacroix-Triki, B. Pistilli & G. Gessain. ESMO Real World Data and Digital Oncology 2026.Episode Summary: In this journal club episode, we step into the reality of computational pathology and explore how artificial intelligence is fundamentally transforming breast cancer diagnostics. We examine a comprehensive review detailing how AI not only assists overburdened healthcare systems but also unlocks invisible genomic data straight from a standard $5 hematoxylin-eosin (H&E) glass slide. What happens when a machine can predict complex DNA mutations just by evaluating the structural architecture of cells?In This Episode, We Cover:• The Diagnostic Bottleneck: Understanding the critical worldwide shortage of pathologists colliding with a projected 3.2 million global breast cancer diagnoses by 2050, and why the system is under unprecedented strain.• The Biomarker Battle: Why the human visual cortex struggles to quantify faint immunohistochemistry stains, and how AI acts as a perfect "digital colorimeter". We discuss its near-perfect concordance in assessing crucial biomarkers like Ki-67, ER, PR, PD-L1, and the newly established HER2-low status.• Seeing the Invisible (Predictive AI): How deep learning transcends visual diagnostics to predict treatment outcomes, such as a patient's response to neoadjuvant chemotherapy. We also discuss AI's ability to infer Homologous Recombination Deficiency (HRD) and BRCA1/2 mutations by identifying macroscopic footprints like laminated fibrosis.• Decoding Genomic Assays: The potential to replace expensive, tissue-consuming genomic tests like Oncotype DX with AI models (such as Orpheus) that predict recurrence risk straight from digitized slides, achieving accuracy that rivals the tests themselves.• Roadblocks to Reality: The major clinical friction preventing global rollout. We discuss the steep infrastructure costs of whole-slide scanners, the danger of AI bias across diverse hospital datasets, and the ethical "black box" problem requiring the evolution of transparent, agent-based AI.Key Takeaway: Computational pathology is moving far beyond basic diagnostic assistance. By successfully reading the structural language of biology, AI proves it can extract costly, invisible molecular data from standard biopsies, fundamentally changing the economics and accessibility of global personalized healthcareSupport the showGet the "Digital Pathology 101" FREE E-book and join us!

Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen #hsfeedback Nachtrag zu japanischen/chinesischen Schriftzeichen von Stefan: Besuch im Leica Museum Titel für einen neuen Workshop: LichtGestalten von Paul: Fotorucksack, der in ein Flugzeug Handgepäck passt Vergrößerungsfaktor in Lightroom von Arne: Vermisster Workshop von Manuel: Fotolabore von Erik: Abspeichern als RAW und JPEG ohne Hintertürchen? von … „#938 – Lizenzgebamsel“ weiterlesen

Chris, Ade and Jeremiah explore the ways new technology can help you make fantastic photos.

Dr. Lawrence Uricchio is Assistant Professor and the Youniss Family Professor of Innovation in the Department of Biology at Tufts University. Research in Lawrence's lab focuses on modeling how evolutionary and ecological processes work. They use a combination of experimental and observational approaches to generate data, and then they develop mathematical models to explain the observations they make in nature. Outside of work, Lawrence is a devoted family man who loves spending time with his nine-year-old son, often shuttling him between soccer practices and games. He also enjoys being outdoors and running, a passion that has stayed with him since his days as a middle-distance runner in high school and college. He completed his bachelor's degree in physics at Carleton College, received master's degrees in biophysical sciences and computer science from the University of Chicago, and was awarded his PhD in bioinformatics from the University of California, San Francisco. While at UCSF, he was named a Discovery Fellow. Afterwards, Lawrence conducted postdoctoral research at Stanford University under a Center for Computational, Evolutionary and Human Genomics (CEHG) Fellowship and an NIH Institutional Research and Academic Career Development Award (IRACDA). He also conducted postdoctoral research at the University of California, Berkeley before joining the faculty at Tufts in 2021. In this interview, Lawrence shares more about his life and science.

Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Neue ID, Start-Stop, Kann sein – muss aber nicht HS Workshops Workshops HS Workshop-Newsletter Aufruf: Interesse an Licht/Mensch Workshop? Statt Werbung DANKE an alle Spender Es gibt kein Scheitern in der Kunst Themen Bericht aus der Sehwerkstatt Tagging mit KI ON1 Restore Alptraum-Generator? … „#937 – Farbdiät“ weiterlesen

In this episode of Data in Biotech, Ross Katz sits down with Robert Abel, Chief Scientific Officer of the Platform at Schrödinger, to explore how physics-based computational modeling is transforming drug discovery.  Robert unpacks why machine learning alone isn't enough to navigate the vast complexity of chemical space - an estimated at 10⁶⁰ possible drug-like molecules - and how integrating atomistic simulations with ML creates a more accurate, reliable, and scalable approach to identifying viable drug candidates. From free energy perturbation calculations to generative AI, Robert offers a rare inside look at how Schrödinger's technology platform is accelerating the path from target identification to clinical candidate and where the field is headed next. What you'll learn in this episode:  >> Why chemical space (~10⁶⁰ molecules) makes purely data-driven ML approaches fundamentally insufficient for drug discovery, and how physics-based sampling solves the training data problem >> How free energy perturbation (FEP) calculations enable quantitative prediction of protein-ligand binding affinities at near-experimental accuracy (~1.2 kcal/mol RMSE) >> How Schrödinger's active learning framework combines physics-based simulations and ML to triage billions of candidate molecules before committing to wet lab synthesis >> Why Schrödinger operates across three business lines; software licensing, collaborative programs, and proprietary drug discovery and how each strengthens the underlying technology platform >> Where the next frontiers lie: routine anti-target selectivity profiling, retrosynthetic AI integration, and the expanding role of generative ML in de novo molecular design Meet our guest: Robert Abel is Chief Scientific Officer, Platform at Schrödinger, where he helps lead the scientific direction behind computational approaches that support modern drug discovery and molecular design. With a PhD in Chemical Physics from Columbia University and a deep background in computational chemistry, he has held multiple senior science leadership roles at Schrödinger, guiding teams that build and scale scientific methods into production-grade platforms used across research and industry. Connect with Robert Abel on LinkedIn  About the host: Ross Katz is Principal and Data Science Lead at CorrDyn. Ross specializes in building intelligent data systems that empower biotech and healthcare organizations to extract insights and drive innovation. Connect with Ross Katz on LinkedIn Connect with us: Follow the podcast for more insightful discussions on the latest in biotech and data science.Subscribe and leave a review if you enjoyed this episode! Sponsored by… This episode is brought to you by CorrDyn, the leader in data-driven solutions for biotech and healthcare. Discover how CorrDyn is helping organizations turn data into breakthroughs at CorrDyn.

In a new episode of the pharmaphorum podcast, recorded at BIO-Europe Spring in Lisbon, Portugal, web editor Nicole Raleigh spoke with Kashif Sadiq, founder and CEO of DenovAI Biotech, a company that believes humanity is on the cusp of a protein design revolution that stands to transform both human health and the world around us. Sadiq discusses the company's springboard from AION Labs - with a first-of-its-kind alliance of AstraZeneca, Merck, Pfizer, Teva, the Israel Biotech Fund, Amiti Ventures, and Amazon Web Services, powered by BioMed X with the support of the Israeli Government. He also explores harnessing the power of artificial intelligence and computational molecular biophysics, developing foundational technology platforms that can design proteins de novo, and describes the trends and insights from the conference itself this year.

A collection of articles published in JNeurosci highlights some of the debates in the field about the details of the computational properties of the prefrontal cortex. Two editors for this special collection, Nandakumar Narayanan and Erin Rich, discuss the role of prefrontal cortex in cognition and behavior. Read through this special collection of articles published in Vol. 45, Issue 37 on Sep. 10, 2025. Find our upcoming webinar schedule here. With special guests: Nandakumar Narayanan and Erin Rich On Neuro Current, we delve into the stories and conversations surrounding research published in the journals of the Society for Neuroscience. Through its publications, JNeurosci, eNeuro, and the History of Neuroscience in Autobiography, SfN promotes discussion, debate, and reflection on the nature of scientific discovery, to advance the understanding of the brain and the nervous system.    Find out more about SfN and connect with us on BlueSky, X, Instagram, and LinkedIn. 

Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow lalalalala… Fusionsenergie wird uns retten! HS Workshops Workshops HS Workshop-Newsletter Statt Werbung DANKE an alle Spender Themen Preview zur Sehwerkstatt 2026 am kommenden Wochenende Einschränkungen können auch nerven: Boris S/W-Jpeg-Adventures featuring das alte 50 1.8 News Erster Discount für Sigma BF Eingestellt: … „#936 – Die Idee ist schlauer als sie sich anhört“ weiterlesen

Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow zuverlässig Dienstags, warum ist der Hintergrund nicht blau, Kekssuche, Bruchware ist Fake HS Workshops Workshops HS Workshop-Newsletter Noch 10 Tage bis zur Sehwerkstatt Statt Werbung DANKE an alle Spender News Nikon: Garantie: Fehlerhafte Teilen können Totalausfall bedeuten David vs. Adobe: Stock-Archiv scheitert vor … „#935 – Pok pok pok pokcasten“ weiterlesen

Chris, Ade and Jeremiah explore the ways new technology can help you make fantastic photos.

In episode 39, Alba Ribera Martinez and Thibault Schrepel talk to Gustavo Augusto Freitas de Lima, Interim President of Brazil's Competition Agency (CADE).Follow the Stanford Computational Antitrust project at https://law.stanford.edu/computationalantitrust

Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Wir werden sehen, das laute, mehr Action in der Bildsprache, Sprechdurchfall HS Workshops Workshops HS Workshop-Newsletter Statt Werbung DANKE an alle Spender HSFeedback Marko: Hat den Kalender bewundert und meldet einen Fehler Detlef: Das 70-200/2.8 ist großartig! (mit Überweisung) Jürgen: Jeff Bridges … „#934 – Toolhopping“ weiterlesen

Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Hier ist nämlich Phase, deshalb haben wir Prestrom… Morsen und CBFunk HS Workshops Workshops HS Workshop-Newsletter Statt Werbung DANKE an alle Spender HSFeedback von Thomas: RapidRaw kostenloser RAW Entwickler von Johannes: Ich habe auf jeder Kamera einen HotShoe-Schutz von Frank: Warum berichtet … „#933 – Hintertürchen“ weiterlesen

The play element at the heart of our interactions with computers—and how it drives the best and the worst manifestations of the information age. Whether we interact with video games or spreadsheets or social media, playing with software shapes every facet of our lives. In Playing Software: Homo Ludens in Computational Culture (MIT Press, 2023), Miguel Sicart delves into why we play with computers, how that play shapes culture and society, and the threat posed by malefactors using play to weaponize everything from conspiracy theories to extractive capitalism. Starting from the controversial idea that software is an essential agent in the information age, Sicart considers our culture in general—and our way of thinking about and creating digital technology in particular—as a consequence of interacting with software's agency through play. As Sicart shows, playing shapes software agency. In turn, software shapes our agency as we adapt and relate to it through play. That play drives the creation of new cultural, social, and political forms. Sicart also reveals the role of make-believe in driving our playful engagement with the digital sphere. From there, he discusses the cybernetic theory of digital play and what we can learn from combining it with the idea that playfulness can mean pleasurable interaction with human and nonhuman agents inside the boundaries of a computational system. Finally, he critiques the instrumentalization of play as a tool wielded by platform capitalism. Rudolf Inderst is a professor of Game Design with a focus on Digital Game Studies at the IU International University of Applied Science, editor of “Game Studies Watchlist”, a weekly messenger newsletter about Game Culture and curator of @gamestudies at tiktok. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/new-books-network

The play element at the heart of our interactions with computers—and how it drives the best and the worst manifestations of the information age. Whether we interact with video games or spreadsheets or social media, playing with software shapes every facet of our lives. In Playing Software: Homo Ludens in Computational Culture (MIT Press, 2023), Miguel Sicart delves into why we play with computers, how that play shapes culture and society, and the threat posed by malefactors using play to weaponize everything from conspiracy theories to extractive capitalism. Starting from the controversial idea that software is an essential agent in the information age, Sicart considers our culture in general—and our way of thinking about and creating digital technology in particular—as a consequence of interacting with software's agency through play. As Sicart shows, playing shapes software agency. In turn, software shapes our agency as we adapt and relate to it through play. That play drives the creation of new cultural, social, and political forms. Sicart also reveals the role of make-believe in driving our playful engagement with the digital sphere. From there, he discusses the cybernetic theory of digital play and what we can learn from combining it with the idea that playfulness can mean pleasurable interaction with human and nonhuman agents inside the boundaries of a computational system. Finally, he critiques the instrumentalization of play as a tool wielded by platform capitalism. Rudolf Inderst is a professor of Game Design with a focus on Digital Game Studies at the IU International University of Applied Science, editor of “Game Studies Watchlist”, a weekly messenger newsletter about Game Culture and curator of @gamestudies at tiktok. Learn more about your ad choices. Visit megaphone.fm/adchoices Support our show by becoming a premium member! https://newbooksnetwork.supportingcast.fm/sociology

Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Hab ihr ’ne App geschrieben, Band-Fotografie, Schuhe putzen HS Workshops Workshops HS Workshop-Newsletter Statt Werbung DANKE an alle Spender HSFeedback von Roland: Rückmeldung über eine Ausstellung, „What the Fake“ Stadtmuseum in Arau. Ebenso „New Realitys“ – im Kalender verzeichnet von Hendrik: bei … „#932 – Nahstelleingrenze“ weiterlesen

Bone loss doesn't start when fractures happen — it begins years earlier. In this episode, we explore how bone aging accelerates during menopause and the emerging role of the gut microbiome as a driver of skeletal decline. Dr. Kara Fitzgerald speaks with research scientists from Sōlaria biō, the company behind Bōndia, about their work studying plant-derived microbes, microbial synergies, and the connections between gut health, inflammation, and bone loss in peri- and postmenopausal women. We also review findings from a randomized, placebo-controlled clinical trial examining a microbiome-based intervention for bone health — and discuss why bone loss may need to be addressed earlier, systemically, and beyond hormones alone. Full show notes + references: https://www.drkarafitzgerald.com/fxmed-podcast/ GUEST DETAILS Alicia Ballok, Ph.D. is Director of Discovery at Sōlaria biō, leading research on plant-derived synbiotics for inflammatory and immune-mediated diseases. Trained in Microbiology and Immunology at Dartmouth, with postdoctoral work at Harvard Medical School and Massachusetts General Hospital, her work focuses on translating host–microbe science into therapeutic innovation. Mark Charbonneau, Ph.D. is Vice President of R&D at Sōlaria biō. He earned his Ph.D. in Computational and Systems Biology at Washington University in St. Louis, studying infant microbiome development and undernutrition. His work spans microbiome research, bioinformatics, and live biotherapeutic innovation. THANKS TO OUR SPONSOR Sōlaria biō: http://bit.ly/SolariaBio EXCLUSIVE OFFER FOR NEW FRONTIERS LISTENERS Looking for a clinically proven way to target bone loss? Bōndia by Sōlaria biō is a groundbreaking blend of plant-derived prebiotics and probiotics shown in a clinical trial to improve bone density outcomes by 85%. Try it for yourself at Sōlaria biō and use code Kara20 for 20% off your order. CONNECT with DrKF Want more? Join our newsletter here: https://www.drkarafitzgerald.com/newsletter/ Or take our pop quiz and test your BioAge! https://www.drkarafitzgerald.com/bioagequiz YouTube: https://tinyurl.com/hjpc8daz Instagram: https://www.instagram.com/drkarafitzgerald/ Facebook: https://www.facebook.com/DrKaraFitzgerald/ DrKF Clinic: Patient consults with DrKF physicians including Younger You Concierge: https://tinyurl.com/yx4fjhkb Younger You Practitioner Training Program: www.drkarafitzgerald.com/trainingyyi/ Younger You book: https://tinyurl.com/mr4d9tym Better Broths and Healing Tonics book: https://tinyurl.com/3644mrfw

Hausmeisterei Video zur Episode Text-/Audio-/Videokommentar einreichen HS-Hörer:innen im Slack treffen Aus der Preshow Teure Hardware, Banderole, KI für Telefonkonferenzen, Döner HS Workshops Workshops HS Workshop-Newsletter Statt Werbung DANKE an alle Spender HSFeedback Von Harald: Artemis II – der Weg zum Mond Robert: Keine Kameraarbeit mehr in der Lokalpresse Manuel: Daten zur Hörerdemographie Followup von Dieter … „#931 – Hochkariert“ weiterlesen