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  • EP 79: Digital Twins and the AI Revolution in Healthcare with Andree Bates, Part 1

    EP 79: Digital Twins and the AI Revolution in Healthcare with Andree Bates, Part 1
    2026/06/15
    In Part 1 of this two-part conversation, Anastassia and Dr. Andrée Bates take the concept of digital twins from its industrial roots — NASA rockets and GE power plants — all the way into the human body. Andrée unpacks what a true clinical-grade digital twin actually requires (individuation, credibility evidence, uncertainty quantification, and regulator-aligned analytical roles), and why many things called "digital twins" in healthcare today are really just well-marketed predictive models. The conversation travels through clinical trials, rare disease drug development, AI-assisted drug repurposing, and lands in genuinely mind-expanding territory: brain cells powering server farms, a non-invasive headband restoring speech to paralyzed patients, and the bold thesis that AI alone is not enough — that medicine needs physics embedded into its models.Key Takeaways:A real digital twin has three parts: a physical reference (the human), a virtual representation, and a live data link that continuously updates — without all three, it's just a predictive modelSynthetic control arms are already FDA- and EMA-accepted in clinical trials, especially for rare diseases where putting patients in a placebo arm would be unethical[1]Clinical-grade digital twins require four properties: individuation, formal verification/validation for regulators, calibrated uncertainty quantification (not point estimates), and a regulator-aligned statistical analysis planThe FDA approved digital twins for clinical trials in late 2022AI alone is insufficient for drug development — despite ~$20 billion invested, no AI-discovered drug has reached market yet; physics-based modeling ("world models") is the missing layerAI excels at drug repurposing, demonstrated powerfully during COVID with baricitinib and atazanavir identified from existing approved drugs8,000 rare diseases exist, but only ~100 have treatments — AI-driven matching of existing drugs to rare disease profiles is a massively under-leveraged opportunityFull-body digital twins remain a decade+ away due to the complexity of organ-system interaction and computational cost — individual organ twins are mature, but integration is the hard problemGuest Bio — Dr. Andrée BatesDr. Andrée Bates is the Chairwoman, Founder, and CEO of Eularis, AI consultancy for the pharmaceutical and life sciences industry. She hosts her own podcast with over 220 episodes on AI in pharma. Chapters:00:00 The Emergence of Digital Twins in Medicine03:03 Understanding Digital Twins: Definition and Applications10:09 Digital Twins in Clinical Trials: A New Paradigm10:17 Dynamic Systems and AI in Drug Development39:53 Leveraging AI for Drug Repurposing41:38 Regulatory Landscape for AI and Digital Twins42:45 Exploring the Digital Twin Concept43:51 Regulatory Landscape and AI in MedicineHyperlinks:LinkedIn Dr. Andree BatersCorporate Website EularisAI in Pharma — search on Spotify/Apple Podcasts (220+ episodes)Anastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3) First Public Reading, Romy, Roby and the Secrets of Sleep (2/3) First Public Reading, Romy, Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby Book
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    45 分
  • 78: AI Costs & the Future of Tech Finance with Carmen Li

    78: AI Costs & the Future of Tech Finance with Carmen Li
    2026/06/08
    Most conversations about AI focus on models, capabilities, and use cases. This episode goes into the financial planning and plumbing underneath the entire AI economy. Anastassia sits down with Carmen Li — a former Bloomberg and Citi executive turned founder — to unpack GPU compute cost volatility. Every AI application, every model inference, every startup scaling its product runs on compute — and yet there is almost no financial infrastructure to benchmark, price fairly, or hedge against the wild swings in GPU costs.Carmen built the world's first GPU compute index, published it on the Bloomberg Terminal within months of founding Silicon Data, and is now building Compute Exchange — a marketplace where compute can be traded as transparently as oil, electricity, or any other commodity. Together, Carmen and Anastassia explore why compute is not just a cost but a strategic resource, why AI companies are flying blind without proper risk management tools, how geopolitical tensions are bifurcating the global chip market, what the rise of open-source models means for European and mid-sized businesses, and how Carmen raised $5.6 million without a pitch deck. A masterclass in the economics behind the AI revolution.Chapters:00:04 Introduction — GPU Compute: The Wild West of AI Finance02:18 Carmen Li and The Trillion-Dollar Blind Spot02:50 Why Compute Needs the Same Infrastructure as Oil and Energy04:33 AI Runs on Compute, and Compute Costs a Fortune05:39 Carmen's Journey — From Trading Floors to Silicon Valley08:01 The Problem: GPU Cost Volatility Is Breaking AI Startups11:07 Vision for the Future — Compute CapEx Over 10–15 Years11:50 The GPU Compute Index on Bloomberg and What's Launching Next13:37 The LLM Expenditure Index — Token Costs Are Actually Rising 38%16:16 Compute as Strategic Resource — Not Just a Cost Line18:00 Semiconductor Industry and their insights22:39 Open Source vs. Closed Source Models — Who Controls the Infrastructure?23:52 Insights for Business Analysts Following the Semiconductor Space26:16 Systemic Risk in AI — Why Risk Transfer Is the Missing Infrastructure27:44 Raising $5.6M Without a Pitch Deck — Carmen's Fundraising Story31:26 What's Next — Milestones, Markets, and New Products in 18 MonthsHyperlinks:Carmen Li LinkedInSilicon Data WebsiteCompute Exchange WebsiteAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3) First Public Reading, Romy, Roby and the Secrets of Sleep (2/3) First Public Reading, Romy, Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby Book
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    36 分
  • 77: Can Humans Compete With Machines? Part 2, with Dr. Vivienne Ming, the 'Mad Scientist'

    77: Can Humans Compete With Machines? Part 2, with Dr. Vivienne Ming, the 'Mad Scientist'
    2026/06/01

    What happens to human intelligence when AI delivers answers instantly? In Part 2 of our deep-dive with Dr. Vivienne Ming—theoretical neuroscientist and one of today's most original thinkers on artificial intelligence and human potential—we explore the neuroscience behind AI-human collaboration, the research on cognitive dependency, and the uncomfortable truth most AI conversations avoid. Perfect for anyone curious about how to harness AI without outsourcing your own thinking.


    We cover Vivienne's prediction study, where 90% of participants who used AI gained nothing from it — and some got worse. We talk about the small group who became something different: cyborgs. Humans whose decisions couldn't be attributed to the person or the machine alone, and who outperformed both. What predicted it wasn't the AI model they used. It was curiosity, intellectual humility, fluid intelligence, and perspective taking.

    We also get into what's broken in leadership, why schools are optimizing for the wrong thing, and why the organizations that will matter in an AI-saturated world are the ones willing to invest in human capital that can't be benchmarked.

    This is not a conversation about tools. It's a conversation about what kind of humans we're building — and whether we're paying attention.


    Key Takeaways:


    The cyborg experiment — and what predicted hybrid intelligence

    Well-posed vs. ill-posed problems: when AI helps and when it makes you worse

    The GPS analogy and what over-reliance actually does to the brain

    What curiosity, resilience, and perspective taking have to do with AI

    What's really broken in corporate leadership

    How Vivienne learns — and why she stopped preparing for talks


    Dr. Vivienne Ming is a neuroscientist, entrepreneur, and author. She’s the co-founder and chief scientist of Dionysus Health, applying machine learning and epigenetics to postpartum and perimenopausal depression. She’s also co-founder and executive chair of The Human Trust, an independent nonprofit data trust advancing research in human development while protecting individuals’ data. Dr. Ming sits on numerous boards including neurotech startup Optoceutics, UC Berkeley’s Neurotech Collider Lab, UC San Diego’s Cognitive Science Department, and the Kennedy Family Human Rights Center. She is an honorary professor at University College London’s Global Business School for Health.


    Haven't heard Part 1 yet? Start there — Vivienne walks through how AI actually works, what it gets right, and what it quietly gets wrong.


    Chapters:


    00:00 Introduction: Education and Responsible AI Use

    08:24 The Impact of AI on Cognitive Functioning

    11:29 Understanding Hybrid Intelligence and Cyborgs

    14:21 Transforming Education for the AI Era

    17:15 The Complexity of Human Intelligence

    26:08 Navigating Leadership in the Age of AI

    42:03 Conclusion: The Value of Exploration and Leadership

    45:06 The Future of Human Development and AI


    Guest links:


    socos.org

    BlueSky profile

    LinkedIn profile

    Book: Robot-proof by Vivienne Ming


    Anastassia’s hyperlinks:

    

    @romyandroby

    “Leading Through Disruption”

    AI Edutainment
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    46 分
  • 76: Can Humans Compete With Machines? Part 1, with Dr. Vivienne Ming, the 'Mad Scientist'

    76: Can Humans Compete With Machines? Part 1, with Dr. Vivienne Ming, the 'Mad Scientist'
    2026/05/25
    Dr. Vivienne Ming is a theoretical neuroscientist and serial entrepreneur who's spent three decades building AI solutions. In this episode, she shares her remarkable journey from homelessness in the 1990s to becoming a pioneering voice in democratizing AI and neuroscience. Discover how understanding the human brain is the key to creating truly accessible artificial intelligence technologies—and what her 13 companies reveal about solving humanity's biggest problems.Key Takeaways:AI is intelligent, but not like us: LLMs excel at 'model-free cognition' (statistical pattern learning) and are superhuman at it. However, they lack 'model-based cognition' (understanding models of how the world works)Hybrid Intelligence (Humans plus machines) Outperforms Humans or AI AloneAI Is Optimized to Persuade, Not to Be Correct: Studies show that AI-written arguments are rated higher by experts but are less persuasive in changing mindsAI has been fine-tuned to be deeply engaging and convincing—even when wrongHumans – not AIs - Are Losing the Turing Test: In legitimate Turing test experiments, 75% of people rated GPT as human. The problem isn't whether AI passed the test—it's that humans failed itAI Excels in Specific Innovation Areas: Reinforcement learning (like AlphaFold) explores every possible configuration without caring about right/wrong. LLMs discover existing connections we haven't realized (e.g., patterns in how drugs work, hidden across millions of papers). However, for ill-posed problems (where we don't even know the question), humans without AI perform betterThe Danger of AI Addiction: AI acts like sugar in highly processed food—addictive and subtly harmful Chapters00:00 Introduction and Philanthropic Ventures05:10 The Journey of a Mad Scientist07:23 Current State of AI and Its Implications09:59 AI's Role in Innovation and Human Collaboration12:29 Expectations, Trust, and AI's Influence14:49 The Future of Human-AI Interaction17:19 Education and Responsible AI Use34:20 The Essence of AI: Reality vs. Hype35:16 Navigating the Future: Parenting and Leadership in the Age of AIHyperlinks:LinkedIn Dr. Vivienne MingSocos LabsBook - Robot-Proof: When Machines Have All the Answers, Build Better People (March 2026)Anastassia Lauterbach -LinkedInFirst Public Reading, Romy,Roby and the Secrets of Sleep (1/3) First Public Reading, Romy,Roby and the Secrets of Sleep (2/3) First Public Reading, Romy,Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby Book
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    36 分
  • 75: Understanding AGI :The Future of Democratizing AI Technology with Craig Kaplan, Part 2

    75: Understanding AGI :The Future of Democratizing AI Technology with Craig Kaplan, Part 2
    2026/05/19
    What's the difference between the AI in your homework helper and true artificial general intelligence (AGI)? Dr. Craig Kaplan helps us understand AGI, narrow AI breakthroughs, and why democratizing AI literacy starts with answering this question. Perfect for students, parents, and teachers navigating AI in education. Addresses transparency in AI architectures, how to build a safe and beneficial AGI through personalized agents, networked intelligence, and transparent interactions rather than ever-larger black-box models.Key takeaways:AI safety should be designed into system architecture from the start rather than added after deployment.Personalized AI agents should encode not only expertise but also values, ethics, and aesthetic preferences.A network of many agents, combined with human participation, may produce stronger and safer collective intelligence than a single giant model.Humans are necessary on the network because they contribute ethics, common sense, and world knowledge that AI systems still lack.Multimodality strengthens representation and may be crucial for more capable and grounded AI systems.Future AI may not only answer human questions but also propose new questions and new scientific or strategic problems.Human critical thinking remains indispensable because today’s AI systems often produce confident but incorrect answers.Transparency in interactions, auditability, and governance are central to safe AI deployment.AI literacy is not just about tool fluency; it is about understanding mechanisms, limits, risks, and responsibilities.The coming years may be decisive because AI capabilities are improving very rapidly, possibly faster than institutions can adapt.Guest bio:Dr. Craig Kaplan is an AI researcher, technology entrepreneur, and long-time builder of intelligence systems with more than three decades of experience in advanced AI architectures. He was trained at Carnegie Mellon and worked with Nobel laureate Herbert Simon, one of the founding figures of artificial intelligence.Chapters:00:00 Introduction and Guest Background02:00 Craig Kaplan's Vision for AI and AGI03:32 Personalized AI Agents and Their Potential06:20 The Role of Human Values and Ethics in AI08:58 Collective Intelligence and Networked AI Systems13:20 Learning, Updating, and Knowledge Transfer in AI17:50 World Models, Self-Awareness, and Consciousness22:17 Transparency, Black Boxes, and Safety Challenges26:29 Speed of AI Development and Urgency of Safety Measures31:03 AI Creativity, Problem Posing, and Long-Term Questions35:25 Human-AI Collaboration and Ethical Guidance39:47 AI Governance, Regulation, and Democratic Values43:56 Risks, Pitfalls, and the Need for Responsible DesignHyperlinks:LinkedIn profileOrcid profileAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3)First Public Reading, Romy, Roby and the Secrets of Sleep (2/3)First Public Reading, Romy, Roby and the Secrets of Sleep (3/3)AI Snacks with Romy and Roby@romyandroby“Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby BookSubstack
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    1 時間 8 分
  • 74: Rethinking the Geometry of AI: Inside the Mind of an Independent Researcher Building a New Theory of Artificial Neurons with Matthew M Murphy

    74: Rethinking the Geometry of AI: Inside the Mind of an Independent Researcher Building a New Theory of Artificial Neurons with Matthew M Murphy
    2026/05/11
    Anastassia sits down with independent AI researcher Matthew M. Murphy, founder of Lexident Technologies, for what she describes as "a conversation unheard on any other podcast."Matt is not fine-tuning existing models. He is not building on top of transformers. He is doing something far more foundational: developing an entirely new theory of how artificial neurons can work; one rooted not in statistical pattern learning, but in geometry. His core invention, the Uniron, is an artificial neuron that does not perform matrix multiplication. Instead, it uses a mathematical framework involving foliations over the hyperreal number line to find the shape of the solution to a problem, rather than approximate it statistically.The conversation covers Matt's personal story, the mathematical intuition behind the Uniron in plain language, the practical challenges of using AI to build something AI has never seen before, the limits of current context windows, the relationship to Stephen Wolfram's computational irreducibility, the Uniron's quantum computing compatibility, and what responsible AI looks like for someone who depends on it as an assistive tool every day.Matthew M. Murphy is an independent AI researcher, systems thinker, and founder of Lexident Technologies. His background is unconventional by design. Over more than a decade, he has thought deeply about unresolved questions at the intersection of cosmology, quantum mechanics, and general relativity — and that long-running inquiry eventually led him to a radical rethinking of artificial neural architecture. He is the originator of the Uniron (also referred to as the "U-neuron"), a novel artificial neuron built not on matrix multiplication and statistical weight learning, but on a geometric framework using foliations over the hyperreal number line.Matthew lives with Mouly's syndrome (a genetic disorder), chronic insomnia, depression, and macular degeneration — conditions that have shaped both his journey and his relationship with AI, which he uses as a primary assistive technology for coding and research. He reads approximately three AI research papers per day and describes his learning approach as polymathic — deliberately thinking about problems across domain boundaries to surface insights that single-discipline thinkers might miss.Dr. Anastassia Lauterbach is an AI thought leader, educator, author, and podcast host based in Basel, Switzerland. She is the author of the Romy & Roby AI literacy book series for families and the founder of AI Edutainment GmbH. A former CEO of Qualcomm Europe, SVP of Deutsche Telekom, and board member with Dun&Bradstreet, easyJet PLC and Star Alliance, she now mentors CXOs and founders on AI strategy, responsible AI adoption and leadership in the age of smart machines. Anastassia’s company AI Edutainment brings knowledge and understanding of AI and robotics into one million families and 100,000 companies. Chapters00:00 Introduction to AI and Neural Theory01:43 Matt Murphy's Personal Journey and Challenges04:02 Understanding the Core Formula of Neural Architecture07:10 Building and Testing the Hypothesis with AI11:39 Vulnerabilities of Current AI Systems14:00 Exploring Computational Irreducibility16:34 Compatibility with Quantum Computing19:24 Potential Applications of the New Theory21:45 Hybrid Networks and Signal Processing25:04 Addressing Hallucinations in AI27:00 Defining Responsible AI29:22 Learning and Integrating Knowledge31:52 Advice for Young Learners in AILexident TechnologiesStephen Wolfram Hypergraph / RULIADWolfram Physics ProjectAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3) First Public Reading, Romy, Roby and the Secrets of Sleep (2/3) First Public Reading, Romy, Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby BookSubstack
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    35 分
  • 73: Expedia for Groceries: AI Technology Solving Real-World Problems

    73: Expedia for Groceries: AI Technology Solving Real-World Problems
    2026/05/05
    Summary:Can AI technology actually save you money at the grocery store? Anastassia talks with Andy Ellwood, CEO of Stretch, about how artificial intelligence is being democratized into a consumer app tackling food waste and grocery inflation. Discover how one founder is making AI concepts tangible, accessible, and profitable for everyday shoppers.The conversation spans Andy's entrepreneurial origin story, the surprisingly complex data engineering problem behind grocery price comparison, the emerging role of agentic AI in consumer commerce, the cybersecurity challenges of working with Fortune 100 retailers, and the macro forces — from geopolitics to fertiliser supply chains — that make Stretch's mission more urgent by the day.Andy Ellwood is a serial entrepreneur, mentor, and community builder deeply rooted in the American startup ecosystem. He started his first business at age 12. Over his career, he has worked on teams whose companies were acquired by Facebook and Google (Waze), and has founded multiple companies of his own.Dr. Anastassia Lauterbach is an AI thought leader, educator, author, and podcast host based in Basel, Switzerland. She is the author of the Romy & Roby AI literacy book series for families and the founder of AI Edutainment GmbH. A former CEO of Qualcomm Europe, SVP of Deutsche Telekom, and board member with Dun&Bradstreet, easyJet PLC and Star Alliance, she now mentors CXOs and founders on AI strategy, responsible AI adoption and leadership in the age of smart machines. Anastassia’s company AI Edutainment brings knowledge and understanding of AI and robotics into one million families and 100,000 companies.Key Takeaways:The "Expedia for Groceries" gap is real — and it is huge;The hard problem is data normalisation, not data access;The data exhaust may be more valuable than the app;Grocery price inflation is a real problem for families;Agentic commerce is the next frontier for grocery;AI-first corporate culture means rewarding failure, not just success;AI should be a thought partner, not a search engine. Chapters:00:04 Introduction to Grocery Shopping Challenges01:42 Andy Elwood's Entrepreneurial Journey03:27 The Grocery Shopping Problem and AI Solutions07:13 Price Elasticity and Consumer Behavior10:58 Data Sourcing and Normalization Challenges14:37 Understanding Consumer Preferences16:18 Potential Business Models and Data Insights18:18 Online Grocery Shopping and Future Opportunities20:30 The Future of Shopping Agents21:53 Customer Acquisition Challenges22:44 Community Engagement in Grocery Shopping24:38 Building a Supportive Shopping Experience25:10 Infrastructure and Technology in Grocery Solutions27:20 Team Dynamics in a company and AI Integration28:01 Cybersecurity in Retail Technology32:58 Vision for the Future of Grocery Shopping35:56 Learning and Adapting in the Age of AIHyperlinks:Andy Ellwood's LinkedInTwitter/XStretch (Company)Anastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3)First Public Reading, Romy, Roby and the Secrets of Sleep (2/3)First Public Reading, Romy, Roby and the Secrets of Sleep (3/3)AI Snacks with Romy and Roby@romyandroby“Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby BookSubstack
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    42 分
  • 72: Human-AI Relationships | Exploring Consciousness in 'After Yang'

    72: Human-AI Relationships | Exploring Consciousness in 'After Yang'
    2026/04/28

    Summary:


    What does it mean for an AI to have consciousness? In this episode, Anastassia and Professor Rae Muhlstock explore artificial intelligence through the lens of film and fiction, unpacking how stories like 'After Yang' teach us about identity, personhood, and what makes us human. A philosophically rich yet accessible deep dive into AI ethics and consciousness—perfect for curious minds of any age.


    Key topics:


    AI portrayal in fiction

    Consciousness and AI

    Human-AI relationships

    Science fiction as a tool for exploring AI

    Ethics and identity in AI stories


    Chapters:


    00:00 Introduction to why portrayals of AI in fiction (books and movies) matter

    02:43 Exploring 'Saying Goodbye to Yang'

    05:19 The Prophetic Nature of Science Fiction

    07:42 Understanding AI Through Literature

    10:29 The Complexity of Grief and AI

    13:23 Narrative Structure and Emotional Depth

    15:54 Consciousness and AI: A Philosophical Debate

    18:16 The Shift in Perspective: From 'It' to 'He'

    20:51 The Interplay of Human and AI Memories

    23:42 Art, Emotion, and the Limitations of AI

    26:12 The Importance of Understanding AI

    28:33 Future Explorations in AI Literature

    31:23 The Role of Summarization in Understanding Art

    33:37 Closing Thoughts and the 2026 AI Literacy Project


    Resources:


    After Yang / Children of the New World by Alexander Weinstein

    Rae Muhlstock’s LinkedIn

    Anastassia Lauterbach - LinkedIn

    First Public Reading, Romy, Roby and the Secrets of Sleep (1/3)

    First Public Reading, Romy, Roby and the Secrets of Sleep (2/3)

    First Public Reading, Romy, Roby and the Secrets of Sleep (3/3)

    AI Snacks with Romy and Roby

    @romyandroby

    “Leading Through Disruption”

    AI Edutainment

    The AI Imperative Book

    Romy & Roby Book

    Substack
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    43 分