Couverture de AI Snacks With Romy & Roby: Democratizing AI Technologies

AI Snacks With Romy & Roby: Democratizing AI Technologies

AI Snacks With Romy & Roby: Democratizing AI Technologies

De : Dr. Anastassia Lauterbach: Democratizing AI Expert
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AI Snacks with Romy&Roby is a podcast that translates AI and robotics technologies from complex scientific concepts into easy-to-understand discussions, making them accessible for teens, parents, teachers, and anyone curious about AI. Through real-world stories and expert interviews, the show is dedicated to democratizing AI knowledge and empowering the general population to understand how AI is developed and applied in everyday life. The podcast is part of the Romy&Roby and AI Edutainment universe.Copyright 2024
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  • 97: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 2

    97: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 2
    Oct 20 2026
    SummaryPart 2 builds on the previous episode, describing how to establish AI governance in an organization, and what AI literacy truly means for business results.Anthony Scriffignano, PhD is one of the world's best data scientists — a thinker who has spent more than 40 years at the intersection of linguistics, advanced algorithms, business intelligence, and the very human problem of making good decisions from imperfect information. Anthony is currently a Distinguished Fellow and member of the Alfred Lee Loomis Innovation Council at The Stimson Center. He also serves on advisory committees in government, the private sector, and academia, and has briefed the National Security Telecommunications Advisory Committee and contributed to reports to the President on Big Data Analytics and Emerging Technologies. He served on the OECD Network of Experts group on implementing Trustworthy AI.His most recent publication is a co-authored chapter — "Illuminating the Invisible: AI's Role in Understanding Complex, Opaque, and Dynamic Ecosystems" — in the Springer volume Artificial Intelligence Revolution (2026, edited by Max Rangeley and Nicholas Fairfax), co-written with Dr. David Bray.Key Takeaways:Compliance is what you must do; governance is what you should do. Governance costs money, time, and opportunity. The right question is not "how do we maximise governance?" but "where do we sit in the continuum between maximum safety and maximum agility, and are we there on purpose?" The human-in-the-loop section challenges the common assumption that more humans equals better decisions. Anthony identifies three limits: humans cannot always make decisions fast enough; even the same human given the same decision repeatedly will not always make the same call; and humans have bias, fatigue, and optimism. The emerging solution is cognitive AI that watches experts making decisions, converges on patterns, and eventually can substitute a synthetic human into the loop for low-stakes votes.Agentic AI is described as a system of semi-autonomous entities, each doing a small piece of a problem, orchestrated together, some inside the enterprise and some outside. Auditing every individual agent is not feasible. The answer is a clinical layer that monitors the health of the agentic ecosystem as a whole — taking the pulse, measuring blood pressure — and raises flags when the system-level behaviour deviates from its guardrails. The language to do this does not fully exist yet.On shadow AI: Anastassia's survey found 64% of respondents in SMEa are using AI tools their employer has not sanctioned. Anthony suspects the real number is higher and points to non-response bias. His prescription is not HR enforcement, but understanding the underserved need: if employees are using shadow AI, the organisation has failed to provide them with the tools they need. Create a safe sandbox, serve the need, and watch what they build.Chinese LLM approach takes a fundamentally different approach from the US— leaner, faster, cheaper, focused rather than voracious.AI literacy versus AI fluency. Fluency — knowing the buzzwords, knowing how to prompt — is the lowest form of useful knowledge. Literacy means knowing what you do not know: understanding the limits of the tool, the provenance of the output, and when you should not trust it. Hyperlinks:Anthony’s LinkedInStimson Center Fellow Profile https://www.stimson.org/ppl/anthony-scriffignano/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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    43 min
  • 96: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 1

    96: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 1
    Oct 13 2026
    SummaryThe conversation focuses on how to build a data strategy addressing the needs of a business first, not what is available on the market in terms of an AI model.Anthony Scriffignano, PhD is one of the world's best data scientists — a thinker who has spent more than 40 years at the intersection of linguistics, advanced algorithms, business intelligence, and the very human problem of making good decisions from imperfect information. Anthony is currently a Distinguished Fellow and member of the Alfred Lee Loomis Innovation Council at The Stimson Center. He also serves on advisory committees in government, the private sector, and academia, and has briefed the National Security Telecommunications Advisory Committee and contributed to reports to the President on Big Data Analytics and Emerging Technologies. He served on the OECD Network of Experts group on implementing Trustworthy AI.His most recent publication is a co-authored chapter — "Illuminating the Invisible: AI's Role in Understanding Complex, Opaque, and Dynamic Ecosystems" — in the Springer volume Artificial Intelligence Revolution (2026, edited by Max Rangeley and Nicholas Fairfax), co-written with Dr. David Bray.Key Takeaways:Data before models. Most AI failures begin in the data, not in the model. Ask the three Ps before you touch a model: Provenance, Permissible use, Perturbation.Decision elasticity. The right question is not "is this data perfect?" but "how wrong could it be and would I still make the same call?" If you cannot answer that, you are not ready to decide.The walled garden method. You can detect disruption inside a system you cannot observe directly by modelling the ecosystem immediately outside it — the haversine annulus, Z-scores, Jensen-Shannon divergence.Start with needs, not tools. Four-box needs analysis: known met, known unmet, unknown met (the danger box), unknown unmet (the blue sky boxSynthetic data compounds error. Pre-LLM synthetic data let you estimate error. Post-LLM, models are generating training data that other models consume, and error compounds multiplicatively rather than averaging out.Hyperlinks:Anthony’s LinkedInStimson Center Fellow Profile https://www.stimson.org/ppl/anthony-scriffignano/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 BookReferenced Concepts:The haversine annulus — a geodesically corrected ring around a walled garden used to model its immediately dependent ecosystem.Jensen-Shannon divergence — an information-theoretic measure used to compress multiple differently-scaled signals into a single change detector.Bayesian inference / updating: start with conditional probability, update as evidence accumulates.Canonical models of malfeasance — Anthony's term for the well-documented patterns of fraud that recur around disasters, insurance events, and public contracts.Dispositive threshold — the point at which you have enough data to make a decision — but just because you can does not mean you should.Shadow AI — unsanctioned AI tools used by employees without employer knowledge or governance; 64% prevalence in Anastassia's SME survey.
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    45 min
  • 95: Science Fiction, Mars & AI Consciousness — What Realistic Sci-Fi Teaches Us with Peter Cawdron

    95: Science Fiction, Mars & AI Consciousness — What Realistic Sci-Fi Teaches Us with Peter Cawdron
    Oct 6 2026

    In episode 95 of AI Snacks with Romy and Roby, Dr. Anastassia Lauterbach sits down with science fiction author Peter Cawdron — 37 books, NASA and SpaceX consultations, and a First Contact series 36 novels deep — to explore what realistic science actually tells us about living on Mars, what AI can and cannot do, and what it would take for a machine to develop genuine values. The conversation moves from the brutal physics of Martian habitation to the Chinese Room thought experiment, from Maslow's hierarchy of needs to the moral arc of the universe. A rare episode where science fiction, engineering, and philosophy arrive at the same question: what makes us human, and can we build that into a machine?


    Peter Cawdron is a science fiction author based on the Sunshine Coast, Queensland, Australia, originally from Auckland, New Zealand. He is the author of 37 books, known for grounding his fiction in the hardest available science — consulting with NASA flight controllers, SpaceX engineers, and space mission specialists to ensure his stories adhere as closely as possible to physical reality.


    Key Topics


    • The influence of real science on sci-fi storytelling and how experts shape the research process
    • Living on Mars — radiation, soil toxicity, underground habitats, and the engineering of survival
    • The Chinese Room thought experiment and why LLMs are not reasoning
    • The Gutenberg Press phase of AI — transformational but still to be determined
    • Jevons' Paradox and why AI may create more jobs than it eliminates
    • Artificial intelligence vs. artificial consciousness — a fundamental distinction
    • Maslow's hierarchy of needs and why AI is stuck at the base
    • The Warren Buffett problem — why digital avatars are not the person
    • A proposed test for AGI — give it all human knowledge up to 1900 and ask it to find Einstein
    • How to give AI values, morals, and culture — and why it matters for avoiding a Terminator future
    • The seesaw between selfishness and cooperation that defines human civilization


    Chapters


    00:00 Introduction to the episode and guest


    01:01 Peter Cawdron's background and journey into sci-fi


    02:17 The role of science and curiosity in human progress


    04:07 Historical perspective on science and its impact on society


    05:31 How real science influences sci-fi writing


    08:02 Researching realistic space habitats and challenges on Mars


    10:30 The plausibility of colonizing Mars and environmental challenges


    17:36 AI as an existential threat and the concept of dumb AI


    19:18 The exponential growth of AI and its societal implications


    21:33 AI as a Gutenberg Press revolution and its future impact


    25:14 The importance of values, morals, and culture in AI development


    30:28 The Chinese Room thought experiment and AI understanding


    35:42 Characters and themes of digital avatars and grief


    41:30 Consciousness, self-awareness, and the brain's complexity


    44:06 AI morality, values, and the risk of Terminator-like futures


    48:47 Human traits — loyalty, trust, and the essence of humanity


    53:45 Upcoming projects and books by Peter Cawdron


    Guest Links


    • Peter Cawdron on Amazon
    • First Contact series
    • To Live and Die on Mars (August 2026)
    • To Live and Die on Earth (September 2026)
    • The Song of the Sirens (upcoming)


    Follow AI Snacks with Romy and Roby

    • Podcast: romyandroby.ai
    • Instagram: @romyandroby
    • LinkedIn: Dr. Anastassia Lauterbach
    • Book: Romy, Roby and the Secrets of Sleep — available on Amazon
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    57 min
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