The AI Fundamentalists

• Metaphysics and modern AI: What is space and time?

  We explore how space and time form a single fabric, testing our daily beliefs through questions about free-fall, black holes, speed, and momentum to reveal what models get right and where they break. To help us, we’re excited to have our friend David Theriault, a science and sci-fi afficionado; and our resident astrophysicist, Rachel Losacco, to talk about practical exploration in space and time. They'll even unpack a few concerns they have about how space and time were depicted in the movie Interstellar (2014).Highlights:• Introduction: Why fundamentals beat shortcuts in science and AI• Time as experience versus physical parameter• Plato’s ideals versus Aristotle’s change as framing tools• Free-fall, G-forces, and what we actually feel• Gravity wells, curvature, and moving through space-time• Black holes, tidal forces, and spaghettification• Momentum and speed: Laser probe, photon momentum, and braking limits• Doppler shifts, time dilation, and length contraction• Why light’s speed stays constant across frames• Modeling causality and preparing for the next paradigmThis episode about space and time is the second in our series about metaphysics and modern AI. Each topic in the series is leading to the fundamental question, "Should AI try to think?" Step away from your keyboard and enjoy this journey with us. Previous episodes:Introduction: Metaphysics and modern AIWhat is reality?What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

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• Metaphysics and modern AI: What is reality?

  In the first episode of our series on metaphysics, Michael Herman joins us from Episode #14 on “What is consciousness?” to discuss reality. More specifically, the question of objects in reality.  The team explores Plato’s forms, Aristotle’s realism, emergence, and embodiment to determine whether AI models can approximate from what humans uniquely experience.Defining objects via properties, perception, and persistenceBanana and circle examples for identity and idealsPlato versus Aristotle on forms and realismShip of Theseus and continuity through changeSamples, complexes, and emergence in systemsEmbodiment, consciousness, and why LLMs lack lived unityExistentialist focus on subjective reality and meaningWhy metaphysics matters for AI governance and safetyJoin us for the next part of the metaphysics series to explore space and time. Subscribe now.What we're reading:[Mumford's] Metaphysics: A Very Short Introduction (Andrew)What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

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• Metaphysics and modern AI: What is thinking? - Series Intro

  This episode is the intro to a special project by The AI Fundamentalists’ hosts and friends. We hope you're ready for a metaphysics mini‑series to explore what thinking and reasoning really mean and how those definitions should shape AI research. Join us for thought-provoking discussions as we tackle basic questions: What is metaphysics and its relevance to AI? What constitutes reality? What defines thinking? How do we understand time? And perhaps most importantly, should AI systems attempt to "think," or are we approaching the entire concept incorrectly? Show notes:• Why metaphysics matters for AI foundations• Definitions of thinking from peers and what they imply• Mixture‑of‑experts, ranking, and the illusion of reasoning• Turing test limits versus deliberation and causality• Towers of Hanoi, agentic workflows, and brittle stepwise reasoning• Math, context, and multi‑component system failures• Proposed plan for the series and areas to explore• Invitation for resources, critiques, and future guestsWe hope you enjoy this philosophical journey to examine the intersection of ancient philosophical questions and cutting-edge technology.What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

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• AI in practice: Guardrails and security for LLMs

  In this episode, we talk about practical guardrails for LLMs with data scientist Nicholas Brathwaite. We focus on how to stop PII leaks, retrieve data, and evaluate safety with real limits. We weigh managed solutions like AWS Bedrock against open-source approaches and discuss when to skip LLMs altogether.• Why guardrails matter for PII, secrets, and access control• Where to place controls across prompt, training, and output• Prompt injection, jailbreaks, and adversarial handling• RAG design with vector DB separation and permissions• Evaluation methods, risk scoring, and cost trade-offs• AWS Bedrock guardrails vs open-source customization• Domain-adapted safety models and policy matching• When deterministic systems beat LLM complexityThis episode is part of our "AI in Practice” series, where we invite guests to talk about the reality of their work in AI. From hands-on development to scientific research, be sure to check out other episodes under this heading in our listings.Related research:Building trustworthy AI: Guardrail technologies and strategies (N. Brathwaite)Nic's GitHubWhat did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

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• AI in practice: LLMs, psychology research, and mental health

  We’re excited to have Adi Ganesan, a PhD researcher at Stony Brook University, the University of Pennsylvania, and Vanderbilt, on the show. We’ll talk about how large language models LLMs) are being tested and used in psychology, citing examples from mental health research. Fun fact: Adi was Sid's research partner during his Ph.D. program.Discussion highlightsLanguage models struggle with certain aspects of therapy including being over-eager to solve problems rather than building understandingCurrent models are poor at detecting psychomotor symptoms from text alone but are oversensitive to suicidality markersCognitive reframing assistance represents a promising application where LLMs can help identify thought trapsProper evaluation frameworks must include privacy, security, effectiveness, and appropriate engagement levelsTheory of mind remains a significant challenge for LLMs in therapeutic contexts; example: The Sally-Anne Test.Responsible implementation requires staged evaluation before patient-facing deploymentResourcesTo learn more about Adi's research and topics discussed in this episode, check out the following resources:Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluationTherapist Behaviors paper: [2401.00820] A Computational Framework for Behavioral Assessment of LLM Therapists Cognitive reframing paper: Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction - ACL Anthology Faux Pas paper: Testing theory of mind in large language models and humans | Nature Human Behaviour READI: Readiness Evaluation for Artificial Intelligence-Mental Health Deployment and Implementation (READI): A Review and Proposed Framework Large language models could change the future of behavioral healthcare: A proposal for responsible development and evaluation | npj Mental Health Research GPT-4’s Schema of Depression: Explaining GPT-4’s Schema of Depression Using Machine Behavior AnalysisAdi’s Profile: Adithya V Ganesan - Google Scholar What did you think? Let us know.Do you have a question or a discussion topic for the AI Fundamentalists? Connect with them to comment on your favorite topics: LinkedIn - Episode summaries, shares of cited articles, and more. YouTube - Was it something that we said? Good. Share your favorite quotes. Visit our page - see past episodes and submit your feedback! It continues to inspire future episodes.

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