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 guests

We hope you enjoy this philosophical journey to examine the intersection of ancient philosophical questions and cutting-edge technology.

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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 complexity

This 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 GitHub

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.

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 highlights

• Language models struggle with certain aspects of therapy including being over-eager to solve problems rather than building understanding
• Current models are poor at detecting psychomotor symptoms from text alone but are oversensitive to suicidality markers
• Cognitive reframing assistance represents a promising application where LLMs can help identify thought traps
• Proper evaluation frameworks must include privacy, security, effectiveness, and appropriate engagement levels
• Theory of mind remains a significant challenge for LLMs in therapeutic contexts; example: The Sally-Anne Test.
• Responsible implementation requires staged evaluation before patient-facing deployment

Resources

To 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 evaluation
• Therapist 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 Analysis
• Adi’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.