In this episode we conclude our series about Metaphysics and modern AI, we explore the definitions of consciousness, reasoning, and thinking to understand if AI possesses these traits. From examining legal accountability and the concept of personhood to analyzing human cognitive frameworks, we map out the differences between actual contemplative problem-solving and probabilistic pattern recognition. The episode covers:

• Defining consciousness, reasoning, and what it means to be a "thinking thing"
• The Turing Test as a low bar and why natural language capabilities create the illusion of intelligence
• Accountability and agency: Why AI models like Claude are not legally recognized as persons
• Daniel Kahneman’s System 1 (fast heuristics) vs. System 2 (contemplative reasoning) thinking
• Why LLMs function primarily as System 1 pattern recognizers rather than true reasoners
• Complex systems, Descartes' dualism, and whether thinking is an emergent property requiring a physical body
• How chatbots use psychological mirroring, filler words, and pauses to trick human biases
• The dangers of anthropomorphizing AI driven by fear of change or financial incentives

This is the final episode in our metaphysics and AI series. You can find the previous episodes here:

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

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.

As the AI landscape evolves, the methods we use to process structured data are undergoing a silent revolution. Join us to explore how Tabular Foundation Models (TFMs) are challenging the decade-long reign of tree-based algorithms, why the traditional "train and predict" workflow is being replaced by "in-context learning," and what this shift means for the future of resilient modeling.

To help us, Christoph Molnar, renowned expert in machine learning interpretability and author of the Mindful Modeler newsletter, joins us to share his perspective on the emergence of tabular transformers, the surprising power of synthetic data, and how to maintain model safety in a world without parameter updates.

• The decline of the "fit and predict" paradigm in tabular data
• Transformer architectures vs. traditional models like XGBoost and LightGBM
• In-context learning: Predicting without traditional training steps
• The role of Structural Causal Models (SCMs) in generating training data
• Why models trained on "math and probability" succeed on real-world datasets
• Hardware accessibility and running foundation models on local MacBooks
• Integrating SHAP values and conformal prediction for model interpretability
• The future of the data science workflow: One tool among many or a total shift?

This episode is full of technical insights and forward-looking predictions that are sure to change how you approach your next dataset. As we move into a new era of AI, it’s the perfect time to explore the fundamentals of the next frontier!

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.

As information sources have become abundant and attention spans have shortened in the age of AI, we take on the lost art of reading. Join us to explore why reading rates are falling, how that shift affects judgment and opportunity, and how interdisciplinary books help us see patterns across history, economics, and technology.

To help us, Alisa Rusanoff, CEO of Eltech AI, joins us to share her perspective on reading, debate volume versus depth, and offer practical ways to reclaim attention and read with intention.

• Evidence on declining reading rates among adults, teens and children
• Noise versus signal in the attention economy
• Mental models and interdisciplinary synthesis for better decisions
• AI’s limits and why human integration still matters
• Cycles in debt, trade, demography, and geopolitics
• Fiction as a cultural sensor for lived experience
• Wealth gaps, polarization and the need for critical thinking
• Practical habits to train feeds and protect reading time
• Challenge to read, reflect, and apply insights

For people worried if they are reading enough:

• Reading just 1 book a year puts you in the top 60% of readers
• Read 4 books a year to be in the top 50% of readers
• Read 10 books a year to be in the top 20% of readers
• For those looking to be in the top 5% of readers, expect to read at least 50 books

This episode is full of research and fun connections that are sure to make you think positively about your commitment to reading. At the time of this episode, it's not too late to join the top 20% in 2026!

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.