In this episode, we look at the actuarial principles that make models safer: parallel modeling, small data with provenance, and real-time human supervision. To help us, long-time insurtech and startup advisor David Sandberg, FSA, MAAA, CERA, joins us to share more about his actuarial expertise in data management and AI.

We also challenge the hype around AI by reframing it as a prediction machine and putting human judgment at the beginning, middle, and end. By the end, you might think about “human-in-the-loop” in a whole new way.

• Actuarial valuation debates and why parallel models win
• AI’s real value: enhance and accelerate the growth of human capital
• Transparency, accountability, and enforceable standards
• Prediction versus decision and learning from actual-to-expected
• Small data as interpretable, traceable fuel for insight
• Drift, regime shifts, and limits of regression and LLMs
• Mapping decisions, setting risk appetite, and enterprise risk management (ERM) for AI
• Where humans belong: the beginning, middle, and end of the system
• Agentic AI complexity versus validated end-to-end systems
• Training judgment with tools that force critique and citation

Cultural references:

• Foundation, AppleTV
• The Feeling of Power, Isaac Asimov
• Player Piano, Kurt Vonnegut

For more information, see Actuarial and data science: Bridging the gap.

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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 paradigm

This 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 AI
• What is reality?

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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.
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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 persistence
• Banana and circle examples for identity and ideals
• Plato versus Aristotle on forms and realism
• Ship of Theseus and continuity through change
• Samples, complexes, and emergence in systems
• Embodiment, consciousness, and why LLMs lack lived unity
• Existentialist focus on subjective reality and meaning
• Why metaphysics matters for AI governance and safety
  
  

Join 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.