The AI Fundamentalists

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 recogniti...

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 mo...

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 p...

In this episode of our series about Metaphysics and modern AI, we break causality down to first principles and explain how to tell factual mechanisms from convincing correlations. From gold-standard Randomized Control Trials (RCT) to natural experiments and counterfactuals, we map the tools that build trustworthy models and safer AI. Defining causes, effects, and common causal structures Gestalt t...

In this episode, Dr. Sebastian (Seb) Benthall joins us to discuss research from his and Andrew's paper entitled “ Validity Is What You Need ” for agentic AI that actually works in the real world.  Our discussion connects systems engineering, mechanism design, and requirements to multi‑step AI that creates enterprise impact to achieve measurable outcomes. Defining agentic AI beyond LLM hype Li...

Here it is! We review the year where scaling large AI models hit its ceiling, Google reclaimed momentum with efficient vertical integration, and the market shifted from hype to viability.  Join us as we talk about why human-in-the-loop is failing, why generative AI agents validating other agents compounds errors, and how small expert data quietly beat the big models. • Google’s resurgence with Gem...

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 a...

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...

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, percep...

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...

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...

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...

The release of OpenAI GPT-5 marks a significant turning point in AI development, but maybe not the one most enthusiasts had envisioned. The latest version seems to reveal the natural ceiling of current language model capabilities with incremental rather than revolutionary improvements over GPT-4.  Sid and Andrew call back to some of the model-building basics that have led to this point to give the...

Sid Mangalik and Andrew Clark explore the unique governance challenges of agentic AI systems, highlighting the compounding error rates, security risks, and hidden costs that organizations must address when implementing multi-step AI processes.  Show notes: • Agentic AI systems require governance at every step: perception, reasoning, action, and learning • Error rates compound dramatically in multi...

We continue with our series about building agentic AI systems from the ground up and for desired accuracy.  In this episode, we explore linear programming and optimization methods that enable reliable decision-making within constraints.  Show notes: Linear programming allows us to solve problems with multiple constraints, like finding optimal flights that meet budget requirements The Lagrange mult...

The hosts look at utility functions as the mathematical basis for making AI systems. They use the example of a travel agent that doesn’t get tired and can be increased indefinitely to meet increasing customer demand. They also discuss the difference between this structured, economic-based approach with the problems of using large language models for multi-step tasks. This episode is part 2 of our...

What if we've been approaching AI agents all wrong? While the tech world obsesses over larger language models (LLMs) and prompt engineering, there'a a foundational approach that could revolutionize how we build trustworthy AI systems: mechanism design. This episode kicks off an exciting series where we're building AI agents "the hard way"—using principles from game theory...

Dr. Michael Zargham provides a systems engineering perspective on AI agents, emphasizing accountability structures and the relationship between principals who deploy agents and the agents themselves. In this episode, he brings clarity to the often misunderstood concept of agents in AI by grounding them in established engineering principles rather than treating them as mysterious or elusive entitie...

Part 2 of this series could have easily been renamed "AI for science: The expert’s guide to practical machine learning.” We continue our discussion with Christoph Molnar and Timo Freiesleben to look at how scientists can apply supervised machine learning techniques from the previous episode into their research. Introduction to supervised ML for science (0:00)  Welcome back to Christoph Molnar...

Machine learning is transforming scientific research across disciplines, but many scientists remain skeptical about using approaches that focus on prediction over causal understanding.  That’s why we are excited to have Christoph Molnar return to the podcast with Timo Freiesleben. They are co-authors of " Supervised Machine Learning for Science: How to Stop Worrying and Love your Black Box .&...

Unlock the secrets to AI's modeling paradigms. We emphasize the importance of modeling practices, how they interact, and how they should be considered in relation to each other before you act. Using the right tool for the right job is key. We hope you enjoy these examples of where the greatest AI and machine learning techniques exist in your routine today. More AI agent disruptors (0:56) Prox...

Agentic AI is the latest foray into big-bet promises for businesses and society at large. While promising autonomy and efficiency, AI agents raise fundamental questions about their accuracy, governance, and the potential pitfalls of over-reliance on automation.  Does this story sound vaguely familiar? Hold that thought. This discussion about the over-under of certain promises is for you. Show Note...

What if privacy could be as dynamic and socially aware as the communities it aims to protect? Sebastian Benthall, a senior research fellow from NYU’s Information Law Institute, shows us how privacy is complex. He uses Helen Nissenbaum’s work with contextual integrity and concepts in differential privacy to explain the complexity of privacy. Our talk explains how privacy is not just about protectin...

What if the secret to successful AI governance lies in understanding the evolution of model documentation? In this episode, our hosts challenge the common belief that model cards marked the start of documentation in AI. We explore model documentation practices, from their crucial beginnings in fields like finance to their adaptation in Silicon Valley. Our discussion also highlights the important r...

Are businesses ready for large language models as a path to AI? In this episode, the hosts reflect on the past year of what has changed and what hasn’t changed in the world of LLMs. Join us as we debunk the latest myths and emphasize the importance of robust risk management in AI integration. The good news is that many decisions about adoption have forced businesses to discuss their future and imp...