Have you ever wondered if AI models actually understand the words they generate, or if they are just really good at faking it?

On this episode of The AITEC Podcast, Roberto García and Sam Bennett are joined by philosopher Jacob Browning (Baruch College, CUNY) to unpack his article, Intentionality All-Stars Redux: Do language models know what they are talking about?

Using a clever baseball diamond metaphor and drawing on the philosophy of Immanuel Kant, Jacob explains why Large Language Models lack the "intentionality" required for genuine comprehension. We cover:

 * First Base (Formal Competence): Why LLMs struggle with basic logic and negation, revealing the absence of an underlying logical engine.
 * Second Base (Rationality): Why true understanding requires purposive behavior, and how LLMs hilariously fail at "intuitive physics" (like trying to inflate a couch to get it onto a roof).
 * Shortstop (Objectivity and World Models): Why genuine understanding requires grasping an objective, mind-independent world that determines whether sentences are true or false. This position explores how LLMs lack a coherent "world model," causing them to fail at tasks that require intuitive physics and planning for counterfactual situations (like predicting where a billiard ball will go or playing simple video games).
 * Third Base (The Unified Self): Why making a claim requires a persistent self that takes responsibility for its beliefs—something a next-token predictor simply cannot do.

Whether you're exploring the intersection of AI, technology, and ethics, or just trying to figure out if your chatbot actually knows what it's saying, this conversation will give you the philosophical toolkit to see through the illusion.

#31: Jacob Browning: Unmasking the Fake Minds of Large Language Models

If God is perfectly good, then this world demands an explanation—or a surrender of belief.

Topics discussed:
 * The relationship between intellectualism and belief in the supernatural
 * A Pyrrhonian-style move: examining both sides of the “religion vs. reason” debate
 * Early Christianity as a countercultural and sometimes anti-intellectual movement
 * Introduction to Baron d’Holbach—an early, explicit defender of atheism
 * The notion of the God of the Gaps
 * The Problem of Evil argument against God’s existence

Lesson 101.2.1: The Problem of Evil, Part I

Philosopher Andrea Pinotti joins us to discuss At the Threshold of the Image: From Narcissus to Virtual Reality. What begins as a conversation about image theory quickly becomes a sweeping exploration of immersion, identity, and the strange pull of simulated worlds. Why do we long to enter the image? What do we gain—and lose—when the frame disappears? Pinotti guides us from Paleolithic caves to VR headsets, through myths of Narcissus and Pygmalion, to Black Mirror’s digital afterlives. Along the way, we consider how virtual environments blur fiction and reality, evoke religious promises, and reshape what it means to be human.If you've ever wondered why virtual reality feels so real—or so dangerous—this episode is for you.For more, visit ethicscircle.org. 

#30 Andrea Pinotti: Beyond the Frame—Virtual Reality, Narcissus, and the Desire to Enter the Image

If we can’t prove whether God exists, should we treat belief like a gamble—and choose the option with the best payoff?

Topics discussed:

 * The philosophical dilemma of whether belief in God should be based on reason, faith, or skepticism.
 * Major positions on the existence of God: rational theism, fideism, deism, atheism, non-theism, and agnosticism.
 * The life and intellectual background of Blaise Pascal, mathematician, theologian, and pioneer of probability theory.
 * The historical origins of probability theory, including early work by gamblers like Gerolamo Cardano and Pascal’s correspondence with Fermat.
 * The concept of expected utility and how decision theory evaluates rational choices under uncertainty.
 * The structure of Pascal’s Wager, which argues that believing in God maximizes expected value because of the possibility of infinite reward.
 * Mathematical objections to the wager, including the Many Gods objection and the Zero Probability objection.
 * Psychological and ethical objections, including doxastic involuntarism, the moral integrity objection, and the Many Hells objection.

Lesson 1.8: The Ace of Spades

Stop treating AI like a search engine and start using it as a "cognitive architect" to bridge the gap between just having information and actually building expertise.

Topics discussed:

 * The Knowledge Illusion: We often fail to distinguish between knowledge stored in our own heads and information readily available in our environment, leading to a false sense of expertise.
 * Illusion of Explanatory Depth: Instant access to the internet can make us feel more confident in our understanding of a topic than we actually are; forcing ourselves to explain a concept is the best way to break this spell.
 * Setting the Stage: Effective learning begins with isolating core concepts and stripping away non-essential content to reduce cognitive load.
 * Deep Processing: Based on the principle that "memory is the residue of thought," learning happens when we actively think about the meaning and implications of information.
 * AI-Driven Retrieval Practice:
   Free Recall Loop: Using AI to analyze what you remember from memory and identify gaps or misunderstandings.
   Pre-flight Diagnostics: Tasking AI to quiz you on foundational terms to ensure proficiency before moving to complex material.
   Borderline Case Testing: Improving conceptual clarity by asking AI for clear examples, non-examples, and tricky "borderline" cases.
 * The Power of Chunking: Organizing disparate facts into small, meaningful units (like how a chess grandmaster sees patterns rather than individual pieces) to make complex material easier to retain.

Lesson 2: Foundational Fluency with AI

During the Age of Enlightenment, the tools of reason that had been used to support faith for centuries began to erode the very foundations of religious authority.

Topics Discussed:

 * The Fragility of the Cartesian Bridge: Examining why Descartes' reliance on a benevolent God to escape skepticism left his project vulnerable to the "Great Erosion" of the 18th century.
 * Naturalizing the Supernatural: The Enlightenment shift from divine intervention to naturalistic explanations for phenomena like lightning, witchcraft, and demonic possession.
 * The Deep Time Crisis: How geological discoveries and "cooling experiments" challenged the traditional biblical chronology of James Usher.
 * Biblical Criticism and Authorial Doubt: The birth of modern textual analysis as Spinoza, Newton, and Reimarus began to treat sacred texts as human historical records.
 * The Rise of Materialism and Atheism: Analyzing the emergence of open atheism and materialism in the radical works of Jean Meslier and Baron d’Holbach.

Lesson 1.7: The Great Erosion

Have you ever felt like ChatGPT genuinely understands you? What if the reality is that it doesn't even have the foundational capacity to "speak" to you at all?

On this episode of The AITEC Podcast, Roberto Carlos García and Sam Bennett sit down with philosopher Justin Tiehen (University of Puget Sound) to unpack his fascinating new paper, LLM's Lack a Theory of Mind and So Can't Perform Speech Acts--A Causal Argument.

Justin takes us on a deep dive into the philosophy of mind to explain why current Large Language Models, despite their impressive output, are essentially just faking it. We explore why next-token predictors are completely missing the causal architecture required to have a "Theory of Mind," and why, without that, they are fundamentally incapable of making assertions, giving orders, or performing true speech acts.

Key Takeaways from this Episode:

 * The Ladder of Causation: Why AI is stuck observing statistical correlations and cannot grasp true causal interventions or counterfactuals (drawing on Judea Pearl’s work).
 * The Speech Act Problem: Why performing a true "speech act" requires the deliberate intention to influence another person's mind.
 * Cheating the Benchmarks: How LLMs "cheat" on psychological exams like the Sally-Anne false-belief test simply by memorizing statistical patterns in text.
 * The Threat of AI Blackmail: What it would actually look like if an AI possessed a Theory of Mind and strategically tried to manipulate human behavior to achieve its goals.

Whether you are deeply invested in the philosophy of language or just trying to figure out how much you should trust your favorite AI assistant, this conversation will completely reframe how you view generative AI.

Learn more about our work and join the conversation at ethicscircle.org [https://www.google.com/search?q=http://ethicscircle.org].

#29 Justin Tiehen: Why AI Can't Make a Promise—The Hidden Limits of Large Language Models

As we offload our cognitive labor to machines, are we inadvertently erasing the 'road to expertise' and deskilling the very traits that define high-level thinking?

Topics discussed:

 * The Hidden Cost of Deskilling: Why human proficiency and cognitive involvement decline when we outsource mental tasks to external systems.
   
 * The "Levelling" Illusion: How GenAI allows users to perform at a high level without actual domain mastery, effectively breaking the ladder to expertise.
   
 * The Multiplier Effect: Why the biggest performance boosts are reserved for those who already possess the skills to critically evaluate and edit machine output.
   
 * AI as a Cognitive Amplifier: A three-stage framework—Foundational Fluency, Application, and Active Engagement—to ensure technology extends thinking rather than replacing it.

Lesson 1: Overreliance on AI

Locke promises a “blank slate” mind—but once you follow the logic, his empiricism starts sliding toward skepticism.

Topics discussed:
 * Three “contenders” for what knowledge is: Descartes (JTB + foundationalism); Bacon (knowledge as power); Locke (empiricism)
 * Locke’s tabula rasa (blank slate) and why he rejects innate ideas
 * How Locke tries to calm intellectual arrogance: learn the limits of the human mind
 * Objections to Locke: Berkeley’s skeptical pressure, Hume’s problem of induction, and modern cognitive-science pushback on the blank slate
 * How each theory hits a wall (utility vs certainty vs “reliable enough”)
 * Pyrrhonian skepticism as a therapy for overconfidence in theory

Lesson 1.6: The Blank Slate

In this episode of the AITEC Podcast, we’re joined by philosopher Mathilda Marie Mulert, a doctoral researcher at the Oxford Internet Institute, to explore one of the most difficult questions in contemporary tech ethics: when, if ever, is it morally permissible to simulate sexual violence? 

Drawing on her recent work on simulation ethics, Mulert examines video games, virtual environments, sex robots, and consensual role-play to challenge the assumption that “it’s just pretend.” We discuss the Gamers’ Dilemma, the limits of consent, and why moral context—not just content—matters when evaluating simulated wrongdoing.

This conversation is philosophical, careful, and candid. Listener discretion is advised.

Links:

 * Mathilda’s Oxford Internet Institute Webpage [https://www.oii.ox.ac.uk/people/profiles/mathilda-mulert/]
 * Mathilda’s recent article [https://link.springer.com/article/10.1007/s10676-025-09878-7]

For more, visit ethicscircle.org [https://www.ethicscircle.org/]. 

#28 Mathilda Marie Mulert: Sex Robots, Simulation, and the Question of Moral Harm

Welcome to AITEC Podcast, where we explore the ethical side of AI and emerging tech.

We call our little group the AI and Technology Ethics Circle (AITEC). Visit ethicscircle.org [https://www.ethicscircle.org/] for more info. 

AITEC Philosophy Podcast

These are PHIL courses taught by R. C. M. García at El Camino College. 

Roberto Carlos

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