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

In this episode of the AITEC podcast, Sam Bennett and Roberto Carlos speak with Matheus Ferreira de Barros, a philosopher of technology at PUC-Rio and the Federal University of Rio de Janeiro, about the work of Peter Sloterdijk. Ferreira de Barros introduces Sloterdijk’s philosophy of technology, focusing on the idea that human beings and technology co-evolve and that technology plays a constitutive role in human life rather than merely serving as an external tool.The conversation explores Sloterdijk’s Spheres project, including his account of insulation, distance from nature, and the creation of protective interiors that stabilize human existence at biological, psychological, and symbolic levels. The discussion also examines the loss of large-scale meaning structures in modernity, the role of religion and culture as technologies of existential security, and how contemporary technologies, including AI, may both disrupt and reshape the spheres through which human life becomes livable.

#27 Matheus Ferreira de Barros: Technology, Spheres, and the Human Being

If our senses can deceive us and our reasoning can outrun experience, which should we trust as the true foundation of knowledge: reason alone, or the evidence of the world?

Topics discussed:
 * How radical skepticism (including modern versions like simulations) motivates Descartes’ turn inward
 * A reconstruction of René Descartes’ Cogito argument as a proposed foundation of certainty
 * The difficulty of moving from Descartes’ foundational truths to knowledge of the external world
 * Philosophical objections to Descartes’ inference from thinking occurs to a unified self exists, including Nagasena’s chariot analogy
 * The role of innate ideas and God in Descartes’ attempt to bridge the epistemic gap
 * John Locke’s rejection of innate ideas and the blank slate hypothesis
 * Locke’s empiricism, including simple vs. complex ideas and indirect realism
 * The tension between reason and the senses as competing sources of knowledge
 * The emerging dilemma between rationalism and empiricism as rival epistemic frameworks

Lesson 1.5: Reason and the Senses

Descartes responds to radical uncertainty with an audacious plan: find one belief so secure that nothing—not even deception itself—can shake it.

Topics Discussed:
 * The historical and intellectual crises of early 17th-century Europe that shaped Descartes’ project
 * The motivation behind Descartes’ method of doubt as a response to skepticism
 * The regress problem and why it pressures philosophers toward foundationalism
 * Descartes’ strategy of withholding belief from anything that can be doubted
 * Arguments from sense deception, dreaming, and the evil demon
 * Why even mathematics and logic become targets of radical doubt
 * The search for indubitable foundations as the starting point for knowledge
 * How Descartes’ internal, first-person approach marks a turning point in modern epistemology

Lesson 1.4: The Method of Doubt

In this episode of the AITEC podcast, Sam Bennett speaks with philosopher of mind and AI researcher Iwan Williams about his paper “Intention-like representations in language models?” Williams is a postdoctoral researcher at the University of Copenhagen and received his PhD from Monash University.

The conversation explores whether large language models exhibit internal representations that resemble intentions, as distinct from beliefs or credences. Focusing on features such as directive function, planning, and commitment, Williams evaluates several empirical case studies and explains why current models may appear intention-like in some respects while falling short in others. The discussion also considers why intentions matter for communication, safety, and our broader understanding of artificial intelligence.

For more, visit ethicscircle.org. 

#26 Iwan Williams: Do Language Models Have Intentions?

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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