AI, Philosophy & Meaning: Mind, Truth, and What Machines Miss
This course doesn’t start with code. It starts with Plato, Frege, and Turing. It doesn’t ask what machines can do, but what they can never be. Through cinematic lectures and deep theory, we explore the limits of computation, the fragility of meaning, and the epistemic boundary machines can’t cross.
Whether you’re a student of philosophy, a tech skeptic, a curious designer, or simply someone who suspects that something is off when machines sound too smart, this course will give you the tools to think before, and beyond, the algorithm.
What you’ll learn
- Understand how Frege, Gödel, Penrose, and Tarski shaped logic, truth, and the philosophical foundations of artificial intelligence..
- Analyze the meaning crisis in mind and language theories through Quine, Davidson, and Millikan’s critiques of semantics and representation..
- Explore Tarski’s theory of truth and its lasting impact on formal semantics, logic, and the language structures used in AI systems..
- Engage with the instructor’s original work on normativity, indeterminacy, and the limits of AI’s ability to generate meaning..
- Apply key philosophical ideas to reflect critically on the nature of machines, cognition, language, and the assumptions behind modern AI..
- Understand how philosophical logic and semantic theories challenge the idea that machines can fully replicate human thought..
- Discover why meaning, not just computation, is central to understanding intelligence — and how philosophy exposes what AI still cannot grasp..
Course Content
- Preface –> 2 lectures • 6min.
- Introduction: Inheritance Before Innovation –> 1 lecture • 10min.
- Framing the Debate: Mimicry, Machines, and Meaning –> 4 lectures • 28min.
- The Why That Machines Can’t Reach: Insight, Proof, and the Edge of Mind –> 2 lectures • 16min.
- Conclusion –> 1 lecture • 7min.
Requirements
This course doesn’t start with code. It starts with Plato, Frege, and Turing. It doesn’t ask what machines can do, but what they can never be. Through cinematic lectures and deep theory, we explore the limits of computation, the fragility of meaning, and the epistemic boundary machines can’t cross.
Whether you’re a student of philosophy, a tech skeptic, a curious designer, or simply someone who suspects that something is off when machines sound too smart, this course will give you the tools to think before, and beyond, the algorithm.
Learn why:
- Syntax ≠ Semantics
- Getting it “right” isn’t the same as understanding
- Normativity and justification are not programmableIncludes a downloadable PDF Course Map, summarizing:
- The 5 main philosophical insights
- The 3 core contributions of the course
- What students will learn
- Key philosophical debates you can now enter
- Contemporary thinkers you’ll meet along the wayNew! One module includes a staged debate — a dramatized philosophical confrontation between two voices: one defending computationalism, the other arguing that true intelligence requires epistemic access — not just processing power. It’s not just lecture. It’s live argument.Course Map: Before the Algorithm – Philosophy & Semantics of AI
SECTION 1 – PREFACE AND OVERVIEW
- Class 1: Before the First Line of Code – A Philosophical Prelude Introduces key conceptual tensions that drive the course. Includes peer-reviewed article on Kant and AI.
- Class 2: Course Map Overview and Audio File: From Symbolic Logic to Neural Nets: Why AI Still Doesn’t UnderstandWe explore:
- The transition from symbolic logic (Carnap, Tarski, Chomsky) to machine learning and deep neural nets
- Concepts like structural isomorphism, emergent behavior, and few-shot learning
- Why pattern recognition ≠ conceptual grasp
- How thinkers like Quine, Brandom, Kant, and Searle help expose the semantic gap
- The illusion of intelligence produced by scaling syntax without grounding semantics
Ultimately, this class shows that today’s machines don’t just reflect meaning — they morph it. And that morphing comes with epistemic costs.
SECTION 2 – INTRODUCTION: INHERITANCE BEFORE INNOVATION
- Class 3: The Thought Code – Why Philosophy Still Holds the Key to AI Examines how Plato, Frege, and Turing shaped the conceptual foundations of AI. Includes PDFs, prompts, and quiz.
SECTION 3 – FRAMING THE DEBATE: MIMICRY, MACHINES, AND MEANING
- Class 4: Mimicry, Machines, and Meaning – Framing the Debate A cinematic essay on normativity, functionalism, and behavioral equivalence. Includes comprehension quiz.
- Class 5: Where Syntax Breaks – Semantics, Failure, and the Human Trace Investigates meaning beyond syntactic success through failure, disorientation, and normativity.
- Class 6: Truth-Conditional Semantics and the Limits of Computational Meaning Challenges Davidson and Lewis’s model of meaning. Introduces epistemic critiques. Includes PDF article and quiz.
- Class 7: Not Just True — But Worth Saying: Truth, Assertion, and Strategic Weight Based on the Cognitio article. Investigates the cost of assertion, epistemic traction, and communicative risk. Includes exercises and downloadables.
SECTION 4 – THE WHY THAT MACHINES CAN’T REACH: INSIGHT, PROOF, AND THE EDGE OF MIND
- Class 8: To Know Why – Penrose, Gödel, and the Limits of Machine Insight Engages Penrose’s argument on instantiability, Gödel’s theorems, and the boundary of formal systems.
- Class 9: The Shape of Failure – Machines, Error, and Epistemic Absence Expands Dummett’s critique of truth-conditional semantics. Discusses semantic failure, normativity, and justification.
SECTION 5 – CONCLUSION
- Class 11: Where the Algorithm Ends – Meaning, Commitment, and What AI Still Misses Revisits the soul-mechanism, mimicry-meaning, and truth-formalism debates. Offers final philosophical framing.
Included Materials Across the Course:
- Peer-reviewed articles (Cognitio, Philósophos, Pólemos)
- Conceptual summaries and reading guides
- Reflective and analytical exercises
- Philosophical quizzes for each critical transition
Final Outcome: You will learn to position AI not only in technical terms, but within the deeper philosophical terrain of understanding, commitment, and the semantic conditions that define what it means to mean.
About the Instructor
Hi, I’m Lucas Vollet — PhD in Philosophy, with articles published in Husserl Studies, Studia Kantiana, and Cognitio
My focus is on the intersection of mind, language, and epistemology, and how these debates are transforming in the age of AI.By the end of this course, you’ll be able to:
Explain the conceptual history that underlies AI Spot the limits of syntactic and truth-based models of meaning
Articulate what machines miss — even when they get things “right”
Enter live debates in philosophy of mind and language
Ethically and intellectually position AI in your own worldviewA Word for the Indecisive
Let’s be honest: I’m not a well-known course creator. You didn’t land here because of flashy ads or bestselling instructor badges. And maybe that’s why I owe you something more than a sales pitch — a clear reason to keep reading.
This course doesn’t just repeat standard AI ethics or rehearse popular philosophy-of-mind summaries. It’s built on years of academic work, published research, and philosophical training focused on one central question: What happens to meaning when intelligence becomes mechanical?
What I’m offering is not just information, but orientation. You’ll leave this course with:
- A conceptual backbone to understand AI not just as a tool, but as the latest echo in a long philosophical conversation.
- The ability to detect the hidden assumptions behind how AI is framed — especially concerning truth, normativity, and the reduction of meaning to structure.
- A stance. You’re not just observing a debate. You’re preparing to take part in it.
After spending months building this course — drawing from my full repertoire of study and years of reflection on these debates — I had to ask myself honestly: what exactly am I offering?
And here’s the answer, focused not on the doubts I had, but on the strengths I trust: I’m offering not just content, but philosophical positioning.
This is a course for those who don’t want to just keep up with AI — but want to know where to stand when it accelerates.