Learn to think rigorously about whether machines can truly think

If you've picked up this page, I suspect you already sense that something is missing from most public conversations about artificial intelligence. The discourse oscillates between utopian excitement and apocalyptic dread, and almost none of it pauses long enough to ask the prior question: what would it actually mean for a machine to think, to understand, to be conscious — or to matter morally? That's the question I've built this course around, because I believe it's the one that makes all the others tractable.

Philosophy of mind has been grappling with the nature of cognition, consciousness, and personhood for decades — long before "AI" became a household word. The tools are there: thought experiments that isolate exactly what's at stake, conceptual distinctions that cut through the noise, argumentative frameworks that let you hold a position and defend it under pressure. What this course does is put those tools in your hands and train you to use them on the questions that matter most right now.

We move through the terrain deliberately. We start with Turing — not as a historical curiosity, but as a still-live philosophical provocation — and we learn to read his argument carefully enough to see what it genuinely establishes and what it quietly assumes. We spend serious time inside Searle's Chinese Room, which I find is the moment most students realise how hard the problem of understanding really is. We work through the Hard Problem of consciousness with the same care, because if you don't understand why Chalmers thinks consciousness resists functional explanation, you can't properly evaluate any claim about machine sentience. Every step is an exercise in precision, not just exposure.

I won't pretend this course is light reading — it isn't, and I wouldn't insult your intelligence by making it so. But I also won't leave you alone in the difficulty. Each argument is unpacked before it's contested, and each objection is given its full weight before we assess it. The structure is Socratic: we build positions together, then pressure-test them together. By the final module — where you construct and defend your own stance on machine personhood — you'll find that the philosophical vocabulary you've built isn't jargon. It's precision. It's the difference between having a feeling about a question and having an argument.

These are not academic puzzles. Whether machines can bear moral responsibility affects how we design, regulate, and relate to AI systems right now. Whether personhood could extend to artificial minds affects the moral community we're building. I think those questions deserve more than hot takes and headlines, and I think you do too. Come and think this through properly — it's the only way the thinking is worth doing.