u/depressed_genie

Hey everyone.

The two dominant philosophical positions on psychedelic experience are well-trodden here. One says the experience is an elaborate hallucination produced by serotonergic disruption, with no privileged access to anything outside the brain. The other says it is contact with a separate metaphysical realm and treats neuroscience as a distraction. Both miss something. The first cannot explain the consistency of the noetic conviction across millions of people who otherwise disagree about everything. The second commits to a metaphysics that is doing more work than the evidence will support.

I was listening to this interview with Danny Forde, a philosopher at University College Cork. His position is realist phenomenology applied to psychedelic experience. The framework comes from the Munich-Göttingen Circle around Scheler, Stein, and Ingarden, who held that essences are mind-independent without floating in a separate realm. On this reading, the ego usually filters perception through narrative and pragmatic concerns. Psychedelics drop that filter for a few hours. What remains is the same world you always had, perceived without the editing.

That is a much harder position to dismiss than vague mysticism, because it commits to no extra furniture in the universe. It also leaves the neuroscience intact. The mechanism is consistent with serotonergic disruption. The disagreement is over what the disrupted state is actually seeing.

Most arguments about whether LLMs understand anything treat intelligence as a unitary capacity. Stanovich's tripartite division of mind (autonomous, algorithmic, reflective) has been around for two decades and rarely shows up in the AI debate, which is strange because it cuts the question cleanly. The autonomous layer is the reflexive, intuitive system. The algorithmic layer is raw computational capacity, which is what IQ tests target and what LLMs do extraordinarily well. The reflective layer is something else: it is truth-oriented, metacognitive, and capable of evaluating the algorithmic processes running beneath it. The question worth pressing is whether current architectures can ever reach the reflective layer or whether they are stuck producing high-fidelity imitations of its outputs from one layer down.

I recently gave a talk at the 6th International Conference on Philosophy of Mind in Porto arguing the second. You can watch it here.

The empirical side of Stanovich's program supports the structural separation. Stanovich and West, and more recently Burgoyne and colleagues, have shown intelligence and rationality share only around thirty percent variance, with the overlap shrinking further once attention is partialled out. The result tells us something beyond raw intelligence is operating in human cognition. That something is what allows an agent to step outside the current frame, ask whether the frame is right, and reorient toward truth. LLMs cannot do this in the relevant sense. They can produce text that looks like metacognition, but the system has no truth-orientation because it has no stakes in any world. Frankfurt's analysis of bullshit (as distinct from lying) applies in the technical sense Hicks and Bender have pressed: the output is indifferent to truth.

If the tripartite frame is right, the productive question is whether the gap between layers is bridgeable by scaling or whether it is constitutive. Is anyone in philosophy of mind doing serious work on whether the reflective layer is in principle implementable in architectures with no embodied existence, or is the embodied-cognition objection now treated as settled here?

I have decided to read 7 books every month this year, Any recommendations for may?

Hey everyone.

Something I keep running into in philosophy-of-religion discussions of AI. The public arguments are framed as ethics or policy, but four distinct theological frames keep surfacing underneath the secular vocabulary. Either technology is an instrument of supernatural encounter, a collaborator that magically extends human capacity, something that makes humans godlike, or something that becomes a god itself. Once you see the frames, the most technocratic writing on AI starts to read differently.

I was listening to this interview with Heidi Campbell, a Texas A&M professor who has studied the religion-technology interface for thirty years. She draws those four models out of sci-fi and post-humanist discourse, and traces religious vocabulary around technology back to the Industrial Revolution, when machinery was first offered as a replacement answer to questions religion used to handle. On her reading, post-humanism is the third model, the one where technology makes humans godlike, and current AI rhetoric is drifting toward the fourth.

The part I find most clarifying is that even strongly secular arguments about alignment, existential risk, or superintelligence map onto those four frames without much strain. Does the taxonomy hold when stress-tested against recent AI writing, and which figures in philosophy of religion are taking these theological undercurrents seriously enough to read?

Hey everyone.

Lately I have been thinking about how much of current humanities discussion of AI operates at a level of technical vagueness that would not be tolerated in any other methodological context. We ask careful questions about archives, corpora, tool selection, and bias in annotation schemas. Then we slide into "AI" as if it were a single object, when the systems differ in kind. A predictive model, a generative model, and an agentic system raise different methodological and ethical questions, and collapsing them leads to arguments that cannot land.

I host a podcast about meaning and the human condition, covering philosophy, cognitive science and religion, and my most recent episode was with Heidi Campbell, who built digital religion as a subfield inside the broader digital humanities and is now worrying about exactly this problem. You can watch here if you like (starts at 40:14): https://youtu.be/Q20Y5fVb5Jw?t=2414

Campbell argues that her Religious Social Shaping of Technology model, developed over 30 years of fieldwork, is one example of how humanities disciplines can move from descriptive to predictive engagement with technology. Its four stages have been validated across Jewish, Muslim, and Christian cases. Her bigger worry now is that the humanities, including the digital humanities, are arriving at AI without the vocabulary to separate predictive, generative, and agentic systems, and the gap produces output that reads as confident but is technically imprecise. She thinks the next decade will need a baseline AI literacy in digital humanities comparable to what corpus linguistics required in the 2000s, and that departments underestimating this will struggle to produce work that survives scrutiny from either the humanities or the technical side.

That tracks what I see in graduate-level humanities training, where tool literacy has crept in but system-type literacy has not. What does a realistic AI-literacy curriculum look like for digital humanities students, and which programs or scholars are modeling this best. I want to cover the methodology of AI-era humanities work more on the podcast, so suggestions for researchers doing serious digital humanities work with real technical grounding would be welcome.