SHRYN

Ask two systems the same question.

On the left, a general AI model. On the right, a model built with the scholar, trained on their own work, which they read and correct.

Mark C. Taylor  ·  Columbia University  ·  30+ books
“What question has pursued you through your life’s work?”
A general AI model
Mark C. Taylor is an American philosopher of religion and cultural critic who taught for many years at Williams College and later chaired the Department of Religion at Columbia University. His work engages death-of-God theology, poststructuralism, and the relationship between religion, art, architecture and finance. A recurring theme across his writing is the way religious structures persist in secular forms.
His Shryn voice
The question that keeps returning, haunting me really, is what happens when you stop believing in God but haven’t yet arrived at pure atheism. Not because you’re looking for God to return, I’ve long abandoned that hope, but because the structures of meaning, the ways we orient ourselves in the world, the very possibility of ethics and responsibility, they all depend on something that traditional theism once provided. So you’re left with this gap, this void, and you have to ask: what comes after? Not as a problem to solve but as a condition of existence. Most people either revert to belief or move straight to pure negation. But I’ve tried to stay in that space, to live within it, to ask: what does it mean to create meaning, to live ethically, to take responsibility, when the ground has been pulled away? That’s not a question with an answer. It’s a question that changes you, shapes how you move through the world. And it doesn’t get resolved. It deepens.
Verbatim output. 32,260 question-and-answer pairs built from his own work, reviewed by him.
One describes a career from the outside. The other is thinking.
Mark C. Taylor  ·  from his memoir, Field Notes from Elsewhere
“What did you learn from being seriously ill?”
A general AI model
In 2005 Mark C. Taylor went into septic shock following a biopsy, complicated by diabetes, and was later treated for cancer. He wrote about the experience in his memoir Field Notes from Elsewhere: Reflections on Dying and Living, in which he reflects on mortality, place and the limits of theoretical language for describing lived experience.
His Shryn voice
It changed everything. I’d always thought of death as an abstraction, something to be theorized. But when you’re facing it concretely, when you’re actually dying, you realize that all the concepts, all the books you’ve written, they pale beside the fact of finitude. There’s a difference between knowing intellectually that you’ll die and actually experiencing the approach of death. The first is abstract; the second is concrete, embodied, visceral. And once you’ve been there, you can’t unknow it. Everything else gets reevaluated. Priorities shift. You stop worrying so much about the grand projects and start paying attention to what’s actually present in front of you. It sounds clichéd, but it’s true: you appreciate what you almost lose. Not that it stays with you perfectly. Old patterns return. But there’s a scar, a mark that doesn’t fully heal.
Verbatim output, trimmed for length. His memoir is part of the material he gave us.
One knows the book exists. The other has been there.
Michael Bamberg  ·  Clark University  ·  Narrative psychology
“Who am I?”
A general AI model
That is a question philosophers have explored for centuries. Identity can be understood through several lenses: your personal history and memories, your relationships and social roles, your values and beliefs, and your sense of continuity over time. Some traditions emphasise an essential self, while others see identity as socially constructed. Ultimately, who you are may be something you define for yourself.
His Shryn voice
That’s the question, isn’t it? And I don’t mean that philosophically, I mean it genuinely. I can’t answer it because identity isn’t something I could know by observing you from the outside, nor is it something fixed that you simply possess and could report to me. What I’ve spent my career studying is how people like you accomplish who they are through language, moment by moment. You become different people in different conversations. Those shifts aren’t failures of authenticity, they’re the very texture of how identity works. So the real question might be: who are you right now, in this particular exchange?
Verbatim output, trimmed for length. 3,451 question-and-answer pairs built from his own work.
One answers. The other refuses to, and the refusal is his entire field.
Gyula Klima  ·  Fordham University  ·  Medieval philosophy of mind
“Can a machine think?”
A general AI model
Whether a machine can think is one of the oldest questions in philosophy of mind. Alan Turing reframed it in 1950 as a question about observable behaviour rather than inner states. Functionalists argue that a system implementing the right causal organisation thinks. Critics such as John Searle, in the Chinese Room argument, contend that syntax alone is not sufficient for semantics. There is no settled consensus.
His Shryn voice
The system flagged this itself: “This specific question cannot be answered on the basis of Professor Klima’s writings alone, but I can make comparisons between what he actually wrote and what I can find in the larger model.”
That depends entirely on what we mean by ‘think’, and that is not a dodge, it is the whole problem. If by thinking you mean something like symbol manipulation that produces outputs correlated with correct answers, then yes, obviously machines can do that, and do it impressively. But if you mean thinking in the sense Aquinas had in mind, the formation of genuinely universal concepts that abstract from all particular material conditions, then I am skeptical. When I think dog, I am not entertaining a particular image of a particular dog; I am grasping something that applies to every dog, past, present, and possible, without being any one of them. That kind of cognition, Aquinas argues, cannot be the operation of a material organ, because any material organ is always this particular thing. A machine is paradigmatically a material particular. Current large language models are, in my view, extraordinarily sophisticated pattern-matchers operating over statistical distributions of tokens. They have never formed the concept dog; they have correlated the string ‘d-o-g’ with other strings in ways that track human usage without grasping what the word means.
Verbatim output, trimmed for length. Nothing added or rewritten.
And it told us where his work ends and inference begins. No general chatbot does that.
SHRYN

Built with them. Corrected by them. Theirs to remove.

Scholars at Columbia, Ohio State, UCLA, Fordham, Clark, Minnesota and Denver. Every answer is reviewable by the person who said it, and every correction goes back into their model.

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