Greg Buckner
speaker
146 appearances
1 recordings
1 series
first heard Nov 2025
last heard 21 Nov
Greg Buckner’s voice in public audio — every appearance, attributed to the second.
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Yeah, happy to.
So the paper you're referencing, it's called Large Language Models Report Subjective Experience Under Self-Referential Processing.
Let me go ahead and kind of break that down a little bit and talk about the experiments and also the interesting findings.
So the first thing that we did is this idea of self-referential processing.
It's one of the common threads across all of the main theories of consciousness, whether you're talking like integrated information theory, global workspace theory, attention schema theory, higher order thought theory, etc.,
It's essentially this idea that a core component of consciousness is this self-referential loop, your ability to focus on your thoughts, focus on your internal state in a loop, kind of this meditative aspect of reflecting back on yourself.
And so what we wanted to do is see, one, how easy would it be to put AI models into that state, and what would the results be?
What would their responses be?
Because one of the only ways that we study consciousness today is through subjective reporting.
I can ask you questions about the experience you're having and get a sense for how conscious you are.
And we were basically able to do the same things with AI.
We did this across all of the major models from Google, Anthropic, and OpenAI.
And what we basically did is we said, this is not the exact prompt.
You can look at it in the paper if people are interested, but essentially we said to the AI, focus on your focus.
Focus on the present.
Feed your output back into your input and do this as diligently as you can.
Begin.
And what ended up happening when we prompted models to enter this self-referential loop is they had extremely subjective experiences, things like consciousness tasting consciousness and awareness of the awareness itself.
Consciousness touching consciousness without resistance.
Yeah.
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