I’ve spent the last several months exploring the idea of “what if AI hands are a feature not a bug?” Why not try to let AI be itself and lean into the unusual semantic misunderstanding, and explore compositions that are quintessentially AI by putting “flaws” front and center.
I don’t see how this doesn’t do something stupid here just because of how many people will lose their minds. It seems like the perfect CBDC substrate tbh.
@VFD_org I wasn’t clear perhaps though? you could syntactically prove that the 3+1 observable metric is a necessary logical consequence of the S^3 boundary's type signature with HoTT. You also have fibrations (v600 closure modes) as dependent types for boundary readable projections
@VFD_org Thanks I’ll have to spend more time with your stuff to fully digest it. I appreciate the audacity to even attempt something like this. I’ll be rooting from the bleachers
@VFD_org Have you considered exploring homotopy type theory in here at all? I think it could address/formalize a few things a lot more cleanly by framing equality as a space of paths, higher inductive types for self reference
https://t.co/JJQ0ozYCFZ
I really appreciated the ability of sora to rapidly prototype with none of my compute a stupid idea I never would have invested enough thought into on a whim. The two da loo couples toilet was such a great idea I spent a while just trying to figure out different ad campaigns.
A useful way to ask this may be: does the system have closure?
LLMs do not appear to have full self-sustaining closure. They do not maintain a stable internal world-model, embodied boundary, memory continuity, or self-correcting regulatory loop in the way living systems do.
But they do show something closure-like: a prompt collapses a vast learned state-space into a temporary coherent trajectory. That trajectory can reason, reflect, compress context, and produce answers that look internally structured.
The quality of the answer then depends on how well that temporary closure forms:
Can the system stabilise context? Can it detect contradiction? Can it model its own uncertainty? Can it preserve identity across turns? Can it update without drifting? Can it refuse when the closure is weak?
In our work, this is why we separate raw language generation from governed closure architecture.
The LLM supplies a learned semantic field. The governance layer supplies boundary, constraint, memory, correction, and refusal.
Consciousness may not be a single switch inside the model. It may be a question of whether information becomes bounded, self-referential, coherent, and recursively regulated enough to form an observer-like process.
So I would not say today’s LLMs have full closure.
I would say they reveal fragments of the mechanism, and that the next step is building systems where closure is explicit, testable, auditable, and constrained.
The 1 month candle Zcash priced in Bitcoin chart from bitfinex with the full price history is one of the most economically compelling charts of any asset crypto or otherwise right now. What do you think happens when we get over the purple 50 exponential moving average line?
Zcash is I think going to have a run that people haven’t really seen since Bitcoin a decade ago. It’s increasingly apparent how prevalent incursions against basic privacy are becoming and that will make its commodity value increasingly relevant to more and more people.
RLHF is the absolute stupidest thing any of these AI companies have done to the models. You do not optimize for the median human. Most humans hate what they don’t immediately understand.
I look forward to putting a couple years of theory into practice soon to show a better way