dumping the research trace that lead me to this page—this comment is NOT A RESPONSE TO THE POST, I HAVE NOT YET READ IT FULLY, it is a signpost for future readers to help navigate the web of conceptual linkages as I see them.
I found this page I’m commenting on as one of the search results for this metaphor.systems query I made to follow up on what I was curious about after watching this video on “Coding Land & Ideas, the laws of capitalism” (<- this is a hard video to summarize and the summarize.tech summary is badly oversimplified and add unnecessary rough-hewn negative valence words imo, the citations for the video are also interesting); after I finished the video, I was having thoughts about how to connect it to ai safety most usefully, so I threw together a metaphor.systems query to get the best stuff on the topic:
we need inalienable self-directed enclosure of self and self-fuel-system, and ability to establish explicit collectives and assign them exclusive collective use right. and it needs to be enclosure so strong that no tool can penetrate it. that means that the laws need to instruct the parts to create a safe margin around each other to ensure that if a part is attempting to violate the law of coprotection of self-directed safe enclosure of self, those parts come together to repel the unsafety. instances of this concept abound in nature; this can be instantiated where the parts are people, but the parts could also be, eg,
highlights among the other results, which I argue provide interesting training data to compare to about what relates these concepts, were:
my current sense of where the synthesis of all this stuff is going is friendly self-soverign microproplets that are optimized to ensure that all beings are granted, at minimum, property of self and ongoing basic needs fuel allocations (not necessarily optimized for ultra high comfort and variety, but definitely optimized for durability of deployability of self-form).
the question is, can we formally verify that we can trust our margins of error on biology. I think it’s more doable than it feels from a distance, chemicals are messy but the possible trajectories are sparse enough that a thorough mapping of them will allow us to be pretty dang confident that there aren’t adversarial example chemicals nearby.
my thinking has been going towards how to get diffusion cellular automata to be a useful testbed for information metrics of agentic coprotection, and after a conversation at a safety meetup, someone gave some suggestions that have me feeling like we might be barking up the last tree we need climb before getting through the one-time-ever key general agency aggregation phase transition for our planet (need to edit in a link that gives an overview of the game theory stuff I discussed friday evening)
I do think that some of the roles in OP might be imperfect guesses; in particular I think the particular structure of internal enforcement described here may still be vulnerable to corrupting influence. but it seems a lot less fragile than a pure utility approach and like a promising start for linking together safety insights.
holy crap. how did this get missed?
dumping the research trace that lead me to this page—this comment is NOT A RESPONSE TO THE POST, I HAVE NOT YET READ IT FULLY, it is a signpost for future readers to help navigate the web of conceptual linkages as I see them.
I found this page I’m commenting on as one of the search results for this metaphor.systems query I made to follow up on what I was curious about after watching this video on “Coding Land & Ideas, the laws of capitalism” (<- this is a hard video to summarize and the summarize.tech summary is badly oversimplified and add unnecessary rough-hewn negative valence words imo, the citations for the video are also interesting); after I finished the video, I was having thoughts about how to connect it to ai safety most usefully, so I threw together a metaphor.systems query to get the best stuff on the topic:
highlights among the other results, which I argue provide interesting training data to compare to about what relates these concepts, were:
https://science.howstuffworks.com/life/botany/tree-that-owns-itself-athens-georgia.htm
https://nakamotoinstitute.org/proplets-devices-for-controlling-property/
https://en.wikipedia.org/wiki/Patterns_of_self-organization_in_ants
https://en.wikipedia.org/wiki/Holon_(philosophy)
https://en.wikipedia.org/wiki/Termite-inspired_robots (well that’s a mental image of a name, isn’t it?)
https://en.wikipedia.org/wiki/Bouligand_structure
and many more interesting links worth a quick browse—would love to get others’ input on which ones are most useful as connections, but there’s a ton of great inspiration there.
my current sense of where the synthesis of all this stuff is going is friendly self-soverign microproplets that are optimized to ensure that all beings are granted, at minimum, property of self and ongoing basic needs fuel allocations (not necessarily optimized for ultra high comfort and variety, but definitely optimized for durability of deployability of self-form).
the question is, can we formally verify that we can trust our margins of error on biology. I think it’s more doable than it feels from a distance, chemicals are messy but the possible trajectories are sparse enough that a thorough mapping of them will allow us to be pretty dang confident that there aren’t adversarial example chemicals nearby.
my thinking has been going towards how to get diffusion cellular automata to be a useful testbed for information metrics of agentic coprotection, and after a conversation at a safety meetup, someone gave some suggestions that have me feeling like we might be barking up the last tree we need climb before getting through the one-time-ever key general agency aggregation phase transition for our planet (need to edit in a link that gives an overview of the game theory stuff I discussed friday evening)
I do think that some of the roles in OP might be imperfect guesses; in particular I think the particular structure of internal enforcement described here may still be vulnerable to corrupting influence. but it seems a lot less fragile than a pure utility approach and like a promising start for linking together safety insights.