How about I assume there is some epsilon such that the probability of an agent going off the rails is greater than epsilon in any given year. Why can’t the agent split into multiple ~uncorrelated agents and have them each control some fraction of resources (maybe space) such that one off-the-rails agent can easily be fought and controlled by the others? This should reduce the risk to some fraction of epsilon, right?
(I’m gonna try and stay focused on a single point, specifically the argument that leads up to >99%, because that part seems wrong for quite simple reasons).
How about I assume there is some epsilon such that the probability of an agent going off the rails
Got it. So we are both assuming that there would be some accumulative failure rate [per point 3.].
Why can’t the agent split into multiple ~uncorrelated agents and have them each control some fraction of resources (maybe space) such that one off-the-rails agent can easily be fought and controlled by the others?
I tried to adopt this ~uncorrelated agents framing, and then argue from within that. But I ran up against some problems with this framing:
It assumes there are stable boundaries between “agents” that allows us to mark them as separate entities. This kinda works for us as physically bounded and communication-bottlenecked humans. But in practice it wouldn’t really work to define “agent” separations within a larger machine network maintaining of own existence in the environment. (Also, it is not clear to me how failures of those defined “agent” subsets would necessarily be sufficiently uncorrelated – as an example, if the failure involves one subset hijacking the functioning of another subset, their failures become correlated.)
It assumes that if any (physical or functional) subset of this adaptive machinery happens to gain any edge in influencing the distributed flows of atoms and energy back towards own growth, that the other machinery subsets can robustly “control” for that.
It assumes a macroscale-explanation of physical processes that build up from the microscale. Agreed that the concept of agents owning and directing the allocation of “resources” is a useful abstraction, but it also involves holding a leaky representation of what’s going on. Any argument for control using that representation can turn out not to capture crucial aspects.
It raises the question what “off-the-rails” means here. This gets us into the hashiness model: Consider the space of possible machinery output sequences over time. How large is the subset of output sequences that in their propagation as (cascading) environmental effects would end up lethally disrupting the bodily functioning of humans? How is the accumulative probability of human extinction distributed across the entire output possibility space (or simplified: how mixed are the adjoining lethal and non-lethal possibility subspaces)? Can any necessarily less complex control system connected with/in this machinery actually keep tracking whether possible machinery outputs fall into the lethal sub-space or the non-lethal sub-space?
→ Do those problems makes sense to you as stated? Do you notice anything missing there?
To sum it up, you and I are still talking about a control system [per point 4.]:
However you define the autonomous “agents”, they are still running through code running across connected hardware.
There are limits to the capacity of this aggregate machinery to sense, model, simulate, evaluate, and correct own component effects propagating through a larger environment.
I’m gonna try and stay focused on a single point, specifically the argument that leads up to >99%
I’m also for now leaving aside substrate-needs convergence [point 5]:
That the entire population of nested/connected machine components would be pulled toward a human-lethal attractor state.
How about I assume there is some epsilon such that the probability of an agent going off the rails is greater than epsilon in any given year. Why can’t the agent split into multiple ~uncorrelated agents and have them each control some fraction of resources (maybe space) such that one off-the-rails agent can easily be fought and controlled by the others? This should reduce the risk to some fraction of epsilon, right?
(I’m gonna try and stay focused on a single point, specifically the argument that leads up to >99%, because that part seems wrong for quite simple reasons).
Got it. So we are both assuming that there would be some accumulative failure rate [per point 3.].
I tried to adopt this ~uncorrelated agents framing, and then argue from within that. But I ran up against some problems with this framing:
It assumes there are stable boundaries between “agents” that allows us to mark them as separate entities. This kinda works for us as physically bounded and communication-bottlenecked humans. But in practice it wouldn’t really work to define “agent” separations within a larger machine network maintaining of own existence in the environment.
(Also, it is not clear to me how failures of those defined “agent” subsets would necessarily be sufficiently uncorrelated – as an example, if the failure involves one subset hijacking the functioning of another subset, their failures become correlated.)
It assumes that if any (physical or functional) subset of this adaptive machinery happens to gain any edge in influencing the distributed flows of atoms and energy back towards own growth, that the other machinery subsets can robustly “control” for that.
It assumes a macroscale-explanation of physical processes that build up from the microscale. Agreed that the concept of agents owning and directing the allocation of “resources” is a useful abstraction, but it also involves holding a leaky representation of what’s going on. Any argument for control using that representation can turn out not to capture crucial aspects.
It raises the question what “off-the-rails” means here. This gets us into the hashiness model:
Consider the space of possible machinery output sequences over time. How large is the subset of output sequences that in their propagation as (cascading) environmental effects would end up lethally disrupting the bodily functioning of humans? How is the accumulative probability of human extinction distributed across the entire output possibility space (or simplified: how mixed are the adjoining lethal and non-lethal possibility subspaces)? Can any necessarily less complex control system connected with/in this machinery actually keep tracking whether possible machinery outputs fall into the lethal sub-space or the non-lethal sub-space?
→ Do those problems makes sense to you as stated? Do you notice anything missing there?
To sum it up, you and I are still talking about a control system [per point 4.]:
However you define the autonomous “agents”, they are still running through code running across connected hardware.
There are limits to the capacity of this aggregate machinery to sense, model, simulate, evaluate, and correct own component effects propagating through a larger environment.
I’m also for now leaving aside substrate-needs convergence [point 5]:
That the entire population of nested/connected machine components would be pulled toward a human-lethal attractor state.
I appreciate that you tried. If words are failing us to this extent, I’m going to give up.