Take the task of designing something like a car, internals.
You might start with a rough idea of the specs, and a precise equation for the value of each feature. You have a scaled model for how it needs to look.
You start a search process where you consider many possible ways to arrange the components within the body shell. Say none of the configurations will fit and meet specs.
You send a request up the stack for a scaled up version of the shell. You get it. You arrange the components into possible designs that fit, and then send the candidate design for simulated testing.
The simulated testing reveals a common failure in one of the parts, and all of the available alternatives for that part have a flaw. So you send a request to the “part designer” to give you a part that satisfies these new tightened specs that will not allow the flaw, and ask for a range of alternate packages.
The resulting redesigned part is now too big to fit, so you rearrange the parts again/send a request to the body shell designer for even more space, and so on.
It is many, may iterative interactions where the flow of the process has to go up and down the stack many times. In addition I am describing the flow for one design candidate. It’s actually a large tree of other candidates you should be checking, where each time there was a choice you queue up a message to the next stage for each possible choice you could have made. (and prune, from all the packages in flight in the system, the worst ones)
If you think about how to implement this, one way is data driven. All the “roles” in this network sit there quiescent waiting for an initial data package. All the context of the process is in the message itself, there is no agent “responsible” for the car design getting finished, but a message flow pipeline where after some time you will get valid car design alternatives in the ‘in box’ of the system that sent the request, or a message stating that the process failed from an intractable problem. (there were constraints that could not be satisfied after exhausting every design permutation)
There is no reason these roles cannot be superintelligences, but they get no context. They don’t think or have an internal narrative, they wait forever for a message, but apply superhuman and general skill when given the task. They are stateless microservices, though as they do have superintelligence level neural architectures, they are too fat to be called ‘micro’.
Fair, though it is separable.
Take the task of designing something like a car, internals.
You might start with a rough idea of the specs, and a precise equation for the value of each feature. You have a scaled model for how it needs to look.
You start a search process where you consider many possible ways to arrange the components within the body shell. Say none of the configurations will fit and meet specs.
You send a request up the stack for a scaled up version of the shell. You get it. You arrange the components into possible designs that fit, and then send the candidate design for simulated testing.
The simulated testing reveals a common failure in one of the parts, and all of the available alternatives for that part have a flaw. So you send a request to the “part designer” to give you a part that satisfies these new tightened specs that will not allow the flaw, and ask for a range of alternate packages.
The resulting redesigned part is now too big to fit, so you rearrange the parts again/send a request to the body shell designer for even more space, and so on.
It is many, may iterative interactions where the flow of the process has to go up and down the stack many times. In addition I am describing the flow for one design candidate. It’s actually a large tree of other candidates you should be checking, where each time there was a choice you queue up a message to the next stage for each possible choice you could have made. (and prune, from all the packages in flight in the system, the worst ones)
If you think about how to implement this, one way is data driven. All the “roles” in this network sit there quiescent waiting for an initial data package. All the context of the process is in the message itself, there is no agent “responsible” for the car design getting finished, but a message flow pipeline where after some time you will get valid car design alternatives in the ‘in box’ of the system that sent the request, or a message stating that the process failed from an intractable problem. (there were constraints that could not be satisfied after exhausting every design permutation)
There is no reason these roles cannot be superintelligences, but they get no context. They don’t think or have an internal narrative, they wait forever for a message, but apply superhuman and general skill when given the task. They are stateless microservices, though as they do have superintelligence level neural architectures, they are too fat to be called ‘micro’.