Although I don’t understand what you mean by “conservation of computation”, the distribution of computation, information sources, learning, and representation capacity is important in shaping how and where knowledge is represented.
The idea that general AI capabilities can best be implemented or modeled as “an agent” (an “it” that uses “the search algorithm”) is, I think, both traditional and misguided. A host of tasks require agentic action-in-the-world, but those tasks are diverse and will be performed and learned in parallel (see the CAIS report, www.fhi.ox.ac.uk/reframing). Skill in driving somewhat overlaps with — yet greatly differs from — skill in housecleaning or factory management; learning any of these does not provide deep, state-of-the art knowledge of quantum physics, and can benefit from (but is not a good way to learn) conversational skills that draw on broad human knowledge.
A well-developed QNR store should be thought of as a body of knowledge that potentially approximates the whole of human and AI-learned knowledge, as well as representations of rules/programs/skills/planning strategies for a host of tasks. The architecture of multi-agent systems can provide individual agents with resources that are sufficient for the tasks they perform, but not orders of magnitude more than necessary, shaping how and where knowledge is represented. Difficult problems can be delegated to low-latency AI cloud services. .
There is no “it” in this story, and classic, unitary AI agents don’t seem competitive as service providers — which is to say, don’t seem useful..
I’ve noted the value of potentially opaque neural representations (Transformers, convnets, etc.) in agents that must act skillfully, converse fluently, and so on, but operationalized, localized, task-relevant knowledge and skills complement rather than replace knowledge that is accessible by associative memory over a large, shared store.
Although I don’t understand what you mean by “conservation of computation”, the distribution of computation, information sources, learning, and representation capacity is important in shaping how and where knowledge is represented.
The idea that general AI capabilities can best be implemented or modeled as “an agent” (an “it” that uses “the search algorithm”) is, I think, both traditional and misguided. A host of tasks require agentic action-in-the-world, but those tasks are diverse and will be performed and learned in parallel (see the CAIS report, www.fhi.ox.ac.uk/reframing). Skill in driving somewhat overlaps with — yet greatly differs from — skill in housecleaning or factory management; learning any of these does not provide deep, state-of-the art knowledge of quantum physics, and can benefit from (but is not a good way to learn) conversational skills that draw on broad human knowledge.
A well-developed QNR store should be thought of as a body of knowledge that potentially approximates the whole of human and AI-learned knowledge, as well as representations of rules/programs/skills/planning strategies for a host of tasks. The architecture of multi-agent systems can provide individual agents with resources that are sufficient for the tasks they perform, but not orders of magnitude more than necessary, shaping how and where knowledge is represented. Difficult problems can be delegated to low-latency AI cloud services. .
There is no “it” in this story, and classic, unitary AI agents don’t seem competitive as service providers — which is to say, don’t seem useful..
I’ve noted the value of potentially opaque neural representations (Transformers, convnets, etc.) in agents that must act skillfully, converse fluently, and so on, but operationalized, localized, task-relevant knowledge and skills complement rather than replace knowledge that is accessible by associative memory over a large, shared store.