Hello—I’m just here to drop a somewhat vague/incipient idea for an AI model and see if there are any existing frameworks that could be used.
The general idea is to view agent action and perception as part of the same discrete data stream, and model intelligence as compression of sub-segments of this stream into independent “mechanisms” (patterns of action-perception) which can be used for prediction/action and potentially recombined into more general frameworks as the agent learns.
More precisely, I’m looking for:
The method of pattern representation
An algorithm for inferring initially orthogonal/unrelated patterns from the same data stream
Some manner of meta-learning for recombining mechanisms
One promising suggestion I received elsewhere was to use reservoir computing/liquid state machines for the time series pattern recognition.
(For a conceptually similar model look at Friston’s “Active Inference”.)
Hello—I’m just here to drop a somewhat vague/incipient idea for an AI model and see if there are any existing frameworks that could be used.
The general idea is to view agent action and perception as part of the same discrete data stream, and model intelligence as compression of sub-segments of this stream into independent “mechanisms” (patterns of action-perception) which can be used for prediction/action and potentially recombined into more general frameworks as the agent learns.
More precisely, I’m looking for:
The method of pattern representation
An algorithm for inferring initially orthogonal/unrelated patterns from the same data stream
Some manner of meta-learning for recombining mechanisms
One promising suggestion I received elsewhere was to use reservoir computing/liquid state machines for the time series pattern recognition.
(For a conceptually similar model look at Friston’s “Active Inference”.)