Hello all, this is my first time posting in this forum, so I look forward to your feedback.

First, some brief background info about myself: I have an undergraduate background in Computer Science & Philosophy and am considering pursuing a Master's in AI. Apart from my formal education I have also engaged in a significant degree of self-study. My motivation in participating in this forum is, as you might assume, to share my ideas and discuss topics in artificial intelligence. To start off, I'll share with you an outline for an AGI model.

My proposal here is inspired by compression-theoretical models of information and detection of patterns over string streams. A lot of work has already been done in problem domains like binary stream prediction models, which extract patterns or regularities from an input stream that enable prediction of subsequent values. My idea would be to apply a similar approach to data streams that include both agent percept and action states, where such a sequence of states represents a cybernetic feedback loop between the agent and its environment.

Such an agent while in “exploration mode” would choose essentially random actions, or else in a manner so as to maximize exploration of the search space. As this is done they would track the action-percept stream for patterns or regularities across subsegments. When a pattern or compression of a particular segment is discovered, that segment’s entropy decreases (a consequence of the formal equivocation of entropy-theoretic and compression-theoretic definitions of information), indicating that the agent’s uncertainty in outcomes over the sequence of the action-perception states decreases. Conceptually, this elegantly unifies representation, information, prediction, and agent causality. A particular such pattern could be termed a “mechanism”, and detected mechanisms would be stored by the agent in some representation format. When the agent model is augmented with a utility function that prioritizes the attainment of certain agent states, the agent could make use of its stored mechanisms to more efficiently attain that goal, since each of these represents a tool or augmented action over its base actions. A comprehensive agent would also be capable of some degree of meta-learning, wherein it synthesizes known mechanisms or expands them into something more general when presented with additio...

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