Artificial general intelligence through recursive data compression and grounded reasoning: a position paper
This is an incremental position paper outlining a theoretical approach to AGI, targeting researchers in AI and cognitive science.
The paper proposes a framework for achieving artificial general intelligence (AGI) by developing a general data compression algorithm that recursively reduces data representations and a grounded reasoning system based on flexible hypotheses, aiming to enable resourceful thinking and commonsense reasoning without providing concrete results or numbers.
This paper presents a tentative outline for the construction of an artificial, generally intelligent system (AGI). It is argued that building a general data compression algorithm solving all problems up to a complexity threshold should be the main thrust of research. A measure for partial progress in AGI is suggested. Although the details are far from being clear, some general properties for a general compression algorithm are fleshed out. Its inductive bias should be flexible and adapt to the input data while constantly searching for a simple, orthogonal and complete set of hypotheses explaining the data. It should recursively reduce the size of its representations thereby compressing the data increasingly at every iteration. Abstract Based on that fundamental ability, a grounded reasoning system is proposed. It is argued how grounding and flexible feature bases made of hypotheses allow for resourceful thinking. While the simulation of representation contents on the mental stage accounts for much of the power of propositional logic, compression leads to simple sets of hypotheses that allow the detection and verification of universally quantified statements. Abstract Together, it is highlighted how general compression and grounded reasoning could account for the birth and growth of first concepts about the world and the commonsense reasoning about them.