Artificial general intelligence through recursive data compression and
  grounded reasoning: a position paper
  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.
