Cognitive Bias for Universal Algorithmic Intelligence
  Existing theoretical universal algorithmic intelligence models are not
practically realizable. More pragmatic approach to artificial general
intelligence is based on cognitive architectures, which are, however,
non-universal in sense that they can construct and use models of the
environment only from Turing-incomplete model spaces. We believe that the way
to the real AGI consists in bridging the gap between these two approaches. This
is possible if one considers cognitive functions as a "cognitive bias" (priors
and search heuristics) that should be incorporated into the models of universal
algorithmic intelligence without violating their universality. Earlier reported
results suiting this approach and its overall feasibility are discussed on the
example of perception, planning, knowledge representation, attention, theory of
mind, language, and some others.
