  This paper provides an overview of the SP theory of intelligence and its
central idea that artificial intelligence, mainstream computing, and much of
human perception and cognition, may be understood as information compression.
  The background and origins of the SP theory are described, and the main
elements of the theory, including the key concept of multiple alignment,
borrowed from bioinformatics but with important differences. Associated with
the SP theory is the idea that redundancy in information may be understood as
repetition of patterns, that compression of information may be achieved via the
matching and unification (merging) of patterns, and that computing and
information compression are both fundamentally probabilistic. It appears that
the SP system is Turing-equivalent in the sense that anything that may be
computed with a Turing machine may, in principle, also be computed with an SP
machine.
  One of the main strengths of the SP theory and the multiple alignment concept
is in modelling concepts and phenomena in artificial intelligence. Within that
area, the SP theory provides a simple but versatile means of representing
different kinds of knowledge, it can model both the parsing and production of
natural language, with potential for the understanding and translation of
natural languages, it has strengths in pattern recognition, with potential in
computer vision, it can model several kinds of reasoning, and it has
capabilities in planning, problem solving, and unsupervised learning.
  The paper includes two examples showing how alternative parsings of an
ambiguous sentence may be modelled as multiple alignments, and another example
showing how the concept of multiple alignment may be applied in medical
diagnosis.
