  This article is an overview of the "SP theory of intelligence". The theory
aims to simplify and integrate concepts across artificial intelligence,
mainstream computing and human perception and cognition, with information
compression as a unifying theme. It is conceived as a brain-like system that
receives 'New' information and stores some or all of it in compressed form as
'Old' information. It is realised in the form of a computer model -- a first
version of the SP machine. The concept of "multiple alignment" is a powerful
central idea. Using heuristic techniques, the system builds multiple alignments
that are 'good' in terms of information compression. For each multiple
alignment, probabilities may be calculated. These provide the basis for
calculating the probabilities of inferences. The system learns new structures
from partial matches between patterns. Using heuristic techniques, the system
searches for sets of structures that are 'good' in terms of information
compression. These are normally ones that people judge to be 'natural', in
accordance with the 'DONSVIC' principle -- the discovery of natural structures
via information compression. The SP theory may be applied in several areas
including 'computing', aspects of mathematics and logic, representation of
knowledge, natural language processing, pattern recognition, several kinds of
reasoning, information storage and retrieval, planning and problem solving,
information compression, neuroscience, and human perception and cognition.
Examples include the parsing and production of language including discontinuous
dependencies in syntax, pattern recognition at multiple levels of abstraction
and its integration with part-whole relations, nonmonotonic reasoning and
reasoning with default values, reasoning in Bayesian networks including
'explaining away', causal diagnosis, and the solving of a geometric analogy
problem.
