1,883 research outputs found
Simplification and integration in computing and cognition: the SP theory and the multiple alignment concept
The main purpose of this article is to describe potential benefits and
applications of the SP theory, a unique attempt to simplify and integrate ideas
across artificial intelligence, mainstream computing and human cognition, with
information compression as a unifying theme. The theory, including a concept of
multiple alignment, combines conceptual simplicity with descriptive and
explanatory power in several areas including representation of knowledge,
natural language processing, pattern recognition, several kinds of reasoning,
the storage and retrieval of information, planning and problem solving,
unsupervised learning, information compression, and human perception and
cognition. In the SP machine -- an expression of the SP theory which is
currently realised in the form of computer models -- there is potential for an
overall simplification of computing systems, including software. As a theory
with a broad base of support, the SP theory promises useful insights in many
areas and the integration of structures and functions, both within a given area
and amongst different areas. There are potential benefits in natural language
processing (with potential for the understanding and translation of natural
languages), the need for a versatile intelligence in autonomous robots,
computer vision, intelligent databases, maintaining multiple versions of
documents or web pages, software engineering, criminal investigations, the
management of big data and gaining benefits from it, the semantic web, medical
diagnosis, the detection of computer viruses, the economical transmission of
data, and data fusion. Further development of these ideas would be facilitated
by the creation of a high-parallel, web-based, open-source version of the SP
machine, with a good user interface. This would provide a means for researchers
to explore what can be done with the system and to refine it.Comment: 32 pages, 5 figure
Proposal for the creation of a research facility for the development of the SP machine
This is a proposal to create a research facility for the development of a
high-parallel version of the "SP machine", based on the "SP theory of
intelligence". We envisage that the new version of the SP machine will be an
open-source software virtual machine, derived from the existing "SP computer
model", and hosted on an existing high-performance computer. It will be a means
for researchers everywhere to explore what can be done with the system and to
create new versions of it. The SP system is a unique attempt to simplify and
integrate observations and concepts across artificial intelligence, mainstream
computing, mathematics, and human perception and cognition, with information
compression as a unifying theme. Potential benefits and applications include
helping to solve problems associated with big data; facilitating the
development of autonomous robots; unsupervised learning, natural language
processing, several kinds of reasoning, fuzzy pattern recognition at multiple
levels of abstraction, computer vision, best-match and semantic forms of
information retrieval, software engineering, medical diagnosis, simplification
of computing systems, and the seamless integration of diverse kinds of
knowledge and diverse aspects of intelligence. Additional motivations include
the potential of the SP system to help solve problems in defence, security, and
the detection and prevention of crime; potential in terms of economic, social,
environmental, and academic criteria, and in terms of publicity; and the
potential for international influence in research. The main elements of the
proposed facility are described, including support for the development of
"SP-neural", a neural version of the SP machine. The facility should be
permanent in the sense that it should be available for the foreseeable future,
and it should be designed to facilitate its use by researchers anywhere in the
world.Comment: arXiv admin note: text overlap with arXiv:1508.04087. substantial
text overlap with arXiv:1409.802
The SP theory of intelligence: distinctive features and advantages
This paper highlights distinctive features of the "SP theory of intelligence"
and its apparent advantages compared with some AI-related alternatives.
Distinctive features and advantages are: simplification and integration of
observations and concepts; simplification and integration of structures and
processes in computing systems; the theory is itself a theory of computing; it
can be the basis for new architectures for computers; information compression
via the matching and unification of patterns and, more specifically, via
multiple alignment, is fundamental; transparency in the representation and
processing of knowledge; the discovery of 'natural' structures via information
compression (DONSVIC); interpretations of mathematics; interpretations in human
perception and cognition; and realisation of abstract concepts in terms of
neurons and their inter-connections ("SP-neural"). These things relate to
AI-related alternatives: minimum length encoding and related concepts; deep
learning in neural networks; unified theories of cognition and related
research; universal search; Bayesian networks and more; pattern recognition and
vision; the analysis, production, and translation of natural language;
Unsupervised learning of natural language; exact and inexact forms of
reasoning; representation and processing of diverse forms of knowledge; IBM's
Watson; software engineering; solving problems associated with big data, and in
the development of intelligence in autonomous robots. In conclusion, the SP
system can provide a firm foundation for the long-term development of AI, with
many potential benefits and applications. It may also deliver useful results on
relatively short timescales. A high-parallel, open-source version of the SP
machine, derived from the SP computer model, would be a means for researchers
everywhere to explore what can be done with the system, and to create new
versions of it
Autonomous robots and the SP theory of intelligence
This article is about how the "SP theory of intelligence" and its realisation
in the "SP machine" (both outlined in the article) may help to solve
computer-related problems in the design of autonomous robots, meaning robots
that do not depend on external intelligence or power supplies, are mobile, and
are designed to exhibit as much human-like intelligence as possible. The
article is about: how to increase the computational and energy efficiency of
computers and reduce their bulk; how to achieve human-like versatility in
intelligence; and likewise for human-like adaptability in intelligence. The SP
system has potential for substantial gains in computational and energy
efficiency and reductions in the bulkiness of computers: by reducing the size
of data to be processed; by exploiting statistical information that the system
gathers; and via an updated version of Donald Hebb's concept of a "cell
assembly". Towards human-like versatility in intelligence, the SP system has
strengths in unsupervised learning, natural language processing, pattern
recognition, information retrieval, several kinds of reasoning, planning,
problem solving, and more, with seamless integration amongst structures and
functions. The SP system's strengths in unsupervised learning and other aspects
of intelligence may help to achieve human-like adaptability in intelligence
via: the learning of natural language; learning to see; building 3D models of
objects and of a robot's surroundings; learning regularities in the workings of
a robot and in the robot's environment; exploration and play; learning major
skills; and secondary forms of learning. Also discussed are: how the SP system
may process parallel streams of information; generalisation of knowledge,
correction of over-generalisations, and learning from dirty data; how to cut
the cost of learning; and reinforcements, motivations, goals, and
demonstration
Information Upload and retrieval using SP Theory of Intelligence
In today's technology Cloud computing has become an important aspect and storing of data on cloud is of high importance as the need for virtual space to store massive amount of data has grown during the years. However time taken for uploading and downloading is limited by processing time and thus need arises to solve this issue to handle large data and their processing. Another common problem is de duplication. With the cloud services growing at a rapid rate it is also associated by increasing large volumes of data being stored on remote servers of cloud. But most of the remote stored files are duplicated because of uploading the same file by different users at different locations. A recent survey by EMC says about 75% of the digital data present on cloud are duplicate copies. To overcome these two problems in this paper we are using SP theory of intelligence using lossless compression of information, which makes the big data smaller and thus reduces the problems in storage and management of large amounts of data
Introduction to the SP theory of intelligence
This article provides a brief introduction to the "Theory of Intelligence"
and its realisation in the "SP Computer Model". The overall goal of the SP
programme of research, in accordance with long-established principles in
science, has been the simplification and integration of observations and
concepts across artificial intelligence, mainstream computing, mathematics, and
human learning, perception, and cognition. In broad terms, the SP system is a
brain-like system that takes in "New" information through its senses and stores
some or all of it as "Old" information. A central idea in the system is the
powerful concept of "SP-multiple-alignment", borrowed and adapted from
bioinformatics. This the key to the system's versatility in aspects of
intelligence, in the representation of diverse kinds of knowledge, and in the
seamless integration of diverse aspects of intelligence and diverse kinds of
knowledge, in any combination. There are many potential benefits and
applications of the SP system. It is envisaged that the system will be
developed as the "SP Machine", which will initially be a software virtual
machine, hosted on a high-performance computer, a vehicle for further research
and a step towards the development of an industrial-strength SP Machine
Interpreting Winograd Schemas Via the SP Theory of Intelligence and Its Realisation in the SP Computer Model
In 'Winograd Schema' (WS) sentences like "The city councilmen refused the
demonstrators a permit because they feared violence" and "The city councilmen
refused the demonstrators a permit because they advocated revolution", it is
easy for adults to understand what "they" refers to but can be difficult for AI
systems. This paper describes how the SP System -- outlined in an appendix --
may solve this kind of problem of interpretation. The central idea is that a
knowledge of discontinuous associations amongst linguistic features, and an
ability to recognise such patterns of associations, provides a robust means of
determining what a pronoun like "they" refers to. For any AI system to solve
this kind of problem, it needs appropriate knowledge of relevant syntax and
semantics which, ideally, it should learn for itself. Although the SP System
has some strengths in unsupervised learning, its capabilities in this area are
not yet good enough to learn the kind of knowledge needed to interpret WS
examples, so it must be supplied with such knowledge at the outset. However,
its existing strengths in unsupervised learning suggest that it has potential
to learn the kind of knowledge needed for the interpretation of WS examples. In
particular, it has potential to learn the kind of discontinuous association of
linguistic features mentioned earlier
Application of the SP theory of intelligence to the understanding of natural vision and the development of computer vision
The SP theory of intelligence aims to simplify and integrate concepts in
computing and cognition, with information compression as a unifying theme. This
article discusses how it may be applied to the understanding of natural vision
and the development of computer vision. The theory, which is described quite
fully elsewhere, is described here in outline but with enough detail to ensure
that the rest of the article makes sense.
Low level perceptual features such as edges or corners may be identified by
the extraction of redundancy in uniform areas in a manner that is comparable
with the run-length encoding technique for information compression.
The concept of multiple alignment in the SP theory may be applied to the
recognition of objects, and to scene analysis, with a hierarchy of parts and
sub-parts, and at multiple levels of abstraction.
The theory has potential for the unsupervised learning of visual objects and
classes of objects, and suggests how coherent concepts may be derived from
fragments.
As in natural vision, both recognition and learning in the SP system is
robust in the face of errors of omission, commission and substitution.
The theory suggests how, via vision, we may piece together a knowledge of the
three-dimensional structure of objects and of our environment, it provides an
account of how we may see things that are not objectively present in an image,
and how we recognise something despite variations in the size of its retinal
image. And it has things to say about the phenomena of lightness constancy and
colour constancy, the role of context in recognition, and ambiguities in visual
perception.
A strength of the SP theory is that it provides for the integration of vision
with other sensory modalities and with other aspects of intelligence.Comment: 40 pages, 16 figure
Problems in AI research and how the SP System may help to solve them
This paper describes problems in AI research and how the SP System may help
to solve them. Most of the problems are described by leading researchers in AI
in interviews with science writer Martin Ford, and reported by him in his book
Architects of Intelligence. These problems, each with potential solutions via
SP, are: how to overcome the divide between symbolic and non-symbolic kinds of
knowledge and processing; eliminating large and unexpected errors in
recognition; the challenge of unsupervised learning; the problem of
generalisation, with under- and over-generalisation; learning from a single
exposure or experience; the problem of transfer learning; how to create
learning that is fast, economical in demands for data and computer resources;
the problems of transparency in results and processing; problems in the
processing of natural language; problems in the development of probabilistic
reasoning; the problem of catastrophic forgetting; how to achieve generality
across several aspects of AI. The SP System provides a relatively promising
foundation for the development of artificial general intelligence
Transparency and granularity in the SP Theory of Intelligence and its realisation in the SP Computer Model
This chapter describes how the SP System, meaning the SP Theory of
Intelligence, and its realisation as the SP Computer Model, may promote
transparency and granularity in AI, and some other areas of application. The
chapter describes how transparency in the workings and output of the SP
Computer Model may be achieved via three routes: 1) the program provides a very
full audit trail for such processes as recognition, reasoning, analysis of
language, and so on. There is also an explicit audit trail for the unsupervised
learning of new knowledge; 2) knowledge from the system is likely to be
granular and easy for people to understand; and 3) there are seven principles
for the organisation of knowledge which are central in the workings of the SP
System and also very familiar to people (eg chunking-with-codes, part-whole
hierarchies, and class-inclusion hierarchies), and that kind of familiarity in
the way knowledge is structured by the system, is likely to be important in the
interpretability, explainability, and transparency of that knowledge. Examples
from the SP Computer Model are shown throughout the chapter.Comment: Accepted for publication as a chapter in the book Interpretable
Artificial Intelligence: A Perspective of Granular Computing, to be published
by Springer-Verlag and edited by Witold Pedrycz and Shyi-Ming Che
- …