1,673 research outputs found
On microelectronic self-learning cognitive chip systems
After a brief review of machine learning techniques and applications, this Ph.D. thesis examines several approaches for implementing machine learning architectures and algorithms into hardware within our laboratory.
From this interdisciplinary background support, we have motivations for novel approaches that we intend to follow as an objective of innovative hardware implementations of dynamically self-reconfigurable logic for enhanced self-adaptive, self-(re)organizing and eventually self-assembling machine learning systems, while developing this new particular area of research.
And after reviewing some relevant background of robotic control methods followed by most recent advanced cognitive controllers, this Ph.D. thesis suggests that amongst many well-known ways of designing operational technologies, the design methodologies of those leading-edge high-tech devices such as cognitive chips that may well lead to intelligent machines exhibiting
conscious phenomena should crucially be restricted to extremely well defined constraints.
Roboticists also need those as specifications to help decide upfront on otherwise infinitely free hardware/software design details.
In addition and most importantly, we propose these specifications as methodological guidelines tightly related to ethics and the nowadays well-identified workings of the human body and of its psyche
Connectionist-Symbolic Machine Intelligence using Cellular Automata based Reservoir-Hyperdimensional Computing
We introduce a novel framework of reservoir computing, that is capable of
both connectionist machine intelligence and symbolic computation. Cellular
automaton is used as the reservoir of dynamical systems. Input is randomly
projected onto the initial conditions of automaton cells and nonlinear
computation is performed on the input via application of a rule in the
automaton for a period of time. The evolution of the automaton creates a
space-time volume of the automaton state space, and it is used as the
reservoir. The proposed framework is capable of long short-term memory and it
requires orders of magnitude less computation compared to Echo State Networks.
We prove that cellular automaton reservoir holds a distributed representation
of attribute statistics, which provides a more effective computation than local
representation. It is possible to estimate the kernel for linear cellular
automata via metric learning, that enables a much more efficient distance
computation in support vector machine framework. Also, binary reservoir feature
vectors can be combined using Boolean operations as in hyperdimensional
computing, paving a direct way for concept building and symbolic processing.Comment: Corrected Typos. Responded some comments on section 8. Added appendix
for details. Recurrent architecture emphasize
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Considerations in designing a cybernetic simple 'learning' model; and an overview of the problem of modelling learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Learning is viewed as a central feature of living systems and must be manifested in any artifact that claims to exhibit general intelligence. The central aims of the thesis are twofold: (1) - To review and critically assess the empirical and theoretical aspects of learning as have been addressed in a multitude of disciplines, with the aim of extracting fundamental features and elements. (2) - To develop a more systematic approach to the cybernetic modelling of learning than has been achieved hitherto. In pursuit of aim (1) above the following discussions are included: Historical and Philosophical backgrounds; Natural learning, both physiological and psychological aspects; Hierarchies of learning identified in the evolutionary, functional and developmental senses; An extensive section on the general problem of modelling of learning and the formal tools, is included as a link between aims (1) and (2). Following this a systematic and historically oriented study of cybernetic and other related approaches to the problem of modelling of learning is presented. This then leads to the development of a state-of-the-art general purpose experimental cybernetic learning model. The programming and use of this model is also fully described, including an elaborate scheme for the manifestation of simple learning
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A Survey on Nature-Inspired Medical Image Analysis: A Step Further in Biomedical Data Integration
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