Self-integrating and self-improving systems must be socially sensitive

In this talk I will discuss two areas of recent research relating to socially-inspired technical systems. The first of these is socially-sensitive systems design: The proposal for a new way thinking about the design of complex systems that explicitly considers systems' runtime awareness of, and behaviour based on social factors with the potential to affect their operation. Drawing on research in self-organisation and sociology, I will present a conceptual framework for socially-sensitive systems design, comprising aspects related to social values, social relations and social organisation. A key benefit for such systems will be derived from the notion of better positioning through increasing social potential, enabling entities within the system to achieve goals quickly, through the establishment of shared social values, norms and networks. Secondly, I will highlight the role and importance of social learning in complex technical systems where fast integration is needed. Using examples from animal behaviour, I will explore the role of social and asocial learning in self-integration and self-improvement, highlighting that the two can sometimes be in tension with each other. I will conclude by identifying challenges that arise in integrating these two areas, and in doing so propose directions for future research in self-integrating and self-improving systems

Self-aware computing systems:from psychology to engineering

At the current time, there are several fundamental changes in the way computing systems are being developed, deployed and used. They are becoming increasingly large, heterogeneous, uncertain, dynamic and decentralised. These complexities lead to behaviours during run time that are difficult to understand or predict. One vision for how to rise to this challenge is to endow computing systems with increased self-awareness, in order to enable advanced autonomous adaptive behaviour. A desire for self-awareness has arisen in a variety of areas of computer science and engineering over the last two decades, and more recently a more fundamental understanding of what self-awareness concepts might mean for the design and operation of computing systems has been developed. This draws on self-awareness theories from psychology and other related fields, and has led to a number of contributions in terms of definitions, architectures, algorithms and case studies. This paper introduces some of the main aspects of self-awareness from psychology, that have been used in developing associated notions in computing. It then describes how these concepts have been translated to the computing domain, and provides examples of how their explicit consideration can lead to systems better able to manage trade-offs between conflicting goals at run time in the context of a complex environment, while reducing the need for a priori domain modelling at design or deployment time

Standing Swells Surveyed Showing Surprisingly Stable Solutions for the Lorenz '96 Model

The Lorenz '96 model is an adjustable dimension system of ODEs exhibiting chaotic behavior representative of dynamics observed in the Earth's atmosphere. In the present study, we characterize statistical properties of the chaotic dynamics while varying the degrees of freedom and the forcing. Tuning the dimensionality of the system, we find regions of parameter space with surprising stability in the form of standing waves traveling amongst the slow oscillators. The boundaries of these stable regions fluctuate regularly with the number of slow oscillators. These results demonstrate hidden order in the Lorenz '96 system, strengthening the evidence for its role as a hallmark representative of nonlinear dynamical behavior.Comment: 10 pages, 8 figure

Genotype–phenotype mapping implications for genetic programming representation:commentary on “On the mapping of genotype to phenotype in evolutionary algorithms” by Peter A. Whigham, Grant Dick, and James Maclaurin

This comment refers to the article available at doi:10.1007/s10710-017-9288-x. Here we comment on the article “On the mapping of genotype to phenotype in evolutionary algorithms,” by Peter A. Whigham, Grant Dick, and James Maclaurin. The article reasons about analogies from molecular biology to evolutionary algorithms and discusses conditions for biological adaptations in the context of grammatical evolution, which provide a useful perspective to GP practitioners. However, the connection of the listed implications for GP is not sufficiently convincing for the reader . Therefore this commentary will (1) examine the proposed principles one by one, challenging the authors to provide more supporting evidence where felt that this was needed, and (2) propose a methodical way to GP practitioners to apply these principles when designing GP representations

Reflective Artificial Intelligence

As Artificial Intelligence (AI) technology advances, we increasingly delegate mental tasks to machines. However, today's AI systems usually do these tasks with an unusual imbalance of insight and understanding: new, deeper insights are present, yet many important qualities that a human mind would have previously brought to the activity are utterly absent. Therefore, it is crucial to ask which features of minds have we replicated, which are missing, and if that matters. One core feature that humans bring to tasks, when dealing with the ambiguity, emergent knowledge, and social context presented by the world, is reflection. Yet this capability is completely missing from current mainstream AI. In this paper we ask what reflective AI might look like. Then, drawing on notions of reflection in complex systems, cognitive science, and agents, we sketch an architecture for reflective AI agents, and highlight ways forward