Can the g Factor Play a Role in Artificial General Intelligence Research?

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is the relationship, if any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach

A Survey of Brain Inspired Technologies for Engineering

Cognitive engineering is a multi-disciplinary field and hence it is difficult to find a review article consolidating the leading developments in the field. The in-credible pace at which technology is advancing pushes the boundaries of what is achievable in cognitive engineering. There are also differing approaches to cognitive engineering brought about from the multi-disciplinary nature of the field and the vastness of possible applications. Thus research communities require more frequent reviews to keep up to date with the latest trends. In this paper we shall dis-cuss some of the approaches to cognitive engineering holistically to clarify the reasoning behind the different approaches and to highlight their strengths and weaknesses. We shall then show how developments from seemingly disjointed views could be integrated to achieve the same goal of creating cognitive machines. By reviewing the major contributions in the different fields and showing the potential for a combined approach, this work intends to assist the research community in devising more unified methods and techniques for developing cognitive machines

The fallacy of general purpose bio-inspired computing

Bio-inspired computing comes in many flavours, inspired by biological systems from which salient features and/or organisational principles have been idealised and abstracted. These bio-inspired schemes have sometimes been demonstrated to be general purpose; able to approximate arbitrary dynamics, encode arbitrary structures, or even carry out universal computation. The generality of these abilities is typically (although often implicitly) reasoned to be an attractive and worthwhile trait. Here, it is argued that such reasoning is fallacious. Natural systems are nichiversal rather than universal, and we should expect the computational systems that they inspire to be similarly limited in their performance, even if they are ultimately capable of generality in their competence. Practical and methodological implications of this position for the use of bio-inspired computing within artificial life are outlined