Female Under-Representation in Computing Education and Industry - A Survey of Issues and Interventions

This survey paper examines the issue of female under-representation in computing education and industry, which has been shown from empirical studies to be a problem for over two decades. While various measures and intervention strategies have been implemented to increase the interest of girls in computing education and industry, the level of success has been discouraging. The primary contribution of this paper is to provide an analysis of the extensive research work in this area. It outlines the progressive decline in female representation in computing education. It also presents the key arguments that attempt to explain the decline and intervention strategies. We conclude that there is a need to further explore strategies that will encourage young female learners to interact more with computer educational games

Can biological quantum networks solve NP-hard problems?

There is a widespread view that the human brain is so complex that it cannot be efficiently simulated by universal Turing machines. During the last decades the question has therefore been raised whether we need to consider quantum effects to explain the imagined cognitive power of a conscious mind. This paper presents a personal view of several fields of philosophy and computational neurobiology in an attempt to suggest a realistic picture of how the brain might work as a basis for perception, consciousness and cognition. The purpose is to be able to identify and evaluate instances where quantum effects might play a significant role in cognitive processes. Not surprisingly, the conclusion is that quantum-enhanced cognition and intelligence are very unlikely to be found in biological brains. Quantum effects may certainly influence the functionality of various components and signalling pathways at the molecular level in the brain network, like ion ports, synapses, sensors, and enzymes. This might evidently influence the functionality of some nodes and perhaps even the overall intelligence of the brain network, but hardly give it any dramatically enhanced functionality. So, the conclusion is that biological quantum networks can only approximately solve small instances of NP-hard problems. On the other hand, artificial intelligence and machine learning implemented in complex dynamical systems based on genuine quantum networks can certainly be expected to show enhanced performance and quantum advantage compared with classical networks. Nevertheless, even quantum networks can only be expected to efficiently solve NP-hard problems approximately. In the end it is a question of precision - Nature is approximate.Comment: 38 page