ARE ARTIFICIAL NEURAL NETS AS SMART AS A RAT

The origins of artificial neural networks are related to animal conditioning theory: both are forms of connectionist theory, which in turn derives from the empiricist philosophers' principle of association. The parallel between animal learning and neural nets suggests that interaction between them should benefit both sides.

Determining propeller induced erosion alongside quay walls in harbours using Artificial Neural Networks

In shallow waters, such as those found close to berth structures, the wash from a manoeuvring ship’s propeller can cause erosion of the seabed. This erosion can be increased if the wash intersects a berth structure. A number of researchers have undertaken model studies and used regression analysis to develop predictive relationships for the scouring action. This paper presents an experimental investigation with Artificial Neural Networks (ANN’s), used to analyse the results. The purpose of using ANN’s was to examine the prediction accuracy of the Networks in comparison with previous regression analysis methods. ANN’s were found to provide a more accurate method of predicting propeller wash scour than the equations presented by previous investigators.

Demo: Have a Chat with Sensitive Artificial Listeners

Sensitive Artificial Listeners (SAL) are virtual dialogue partners based on audiovisual analysis and synthesis. Despite their very limited verbal understanding, they intend to engage the user in a conversation by paying attention to the user’s emotions and non- verbal expressions. The SAL characters have their own emotionally defined personality, and attempt to drag the user towards their dom- inant emotion through a combination of verbal and non-verbal ex- pression. The demonstrator shows the publicly available, fully au- tonomous SAL system.