Computational Intelligence: The Grid as a Post-Human Network

Research and design collaborative EZCT Architecture & Design Research has adopted grid computing to produce a series of furniture systems and other small-scale prototypes using genetic algorithms in combination with automated fabrication technologies. Here, cofounder Philippe Morel relates this design practice to the broader technical and social implications of various grid-computing projects, such as the online organisation Folding@Home, which utilises grid computing and distributed communities for the production and exchange of postindustrial knowledge. He argues that these ‘knowledge farms’ which create an ‘ambient factory’, are perhaps the ultimate form of social-economic production, transforming not only the evolution of design but of the communities that produce and eventually consume its products

Intrinsic vs. extrinsic evaluation measures for referring expression generation

In this paper we present research in which we apply (i) the kind of intrinsic evaluation metrics that are characteristic of current comparative HLT evaluation, and (ii) extrinsic, human task-performance evaluations more in keeping with NLG traditions, to 15 systems implementing a language generation task. We analyse the evaluation results and find that there are no significant correlations between intrinsic and extrinsic evaluation measures for this task.peer-reviewe

On the possible Computational Power of the Human Mind

The aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and its natural counterpart. A possible characterization of the different power capabilities of the mind is suggested in terms of the information contained (in its computational complexity) or achievable by it. Such characterization takes advantage of recent results based on natural neural networks (NNN) and the computational power of arbitrary artificial neural networks (ANN). The possible acceptance of neural networks as the model of the human mind's operation makes the aforementioned quite relevant.Comment: Complexity, Science and Society Conference, 2005, University of Liverpool, UK. 23 page

A comparison of the performance of humans and computational models in the classification of facial expression

Recognizing expressions are a key part of human social interaction, and processing of facial expression information is largely automatic for humans, but it is a non-trivial task for a computational system. In the first part of the experiment, we develop computational models capable of differentiating between two human facial expressions. We perform pre-processing by Gabor filters and dimensionality reduction using the methods: Principal Component Analysis, and Curvilinear Component Analysis. Subsequently the faces are classified using a Support Vector Machines. We also asked human subjects to classify these images and then we compared the performance of the humans and the computational models. The main result is that for the Gabor pre-processed model, the probability that an individual face was classified in the given class by the computational model is inversely proportional to the reaction time for the human subjects

A Meta-Theory of Boundary Detection Benchmarks

Human labeled datasets, along with their corresponding evaluation algorithms, play an important role in boundary detection. We here present a psychophysical experiment that addresses the reliability of such benchmarks. To find better remedies to evaluate the performance of any boundary detection algorithm, we propose a computational framework to remove inappropriate human labels and estimate the intrinsic properties of boundaries.Comment: NIPS 2012 Workshop on Human Computation for Science and Computational Sustainabilit