¿Pueden pensar las máquinas? Una evaluación del problema de la inteligencia artificial mediante el equilibrio refractivo

Revisión del problema de la filosofía de la Inteligencia Artificial a la vista del Equilibrio refractivo. La revisión del problema se lleva a cabo para mostrar como "¿pueden pensar las máquinas?" sólo se ha evaluado en los terminos humanos. El equilibrio refractivo se plantea como una herramienta para definir conceptos de tal modo que la experiencia y los preceptos se encuentren en equilibrio, para con él construir una definición de pensar que no esté limitada exclusivamente a "pensar tal y como lo hacen los humanos".

Human papillomavirus detection from human immunodeficiency virus-infected colombian women's paired urine and cervical samples

Infection, coinfection and type-specific human papillomavirus (HPV) distribution was evaluated in human immunodeficiency virus (HIV)-positive women from paired cervical and urine samples. Paired cervical and urine samples (n = 204) were taken from HIV-positive women for identifying HPV-DNA presence by using polymerase chain reaction (PCR) with three generic primer sets (GP5+/6+, MY09/11 and pU1M/2R). HPV-positive samples were typed for six high-risk HPV (HR-HPV) (HPV-16, -18, -31, -33, -45 and -58) and two low-risk (LR-HPV) (HPV-6/11) types. Agreement between paired sample results and diagnostic performance was evaluated. HPV infection prevalence was 70.6% in cervical and 63.2% in urine samples. HPV-16 was the most prevalent HPV type in both types of sample (66.7% in cervical samples and 62.0% in urine) followed by HPV-31(47.2%) in cervical samples and HPV-58 (35.7%) in urine samples. There was 55.4% coinfection (infection by more than one type of HPV) in cervical samples and 40.2% in urine samples. Abnormal Papanicolau smears were observed in 25.3% of the women, presenting significant association with HPV-DNA being identified in urine samples. There was poor agreement of cervical and urine sample results in generic and type-specific detection of HPV. Urine samples provided the best diagnosis when taking cytological findings as reference. In conclusion including urine samples could be a good strategy for ensuring adherence to screening programs aimed at reducing the impact of cervical cancer, since this sample is easy to obtain and showed good diagnostic performance.

An artificial economy based on reinforcement learning and agent based modeling

In this paper, we employ techniques from artificial intelligence such as reinforcement learning and agent based modeling as building blocks of a computational model for an economy based on conventions. First we model the interaction among firms in the private sector. These firms behave in an information environment based on conventions, meaning that a firm is likely to behave as its neighbors if it observes that their actions lead to a good pay off. On the other hand, we propose the use of reinforcement learning as a computational model for the role of the government in the economy, as the agent that determines the fiscal policy, and whose objective is to maximize the growth of the economy. We present the implementation of a simulator of the proposed model based on SWARM, that employs the SARSA(λ) algorithm combined with a multilayer perceptron as the function approximation for the action value function.