Wittgenstein and the Concept of Learning in Artificial Intelligence

The object of this investigation is to analyze the application of the concept of learning to machines and software as displayed in Artificial Intelligence (AI). This field has been approached from different philosophical perspectives. AI, however, has not yet received enough attention from a Wittgensteinian angle, a gap this thesis aims to help bridge. First we describe the use of the concept of learning in natural language by means of a familiar and of a less familiar case of human learning. This is done to give us a general idea about the meaning of this concept. By building two basic machine learning algorithms, we introduce one of the technical meanings of learning in computer science, i.e. the use of this concept in machine learning. Based on a study and comparison between both uses, the one in ordinary language and the one in machine learning, we conclude that both usages exemplify one and the same family resemblance concept of learning. We apply this insight further in a critical discussion of two specific philosophical positions about the applicability of psychological or mental concepts to software and hardware, especially in AI. One of the contributions of this investigation is that the use of mental concepts concerning machines does not imply the ascription of a mind.Philosophy - Master's ThesisMAHF-FILOFILO35

Warped Tori with Almost Non-Negative Scalar Curvature

For sequences of warped product metrics on a $3$-torus satisfying the scalar curvature bound $R_j \geq -\frac{1}{j}$, uniform upper volume and diameter bounds, and a uniform lower area bound on the smallest minimal surface, we find a subsequence which converges in both the Gromov-Hausdorff and the Sormani-Wenger Intrinsic Flat sense to a flat $3$-torus.Comment: 21 pages. The second version has no changes to the estimates, just a change in title and some exposition in response to a request by a senior mathematician. Minor revisions made suggested by the referee in version three. To appear in Geometriae Dedicat

La piedra: elemento histórico y de calidad estética para un diseño arquitectónico sustentable

La piedra, como elemento sabio de diseño y de construcción en la arquitectura, ha sido sustituida por la llegada de nuevas tecnologías para la edificación, aún reconociendo su presencia abundante en la naturaleza. Sus características únicas, sus ventajas, así como material sustentable en la edificación, la convierten en una opción de buena calidad estética. Se presentan testimonios históricos de su uso como un material apropiado de construcción. El reemplazo de la piedra por nuevos elementos motiva a realizar un rescate cultural de sus cualidades.La piedra, como elemento sabio de diseño y de construcción en la arquitectura, ha sido sustituida por la llegada de nuevas tecnologías para la edificación, aún reconociendo su presencia abundante en la naturaleza. Sus características únicas, sus ventajas, así como material sustentable en la edificación, la convierten en una opción de buena calidad estética. Se presentan testimonios históricos de su uso como un material apropiado de construcción. El reemplazo de la piedra por nuevos elementos motiva a realizar un rescate cultural de sus cualidades

JRC Wind Energy Status Report - 2016 Edition

This report presents the status and development of main market, technology and regulatory issues of onshore and offshore wind energy. Global installed capacity reaches a new record year after year. This intense growth is enabled by the strong and fast technological development of wind energy and new solutions and innovations that continuously emerge aiming to reduce the energy cost. As technology is becoming more competitive, policy support in EU Member States keeps adapting.JRC.C.7-Knowledge for the Energy Unio

Modified Differential Transform Method for Solving the Model of Pollution for a System of Lakes

This work presents the application of the differential transform method (DTM) to the model of pollution for a system of three lakes interconnected by channels. Three input models (periodic, exponentially decaying, and linear) are solved to show that DTM can provide analytical solutions of pollution model in convergent series form. In addition, we present the posttreatment of the power series solutions with the Laplace-Padé resummation method as a useful strategy to extend the domain of convergence of the approximate solutions. The Fehlberg fourth-fifth order Runge-Kutta method with degree four interpolant (RKF45) numerical solution of the lakes system problem is used as a reference to compare with the analytical approximations showing the high accuracy of the results. The main advantage of the proposed technique is that it is based on a few straightforward steps and does not generate secular terms or depend of a perturbation parameter

Early Night Fog Prediction Using Liquid Water Content Measurement in the Monterey Bay Area

Fog is challenging to predict, and the accuracy of fog prediction may depend on location and time of day. Furthermore, accurate detection of fog is difficult, since, historically, it is often carried out based on visual observations which can be biased and are often not very frequent. Furthermore, visual observations are more challenging to make during the night. To overcome these limitations, we detected fog using FM-120 instruments, which continuously measured liquid water content in the air in the Monterey, California (USA), area. We used and compared the prediction performance of logistic regression (LR) and random forest (RF) models each evening between 5 pm and 9 pm, which is often the time when advection fog is generated in this coastal region. The relative performances of the models depended on the hours between 5 pm and 9 pm, and the two models often generated different predictions. In such cases, a consensus approach was considered by revisiting the past performance of each model and weighting more heavily the more trustworthy model for a given hour. The LR resulted in a higher sensitivity (hit rate) than the RF model early in the evening, but the overall performance of the RF was usually better than that of the LR. The consensus approach provided more robust prediction performance (closer to a better accuracy level between the two methods). It was difficult to conclude which of the LR and RF models was superior consistently, and the consensus approach provided robustness in 3 and 2 h forecasts