Edsger Dijkstra. The Man Who Carried Computer Science on His Shoulders

This a biographical essay about Edsger Wybe Dijkstra.Comment: 12 pages. Originally appeared in Inference, Volume 5, Issue 3, 2020, see https://inference-review.com/article/the-man-who-carried-computer-science-on-his-shoulder

Ideologies of computer scientists and technologists (Correctness beyond reason)

Ideologies of computer scientists and technologist

Uniform Bipartition in the Population Protocol Model with Arbitrary Communication Graphs

In this paper, we focus on the uniform bipartition problem in the population protocol model. This problem aims to divide a population into two groups of equal size. In particular, we consider the problem in the context of arbitrary communication graphs. As a result, we investigate the solvability of the uniform bipartition problem with arbitrary communication graphs when agents in the population have designated initial states, under various assumptions such as the existence of a base station, symmetry of the protocol, and fairness of the execution. When the problem is solvable, we present protocols for uniform bipartition. When global fairness is assumed, the space complexity of our solutions is tight

Artificial Intelligence (and Christianity) : Who? What? Where? When? Why? and How?

Open Access via the Sage AgreementPeer reviewedPublisher PD

Deep Neuroevolution: Smart City Applications

Particularmente, la contribución de esta tesis se centra en cuatro aspectos: Primero, proponemos la técnica Mean Absolute Error Random Sampling (MRS) para estimar el rendimiento de una RNN, la cual se basa en la distribución del error observado en un muestreo aleatorio. Nuestros resultados muestran que MRS es una estimación fiable y de bajo coste computacional para predecir el rendimiento de una RNN. Segundo, diseñamos un algoritmo evolutivo (RESN) que explota MRS para optimizar la arquitectura de una RNN. RESN muestra resultados competitivos a la vez que reduce significativamente el tiempo. Tercero, en el contexto de la aplicación, proponemos soluciones para problemas de movilidad, electricidad y gestión de residuos inteligente, y hemos revisado el estado del arte de la ciudad inteligente y su relación con la informática. Cuarto, hemos desarrollado la biblioteca de software Deep Learning OPTimization (DLOPT), la cual está disponible bajo la licencia GNU GPL v3. Ésta contiene la mayor parte del trabajo realizado en esta tesis.El interés por desarrollar redes neuronales artificiales ha resurgido de la mano del Aprendizaje Profundo. En términos simples, el aprendizaje profundo consiste en diseñar y entrenar una red neuronal de gran complejidad y tamaño con una inmensa cantidad de datos. Esta creciente complejidad propone nuevos desafíos, siendo de especial relevancia la optimización del diseño dado un problema. Tradicionalmente, este problema ha sido resuelto en una combinación de conocimiento experto (humano) con prueba y error. Sin embargo, conforme la complejidad aumenta, este acercamiento se vuelve ineficiente (e impracticable). Esta tesis doctoral aborda el diseño de redes neuronales recurrentes (RNN), un tipo de red neuronal profunda, desde la neuroevolución. Concretamente, se combinan técnicas de aprendizaje automático con metaheurísticas avanzadas, con el fin de proveer una solución eficaz y eficiente. Por otra parte, se aplican las técnicas desarrolladas a problemas de la ciudad inteligente

Data, Debt & Daemons: Systemic Asymmetries on Spaceship Earth

Day by day, the rate at which we create new data increases exponentially. Our capacity to learn cannot keep up. We are tiny members of a vast universal network, incapable of discerning cause and effect. Instead, we develop simplified narratives, leaving ourselves misguided yet complacent. The information management trade of both physical and intellectual property has become more vital to global economies than ever, replacing physical resources and manufacturing. Through our deepening reliance on specialization, we forfeit agency over our own homes while accruing unprecedented debt. Housing costs have risen dramatically compared to wages, despite reportedly successful economies. Citizens were supposed to have the ability to participate in financial markets using their property as collateral. This seduced many into the ideologies of unregulated capitalism. However, by the 21st century, these systems had become unrecognizable mutilations of their intended designs. The momentum we have gathered in the past century has thrust us on an unsustainable trajectory we have little hope of predicting. We laid the foundation for Western economic dominance with technology, monetary policy, and globalization, but we did so using incentive structures that exacerbated wealth inequality. These systems integrate digital technology into both our physical and virtual spaces, operating on invisible planes that bypass our senses. The radical novelty of computers has entangled us in niche engineered concepts that few understand. They create a lack of accountability in Big Tech that policy-makers are ill-prepared for. We cannot ensure an equitable distribution of the leverage or stakes when we entrust brokers, politicians, traders, and captains of industry to make complex decisions for us without bearing the risks of their consequences. Our long-term welfare, including our future habitation on this planet, is not visceral enough to force effective reform. Both our physical and our digital spaces are designed, built, evaluated, and monitored on asymmetric principles, causing disasters that future generations and the least fortunate always pay for. How did we normalize this moral hazard? How can digital systems born out of frustration with modern policy combat these issues, without disrupting the benefits of a techno-utopia? How can they promote efficiency, security, and transparency in the spaces we call home