Hierarchical Prediction Machines and Big Data Analytics

An emerging consensus in cognitive science views the biological brain as a hierarchically-organized predictive processing system. This is a system in which higher-order regions are continuously attempting to predict the activity of lower-order regions at a variety of (increasingly abstract) spatial and temporal scales. The brain is thus revealed as a hierarchical prediction machine that is constantly engaged in the effort to predict the flow of information originating from the sensory surfaces. Such a view seems to afford a great deal of explanatory leverage when it comes to a broad swathe of seemingly disparate psychological phenomena (e.g., learning, memory, perception, action, emotion, planning, reason, imagination, and conscious experience). In the most positive case, the predictive processing story seems to provide our first glimpse at what a unified (computationally-tractable and neurobiological plausible) account of human psychology might look like. This obviously marks out one reason why such models should be the focus of current empirical and theoretical attention. Another reason, however, is rooted in the potential of such models to advance the current state-of-the-art in machine intelligence and machine learning. Interestingly, the vision of the brain as a hierarchical prediction machine is one that establishes contact with work that goes under the heading of 'deep learning'. Deep learning systems thus often attempt to make use of predictive processing schemes and (increasingly abstract) generative models as a means of supporting the analysis of large data sets. But are such computational systems sufficient (by themselves) to provide a route to general human-level analytic capabilities? I will argue that they are not and that closer attention to a broader range of forces and factors (many of which are not confined to the neural realm) may be required to understand what it is that gives human cognition its distinctive (and largely unique) flavour. The vision that emerges is one of 'homomimetic deep learning systems', systems that situate a hierarchically-organized predictive processing core within a larger nexus of developmental, behavioural, symbolic, technological and social influences. Relative to that vision, I suggest that we should see the Web as a form of 'cognitive ecology', one that is as much involved with the transformation of machine intelligence as it is with the progressive reshaping of our own cognitive capabilities.

Elementos para la relación entre cultura organizacional y estrategia: caso Google

Desde el año 360 a.C. se comienza a hablar de la importancia de la estrategia para el éxito de las empresas, argumentando que a través de ella, se establece una visión y un método para alcanzarla. Sin embargo, desde los años ochenta el tema de cultura organizacional empieza a surgir como un elemento clave dentro de las compañías. El talento humano pasa a tener importancia dentro del panorama organizacional como un recurso valioso, cuya gestión puede resultar convirtiéndose en una ventaja competitiva para las compañías dentro de su sector de mercado. A pesar de que estos conceptos son subjetivos, al revisar las diferentes opiniones de autores representativos en cada uno de estos temas, se determina que se encuentran elementos en común entre ellos. Para el caso de cultura organizacional, son la identidad y diferenciación, los sistemas simbólicos, y los comportamientos uniformes. Para el caso de la estrategia, dichos elementos son el dinamismo, la asignación de recursos, y el patrón con propósito a largo plazo. Si tanto la estrategia como la cultura organizacional, independientemente aportan al progreso de las compañías, se buscó establecer, a partir de la fijación de los elementos mencionados, una definición de la relación entre ellos. Para lograrlo, se utilizó un ejemplo específico, el caso de la empresa Google Inc. Se discutieron tres estrategias que esta compañía ha desarrollado en materia relacionada con la construcción de su propia cultura organizacional.

L'ús de Google Earth a l'aula. 'El uso de Google Earth en el aula'.

Monográfico con el título: 'Ensenyar geografia en un món en transformació'. Resumen tomado de la publicación

Can more education be bad? : some simple analytics on financing better education for development. '¿Puede más educación ser mala? : un análisis sencillo sobre la financiación de una mejor educación para el desarrollo'.

Resumen tomado de la publicación

La generaci??n Google : evoluci??n en las predisposiciones y comportamientos informativos de los j??venes

Monogr??fico con el t??tulo: 'Infocomunicaci??n y educaci??n social'

Una aproximación didáctica a la contratación bursátil a través de un juego de rol en Google-Docs.

Resumen basado en el de la publicación

Uso de Google Docs como herramienta de construcción colaborativa tomando en cuenta los estilos de aprendizaje.

Resumen basado en el de la publicación

Integración de APIs políglotas de mapas en Google Web Toolkit: IDELabMapstraction GWT

En este trabajo se ha integrado la librería IDELabMapstraction como un nuevo módulo para GWT, denominado IDELabMapstractionGWT, en el que también se han diseñado distintos controles genéricos que se pueden asociar al mapa para que los usuarios puedan interactuar con él de forma intuitiva. Como resultado de esta integración se ha creado el visor MirameDuero, un completo visor web realizado en colaboración con la Confederación Hidrográfica del Duero

Utilización y aplicación de la herramienta "google docs" en la docencia universitaria dentro del marco del EEES

Como consecuencia de la realización del curso 'Aplicaciones de la Web 2.0 en la Investigación y la Docencia' organizado por La Fundación General Universidad de Granada-Empresa en el curso académico 2009-2010, los autores del presente trabajo presentamos el Proyecto de Innovación Docente concedido por el Vicerrectorado para la Garantía de la calidad de la Universidad de Granada,titulado 'Utilización de la herramienta “Google Docs” en la Docencia Universitaria dentro del marco del EEES. Creemos que el proyecto tiene un diseño aplicable a cualquier asignatura de la actual Licenciatura y/o Grado de Farmacia. En este trabajo se expone la experiencia piloto llevada a cabo en la asignatura de Química Farmacéutica, incluyendo el desarrollo, objetivos, metodología, resultados y las conclusiones que se están obteniendo en el trascurso del mismo

Como aprender História e Geografia no 8º ano de escolaridade, usando o Google Earth ?

Actualmente no Ensino Básico as disciplinas de História e Geografia são vistas, maioritariamente , como desinteressantes e pouco úteis a grande parte dos alunos. Variadas razões podem ser apontadas, como a excessiva memorização de conteúdos, cultivados por um ensino expositivo enquanto forma de ensino com maiores adeptos pelos professores nas escolas. Aliada a esta memorização surge, inevitavelmente, a pouca retenção na memória no tempo dos conhecimentos adquiridos a estas disciplinas. Partindo dos pressupostos teóricos sobre os métodos de ensino mais inovadores, que sugerem a realização de experiências educativas com uma metodologia activa e um ensino por competências , efectuou-se uma experiência de aprendizagem em que se utilizou a ferramenta Google Earth nas aulas de História e Geografia do 8º ano de escolaridade. Pretendeu-se aferir se, com recurso às novas tecnologias, os alunos interiorizariam melhor conceitos relativos ás duas áreas do saber.

GIS in the cloud: implementing a web map service on Google App Engine

Many producers of geographic information are now disseminating their data using open web service protocols, notably those published by the Open Geospatial Consortium. There are many challenges inherent in running robust and reliable services at reasonable cost. Cloud computing provides a new kind of scalable infrastructure that could address many of these challenges. In this study we implement a Web Map Service for raster imagery within the Google App Engine environment. We discuss the challenges of developing GIS applications within this framework and the performance characteristics of the implementation. Results show that the application scales well to multiple simultaneous users and performance will be adequate for many applications, although concerns remain over issues such as latency spikes. We discuss the feasibility of implementing services within the free usage quotas of Google App Engine and the possibility of extending the approaches in this paper to other GIS applications.

Analysing Google rankings through search engine optimization data

Purpose - The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google. Design/methodology/approach - The paper presents the results of a study into 50 highly optimized web pages that were created as part of a Search Engine Optimization competition. The study focuses on the most popular techniques that were used to rank highest in this competition, and includes an analysis on the use of PageRank, number of pages, number of in-links, domain age and the use of third party sites such as directories and social bookmarking sites. A separate study was made into 50 non-optimized web pages for comparison. Findings - The paper provides insight into the techniques that successful Search Engine Optimizers use to ensure a page ranks highly in Google. Recognizes the importance of PageRank and links as well as directories and social bookmarking sites. Research limitations/implications - Only the top 50 web sites for a specific query were analyzed. Analysing more web sites and comparing with similar studies in different competition would provide more concrete results. Practical implications - The paper offers a revealing insight into the techniques used by industry experts to rank highly in Google, and the success or other-wise of those techniques. Originality/value - This paper fulfils an identified need for web sites and e-commerce sites keen to attract a wider web audience.

Non-display uses of digital works: Google Books and beyond

With the advent of mass digitization projects, such as the Google Book Search, a peculiar shift has occurred in the way that copyright works are dealt with. Contrary to what has so far been the case, works are turned into machine-readable data to be automatically processed for various purposes without the expression of works being displayed to the public. In the Google Book Settlement Agreement, this new kind of usage is referred to as ‘non-display uses’ of digital works. The legitimacy of these uses has not yet been tested by Courts and does not comfortably fit in the current copyright doctrine, plainly because the works are not used as works but as something else, namely as data. Since non-display uses may prove to be a very lucrative market in the near future, with the potential to affect the way people use copyright works, we examine non-display uses under the prism of copyright principles to determine the boundaries of their legitimacy. Through this examination, we provide a categorization of the activities carried out under the heading of ‘non-display uses’, we examine their lawfulness under the current copyright doctrine and approach the phenomenon from the spectrum of data protection law that could apply, by analogy, to the use of copyright works as processable data.

The future of video analytics for surveillance and its ethical implications

The current state of the art and direction of research in computer vision aimed at automating the analysis of CCTV images is presented. This includes low level identification of objects within the field of view of cameras, following those objects over time and between cameras, and the interpretation of those objects’ appearance and movements with respect to models of behaviour (and therefore intentions inferred). The potential ethical problems (and some potential opportunities) such developments may pose if and when deployed in the real world are presented, and suggestions made as to the necessary new regulations which will be needed if such systems are not to further enhance the power of the surveillers against the surveilled.