Faith in the Algorithm, Part 1: Beyond the Turing Test

Since the Turing test was first proposed by Alan Turing in 1950, the primary goal of artificial intelligence has been predicated on the ability for computers to imitate human behavior. However, the majority of uses for the computer can be said to fall outside the domain of human abilities and it is exactly outside of this domain where computers have demonstrated their greatest contribution to intelligence. Another goal for artificial intelligence is one that is not predicated on human mimicry, but instead, on human amplification. This article surveys various systems that contribute to the advancement of human and social intelligence

Mind, Computational Thinking & Neural Network

Mind, Computational Thinking & Neural Networ

Redes de información y propiedades sistémicas. Una perspectiva epistemológica

[EN] In this article I present a proposal for a new epistemic paradigm for the interpretation of complex reticular phenomena: the information network. Starting from an analysis of the concept of network in different contexts, such as in the case of an artificial neuronal network, the signal network of a swarm intelligence or the synaptic network in the brain, the present work has the ambition to identify the common features of all this kinds of net and to start delineating a general epistemic paradigm. The strongest idea of this essay is that most important thing in a net in not its architecture, but the information content it conveys: any information is here presented as a set of signs, hence, any information network constitutes a semiotic system (which is particularly evident in a swarm intelligence). The nodes of a net can be seen as the agents of a system: each agent locally manipulates signs, modifying in this way its environment (the very semiotic system it belongs to, the information network). Therefore, I argue that the very information structure influences local responses of the individual agents, feedbacks the system and self-organize. [ES] En este artículo se acuña una propuesta para un nuevo paradigma epistémico para la interpretación de fenómenos complejos reticulares: la red de informaciones. Partiendo de un análisis del concepto de red en contextos distintos, como es el caso de la red neuronal artificial, de la red de señales en una inteligencia de enjambre o de la red sináptica en el cerebro, el presente escrito tiene la ambición de detectar las características comunes a estos retículos y empezar a delinear un paradigma epistémico general. La idea fuerte del ensayo es que lo que cuenta en una red no es su arquitectura física, sino el contenido de información que ésta vehicula: se presenta aquí toda información como un conjunto de signos, por ende, toda red de información constituye un sistema semiótico (lo cual es particularmente evidente en una inteligencia de enjambre). Los nodos de una red pueden ser vistos como agentes de un sistema: todo agente manipula signos localmente, alterando así su entorno (el propio sistema semiótico del que forma parte, la red de informaciones). Se defiende, entonces, que es la propria información la que afecta las respuestas locales de los agentes individuales, retroalimenta el sistema y se auto-organiza

A novel memory-based pattern recognition system

This thesis proposes a novel method for learning and pattern recognition. The algorithm presented relies entirely on memory arranged in a custom hierarchical data structure which shifts the workload from the processor to memory. The structure and functionality draw on biology and neuroscience for inspiration while not losing sight of the inherent strengths and limitations of modern computers. A hierarchy of learned nodes is built, stored, and used for recognition without the need for complicated math or statistics. Recognition and prediction are inherent to the hierarchy and require little additional computation, even for matching of partial patterns. The experiments and results presented empirically demonstrate the robustness of memory-based recognition of images