Describing the FPGA-Based Hardware Architecture of Systemic Computation (HAoS)

his paper presents HAoS, the first hardware architecture of the bio-inspired computational paradigm known as Systemic Computation (SC). SC was designed to support the modelling of biological processes inherently by defining a massively parallel non-conventional computer architecture and a model of natural behaviour. In this work we describe a novel custom digital design, which addresses the SC architecture parallelism requirement by exploiting the inbuilt parallelism of a Field Programmable Gate Array (FPGA) and by using the highly efficient matching capability of a Ternary Content Addressable Memory (TCAM). Basic processing capabilities are embedded in HAoS in order to minimize time-demanding data transfers. Its custom instruction set can be expanded based on user requirements, since the optional use of a CPU provides high-level processing support if required. We demonstrate a functional simulation-verified prototype, which takes into consideration programmability and scalability, and review various communication interfaces between HAoS and the CPU. Analysis shows that the proposed architecture provides an effective solution in terms of efficiency versus flexibility trade-off and can potentially outperform prior implementations

From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

Using a Variational Autoencoder to Learn Valid Search Spaces of Safely Monitored Autonomous Robots for Last-Mile Delivery

The use of autonomous robots for delivery of goods to customers is an exciting new way to provide a reliable and sustainable service. However, in the real world, autonomous robots still require human supervision for safety reasons. We tackle the real-world problem of optimizing autonomous robot timings to maximize deliveries, while ensuring that there are never too many robots running simultaneously so that they can be monitored safely. We assess the use of a recent hybrid machine-learning-optimization approach COIL (constrained optimization in learned latent space) and compare it with a baseline genetic algorithm for the purposes of exploring variations of this problem. We also investigate new methods for improving the speed and efficiency of COIL. We show that only COIL can find valid solutions where appropriate numbers of robots run simultaneously for all problem variations tested. We also show that when COIL has learned its latent representation, it can optimize 10% faster than the GA, making it a good choice for daily re-optimization of robots where delivery requests for each day are allocated to robots while maintaining safe numbers of robots running at once

COIL: Constrained optimization in learned latent space: learning representations for valid solutions

Constrained optimization problems can be difficult because their search spaces have properties not conducive to search, e.g., multimodality, discontinuities, or deception. To address such difficulties, considerable research has been performed on creating novel evolutionary algorithms or specialized genetic operators. However, if the representation that defined the search space could be altered such that it only permitted valid solutions that satisfied the constraints, the task of finding the optimal would be made more feasible without any need for specialized optimization algorithms. We propose Constrained Optimization in Latent Space (COIL), which uses a VAE to generate a learned latent representation from a dataset comprising samples from the valid region of the search space according to a constraint, thus enabling the optimizer to find the objective in the new space defined by the learned representation. Preliminary experiments show promise: compared to an identical GA using a standard representation that cannot meet the constraints or find fit solutions, COIL with its learned latent representation can perfectly satisfy different types of constraints while finding high-fitness solutions

¿Debemos temer a la inteligencia artificial?: análisis en profundidad

El ISBN corresponde a la versión electrónica del documentoDesde hace ya algunos años, la inteligencia artificial (IA) ha estado cobrando impulso. Una oleada de programas que sacan el máximo rendimiento a los procesadores de última generación están obteniendo resultandos espectaculares. Una de las aplicaciones más destacadas de la IA es el reconocimiento de voz: si bien los primeros modelos eran extraños y se caracterizaban por defectos constantes, ahora son capaces de responder correctamente a todo tipo de solicitudes de los usuarios en las más diversas situaciones. En el ámbito del reconocimiento de imagen también se están logrando avances notables, con programas capaces de reconocer figuras —e incluso gatos— en vídeos en línea que ahora se están adaptando para que el software controle los coches autónomos que invadirán nuestras calles en los próximos años. A día de hoy no podemos imaginar un futuro en Europa sin una IA avanzada que influya cada vez en más facetas de nuestra vida, desde el trabajo a la medicina, y desde la educación a las relaciones interpersonales