Over the past few years, artificial intelligence (AI) tools, particularly deep neural networks, have achieved remarkable results on a number of tasks. However, recent studies have found that these computational techniques have a number of limitations. In a recent...
Machine learning & AI
Exploring the potential of near-sensor and in-sensor computing systems
As the number of devices connected to the internet continues to increase, so does the amount of redundant data transfer between different sensory terminals and computing units. Computing approaches that intervene in the vicinity of or inside sensory networks could...
LUCIDGames: A technique to plan adaptive trajectories for autonomous vehicles
While many self-driving vehicles have achieved remarkable performance in simulations or initial trials, when tested on real streets, they are often unable to adapt their trajectories or movements based on those of other vehicles or agents in their surroundings. This...
A memory-augmented, artificial neural network-based architecture
Over the past decade or so, researchers have developed a variety of computational models based on artificial neural networks (ANNs). While many of these models have been found to perform well on specific tasks, they are not always able to identify iterative,...
An open-source and low-cost robotic arm for online education
Researchers at Tecnologico de Monterrey in Mexico have recently created a low-cost robotic arm that could enhance online robotics education, allowing teachers to remotely demonstrate theoretical concepts explained during their lessons. This robotic arm, presented in a...
Using deep learning to infer the socioeconomic status of people in different urban areas
Deep learning algorithms have proved to be promising tools to tackle a variety of real-world problems, especially those that require the analysis of vast amounts of data. In contrast with other computational techniques, in fact, these algorithms can learn to make...
RealAnt: A low-cost quadruped robot that can learn via reinforcement learning
RealAnt: an open-source low-cost quadruped robot for real-world reinforcement learning research. Credit: Ote Robotics Ltd, CC BY 4.0 license. Over the past decade or so, roboticists and computer scientists have tried to use reinforcement learning (RL) approaches to...
RealitySketch: An AR interface to create responsive sketches
Researchers at University of Calgary, Adobe Research and University of Colorado Boulder have recently created an augmented reality (AR) interface that can be used to produce responsive sketches, graphics and visualizations. Their work, initially pre-published on...
Exploring the use of artificial intelligence in architecture
Over the past few decades, artificial intelligence (AI) tools have been used to analyze data or complete basic tasks in an increasing number of fields, ranging from computer science to manufacturing, medicine, physics, biology and even artistic disciplines....
Previewed Reality: A system that allows users to predict future changes in their environment
When robots and humans interact in a shared environment, it is important for them to move in ways that prevent collisions or accidents. To reduce the risk of collisions, roboticists have developed numerous of techniques that monitor an environment, predict the future...