AI-powered robots are generally trained in simulation environments before they are tested and introduced in real-world settings. These environments allow developers to safely test their machine learning techniques on a variety of robots and in numerous possible...
Researchers at IBM Research Zürich and ETH Zürich have recently created a new architecture that combines two of the most renowned artificial intelligence approaches, namely deep neural networks and vector-symbolic models. Their architecture, presented in Nature...
Graph neural networks (GNNs) are promising machine learning architectures designed to analyze data that can be represented as graphs. These architectures achieved very promising results on a variety of real-world applications, including drug discovery, social network...
At the end of November 2022, the San Francisco-based company OpenAI launched its prototype of ChatGPT, an artificial intelligence (AI)-based chatbot that can answer a wide range of questions in short periods of time. Since then, users worldwide have been testing the...
Humans innately learn to adapt their movements based on the materials they are handling and the tasks that they are trying to complete. When chopping specific fruits or vegetables, for instance, they might learn to cut around harder parts, such as avocado or peach...
In recent years, computer scientists have developed increasingly advanced algorithms for controlling the movements of robotic agents. These include model predictive control (MPC) techniques, which use a model of the agent's dynamics to optimize its future behavior...
Roboticists have developed many advanced systems over the past decade or so, yet most of these systems still require some degree of human supervision. Ideally, future robots should explore unknown environments autonomously and independently, continuously collecting...
As the use of machine learning (ML) algorithms continues to grow, computer scientists worldwide are constantly trying to identify and address ways in which these algorithms could be used maliciously or inappropriately. Due to their advanced data analysis capabilities,...
While robots have become increasingly advanced over the past few years, most of them are still unable to reliably navigate very crowded spaces, such as public areas or roads in urban environments. To be implemented on a large-scale and in the smart cities of the...
Domain adaptation approaches are techniques designed to improve the performance of computational models in specific target domains. These techniques are particularly valuable for tackling problems for which there is only a limited amount of relevant annotated data and...