Reactive machines are artificial intelligence models that continuously interact with their environment, without maintaining an internal representation of it. These models rely on rules and heuristics to make real-time decisions and adjust to changing environmental conditions. Reactive machines are particularly useful in real-time applications, such as robot control systems and autonomous navigation systems, as they can make fast and accurate decisions without the need to process large amounts of data or maintain long-term memory. However, reactive machines are limited in their ability to learn from experience and adapt to new and unfamiliar situations. They are therefore used in combination with other machine learning approaches, such as reinforcement learning and supervised learning, to improve their adaptive and decision-making capabilities.
Credit scoring is a system used to rate credits and thus try to automate the decision making process at the time of purchasing a loan, and to [...]
Read More »The content of this article synthesizes part of the chapter "Concept and brief history of Artificial Intelligence" of the thesis Generation of Artificial [...]
Read More »Artificial intelligence (AI) solutions are valuable in reducing product returns. Through data analysis and decision [...]
Read More »A few days ago we were able to attend a pioneering event in the world of Retail, the Retail Future 2022 fair. In its fifth edition, and under the slogan "Challenge [...]
Read More »