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.
Normally the acronym NPLs (Non Performing Loans) is used in the financial sector and is a reality in Spanish banks as well as in banks [...].
Read More »The financial sector is constantly implementing new technologies to modernize and digitize its functions. One of the reasons for this is the processing of [...]
Read More »Artificial intelligence (AI), Machine Learning (ML) and data analytics are rapidly changing and having a major impact on our business.
Read More »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 »
