Daniel Leite, a control and automation engineer, is an avid runner who frequently participates in street running events when he is not engaged in his scientific research. He completed his undergraduate and master’s degrees in electrical engineering at the Pontifical Catholic University of Minas Gerais. He further pursued his doctorate in electrical engineering at the State University of Campinas. Daniel also spent time at the University of Ljubljana, Slovenia, for his post-doctoral studies, and the Federal University of Minas Gerais. His research primarily focuses on the exploration of novel artificial intelligence algorithms. He enjoys playing chess and traveling to unwind outside his professional pursuits. He has had the opportunity to visit six continents so far.
The process of deriving mathematical models for increasingly intricate systems has become a challenging task. These models serve multiple purposes, such as predicting future behavior, identifying patterns, and controlling robots and virtual agents. The key question is whether “smart” algorithms can learn the laws governing the interaction between system variables in real-time. We propose the development of algorithms that autonomously construct models for general purposes. These algorithms are designed to process various data streams, including data from images, movements, sounds, electrodes, social networks, and human language. Unlike other artificial intelligence algorithms, the proposed algorithms capture granules of information to facilitate approximate reasoning. These models enhance their performance autonomously, drawing on past experiences and interactions with the environment and agents. We have laid the groundwork for instilling more intelligence in software and robots. Our goal is to push the boundaries of machine intelligence towards more realistic scenarios, assuming an evolving perception of the world and granules of information in abstract spaces.