In this book, written in Portuguese, we discuss what ill-posed problems are and how the regularization method is used to solve them. In the form of questions and answers, we reflect on the origins and future of regularization, relating the similarities and differences of its meaning in different areas, including inverse problems, statistics, machine learning, and deep learning.

Artificial intelligence (AI) has been one of the main drivers for the development of cutting-edge technologies that are impacting society at different levels [1-3]. To harness the benefits of AI, while mitigating the risks, governments are developing National Strategies, seeking geopolitical protagonism and leveraging economic, social and cultural progress [4]. Launched in 2017, the Pan-Canadian Artificial Intelligence Strategy [5] was the first national strategy with the goal of guiding the priorities of AI policy at the country level [6]. Finland also developed its national AI strategy in 2017, closely followed by Japan, France, Germany, and the United Kingdom in 2018.

This study focuses on Intelligent Fault Diagnosis (IFD) in rotating machinery utilizing a single microphone and a data-driven methodology, effectively diagnosing 42 classes of fault types and severities. The research leverages sound data from the imbalanced MaFaulDa dataset, aiming to strike a balance between high performance and low resource consumption. The testing phase encompassed a variety of configurations, including sampling, quantization, signal normalization, silence removal, Wiener filtering, data scaling, windowing, augmentation, and classifier tuning using XGBoost. Through the analysis of time, frequency, mel-frequency, and statistical features, we achieved an impressive accuracy of 99.54% and an F-Beta score of 99.52% with just 6 boosting trees at an 8 kHz, 8-bit configuration. Moreover, when utilizing only MFCCs along with their first- and second-order deltas, we recorded an accuracy of 97.83% and an F-Beta score of 97.67%. Lastly, by implementing a greedy wrapper approach, we obtained a remarkable accuracy of 96.82% and an F-Beta score of 98.86% using 50 selected features, nearly all of which were first- and second-order deltas of the MFCCs.

Several public security systems depend directly on human action in numerous stages of its operation. The monitoring of public areas, for instance, is usually done with the use of cameras spread over the busiest places in large urban centers. In general, these systems depend on an operator to pay attention to the images so that the agencies responsible for security can be activated when events such as thefts, vandalism, and traffic accidents are observed. Considering the amount of information to which the operator is exposed, there is a high probability that surveillance failures will occur, even if the patrol center has a large team [1]. Although the operators are attentive at all times, this type of monitoring has some disadvantages: the images are limited to the direction in which the camera points and have low visibility at dusk and in cases of rain or bright light. Besides, events such as gunshots, alarms, distress calls, among others, are much more noticeable in the auditory field than in the visual [2, 3]. In this sense, the monitoring of risk areas could be done through the use of audio processing techniques, reducing the need for human participation in the surveillance process, and making public security systems more efficient [4]. To support this argument, it is worth recalling two very favorable characteristics concerning these signals: initially, the sound consumes less bandwidth in the transmission of information, reducing the need for high transmission rates, as in the case of high definition images; in addition, sound processing techniques require, in general, less computational power than techniques for video processing and analysis, which would enable the implementation of simpler and therefore less costly embedded systems [3, 5].