The field of artificial intelligence is moving towards increased uncertainty awareness and robustness, particularly in safety-critical domains. Researchers are exploring novel methods to improve the calibration of predicted probabilities and robustness against out-of-distribution data. One notable direction is the integration of uncertainty-aware techniques with existing algorithms, such as genetic programming and neural networks. This approach has shown promising results in improving accuracy and calibration in various applications, including gesture recognition and financial trading strategy evolution. Additionally, advancements in evolutionary algorithms, such as the use of caching and mating preferences, are enhancing the efficiency and diversity of solutions. Noteworthy papers include: UAC, which proposes a novel two-step method for uncertainty-aware calibration of neural networks, and Evolving Financial Trading Strategies with Vectorial Genetic Programming, which introduces new variants of Vectorial Genetic Programming for evolving profitable trading strategies. Improved Compact Genetic Algorithms with Efficient Caching is also notable for its enhancement of the algorithm's time efficiency through caching.