The field of machine learning and artificial intelligence is rapidly advancing, with a focus on improving the interpretability and explainability of complex models. Recent research has explored the intersection of connectionist and symbolic approaches to artificial intelligence, aiming to derive interpretable symbolic models from feedforward neural networks. Additionally, there is a growing interest in developing mathematical frameworks for semantic communication, which can enhance transmission efficiency and reliability by leveraging machine learning and knowledge bases. Another area of research is the development of new algorithms and techniques for training and analyzing neural networks, such as the Forward-Forward algorithm and the disentanglement of polysemantic channels in convolutional neural networks. Noteworthy papers in this area include: auto-fpt, which introduces a lightweight Python and SymPy-based tool for automating free probability theory calculations. Deriving Equivalent Symbol-Based Decision Models from Feedforward Neural Networks, which proposes a systematic methodology for bridging neural and symbolic paradigms. Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks, which investigates the integration of graph neural networks with qualitative explainable graphs for scene understanding in automated driving.