The field of deep learning is moving towards developing more interpretable and explainable models. Researchers are focusing on creating models that can provide insights into their decision-making processes, making them more trustworthy and reliable. One of the key directions is the development of semantic autoencoders that can factorize inputs into multiple interpretable components. Another area of research is the use of restricted receptive fields to improve the interpretability of face verification models. Additionally, there is a growing interest in using programmable priors to sculpt latent spaces and achieve disentanglement. Noteworthy papers in this area include: FACE, which proposes a novel framework for faithful automatic concept extraction, and Sculpting Latent Spaces With MMD, which introduces a programmable prior framework for disentanglement. These papers demonstrate innovative approaches to advancing the field of deep learning and improving the interpretability of models.