The field of artificial intelligence is undergoing a significant transformation, driven by the convergence of symbolic and subsymbolic approaches. Recent developments have focused on enhancing symbolic machine learning with subsymbolic representations, leading to improved performance in various tasks. This trend is evident in multiple research areas, including disease management, physics-informed machine learning, inverse problems, sequence alignment, and machine learning interpretability. Notably, the introduction of novel frameworks such as Contextual Analog Logic with Multimodality (CALM) and co-creative learning via Metropolis-Hastings interaction has demonstrated potential in integrating multiple sources of information and enabling flexible decision-making. Furthermore, advancements in autoencoders, explainable AI, and human-AI collaboration are contributing to the development of more transparent, trustworthy, and reliable AI systems. As research continues to evolve, it is essential to address concerns regarding automation bias, deskilling, and research misconduct, and to prioritize transparency, explainability, and environmental sustainability in AI systems. Overall, the convergence of symbolic and subsymbolic approaches is poised to revolutionize various fields, from healthcare and materials science to education and social science research, and will require a multidisciplinary effort to fully realize its potential.