Recent advances in music AI are focusing on enhancing the quality and interpretability of generated music while addressing practical challenges in the industry. A notable trend is the development of semi-supervised methods for automatic drum transcription, which leverage unlabeled audio to create high-quality datasets, thus reducing reliance on scarce paired audio-MIDI data. Concurrently, the introduction of structured datasets like ConceptCaps is improving the interpretability of music models, enabling clearer semantic understanding and better alignment between audio and textual descriptions. Innovations in piano accompaniment generation are also emerging, with models that effectively preserve musical coherence while adhering to lead sheet constraints. Additionally, efforts to streamline foundation models for music information retrieval are making these technologies more accessible and cost-effective. As the field matures, these developments are likely to facilitate applications in music production, copyright enforcement, and personalized music experiences, addressing both creative and commercial needs.