The field of artificial intelligence and language models is moving towards more human-centric and interactive approaches. Researchers are exploring ways to make AI systems more transparent, explainable, and aligned with human goals and values. One notable direction is the development of systems that can infer and adapt to user intentions, providing more effective and personalized support. Another area of focus is the creation of frameworks and tools that enable more efficient and effective fine-tuning of large language models, allowing them to better capture subtle nuances and complexities of human language. Noteworthy papers in this area include: PromptFlow, which proposes a modular training framework for prompt engineering, and Just-In-Time Objectives, which demonstrates an architecture for automatically inducing user objectives and steering downstream AI systems. These advancements have the potential to significantly improve the usability and effectiveness of AI systems in a wide range of applications.