#AAAI2024 workshops round-up 3: human-centric representation learning, and AI to accelerate science and engineering

Accepted papers spanned a diverse range of topics in cutting edge AI research and applications. This included computer vision, multimodal learning, fairness and ethics considerations, interpretability and explainability of models, learning effective representations, continual learning, generative modeling techniques, and novel applications in healthcare among others. We gave awards to three papers which share the common goal of aligning AI models, especially large language models, with human values, preferences and social intelligence. One proposes techniques for improved controllability of language model outputs through activation steering, allowing humans to guide model behavior. Another explores hybrid natural language and feedback signals to fine-tune models towards satisfying human feedback during training itself.