Representation Alignment

Representation alignment focuses on aligning the internal representations of different systems, such as humans and AI models, to improve understanding, trust, and collaboration. Current research explores this through various methods, including aligning feature maps in deep neural networks, optimizing large language models based on human feedback, and developing metrics to quantify representational similarity across modalities (e.g., EEG signals and language). This work is crucial for enhancing AI trustworthiness, improving the efficiency of AI training, and enabling more effective human-AI interaction across diverse applications, from autonomous driving to personalized medicine.