Foundation Transformer

Foundation Transformers aim to create a single, universally applicable architecture for various machine learning tasks across different modalities (text, images, audio). Current research focuses on improving training stability and efficiency, exploring techniques like binary neural networks and cross-modal knowledge transfer using autoencoders, and investigating the compositional generalization capabilities of these models. This pursuit of a unified architecture promises to simplify model development, reduce computational costs, and potentially unlock more robust and generalizable AI systems.