Transformer Era

The "Transformer Era" in machine learning signifies the widespread adoption of transformer-based architectures, initially designed for natural language processing, across diverse domains like computer vision and audio processing. Current research focuses on optimizing transformer training efficiency (e.g., through techniques like early-bird tickets), addressing limitations in handling long sequences (exploring hybrid models combining transformers with recurrent networks or novel state-space models), and comparing the performance and cost-effectiveness of large language models against earlier, smaller transformer models for various tasks. This era's impact stems from enabling significant advancements in numerous applications, ranging from improved text classification and image captioning to more efficient 3D human pose estimation and enhanced speech emotion recognition, while also raising important questions about resource consumption and model robustness.