Every architecture, whether a transformer, MoE or U-Net, has its own strengths and suits different types of tasks. Transformers remain the primary choice for processing sequential data such as text and program code. MoE optimizes computing resources, delivering high performance on specific tasks.
Diffusion models based on U-Net are paving the way to new achievements in image generation, creating content that used to be available only to humans. Together these architectures represent the future of artificial intelligence and open up enormous opportunities for further development.