2015, in 7 pieces

The launch of the organization that would become the starting point for the GPT series and the shift to commercialization

Made training of very deep networks possible through residual connections. A basic component of most later architectures, including the Transformer

Opened up an internal system, kicking off the framework competition and making deep learning tools an industry standard

Proposed for medical imaging, but later became the basic backbone of generative work such as diffusion models

Formalized distillation, which transfers a large model's knowledge to a small model. The root of today's small-model lineage

The point at which deep reinforcement learning passed peer review and was recognized as an official achievement

Stabilized and sped up the training of deep networks and was adopted as standard in most architectures