Deep learning architectures have been pivotal in AI advancements.

CNNs, RNNs, GANs, Transformers, and Encoder-Decoder models are notable examples.

Transformers, introduced in 2017, are distinguished by their use of attention-weighting and FFNs.

The AI field has seen exponential growth with the advent of transformers.

Transformers are expected to drive future AI developments.