The Architecture of Gen AI: Transformers & Beyond

To understand Gen AI, one must understand the Transformer architecture. Introduced in 2017, the Transformer replaced Recurrent Neural Networks (RNNs) as the standard for processing sequential data. Unlike RNNs, which process data step-by-step, Transformers use Parallel Processing, allowing them to train on much larger datasets (like the entire internet).

The Power of Self-Attention

The core innovation of the Transformer is the "Self-Attention" mechanism. This allows the model to look at every other word in a sentence to determine which words are most relevant to the current one. For example, in the sentence "The bank was closed because it was a holiday," self-attention helps the model realize that "it" refers to the "bank," not the "holiday."

Scaling Laws and Emergent Abilities

As we scale these models—adding more parameters and more data—we see "emergent abilities." These are capabilities that the model didn't show at smaller scales, such as complex reasoning, multi-step planning, and coding proficiency. This phenomenon has driven the race for larger and larger models, leading to GPT-4, Gemini 1.5, and beyond.

Beyond the Transformer

While Transformers dominate today, researchers are already looking at what's next. State Space Models (SSMs) like Mamba offer the potential for even faster processing of extremely long sequences, which could eventually replace Transformers in specific high-bandwidth applications.