Transformer Architecture
The revolutionary neural network architecture from 2017 ("Attention Is All You Need") that replaced RNNs and forms the foundation of all modern LLMs like GPT, Claude, Gemini.
Transformers are the architecture behind the AI revolution in marketing: Every LLM, every chatbot, every content AI uses transformers.
Explanation
Transformers use stacked attention layers instead of sequential processing. They can be trained in parallel (GPU-friendly) and process arbitrarily long contexts through attention weights. Variants: Encoder-only (BERT), Decoder-only (GPT), Encoder-Decoder (T5).
Marketing Relevance
Transformers are the architecture behind the AI revolution in marketing: Every LLM, every chatbot, every content AI uses transformers. Understanding the architecture helps understand strengths and limitations.
Example
GPT-4 is a decoder-only transformer with ~1.7 trillion parameters, trained on the internet. BERT is an encoder-only transformer, optimal for classification. T5 combines both for translation and summarization.
Common Pitfalls
High computational cost for long contexts. No real "understanding", only statistical patterns. Prone to hallucinations. Training costs in millions.
Origin & History
Transformer Architecture is an established concept in the field of Artificial Intelligence. The concept has evolved alongside the growing importance of AI and data-driven methods.