In 2017, the now-legendary paper “Attention is All You Need” introduced the Transformer model - ushering in a new era of artificial intelligence. But beneath the surface of self-attention and generative power lies a deeply mathematical story. This talk traces the evolution of transformer models from their inception to the present, through the lens of mathematical innovation.

We’ll explore:

• How linear algebra, probability, and optimization form the backbone of self-attention mechanisms.
• How mathematical simplifications and approximations enabled the scalability of transformer models - from the original paper to architectures like GPT, BERT, and beyond.
• How vector-based algorithms and similarity metrics drive retrieval-augmented generation (RAG), powering modern search, recommendation, and hybrid AI systems.

With industrial applications in NLP, vision, protein folding, and robotics, transformer models are not just academic—they’re fundamentally reshaping industries. This session connects the theoretical rigor of mathematics with the transformative impact of AI technologies, demonstrating how equations continue to engineer the future.