In this episode, we break down the Transformer architecture—how it works, why it replaced RNNs and LSTMs, and why it underpins modern AI systems. We explain how attention enabled models to capture global context in parallel, removing the memory and speed limits of earlier sequence models.

We cover the core components of the Transformer, including self-attention, queries, keys, and values, multi-head attention, positional encoding, and the encoder–decoder design. We also show how this architecture evolved into encoder-only models like BERT, decoder-only models like GPT, and why Transformers became a general-purpose engine across language, vision, audio, and time-series data.

This episode covers:

• Why RNNs and LSTMs hit hard limits in speed and memory

• How attention enables global context and parallel computation

• Encoder–decoder roles and cross-attention• Queries, keys, and values explained intuitively

• Multi-head attention and positional encoding

• Residual connections and layer normalization

• Encoder-only (BERT), decoder-only (GPT), and seq-to-seq models

• Vision Transformers, audio models, and long-range forecasting

• Why the Transformer defines the modern AI era

This episode is part of the Adapticx AI Podcast. Listen via the link provided or search “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.

Sources and Further Reading

Additional references and extended material are available at:

https://adapticx.co.uk