In this episode, we explore the attention mechanism—why it was invented, how it works, and why it became the defining breakthrough behind modern AI systems. At its core, attention allows models to instantly focus on the most relevant parts of a sequence, solving long-standing problems in memory, context, and scale.

We examine why earlier models like RNNs and LSTMs struggled with long-range dependencies and slow training, and how attention removed recurrence entirely, enabling global context and massive parallelism. This shift made large-scale training practical and laid the foundation for the Transformer architecture.

Key topics include:

• Why sequential memory models hit a hard limit

• How attention provides global context in one step

• Queries, keys, and values as a relevance mechanism

• Multi-head attention and richer representations

• The quadratic cost of attention and sparse alternatives

• Why attention reshaped NLP, vision, and multimodal AI

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

This trailer introduces Season 5 of the Adapticx Podcast, where we begin the story of large language models. After tracing AI’s evolution from rules to neural networks and attention, this season focuses on the breakthrough that changed everything: the Transformer.

We preview how “Attention Is All You Need” reshaped language modeling, enabled large-scale training, and led to early models like BERT, GPT-1, GPT-2, and T5. We also introduce scaling laws—the insight that performance grows predictably with data, compute, and model size.

This episode sets the direction for the season and explains why the Transformer marks the start of the modern LLM 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

In this episode, we trace how neural networks learned to model sequences—starting with recurrent neural networks, progressing through LSTMs and GRUs, and culminating in the attention mechanism and transformers. This journey explains how NLP moved from fragile, short-term memory systems to architectures capable of modeling global context at scale, forming the backbone of modern large language models.

This episode covers:

• Why feed-forward networks fail on ordered data like text and time series

• The origin of recurrence and sequence memory in RNNs • Backpropagation Through Time and the limits of unrolled sequences

• Vanishing gradients and why basic RNNs forget long-range dependencies

• How LSTMs and GRUs use gates to preserve and control memory

• Encoder–decoder models and early neural machine translation

• Why recurrence fundamentally limits parallelism on GPUs

• The emergence of attention as a solution to context bottlenecks

• Queries, keys, and values as a mechanism for global relevance

• How transformers remove recurrence to enable full parallelism

• Positional encoding and multi-head attention

• Real-world impact on translation, time series, and reinforcement learning

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

All referenced materials and extended resources are available at:

https://adapticx.co.uk