Transformer models part I: The building block of the modern A.I.

"Transformers have become the universal currency of AI. It's now the standard and in many ways, it's eating the world of machine learning." — Dr. Fei-Fei Li, Co-Director of Stanford's Human-Centered AI Institute

Transformer models were first introduced in 2017 by Google in their revolutionary paper, "Attention Is All You Need." This publication was a significant achievement in the history of AI development. In less than five years, the transformer architecture proved to be a revolutionary breakthrough, quickly becoming the foundational building block for some of the most influential AI models ever created, including famous Large Language Models (LLMs) like Chat-GPT, Gemini, and many others.

RNN (Recurrent Neural Network): The predecessor of the Transformer architecture        

"We were stuck in this loop of sequential models, with the idea that the only way to process language was word by word. The problem was that you just couldn't scale it." — Dr. Richard Socher, former Chief Scientist at Salesforce

Before the 2017 transformer paper, the RNN architecture was a common solution, and indeed a building block for various NLP tasks, sequential data, and tasks like language-to-speech models. Since 1980s, The RNN architecture was a formidable and reliable architecture until 2017, when the transformer model arrived like a storm, establishing a new development paradigm that was not only more reliable, but also significantly faster and more efficient. The RNN operated on a sequential basis, processing data word by word It also featured a hidden state that acted as a sort of short-term memory, where the model would store information about the words it had processed The process was a continuous cycle: a word would be processed, the hidden state would be updated, and then the next word would be processed. This is the very nature of RNN's sequential processing, word by word. "This sequential bottleneck was a major downfall of the RNN architecture, directly paving the way for the transformer's sudden rise.

The succession of transformer architecture        

"The 'Attention Is All You Need' paper was a pivotal moment. It showed us that we could completely abandon sequential processing and rely on a mechanism that was both more powerful and highly parallelizable." — Dr. Andrew Ng, Founder of DeepLearning.AI

The RNN's sequential data processing was a primary reason for the transformer's rise. Because the architecture had to wait for one word to be processed before moving to the next, its data processing was slow and inefficient, especially for large datasets.  or the same reason, RNNs could not fully leverage the power of GPUs, which are designed for parallel computation. In contrast, transformer models were explicitly designed to utilize this parallel processing, immediately leaving the RNN architecture behind in terms of speed and efficiency. Another reason was the vanishing gradient problem of the RNN which enabled the transformer model to take the high road. For a huge data the architecture had to process huge amounts of words During backpropagation, the gradients (the values used to update the weights) would get progressively smaller as they traveled backward. This would eventually cause them to vanish as they reached the starting layers, preventing those layers from learning. However, to address this, Long Short-Term Memory (LSTM) Networks were introduced by Sepp Hochreiter and Jürgen Schmidhuber in 1997. They introduced a gating mechanism in this updated RNN architecture, to solve the problem of vanishing gradient. However, despite these improvements, the fundamental sequential bottleneck remained. This is where "Attention Is All You Need" made its radical departure.

"The transformer architecture is not just a leap forward for natural language processing; it's a testament to the fact that sometimes, the most revolutionary ideas are born from the simplest, yet most radical, departures from the norm." — Dr. Geoffrey Hinton, Turing Award Laureate

In Part II of this article, we will explore the intricate architecture of the transformer model, detailing its key components and its revolutionary impact on the modern era of AI.