Demystifying the Transformer Model and GPT: Decoding AI

Welcome to another edition of "Decoding AI: A Deep Dive," where we tackle fascinating technical aspects in artificial intelligence. Today, we are unraveling the secrets of the Generative Pretrained Transformer, popularly known as GPT, one of the most powerful language models residing in the AI realm.

Firstly, we need to understand the architecture that GPT is built upon - the Transformer model. Introduced by Google in 2017, the Transformer model changed the way Intelligent Machines understand human languages. This piece of tech wizardry revolutionized a Neural Network's capacity to understand context, unlike earlier models that struggled to grasp the whole meaning of sentences. The Transformer model achieved this by doing away with recurrence and convolutions, instead opting for Attention Mechanisms that weigh different words in the input for relevance and meaning.

Dissecting GPT, we find that it essentially extends the Transformer model's application from translation tasks to tasks that require the generation of original, context-aware content. The potency of each GPT model is captured by 'training parameters'. This can be thought of as instruction points given to the AI, allowing it to learn patterns and extract meaning.

The debut version of this series - GPT-1, demonstrated a promising 110 million parameters. This was succeeded by GPT-2, which boasted a monumental increase to 1.5 billion parameters. The most recent and ground-breaking offering, GPT-3, pushed the boundaries further, carrying an enormous 175 billion parameters.

GPT models can be employed in a myriad of applications beyond just convenient AI helpers like Siri and Alexa. They're used in automatic report generation in various industries, where they draft comprehensive and coherent documents. They're employed in healthcare to review lengthy medical records and deliver relevant summaries for busy doctors. On a more day-to-day level, GPT-3 can even write emails for you, making your communication more efficient and error-free.

However, there are some limitations associated with these models. Besides the vast compute and data resources required to train such large models, they can occasionally produce text that may be biased, largely due to the selection of training data. The task remains for researchers to continually refine these models, ensuring the generated content is accurate and unbiased.

In conclusion, the Transformer model and its advanced versions - the GPT models, serve as a prime example of what AI is capable of today. Providing a deeper understanding of language generation, they set new benchmarks in the field of Artificial Intelligence. We look forward to welcoming you on the next journey of our series as we continue to 'decode' more complex AI topics.