12 AI BASICS Large Language Models Transformers

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Link to my YT channel SINSAVK AI FOR BEGINNERS https://www.youtube.com/channel/UCWYy-VfH3A92kS4HNWZXsMA Transformers are the core technology behind most modern large language models. They are the neural network architecture that made it possible for AI systems to understand and generate human language at a level that was not achievable with earlier approaches. Many well-known AI systems that can write text, answer questions, translate languages, or summarize documents are built using transformer-based models. To understand why transformers are so important, it helps to briefly look at how earlier language models worked. Before transformers became popular, many natural language processing systems relied on architectures such as recurrent neural networks, often called RNNs, and their improved versions known as LSTMs. These models processed text one word at a time in sequence. While they were useful, they had difficulty capturing relationships between words that were far apart in a sentence or paragraph. As sentences became longer, the models often struggled to remember earlier context. The transformer architecture was introduced in 2017 and quickly changed the direction of AI research. The key innovation of transformers is something called the attention mechanism. Attention allows the model to examine all parts of a sentence at the same time and determine which words are most relevant to each other. For example, consider the sentence “The scientist presented the discovery because it was important.” The word “it” refers to “the discovery,” not “the scientist.” A transformer model can use attention to identify that relationship by examining connections between words across the entire sentence. This ability to focus on relevant parts of the text helps the model understand context much more effectively. In a transformer model, each word or token in a sentence is first converted into a numerical representation called an embedding. These embeddings capture semantic relatio

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