Transformers are changing the AI landscape, and it all began with the groundbreaking paper "Attention is All You Need." Today, I explore the Introduction and Background sections of the paper, uncovering the limitations of traditional RNNs, the power of self-attention, and the importance of parallelization in modern AI models. Dive in to learn how Transformers revolutionized sequence modeling and transduction tasks! 1. Introduction Sentence 1: Recurrent neural networks, long short-term memory [13] and gated recurrent [7] neural networks in particular, have been firmly established as state-of-the-art approaches in sequence modeling and transduction problems such as language modeling and machine translation [35, 2, 5]. Explanation (like for an elementary school student): There are special types of AI models called Recurrent Neural Networks (RNNs) that are like people who can remember things from the past while working on something new. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs) are improved versions of RNNs. These models are the best performers (state-of-the-art) for tasks where you need to process sequences, like predicting the next word in a sentence (language modeling) or translating text from one language to another (machine translation). Key terms explained: Recurrent Neural Networks (RNNs): Models designed to handle sequential data (like sentences, time series). Analogy: Imagine reading a book where each sentence depends on the one before it. An RNN processes the book one sentence at a time, remembering earlier ones. Further Reading: RNNs on Wikipedia Long Short-Term Memory (LSTM): A type of RNN that solves the problem of forgetting important past information. Analogy: LSTMs are like a memory-keeper that…