History of NLP and LLM Evolution - Rule Based Systems to ChatGPT

In this video, we explore the complete history and evolution of Natural Language Processing and Large Language Models from early rule-based systems to modern transformer-based AI models like GPT and ChatGPT. Here is the GitHub repo link: https://github.com/switch2ai You can download all the code, scripts, and documents from the above GitHub repository. We begin by understanding the earliest stage of NLP development. From 1950 to 1980, most NLP systems were rule-based. Linguists and researchers manually wrote grammar rules and language patterns that computers could follow. These systems relied heavily on dictionaries, handcrafted linguistic rules, and symbolic processing. While these approaches worked for simple tasks, they struggled with ambiguity and scalability. Between 1980 and 2000, the field shifted toward statistical approaches and machine learning. Instead of manually defining language rules, models began learning patterns from data using probability-based methods such as Hidden Markov Models and n-gram language models. In 1987, Recurrent Neural Networks (RNNs) were introduced. RNNs were designed to process sequential data where the order of words matters. Unlike traditional neural networks, RNNs pass information through time steps, allowing them to capture sequential dependencies. However, RNNs struggled with long-term dependencies due to the vanishing gradient problem. To solve this issue, Long Short-Term Memory networks (LSTM) were introduced in 1997. LSTMs improved sequence modeling by maintaining long-term memory through cell states and gating mechanisms. The 2010s marked a major breakthrough period in deep learning due to two key factors: the availability of large datasets and the rise of GPU computing. GPUs allowed deep neural networks to train much faster on large-scale data. In 2013, AlexNet demonstrated the power of deep learning in computer vision by achieving groundbreaking results on the ImageNet dataset. Around the same time, Word2Vec w