Rise of GPT and Large Language Models (LLMs)

Transformer models have had a profound impact on language translation, completely replacing long short-term memory (LSTM) models for this task. This success led to the idea of applying transformers to general language processing. Rather than focusing solely on sequence-to-sequence tasks like translation, transformers can be trained to predict the next word based on the preceding words in a text.

For example, consider the sentence: “He was walking on the ...”. Given the context, it's highly likely that the next word would be "road," a guess that can be made intuitively. The goal was to replicate this type of intuition in AI.

This challenge was addressed by modifying the transformer architecture. One key change was removing the encoder, which is unnecessary for predicting the next word. The model is trained on vast amounts of data from the web and other sources to predict the next word, and then fine-tuned for specific tasks like answering questions or summarizing text through supervised learning. This approach led to the development of the Generative Pretrained Transformer (GPT), which forms the foundation of most modern large language models, including ChatGPT.

The GPT model has significantly advanced the field of language processing and AI in recent years, igniting a competitive race across industries to capitalize on its potential by investing tens of billions of dollars. While it's still too early to determine which sectors will benefit the most, industries heavily reliant on text—such as call centers and law firms—seem to have the most immediate advantages. In contrast, fields that require complex mathematical modeling, like most engineering disciplines, may find fewer applications. However, given the rapid pace of development, these trends could change quickly.