ChatGPT, possibly the most famous LLM, has immediately skyrocketed in popularity due to the fact that natural language is such a, well, natural interface that has made the recent breakthroughs in Artificial Intelligence accessible to everyone. Nevertheless, how LLMs work is still less commonly understood, unless you are a Data Scientist or in another AI-related role. In this article, I will try to change that.
all, the powerful LLMs we have today are a culmination of decades of research in AI.
The field of AI is often visualized in layers:
- Artificial Intelligence (AI) is very a broad term, but generally it deals with intelligent machines.
- Machine Learning (ML) is a subfield of AI that specifically focuses on pattern recognition in data. As you can imagine, once you recoginze a pattern, you can apply that pattern to new observations. That’s the essence of the idea, but we will get to that in just a bit.
- Deep Learning is the field within ML that is focused on unstructured data, which includes text and images. It relies on artificial neural networks, a method that is (loosely) inspired by the human brain.
- Large Language Models (LLMs) deal with text specifically, and that will be the focus of this article.
As we go, we’ll pick up the relevant pieces from each of those layers. We’ll skip only the most outer one, Artificial Intelligence (as it is too general anyway) and head straight into what is Machine Learning.