Artificial Intelligence (AI)
In short
The word AI has been used more and more recently and the majority of people don’t actually understand what it means. As of today, the most common meaning behind it refers to models like ChatGPT, Gemini, Claude and others.
You ask questions or say words, and they respond back to you accordingly — sometimes in a wizard way giving you the exact answer you were looking for, other times making you question why you’re still using them in the first place.
The field of AI is quite broad. A subfield of AI could be considered Machine Learning, the idea being that you use data to learn behaviors and patterns. A subfield of that is Deep Learning, which came up as a method for models to learn more complex patterns at the cost of much more data needed. Then you have Computer Vision for everything visual (images, videos), NLP for text, Data Engineering for managing and cleaning data, Data Analysis for drawing conclusions, Data Science as a kind of full stack of the AI field, and Big Data when simple tools don’t cut it anymore.
The AI field is confusing partly because there’s no clearly defined way of categorizing things. And then AI Engineering came along, which people use to refer to the work involving LLMs basically.
Related
- Model - the core building block
- Machine Learning - subfield of AI
- Deep Learning - subfield of ML
- LLMs - what most people mean by “AI” today