Building Intuition: Generative AI and Predictive (“Traditional”) AI
TL;DR: generative AI produces new content in some form, predictive AI produces predictions about its input
2023 was undoubtedly THE year of generative AI, but what is generative AI? How is it different from what we more commonly know as AI? And since when did neural networks get termed “traditional” AI despite these techniques only being viable within the last 10 years? Let’s take a look at how all this happened.
What is Generative AI?
Generative AI consists of models focused on generating new output. Examples of techniques that are considered generative AI include generative adversarial networks (GANs), variational autoencoders (VAE), and transformer models.
Generative models are trained on data in an “unsupervised” manner. They learn the patterns and structures in their training data. When we interact with these models, they give us predictions based on the data they were trained on and the input we give.
Currently, the most common use cases for generative AI are chatbots through retrieval augmented generation (RAG) and image generation via diffusion models. We’ll touch more on RAG, which may use both traditional models and generative…
