Introduction to Generative Artificial Intelligence

Artificial intelligence (AI) has been making significant strides in recent years, and one of the areas that has seen considerable growth is generative AI. Generative AI is a subfield of artificial intelligence (AI) and deep learning (DL) that focuses on generating new content, such as images, text, music, and video, by using algorithms and models that have been trained on existing data using ML techniques.

The relationship between generative AI and related fields

To better understand the relationship between artificial intelligence (AI), machine learning (ML), deep learning (DL), and generative AI, consider AI as the foundation, while ML, DL, and generative AI represent increasingly specialized and focused areas of study and application:

• Artificial intelligence (AI) represents the broad field of creating systems that can perform tasks, demonstrate human intelligence and ability, and interact with the ecosystem.

• Machine learning (ML) is a branch that focuses on creating algorithms and models that enable those systems to learn and improve themselves with time and training. ML models learn from existing data and automatically update their parameters as they grow.

• Deep learning (DL) is a sub-branch of ML, in the sense that it encompasses deep ML models. Those deep models are called neural networks and are particularly suitable in domains such as computer vision (CV) or natural language processing (NLP). When we talk about ML and DL models, we typically refer to discriminative models, whose aim is that of making predictions or inferencing patterns on top of data.

• Generative AI is a further sub-branch of DL, which doesn’t use deep neural networks to cluster, classify, or make predictions on existing data: it uses those powerful neural network models to generate brand new content, from images to natural language, from music to video.

The following figure shows how these areas of research are related to each other:

Generative AI models can be trained on vast amounts of data, and then they can generate new examples from scratch using patterns in that data. This generative process is different from discriminative models, which are trained to predict the class or label of a given example.