What is generative AI?

  Generative AI (GenAI) is an Artificial Intelligence (AI) technology that automatically generates content in response to prompts written in natural language conversational interfaces.

Rather than simply curating existing webpages, by drawing on existing content, GenAI actually produces new content. 

• The content can appear in formats that comprise all symbolic representations of human thinking: texts written in natural language, images (including photographs to digital paintings and cartoons), videos, music and software code. 

• GenAI is trained using data collected from webpages, social media conversations and other online media. It generates its content by statistically analysing the distributions of words, pixels 

or other elements in the data that it has ingested and identifying and repeating common patterns (for example, which words typically follow which other words).

• While GenAI can produce new content, it cannot generate new ideas or solutions to real-world challenges, as it does not understand real-world objects or social relations that underpin language.

How generative AI works?

The specific technologies behind GenAI are part of the family of AI technologies called Machine Learning which uses algorithms to enable it to continuously and automatically improve its performance from data. 

The type of Machine Learning which has led to many of the advances in AI that we have seen in recent years, such as the use of AI for facial recognition, is known as Artificial Neural Networks (ANNs), which are inspired by how the human brain works and its synaptic connections between neurons. There are many types of ANNs.

Text generative AI uses a type of ANN known as a General-purpose Transformer, and a type of General-purpose Transformer called a Large Language Model. This is why AI Text GenAI systems are often referred to as Large Language Models (LLMs). The type of LLM used by text GenAI is known as a Generative Pre-trained Transformer, or GPT (hence the ‘GPT’ in ‘ChatGPT’).

Image GenAI and music GenAI typically use a different type of ANN known as Generative Adversarial Networks (GANs) which can also be combined with Variational Autoencoders. GANs have two parts (two ‘adversaries’) —  the ‘generator’ and the ‘discriminator’. In the case of image GANs, the generator creates a random image in response to a prompt, and the discriminator tries to distinguish between this generated image and real images. The generator then uses the result of the discriminator to adjust its parameters, in order to create another image. The process is repeated, possibly thousands of times, with the generator making more and more realistic images that the discriminator is less and less able to distinguish from real images. For example, a successful GAN trained on a dataset of thousands of landscape photographs might generate new but unreal images of landscapes that are almost indistinguishable from real photographs. 

Meanwhile, a GAN trained on a dataset of popular music (or even music by a single artist) might generate new pieces of music that follow the structure and complexity of the original music.