Generative AI, sometimes written shorthand as ‘gen AI’, refers to a subset of artificial intelligence technologies that are capable of generating new content that is similar to, but not identical to, the data they were trained on. This can include text, images, music, and even code. The ‘generative’ aspect implies that the AI is not simply analyzing and interpreting data, but actively creating new data that did not previously exist.

Common techniques in generative AI:

1/ Generative adversarial networks (GANs): These consist of two neural networks, the generator and the discriminator, which are trained simultaneously. The generator creates new data instances while the discriminator evaluates them against real data. The two networks essentially play a game, with the generator trying to produce increasingly convincing data, and the discriminator trying to get better at distinguishing real data from the fakes. Over time, the generator learns to produce very realistic data.

2/ Variational autoencoders (VAEs): These are a type of autoencoder that generates data by learning the distribution of the input data. They work by encoding data into a latent space representation and then decoding from this space to generate new data instances that have similar characteristics to the input data.

3/ Transformers: Used mainly in natural language processing (NLP), transformers like generative pre-trained transformers (GPTs) can generate human-like text by predicting the next word in a sequence based on the context of previous words.

Generative AI has a wide range of applications:

• In the visual arts, it can generate new images or modify existing ones, such as creating new fashion designs or realistic-looking faces of non-existent people.

• In the music industry, it can compose new pieces of music in various styles.

• In natural language processing, it can generate coherent and contextually relevant text, which can be used for tasks such as writing assistance, creating chatbot responses, and more.

• In the gaming industry, it can be used to create new level designs, textures and other content.

• In research and development, it can be used to discover new drugs by generating and evaluating potential molecular structures.

Generative AI represents a significant advancement in artificial intelligence, enabling machines to create original content that can closely resemble human creations. Its applications span various industries, driving innovation and transforming how we generate and interact with digital content.