Firstly, the term ‘generative’ implies the ability to generate or create, which is made possible by an invention called the Attention Transformer (Vaswani et al., 2017). This was utterly ground-breaking and deep learning was completely revolutionised.

For many years since then, Large Language Models (LLMs) were consuming, “a total data size exceeding 774.5 TB for pre-training corpora”, (Liu et al., 2024), and were handling the information in a completely new way to classical computers. OpenAI, ironically not, introduced ChatGPT (Generative Pre-Trained Transformer) in November 2022, and the consensus was amazement at its ability. Their LLM generated streams of text within seconds that resembled professional essays of expertise (See Fig 2.1).

Other modalities were emerging to make bespoke images from words, such as the diffusion models: MidJourney or DALL-E; also GANs (Generative Adversarial Networks) work best at duplicating images for higher quality. VAEs (Variational Autoencoders) can work with text, image audio and video using an encoder-decoder infrastructure. Google’s BERT (Bidirectional Encoder Representations from Transformers) excels at finding context in text (Abdullahi, 2024).

Currently, affordable software subscriptions are making this technology accessible to everyone. For example, Suno can take a prompt and auto-generate a published song within one minute, see Fig 2.2 (Suno, 2024).

Moving forward, GenAI software like SORA and VEO will confidently stride into the Box-office with hyper-realistic movies and soundtracks. Disney has been working at ‘replacing humans’ for a decade (Dams, 2022).

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