The Basics: What Is Generative AI?

Generative AI services in simple terms, refers to a category of artificial intelligence models that are designed to create new content. Unlike traditional AI models that perform tasks like classifying images or making predictions, generative AI produces new, often unique outputs based on what it’s learned. These outputs can range from written articles and music compositions to images, videos, and even 3D objects.

To get a sense of just how impressive generative AI is, think about this: with the right prompts, AI can write stories that sound as though a human penned them, or generate artwork that might hang in a modern art gallery. It’s exciting stuff, but it’s built on some rather complex technology.

Key Techniques That Power Generative AI

At the heart of generative AI are several key techniques that allow models to learn and generate content:

1. Neural Networks: These are the building blocks of most AI systems, inspired by the way the human brain works. Neural networks consist of layers of interconnected nodes, or “neurons,” that process data and learn to recognise patterns. Deep learning, a subset of machine learning, uses neural networks with multiple layers to handle complex tasks, such as understanding language or identifying objects in images.

2. Generative Adversarial Networks (GANs): GANs are among the most innovative techniques in generative AI. Invented by Ian Goodfellow in 2014, GANs consist of two competing networks: the generator and the discriminator. The generator creates content (like an image or piece of music), while the discriminator evaluates it to determine if it looks real or fake. The two networks engage in a back-and-forth “game,” with the generator getting better at producing convincing content and the discriminator getting better at spotting fakes. This cat-and-mouse dynamic is what makes GANs so powerful, and they’ve been used to generate everything from photorealistic images to lifelike deepfakes.

3. Transformer Models: Transformers have been game-changers in the world of natural language processing (NLP). You might have heard of models like GPT (Generative Pre-trained Transformer) and BERT, which are built on this architecture. Transformers excel at processing and understanding text because they can consider the context of each word in a sentence, rather than looking at words in isolation. This makes them incredibly effective at generating coherent, human-like text. GPT-3 and GPT-4, for example, can write essays, answer questions, and even hold a conversation that feels remarkably real.

4. Variational Autoencoders (VAEs): VAEs are another type of generative model used to create data like images. They work by compressing the input data into a simpler form (an encoding) and then trying to reconstruct the original data from this encoding. The result is a model that can generate new data similar to what it was trained on, but with some built-in variation. VAEs are often used for tasks like image synthesis or creating realistic variations of existing images.