Key AI Terms
Artificial Intelligence includes a variety of methods and technologies that enable machines to carry out tasks that typically require human intelligence. Understanding a few key terms helps to demystify how AI works and what makes certain types of AI particularly powerful today.
Machine learning is one of the foundational techniques within artificial intelligence. Rather than being explicitly programmed with rules, a machine learning system learns patterns and behaviours from data. It builds models that can make predictions or decisions based on that data. The more high-quality data the system receives, the more accurately it can learn and adapt. Common uses include recommendation systems, fraud detection, and image recognition. Machine learning can be supervised, where the system is trained on labelled examples, or unsupervised, where it identifies hidden patterns without predefined outputs. There are also reinforcement learning methods, where the system learns through trial and error, receiving feedback in the form of rewards or penalties.
Generative AI is a branch of machine learning that focuses on creating new content. Instead of just analysing or categorising existing data, generative AI can produce entirely new outputs that resemble its training data. This could include writing text, generating images, composing music, creating software code, or even designing product mockups. The goal is to generate outputs that are realistic, coherent, and useful. Generative AI models are trained on vast datasets and learn the structures and patterns that define the data. Once trained, they can generate new data points that fit within those structures. These models do not create content through understanding but by predicting what comes next based on what they have seen.
Large language models, often abbreviated as LLMs, are a type of generative AI specifically trained on textual data. They analyse enormous volumes of text from books, articles, websites, and other sources to learn the structure and meaning of human language. LLMs operate using a method called next-token prediction. This means they generate language one word or token at a time by calculating the probability of what is most likely to follow, based on the prompt and everything they have previously generated. While LLMs do not understand language in a human sense, they can produce text that feels natural, fluent, and contextually appropriate. Their ability to summarise information, answer questions, and generate content makes them valuable in a wide range of product and business contexts. However, they are limited by their training data and may produce inaccurate or biased content if not used carefully.
Diffusion models represent another major innovation in generative AI, particularly for generating images, audio, and video. These models work by learning to reverse the process of noise. During training, they take clean data, such as an image, and gradually add random noise until it becomes completely unrecognisable. The model then learns to reverse this process by gradually removing the noise to recreate the original content. Once trained, a diffusion model can start from random noise and generate entirely new content by progressively denoising it. This process results in highly realistic and creative outputs. It is particularly effective for visual media, allowing for the generation of detailed and imaginative images that were never directly seen during training. Some of the most popular image generation tools today rely on diffusion models.
Together, these terms represent important building blocks of modern AI. Machine learning provides the core mechanism for learning from data. Generative AI expands that ability to produce new content. Large language models apply those principles specifically to language, while diffusion models do the same for visual and auditory data. Each of these technologies has different strengths, applications, and limitations. Understanding how they work enables more informed decisions about when and how to use them.
The field of AI continues to develop rapidly, with new techniques building on these foundations. By grasping these core concepts, it becomes easier to navigate the evolving AI landscape and apply the right tools to the right problems. Being precise with terminology also improves communication with technical teams and helps clarify expectations about what AI can and cannot do. These terms are central to understanding both the current capabilities and the future potential of AI in product development and beyond.