New to Ai? Here are 10 terms that will make you an expert

Editorial Team Artificialintelligence-tech

Introduction

Since you’re a beginner, its best to start with generative AI. It’s the fastest-growing area of AI and is already used by students, professionals , and everyday people.

Generative AI creates new content—such as text, images, audio, video, and code—by learning from existing data. Knowing the key terms makes it easier to understand and use tools like ChatGPT, Claude, Jasper, Gemini.

This beginner-friendly glossary explains 10 essential concepts, starting with:

1. Artificial intelligence (AI)

Artificial intelligence is the broad field of computer science that aims to build systems capable of tasks that normally require human intelligence, such as reasoning, pattern recognition, planning and language understanding. AI systems use algorithms and data to solve complex problems, adapt to new information and automate decision‑making in areas like healthcare, finance, manufacturing and education.​

2. Machine learning (ML)

Machine learning is a key branch of AI where models learn from examples instead of following only hand‑coded rules. Algorithms find patterns in data and then use those patterns to make predictions or decisions, like sorting emails, predicting demand, or recommending products. Common approaches include supervised learning (using labelled data), unsupervised learning (finding structure without labels) and reinforcement learning (learning by trial and error with rewards).​

3. Deep learning (DL)

Deep learning is a type of machine learning that uses neural networks with many layers to learn complex patterns. These models automatically extract features from raw data like images, sound, and text, which is why they power applications such as image recognition and many generative AI systems. Deep learning has enabled breakthroughs in areas where traditional algorithms struggled with scale and complexity.​

4. Neural networks

Neural networks are model architectures loosely inspired by the human brain. They are built from layers of simple processing units (neurons) connected by weighted links, and they learn by adjusting those weights to reduce prediction errors. With enough data, neural networks can learn complex patterns, which makes them useful for things like image recognition, speech, and understanding text.​

5. Generative AI

Generative AI refers to models that can create new content, like text, images, video, music or code, that resembles their training data. Instead of just sorting or analysing data, generative models produce original outputs based on a prompt. This makes it useful for tasks like writing content, exploring designs, running simulations, and creating extra data, often using deep learning models such as transformers or GANs.

6. Generative adversarial  networks (GANs)

Generative adversarial networks (GANs) are a type of deep learning models designed specifically for generating realistic new data. A GAN contains two neural networks: a generator that creates fake samples and a discriminator that tries to tell real data from fake. By competing with each other, the generator learns to produce very convincing images, videos, or audio, which is why GANs are popular for image creation and enhancement.

7. Natural language  processing (NLP)

Natural language processing (NLP) is a part of AI that helps computers understand and use human language. It lets machines read, interpret, create, and respond to text or speech. NLP is used for sentiment analysis, document sorting, translation, chatbots, search and more. Many generative AI applications, including large language models, rely on NLP to convert raw text into structured representations and back again.​

8. Transformers

Transformers are a type of deep learning model that revolutionised NLP and now power most generative AI. They use self-attention, which helps the model decide which words (or tokens) in a sentence matter most, even if they are far apart. Unlike older models that read text one word at a time, transformers process words simultaniously, making them faster and better at understanding long‑range context across text, images, and other data.

9. Generative pre‑trained  transformers (GPT)

Generative pre-trained transformers (GPTs) are large AI models based on the transformer design and trained on huge amounts of text. In the pre-training phase, they learn how language works by predicting the next word in a sequence. After that, they can be used for tasks like summarising text, translating languages, answering questions, or writing code. Because they are trained at such a large scale, GPT models can produce clear, context‑aware text that sounds human-like.

10. Tokenisation, Word2Vec  and BERT

Tokenisation means breaking text into smaller pieces, such as words, characters or subwords so NLP & generative AI models can understand it. Methods like Word2Vec turn words into numbers based on how they are used, which helps the model understand that some words are related (for example, “king” and “queen”). More advanced models like BERT look at all the words in a sentence at the same time, instead of one by one, giving them a better understanding of meaning and improving tasks like question answering and sentiment analysis.

Concusion

This generative AI glossary covered 10 fundamental terms:

Together, these concepts describe how modern AI systems understand data and generate new content across text, image, audio and beyond. By understanding these terms, whether your a student, professional or business leader you will be able to use & evaluate generative AI tools better and make better decisions about how to apply them in real‑world projects.

Ready to put Generative AI into action? Check out our AI Market place where we’ve selected the best models for tasks you might need doing such as writing long/short content, quick original images, calculations or even videos and music.