Introduction to Generative AI

Over the recent past a relatively science fiction term has now become science fact, along with all the hype and scaremongering that goes along with anything new. In this series of 5 articles we’ll be looking into how Generative AI works, the key concepts and jargon used, ending with some of the benefits and risks of Generative AI both now and in the future.

Generative AI is a type of artificial intelligence that can create new content, such as text, images, music, or even code. It’s called “generative” because it generates things, much like how a human might write a story or paint a picture.

Key Concepts and Jargon Explained:

1. Artificial Intelligence (AI):
   • Definition: A field of computer science that focuses on creating systems capable of performing tasks that usually require human intelligence.
   • Explanation: This includes activities like understanding language, recognising patterns, solving problems, and making decisions.
2. Generative Models:
   • Definition: A kind of AI model designed to create new data instances that resemble the training data.
   • Explanation: If the training data is a set of paintings, a generative model can produce new images that look like those paintings.
3. Training Data:
   • Definition: The data used to teach an AI model how to perform its task.
   • Explanation: For generative AI, this could be a collection of text, images, or any other type of content the AI will learn to generate.
4. Neural Networks:
   • Definition: Computer systems modelled after the human brain’s network of neurons, designed to recognise patterns and learn from data.
   • Explanation: These networks help generative AI understand and create content by learning from the examples in the training data.
5. GPT (Generative Pre-trained Transformer):
   • Definition: A specific type of generative AI model that is very good at understanding and generating human-like text.
   • Explanation: GPT models, like ChatGPT, learn from large amounts of text data and can produce coherent and contextually relevant sentences.

How Generative AI Works:

Imagine teaching a child to write stories by giving them lots of books to read. Over time, they learn the patterns of storytelling, such as grammar, structure, and style. Generative AI works similarly but uses algorithms and neural networks instead of human learning.

1. Learning Phase:

• The AI model is trained on a large dataset, absorbing patterns, styles, and structures from this data.

1. Generation Phase:

• When asked to create something new, the AI uses what it learned to produce original content that mimics the patterns from the training data.

Example in Simple Terms:

Suppose you have a generative AI trained on thousands of recipes. You could ask it to create a new recipe for a chocolate cake, and it would generate an original recipe by combining elements from the recipes it learned from.

In summary, generative AI is like a super-smart creative assistant that learns from vast amounts of information and can create new content based on that learning.

Now we’ll start looking at these key concepts in more detail starting with some basics of Artificial intelligence.

Artificial Intelligence (AI)

Definition: Artificial intelligence (AI) is a branch of computer science that aims to create machines or software that can perform tasks that typically require human intelligence.

Explanation

To understand AI, think of it as teaching computers to be smart. Normally, computers follow specific instructions to perform tasks. With AI, we want computers to do more than just follow instructions; we want them to think, learn, and make decisions like humans.

Key Concepts:

1. Intelligence:
   • Definition: The ability to learn, understand, and make decisions.
   • Explanation: Humans use their intelligence to solve problems, learn from experience, and adapt to new situations.
2. Algorithm:
   • Definition: A step-by-step set of instructions that a computer follows to perform a task.
   • Explanation: Think of an algorithm like a recipe in a cookbook. It tells the computer exactly what steps to take to complete a task.
3. Machine Learning (ML):
   • Definition: A type of AI that allows computers to learn from data without being explicitly programmed.
   • Explanation: Instead of telling the computer exactly what to do, we give it lots of examples, and it figures out patterns and makes decisions based on those patterns.
4. Data:
   • Definition: Information that the computer uses to learn.
   • Explanation: This could be anything from numbers and text to images and sounds.

How AI Works:

Imagine teaching a child to recognise animals. You show them lots of pictures of different animals and tell them the names of each one. Over time, the child learns to identify the animals on their own. AI works similarly but uses algorithms and data.

1. Data Collection:
   • Collect lots of examples (data) related to the task we want the AI to learn.
2. Training:
   • Use this data to train the AI, allowing it to recognise patterns and make decisions.
3. Application:
   • Once trained, the AI can use what it has learned to perform tasks, make predictions, or recognise new information.

Example in Simple Terms:

Imagine you have a smartphone with a voice assistant, like Siri or Google Assistant. When you ask it a question, it understands your words, searches for the answer, and responds to you. This process involves AI: it understands language, processes the information, and gives you a useful response.

In summary, artificial intelligence is about creating smart machines that can learn, think, and make decisions, helping us with tasks that typically require human intelligence.

In the next articles we’ll be considering the other concepts involved in Generative AI, that being Generative Models and Training Data.