Introduction to Generative AI (part 3)

In the previous articles in this series we introduced 3 of the key concepts of Generative AI, now the final 2 concepts, Neural Networks and Generative Process Transformation (or GPT for short).

Neural Networks

Definition: Neural networks are a type of artificial intelligence (AI) system designed to mimic the way the human brain works. They consist of layers of connected units (called “neurons”) that process information and learn from data.

Explanation

Neural networks get their name because they work similarly to the way our brains function. In the human brain, we have billions of neurons (nerve cells) that send signals to each other, helping us think, learn, and make decisions. Neural networks in AI do something similar but with math and data instead of biological cells.

How Neural Networks Work:

• Neurons (Nodes):
  • Just like in the brain, a neural network has individual “neurons” or nodes. Each neuron processes a bit of information and passes it along to the next layer of neurons.
• Layers:
  • Neural networks are organised into layers.
    • Input layer: This is where the network receives data, like an image or a sentence.
    • Output layer: This is where the network produces its final result, like identifying an object in a picture or generating a new sentence.
    • Hidden layers: These layers process the information, finding patterns or features.
• Connections:
  • Neurons in one layer are connected to neurons in the next layer. These connections have different “strengths” (called weights) that control how much influence one neuron has on another.
• Learning Process:
  • During training, the network is shown examples (the training data) and adjusts the weights of the connections based on whether its output is correct or incorrect. This process, called backpropagation, helps the network learn and improve over time.

Key Concepts:

• Input Layer:
  • Definition: The first layer where the data enters the network.
  • Explanation: If the task is to recognise a cat, the input could be a picture of a cat. The picture is turned into numbers that the network can understand.
• Hidden Layers:
  • Definition: Layers between the input and output where the network processes and learns from the data.
  • Explanation: This is where the network learns to recognise patterns, like the shape of a cat’s ears or the texture of its fur.
• Output Layer:
  • Definition: The final layer that gives the result.
  • Explanation: In our cat example, the output layer would produce the answer: “This is a cat” or “This is not a cat.”
• Weights:
  • Definition: Numbers that determine how strongly one neuron influences another.
  • Explanation: By adjusting these weights, the network learns how to recognise patterns or make decisions.

Example in Simple Terms:

Imagine you’re teaching someone to recognize different fruits. You show them many pictures of apples, oranges, and bananas (the input). Over time, they learn to look for specific patterns: an apple is round and red, an orange has a bumpy surface, and a banana is long and yellow (the hidden layers processing the information). After enough practice, they can correctly identify each fruit (the output).

In a neural network, the same process happens: it takes input, processes it through many layers, and then produces a result.

Summary:

Neural networks are a powerful AI system that learns from data by mimicking how the human brain processes information. It uses layers of connected “neurons” to recognise patterns, solve problems, or generate new content. The more it learns, the better it gets at its task.

GPT (Generative Pre-trained Transformer)

Definition: GPT is a type of artificial intelligence model designed to understand and generate human-like text. It’s called “Generative” because it can create new text, “Pre-trained” because it learns from a large amount of text before being fine-tuned for specific tasks, and “Transformer” refers to the technology that helps it process and understand language.

Explanation

GPT is a kind of generative AI that focuses specifically on text, like writing stories, answering questions, or carrying on conversations. It’s widely known because it can produce text that sounds very natural and human-like.

Breaking It Down:

1. Generative:
   • Definition: Refers to the model’s ability to generate or create new content.
   • Explanation: GPT can take an input (like a question or prompt) and generate a meaningful response, whether it’s a sentence, paragraph, or even an entire article.
2. Pre-trained:
   • Definition: The model is initially trained on a large amount of data before being used for specific tasks.
   • Explanation: GPT is trained on massive datasets that include books, websites, and other forms of text. This helps it learn the structure of language, grammar, facts, and even some level of reasoning.
3. Transformer:
   • Definition: A type of AI architecture used in language models, designed to process and understand sequences of data (like sentences).
   • Explanation: Transformers help the model pay attention to the relationships between words in a sentence. For example, in the sentence “The cat sat on the mat,” it understands that “cat” is the subject and “sat” is the action.

How GPT Works:

1. Pre-training:
   • GPT is exposed to large amounts of text data (like books, articles, and websites). During this phase, it learns the structure of language, how words relate to each other, and general facts about the world.
2. Fine-tuning:
   • After pre-training, GPT can be fine-tuned for specific tasks, like answering questions, writing code, or even chatting. This is done by giving it additional training on more focused data.
3. Text Generation:
   • When you give GPT a prompt or a question, it processes that input and generates text based on what it has learned. It tries to predict the next word or sentence that should follow, producing coherent and relevant responses.

Example in Simple Terms:

Imagine you’ve read thousands of books on every topic—science, history, literature, and more. Someone asks you a question about space travel, and you use all the knowledge from those books to give a detailed, well-written answer. That’s what GPT does with text. It’s like a super-smart assistant that’s read almost everything and can generate a well-formed response based on the question or prompt you give it.

Key Concepts Related to GPT:

• Natural Language Processing (NLP):
  • Definition: A field of AI that focuses on helping machines understand and generate human language.
  • Explanation: GPT uses NLP to understand the meaning of text and respond in a way that makes sense in conversation.
• Context:
  • Definition: The surrounding words or sentences that help clarify meaning.
  • Explanation: GPT pays attention to the context of the conversation or text to produce relevant and meaningful responses. It doesn’t just reply with random sentences but considers the overall topic and flow of the conversation.

Example in Simple Terms:

If you ask GPT, “What is the capital of France?” it will reply with “Paris” because it has learned from its training data that Paris is the capital of France. If you then ask, “Can you tell me more about Paris?” it understands from the context that you’re still talking about the city, not something else named Paris.

Summary:

GPT (Generative Pre-trained Transformer) is a powerful AI model designed to understand and generate human-like text. It’s trained on massive amounts of data and can produce responses based on the input it receives, making it useful for tasks like answering questions, writing content, and even holding conversations.

Next up

That’s the key concepts taken care of. In the next article we’ll be looking at how AI works, the process involved.