Glossary of AI Terms

I've created this glossary to explain the key AI concepts featured on the AI Term cards. Each term builds on the previous one, to show you how these concepts are related.

Related resources:

• The cards are part of the Starter Kit
• An activity to learn the vocabulary: the AI Term card challenge

Artificial Intelligence (AI)

AI is the field of creating machines that can perform tasks usually requiring human intelligence.

AI is the broadest concept, covering all technologies that allow machines to mimic human abilities like problem-solving, learning, and decision-making. It includes both machine learning and deep learning as specific approaches to achieving these goals.

Machine Learning

Machine learning is a part of AI where machines learn patterns from data to make decisions.

Machine learning is a specific method within AI. Instead of being explicitly programmed, machines are trained by "learning" from data. Neural networks are one of the techniques used in machine learning, enabling it to handle more complex tasks.

Neural Networks

Neural networks are computational models inspired by how the human brain works, used in machine learning.

Neural networks are made up of layers of connected nodes (like artificial neurons). They process data step by step to recognize patterns and make predictions. Neural networks are the foundation for deep learning, which adds many layers to handle more complex problems.

Deep Learning

Deep learning is a type of machine learning that uses neural networks with many layers (hence "deep").

Deep learning builds on neural networks by adding multiple layers that process data in increasingly complex ways. This allows it to handle large amounts of unstructured data like images, text, and audio, making it more powerful than traditional machine learning methods.

Transformers

Transformers are a type of deep learning model designed for working with sequences, like text or speech.

Transformers are a modern innovation in deep learning. They use neural networks with attention mechanisms to process sequential data efficiently. Transformers are the foundation for models like GPT, which excel at tasks such as language translation, summarization, and conversation.