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AI Concepts184
Plain-English explanations of the terms you keep encountering in the news.
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Graph Contrastive Learning
A self-supervised approach to learning graph representations by training a model to recognise that augmented views of the same graph are similar, without requiring labelled data.
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Analogy
Learning to recognise a friends face from different photographs taken in different lighting, from different angles, with different expressions. The photographs are augmented views of the same underlying face. Contrastive learning for graphs applies the same intuition: augmented views of the same graph should look similar to a good encoder.
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AI Foundations
The basics before anything else - what AI and machine learning actually are, how neural networks learn, and the building blocks every other concept assumes you know.
LLM Architectures
The building blocks inside large language models - how they store knowledge, process text, and generate responses.
Training & Alignment
How models are built, fine-tuned, and taught to behave the way people actually want them to.
Generative & Multimodal
AI that creates images, audio, and video - and systems that reason across multiple types of input at once.
Agentic AI
AI that plans, acts, and uses tools autonomously - moving beyond question-and-answer into doing.
AI Safety & Alignment
The field dedicated to making AI systems behave reliably, honestly, and without causing unintended harm.
MLOps & Infrastructure
The engineering discipline of running AI in production - reliably, efficiently, and at scale.
Reinforcement Learning & Robotics
AI that learns by doing - and the systems that let it operate in the physical world.
Graph Neural Networks
A class of models built for data with relationships - social networks, molecules, maps, and knowledge graphs.
Specialized Domains
AI applied to medicine, science, audio, and other fields where domain knowledge changes everything.