Understanding AI

In this section, you’ll find tools and information to help you better understand AI, its impact, and how it shapes everyday life.

We’ve included:

• A glossary to explain important terms and concepts
• Some myth busters to help you know the fact from the fiction
• Statistics and real-world examples showing AI in action

Explore these resources to feel more informed and confident navigating a world shaped by AI.

So, what is AI?

Because tech has been historically masculinised, you might be imagining a vaguely man-shaped robot. 

But this representation of AI is dangerous, because it tells BIPOC people and women that AI is not built by or for them. AI is shaped by the data we give it, and if only actors empowered by the patriarchy and white supremacy feed into it, it will copy the biases in our society today.

We want to empower young women to truly understand AI. 

‘’This isn’t just a tech issue, it’s a safeguarding one. My mission is to ensure that young women feel informed, empowered, and protected in an increasingly digital world.“
– Lauren Caskie, Young Woman Leader

Artificial Intelligence

A term used to describe a group of technologies that allow computers to perform tasks previously associated with human intelligence.

– Scottish AI Alliance

The building blocks of AI are:

• Data – The information AI learns from.
• Algorithms – The rules that tell AI how to process and interpret the data.
• Models – The trained system that uses data and algorithms to perform tasks.

Examples of AI

AI is everywhere in our everyday lives.

Machine learning

At its core, machine learning uses algorithms and statistical models to identify patterns in data and make predictions. It works in the same way predictive text does.

• Supervised learning – when a programme is trained using labelled data, meaning humans provide examples with known answers – only positive feedback is given.

• Unsupervised learning – when AI teaches itself without humans. 
• Reinforced learning – when humans supervise and give both positive and negative feedback.

Generative AI uses machine learning techniques to create new data that shares characteristics of its training data, often producing outputs that are nearly indistinguishable from human-created content. ChatGPT, Microsoft Co-pilot and Google Gemini are all examples of generative AI.

Large Language Models (LLMs) are a widely used form of generative AI. They’re trained on extensive text data to refine their predictions, allowing them to generate fluent, human-like responses. However, their outputs are based purely on probability, not comprehension. This is why AI models can “hallucinate” and produce fake data.

Thanks to the Scottish AI Alliance for granting us special access to their Living with AI online learning platform. We’ve written more about our experience accessing the course as part of this guide.