What is AI?

Understand the basics of AI, how it works and some of its limitations and risks

AI, or Artificial Intelligence, refers to computer systems that can perform tasks that would usually require human intelligence. These tasks include understanding language, recognising images, making decisions and learning from data.

For charities and community causes, AI can help enormously with things like copywriting, analysing data, creating reports, creating donor pitches and generating content for social media.

The AI models you are likely most come as chat assistants - you access a website or download an app and communicate with the AI through words, just like sending a text to a friend. You can upload files or send it links to websites, but most of the time you will be instructing the AI to do things through a chat window.

Examples of AI in everyday life:

• Virtual assistants like Siri or Alexa.
• Social media algorithms recommending posts.
• Translation tools like Google Translate.

AI models are programmes trained to perform specific tasks by learning patterns from data. For example, an AI model trained on thousands of images of cats can recognise a cat in a new picture.

Here’s a simple analogy:
Think of an AI model as a very smart apprentice. You train the apprentice by showing them examples (data) and teaching them to recognise patterns (training). Over time, the apprentice learns to apply this knowledge to new situations.

How AI models work:
Training: The model is fed large amounts of data. For example, to build a chatbot, the AI is trained on conversations.
Learning: The AI identifies patterns in the data (e.g. what makes a sentence sound polite or helpful).
Making Predictions: When given new input, the AI uses its learned patterns to provide an output (e.g. answering a question or generating a piece of text).

Important to Understand

AI is not sentient. This means it does not have feelings, intentions or understanding like a human person does. It cannot think or make decisions on its own—it only responds based on the patterns it has learned from data. When an AI gives an answer or makes a suggestion, it’s not because it “cares” or has “opinions,” but because it has calculated the most likely response based on its training.