AI Basics

Artificial intelligence (AI) is becoming part of how people learn, teach, work, create, communicate, and solve problems. This page introduces key AI concepts in plain language and provides a starting point for understanding how AI and generative AI differ.

What Is AI?

Artificial intelligence, often called AI, refers to computer systems designed to perform tasks that typically require human intelligence. These tasks can include learning from information, recognizing patterns, communicating in natural language, making predictions, classifying information, and supporting decision-making.

The term “artificial intelligence” was introduced in the 1950s, but AI has developed over many decades. Today, AI appears in many everyday tools and services, from search engines and recommendation systems to chatbots, image tools, translation tools, accessibility tools, and workplace productivity platforms.

AI systems do not “think” or understand the world in the same way people do. They process data, identify patterns, and generate outputs based on how they were designed and trained. Human judgment, context, and accountability remain essential.

What Is Generative AI?

Generative AI is a type of AI that creates new content based on patterns learned from large datasets. It can generate text, images, code, audio, video, summaries, ideas, outlines, and other types of content.

Tools such as ChatGPT, Microsoft Copilot, Gemini, Claude, and image generators are examples of generative AI tools. They can respond to prompts, revise text, summarize information, generate examples, and support brainstorming or planning.

Generative AI produces probable outputs based on patterns. It does not automatically verify truth, accuracy, context, or quality. Outputs should always be reviewed, checked, and adapted by a person before being used.

AI vs. Generative AI

AI is the broader field. Generative AI is one part of that field.

Today, when people talk about “AI” in everyday conversation, they are often referring to generative AI tools such as ChatGPT, Microsoft Copilot, Gemini, or Claude. These tools have made AI more visible and accessible, but they represent only one part of the larger AI landscape.

A simple way to think about it:

• Artificial Intelligence is the broadest category. It includes systems that perform tasks associated with human intelligence.
• Machine Learning is a subset of AI. It involves systems that learn patterns from data and improve performance over time.
• Neural Networks are a type of machine learning model inspired by connected layers of processing units.
• Deep Learning is a subset of machine learning that uses multi-layered neural networks to model complex patterns in data.
• Generative AI is a type of AI that creates new content, such as text, images, code, audio, or video.

In other words, AI is the big bubble. Machine learning sits inside that bubble. Neural networks and deep learning sit inside machine learning. Generative AI builds on these approaches to produce new content. For more definitions, visit the AI Glossary.

Common AI Tools

AI tools now support a wide range of tasks, including writing, summarizing, searching, coding, designing, translating, tutoring, note-taking, data analysis, image generation, video creation, and workflow automation.

Some common types of AI tools include:

• AI chatbots: ChatGPT, Microsoft Copilot, Gemini, Claude, Perplexity
• Writing and editing tools: Grammarly, Microsoft Editor, Wordtune
• Image generation tools: DALL-E, Adobe Firefly, Midjourney, Canva AI
• Coding tools: GitHub Copilot, Cursor, Replit AI
• Meeting and transcription tools: Microsoft Teams AI features, Otter.ai, Fireflies.ai
• Research and discovery tools: Elicit, Perplexity, Semantic Scholar AI features
• Productivity and workflow tools: Microsoft Copilot, Notion AI, Zapier AI

The AI tool landscape is growing quickly. External directories such as There’s An AI For That list tens of thousands AI tools across many categories. These directories can be useful for exploration, but they are not approval lists. Inclusion in a directory does not mean a tool is approved, endorsed, secure, accessible, or supported by George Brown Polytechnic.

For George Brown-related work, start with institutionally supported tools such as Microsoft Copilot Chat and ChatGPT Edu, where available.

Why AI Matters Now

AI is no longer a niche technology. It is becoming part of everyday learning, work, communication, and decision-making across education and industry.

Recent reports show that more than one billion people now use standalone generative AI tools every month, and Stanford’s 2025 AI Index reported that 78 per cent of organizations used AI in 2024, up from 55 per cent the previous year. These trends show that AI adoption is accelerating across daily life and the workplace.

AI use is also growing across post-secondary education. A 2024 Digital Education Council survey of 3,839 students across 16 countries found that 86 per cent of respondents used AI in their studies. This matters for George Brown Polytechnic because students and employees are already encountering AI in learning, work, services, and professional practice.

The goal is not to use AI for everything. The goal is to understand where AI can add value, where it introduces risk, and how to use it in ways that are responsible, effective, and human-first.

Start Learning

To continue learning about AI at George Brown Polytechnic, visit:

• AI Glossary for plain-language definitions of common AI terms
• Using AI Tools Responsibly for guidance on privacy, security, responsible use, and external tools
• Microsoft Copilot for information about Copilot Chat
• ChatGPT for information about ChatGPT Edu access
• AI Training & Programs for learning opportunities, courses, and professional development