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AI Foundations

Understand what AI tools do, how their parts work together and how to judge their answers and costs.

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What You Will Learn

Course Introduction

1 min 22 sec

Read the video transcript

Welcome to AI Foundations from Learn with Q. These twelve lessons explain AI tools, their answers and their costs. Our central question is: how do we explain what an AI system does, follow the work through its parts, and judge whether the result is useful? These ideas help you understand AI products and connect

to courses on chips, costs, models and business. We start by distinguishing AI capabilities from the products that use them. Then we look at how computers do work; how data, parameters, and instructions differ; and how training differs from inference. From there, we distinguish a prediction or generated output from an action, follow one request,

and learn to read an API request and response. No programming background is assumed. The final foundation lessons help us reason about units and percentages, interpret diagrams, examine evidence, and distinguish value, cost, and successful completion. Your first project connects the ideas in a request map and an assumptions ledger. By the end of this course,

you should be able to explain these relationships in your own example. For more on learning from examples, explore How Machine Learning Learns. For now, begin with Lesson one: what do we mean by artificial intelligence?

About This Course

Your starting point

The course starts with the basics and explains technical terms as they appear. You already have experience using products and making decisions. We will build from there, connecting what you see in an AI app to the systems that make it work. Use each new term to explain the example in front of you. You can return to the definitions as needed.

By the end of this course, you should be able to follow an AI request, distinguish a model from a product, explain training and inference, read a basic diagram, and question a claim. You will also begin two pieces of work: a map showing the connections in an AI system, and a notebook of assumptions and questions to revisit.

Take the lessons at a pace that works for you. Read or watch, work through the example, and try the checkpoint. If something in a later module is unclear, come back to the relevant foundation. You can revisit one idea without restarting the course.

Lessons

  1. What Do We Mean By Artificial Intelligence?See what makes a tool an AI tool, using examples you already know. This gives you a starting point for the rest of AI Foundations.
  2. A Model Is One Component Of A ProductFind out what the AI model does inside a chatbot or app, and what the rest of the product has to handle.
  3. How Computers Turn Information Into WorkFollow how a computer stores information and carries out instructions—the basic work behind every AI tool.
  4. Data, Parameters, And Instructions Are DifferentTell apart the information an AI receives, the patterns it has learned and the instructions you give it. This helps explain why answers change.
  5. Training And Inference, Step By StepSee how an AI model learns from examples, then uses what it learned to answer a new question.
  6. Prediction, Generation, And ActionCompare AI that predicts a result, creates something or takes an action, so you can recognise what a tool is being asked to do.
  7. Follow One Request Through The SystemFollow a question from the person asking it to the AI answer. See which parts of the app and its services do the work.
  8. APIs, JSON, And Practical Technical LiteracySee how apps ask other services for information. Read a simple message to understand how an AI product connects its parts.
  9. Units, Percentages, And DenominatorsCheck the numbers in AI claims, from percentage improvements to computing costs, so you can tell whether comparisons are fair.
  10. What A Diagram Can And Cannot Tell YouRead a simple drawing of how an AI app works. Spot missing steps that could explain wrong answers, delays or unexpected costs.
  11. Read Evidence Before Drawing ConclusionsCheck who made an AI claim, what they tested and what the result supports, so you can judge claims more carefully.
  12. Value, Cost, And Successful CompletionWork out what an AI task really costs, including failed attempts and human checks, and compare that cost with the benefit.