SUBJECTAI Foundations
Artificial Intelligence
Artificial intelligence is changing how people work, learn, create, communicate, and solve problems. This structured learning path takes an absolute beginner toward confident practical understanding.
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Prefer the whole picture first? See the Artificial Intelligence study guide — full lesson outline, key terms, and practice on one page.
AI Foundations — Lessons
- 1
What Is Artificial Intelligence?
AI is the field of building systems that perform tasks associated with intelligence. Learn how it differs from rule-based software and how AI, machine learning, and deep learning nest.
13 min · beginner - 2
The History of Artificial Intelligence
Why AI progressed in waves — from the 1956 Dartmouth workshop through the AI winters and expert systems to deep learning, Transformers, and generative AI.
15 min · beginner - 3
Types of Artificial Intelligence
Narrow AI, AGI, and superintelligence — what exists today, what remains hypothetical, and why strong performance at one task is not general intelligence.
15 min · beginner - 4
How Artificial Intelligence Works
The AI lifecycle from problem definition to monitoring — why training and inference differ, why models must be tested on unseen data, and why deployed performance decays.
18 min · beginner - 5
AI Terminology
The working vocabulary of AI — model, parameter, token, prompt, context, embedding, fine-tuning, and hallucination — with the distinctions people most often get wrong.
18 min · beginner - 6
AI Myths vs. Facts
Seven persistent misconceptions about AI — thinking like humans, knowing everything, being objective, replacing all jobs — and what the evidence actually supports.
15 min · beginner - 7
The AI Timeline
From the 1943 mathematical neuron to generative AI — the milestones, the two AI winters, and why each wave of progress needed data and hardware as much as ideas.
15 min · beginner - 8
Artificial Intelligence in Everyday Life
Where AI already sits in phones, search, finance, healthcare, and transport — the benefits, the risks worth tracking, and why the most effective systems go unnoticed.
18 min · beginner
Modules in this subject
AI Foundations
8 lessons · ~2.5h
Not started yet
Machine Learning
7 lessons · ~6-8h
Not started yet
Generative AI
7 lessons · ~6-8h
Not started yet
Deep Learning
7 lessons · ~6-8h
Not started yet
AI Applications
7 lessons · ~6-8h
Not started yet
AI Ethics and Society
7 lessons · ~6-8h
Not started yet
Building with AI
7 lessons · ~6-8h
Not started yet
Mastery
Coming soon
What you'll be able to do
- Explain the foundations of AI.
- Understand machine learning and deep learning.
- Describe large language models, generative AI, agents, and retrieval.
- Evaluate AI tools and limitations.
- Recognize ethical, legal, privacy, and safety concerns.
- Build practical AI-powered projects.
Concept map
How the core concepts in Artificial Intelligence relate to one another.
- Machine Learningis part ofArtificial Intelligence
- Deep Learningis part ofMachine Learning
- Narrow AIis aArtificial Intelligence
- Artificial General Intelligencerelates toArtificial Intelligence
- Trainingis explained byModel
- Datasetis applied inTraining
- Inferenceis applied inModel
- Transformeris aDeep Learning
Artificial Intelligence: frequently asked questions
- Is all AI just machine learning?
- No. Machine learning is one branch of AI, and deep learning sits inside that. AI also includes approaches that learn nothing at all — a chess engine that searches possible moves is still AI. All machine learning is AI, but not all AI is machine learning.
- If an AI answer sounds confident, is it correct?
- Not necessarily. Fluency is a property of the writing, not the facts. Models can be outdated, incomplete, or simply wrong while presenting all three in the same assured tone. Confident, polished phrasing is no evidence of accuracy, so verify any claim that matters.
- Does being computer-based make AI unbiased?
- No. Models inherit patterns and imbalances from their training data, labeling choices, and whatever their objective rewarded. A system trained on past human decisions reproduces the biases in them — consistently and at scale. Computation is not neutrality; it can repeat old skews more efficiently than people did.
- Are bigger AI models always better?
- No. Size is a cost as well as a capability. Larger models are pricier to run, slower, and harder to inspect. Smaller specialized models are often cheaper, faster, more private, and better suited to a specific job. The right model is usually the smallest one that meets the requirement.
- Does chatting with an AI retrain it?
- Usually not. Most interactions are inference — using an already-trained model, which does not change it. Training is a separate, occasional, resource-heavy phase. Whether your data is stored or later used for improvement depends entirely on the service and its settings, so it is worth checking rather than assuming.
- Will AI replace whole jobs entirely?
- Rarely wholesale. AI automates tasks, and occupations are bundles of tasks. Effects vary widely by sector and usually mean role redesign, productivity changes, some displacement, and new kinds of work. Sweeping predictions of mass unemployment — or of no effect at all — tend to ignore the actual tasks involved.
- Do you need to code to use AI?
- No. Applying AI well is increasingly a domain-expertise problem rather than a coding one. The scarce skill is knowing what a good output looks like and being able to tell when one is wrong. Non-programmers with deep subject knowledge often put AI to its most effective use.