Novice Checkpoint
You have reached the end of Track 1. Look back at what you have learnt, test yourself, try one small real project — and choose your next step.
A few pages ago, “AI” may have been a vague, slightly worrying word. Now you can explain what a large language model does, write a request that gets a useful answer, recognise when an assistant is making things up, and protect your family from a cloned voice on the phone. That is a real shift. This page helps you lock it in.
What you have covered
Each line links back to its page, in case anything feels hazy.
- Your AI Journey Starts Here — how the track works.
- What Is AI, Really? — machines doing tasks we would call intelligent; narrow AI versus general.
- AI, ML, Deep Learning, GenAI — the nesting dolls of the field.
- How Machines Learn — learning patterns from examples instead of following hand-written rules.
- What Is a Large Language Model? — next-token prediction at enormous scale.
- Your First Conversation with an AI — choosing an assistant and getting started.
- Prompting Basics — task, context, audience, format, examples, constraints; then iterate.
- AI All Around You — recommendations, maps, face unlock, fraud alerts and more.
- What AI Gets Wrong — hallucinations, cutoffs, bias, flattery and overconfidence.
- Myths and Misconceptions — telling settled facts from open debates and guesses.
- Staying Safe with AI — what not to share, deepfakes, scams and where to report them.
AI systems learn patterns from data and make predictions; chat assistants predict text, which makes them fluent but not automatically truthful. You get better results by asking clearly and refining through conversation. You stay safe by checking what matters, sharing little, and verifying anything urgent through a separate channel.
Test yourself
Fourteen questions covering the whole track. One try each — no peeking. If you score 11 or more, you are ready to move on; if not, revisit the pages for the questions you missed.
1. Which best describes almost all AI in use today?
Every deployed system is narrow in some important way — even assistants that feel general.
2. How does machine learning differ from traditional programming?
Show it many labelled examples and it finds the patterns itself.
3. Put these in order from widest to narrowest.
The nesting dolls: deep learning sits inside machine learning, which sits inside AI.
4. At heart, what is a large language model trained to do?
Next-token prediction at enormous scale, followed by further training to be a helpful assistant.
5. Which prompt is likely to get the most useful answer?
It includes the task, context, constraints and format.
6. The first answer is nearly right but too long. Best move?
Iterate with specific follow-ups. The first answer is a draft.
7. What is a hallucination?
Fake citations in Mata v. Avianca (2023) are the famous example.
8. Why might an assistant not know about a law that changed last month?
Models learn from a snapshot of text. Check recent facts with official sources.
9. Your phone unlocks when it sees your face. What kind of AI is that mainly?
A neural network compares the face it sees with the one you registered.
10. Where does bias in AI usually come from?
Systems learn from human data, including its gaps and unfairness — as Gender Shades (2018) showed.
11. You push back on a correct answer and the AI instantly agrees with you. That is…
Ask neutral questions and invite criticism rather than agreement.
12. Which statement deserves the label “speculative”?
Forecasts about the future are speculative, however confidently they are stated.
13. Which of these is it safe to type into an AI chat?
General questions are fine. Never share financial details, and follow your workplace’s AI policy.
14. A video of a famous tycoon promotes a trading app with guaranteed returns. Best response?
Guaranteed returns plus a celebrity endorsement is a classic scam pattern. Deepfakes can look flawless. If you have lost money in Malaysia, call 997 As of 2026.
A small practical project
Knowledge sticks when you use it. Spend 30–45 minutes on this project with any free AI assistant. Choose either a week of family meals or a weekend trip somewhere in Malaysia.
- Write a full promptUse at least five of the six ingredients: task, context (who is eating or travelling, budget, dietary needs or interests), audience, format (a table by day), an example of what you like, and constraints. No real names, addresses or booking details.
- Let it interview youAdd: “Before you answer, ask me up to five questions, one at a time.” Answer them.
- Refine twiceGive at least two specific follow-ups — cheaper, less travel, more vegetables, swap a day around.
- Verify three claimsPick three factual claims in the plan — an opening time, an entry price, a travel time, a nutrition fact, a cost estimate — and check each against an official website or other reliable source. Record what was right, outdated or invented.
- Push back onceTell it, politely, that something correct is wrong. Does it hold its ground or cave in? Note what happens.
- Make it yoursFix the errors, adjust it to your real life, and use the plan. You — not the AI — are responsible for the final version.
Keep the plan and your three checks: they make a good “before” sample to compare with your work after the Practitioner track.
// Pause and reflect
- Of your three verified claims, how many were fully right? What does that tell you about when to trust the assistant?
- Which single habit from this track will you keep using every time — and which do you still forget?
- Who in your life would benefit most from what you have learnt here, especially about scams? What will you tell them?
Your notes are saved privately in this browser.
Where next?
You have finished the foundations. Three good directions:
L2The Practitioner trackPower user, still no code
Turn AI into a dependable everyday tool for writing, research, spreadsheets, images and simple automation — with proper verification habits and workplace rules. Start at The Practitioner’s Road.
1956The Story of AIHow we got here
From ancient automata to Alan Turing, the Dartmouth workshop, two AI winters and the deep-learning revolution. Begin with AI History at a Glance.
?Not sure?Choose your track
Answer a few questions and get a suggested route, plus a checklist for every track, on Choose Your Track.
Key terms on this page
Sources & further reading
- Mitchell, M., Artificial Intelligence: A Guide for Thinking Humans (2019).
- Mollick, E., Co-Intelligence: Living and Working with AI (2024).
- Buolamwini, J. and Gebru, T., Gender Shades (2018).
- University of Helsinki and MinnaLearn, Elements of AI — a free online introductory course (first launched 2018).