AI Basics Quiz: What Do You Actually Know About AI?

What fundamentally changed about AI in late 2022 with ChatGPT's release, compared to AI that existed before?

ChatGPT was the first AI to run on computers; previous AI required specialized hardware AI was invented in 2022 — there was no meaningful AI before ChatGPT The 2022 breakthrough was AI becoming conscious and able to truly understand language Generative AI crossed a quality and accessibility threshold — anyone could use it for genuinely useful work, without technical expertise, for the first time

What is the transformer architecture, and why does it matter for modern AI?

A type of electrical transformer that powers AI data centers more efficiently A hardware chip designed specifically for Google's AI research A programming language used to build ChatGPT and similar AI products A 2017 research breakthrough that enabled models to track how words relate across long text — making it possible to train AI on vast amounts of text at a scale that produces today's LLMs

When would you want a LOWER temperature setting for an AI task vs. a HIGHER temperature?

Temperature only affects response speed, not content — lower is faster, higher is slower Temperature controls language formality: low for formal writing, high for casual writing Lower for precise tasks (data extraction, code, factual answers) where consistency matters; higher for creative tasks (brainstorming, varied options, creative writing) where variation is desirable Higher temperature always produces better quality — you should maximize it for all tasks

Which of the following is an example of AI that predates ChatGPT and has been running invisibly for years?

A credit card fraud detection system that flags unusual transaction patterns — a classic machine learning application deployed by banks for over a decade A dropdown menu that shows you your most recently visited websites ChatGPT Plus, which was the first commercial AI product available to consumers A basic spreadsheet formula that calculates the sum of a column

What is the core training objective of a large language model?

Predict what word (token) comes next, given all the words before it — repeated billions of times across vast text to build pattern knowledge Build a logical reasoning engine that can prove mathematical theorems Memorize every fact in its training data so it can retrieve them accurately Create a database of question-answer pairs to look up when asked something