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

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

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 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

What is the key difference between 'narrow AI' and 'general-purpose AI'?

Narrow AI runs on phones; general-purpose AI requires large servers Narrow AI is designed for one specific task (spam filtering, recommendations); general-purpose AI handles many different tasks through a single model that users direct with language Narrow AI is for small companies; general-purpose AI is only for enterprise use Narrow AI is cheaper; general-purpose AI is always more expensive per use

What is AI 'hallucination'?

When AI becomes confused and stops generating responses When AI generates plausible-sounding but factually incorrect content — stated confidently, without any signal to the user that the information may be wrong When AI translates text incorrectly because it doesn't know the language When AI produces visual artifacts or distorted images

What does a 'context window' determine in an AI conversation?

The number of different topics an AI can discuss in a single session The time window during which a conversation is saved before being deleted The size of the screen where AI output is displayed How much text the AI can 'see' and refer to at once — including your messages, shared documents, and the AI's own replies. Text outside the window is effectively forgotten

What does the history of AI 'winters' and breakthrough cycles tell us about the current AI era?

AI winters prove that AI never delivers; the current excitement will also disappear AI has had repeated cycles of hype and disappointment — the current era may be different in scale, but calibrated skepticism and attention to real results (not promises) remains the right posture The history of AI winters is irrelevant — current AI is categorically different from all prior AI Because we've had AI winters before, today's AI is definitely not as advanced as claimed