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

What made ChatGPT feel fundamentally different from previous AI products like Siri or Alexa?

ChatGPT was different because it ran on quantum computers unavailable to earlier AI ChatGPT was a general-purpose system — one interface could write, analyze, code, and answer questions across any domain, unlike assistants built for narrow command-response tasks ChatGPT was the first AI ever to understand English language ChatGPT was the first AI to use the internet in real time to answer questions

What does the Gartner Hype Cycle predict about technologies that go through a 'trough of disillusionment'?

Most eventually reach a 'plateau of productivity' where real, consistent value is delivered — the trough represents reality calibration, not permanent failure Technologies that enter the trough of disillusionment never recover and should be abandoned The Hype Cycle predicts AI will be replaced by a completely different technology by 2027 The trough of disillusionment only applies to hardware technologies, not software like AI

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

The size of the screen where AI output is displayed 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 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 is the recommended approach when starting with AI tools for the first time?

Wait until AI tools become more mature and reliable before starting to use them Take a certification course before using any AI tool in a professional context Try every major AI tool simultaneously to quickly determine which is best Pick one tool and use it consistently on real work for 30 days, building habits through hands-on experimentation rather than reading about AI or switching between tools

What is a 'token' in the context of AI language models?

A single character of text — AI processes text one character at a time A security credential required to access AI APIs A unit of text roughly corresponding to a word or word fragment — the basic unit AI models use to process and generate language, and how usage and pricing are measured A cryptocurrency used to pay for AI model usage

What happens when a conversation exceeds an AI's context window limit?

Exceeding the context window triggers an automatic upgrade to a larger model The AI stops responding and requires a new conversation to be started The AI stores the overflow in external memory and retrieves it when needed The AI loses access to text that falls outside the window — it effectively 'forgets' earlier parts of the conversation and may give inconsistent responses

How quickly did ChatGPT reach 1 million users after its release in November 2022?

ChatGPT has never reached 1 million users — it is primarily used by researchers 1 year — mainstream adoption in consumer AI has historically been slow 6 months — it grew slowly at first before viral spread 5 days — the fastest product in history to that point to cross 1 million users

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 hardware chip designed specifically for Google's AI research A type of electrical transformer that powers AI data centers more efficiently A programming language used to build ChatGPT and similar AI products

What is 'hallucination' in the context of AI?

Hallucination is a technical term for when AI models become overloaded and crash When AI produces visual hallucinations or images instead of text When AI becomes confused and stops responding to questions When AI generates plausible-sounding but factually incorrect information — because the model predicts likely text, not verified facts

For which type of task would AI hallucination risk be LOWEST?

Describing a competitor's product features from their latest press release Looking up the exact revenue of a private company last quarter Finding the specific statute number of a regulation passed in 2025 Brainstorming a list of marketing tagline ideas — there are no 'correct' facts to get wrong; creativity and fluency are the objective

How did the relationship between users and AI change with generative AI tools like ChatGPT?

Users lost control — AI now makes all decisions without user input Users shifted from passive recipients of AI decisions (algorithms deciding what you see) to active operators directing AI to produce content and analysis on their behalf The relationship didn't change — generative AI works the same way as recommendation algorithms Users became more passive — generative AI automates everything without requiring input

What usage pattern consistently predicts high-value AI adoption?

Starting with a specific, recurring real problem; using AI for first drafts with human review; building task-specific prompting habits; knowing which tasks to keep fully human Using AI to replace human involvement in all written tasks entirely Waiting for AI tools to mature further before integrating them into real workflows Trying every new AI tool as soon as it launches to stay current

What is AI 'hallucination'?

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 When AI becomes confused and stops generating responses

What are 'parameters' in a large language model?

Billions of numerical values adjusted during training that encode the model's learned patterns — they represent what the model 'knows' about language, facts, and reasoning The physical memory chips inside the servers that run the AI model The rules that prevent AI from producing harmful content The settings menu where users configure how the AI responds

Which of these everyday products uses machine learning or AI?

A physical thermostat with a timer — it executes pre-set rules without learning A basic alarm clock — it does only what it's explicitly programmed to do Netflix's recommendation engine — it uses machine learning to predict what content you'll enjoy based on your viewing patterns A standard calculator — it follows fixed mathematical rules with no learning

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

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 AI was invented in 2022 — there was no meaningful AI before ChatGPT ChatGPT was the first AI to run on computers; previous AI required specialized hardware

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

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

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

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 runs on phones; general-purpose AI requires large servers 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

Which of the three habits of effective AI users is most important for working with AI in a professional context?

Using AI for every task, no matter how simple, to build maximum fluency Keeping the judgment — AI generates options and drafts; the human remains accountable for decisions and final outputs Delegating final decisions to AI to remove human bias from the process Never verifying AI output to avoid slowing down the productivity gains

What is the '80/20 trap' in AI adoption?

AI handles 80% of a task well but fails at the remaining 20% in ways requiring human correction — making autonomous completion risky, but AI-assisted draft work highly valuable AI can only be used for 20% of any professional's tasks; 80% must remain fully human Only 20% of AI tools are reliable; 80% are experimental and should not be used at work AI tools cost 80% more than traditional software and deliver only 20% of the value