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

What is 'calibration failure' in the context of AI hallucination?

When an AI model is calibrated to a different language than the user's The model's expressed confidence doesn't match its actual accuracy — AI states invented information in the same confident tone as verified facts, giving users no signal that something is wrong When an AI's internal clock is set to the wrong time zone The process of adjusting an AI model to produce shorter responses

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 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 Using AI to replace human involvement in all written tasks entirely

After completing AI Basics, what is the recommended next course in the AI Readiness curriculum?

AI Landscape Essentials — which builds on this foundation to cover specific AI models, their capabilities, and how to choose between them for different use cases The Vibe Coding Economy course — which is the advanced technical course and assumes significant AI experience There is no recommended next step — AI Basics is a standalone course with no further progression The AI Economy course — which focuses on macroeconomic trends and is best taken after building practical AI skills

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

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 AI winters prove that AI never delivers; the current excitement will also disappear 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

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

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 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

What are 'parameters' in a large language model?

The physical memory chips inside the servers that run the AI model The settings menu where users configure how the AI responds The rules that prevent AI from producing harmful content 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

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

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 The 2022 breakthrough was AI becoming conscious and able to truly understand language

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

Only 20% of AI tools are reliable; 80% are experimental and should not be used at work 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 AI tools cost 80% more than traditional software and deliver only 20% of the value

According to McKinsey's 2025 survey, approximately what fraction of organizations report regular AI use in at least one business function?

About 25% — AI adoption is still in early phases for most organizations About 50% — half of organizations have started; half remain uninvolved Less than 10% — AI is still primarily used only by technology companies 88% — AI is now mainstream in organizations, not experimental

Why is 'Artificial Intelligence' considered a misleading name by many researchers?

AI is misleading because the system is not artificial — it uses real human data AI systems don't think or understand — they perform sophisticated pattern recognition and statistical prediction, which produces human-seeming outputs without human-like comprehension The name is misleading because AI is more intelligent than humans in every domain There is no controversy about the name — it accurately describes how the systems work

Which of the following is the correct relationship between AI, machine learning, and generative AI?

Generative AI came before machine learning, which came before AI Generative AI is a type of machine learning, which is a type of AI — they are nested categories, not separate things AI is only about robots; machine learning is about software; generative AI is a marketing term Machine learning and generative AI are the same thing with different names

Even the most capable AI models in 2026 cannot do which of the following?

Summarize a long document into key bullet points Write code that executes correctly in common programming languages Produce grammatically correct text in English Reliably verify whether their own output is factually correct — models can produce false information with the same confidence as true information

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

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

What is the 'thinking partner' use case for AI?

Using AI to make decisions for you when you're uncertain what to do Asking AI to agree with your existing conclusion to build confidence before a presentation Using AI to stress-test plans, identify weaknesses in arguments, and explore a decision from multiple angles — not for the answer, but to sharpen your own thinking A specific AI product designed for therapists and mental health professionals

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

Temperature controls language formality: low for formal writing, high for casual writing Temperature only affects response speed, not content — lower is faster, higher is slower Higher temperature always produces better quality — you should maximize it for all tasks 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

What is AI 'hallucination'?

When AI produces visual artifacts or distorted images 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

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

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

Which of these everyday products uses machine learning or AI?

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

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

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

Which of the following describes a realistic and high-value use of AI in 2026?

Using AI to replace all written communication with a client without any human involvement Using AI to make final legal or medical decisions without a professional reviewing them Delegating an entire client project to an AI agent with no human review of outputs Using AI to produce a first draft of a client proposal, then editing it with domain knowledge and relationship context the AI doesn't have