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

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 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 Narrow AI runs on phones; general-purpose AI requires large servers

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 Delegating final decisions to AI to remove human bias from the process Never verifying AI output to avoid slowing down the productivity gains Keeping the judgment — AI generates options and drafts; the human remains accountable for decisions and final outputs

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

The AI Economy course — which focuses on macroeconomic trends and is best taken after building practical AI skills There is no recommended next step — AI Basics is a standalone course with no further progression 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

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

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

Why does an AI model perform better on common tasks than on rare or highly specialized knowledge?

Rare topics require more parameters, and models have a fixed number available Common tasks are hard-coded into AI models; rare tasks require internet access Common tasks appear more frequently in training data, so the model has seen more examples to learn from — rare or specialized topics have fewer training examples, increasing error and hallucination risk AI models are intentionally limited on specialized topics to avoid replacing experts

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

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

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

ChatGPT was the first AI to use the internet in real time to answer questions ChatGPT was the first AI ever to understand English language 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 different because it ran on quantum computers unavailable to earlier AI

What is AI 'hallucination'?

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 When AI generates plausible-sounding but factually incorrect content — stated confidently, without any signal to the user that the information may be wrong

Which of the following tasks is AI MOST reliable at?

Accurately citing a specific legal case from last month — AI has knowledge cutoffs and hallucination risk with specific facts Calculating the exact compound interest on a complex loan schedule — mathematical precision is a known AI weakness Producing a well-structured first draft of a professional email — AI draws on vast writing patterns and excels at fluent, structured text generation Telling you whether a news article published yesterday is accurate — AI can't verify real-world current facts

A lawyer asks an AI for case citations supporting a specific legal argument. What is the risk, and what should they do?

There is no risk — AI was trained on legal databases and citations are reliably accurate The risk is only if the lawyer is in a jurisdiction not covered by US law training data The risk is low if the AI is asked to cite recent cases from 2024 or later High hallucination risk — AI may generate convincing-looking but nonexistent case citations. Every citation must be verified in a legal database before use