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

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

The risk is low if the AI is asked to cite recent cases from 2024 or later 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 High hallucination risk — AI may generate convincing-looking but nonexistent case citations. Every citation must be verified in a legal database before use

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

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

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

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

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 Asking AI to agree with your existing conclusion to build confidence before a presentation Using AI to make decisions for you when you're uncertain what to do A specific AI product designed for therapists and mental health professionals

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 Because we've had AI winters before, today's AI is definitely not as advanced as claimed 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

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

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 Common tasks are hard-coded into AI models; rare tasks require internet access Rare topics require more parameters, and models have a fixed number available AI models are intentionally limited on specialized topics to avoid replacing experts

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

The Vibe Coding Economy course — which is the advanced technical course and assumes significant AI experience 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 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

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

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

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 security credential required to access AI APIs A single character of text — AI processes text one character at a time A cryptocurrency used to pay for AI model usage

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

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 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 Users lost control — AI now makes all decisions without user input