Future of AI Quiz

Answers and explanations appear as you take the quiz.

1. What should a useful AI forecast state?
   • Assumptions, time horizon, and evidence
   • Only a confident conclusion
   • The model used to write it
   • A precise launch date regardless of uncertainty
2. A controlled benchmark improves. What does that establish about widespread product adoption?
   • That all organizations can deploy it now
   • That infrastructure and maintenance are solved
   • Not when or how widely adoption will happen by itself
   • That future reliability is guaranteed
3. Why use faster, slower, and uneven-progress scenarios?
   • They identify which forecast is guaranteed
   • They make planning robust when progress is uncertain across tasks
   • They eliminate the need for indicators
   • They convert a scenario into a probability
4. Which indicator better updates a capability assessment than a launch announcement?
   • A company product announcement alone
   • A funding round
   • A marketing forecast
   • Independently reproduced task performance and sustained reliability
5. Why record a forecast and what would count as a miss?
   • To ensure the forecast becomes correct
   • To keep the prediction secret
   • To assess it against the stated outcome without changing the definition afterward
   • To avoid updating assumptions
6. What does uneven capability growth imply?
   • Progress on one benchmark does not prove the same progress in other domains
   • Every task improves at the same rate
   • One benchmark predicts physical interaction reliability
   • A capability cannot be measured
7. What is a robust decision under multiple plausible futures?
   • One that depends on the fastest forecast being correct
   • A decision made only after certainty arrives
   • One that has no current evidence
   • One that remains useful across several scenarios
8. Why review forecasts over time?
   • Forecasts become facts after publication
   • New evidence can update an assessment and reveal whether prior predictions were calibrated
   • Predictions should never change
   • A later review hides the original assumptions