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Майбутнє AI Quiz
Тестує припущення, сценарії, оновлення доказів, відтворювані результати, нерівномірний прогрес, прогнозні записи та надійні рішення.
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Яким має бути корисний прогноз AI?
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Відповіді та пояснення з’являються під час виконання тесту.
- Яким має бути корисний прогноз AI?
- Припущення, часовий горизонт і докази
- Тільки впевнений висновок
- Модель, яка його написала
- Точна дата запуску, незалежно від невизначеності
- 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
- 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
- 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
- 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
- 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
- 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
- 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