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AI خرافات کوئز

روانی سے جوابات، ڈیٹا اور ماڈل سائز کے مفروضوں، ڈیمو، بینچ مارکس، آٹومیشن کی ذمہ داری، اور دائرہ کار کے پیچھے ثبوت کی جانچ کرتا ہے۔

متعلقہ گائیڈ کے راستےAI خرافات
سوال 1 کی 8

A model answers fluently and names a source. What should a reader still check?

ہر سوال کا جائزہ لیں۔8 سوالات

جب آپ کوئز دیتے ہیں تو جوابات اور وضاحتیں ظاہر ہوتی ہیں۔

  1. A model answers fluently and names a source. What should a reader still check?
    • Only whether the answer is polite
    • Whether the response is long enough
    • Whether the model used a large context window
    • Whether the source exists and supports the claim
  2. Does more training data guarantee a better system?
    • No; data can be irrelevant or systematically flawed
    • Yes, if it comes from the internet
    • Yes, regardless of the task
    • No model can learn from more data
  3. Why compare a larger model with smaller or simpler alternatives?
    • Larger models always have worse accuracy
    • It may cost more without meeting the task’s needs
    • A benchmark score cannot be measured
    • Model size determines deployment law
  4. A demo solves one impressive case. What conclusion is justified?
    • That the system can handle every difficult case
    • That it will be reliable in production
    • Only that the demonstrated setup handled that case; more tests are needed
    • That no tool or context contributed
  5. Does automation remove human responsibility for the system?
    • Yes, when outputs are generated automatically
    • Yes, if a vendor hosts the model
    • Only if a model has a high score
    • No; people choose goals, data, interfaces, permissions, and deployment conditions
  6. What does one failure tell you about a system?
    • It reveals a failure worth testing, but not that every use is ineffective
    • That the system is useless for all tasks
    • That the model is conscious
    • That all future versions will fail identically
  7. What is a benchmark result distinct from?
    • A measured score
    • A future prediction or a statement about consciousness
    • An evaluation setup
    • A task-specific test
  8. How should a broad capability claim be phrased?
    • As strongly as possible to attract attention
    • As a guarantee based on a single score
    • At the scope the supporting evidence actually covers
    • Without naming the test conditions