Maswali yanayounganishwa na mwongozo · Kati Kiwango

Maswali ya AI katika Sayansi

Tathmini AI ya kisayansi kupitia muundo wa maswali, utenganishaji wa data, kutokuwa na uhakika, asili, majaribio ya zana mtambuka na ushahidi wa madai.

Njia za mwongozo zinazohusianaSayansi ya AI
Swali 1 ya 8

Before selecting a scientific AI method, what should a researcher define?

Hakiki kila swali8 maswali

Majibu na maelezo huonekana unapojibu maswali.

  1. Before selecting a scientific AI method, what should a researcher define?
    • The scientific question and what observation would disconfirm the claim
    • Only the model architecture
    • The desired publication date
    • A result that cannot be challenged
  2. Why might a random train/test split overstate generalization when readings are correlated?
    • The test set becomes larger
    • Data from the same subject, instrument, or experiment can appear across splits
    • Random splits remove all uncertainty
    • The model cannot train on numbers
  3. What should a researcher record to support reproducibility?
    • Only the model’s final prediction
    • Only the paper title
    • Preprocessing, model versions, relevant random seeds, and evaluation material
    • The number of plots generated
  4. What is required before treating an AI-generated hypothesis as a supported scientific claim?
    • As soon as the model explains it confidently
    • Whenever it matches a familiar theory
    • Only when the model’s score is high
    • After appropriate experiments or independent analysis support it
  5. A model performs well on random readings from instrument A but poorly on instrument B. What should the team do?
    • Report both results and investigate calibration or measurement differences
    • Report only the better score
    • Claim general performance based on the random split
    • Remove instrument B from the record
  6. Why separate what is measured from what is inferred?
    • It removes the need for replication
    • It clarifies which parts are observations and which are model conclusions
    • It guarantees a causal explanation
    • It makes missing data irrelevant
  7. What should a researcher understand about calibration, confidence intervals, and prediction intervals?
    • They are interchangeable scores
    • Use only the largest value
    • They answer different questions
    • Replace them with the model name
  8. What should be preserved from source data through a published figure?
    • Only a screenshot of the chart
    • The model’s general description
    • A list of software logos
    • The path from source data to figure, table, or manuscript