Gidauniyar AI

• Explain training and inference in plain language.
• Separate demonstrated capability from marketing claims.
• Evaluate AI outputs using evidence, uncertainty, and fit-for-purpose tests.

Prerequisites:None.

1. 1

   What AI is

   Sakamakon: Distinguish AI, machine learning, and ordinary software.

   Ayyuka: Classify five everyday systems and explain which definition of AI you used.

   AI concepts and boundaries
   Menene AI? →Tushen Koyon Inji →
2. 2

   How systems learn

   Sakamakon: Describe the role of examples, objectives, and generalization.

   Ayyuka: Diagnose why a model can perform well in testing but fail for a new population.

   Data, training, and generalization
   Yadda AI ke Koyi →Tushen Koyon Inji →AI horo →
3. 3

   Models and inference

   Sakamakon: Explain what happens when a trained model produces an output.

   Ayyuka: Trace one user request from input through model output and human review.

   Model, inference, da iyakokin
   AI Model ya bayyana →AI Inference →Hanyoyin Sadarwar Jijiya →
4. 4

   Evidence and uncertainty

   Sakamakon: Check claims, benchmarks, and confident-sounding outputs.

   Ayyuka: Create a claim ledger for one AI product announcement.

   Kimantawa da Benchmark LiteracyUncertainty and confidence calibrationSource da da'awar tabbatar
   AI Benchmarks →AI Hallucinations →AI Tatsuniyoyi →