人工智能基金会

• 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

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

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

   AI concepts and boundaries
   什么是人工智能？ →机器学习基础知识 →
2. 2

   How systems learn

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

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

   Data, training, and generalization
   人工智能如何学习 →机器学习基础知识 →人工智能培训 →
3. 3

   Models and inference

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

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

   Models, inference, and limitations
   人工智能模型解释 →人工智能推理 →神经网络 →
4. 4

   Evidence and uncertainty

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

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

   Evaluation and benchmark literacyUncertainty and confidence calibrationSource and claim verification
   人工智能基准 →人工智能幻觉 →人工智能神话 →