Do you really know about AI? — Part 3

Daniel Deutsch
Feb 10 · 3 min read
Photo by Markus Spiske — https://unsplash.com/photos/FXFz-sW0uwo

More and more people are getting into the field of machine learning and AI. There are many ways to acquire knowledge in this field. But do you really know about the basics in the field of AI? During my studies, I came across various topics and formulated questions. Can you answer them? This is part 3, the last part of a series of questions.

9.Natural Language Processing

  1. What is Natural Language Processing?
  2. What is ELIZA and how does it work? Why is its simple strategy successful?
  3. What is PARRY and what is its difference to ELIZA?
  4. What is STUDENT and what was its specialty?
  5. What are the fundamental problems of natural language that have to be addressed in NLP?
  6. What are the four fundamental NLP tasks?
  7. Explain word segmentation, part-of-speech tagging, syntactic analysis, and semantic analysis.
  8. What is Word2Vec?
  9. Name two variants of Word2Vec.
  10. How does a vector-space model work?
  11. Name three other NLP tasks apart from conversational agents.
  12. What is the difference between information retrieval and information extraction?
  13. What is the Bag of Words Model?
  14. What are class-conditional probabilities in text classification?
  15. What are semantic embeddings?
  16. Why is machine translation hard?
  17. What is end-to-end learning in the context of NLP?

10.Interpretability in AI

  1. What is interpretability?
  2. Why is interpretability in AI important?
  3. What is a bias in learning generalization? Name an example.
  4. What is the comprehensibility postulate by Michalski (1983)?
  5. What distinguishing criteria has Michie (1988) developed for Machine Learning?
  6. What is the difference between Knowledge Discovery in Databases (KDD) and Machine Learning? (Tipp: Piatetsky-Shapiro)
  7. What does Data Mining do? (Tipp: Fayyad et al. 1996)
  8. How can you measure interpretability? What does complexity mean in this context?
  9. What are cognitive biases? Name an example.
  10. Why are they important in terms of interpretability?
  11. What are representativeness heuristics?
  12. How do understandability and rule length work together?
  13. What are discriminative rules?
  14. What are characteristic rules?
  15. What is Occam’s Razor?
  16. What is the Minimum Description Length Principle?
  17. Why do humans sometimes prefer longer explanations? Think about Kolmogorov Directions.
  18. What is recognition heuristic?
  19. How can you interpret black-box models?
  20. What is LIME in the context of interpretability? What is its algorithmic approach?

11.Philosophical Foundations

  1. When is a machine considered intelligent?
  2. What did Turing want to demonstrate with his test?
  3. What is the Loebner Competition?
  4. What are CAPTCHAs? What does the acronym stand for? Give an example.
  5. What is the Physical Symbol Systems Hypothesis (PSS)?
  6. Explain the idea of the Grandmother Cell or the Pamela Anderson Cells in the PSS context.
  7. Name three objections to strong AI.
  8. What is Gödel’s Incompleteness Theorem?
  9. What is the Chinese Room experiment?
  10. What is the Chinese Room Argument?
  11. What is the difference between weak and strong AI? What is the public perception of it?
  12. What is the mind-body problem? What does it mean in the context of AI?
  13. What is the purpose of the Brain-prothesis experiment?

About

Daniel is an entrepreneur, software developer, and lawyer. His knowledge and interests evolve around business law and programming machine learning applications. To the core, he considers himself a problem solver of complex environments, which is reflected in his various projects. Don’t hesitate to get in touch if you have ideas, projects or problems.

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Daniel Deutsch

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Business Law and Machine Learning. Pushing the limits to make the world a better place. Open for Projects of any kind.

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