Do you really know about AI? — Part 1

Daniel Deutsch
Feb 7 · 4 min read

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 will be part 1 of a series of questions.


  1. What is AI? How can you define it?
  2. What does “systems that think like humans” mean?
  3. What does “systems that think rationally” mean?
  4. What is a “Turing Test” and what is an “Imitation Game”?
  5. What is the problem of the Turing Test?
  6. What does “systems that act like humans” mean?
  7. What does “systems that act rationally” mean?
  8. What is an algorithm?
  9. Name two current trends in AI.
  10. Is AI and Cognitive Science the same thing?
  11. Is AI Deep Learning?
  12. What are the most important subfields of AI?


  1. What fields have contributed to AI in the form of ideas, viewpoints, and techniques?
  2. What was the first purpose of automated tools for Aristotle?
  3. Name three examples of early examples for automation inventions.
  4. Who invented the first difference- and analytical engine?
  5. What is the origin of the word “robot”?
  6. Why was there a setback for logical reasoning after the mathematical analysis of logic?
  7. Name the laws of robotics by Asimov.
  8. When was the first Turing-complete computer ENIAC finished?
  9. When was the concept of neural networks first introduced? (Tip: by McCulloch and Pitts)
  10. What was the inspiration for artificial neural networks and which two components are the most important ones from it? What is the advantage in terms of calculation?
  11. Why is the number “seven, plus or minus two” considered magical in cognitive science?
  12. When was the “official” birth of the term AI? (It was at the Dartmouth conference)
  13. Name three of the “founding fathers” of AI.
  14. Why were there great expectations for AI and why was it followed by a depression?
  15. What is the difference between symbolic and subsymbolic AI?
  16. Name three areas where a state of the art use of AI has been developed.
  17. Why did Deep Learning start to bring breakthroughs only since 2010?
  18. Herb Simon answered “Why AI” with two main purposes. What were those?

3.Puzzles and Search

  1. What two components are needed for defining a search problem?
  2. What is a state description?
  3. Explain the basic procedure of a simple exhaustive search.
  4. Explain the basic procedure of a local search. What are its advantages or disadvantages?
  5. What is Heuristic? What is its purpose in search algorithms?
  6. Explain the hill-climbing search (aka greedy local search). What is its algorithm and what is the main problem?
  7. What is a state-space landscape? What are its main components?
  8. What are two randomized hill-climbing variants? Describe the algorithm.
  9. What is a Beam Search? Describe its algorithm.
  10. What is a Beam size?
  11. Compare Beam search to k-parallel hill-climbing. What is the difference?
  12. What is the difference between Beam search and stochastic Beam search?
  13. What is a simulated annealing search? What is the algorithmic approach?
  14. What is a genetic algorithm? What is the algorithmic approach?
  15. Why are genetic algorithms so popular?
  16. What are constraint satisfaction problems? What two components characterize this type of search problem?
  17. Name two real-world constraint satisfaction problems.
  18. What is a constraint graph? What are its nodes and edges?
  19. What types of constraints exist?
  20. What are the basic approaches to solve constraint satisfaction problems?
  21. What is a backtracking search? What is the algorithmic approach?
  22. What are general-purpose heuristics? Name examples.
  23. What is constraint propagation? What is the concept behind forward checking?
  24. What is arc consistency? How does it fit into the idea of constraint propagation?
  25. How would you integrate constraint propagation with backtracking search?
  26. Explain how local search can be done for constraint satisfaction problems.
  27. What problem-solving technique is meant by “divide et impera”?

4.Game Playing

  1. Why are games an important field for AI?
  2. What are the differences between adversary and no-adversary problem-solving?
  3. What is the Zermelo’s Theorem?
  4. What is the Retrograde Analysis algorithm?
  5. Why is Retrograde Analysis too complex for most games?
  6. What is a minmax search and what is an efficient variant of it called?
  7. Why can a perfect chess game not be solved in practice?
  8. What was Shannon’s idea for solving imperfect real-world decisions?
  9. What is the idea behind brute force and selective search, and what are its differences?
  10. What is an evaluation function in the context of selective search?
  11. What is the difference between a heuristic evaluation function?
  12. What are linear evaluation functions? What are the advantages and disadvantages?
  13. What is a Quiescence Search?
  14. What is the horizon effect in the context of a fixed-depth search?
  15. What is forward pruning?
  16. What are Transposition tables? How do they work?
  17. What are games where human supremacy still exists?
  18. What is a game tree and how is it constructed?
  19. Do deeper searches necessarily help in problem-solving?
  20. Name two key events in game playing, where computers beat humans.


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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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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