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AI AllOther (6) Solving a constraint satisfaction problem on a finite domain is an/a ___________ problem with respect to the domain size. a) P complete b) NP complete c) NP hard d) Domain dependent
1 2379____________ is/are useful when the original formulation of a problem is altered in some way, typically because the set of constraints to consider evolves because of the environment. a) Static CSPs b) Dynamic CSPs c) Flexible CSPs d) None of the above
1 3916Fuzzy logic is a form of a) Two-valued logic b) Crisp set logic c) Many-valued logic d) Binary set logic
1 2366The truth values of traditional set theory is ____________ and that of fuzzy set is __________ a) Either 0 or 1, between 0 & 1 b) Between 0 & 1, either 0 or 1 c) Between 0 & 1, between 0 & 1 d) Either 0 or 1, either 0 or 1
1 8249Fuzzy logic is extension of Crisp set with an extension of handling the concept of Partial Truth. a) True b) False
1 4096The room temperature is hot. Here the hot (use of linguistic variable is used) can be represented by _______ . a) Fuzzy Set b) Crisp Set
1 4986The values of the set membership is represented by a) Discrete Set b) Degree of truth c) Probabilities d) Both b & c
1 10235What is meant by probability density function? a) Probability distributions b) Continuous variable c) Discrete variable d) Probability distributions for Continuous variables
1 1968Japanese were the first to utilize fuzzy logic practically on high-speed trains in Sendai. a) True b) False
1 3068Which of the following is used for probability theory sentences? a) Conditional logic b) Logic c) Extension of propositional logic d) None of the mentioned
1 3259Fuzzy Set theory defines fuzzy operators. Choose the fuzzy operators from the following. a) AND b) OR c) NOT d) EX-OR
1 5525There are also other operators, more linguistic in nature, called __________ that can be applied to fuzzy set theory. a) Hedges b) Lingual Variable c) Fuzz Variable d) None of the mentioned
1 3060Where does the Bayes rule can be used? a) Solving queries b) Increasing complexity c) Decreasing complexity d) Answering probabilistic query
1 3251
Provide examples of industries where fairness in AI is particularly critical.
Discuss how AI is used to identify vulnerabilities.
What techniques can improve the explainability of AI models?
What methods are used to make AI decisions more transparent?
Explain how AI models predict stock market trends.
Why is it beneficial to run AI models on edge devices (IoT)?
What challenges do organizations face in implementing fairness in AI models?
What ethical concerns arise when AI models are treated as "black boxes"?
What are the apis outside tensorflow project?
What are the advantages of low-power AI models?
How does XAI address regulatory compliance issues?
How does AI intersect with human bias and societal inequities?
How can you optimize AI models for edge deployment?
What are some open problems you find interesting?
How do you measure fairness in an AI model?