AI

1
AI ES Quiz1Introduction

• For MTech I Sem students

2
1. Turing Tests are for

• Testing artificial intelligence
• Testing a computer
• Testing humans
• Testing a machine can function as good as human

3
2. Who coined the term AI

• John McCarthy of Stanford
• Marvin Minsky
• Semyour Cray
• Bill Gates

4
3. One of the following problem does not require
AI

• Face Recognition
• Speech Recognition
• Dictionary
• Language Translator

5
4. A Knowledge Engineer

• Builds a database from data
• Captures and represents knowledge
• Designs Expert system
• Uses Knowledge Management

6
5. Expert systems have following characteristics
except

• They acquire and represent knowledge
• They have inferencing capability
• They can learn new inferencing
• They can reason the output

7
6. AI deals with

• symbolic, non-algorithmic problem solving
  techniques
• Knowledge acquisition, representation,
  inferencing
• Algorithmic Learning from data
• Study of the brain

8
7.The various categories of knowledge are the
following except

• Declarative
• Procedural
• Metaknowledge
• New knowledge

9
8.Metaknowledge is

• knowledge relates to a specific object. Includes
  information about the meaning, roles,
  environment, resources, activities, associations
  and outcomes of the object
• knowledge relates to the procedures employed in
  the problem-solving process
• Knowledge about Knowledge
• knowledge about the operation of knowledge-based
  systems

10
9. Prolog uses the

• Proposition logic
• Predicate logic
• Proposition calculus
• Predicate Calculus

11
10. A Semantic Network is a

• Graphic dipiction of knowledge with nodes and
  links representing hierarchical relationships
  between objects
• Decision trees
• O-A-V Triplets
• Cognitive maps

12
11. Uncertainity stands for

• Dealing with degree of truthness and degree of
  falseness
• When a user cannot give a precise answer
• Imprecise knowledge
• Incomplete information

13
12. Neural Networks are mathematical models of

• Human nervous system
• Brain
• Spinal chord
• cognition

14
13. The following is the sigmoid function

• Sigm ( y ) 1 / (1 e-y)
• Sigm( y) 1-y
• Sigm(y) 1/ ( 1y)
• Sigm(y) y

15
14. Neural Networks have following properties
except

• Learn from training data
• Use learning algorithms
• Reason the output
• Have densely connected neurons

16
15 Back propagation Network learns by

• Back propagating the error and adjusting the
  weights accordingly
• Back propagating the data from training examples
• Back propagating the outputs and iteratively
  adjusting the weights
• Repeated presentation of training inputs

17
16. The difference between ES and ANN are

• ANNs learn from training data
• ES can provide reasoning of the output
• ANNs use numeric, algorithmic approach
• ANNs do not store knowledge as rules

18
17. The difference between associative memory and
content addressable memory are

• Both are same
• Associative memory can use the part of data to
  recollect the data
• Same as random access memory
• Same as in hard disks

19
18. In unsupervised learning the network learns by

• Using the data
• Using the training examples
• Using the knowledge rules
• Using the inferencing

20
19. Examples for unsupervised learning NNs are

• Multilayer perceptrons
• Hopfield networks
• Kahonens Self organizing feature map
• Adaptive resonance theory networks

21
20. Genetic Algorithms have following operators
except

• Reproduction
• Cross-over
• Mutation
• training

22
21. Fuzzy sets differs from crisp sets by

• Having a degree of memberships for each element
• Subsets
• Operations on the sets
• No difference

23
22. Fuzzy logic is useful in

• Dealing with uncertainity, imprecision
• Dealing with precise data
• Obscure data
• Random data

24
(No Transcript)