Artificial Intelligence Interview Questions
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1) What is Artificial Intelligence?
Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.
2) What is an artificial intelligence Neural Networks?
Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.
3) What are the various areas where AI (Artificial Intelligence) can be used?
Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.
4)What is the difference between strong AI and weak AI?
Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to humans. Weak AI simply states that some “thinking-like” features can be added to computers to make them more useful tools… and this has already started to happen (witness expert systems, drive-by-wire cars and speech recognition software). What does ‘think’ and ‘thinking-like’ mean? That’s a matter of much debate.
5) Mention the difference between statistical AI and Classical AI ?
Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
6)What is a top-down parser?
A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.
7)What are the various areas where AI (Artificial Intelligence) can be used?
Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.
8)What is alternate, artificial, compound and natural key?
Alternate Key: Excluding primary keys all candidate keys are known as Alternate Keys.
Artificial Key: If no obvious key either stands alone or compound is available, then the last resort is to, simply create a key, by assigning a number to each record or occurrence. This is known as artificial key.
Compound Key: When there is no single data element that uniquely defines the occurrence within a construct, then integrating multiple elements to create a unique identifier for the construct is known as Compound Key.
Natural Key: Natural key is one of the data element that is stored within a construct, and which is utilized as the primary key.
9)A* algorithm is based on which search method?
A* algorithm is based on best first search method, as it gives an idea of optimization and quick choose of path, and all characteristics lie in A* algorithm.
10)Mention the difference between breadth first search and best first search in artificial intelligence?
These are the two strategies which are quite similar. In best first search, we expand the nodes in accordance with the evaluation function. While, in breadth first search a node is expanded in accordance to the cost function of the parent node.
11) What is FOPL stands for and explain its role in Artificial Intelligence?
FOPL stands for First Order Predicate Logic, Predicate Logic provides
a) A language to express assertions about certain “World”
b) An inference system to deductive apparatus whereby we may draw conclusions from such assertion
c) A semantic based on set theory
12)What does the language of FOPL consists of
a) A set of constant symbols
b) A set of variables
c) A set of predicate symbols
d) A set of function symbols
e) The logical connective
f) The Universal Quantifier and Existential Qualifier
g) A special binary relation of equality
13) Which search algorithm will use a limited amount of memory in online search?
RBFE and SMA* will solve any kind of problem that A* can’t by using a limited amount of memory.
14)While creating Bayesian Network what is the consequence between a node and its predecessors?
While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.
15)To answer any query how the Bayesian network can be used?
If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.
16)In top-down inductive learning methods how many literals are available? What are they?
There are three literals available in top-down inductive learning methods they are
a) Predicates
b) Equality and Inequality
c) Arithmetic Literals
17)What is Hidden Markov Model (HMMs) is used?
Hidden Markov Models are a ubiquitous tool for modelling time series data or to model sequence behaviour. They are used in almost all current speech recognition systems.
18)How logical inference can be solved in Propositional Logic?
In Propositional Logic, Logical Inference algorithm can be solved by using
a) Logical Equivalence
b) Validity
c) Satisfying ability
19)Which process makes different logical expression looks identical?
‘Unification’ process makes different logical expressions identical. Lifted inferences require finding substitute which can make a different expression looks identical. This process is called unification.
20)Which property is considered as not a desirable property of a logical rule-based system?
“Attachment” is considered as not a desirable property of a logical rule based system.
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