Artificial Intelligence Interview Questions Set 1
Q.1. What is Artificial Intelligence?
Answer:- Artificial Intelligence is a field of computer science wherein the cognitive functions of the human brain are studied and tried to be replicated on a machine/system. Artificial Intelligence is today widely used for various applications like computer vision, speech recognition, decision-making, perception, reasoning, cognitive capabilities, and so on.
Q.2. What is Neural Network in Artificial Intelligence?
Answer:- In artificial intelligence, neural network is an emulation of a biological neural system, which receives the data, process the data and gives the output based on the algorithm and empirical data.
Q.3. What is the difference between AI, Machine Learning and Deep Learning?
Answer:-
| Artificial Intelligence |
Machine Learning |
Deep Learning |
| AI represents simulated intelligence in machines. |
Machine learning is the practice of getting machines to make decisions without being programmed. |
Deep learning is the process of using artificial neural networks to solve complex problems. |
| AI is a subset of Data Science. |
Machine learning is a subset of AI and Data Science. |
Deep Learning is a subset of Machine Learning, AI and Data Science. |
| Aim is to build machines which are capable of thinking like humans. |
Aim is to make machines lean through data so that they can solve problems. |
Aim is to build neural networks that automatically discover patterns for feature detection. |
Q.4. What is the difference between Strong Artificial Intelligence and Weak Artificial Intelligence?
Answer:-
| Weak AI |
Strong AI |
| Narrow application, with very limited scope |
Widely applied, with vast scope |
| Good at specific tasks |
Incredible human-level intelligence |
| Uses supervised and unsupervised learning to process data |
Uses clustering and association to process data |
| E.g. Siri, Alexa, etc. |
E.g. Advanced Robotics |
Q.5. List some applications of AI.
Answer:-
- Natural language processing
- Chat bots
- Sentiment analysis
- Sales prediction
- Self-driving cars
- Facial expression recognition
- Image tagging
Q.6. What is alternate, artificial, compound and natural key?
Answer:- 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.
Q.7. What is an expert system? What are the characteristics of an expert system?
Answer:- An expert system is an Artificial Intelligence program that has expert-level knowledge about a specific area and how to utilize its information to react appropriately. These systems have the expertise to substitute a human expert. Their characteristics include:
- High performance
- Adequate response time
- Reliability
- Understandability
Q.8. What is a heuristic function?
Answer:- A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.
Q.9. Mention the difference between breadth first search and best first search in artificial intelligence?
Answer:- 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.
Q.10. Explain Alpha–Beta pruning.
Answer:- Alpha–Beta pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to ‘n’ depths and can prune the entire sub trees and leaves.
Q.11. What Are The Properties Of A Good Knowledge Representation System?
Answer:- A good knowledge representation system must have following properties:
- Representation Adequacy: It must be able to represent all knowledge required in a particular domain
- Inferential Adequacy: It must be able to derive knowledge representation structures such as symbols when new knowledge is inferred from old knowledge
- Inferential Efficiency: It must be able to incorporate additional information into knowledge structures which may help inference process to move in promising direction
- Acquisitional Efficiency: It must be able to incorporate new information.
Q.12. What Are The Techniques To Represent Knowledge?
Answer:- There are four techniques to represent knowledge:
- Relational knowledge: In this representation, knowledge is represented as a set of relations, similar to relations that are used in the database
- Inheritable knowledge: In this representation, knowledge is represented using objects, their attributes and the values of the attributes
- Inferential knowledge: In this representation, knowledge is represented in the form of first-order predicate logic
- Procedural knowledge: In this representation, knowledge is represented as a set of rules and a rule describes an action to be performed when a condition is met.
Q.13. How are game theory and AI related?
Answer:- AI system uses game theory for enhancement; it requires more than one participant which narrows the field quite a bit. The two fundamental roles are as follows:
- Participant design: Game theory is used to enhance the decision of a participant to get maximum utility.
- Mechanism design: Inverse game theory designs a game for a group of intelligent participants, e.g., auctions.
Q.14. What is Turing test?
Answer:- The Turing test is a method to test a machine’s ability to match the human-level intelligence. A machine is used to challenge human intelligence, and when it passes the test it is considered intelligent. Yet a machine could be viewed as intelligent without sufficiently knowing how to mimic a human.
Q.15. What is a fuzzy logic?
Answer:- Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF-THEN rules.
Q.16. List the applications of fuzzy logic.
Answer:-
- Facial pattern recognition
- Air conditioners, washing machines, and vacuum cleaners
- Anti skid braking systems and transmission systems
- Control of subway systems and unmanned helicopters
- Weather forecasting systems
- Project risk assessment
- Medical diagnosis and treatment plans
- Stock trading
Q.17. How Many Types Of Entities Are There In Knowledge Representation?
Answer:- There are two types of entities in knowledge representation:
- Facts: These are truths that need to be represented
- Symbols: It is a form of representation of facts and it is manipulated by the programs to derive new facts.
Q.18. What Is The Difference Between Classical Ai And Statistical Ai?
Answer:- Statistical AI, arising from machine learning, tends to be more concerned with "inductive" thought: given a set of patterns, induce the trend. Classical AI, on the other hand, is more concerned with "deductive" thought: given a set of constraints, deduce a conclusion. Another difference, as mentioned in the previous question, is that C++ tends to be a favorite language for statistical AI while LISP dominates in classical AI.
Q.19. What are the features of AI?
Answer:- AI system shares the following features:
- Adaptive: AI system is adaptive as they observe results and improve their performance.
- Reactive: They react to changing conditions as they continuously monitor their inputs and when certain condition apply, they react.
- Concurrent: They are capable to handle multiple users simultaneously
- Forward-Looking: They explore through a space of possible scenarios to reach the goal.
Q.20. What is the Minimax Algorithm? Explain the terminologies involved in a Minimax problem.
Answer:- Minimax is a recursive algorithm used to select an optimal move for a player assuming that the other player is also playing optimally.
A game can be defined as a search problem with the following components:
- Game Tree: A tree structure containing all the possible moves.
- Initial state: The initial position of the board and showing whose move it is.
- Successor function: It defines the possible legal moves a player can make.
- Terminal state: It is the position of the board when the game ends.
- Utility function: It is a function which assigns a numeric value for the outcome of a game.
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