Preparing for an Artificial Intelligence (AI) interview requires a solid grasp of key concepts and techniques in this dynamic field. This guide offers a collection of AI interview questions and answers, covering topics like machine learning, natural language processing, computer vision, and ethics. By familiarizing yourself with these questions and formulating thoughtful responses, you can demonstrate your proficiency in AI and enhance your chances of success in the interview.
Following are Important AI Interview Questions and Answers you should know to crack AI Interview:
1. What is Artificial Intelligence?
AI is a subset of computer science. Here the cognitive functions of human brain is analysed and attempted to be replicated on a machine or system. It is widely used for several applications including computer vision, speech recognition, reasoning, decision-making etc.
List some applications of AI.
- Natural language processing
- Sentiment analysis
- Sales Forecasting
- Chat bots
- Self-driving cars
- Facial expression recognition
List the programming languages in AI.
2. What is Tower of Hanoi?
Being a mathematical puzzle, the Tower of Hanoi shows how recursion might be used as a device in forming an algorithm to handle a specific problem. Breath first search algorithm and decision tree are applied to solve Tower of Hanoi with the help of AI.
3. What is Turing test?
The Turing test is a method that tests the machine’s ability to correspond to the human level intelligence. A machine is here a challenge to human intelligence. When the machine passes the test it is regarded as intelligent.
4. What is an expert system & characteristics of expert system?
An artificial intelligence program that consist of expert-level knowledge about a particular area and knows how to use its details for responding in the correct manner is called as an expert system. These systems contain the expertise to be the alternative to human expert. The characteristics are:
- High performance
- Sufficient response time
- Dependability
- Comprehendible
5. Explain types of Artificial Intelligence?
The two types of artificial intelligence are:
Strong artificial intelligence
- Its essence lies in the formation of real intelligence artificially. Besides, strong AI has the belief that machines can be made to perceive things.
Two types of strong AI:
- Human-like AI-Here the computer program thinks and rationalizes at the level of human being.
- Non-human-like AI-Here the computer program gets a non-human means of thinking an rationalizing.
Weak artificial intelligence
Her there is a foundation that there AI will always be a simulation of human cognitive function, and that computers can only seem like thinking but are not really conscious.
6. What are intelligent agents and how are they used in AI?
Being autonomous entities, intelligent agents make use of sensors to comprehend what is taking place. Later they use actuators to carry out their tasks or objectives. Their role is to be either simple or complex.
7. What is TensorFlow and what is it used for?
Being an open-source software library developed by the Google Brain team, TensorFlow is applied in machine learning and neural networks research. It is applied for data-flow programming. It makes it much easier to build specific AI features into applications comprising speech recognition and natural language processing.
8. What is machine learning and how does it relate to AI?
Being a subset of AI, machine learning functions on the idea that machines will learn and become good at tasks with the passage of time. The key is in humans not frequently inputting parameters.
9. Explain the difference between statistical AI and Classical AI?
Statistical AI arises from a machine learning concept that has more focus on “inductive” thought with the given set of patterns to induce the trend. While Classical AI iout “deductive” thought that has given a set of constraints to deduce the conclusion.
10. Define Alternate key and Artificial Key
An alternate key is also referred to as a secondary key that excludes the primary keys of all candidate keys. An artificial key is the creation of a key by assigning severa or occurrences when there is no obvious key or standalone or compound key available for access.
11. Explain the terms Compound key and Natural key
A compound key is the one used to create a unique identifier by integrating multiple elements for the construct. It is created when there will be no single data element that the occurrence uniquely within a construct. The natural key is the data element that is stored in a construct for utilizing the primary key.
12. What is the production rule comprised of?
Production rule consists of a sequence of steps and a set of rules about the behaviour of a system along with the necessary mechanism to follow the rules to respond as per the state.
13. Describe the best way to handle game playing problem
The heuristic approach is considered the best way to go for game-playing problems as it uses the intelligence guesswork technique. An example of this is the chess game between robot and human. It uses the brute force computation that looks at thousands of positions in the game.
14. Explain the search method used in the A* algorithm
A* algorithm used the best search method as it provides the idea for optimization and quick choose path along with all characteristics of the A* algorithm.
15. Explain the partial order/planning invoke.
A partial order is a plan that involves searching over the possible plans rather than searching over the possible situation and it constructs a planned piece by piece.
16. Define “Generality
Generality is the term that measures of ease to find which method to be adopted for different domains of applications.
17. Explain top-down parser
A top-down parser starts by hypothesizing a sentence to predict lower-level constituents successfully until all pre-terminal symbols are written completely.
18. Explain frames and scripts
Frames in AI are used to divide knowledge into substructure through representing the “stereotyped situations”. These are variants of the semantic network that is on the popular ways to present non-procedural knowledge in an expert system.
19. Define FOPL and explain its role in Artificial Intelligence
FOPL is the acronym of First Order Predictive Logic. It provides AI a language to convey assertions about certain “World”, an inference system to deductive apparatuhat concludes assertions, and a semantic-based on set theory.
20. What FOPL contains?
FOPL consists of a set of constant symbols, a set of variables, a set of predictive symbols, a set of function symbols, logical connective, universal quantifier, existentiaand a special binary relation of equality.
21. Where can the Bayes rule be used in Artificial Intelligence?
Bayes rule can be used when to answer the probabilistic queries conditioned on one piece of evidence in Artificial Intelligence.
22. How many terms are needed to build a Bayes model?
One conditional probability and two unconditional probabilities are required to build a Bayes model for Artificial Intelligence.
23. What consequence can be expected between a node and its predecessors while developing a Bayesian Network?
When developing a Bayesian network, a node can be conditionally independent of its predecessors.
24. What algorithm can be used to solve temporal probabilistic reasoning?
Hidden Markov Model (HMM) algorithm can be used to solve temporal probabilistic reasoning as it is independent of the transition and sensor model.
25. Define compositional semantics?
The process of deriving the meaning of P*Q from P, Q, and * is called compositional semantics.
26. What is the most straightforward approach for implementing a planning algorithm?
The state-space search takes account of everything to find a solution and that’s why it would be the most straightforward approach for implementing planning algorithms iial Intelligence.
27. Can logical inference solve propositional logic and how?
A logical inference algorithm can solve propositional logic using logical equivalence, validity, and satisfying ability.
28. What are the literals available in top-down inductive learning?
There are three literals available such as predicates, equality, inequality, and arithmetic literals for top-down inductive learning.
Conclusion
In conclusion, mastering AI interview questions and answers is crucial for success in AI interviews. By understanding the core concepts, algorithms, and real-world applications, you can confidently demonstrate your expertise. Remember to think critically and showcase your ability to apply AI principles effectively. Stay updated with the latest advancements in AI to further enhance your chances of success. With this comprehensive guide, you are well-prepared to showcase your knowledge and skills in Artificial Intelligence interviews.
