Artificial Intelligence Interview Questions

Artificial Intelligence (AI) is a well-known term for everyone as it plays the most important role in our daily life. From roots to robots, we can observe the development of AI. As AI solutions were spreading all over the world, a career in the AI field leads to a great extent. Today, I would like to take you through Artificial Intelligence Interview Questions 2023 which are curated by AI experts. Let’s get started.

We have categorized Artificial Intelligence Interview Questions into 2 levels they are:

• Freshers
• Experienced

Frequently Asked Artificial Intelligence Interview Questions

1. What is Artificial Intelligence?
2. What are the programming languages used for Artificial Intelligence?
3. What are the stages of learning AI?
4. What is an intelligent agent in Artificial Intelligence?
5. What is the difference between Strong AI and Weak AI?
6. What are the characteristics of expert systems?
7. What are the various domains of Artificial Intelligence?
8. What is Natural Language Processing?
9. What is Game Theory in AI?
10. What is an Artificial Neural Network?

AI Interview Questions and Answers For Freshers

1: What is Artificial Intelligence?

Artificial Intelligence (AI) is a stream of computer science that enhances the intelligent machines that work and react like humans. The ability of a machine to simulate intelligent human behavior. AI is commonly used for various applications such as decision-making, speech recognition, perception, cognitive abilities, computer vision, and many more.

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2: List out the various applications of AI?

The applications of AI are as follows:

1. Chatbots
2. Self-driving cars
3. Image tagging
4. AI in healthcare
5. AI in eCommerce
6. Human Resource Management
7. Intelligent Cybersecurity
8. AI to enhance workplace communication
9. Facial expression recognition
10. Natural Language Processing, and many more.

3: What are the programming languages used for Artificial Intelligence?

The following are the best AI programming languages used for Artificial Intelligence:

1. Python
2. Java
3. R
4. Prolog
5. Lisp
6. AIML
7. STRIPS
8. Julia.

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4: How many Types of Artificial Intelligence are there? What are they?

There are four types of Artificial Intelligence as follows:

1. Reactive Machines AI
2. Limited Memory AI
3. Theory of Mind AI
4. Self Aware AI.

5: What are the stages of learning AI?

The following are the stages of learning AI:

• Artificial General Intelligence (AGI): It is also known as Strong AI, which is considered a threat to many scientists' human existence. It is an evolution of AI where machines can think and make decisions just like humans.
• Artificial Normal Intelligence (ANI): It is also known as Weak AI that can perform only a defined activity set. It does not perform any thinking ability; instead, it performs a set of pre-defined functions.
• Artificial Super Intelligence (ASI): ASI can perform everything that a human can do. Alpha 2 is an example of ASI, which is the first humanoid ASI robot.

[ Related Article: Top 5 Reasons to Learn Artificial Intelligence ]

6: What is an intelligent agent in Artificial Intelligence?

Ans: An Intelligent Agent (IA) refers to an autonomous entity that acts as a directing its activity to achieve goals as an agent upon an environment using observation by actuators and sensors as an intelligent. 

7: What is the difference between Strong AI and Weak AI?

With strong AI, machines can think and perform tasks as their own, as humans do. With Weak AI, the machines cannot perform tasks independently; instead, it depends heavily on human interference. 

Strong AI has a complex algorithm that helps it act in various situations, whereas Weak AIs are pre-programmed by humans.

8: Define an expert system in AI?

In Artificial Intelligence, an expert system is a computer system that imitates human experts' decision-making ability. Expert systems are developed to solve problems by reasoning the bodies of knowledge, represented mainly as an if-then formula instead of conventional procedural code.

9: What are the characteristics of expert systems?

An expert system is designed to have the following characteristics:

• High-level performance
• Good Reliability
• Adequate Response time
• Linked with Metaknowledge
• Domain Specificity
• Understandable
• Justified Reasoning
• Expertise knowledge
• Special Programming Languages
• Use of symbolic representations.

[ Check out Top 10 Artificial Intelligence Technologies ]

10: What is A* Search Algorithms in AI?

A* is formulated with weighted graphs, which means it can find the best path involving the smallest cost in terms of time and distance. This makes A* algorithm in artificial intelligence an informed search algorithm for best-first search. A* search algorithm separates it from other traversal techniques as it has a brain.

11: What are the various domains of Artificial Intelligence?

The following are the various domains of Artificial Intelligence:

• Machine Learning
• Robotics
• Fuzzy logic systems
• Neural Networks
• Expert Systems
• Natural Language Processing.

12: What is Machine Learning?

Machine Learning is an application of Artificial Intelligence (AI) that offers systems the ability to learn and improve from experience automatically without being programmed externally. It focuses on enhancing computer programs that can access the data and use it to learn accordingly. To make it simple, Machine Learning is a subset of AI that uses data to enhance machines in solving complex problems.

13: What are the different types of Machine Learning (ML)?

There are three different types of Machine Learning:

1. Supervised learning
2. Reinforced learning
3. Unsupervised learning.

14: What is the difference between AI and ML?

Artificial Intelligence	Machine Learning
Artificial intelligence is a technology that enables a system to imitate human behaviour.	Machine Learning is a subset of an AI that enables a machine to learn from experience automatically.
Machine Learning and Deep Learning are the two main subsets of AI.	Deep Learning is the main subset of Machine Learning.
It includes self-correction and reasoning and learning.	It includes self-correction and learning when introduced with new data.
AI deals with unstructured, structured, and semi-structured data.	Machine Learning deals with semi-structured and structured data.
AI has an extensive range of scope.	Machine Learning has a limited scope.

[ Learn More About Machine Learning vs AI ]

15: What is Natural Language Processing?

Natural Language Processing (NLP) is a subfield of Artificial Intelligence, computer science, and linguistics concerned with the communications between human language and computer language that helps in specific how to program computers to analyze and process large amounts of natural language data.

AI Interview Questions For Experienced

16: What is an Artificial Neural Network?

An Artificial Neural Network (ANN) is a computing system designed to simulate the human brain's analysis and processes of information. It acts as a foundation of AI and solves the problems that prove difficult or impossible or statistical standards. ANN has a self-learning capability that allows users to produce better results.

17: What are the commonly used types of neural networks in AI?

The following are the three commonly used types of neural networks in AI:

1. Recurrent neural networks
2. Feedforward neural networks
3. Convolution neural networks.

18: What is Deep Learning?

Deep Learning is an AI function that imitates the working of the human brain in processing data for use in recognizing speech, making decisions, detecting objects, and translating languages. It can learn without human guidance, drawing from data that is both unlabeled and unstructured.

19: What are Fuzzy Logic systems in AI?

Fuzzy logic (FL) is a method of reasoning that represents human reasoning. The approach of FL imitates the process of decision-making done by humans, including all possibilities between YES or NO's digital values. It works on the levels of possibilities of input to achieve a definite output.

20: List out various search algorithms in AI?

The various search algorithms in AI are: 

• Breadth-First Search
• Bidirectional Search
• Depth-First Search
• Uniform Cost Search
• Heuristic Evaluation Functions
• Pure Heuristic Search
• Iterative Deepening Depth-First Search
• Comparison of various Algorithms Complexities
• Local Search Algorithms.

[ Related Article: Genetic Algorithm in Artificial Intelligence ]

21: Which search method takes less memory?

The Depth-First Search method takes less memory.

22: What are the steps involved in Machine Learning?

The steps that are involved in Machine Learning are as follows:

• Data collection
• Data preparation
• Choosing an appropriate model
• Training the dataset
• Evaluation
• Parameter tuning
• Prediction.

23: List out the two various kinds of steps that we make in constructing a plan?

The following are two various steps that we make in constructing a plan:

• Add an operator
• Add ordering constraints between operators.

24: What are the advantages of an expert system?

The following are the advantages of an expert system:

• Memory
• Fast response
• Consistency
• Logic
• Ability to reason
• Unbiased in nature
• Diligence.

[ Check out Why AI is Important? ]

25: What are the various components of the Expert system?

An expert system includes three components of Expert System:

• User Interface: The User Interface allows the user to communicate with the expert system to find the solutions for a complex problem
• Knowledge Base: The Knowledge Base is a kind of Storage that is used to store high-quality and domain-specific knowledge
• Inference Engine: It is the main processing unit of an expert system. It enables various inference rules to the knowledge base to bring a conclusion. The system extracts the information from the KB with the inference engine.

26: What is Game Theory in AI?

In the context of AI and DL systems, the game theory allows some of the key capabilities required in multi-agent environments in which various AI programs are required to interact to meet the goals. 

Game Theory is a branch of mathematics used to develop the strategic interactions between multiple players with pre-defined rules and the outcomes. It is also used to define several instances in our daily life and machine learning models.

27: What is the Turing test?

The Turing Test is a process of inquiry in Artificial Intelligence (AI) to determine whether a computer or not, which is capable of thinking like a human being. The Turing test is named after Alan Turing, is the method of testing a machine’s human-level intelligence.

28: Explain Markov’s Decision Processes in Artificial Intelligence?

Markov Decision Processes (MDPs) is a mathematical framework for designing sequential decision problems under uncertainty and Reinforcement Learning problems. The main goal of this process is to achieve maximum positive rewards by choosing the optimum policy.

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29: list out the elements of MDP?

The following are the four elements of MDP:

• A set of finite states S
• A set of finite actions A
• Rewards
• Policy Pa.

In this process, the agent implements an Action A to transition from the start state to the End state, and while performing these actions, the agent receives few rewards. The series of actions taken by the agent can be defined as a policy.

30: What is FOPL in Artificial Intelligence?

The First Order Predicate Logic (FOPL) offers the following:

• A language to express assertions about a certain “World.”
• A semantic-based on set theory
• An inference system to deductive apparatus so that we can conclude from the assertions.

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31: What does the language of FOPL include?

The language of FOPL includes the following:

• A set of variables
• A set of function symbols
• A set of constant symbols
• A set of predicate symbols
• A special binary relation of equality
• The Universal Quantifier and Existential Quantifier
• The logical connective.

32: List out a few machine learning algorithms?

The following are the list of a few machine learning algorithms:

• Linear regression
• Logistics regression
• Naive Bayes
• Decision tree learning
• Random forest
• Decision making
• K-means clustering
• Reinforcement learning
• Artificial neural networks.

[ Check out Top 10 Machine Learning Algorithms ]

33: What is K-means clustering?

K-means clustering is an unsupervised learning algorithm used to identify data objects clusters in a dataset. K-means attempts to classify data without having trained with labeled data. Once the algorithm has been executed, and the groups are defined, users assign any new data to the most relevant group.

34: What are the real-world examples of K-means?

The following are the real-world examples of K-Means:

• Astronomy
• Search engines
• Computer vision
• Customer profiling
• Market segmentation.

35: What is overfitting?

Overfitting refers to a design that develops the training data. It occurs when a model learns the noise and detail available in the training data; thus, it negatively impacts the performance of the model on the new data.

36:What are methods used to avoid overfitting in neural networks?

The following are methods used to avoid overfitting in neural networks:

• Cross-validation
• Early stopping
• Remove features
• Ensembling
• Train with more data
• Regularization.

37: What is the Hidden Markov Model (HMM)?

Hidden Markov Model is a statistical model used to describe the evolution of observable events that depend on internal factors, that are not observable directly. We can call the observed event a symbol and the invisible factor underlying the observation of a state.

38: What are the applications of Machine Learning?

The following are the applications of Machine Learning:

• Bio-informatics
• Image, face, and speech recognition
• Fraud detection
• Market segmentation
• Manufacturing and inventory management, and many more.

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39: What is TensorFlow?

TensorFlow is an end-to-end open-source library for numerical computation and large-scale machine learning. It includes a flexible ecosystem of tools, comprehensive, and community resources that allow developers to build and deploy the application easily. It holds together a combination of deep learning and machine learning algorithms and models to make them useful in a common metaphor.

40: What is a Hash table?

A Hash Table is a data structure used to store data in an associative way. In this table, data is stored in an array format where each data has its respective unique index value. 

Conclusion

On winding up, these TOP frequently asked interview questions will help you to crack the interview easily. There is more job openings and this was the best time to grab an opportunity. All the best! Thanks for learning!