Artificial intelligence interview questions

Hey! Looking to grab the opportunities in Multinational organizations? Especially into Artificial intelligence? Well, then you have landed in the right place. We are here to help you to grab the upcoming interviews for the job roles in Artificial intelligence. In this article, you will review the Artificial intelligence interview questions. This article includes the frequently asked interview questions that are curated by the experts for both freshers and experienced. Let us get started now!

Categories Of Artificial intelligence Interview Questions

Here are the most frequently asked artificial intelligence interview questions in 2021

Q1) What do you understand by Artificial Intelligence?

Ans: Artificial intelligence is referred to as a computer science technology that primarily emphasizes or focuses on creating an intelligent machine that can work and mimic human behavior. In generic terms, Intelligent machines are referred to as those machines that are capable of behaving like a human, think like a human, and also capable of making decisions. It is made up of two words, "Artificial" and "Intelligence," which refers to the "man-made thinking ability."By using artificial intelligence, there is no need to pre-program the machine to perform a particular task; instead, we can also create a machine using the programmed algorithms, and it will be capable of working on its own.

Q2) Briefly explain the different types of AI?

Ans: Below listed are the different types of artificial intelligence.

  • Limited Memory AI: This type of Artificial intelligence is used in self-driving cars. They are capable of detecting the movement of vehicles that are present around them constantly and add them to their memory.
  • Reactive Machines AI: Based on present actions, the reactive machines are those machines that cannot use the previous experiences to form the current decisions and simultaneously update their memory. Example: Deep Blue
  • Self-Aware AI: These are the AIs that possess human-like consciousness and reactions. Such machines have the ability to form self-driven actions.
  • Artificial Narrow Intelligence (ANI): These are the general-purpose AI, that is used in building virtual assistants like Siri.
  • Theory of Mind AI: The theory of mind AI is an Advanced AI that possesses the capability to understand emotions, people, and other things in the real world.
  • Artificial Superhuman Intelligence (ASI): It is the AI that possesses the ability to do everything that a human can do and more. One example is the Alpha 2 which was the first humanoid ASI robot.
  • Artificial General Intelligence (AGI): This AGI is also known as strong AI. An example is the Pillo robot that is capable of answering questions related to health.

Do you want to Master AI? Then enroll in "Artificial Intelligence Training" This course will help you to master Artificial Intelligence

Q3) Briefly explain how you think that Machine Learning is related to Artificial Intelligence?

Ans: Artificial Intelligence is referred to as a technique that is responsible for enabling machines to mimic human behavior while Machine Learning is a subset of Artificial Intelligence. Machine learning is referred to as the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so. Therefore it is known that Machine Learning is a technique that is used to implement Artificial Intelligence.

Q4) What do you understand by the term Deep Learning in AI?

Ans: Deep learning refers to the process that imitates the way our brain works i.e. it learns from experiences. Deep learning makes use of the concepts of neural networks in order to solve complex problems. Any Deep neural network will include three different  types of layers:

Input Layer: the input layer is responsible for receiving all the inputs and forwards them to the hidden layer to perform the analysis.

Hidden Layer: In the hidden layer, various computations are carried out and the result will be transferred to the output layer. There can be any number of hidden layers, based on the problem you are referring to or trying to solve.

Output Layer: The output layer is designed for transferring information from the neural network to the outside world.

Q5) What are Bayesian Networks?

Ans: A Bayesian network is referred to as a statistical model that is represented as a set of variables and their conditional dependencies will be represented in the form of a directed acyclic graph. 

Whenever an event occurs, the Bayesian Networks can be used in order to predict the likelihood that any one of several possible known causes was the contributing factor.

Let us take an example, a Bayesian network can be used to study and understand the relationship between diseases and symptoms. The Bayesian network is ideal for analyzing and computing the probabilities of the presence of various diseases based on the symptoms.

Q6) What are hyperparameters in Deep Neural Networks?

Ans: Hyperparameters are referred to as the variables that are used to define the structure of the network. Let us understand using an example, variables such as the learning rate, define how the network is trained. They are used for defining the number of hidden layers that must be present in a network. More number of hidden units can improve and increase the accuracy of the network, however, a lesser number of units may cause issues like underfitting.

Q7) Why are the Deep Learning frameworks such as Keras, TensorFlow, and PyTorch used?

Ans: Below is an explanation of the different deep learning frameworks used.

Keras: Keras is referred to as an open-source neural network library that is written in the Python programming language. It is designed in order to enable fast experimentation with deep neural networks.

TensorFlow: TensorFlow is also one of the open-source software libraries which are used for dataflow programming. Apart from this, it is also used for machine learning applications like neural networks.

Pytorch: PyTorch is another open-source machine learning library for Python, that is based on Torch. This is used for applications such as natural language processing.

Q8) Briefly give an idea about the components of Expert Systems?

Ans: Below listed are the different components of expert systems.

Knowledge Base: It consists of domain-specific and high-quality knowledge.

Inference Engine: It is responsible for acquiring and manipulating the knowledge from the knowledge base to arrive at a particular solution.

User Interface: The user interface is responsible for establishing and providing an interaction between the user and the Expert System itself.

Q9) What do you think is better for the mage classification? Supervised or unsupervised classification? Justify your answer.

Ans: In supervised classification, the images will be manually fed and interpreted by the Machine Learning expert in order to create feature classes.

In the unsupervised classification, the Machine Learning software will be creating the feature classes based on the values of the image pixels.. Hence, it is recommended to choose supervised classification for image classification in terms of accuracy.

Q10) What is meant by the Minimax Algorithm? Explain the different terminologies that are involved in a Minimax problem.

Ans: Minimax is called a recursive algorithm that provides the flexibility to select an optimal move for a player thinking that the other player is also playing optimally. A game can be defined as a search problem that includes the following components:

Game Tree: The game tree includes a tree structure that contains all the possible moves.

Initial state: The initial state represents the initial position of the board and showing whose move it is.

Successor function: The successor function is used for defining the possible legal moves a player can make.

Terminal state: The terminal state refers to the position of the board when the game ends.

Utility function: The utility function is a function that will assign a numeric value for the outcome of a game.

Critical Questions About Artificial Intelligence

Q11) Which programming language is used for AI?

Ans: Below is a few of the programming languages that are widely used for the development of Artificial Intelligence:

  • Python
  • Java
  • Lisp
  • R
  • Prolog

Among the above five languages, Python is the most used language for AI development because of its simplicity and availability of lots of libraries, such as Numpy, Pandas, etc.

Q12) What do you know about the parametric and non-parametric models?

Ans: Machine learning includes two types of models called Parametric and Non-parametric. In these models, the parameters are the predictor variables that are used in order to build the machine learning model. Below is the explanation of these models.

Parametric Model: The parametric models are those models that make use of a fixed number of the parameters in order to create the ML model. It will take strong assumptions about the data into consideration. The examples of the parametric models are Perceptron, Linear regression, Logistic Regression, Naïve Bayes,, etc.

Non-Parametric Model: The non-parametric model makes use of the flexible numbers of parameters. It will take few assumptions about the data into consideration. These models will be more compatible and good for higher data and no prior knowledge. The examples of the non-parametric models are  SVM with Gaussian kernels, Decision Tree, K-Nearest Neighbour, etc.

Q13) What do you know about overfitting? How is it possible to overcome it in Machine Learning?

Ans: Whenever the machine learning algorithm will try to capture all the data points, and hence, as a result, it will also capture noise, then overfitting occurs in the model. Due to this overfitting issue, the algorithm will be showing the low bias, but the high variance in the output. Overfitting is one of the main concerns in machine learning.

Below are the Methods used to avoid Overfitting in ML:

  • Cross-Validation
  • Ensembling
  • Training With more data
  • Early Stopping the training.
  • Removing Unnecessary Features
  • Regularization

Q14) What are the eigenvalues and eigenvectors?

Ans: The two main concepts of Linear algebra are Eigenvectors and eigenvalues. Eigenvectors are considered unit vectors that have a magnitude equal to 1.0.

Eigenvalues are referred to as the coefficients that are applied to the eigenvectors, or these are also called the magnitude by which the eigenvector is scaled.

Q15) What is a Chatbot?

Ans: A chatbot is referred to as the Artificial intelligence software or agent that is capable of simulating a conversation with humans or users using Natural language processing. The conversation can be achieved through a website, application, or messaging apps. These chatbots are also named digital assistants and are capable of interacting with humans in the form of text or through voice. The AI chatbots are broadly used in most of businesses to provide 24*7 virtual customer support to their customers, such as HDFC Eva chatbot, Vainubot, etc.

Q16) List out the different techniques of knowledge representation in AI?

Ans: The Knowledge representation techniques are listed below:

  • Logical Representation
  • Semantic Network Representation
  • Frame Representation
  • Production Rules

Q17) What are the different areas where AI has a great impact?

Ans: Below listed are some areas where AI has a great impact:

  • Autonomous Transportation
  • Education-system powered by AI.
  • Predictive Policing
  • Healthcare
  • Entertainment
  • Space Exploration, etc

Q18) What do you understand by the term market-basket analysis?

Ans: The market-basket analysis is referred to as a popular technique that is used to identify or find the associations that exist between the items. This analysis is frequently used by big retailers in order to gain maximum profit. In this approach, you will need to find out the combinations of items that can be frequently bought together.

Let us take an example if a person buys bread, there are most of the chances that he will buy butter also. Hence, trying to understand such correlations will help the retailers to grow, improve and expand their business by providing relevant offers to their customers.

Q19) What is the inference engine, and why it is used in AI?

Ans: The inference engine is the part of an intelligent system in artificial intelligence,  that is capable of deriving new information from the knowledge base by applying some logical rules.

It mainly works in two modes:

Backward Chaining: The backward chaining begins with the goal and proceeds backward to deduce the facts that support the goal.

Forward Chaining: The forward chain begins with known facts, and asserts new facts.

Q20) How are Computer Vision and AI-related?

Ans: Computer Vision is one of the fields in Artificial Intelligence that is used to obtain information from images or multi-dimensional data. Machine Learning algorithms such as K-means are specifically used for Image Segmentation, Support Vector Machine is used for Image Classification, and so on. Therefore, Computer Vision utilizes AI technologies to resolve complex problems such as Object Detection, Image Processing, etc.

Artificial Intelligence Test Questions

Q21) Explain Fuzzy Logic architecture.

Ans: Below is a brief explanation of the architecture of fuzzy logic.

Fuzzification Module: The system inputs are fed into the Fuzzifier, which is responsible for transforming the inputs into fuzzy sets.

Knowledge Base: This component is responsible for storing analytic measures such as IF-THEN rules that are provided by experts.

Inference Engine: The inference engine is responsible for simulating the human reasoning process by making fuzzy inferences on the inputs and IF-THEN rules.

Defuzzification Module: The defuzzification module helps in the transformation of the fuzzy set obtained by the inference engine into a crisp value.

Q22) What is Q-Learning?

Ans: Q-learning is referred to as a Reinforcement Learning algorithm which includes an agent that tries to learn the optimal policy based on past experiences with the environment.

Q23) Illustrate the differences between statistical AI and Classical AI?

Ans: Statistical AI is more focused on the “inductive” thought like given a set of patterns, induce the trend, etc.  In contrast to statistical AI, the classical AI is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion, etc.

Q24) Briefly give an idea of the best way to go for a Game playing problem?

Ans: The Heuristic approach is one the best ways to go for game-playing problems, as it will make use of the techniques that are based on intelligent guesswork. Let us take an example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.

Q25) Illustrate the differences between breadth-first search and best-first search in artificial intelligence?

Ans: These are the two strategies that would look quite similar. In the best first search, the nodes will be expanded in accordance with the evaluation function. While in breadth-first search a node is expanded based on the cost function of the parent node.

Q26) What is Prolog in AI?

Ans: In AI, Prolog is a programming language that is based on logic.

Q27) What is a uniform cost search algorithm?

Ans: The uniform cost search allows you to perform sorting by increasing the cost of the path to a node. It expands the least cost node. This algorithm is similar to BFS if each iteration includes the same cost. It investigates the ways in expanding the order of cost.

Q28) What is FOPL?

Ans: FOPL stands for First-order predicate logic, which is referred to as a collection of formal systems, in which each and every statement will be divided into a subject and a predicate. The predicate refers to only one subject, and it can be either modified or defined by the properties of the subject.

Q29) List the different algorithm techniques in Machine Learning.

Ans: Below is the different algorithm techniques that can be used in machine learning.

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning
  • Transduction
  • Learning to Learn

Q30) List the extraction techniques used for dimensionality reduction.

Ans: Below listed are the different types of extraction techniques that are used for dimensionality reduction.

  • Independent component analysis
  • Principal component analysis
  • Kernel-based principal component analysis

Conclusion: 

We are at the end of the article now. By now, you might have got an idea of the interview questions that will be asked in an interview for the job roles in Artificial intelligence. As most organizations are looking for individuals who are expertized and have knowledge on the subject, you can crack the interview and attain the best career that you are looking for. All the best.