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About Artificial Intelligence

In computer science, Artificial Intelligence (AI) is the machine intelligence, which is demonstrated by machines, in contrast to the natural intelligence displayed by humans. Artificial Intelligence is also described cognitive functions that humans connect with the human mind to learn and solve problem. Contemporary machine capabilities usually classify AI as considering human speech, competing in strategic game systems, operating cars, routing content delivery networks, and military simulations.

Artificial Intelligence Interview Questions And Answers

1. What is Artificial Intelligence?

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.

2. What are the different types of AI?

  • Reactive Machines AI: Based on present actions, it cannot use previous experiences to form current decisions and simultaneously update their memory.
    Example: Deep Blue
  • Limited Memory AI: Used in self-driving cars. They detect the movement of vehicles around them constantly and add it to their memory.
  • Theory of Mind AI: Advanced AI that has the ability to understand emotions, people and other things in the real world.
  • Self Aware AI: AIs that posses human-like consciousness and reactions. Such machines have the ability to form self-driven actions.
  • Artificial Narrow Intelligence (ANI): General purpose AI, used in building virtual assistants like Siri.
  • Artificial General Intelligence (AGI): Also known as strong AI. An example is the Pillo robot that answers questions related to health.
  • Artificial Superhuman Intelligence (ASI): AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.

3. Explain the different domains of Artificial Intelligence.

  • Machine Learning: It’s the science of getting computers to act by feeding them data so that they can learn a few tricks on their own, without being explicitly programmed to do so.
  • Neural Networks: They are a set of algorithms and techniques, modeled in accordance with the human brain. Neural Networks are designed to solve complex and advanced machine learning problems.
  • Robotics: Robotics is a subset of AI, which includes different branches and application of robots. These Robots are artificial agents acting in a real-world environment. An AI Robot works by manipulating the objects in it’s surrounding, by perceiving, moving and taking relevant actions.
  • Expert Systems: An expert system is a computer system that mimics the decision-making ability of a human. It is a computer program that uses artificial intelligence (AI) technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.
  • Fuzzy Logic Systems: Fuzzy logic is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0) boolean logic on which the modern computer is based. Fuzzy logic Systems can take imprecise, distorted, noisy input information.
  • Natural Language Processing: Natural Language Processing (NLP) refers to the Artificial Intelligence method that analyses natural human language to derive useful insights in order to solve problems.

4. Explain the commonly used Artificial Neural Networks.

Feedforward Neural Network

  • The simplest form of ANN, where the data or the input travels in one direction.
  • The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers.

Convolutional Neural Network

  • Here, input features are taken in batch wise like a filter. This will help the network to remember the images in parts and can compute the operations.
  • Mainly used for signal and image processing

Recurrent Neural Network(RNN) – Long Short Term Memory

  • Works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer.
  • Here, you let the neural network to work on the front propagation and remember what information it needs for later use
  • This way each neuron will remember some information it had in the previous time-step.


  • These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them.
  • The output layer has the same number of units as the input layer. Its purpose is to reconstruct its own inputs.
  • Typically for the purpose of dimensionality reduction and for learning generative models of data.

5. What are hyperparameters in Deep Neural Networks?

  • Hyperparameters are variables that define the structure of the network. For example, variables such as the learning rate, define how the network is trained.
  • They are used to define the number of hidden layers that must be present in a network.
  • More hidden units can increase the accuracy of the network, whereas a lesser number of units may cause underfitting.

6. Explain the different algorithms used for hyperparameter optimization.

Grid Search

Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques.

Random Search

It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.

Bayesian Optimization

This includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called surrogate model (Gaussian Process). Bayesian Optimization uses Gaussian Process (GP) function to get posterior functions to make predictions based on prior functions.

7. How does data overfitting occur and how can it be fixed?

Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data. This causes an algorithm to show low bias but high variance in the outcome.

Overfitting can be prevented by using the following methodologies:

Cross-validation: The idea behind cross-validation is to split the training data in order to generate multiple mini train-test splits. These splits can then be used to tune your model.

More training data: Feeding more data to the machine learning model can help in better analysis and classification. However, this does not always work.

Remove features: Many times, the data set contains irrelevant features or predictor variables that are not needed for analysis. Such features only increase the complexity of the model, thus leading to possibilities of data overfitting. Therefore, such redundant variables must be removed.

Early stopping: A machine learning model is trained iteratively, this allows us to check how well each iteration of the model performs. But after a certain number of iterations, the model’s performance starts to saturate. Further training will result in overfitting, thus one must know where to stop the training. This can be achieved by a mechanism called early stopping.

Regularization: Regularization can be done in n number of ways, the method will depend on the type of learner you’re implementing. For example, pruning is performed on decision trees, the dropout technique is used on neural networks and parameter tuning can also be applied to solve overfitting issues.

Use Ensemble models: Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. This is one of the best ways to prevent overfitting. An example is Random Forest, it uses an ensemble of decision trees to make more accurate predictions and to avoid overfitting.

8. What is the purpose of Deep Learning frameworks such as Keras, TensorFlow, and PyTorch?

  • Keras is an open source neural network library written in Python. It is designed to enable fast experimentation with deep neural networks.
  • TensorFlow is an open-source software library for dataflow programming. It is used for machine learning applications like neural networks.
  • PyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing.

9. Which is better for image classification? Supervised or unsupervised classification? Justify.

  • In supervised classification, the images are manually fed and interpreted by the Machine Learning expert to create feature classes.
  • In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values.

Therefore, it is better to choose supervised classification for image classification in terms of accuracy.

10. What is the Minimax Algorithm? Explain the terminologies involved in a Minimax problem.

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.

11. What is Turing test?

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.

12. What is an A* algorithm search method?

A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.

13. How are game theory and AI related?

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.

14. What is a fuzzy logic?

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.

15. List the applications of fuzzy logic.

  • Facial pattern recognition
  • Air conditioners, washing machines, and vacuum cleaners
  • Antiskid 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

16. What is a partial-order planning?

A problem has to be solved in a sequential approach to attain the goal. The partial-order plan specifies all actions that need to be undertaken but specifies an order of the actions only when required.

17. What is FOPL?

First-order predicate logic is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.

18. Differentiate between parametric and non-parametric models.

Differentiation Based on Parametric Model Non-parametric Model
Benefits Simple, fast, and less data Flexibility, power, and performance
Limitations Constrained, limited complexity, and poor fit More data, slower, and overfitting
Features A finite number of parameters to predict new data Unbounded number of parameters
Algorithm Logistic regression, linear discriminant analysis, perceptron, and Naive Bayes K-nearest neighbors, decision trees like CART and C4.5, and support vector machines





19. What is Naive Bayes?

Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.

20. Which algorithm in ‘Unification and Lifting’ takes two sentences and returns a unifier?

In ‘Unification and Lifting’ the algorithm that takes two sentences and returns a unifier is ‘Unify’ algorithm.

21. Which is the most straight forward approach for planning algorithm?

State space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution.

22. In HMM, where does the additional variable is added?

While staying within the HMM network, the additional state variables can be added to a temporal model.

23. In Artificial Intelligence, what do semantic analyses used for?

In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.

24. What is meant by compositional semantics?

The process of determining the meaning of P*Q from P,Q and* is known as Compositional Semantics.

25. Which algorithm is used for solving temporal probabilistic reasoning?

To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.

26. In speech recognition what kind of signal is used?

In speech recognition, Acoustic signal is used to identify a sequence of words.

27. 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.

28. 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.

29. What Are Frames And Scripts In “artificial Intelligence”?

Frames are a variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. A frame which is an artificial data structure is used to divide knowledge into substructure by representing “stereotyped situations’. Scripts are similar to frames, except the values that fill the slots must be ordered. Scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.

30. In ‘artificial Intelligence’ Where You Can Use The Bayes Rule?

In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.

31. What Is A Heuristic Function?

A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.

32. What Does A Production Rule Consist Of?

The production rule comprises of a set of rule and a sequence of steps.

33. What is collaborative filtering?

Collaborative filtering can be described as a process of finding patterns from available information to build personalized recommendations. You can find collaborative filtering in action when you visit websites like Amazon and IMDB.

Also known as social filtering, this approach essentially makes suggestions based on the recommendations and preferences of other people who share similar interests.

34. What’s selection bias? What other types of biases could you encounter during sampling?

When you’re dealing with a non-random sample, selection bias will occur due to flaws in the selection process. This happens when a subset of the data is consistently excluded because of a particular attribute. This exclusion will distort results and influence the statistical significance of the test.

Other types of biases include survivorship bias and undercoverage bias. It’s important to always consider and reduce such biases because you’ll want your smart algorithms to make accurate predictions based on the data.

35. What’s an eigenvalue? What about an eigenvector?

The directions along which a particular linear transformation compresses, flips, or stretches is called eigenvalue. Eigenvectors are used to understand these linear transformations.

For example, to make better sense of the covariance of the covariance matrix, the eigenvector will help identify the direction in which the covariances are going. The eigenvalues will express the importance of each feature.

Eigenvalues and eigenvectors are both critical to computer vision and ML applications. The most popular of these is known as principal component analysis for dimensionality reduction (e.g., eigenfaces for face recognition).

Career scopes and salary scale

Artificial Intelligence, Machine Learning, Software programming, etc are the fields require thousands of talented professionals to handle their upcoming projects. Joining the field right now will empower your career with high salary and position. Artificial Intelligence contenders having in-depth knowledge and skill-based training in Artificial Intelligence are able realize their smooth ways to accomplish their career goals. A candidate of Artificial Intelligence applicant gets a minimum salary of 40, 000 dollars per annum, which get to double with experience. However, the salaries are very dependent upon the location, business, and the company’s requirements.


The article on Artificial Intelligence interview questions have been drafted by team of specialist to train fresh minds in understanding the tact and technique of interview questions. Often, interviewers ask a simple question making it more elaborative and confusing. This article has tried providing answers to entire advanced Artificial Intelligence interview questions. It also has answers for Artificial Intelligence interview questions for experienced professionals. If any students still require more explanation, then they may drop in a message to our experts regarding Artificial Intelligence interview questions for experienced professionals. Join Artificial Intelligence Training in NoidaArtificial Intelligence Training in DelhiArtificial Intelligence Training in Gurgaon

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