Top 35 Artificial Intelligence Interview Questions and Answers

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Doesn’t the mere mention of AI sound like a robot’s vernacular and the unfathomable rocket science?! What if I told Artificial Intelligence is amongst us already, helping us in every walk of life?! The Uber you book in a hurry, the smartwatch measuring your calories, the multiple Swiggy indulgences, the video calls, the CCTV at your apartment, the GPS in your mobile, the bills you pay, and the Alexa in your tv. Artificial Intelligence is already our family.

Listed in this blog are the carefully researched and chosen Top 35 Artificial Intelligence Interview Questions and answers for you to easily crack that challenging interview.

What is Artificial Intelligence?

Artificial Intelligence is programming a computer to learn and think like humans. Everything can be transformed into Artificial intelligence by moving the dependence on the human capability to a programmed machine.

Artificial Intelligence is for all

The language of Artificial Intelligence (AI) is no longer an alien. To emphasize, it is everywhere making our mere functioning possible. AI is rapidly pervading across many industries and therefore influencing the very aspect of our daily life.

Furthermore, whether you are a professional or an entrepreneur, or a student the need for Artificial Intelligence is of primary importance to transform your career and to maneuver the Artificial Intelligence Interview Questions.

John McCarthy who coined the term Artificial Intelligence said “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it”. An interesting and effective Artificial Intelligence course can make it possible for you.

Important Learnings from an Artificial Intelligence course

• Understand AI & ML theories.
• Know in-depth about the meaning, scope, applications, purpose, and impacts of Artificial Intelligence.
• Master the basic concepts of DL(Deep Learning) and ML(Machine Learning).
• Gain knowledge of the ML workflow in addition to how to execute the strategies effectively.
• Know the usage of performance metrics and identify their important methods.
• Learn the variations in unsupervised, supervised, and reinforced learning.
• Comprehend how to cluster and classify algorithms that can help in identifying Artificial Intelligence business applications.
• Learn about Neural Networks and Natural Language Processing.
• Knowledge of Word embedding, Dense Encoding, Sequential models and

Different platforms where Artificial Intelligence can be used

• Infosys Nia
• IBM Watson
• H2O
• Polyaxon
• PredictionIO
• Amazon AI services
• Tensorflow
• Google AI services
• Microsoft Azure AI platform

Benefits of using Artificial Intelligence applications

• Surpass the many risky limitations of human capability
• Forego human errors to reach maximum accuracy.
• Performs repetitive and monotonous chores with precision.
• Digital Assistance to save time and effort and better communication
• Programmed technology to make decisions faster.
• Supporting our daily requests and fulfilling them error-free.
• AI powers many inventions in different domains.
• AI improves user experience.
• Crack the tough Artificial Intelligence Interview questions
• Comes to humanitarian aid in times of emergency or disaster.
• AI is a versatile arena for a lucrative career.

Need for the Artificial Intelligence interview questions and answers

All the knowledge you accumulated by taking an AI course and the experience you gained that makes you the most sought-after candidate is put to a grueling test by the Artificial Intelligence Interview questions. Prep yourself well with the most popular Top 35 Artificial Intelligence Interview Questions and answers.

Here we have covered topics such as AI programming applications and languages, Turing test, expert system, ML algorithm techniques, Perceptron, KNN, LSTM, autoencoder details of various search algorithms, game theory, fuzzy logic, inductive, deductive, and abductive Machine Learning and other related topics.

These Artificial Intelligence Interview Questions and answers are segmented as few Q&A for beginners, for experienced candidates, and some scenario-based Artificial Intelligence Interview questions.

Top 35 Artificial Intelligence Interview Questions and answers

1. What is Artificial Intelligence?

Tip: Summarise your know-how in just a few sentences

Ans: Artificial Intelligence is a field of programming wherein the cognitive functions of the human brain are analyzed and replicated on a machine/system. Artificial Intelligence is widely used in various applications like computer vision, decision-making, perception, reasoning, cognitive capabilities, and speech recognition to name a few.

2. Name some real-life applications of Artificial Intelligence?

Tip: Name the most common ones to show your understanding

Ans: Online shopping sites, Smart cars, Booking sites, Healthcare sector, Security Systems with face or voice recognition, NLP, and social media.

3. What are the programming languages used in Artificial Intelligence?

Ans: Python, R, Lisp, Prolog, and Java are some of the programming languages used in Artificial Intelligence

4. Give a briefing about ANN (Artificial Neural Network) and explain its layers

Ans: Artificial Neural Network is a derivational model that is built similar to the structure of the human Biological Neural Network (BNN). The human brain contains billions and billions of neurons to collect and process information, and derive meaningful solutions from it.

These neurons are using electro-chemical signals to communicate. Likewise, ANN also has artificial neurons which are called nodes. These nodes are connected with other nodes thereby, forming a complex relationship between the output and the input.

Artificial Neural Network has three layers:

Input Layer:  The neurons here take input from external sources like files, data sets, images, videos, and sensors. It transfers the data from the outside to the Neural Network and no computation is performed here.

Hidden Layer: This layer receives data from the input layer and uses it to arrive at solutions and to train other Machine Learning models.

Output layer: Once the processing is over, the data gets transferred to the output layer, ready to be delivered to the outside world.

5. How many types of Artificial Intelligence are there?

There are seven types of Artificial Intelligence, which are:

• Reactive Machines– These types of machines react very well in a current situation. But they cannot store experiences or memories.

Eg. IBM’s Deep Blue system and Google’s Alpha go.

• Limited memory– These machines can store experiences, although only for a limited.

Eg: smart cars that can store information about other cars (speed or routes or fuel info)

• Weak AI or Narrow AI– These machines are designed to perform specifically allocated tasks and cannot perform beyond their limitations.

Eg: Apple’s Siri and IBM’s Watson

• General AI– These Ais can perform intellectual tasks like human beings. At present, there isn’t any AI that falls under this category.
• Super AI– This type of AI can surpass the intelligence of humans and can outperform them. However, Super AI is still a hypothetical concept.
• Theory of mind- It is still a theory that the AI might be able to understand human emotions and beliefs, and society, and interact just like us.
• Self-awareness- Self-awareness AI is the magical future of AI. They are expected to be super-intelligent, carrying their own emotions, and self-awareness.

6. Explain Deep Learning?

The word ‘deep’ here signifies the number of hidden layers that the neural network has. Deep learning models are built in a way to train and manage themselves.

7. What are the common misunderstandings about AI?

Some misunderstandings about Artificial Intelligence are:

That machines can learn from themselves – though they are programmed to learn, they are still not advanced enough to take decisions by themselves.

Artificial Intelligence and Machine learning are the same – While AI is about creating and programming devices, machine learning is just a subsect of AI.

AI can take over humans someday – They might surpass human capabilities and intelligence but to enslave humanity is a fictional concept.

8. Name a few platforms for Artificial Intelligence development

IBM Watson, Google AI services, Infosys Nia, Microsoft Azure AI platform, Amazon AI services.

9. How can the future benefit from AI?

• It will be the pivotal point of research and development for healthcare, science, and space.
• AI will be the door to emerging greater technology.
• To manage and study a massive amount of data to arrive at an optimal solution.

10. State the difference between Artificial Intelligence, Machine Learning, and Deep Learning

Artificial Intelligence – It is the programming of machines to function like a human brain. It is a subset of data science. Eg: Google search engine

Machine Learning – It is arriving at informed decisions with the collected data. It is a subset of AI. Eg: Image recognition.

Deep Learning – It uses ANN to solve complex problems. It is a subset of Machine Learning. Eg: An automatic car driving system.

11. What is Markov’s Decision process?

MDP helps to solve a reinforcement learning problem. It aims to gain maximum positive rewards by selecting the best policy. MDP has four elements: A set of finite states S, A set of finite actions A, Policy Pa, and Rewards.

12. What is the Hidden Markov model?

1. HMM is used for temporal data and is used in various applications like temporal pattern recognition, reinforcement learning, and others.
2. It is a statistical model to represent the probability distributions for a loop of observations.
3. The word hidden refers to the hidden property it takes to hide the process at a certain time from the observer, and Markov refers to the process that satisfies the Markov property.

13. Define Hyperparameter

These parameters are outside the model that manages the complete training process. Eg: Hidden layers, Learning rate, Activation functions, etc.

14. Define reward maximization

In Reinforcement learning, a reward is a positive note for taking action in the transition from one state to another. For every good action for an optimal policy, a reward is given. Therefore, by applying more optimal policies the rewards get maximized.

15. Explain the relevance of Computer vision in Artificial Intelligence

1. To arrive at relevant interpretations from images or other visual stimulation.
2. A large amount of visual data is processed and analyzed to arrive at a pattern.
3. Computer vision algorithms solely depend on pattern recognition.
4. Eg: Facial recognition systems, Augmented reality, and the Content organization in Apple.

16. Explain the relevance of Turing tests in AI

The test is performed to check if computers can replicate human responses in said conditions. It involves three entities, Computer, Human Responder, and the Human Interrogator.

17. How does Reinforcement Learning work?

It is a reward-based model to encourage the machine to find the best possible solution. In this feedback-based system, sequential decision-making is required without any labeled data. Negative feedback is provided when the machine does not do well.

18. Define fuzzy logic and its applications?

It is a subset of Artificial Intelligence that is a form of many-valued logic. IF-THEN rules are used to represent Fuzzy Logic.

Applications eg: Stock market, Weather Forecast systems, Home Appliances, Health diagnosis, and treatment programs.

19. What is NLP and what are its components?

Natural Language Processing is a branch of Artificial Intelligence that allows machines to understand and interpret human language. The 2 main components are:

Natural Language Understanding (NLU) – To make representations with the input

Natural Language Generation (NLG) – Sentence Planning, Text Planning, and Text Realization.

20. What is Minimax Algorithm?

It is used in game theory as a backtracking algorithm. There are two players, MAX and MIN. It gives optimal moves for a player assuming that the other player is playing optimally.

The terminologies used are Game tree, Initial State, Terminal State, and Utility Function.

21. Name the difference between inductive, deductive, and abductive Machine Learning.

Inductive Machine Learning – Conclusion from a set of instructions. Eg: KNN (K-nearest neighbor) or SVM (Support Vector Machine).

Deductive Machine Learning – Improves conclusion based on earlier decisions. Eg: A decision tree is used in Machine Learning Algorithm.

Abductive Machine Learning – Various instances are considered to conclude. Eg: Deep Neural Networks.

22. What are the different algorithm techniques in Machine Learning.

The most commonly used Machine Learning Algorithms are:

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

23. What is a Backpropagation Algorithm?

To manage messy data and detect unrecognized patterns Backpropagation Algorithm is used. It uses a standard approach for training ML models. It is mainly used in image processing, optical character recognition, and speed recognition.

The below values are included with the algorithm before data processing:

Dataset: It is used for training a model.

Weights: It transforms the input in the hidden layer, in a neural network.

Target Attributes: the algorithm’s Output value.

Biases: Bias values are added to the sum calculated.

24. How many types of agents are there in Artificial Intelligence?

Simple Reflex Agents: They act only on the current situation and ignore the past interaction with the environment.

Model-Based Reflex Agents: They act according to predefined models also keeping track of internal conditions, that can be altered as per the changes in the environment.

Goal-Based Agents: These agents react as per the goals given to them and the aim is to reach that goal.

Utility-based Agents: These agents take action according to utilities of choices, to make the easiest and safest route to the goal.

Learning Agents: These agents learn from their experiences.

25. How is Game theory important in Artificial Intelligence?

• Forms a model of interactions between two or more players.
• Game theory is used in AI to enable the key capabilities in the multi-agent environment.
• Logical online games need algorithms that are applied with AI.

26. What is knowledge representation in AI?

Knowledge about the world that is needed by the AI agents to solve complex problems is called Knowledge representation.

The elements used are: Objects, Events, Meta-Knowledge, Facts, Performance and Knowledge-base

27. What are the techniques used to avoid overfitting?

Overfitting can be avoided using any of the below methods:

• More data training.
• Regularization: Add a penalty term with the cost.
• Ensemble learning is combining predictions from other models.
• Cross-validation: To evaluate machine learning models it is a resampling technique.
• Removal of features: Including outliers by removing unnecessary features.
• Stopping early: In an iterative method, it is a type of regularization.

28. What is Naive Bayes?

It is an influential algorithm in predictive modeling with a set of algorithms based on the Bayes Theorem that carries a common principle. The primary Naive Bayes hypothesis is that every feature is independently and equally related to the outcome.

29. Name the extraction techniques for dimensionality reduction.

The techniques used are:

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

30. What are the steps in Machine Learning?

• Collection of Data
• Preparation of Data
• Choosing a model
• Dataset Training
• Evaluation
• Tuning of parameters and
• Predictions

31. What are the methods used to reduce dimensionality?

It is the process of reducing the number of random variables. Techniques like high correlation filter, random forest, missing values ratio, principal component analysis, etc.

32. What is a heuristic function, and where is it used?

It estimates how close a state is to its goal. This function is used in Informed Search to find the most promising path. It is represented by h(n). For an optimal path between pair of states this function finds the cost, the value which is always positive.

Few scenario-based Artificial Intelligence Interview questions.

33. Imagine you are an entrepreneur; how can you use AI in your business marketing?

AI can be used in Sales forecast, Customer support, Marketing campaigns, Dynamic pricing methods, and Personalized user experience, to get customer feedback and opinions.

34. How will you explain a face detection system to a beginner?

A Face recognition technology to recognize a person by his face. The algorithm works as follows:

• Photo is given as input to the face detection system.
• System scans and analyses various facial features.
• Algorithm converts your face into a faceprint
• The feature vector compares this with a potential match
• System takes an action when a match is found.

35. Why is Chatbot considered the best customer support system?

• It is a program that processes and manages human conversations. It interacts the same way a customer support executive would do.
• The chatbot can give simple single-line replies and also elaborate answers to fulfill the customer’s queries.  
• They are the future of the best and the most satisfying customer support that can drastically bring down the customer service cost to the company.

The entire human race owes it to AI for every invention and breakthrough in science. To emphasize, AI is like the most eligible bachelor in town today. Each one of us needs it and researchers are finding new possibilities to upscale the capabilities of AI.

Therefore, our only responsibility now is not to let the myth ‘rise of the robots’ become a scary reality.

The above-listed Top 35 Artificial Intelligence Interview Questions and answers are done so after carefully considering the demands of the job market. These artificial intelligence questions and answers would not only help an aspiring student to enhance and upgrade his knowledge levels but will also definitely get him the dream AI job.

Recommended reads

Top 10 Artificial Intelligence applications

Artificial Intelligence courses in India

Current and Future of Artificial Intelligence

Types of Artificial Intelligence

A Comprehensive Guide on How to Become an AI Designer

Frequently asked questions

1. Will the knowledge of AI get me a good-paying job?

Definitely, as discussed in this blog AI has become the backbone of so many sectors. So the need for candidates with AI knowledge is high in the job market.

2. Are the above-mentioned questions comprehensive of the AI syllabus

The above-listed Artificial Intelligence interview questions cover almost all the major concepts of AI.

3. Can I get a job if I study the above-mentioned interview questions

These Artificial Intelligence interview questions and answers will act as a perfect supplement addition to your other source of study.

4. Is it expensive to develop an AI application?

Anything that is built from step 1 needs time and money, and so is an AI application. The cost structure depends on the app specifications.

5. What should I do to do well in an AI job interview?

Revise the key concepts and terminologies and answer in a composed manner.

 

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