AI Interview Questions

AI Interview Questions

  • Anupriya Dutta
  • 11th Nov, 2022

Practice Best AI Interview Questions 

AI is the abbreviation for Artificial intelligence. It allowed the simulation of human intelligence in machines. It is referred to as any machine that exhibits traits associated with a human mind such as learning and problem-solving. Artificial intelligence is intelligence activated by machines, unlike the natural intelligence displayed by humans and animals.

Finally, practice here the best 15+ AI Interview Questions and Answers, that is very helpful for freshers & experienced candidates. These AI Interview Questions are very popular and are asked various times in AI Interviews. So, practice these questions to check your final preparation for AI Interview. apart from this, you can also download below the AI Interview Questions PDF, completely free.

AI Interview Questions for Freshers

1) What do you understand by AI?

AI is a domain of computer science that refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions.

2) What is the full form of the AI?

AI stands for Artificial intelligence.

3) When AI was introduced?

AI was introduced in the year 1956, at a conference at Dartmouth College, in Hanover, New Hampshire, where the term "artificial intelligence" was coined.

4) What are the types of AI?

The AI can be categized into the following types:

  • Reactive machines
  • Limited memory
  • Theory of mind
  • Self-awareness

5) What are the applications/uses of AI?

There are a lot of applications of AI. Some of these are:

  • AI in Astronomy
  • AI in Automotive Industry
  • AI in Data Security
  • AI in Finance
  • AI in Healthcare
  • AI in Automation


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6) Describe the various subsets of AI?

The various subsets of AI are Machine Learning, Deep Learning, Natural Language Processing, Robotics, Neural networks, Computer Vision, etc.

7) Describe the intelligent agent in AI?

An intelligent agent is an autonomous entity or entity that makes a decision, that enables artificial intelligence to be put into action. Such that it could conduct operations in the place of users or programs after sensing the environment.

8) Which AI is used for decision making?

Augmented intelligence is the part of AI that is used for decision making.

9) What is different between AI & Machine Learning?

AI is a domain of computer science that refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. Whereas Machine learning is a subset of AI which is about extracting knowledge from the data.

10) Explain the knowledge representation in AI?

Knowledge representation is the field of artificial intelligence or AI dedicated to representing real-world information in a form that a computer system can utilize to solve complex and real-life tasks.

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11) What is the difference between strong AI and weak AI?

Strong AI has a complex algorithm that helps it act in different situations such that the machines are consist of their own mind, whereas weak AIs are pre-programmed by a human and the machines can only simulate human behavior.

12) Explain Statical AI & Classical AI?

Statistical AI is a part of machine learning which is concerned with inductive thought i.e. given a set of patterns, induce the trend. Whereas Classical AI is concerned with deductive thought i.e. given a set of constraints, deduce a conclusion.

13) What is Neural Network in AI?

In Artificial intelligence, a neural network is an interconnected group of nodes, inspired by a simplification of neurons in a brain that is meant to simulate the functioning of a human brain.

14) Explain Bayes rule in AI?

Bayes rule is also known as Bayes' theorem determines the probability of an event with uncertain knowledge. In probability theory, it relates the conditional probability and marginal probabilities of two random events.

15) Describe the areas where AI is used?

There are several areas where AI is used. Some of these are:

  • Manufacturing and Production.
  • Sports Analytics and Activities.
  • Live Stock and Inventory Management.
  • Agriculture and Farming.
  • Security and Surveillance.


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