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Top 23 AI (Artificial Intelligence) Interview Questions and Answers

01/Oct/2020 | 10 minutes to read

QFLES is listing some Essential AI (Artificial Intelligence) Interview Questions and Answers for Freshers and mid level of Experienced Professionals prepared by Industry Experts. All answers for these AI (Artificial Intelligence) interview questions are given based on standard documents and tried to explain in simple and easiest way.

Best Answers to AI (Artificial Intelligence) Interview Questions

These interview questions are targeted for AI (Artificial Intelligence) professionals. These questions will help you to clear your AI interview.

1. What is Artificial Intelligence(AI)?

Artificial Intelligence or AI is the field or branch of computer science that aims to create intelligent machines that behave or work and react like humans. AI is the study of cognitive functions of human brain and tried to implement same on machines. Or Artificial Intelligence is way of making a computer, computer controlled robot, or a computer software which thinks intelligently, like intelligent humans think.

2. List some common Applications of AI? Or give some examples of AI.

Artificial Intelligence or AI is used in many fields as below.

  • Speech Recognition
  • Sentiment Analysis
  • NLP or Natural Language Processing
  • Chatbots
  • Computing
  • Robotics
  • Machine Perception
  • Many other.

3. What are latest technologies used in Artificial Intelligence AI?

There are many Artificial intelligence technologies some of them we are listing here.

  • NLP or Natural Language Processing
  • Machine Learning
  • Speech Recognition
  • RPA (Robotic Process Automation)
  • Image Recognition
  • Virtual Agents
  • Biometrics
  • Decision Management
  • Deep Learning Platforms
  • Content Creation
  • Emotion Recognition
  • There are many other also.

4. What are the most popular AI programming Languages?

These below are the most popular AI programming Languages used by developers to satisfy different needs.

  • Python
  • C++
  • Java
  • LISP
  • Prolog

5. What is the difference between Strong AI and Weak AI?

AI is the capability of machines to perform the tasks just like humans.

  • Weak AI is the artificial intelligence which implements a limited part of mind. Machines can not carry the tasks on their own, they need human interference. iPhone's Siri and Amazon's Alexa are the examples of weak AI.
    Weak AI has less complex algorithm and all actions in this are the preprogrammed by humans like in Siri.
  • Strong AI is the artificial intelligence that can perform the tasks on their own just like humans do. AI in gamings like Poker AI is the example of strong AI that can teach itself to adapt the skills of human opponent.
    Strong AI has complex algorithm that help it to act in different situations. Strong AI has unpredictable responses just like humans as you can not judge the response of a person when you talk to him.

6. What are the sub fields of AI? Or how AI, Machine learning and deep learning are related?

Machine learning and deep learning are the subfields of AI. Artificial Intelligence (AI) contain many subfields as below.

  • Machine Learning is a subfield of AI that trains the machines how to learn. Machine learning is the study of data models and algorithms that software systems use to perform some tasks without explicit instructions relying on the idea that systems can learn from data, identify patterns and make decisions without human intervention or minimal human intervention.
  • A neural network - A interconnected system or circuit of neurons makes a neural network. It could be either organic or artificial in nature. A neural network is a software or hardware that performs the tasks similar to human brain neurons. A neural network is a series of algorithms to solve various business problems such as risk management, customer forecasting, data validation in similar way as humans brain neurons do.
  • Deep learning is the subset of machine learning or is a vast family of machine learning methods that has capability of unsupervised learning from unstructured data. Deep learning is well suited for big data for knowledge discovery and knowledge based applications.
  • Computer vision is the subfield of artificial intelligence that deals with how computer or systems understand the information from images or videos for their proper understanding to predict the visual input similar to human brain. It's AI technology that makes computers to understand image or video inputs to perform other tasks automation, for example: driver less car testing, livestock etc.
  • NLP (Natural Language Processing) is the subfield of AI, computer science or information engineering that deals with how to program computers to process, understand and analyze large amount of natural languages data. NLP is the automation process of manipulating natural languages by softwares.

7. How will you differentiate Artificial Intelligence, Machine learning and Deep learning?

AI is at broader level and all other are the subfields of AI. Please refer my above questions's (6th) answer.

  • AI is the subset of Data Science or the branch of computer science with the aim to create intelligent machines that behave like humans.
  • Machine learning is the subfield of AI that trains the machines how to learn and make decisions without programming so that they can solve the problems.
  • Deep Learning is the subset of machine learning with the capability of unsupervised learning from unstructured data with the aim of building neural networks for automatic discovering patterns for feature detection.

8. What is the Expert System in AI? Explain it's characteristics?

Expert System is a computer program or system that has the ability similar to human expert to make decisions and judgment. Expert system emulates the if-then pattern rather than procedural programming. Expert systems or programs are used to solve complex problems by capturing the knowledge from knowledge base. All knowledge in knowledge base is stored by human experts and Expert Systems are used by Non Expert humans to solve complex problems.
There are following characteristics of Expert System.

  • Expert Systems helps to spread the expertise of a human to many.
  • Expert Systems provide more efficient solutions as knowledge base contains the information from many human expert rather than single human.
  • Cost is reduced with expert systems as less cost involve as compare to consultants for problem solving.
  • As New facts can be reduced based on existing knowledge base facts so expert systems can solve complex problems.
  • Expert system is a permanent computer system as human experts are perishable or limited life.

9. What are the limitations of an Expert System in AI?

Expert system is an permanent and solve complex problems but it has some limitations as below.

  • Expert system can not give correct results from less knowledge base data.
  • Expert system can not provide decision making like humans.
  • It require lot of training to give correct results.

10. What are the advantages and disadvantages of an Expert System in AI?

Expert Systems in AI has certain advantages and disadvantages as following.

  • Expert System Advantage -
  • It gives fast results with less chances of errors.
  • Expert system comes with reduced cost.
  • Expert systems has no effect of emotions like humans.
  • It's permanent solution not perishable.
  • Expert System Disadvantage -
  • Expert system does not care about emotions and does no have any common sense.
  • Expert system takes decision fast but not able to explain the reason behind it.
  • does not support self learning, need to update manually.

11. What is a fuzzy logic in AI (Artificial Intelligence)?

Fuzzy logic is a concept of reasoning similar to human reasoning. Fuzzy logic is the capability of making decisions similar to humans and includes a possible combinations of "YES" and "NO" while making decisions.

12. What are the different types of Artificial Intelligence?

AI provides the capability to develop machines that will act like humans. Based on the degree to which intelligent machines can be create, is used as a criteria to determine the classification of AI. So based on this criteria AI has four types.

  • Reactive Machines - These are most basic AI systems with limited capability. These systems does not have past stored memories and perform actions based on current set of inputs. Alpha Go is an example of reactive machines based AI system.
  • Limited Memory - These systems has the capability of reactive machines and addition to memory store for limited period of time. Self driving Cars are the example of this AI system. Self driving cars can store speed, distance from other cars etc.
  • Theory of Mind - Above two types of AI have been found and in use but Theory of Mind AI is the next level of AI that is available as concept only. Researchers are engaged on this. Theory of mind AI will act like humans means capability of understanding beliefs, emotions and thought process.
  • Self-aware - This AI concept is completely hypothetically. These AI systems will be much intelligent then humans and will have their own consciousness, belief, emotions and thought process. These AI systems will come in next decades.
An Alternate classification that is more generally used in Tech industries or based on capability AI has 3 types.
  • Artificial Narrow Intelligence (ANI)
  • Artificial General Intelligence (AGI)
  • Artificial Super Intelligence (ASI)
For more you can refer AI Types

13. Explain the AI Neural Networks.

Artificial neurons are similar to human brain neurons which is called biological neural networks. Every artificial neuron is connected to other neuron, so when an artificial neuron receives the signals, process it and pass it to next connected neuron.
In fact Deep learning is the new name for the Neural Networks approach of AI. This Artificial Neural Networks is capable of learning unsupervised from unlabeled and unstructured data.

14. Describe the Intelligent Agents.

15. What is the Turing Test.

16. What's a Transfer Flow?.

17. What is the Game Theory in Artificial Intelligence?

18. What's Inverse Game Theory in AI?.

Some General Interview Questions for AI (Artificial Intelligence)

1. How much will you rate your self in AI (Artificial Intelligence)?

When you attend an interview, Interviewer may ask you to rate your self in specific Technology like AI (Artificial Intelligence), So It's depend on your knowledge and work experience in AI (Artificial Intelligence).

2. What challenges did you face while working on AI (Artificial Intelligence)?

This question may be specific to your technology and completely depends on your past work experience. So you need to just explain the challenges you faced related to AI (Artificial Intelligence) in your Project.

3. What was your role in last Project related to AI (Artificial Intelligence)?

It's based on your role and responsibilities assigned to you and what functionality you implemented using AI (Artificial Intelligence) in your project. This question is generally asked in every interview.

4. How much experience do you have in AI (Artificial Intelligence)?

Here you can tell about your overall work experience on AI (Artificial Intelligence).

5. Have you done any AI (Artificial Intelligence) Certification or Training?

It's depend on candidate like you have done any AI (Artificial Intelligence) training or certification. Certifications or trainings are not essential but good to have.


We have covered some frequently asked AI (Artificial Intelligence) Interview Questions and Answers to help you for your Interview. All these Essential AI (Artificial Intelligence) Interview Questions are targeted for mid level of experienced Professionals and freshers.
While attending any AI (Artificial Intelligence) Interview if you face any difficulty to answer any question please write to us at Our IT Expert team will find the best answer and will update on portal. In case if we find any new AI (Artificial Intelligence) questions, we will update the same here.

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