Top 50 Artificial Intelligence Interview Questions

Introduction

Job opportunities in artificial intelligence are at high raise in recent times due to huge demand in various industries. Almost every industry in the market today is getting technically advanced, which is why there is more demand for AI engineers and AI professionals.  A career in AI is promising today and if we are well prepared there can be a bright future.
So let’s get to know more about AI. If you are a fresher, this article will be very helpful to you. It will be a good guide for your interview and a confidence booster.
This article will highlight on the list of questions, generally asked in an interview for an AI professional. So here are the top 50 artificial intelligence interview questions.
Here are the top 50 artificial intelligence interview questions. You will find it very useful.

Top 50 Artificial intelligence interview questions

1. What is the difference between AI and ML?

Artificial Intelligence	Machine Learning
It was practically implemented since 1950	It was practically implemented since 1960
AI is intelligence imitated and fed in machine	ML is machine making decisions without being programmed
AI is sub set of data science	ML is sub set of AI & data science
AI objective is to make machine capable of thinking like human	ML is a problem solving process with the help of data

2. What is the difference between Strong AI & Weak AI?

Strong AI	Weak AI
It is also called artificial general intelligence (AGI)	It is also called narrow AI
Machine performs high ability to solve a problem	It performs limited tasks with a single function
Task that humans can do could be performed by strong AI	It has a limited probability of performance
Examples: Visual recognition, Fraud detection comes under the strong AI category.	Examples: Siri and Google Assistant come under the narrow Ai category.

3. What is Artificial Intelligence? What are real-life examples where we use AI?

Artificial Intelligence is a part of computer science. It automates the machine to perform like humans. AI feeds the programs into machines that do human tasks intelligently. For example, in cyber security and weather forecasting using AI, there is zero error possibility. Space programs AI takes over successfully. Also, the bomb diffuse AI program helps and saves human life.

4. Name some AI applications.

• AI-powered Assistants. 
• Cyber Security. 
• Administrative Tasks Automated to Aid Educators. 
• Creating Smart Content. 
• Voice Assistants. 
• Personalized Learning. 
• Autonomous Vehicles.

5. What is the list of programming languages used in AI?

Python, Prolog, Java, R, Lisp.

6. What are the types of AI?

Artificial Narrow Intelligence (ANI): Simplified purpose AI, used in building virtual assistants like Siri.
Artificial General Intelligence (AGI): Also known as strong AI. An example is the fraud detector. It detects the dangers and informs the user.
Artificial Superhuman Intelligence (ASI): AI that compels the ability to do everything that a human can do and more. An example is the Alpha 2, the first humanoid ASI robot.

7. What is ANN?

There are three layers in the Artificial Neural Network: 
Input Layer: The input layer consists of neurons that take input from independent sources like files, data sets, images, videos, and sensors. This part of the Neural Network doesn’t operate any computing. It only transfers the data from external sources to the Neural Network.
Hidden Layer: The hidden layer acquires the data from the input layer and uses it to derive results and train Machine Learning models. The layer has sub-layers that extract features, make decisions, connect with external sources, and predict future actions based on the situations. 
Output layer: After processing, the data is transferred to the output layer for delivering it to the external environment.
Artificial Neural Network (ANN) consists of artificial neurons called nodes connected with other nodes forming a composite exchange between the output and the input.  

8. What Are Intelligent Agents, and How Are They Used in AI?

Intelligent Agents are automatic sensors that detect the situation and take action to perform the required task. The smart actuators perform the task very precisely as defined. They are applicable in simple, complicated, or advanced tasks as per the requirement.

9. What Is Tensorflow, and What Is It Used For?

Tensorflow is an open-source library developed by the Google Brain team, for deep learning applications. It is written in C++ and java. Tensorflow makes it easy to develop many AI features in applications of neural language and voice recognition.

10. What is an A* algorithm search method?

A* algorithm is a computer algorithm that is exclusively used to find the path for the graph to find the best track between the nodes. 

11. What is a depth-first search algorithm?

Depth-first search is an algorithm for searching graph data structures. The algorithm starts at the root node and analyzes each branch before backtracking. 
The basic idea is to start from the random node and mark the node and move to the adjacent unmarked node and continue this loop until there is no unmarked adjacent node. Then backtrack and check for other unmarked nodes and find them. 

12. What is a breadth-first search algorithm?

BFS is the most commonly used search method for graphs. 
BFS is a search algorithm where the search is started from a selected node and the search of the graph is performed in layers. This method allows the discovery of new points or neighboring nodes. You must then move toward the neighboring nodes.

13. What is a bidirectional search algorithm?

Heuristic indicates the process of finding the shortest path from the present node in the graph to the objective node. The search often takes the shortest path toward the objective node. This regulation is used in bidirectional heuristic searches. The bidirectional search algorithm reduces the search times from the nodes present in the simultaneous directions.

14. What is a uniform cost search algorithm?

The uniform cost search algorithm is the unbraided search algorithm that uses the lowest aggregate cost to find the path from source to destination. 

15. What is the Tower of Hanoi?

Tower of Hanoi is a mathematical puzzle that shows how a self-designed problem can be utilized as a device in building up an algorithm to resolve a particular problem. The Tower of Hanoi can be solved in order by the decision tree and BFS algorithm.

16. What is the Turing test?

The turning test is the process of testing the machine's ability to match human intelligence. Machines challenge human intelligence and when it succeeds it is labeled as intelligent.

17. What is an expert system? 

The expert system is an expert-level artificial intelligence program. It has expert-level knowledge about a certain subject. This program takes care of how appropriately the knowledge is used on a particular subject. Such programs can replace the human expert. the programs are high in performance, very reliable, and efficient.

18. What are the advantages of an expert system?

• Consistency
• Multiple expertise
• Ability to reason
• Fast response
• Unbiased in nature
• Memory
• Diligence
• Logic

19. What is an iterative deepening depth-first search algorithm?

The deep search algorithm repeatedly processes the search till the solution is found. Nodes are generated till the final objective goal is created. The heap of nodes is saved.

20. Explain Alpha–Beta pruning.

Alpha-beta pruning is a search algorithm that works on reducing the number of nodes that are searched by the algorithms in the graph. It can be applied deeply to the root level and can prune the entire structure.

21. What are the AI skills in demand in 2022?

As an AI expert, you must be proficient in any of the following programming languages.

• PYTHON
• C, C++
• MATLAB
• With good command in:
• Communication skills
• Proficiency in programming languages like python
• Analytical Skills
• Expert in mathematics
• Problem-solving skills
• Good knowledge of Machine Learning language 

22. What are the advantages of artificial intelligence?

Reduction of human error

Artificial intelligence with the right algorithms and a wide collection of stored data guarantees tasks with full efficiency and accuracy. Many complex problems are solved with sophisticated solutions without error.
For example, in cyber security and weather forecasting using AI, there is zero error possibility. 

Takes risks instead of humans

The best advantage of artificial intelligence is not risking human lives. With well-defined algorithms and the right program development, a system is designed to perform particular tasks that can prove risky for humans otherwise. The example we can understand is, space programs where no humans are sent, instead, AI takes over the project successfully. 

Repetitive job role

Artificial intelligence makes it easy to perform activities like getting bank updates, transaction details, and email responses can be such boring tasks to do reputedly every day that task is made simple by auto-replies. Documents checks or verification customer care, banks or educational institutes is another best example of AI getting our repetitive jobs.

24/7 support

AI can continuously perform the machining task 24/7 without intervention. 
Example: technology in a machine plant where a technical job can be handled by AI 24/7 or helplines in educational or medical centers that can handle the queries continuously and effectively. 

Digital Assistance

Digital Assistants are used by some big companies for communication and customer support. Some chatbots are designed so well that it gets difficult to detect whether the communication is handled by a human or a chatbot.
Examples we can relate to are mobile applications designed to handle chatbots and client queries or technical problems that are answered and resolved efficiently.

Great concepts

The advanced technology in AI has effectively handled tasks like early detection of any severe disease and providing medical solutions has been proving so beneficial that it has saved many human lives. These were some of the benefits and highlights of AI, now let’s get to know some of the disadvantages of artificial intelligence.

Instant decision

The fast and immediate decisions of artificial intelligence are all pre-defined through the machine learning programs and algorithms which define the AI system. All the possible solutions and results are calculated with the programming language and algorithms.
Technically this proves very beneficial for example, in a development plant where the production of thousands of products is all dependent on the right timing and settings of the software program. With no human involvement, the automated programs leave no scope for human error and take an instant decision of changing the software setting in case it is required.

Application in day-to-day activities

We play our favorite playlist at a particular time using digital assistants like Siri, Alexa, or Google Assistant. We use the GPRS from Google navigation support and we are good to go to explore new locations anywhere in the world.

23. What are the disadvantages of artificial intelligence?

High cost

AI upgrades very frequently, and the applications and software need constant updates. This is a challenging task as it involves huge costs. Software professionals have challenging tasks to perform targeted results. so the development task and implementation become a cost-effective job.

Unemployment

AI assistants perform great tasks in various organizations reducing human intervention. The firms are now getting digitalized and want to utilize automated solutions. This concept has affected badly on human connection. The jobs have been replaced by AI. Thus the unemployment ratio has been increasing with time.

No involvement

This is a clear picture that AI machines perform great tasks with precision.AI does perform assigned tasks but it does not have any inputs or connection with humans. This is a very necessary factor for management and team growth.

No creativity

Machines are designed and programmed to do various tasks. They perform the assigned task as programmed. It fails to add any input or come up with a creative solution for any problem.  So these were some of the advantages and disadvantages of artificial intelligence. It is up to us how we can make sure that AI will be beneficial for us in the future. With good development and the right programming, we can define a better world. Better and innovative solutions in the medical or science field can help to improvise and benefit the human race.
With the right knowledge of AI, we can initiate and step towards building a better tomorrow for us all. Get to know more about artificial intelligence. 

Limited improvement

Artificial intelligence is a set of programs that are pre-defined and programmed to perform a specific task at a specific time. It will efficiently perform the same task over and over without giving upgraded better solutions.
It does repeat the same task and saves an ample amount of time but in case of a technical issue, it will stop and need technical support. Without resolving the issue the task cannot be resumed. 
Humans, on the other hand, can rectify the technical issue and can come up with solutions with the right information can resolve the technical problem, and can resume the technical task with supervision.
Hence artificial intelligence pre-defined programs have no scope for coming up with program improvement.

24. What is Naive Bayes?

The naive Bayes Machine Learning algorithm is a strong algorithm for predictive modeling. It is a set of algorithms based on the Bayes Theorem. The Naive Bayes is relevant as each feature makes a separate and equal contribution to the results.

25. What is perceptron in Machine Learning?

Perception is an artificial neuron. It has a simple layer structure that is not a multilayered neural network. This is because the pre-processing stage of perception does not have any neurons involved. 

26. List the extraction techniques used for dimensionality reduction.

The following are the methods that are used commonly in dimensionality reduction.
Principal Component Analysis (PCA), 
Factor Analysis (FA), 
Linear Discriminant Analysis (LDA) and 
Truncated Singular Value Decomposition (SVD) 

27. What are the techniques used in dimensionality reduction?

The following are the methods that are used commonly in dimensionality reduction.
Principal Component Analysis (PCA), 
Factor Analysis (FA), 
Linear Discriminant Analysis (LDA) and 
Truncated Singular Value Decomposition (SVD) 

28. What is partial-order planning?

Any problem that occurs has to be solved sequentially in proper order to achieve the expected results. Partial-order planning presents a complete layout of the plan in order, with specified steps to be followed to solve a problem.

29. What is FOPL?

First-order predicate logic is a collection of precise and conventional formats. Every section is divided into subjects and significant actions that can modify or define the settings of the subject.

30. Name a few Machine Learning algorithms you know.

• Decision trees
• Support vector machines
• Naive Bayes, and so on
• Logistic regression
• Linear regression

31. List the steps involved in Machine Learning.

• Data collection
• Data preparation
• Choosing an appropriate model
• Training the dataset
• Evaluation
• Parameter tuning
• Predictions

32. What is regularization in Machine Learning?

Regularization indicates the techniques that are used to balance machine learning models to reduce the amended loss function and avoid over fitting or under fitting.

33. What is an F1 score?

F1-score is the consonant mean of precision and recall. It measures the model performance. The F1 score takes into account both false positives and false negatives which combines precision and recall.

34. What are the types of feature selection in Machine Learning?

Wrapper methods, 
Filter methods, and 
Embedded methods 

34. List different methods for sequential supervised learning.

The methods for sequential supervised learning are sliding window methods, recurrent sliding windows, hidden Markov models, conditional random fields, and graph transformer networks.

35. What is the Bias–Variance tradeoff?

Bias is the process that simplifies the predictions made by the model to make the expected function accessible to close value. Variance on the other hand is the amount that the assessment of the target function will differ as the training data will be different. Trade-off indicates the fine line defined between the error rectified by the bias and the variance.

36. What is a vanishing gradient?

With core layers in the network, the value of the product of the subordinate reduces till the partial subordinate of the loss function comes close to zero value and the partial subordinate vanishes. This process is called the vanishing gradient.

37. Define LSTM.

Long short-term memory (LSTM) comes from the multiple areas of deep learning. It deals with the algorithm that tries to imitate the human brain the way it works and to discover the connectivity of the system with sequential data.

38. Name the key components of LSTM.

A common component of LSTM unit is composed of a cell, an input gate, an output gate, and a forget gate. 

40. What is an autoencoder? Name a few applications.

Autoencoders are neural networks that target to copy their inputs to outputs. They are used to learn the complicated and dense form of data.

• Train the model
• As an AI expert, you must be proficient in any of the following programming languages.
• PYTHON
• C, C++
• MATLAB
• With good command in:
• Communication skills
• Proficiency in programming languages like python
• Analytical Skills
• Expert in mathematics
• Problem-solving skills
• Good knowledge of Machine Learning language 
• Decision trees
• Support vector machines
• Naive Bayes, and so on
• Logistic regression
• Linear regression
• Data collection
• Data preparation
• Choosing an appropriate model
• Training the dataset
• Evaluation
• Parameter tuning
• Predictions
  

Applications of autoencoder are as follows:

• Dimensionality Reduction
• Image Compression
• Feature Extraction
• Image generation
• Sequence to sequence prediction
• Recommendation system

41. What are the components of the generative adversarial network (GAN)? How do you deploy it?

Components of GAN:

•  AI-powered Assistants.
• Cyber Security.
• Administrative Tasks Automated to Aid Educators.
• Creating Smart Content.
• Voice Assistants.
• Personalized Learning.
• Autonomous Vehicles.

42. What is the lifetime of a variable?

The lifetime of a variable is the time period in which the variable has accurate memory. Lifetime is also termed as the "allocation method" or "storage duration."

43. What are some of the algorithms used for hyperparameter optimization?

Various algorithms are used for hyperparameter optimization, the important algorithms are mentioned below:

• Bayesian optimization
• Grid search
• Random search
  

44. What are dropouts?

A method that randomly ignores the neurons during the session is called a  dropout. Neurons are dropped out simultaneously. The downstream neurons are temporarily eliminated on the forward pass, and no updates are applicable to the neurons on the backward pass.

• The lifetime of a variable is the time period in which the variable has accurate memory. Lifetime is also termed as the "allocation method" or "storage duration."
• Generator- that generates credible data.
• Discriminator-that analyzes the fake data
  Deployment Steps:
• Validate and finalize the model
• Save the model
• Load the saved model for the next prediction

45. What are the hyperparameters of ANN?

• A hyperparameter is a machine learning parameter whose value is selected before a learning algorithm is trained.
• Learning rate: The learning rate determines the speed in which the network gets to know its parameters.
• Momentum: It is a parameter that helps to come out of the local minima and smoothen the jumps while gradient descent.
• Number of epochs: During the training session the number of times the complete training data is delivered to the network is referred to as the number of epochs. 

46. What are the examples of hyperparameters in machine learning include:

• Model architecture
• Learning rate
• Number of epochs
• Number of branches in a decision tree
• Number of clusters in a clustering algorithm
• Advertisement

47. What are the advantages of neural networks?

• Require statistical training brief
• Detects successfully the nonlinear connection between variables
• Covers all communication between predictor variables
• Availability of multiple training algorithms
• Store information on the entire network
• The ability to work with insufficient knowledge
• Good fault tolerance
• Distributed memory
• Gradual Corruption
• Ability to train machine
• The ability of parallel processing

48. What is the advantage of deep learning?

The advantage of deep learning is that it is able to execute featuring engineering independently. In deep learning, the data is scanned by an algorithm to identify properties that correlate and combine them to promote fast learning.
A hyperparameter is a machine learning parameter whose value is selected before a learning algorithm is trained.

• Learning rate: The learning rate determines the speed in which the network gets to know its parameters.
• Momentum: It is a parameter that helps to come out of the local minima and smoothen the jumps while gradient descent.
• Number of epochs: During the training session the number of times the complete training data is delivered to the network is referred to as the number of epochs. 

49. What are the parameters of a neural network?

The number of neurons per layer, the number of training iterations, et cetera. The critical parameters in terms of training and network capacity are the number of hidden neurons, the learning rate, and the momentum parameter.

50. List the applications of fuzzy logic.

• Air conditioners, washing machines, and vacuum cleaners
• Facial recognition
• Antiskid braking systems and transmission systems
• Control of subway systems 
• Weather forecasting systems
• Medical diagnosis and treatment plans
  

With this, we come to the end of this blog. Hopefully, these Artificial Intelligence Interview Questions will help you crack your AI Interview.

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