Top 30+ Artificial Intelligence Interview Questions and Answers

Skilled AI engineers are in high demand as industries use AI to boost efficiency and innovation. Our carefully developed Top 30+ artificial intelligence interview questions and answers will help you ace your AI interview. Get started with our AI course syllabus. 

Artificial Intelligence Interview Questions and Answers for Freshers

Most of the AI key concepts are covered in these top 30+ interview questions on artificial intelligence:

Interview Questions on AI Foundational Concepts

1. What is artificial intelligence (AI)?

The study of creating intelligent machines, particularly intelligent computer programs, is known as artificial intelligence (AI). It entails developing algorithms that give machines the ability to reason, learn, and make choices.

2. What are the various types of AI?

Artificial intelligence (AI) comes in many forms, such as:

• Artificial Narrow AI (ANI), also known as Weak AI
• Artificial General Intelligence (AGI)
• Super AI
• Reactive machines
• Limited Memory AI
• Natural language processing (NLP)
• Generative AI
• Deep learning
• Expert systems
• Fuzzy logic

3. Define ANI.

The most prevalent kind of AI now in use is artificial narrow intelligence (ANI), also referred to as weak AI. Without the capacity to learn beyond its intended use, ANI is made to carry out certain tasks.

4. Define AGI.

Artificial General Intelligence (AGI), sometimes referred to as Strong AI, is a theoretical idea that might do new jobs without human instruction by drawing on prior knowledge. 

5. What is super AI?

This theoretical concept, also referred to as artificial superintelligence (ASI), has the potential to use and synthesize vast amounts of data and knowledge more effectively than humans.  

6. Define reactive machines.

These are the earliest kind of AI systems; they have little memory and very few capabilities. Only a small number of inputs can cause them to react automatically. 

7. Describe the limited memory AI.

This kind of AI can track particular items or circumstances over time and remember previous occurrences and results.

8. What is deep learning?

This technology, which is utilized in applications like autonomous driving, natural language processing, and picture identification, uses neural networks to simulate how human minds function.  

9. Define expert systems.

These artificial intelligence (AI) computer systems simulate a human expert’s ability to reason and make decisions. Applications such as financial planning, customer service, and medical diagnosis make use of them.  

10. Define fuzzy logic.

This computational method, which is founded on the idea of “degrees of truth,” is similar to human thinking. 

11. Define machine learning.

A branch of artificial intelligence called machine learning uses big datasets to train algorithms to make predictions or judgments. 

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AI Interview Questions on Machine Learning Algorithms

12. What distinguishes supervised learning from unsupervised learning?

The kind of data needed to train the model is the primary distinction between supervised and unsupervised learning:

Supervised Learning: It teaches a model a certain objective using labeled training data. The model adapts to reduce inaccuracy as it gains knowledge of the connections between input and output data.

Example: Based on characteristics like location, size, and number of bedrooms, a model can be trained to forecast home values.

Unsupervised Learning: It employs unlabeled data to discover the data’s structure without specific guidance. To find patterns in the dataset, the model operates on its own.

Example: News stories from many news websites can be grouped by an algorithm into similar categories, such as crime and sports.  

13. What distinguishes regression from classification?

Regression predicts numerical values, whereas classification predicts categorical results. This is the primary distinction between the two methods:

Classification is the process of allocating incoming data to distinct groups, like spam or ham. A label or class from a list of predefined alternatives is the output.

Example: Grouping toys according to their type, color, or shape. 

Regression: To predict age, income, or temperature, for example, regression entails establishing a link between the input and output variables. A real-valued number that can fluctuate within a range is the output.

Examples of regression include describing a person’s height and age using a linear regression model.  

14. What is overfitting and underfitting in AI?

When a model gets overly complicated and performs badly on fresh data, it is said to be overfit. A model that is overly simplistic and misses underlying patterns is said to be underfitting. 

Overfitting: A model is said to overfit when it performs well on training data but poorly on test data. 

• This occurs when a model picks up on the noise and specifics in the training data instead of the underlying patterns. 
• Low bias and high variance might lead to overfitting.  

Underfitting: A model is considered underfitted when it exhibits poor performance on training data and fails to generalize to fresh data. 

• When a model is too basic to identify the underlying patterns in the training data, this occurs. 
• Low variance and excessive bias might lead to underfitting.  

15. What are some techniques to prevent overfitting in AI?

You can employ a mix of strategies and best practices to prevent overfitting in machine learning. The following is a list of important precautions:

• Cross-Validation
• Split Your Data
• Regularization
• Data Augmentation
• Ensemble Learning
• Early Stopping
• Dropout
• Reduce Model Complexity
• Increase Training Data

AI Interview Questions on Deep Learning

16. Explain neural networks in deep learning.

Neural networks are used in deep learning, a branch of machine learning, to teach computers to process information similarly to how the human brain does. Neural networks employ a layered structure with interconnected nodes or neurons.  

Neural network types include the following:

• Recurrent neural networks (RNNs).
• Feed-forward neural networks (FNNs).
• Convolutional neural networks (CNNs).
• Modular neural networks.

17. What is backpropagation in deep learning?

Neural networks are trained using the backpropagation technique, which modifies weights and biases to reduce the discrepancy between expected and actual results.

A machine learning technique called backpropagation uses error correction to train artificial neural networks. A feedback mechanism called backpropagation modifies the weights of a neural network to lower the discrepancy between the output and the right response. 

It includes:

• Providing data: supplying information The network is provided with data.
• Calculating loss: The loss function is determined by comparing the input and output.
• Running error: The error propagates from output to input via the network.
• Updating weights: Until the error is reduced, the weights are updated and the procedure is repeated.  

18. What are activation functions in deep learning?

Neural networks can learn intricate patterns because of activation functions, which give them non-linearity. 

19. What is the problem of the disappearing gradient?

When gradients get smaller as they move backward through the network, it’s known as the vanishing gradient problem. 

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AI Interview Questions on Natural Language Processing (NLP)

20. Explain NLP.

Applications such as chatbots, virtual assistants, and translation services use natural language processing (NLP), a form of artificial intelligence that can comprehend and interpret human languages.  

Natural Language Processing (NLP) is capable of the following tasks:

• Sentiment analysis: It is the process of analyzing text to identify the sentiment or emotion conveyed and categorizing it as neutral, negative, or positive.
• Machine translation: It is the process of automatically translating text between languages.
• Tokenization: By dividing text into discrete words or phrases, or tokens, tokenization enables the computer to examine the structure of the text.
• Name Entity Recognition: Recognizes and categorizes important textual elements, including names of individuals, groups, places, and dates.
• Question answering: answers a user’s question by extracting information from data. 
• Speech recognition: Transcodes spoken words into text for use in automatic transcription services and virtual assistants.
• Speech synthesis: Develop language models that can adjust to different speech patterns using natural language processing (NLP).  
• Statistical NLP: It automatically extracts, categorizes, and labels text and audio data pieces and gives each potential interpretation a statistical likelihood.  

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Artificial Intelligence Questions and Answers for Experienced

AI Interview Questions on Computer Vision

21. Define Computer Vision.

Computers can interpret and comprehend visual information from their surroundings thanks to computer vision. 

Computer vision is focused on:

• Information extraction: Extracting, evaluating, and comprehending valuable information from photos automatically
• Using models and theories: Using models and theories to build computer vision systems
• Task automation: Automating actions that can be performed by the human visual system  

Examples of computer vision are facial recognition, self-driving cars, robotic automation, medical anomaly detection, sports performance analysis, agricultural monitoring, and plant species classification. 

22. What is image classification?

The process of classifying and labeling photographs according to predetermined guidelines is called image classification. It’s a basic task in machine learning and computer vision.  

How it operates: Image classification classifies images using rules derived from their pixels or vectors. 

For example, image classification might identify the pictures of cats, dogs, and bunnies in a collection of pictures. 

Classification types: Supervised and unsupervised are the two general classification techniques. 

• Unsupervised classification identifies spectral classifications without the analyst’s help. 
• Supervised classification classifies an image using training samples. 

Image retrieval: Image categorization is frequently referred to as image retrieval when it gets detailed. Finding related images in a sizable database is known as image retrieval.

Real-world examples: Many well-known services and products, like Facebook’s photo-tagging and Tesla’s self-driving car, use image classification. 

23. Explain object detection in AI.

A computer vision method called object detection finds and identifies things in pictures or videos. 

What it does: Object detection finds things in an image, identifies their location, and determines what kind of object they are.  

How it operates: Algorithms for object detection generate outcomes through machine learning or deep learning. There are numerous uses for object detection, such as:  

• Image and video analysis 
• Robotics 
• Surveillance 
• Medical imaging 
• Self-driving cars 

What distinguishes it from image recognition: Although they are different computer vision problems, object detection and image identification are related.  

Examples of object detection in action include the following:

• Object localization: Drawing a bounding box around an object in a picture allows object localization to identify its location.
• Object classification: Identifying the category to which a recognized object belongs is known as object categorization.
• Video surveillance: Identifies unusual activity and triggers automated alerts using object detection.

Interview Questions on AI Ethics and Bias

24. Which AI-related ethical issues exist?

AI algorithm bias, job displacement, privacy issues, and self-defense. Artificial intelligence (AI) raises many ethical issues, such as:

• Bias: AI can only be as objective as the data and its trainers. Human biases, flawed algorithmic design, and biased training data can all contribute to prejudice.
• Privacy: AI may give rise to worries about monitoring and privacy.
• Human judgment: AI makes one wonder if people can be outthought by robots or if some critical judgments still require human judgment.
• Job displacement: It’s unclear if AI will eventually supplant human labor.
• Fake media: There are concerns about whether AI may exacerbate misinformation and fake media.
• AI Access: AI makes one wonder if malevolent actors should have easy access to the technology.
• Transparency: Creating transparency in decision-making is a question raised by AI.  
• Generative AI: This technology has concerns regarding its potential for abuse, including the production of false information or the propagandizing of false information. 

25. How can we mitigate bias in AI models?

The following strategies can help reduce bias in AI models:

• Assure data quality: Verify that the data is varied and indicative of the actual world. Statistical tests can also be used to detect and measure bias.
• Using a diversified workforce: Hire programmers from a variety of backgrounds to help prevent prejudice.
• Engage people: Make people actively seek out instances of unintentional bias.
• Document and analyze: Keep a record of your data selection and cleaning procedures.
  • Additionally, evaluate the model’s performance regularly and take appropriate action if bias is detected.
• Employing debiasing algorithms can help reduce bias in AI systems without eliminating labels.
• Interact with customers: Provide a way for customers to voice their opinions and concerns regarding automated judgments.
• Make use of open-source resources: The AI Fairness 360 toolkit is an example of an open-source package.
• Establish ethical guidelines for AI: Develop, put into practice, and operationalize ethical guidelines for AI.
• Use both human and machine power: When human review is necessary, use both human and machine power to get the results you want.  

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AI Interview Questions on Practical Considerations

26. What is the role of data in AI?

An essential part of artificial intelligence (AI) systems is data, which gives AI the knowledge it needs to learn, make decisions, and get better over time.

• Training: To identify trends and generate predictions, AI models are trained on datasets of labeled or unlabeled data. For instance, a sizable collection of photos with identified faces is needed to train a facial recognition model.
• Validation and Testing: AI models are validated and tested using different datasets to assess their functionality and predict how well they will function in practical situations.
• Continuous Learning: By learning from new data in real-time or nearly real-time, certain AI systems can adapt to changing conditions.
• Data Reintegration: To increase the accuracy and efficiency of the model, data produced by AI operations is frequently reintegrated into the system.  

For AI systems, data quality is crucial. Incomplete or erroneous data might produce untrustworthy results and inaccurate conclusions. Data quality assurance, which includes procedures to clean and validate data to fix mistakes or inconsistencies, is crucial.

27. List some popular AI libraries and frameworks.

These are a few well-known AI frameworks and libraries:

• TensorFlow: An open-source, free Python library for neural network training and prediction. Deep learning inference is its foundation.
• PyTorch: An open-source Python neural network construction tool. It is renowned for being adaptable and simple to use.
• Keras: TensorFlow is the foundation of this high-level library. It is intended to make deep learning model construction and training simple.
• Theano: An open-source Python library for numerical calculations is called Theano. In the early days of deep learning, it was quite important.
• Torch: A versatile, open-source machine learning library built on top of the LUA programming language is called Torch. It is renowned for being quick and effective.
• Apache Mahout: A machine-learning library based on Apache Hadoop is called Apache Mahout. It’s utilized for scalable tasks utilizing distributed linear algebra.
• Apache Spark: A framework that works with Python, Java, Scala, and R. It is compatible with Hadoop workflows for machine learning methods such as clustering, regression, and classification.
• Amazon Machine Learning: A well-known machine learning framework that enables users to create ready-to-use AI software and applications.  

Hugging Face, OpenAI, Caffe, MXNet, Scikit-Learn, and XGBoost are some other AI frameworks.

28. How can you evaluate the performance of a machine-learning model?

Utilizing metrics such as confusion matrices, F1-score, recall, accuracy, and precision. A machine learning model’s performance can be assessed in the following ways:

• Divide the data into test and training sets: This is a rapid method for obtaining a preliminary assessment of the model’s performance. The model is trained using one portion of the data, and performance is examined using the other.
• Confusion matrix: A tabular form that displays the differences between the actual facts and the predictions of a model. Accuracy, precision, recall, specificity, and AUC-ROC curves can all be measured with it.
• F1 Score: A single statistic that assesses the correctness of a model by combining precision and recall. During model iteration phases, it is helpful for rapidly assessing model performance.  
• Precision: A gauge of the precision of a model’s optimistic forecasts. “Of all the instances that the model predicted as positive, how many were positive?” is the question it addresses.
• ROC curve, or receiver operating characteristic: Plotting the genuine positive rate against the false positive rate at various threshold values is done graphically.
• AUC, or area under the curve: an evaluation of the whole region beneath the ROC curve. It works well for assessing how well a model performs at various classification levels.
• Mean Average Precision (mAP) is a statistic used to assess how well items are detected in object detection.
• Support Scores: How many instances of the class are there in the given dataset?  

29. What distinguishes model inference from model training?

While model inference entails applying the learned model to generate predictions on fresh data, model training entails discovering patterns in data. 

• Ideally, a model doesn’t require more training after it has been taught correctly. However, inference continues. 
• A model continuously applies its training to fresh data and draws new conclusions if it is actively being used. 
• This can be highly costly and requires a significant amount of processing power. 

Explore our deep learning course syllabus for further understanding. 

Interview Questions on AI Applications

30. What are some real-world applications of AI?

Applications of artificial intelligence (AI) in the real world are numerous and include:

Retail: AI is capable of managing inventory, making product recommendations, and personalizing shopping experiences.

Transportation: AI in transportation is utilized in in-car helpers, traffic control, and self-driving automobiles.

Energy: AI can optimize energy storage systems, forecast energy consumption, and increase energy efficiency.

Government: AI in government can deliver citizen services, identify crime, and increase public safety.

Healthcare: AI in healthcare can increase access to services and reduce expenses. Additionally, it can be applied to drug development, tailored therapy, and medical imaging analysis.

Finance: AI can assist in protecting the financial industry from malicious activities. Additionally, it can be applied to risk assessment, fraud detection, and stock market forecasting.

Education: AI in education can tailor lesson programs to each student’s skills and shortcomings.

Agriculture: AI in agriculture can assist farmers with crop monitoring, yield prediction, and pest management. Additionally, it can be applied to autonomous tractors for precision farming.

Manufacturing: AI in manufacturing can be used for many procedures, including design and production.

Security: Advanced biometric authentication, threat detection, and facial recognition surveillance are all applications of security AI.

Games: NPCs in games can be made to behave realistically by using entertainment AI.

Digital Assistants: AI can be utilized by digital assistants such as Siri to carry out tasks for you.  

AI Interview Questions on Advanced Topics

31. What is reinforcement learning?

Through interaction with an environment and incentives or punishments, reinforcement learning teaches agents to make decisions. 

A machine learning method called reinforcement learning (RL) trains software on how to make choices that will lead to the greatest results. It mimics the process of trial and error that people employ to accomplish their objectives.  

Key elements of RL include the following:

How it operates: Without any help from a human user, RL teaches an agent to learn how to do a task through trial and error. 

• To maximize reward, the agent learns to sense and alter the state of the environment through its behaviors.

How it’s applied: Robotics and other decision-making contexts use reinforcement learning. 

• The creation of autonomous cars, which employ RL to better comprehend and traverse challenging settings, is one practical application of RL. 

How it differs from other methods of machine learning: Along with supervised learning and unsupervised learning, reinforcement learning is one of the three fundamental machine learning paradigms. 

• Unsupervised learning seeks to uncover and learn hidden patterns from unlabeled data, whereas supervised learning uses explicitly labeled data to create predictions or classifications.

Its connection to the idea of reinforcement: The framework of reinforcement theory, sometimes referred to as operant conditioning, seeks to minimize a behavior through punishment and promote a behavior through reinforcement. Punishment and reinforcement can be either good or negative.

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32. What is generative AI?

Generative AI of AI produces original content, including virtual worlds, literature, photos, and music. 

Generative artificial intelligence, often known as generative AI, is a subset of AI that can produce original ideas, information, and results. It is capable of learning intricate topics like human language, biology, chemistry, art, and programming languages. 

Conversations, pictures, music, stories, films, designs, artificial intelligence, and deepfakes are all possible with generative AI.

Deep learning algorithms, such as neural networks, are used in generative AI to identify patterns and structures in incoming data and produce new material. To direct the creation of information, it frequently begins with a prompt, which could be a starting query or data set.  

Generative AI is useful for innovative problem-solving and in creative domains. Here are a few instances of applications for generative AI:

• Healthcare: Generative AI can assist with patient record management and drug discovery.
• Customer experience: Dynamic AI bots that respond to consumer questions more like humans can be developed using generative AI.
• Synthetic data: By creating synthetic data, generative models can assist in resolving data issues.  

33. Explain transfer learning in detail.

Utilizing information acquired from one job to enhance performance on a related one is known as transfer learning. 

A machine learning (ML) technique called transfer learning (TL) makes use of a previously trained model to enhance performance on a related task. It’s comparable to how people pick up new skills, like picking up the ukulele after mastering the guitar.  

Examples of transfer learning include the following:

• Image Classification: Using a smaller collection of pictures that emphasize the distinctions between the two, a model that can recognize dogs may be trained to recognize cats.
• Medical Image Analysis: Performance on comparable medical imaging tasks can be enhanced by adapting knowledge about anatomical features or disease patterns.  

TL can be applied to: 

• Reduce time and computational resources: TL enables you to more effectively solve new challenges by utilizing prior training.
• Achieve higher performance: Training using a tiny amount of data is not as effective as using TL to produce much better results.

Both generic and domain-specific knowledge can be transferred during TL. While generic information is more comprehensive and widely applicable, domain-specific knowledge is unique to a given activity or domain.

34. What is adversarial machine learning?

Attacking and defending machine learning models is known as adversarial machine learning. 

The approach of influencing machine learning (ML) systems to get a desired result is known as adversarial machine learning (AML). It can be used to research machine learning models and find their weaknesses so that countermeasures against malevolent attacks can be implemented. 

AML may include:

• Deceptive inputs: Making inputs that are intended to fool the machine learning model into generating inaccurate results is known as deceptive input creation.
  • For instance, an AI system may misidentify an image if imperceptible alterations are made to it.
• Tampering with training data: Changing the training data by adding biases or errors is known as tampering.
• Byzantine attacks: When compute units are compromised, false updates are sent to the central aggregation server.  

ML systems can be attacked via AML in many phases, such as during deployment, testing, and training. Since it can result in data breaches, inaccurate outputs, and other problems, current SecOps teams are becoming increasingly concerned about it.

AML uses an AI system’s decision-making logic to its advantage, setting it apart from more conventional cyber threats like malware or phishing.  

35. What is explainable AI, or XAI?

The goal of XAI is to improve the interpretability and comprehension of AI models.

A collection of techniques and procedures known as explainable AI (XAI) enables people to comprehend and have faith in the output of machine learning (ML) algorithms. 

The goal of XAI is to build a link between the intricacy of ML models and the human requirement for comprehension and confidence. 

XAI can assist with:

• Model enhancement and debugging: ML models can be enhanced and debugged with the aid of XAI.
• Regulatory compliance: XAI can assist in achieving regulatory compliance.
• Increasing trust: XAI can support the development of confidence in the judgments and forecasts made by AI models.
• Resolving issues: XAI can assist in resolving issues and problems about systems development, governance, and user adoption.  

XAI functions by converting AI’s intricate patterns and decision-making procedures into human-understandable formats. 

Bidirectional translation enables AI systems to communicate with humans in a way that they can comprehend.  

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Common Interview Tips to Ace AI Interviews Successfully

• Take part in coding challenges.
• Keep abreast of the most recent developments and trends in AI.
• Be ready to talk about the ethical implications and societal effects of AI. 

Conclusion

We hope you have gained confidence and ideas to get success in AI interviews through these artificial intelligence interview questions and answers. Join SLA for the best AI training in Chennai to learn with hands-on exposure to real-time AI projects.