Top 10 AI-Related Interview Questions

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Robin Sharma

Last Updated

June 10, 2024

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AI interview questions

Cracking the AI interview questions can be a hard nut. Especially for data science enthusiasts and fresh professionals, who are new to the industry. Because AI has rapid advancements and complexities, so to create a steep learning curve can be intimidating.
You can be overwhelmed by the depth and breadth required of the questions you might face in an interview. And being unsure about how to prepare for those questions can be challenging. Without the right guidance, you can feel lost and unprepared. Potentially missing out on high-paying opportunities.
Thus, we have prepared a list of the top 10 AI interview questions to prepare you for the interview. These questions guide you with the technical interview practice, knowledge and confidence with AI interview tips. So, let’s get started →.

AI-Interview Questions

To crack the interview and land the job as a data science professional, here are 10 AI interview questions that will help you prepare better:

What is Artificial Intelligence?

Artificial Intelligence (AI) is a computer science branch. It aims to create machines capable of human intelligent behaviour.
It involves the development of algorithms to enable computers to perform tasks that need human intelligence like:

  •  Visual Perception
  • Speech Recognition
  • Decision-Making
  • Language Translation

You can categorize AI into two parts. Narrow AI (for specific tasks) and general AI (to perform intellectual tasks a human can do).

What are the types of Artificial Intelligence?

AI can be classified into 7 types:

  • Narrow AI: This AI cannot learn on its own and performs very specific tasks.
  • Artificial General Intelligence (AGI): This software is created to think, learn, and act like a human.
  • Artificial Superintelligence: This AI can surpass human knowledge and ability.
  • Reactive AI: It is reactive & able to react instantly to outside inputs. But it cannot learn or retain knowledge for later use.
  • Limited Memory AI: This AI can learn and prepare for tasks in the future by storing knowledge.
  • Theory of Mind AI: AI that can both sense and react to human emotions and carry out jobs that limited memory computers cannot.
  • Self-Aware AI: The ultimate form of AI is self-awareness, or AI with a sense of self and is intelligent enough to understand the feelings of others.

How does Natural Language Processing (NLP) work?

AI interview question - How does Natural Language Processing (NLP) work?
Natural language processing (NLP) is a subfield of artificial intelligence (AI). NLP aims to enable robots to:

  • understand
  • interpret and
  • derive meaning

from human languages.
It combines the principles of computational linguistics, which involves rule-based language structuring, with advancements in statistical analysis, machine learning, and deep learning. This integration allows computers to process and analyze large amounts of natural language data.

What are the different platforms for Artificial Intelligence (AI) development?

For the development of AI, there are numerous platforms and tools accessible, like TensorFlow, PyTorch, Keras, Microsoft Azure, and IBM Watson.

  • TensorFlow: TensorFlow is an open-source software framework designed for numerical calculations using data flow graphs. When working with Python, TensorFlow simplifies and accelerates machine learning processes with its library for numerical computing. It employs a familiar metaphor to make deep learning and machine learning models and computations accessible.
  • PyTorch: PyTorch is a comprehensive framework for developing deep learning models, a subset of machine learning widely used in applications such as image recognition and language processing. Written in Python, it is relatively easy for most machine learning developers to learn and use.
  • Keras: Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to make the implementation of neural networks easy. It also supports multiple backend neural network computations.
  • Microsoft Azure: Microsoft Azure Machine Learning simplifies enterprise machine learning by providing cloud-based advanced analytics. It powers real-time predictive analytics for applications like the Windows Phone personal assistant, Cortana, and includes solutions verified for Xbox and Bing.
  • IBM Watson: Watson is IBM's AI technology, designed to make businesses smarter and enhance employee performance. It includes a variety of advanced APIs, specialized tools, and Software as a Service applications. This means Watson is suited for complex use cases and is built to integrate with the platforms professionals use daily, providing seamless access to the information needed to make informed decisions.

What are the programming languages used for Artificial Intelligence?

These are the popular programming languages used in AI development - Python, R, Java, C++ , LISP and Prolog.

  • Python: Python is a popular general-purpose language for AI development due to its strong data visualization and analytics capabilities. It has an easy-to-learn syntax, making it accessible for data scientists and analysts. Python’s garbage collection feature ensures automatic memory management, and its interpreted execution allows for rapid development without recompilation. 
  • R: R is a key language for statistical computing, widely used in data science. It excels in statistical techniques for AI algorithms, including probabilistic modeling and simulations. The caret package enhances machine learning capabilities with preprocessing and validation, while ggplot2 enables custom data visualizations. 
  • Java: Java integrates AI systems with existing business infrastructures via the JVM, supporting a vast ecosystem of libraries, tools, and cross-platform compatibility. Libraries like DeepLearning4j, Weka, and Mallet offer neural network, machine learning, and natural language processing capabilities.
  • C++: C++ is ideal for deploying AI models in low-latency environments, providing fast execution and memory control. Libraries such as TensorFlow C++ interface and Caffe2 support machine learning and deep learning tasks.
  • Lisp: Lisp is known for rule-based AI applications and logical reasoning, offering metaprogramming capabilities. It remains relevant for symbolic reasoning and expert systems.
  • Prolog: Prolog excels in knowledge representation and reasoning, using a declarative, query-based approach. Libraries like SWI-Prolog and ProbLog support advanced probabilistic reasoning.

Explain the assessment that is used to test the intelligence of a machine.

AI interview questions - Turning Test
The Turning Test is a well-known evaluation of machine intelligence. This was developed by Alan Turing.
You can assess a machine's capacity to display intelligent behaviour, that is indistinguishable from human conduct.
During the test, a computer interface is used to facilitate communication between a human assessor and a machine.
The machine passes the test if the examiner is unable to distinguish it from the human.

What are some real-life applications of Artificial Intelligence?

There are various applications of AI around daily life across various industries. Some examples of it are:

  • Healthcare: AI helps in diagnosis and treatment recommendations for personalized medicine.
  • Finance: In finance, you can detect fraud early and do risk management for your assets.
  • Retail: AI can provide personalized recommendations, help you with inventory management and allow proper customer support with chatbots.
  • Entertainment: AI can recommend content based on your preference.

 

What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?

The difference between AI, machine learning and deep learning is:

  • Artificial intelligence is the ability of a software to perceive, think, act, and adapt.
  • Machine learning refers to algorithms whose performance gets better with time as they are exposed to more data.
  • Deep Learning is a branch of machine learning where large volumes of data are used to train multilayered neural networks.

 

Explain TensorFlow.

TensorFlow (developed by Google) is an open-source library.
It is suitable for numerical computation and machine learning.
You can access a comprehensive ecosystem of tools, libraries, and community resources. Which allows developers to build and deploy machine learning models.
It is designed to be flexible and scalable. To allow easy implementation of machine learning algorithms and deep neural networks.
And, it supports various platforms, including CPUs, GPUs, and TPUs, and can be used from research to production.
TensorFlow is important in AI because it provides an adaptable framework for building and implementing machine learning models.
Thus, it brings the conversion of creative ideas into practical applications by streamlining the process for researchers and developers.

What are Decision Trees?

Decision Trees are a type of supervised learning algorithm utilized for tasks involving classification and regression. They represent decisions and their potential outcomes in a hierarchical tree structure. Nodes in the tree correspond to attribute tests, edges depict the results of these tests, and leaf nodes indicate class labels or final decision outcomes.

Conclusion

To prepare for an interview, these AI interview questions will help you better answer these questions. To prepare for an AI interview, you need to have a LinkedIn profile which will help you get hired. If you want to learn in-demand data science skills and get hands-on in-depth learning, you can join our 12-month executive data science and AI course. This course will provide you with complete details about becoming a data science professional. You will also get AI project experience with this course. By familiarizing yourself with these top 9 AI-related interview questions, you'll face your next interview with confidence. Remember, the key to success is continuous learning and staying updated with the latest knowledge.

Get Free Consultation

AI interview questions

Cracking the AI interview questions can be a hard nut. Especially for data science enthusiasts and fresh professionals, who are new to the industry. Because AI has rapid advancements and complexities, so to create a steep learning curve can be intimidating.
You can be overwhelmed by the depth and breadth required of the questions you might face in an interview. And being unsure about how to prepare for those questions can be challenging. Without the right guidance, you can feel lost and unprepared. Potentially missing out on high-paying opportunities.
Thus, we have prepared a list of the top 10 AI interview questions to prepare you for the interview. These questions guide you with the technical interview practice, knowledge and confidence with AI interview tips. So, let’s get started →.

AI-Interview Questions

To crack the interview and land the job as a data science professional, here are 10 AI interview questions that will help you prepare better:

What is Artificial Intelligence?

Artificial Intelligence (AI) is a computer science branch. It aims to create machines capable of human intelligent behaviour.
It involves the development of algorithms to enable computers to perform tasks that need human intelligence like:

  •  Visual Perception
  • Speech Recognition
  • Decision-Making
  • Language Translation

You can categorize AI into two parts. Narrow AI (for specific tasks) and general AI (to perform intellectual tasks a human can do).

What are the types of Artificial Intelligence?

AI can be classified into 7 types:

  • Narrow AI: This AI cannot learn on its own and performs very specific tasks.
  • Artificial General Intelligence (AGI): This software is created to think, learn, and act like a human.
  • Artificial Superintelligence: This AI can surpass human knowledge and ability.
  • Reactive AI: It is reactive & able to react instantly to outside inputs. But it cannot learn or retain knowledge for later use.
  • Limited Memory AI: This AI can learn and prepare for tasks in the future by storing knowledge.
  • Theory of Mind AI: AI that can both sense and react to human emotions and carry out jobs that limited memory computers cannot.
  • Self-Aware AI: The ultimate form of AI is self-awareness, or AI with a sense of self and is intelligent enough to understand the feelings of others.

How does Natural Language Processing (NLP) work?

AI interview question - How does Natural Language Processing (NLP) work?
Natural language processing (NLP) is a subfield of artificial intelligence (AI). NLP aims to enable robots to:

  • understand
  • interpret and
  • derive meaning

from human languages.
It combines the principles of computational linguistics, which involves rule-based language structuring, with advancements in statistical analysis, machine learning, and deep learning. This integration allows computers to process and analyze large amounts of natural language data.

What are the different platforms for Artificial Intelligence (AI) development?

For the development of AI, there are numerous platforms and tools accessible, like TensorFlow, PyTorch, Keras, Microsoft Azure, and IBM Watson.

  • TensorFlow: TensorFlow is an open-source software framework designed for numerical calculations using data flow graphs. When working with Python, TensorFlow simplifies and accelerates machine learning processes with its library for numerical computing. It employs a familiar metaphor to make deep learning and machine learning models and computations accessible.
  • PyTorch: PyTorch is a comprehensive framework for developing deep learning models, a subset of machine learning widely used in applications such as image recognition and language processing. Written in Python, it is relatively easy for most machine learning developers to learn and use.
  • Keras: Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to make the implementation of neural networks easy. It also supports multiple backend neural network computations.
  • Microsoft Azure: Microsoft Azure Machine Learning simplifies enterprise machine learning by providing cloud-based advanced analytics. It powers real-time predictive analytics for applications like the Windows Phone personal assistant, Cortana, and includes solutions verified for Xbox and Bing.
  • IBM Watson: Watson is IBM's AI technology, designed to make businesses smarter and enhance employee performance. It includes a variety of advanced APIs, specialized tools, and Software as a Service applications. This means Watson is suited for complex use cases and is built to integrate with the platforms professionals use daily, providing seamless access to the information needed to make informed decisions.

What are the programming languages used for Artificial Intelligence?

These are the popular programming languages used in AI development - Python, R, Java, C++ , LISP and Prolog.

  • Python: Python is a popular general-purpose language for AI development due to its strong data visualization and analytics capabilities. It has an easy-to-learn syntax, making it accessible for data scientists and analysts. Python’s garbage collection feature ensures automatic memory management, and its interpreted execution allows for rapid development without recompilation. 
  • R: R is a key language for statistical computing, widely used in data science. It excels in statistical techniques for AI algorithms, including probabilistic modeling and simulations. The caret package enhances machine learning capabilities with preprocessing and validation, while ggplot2 enables custom data visualizations. 
  • Java: Java integrates AI systems with existing business infrastructures via the JVM, supporting a vast ecosystem of libraries, tools, and cross-platform compatibility. Libraries like DeepLearning4j, Weka, and Mallet offer neural network, machine learning, and natural language processing capabilities.
  • C++: C++ is ideal for deploying AI models in low-latency environments, providing fast execution and memory control. Libraries such as TensorFlow C++ interface and Caffe2 support machine learning and deep learning tasks.
  • Lisp: Lisp is known for rule-based AI applications and logical reasoning, offering metaprogramming capabilities. It remains relevant for symbolic reasoning and expert systems.
  • Prolog: Prolog excels in knowledge representation and reasoning, using a declarative, query-based approach. Libraries like SWI-Prolog and ProbLog support advanced probabilistic reasoning.

Explain the assessment that is used to test the intelligence of a machine.

AI interview questions - Turning Test
The Turning Test is a well-known evaluation of machine intelligence. This was developed by Alan Turing.
You can assess a machine's capacity to display intelligent behaviour, that is indistinguishable from human conduct.
During the test, a computer interface is used to facilitate communication between a human assessor and a machine.
The machine passes the test if the examiner is unable to distinguish it from the human.

What are some real-life applications of Artificial Intelligence?

There are various applications of AI around daily life across various industries. Some examples of it are:

  • Healthcare: AI helps in diagnosis and treatment recommendations for personalized medicine.
  • Finance: In finance, you can detect fraud early and do risk management for your assets.
  • Retail: AI can provide personalized recommendations, help you with inventory management and allow proper customer support with chatbots.
  • Entertainment: AI can recommend content based on your preference.

 

What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?

The difference between AI, machine learning and deep learning is:

  • Artificial intelligence is the ability of a software to perceive, think, act, and adapt.
  • Machine learning refers to algorithms whose performance gets better with time as they are exposed to more data.
  • Deep Learning is a branch of machine learning where large volumes of data are used to train multilayered neural networks.

 

Explain TensorFlow.

TensorFlow (developed by Google) is an open-source library.
It is suitable for numerical computation and machine learning.
You can access a comprehensive ecosystem of tools, libraries, and community resources. Which allows developers to build and deploy machine learning models.
It is designed to be flexible and scalable. To allow easy implementation of machine learning algorithms and deep neural networks.
And, it supports various platforms, including CPUs, GPUs, and TPUs, and can be used from research to production.
TensorFlow is important in AI because it provides an adaptable framework for building and implementing machine learning models.
Thus, it brings the conversion of creative ideas into practical applications by streamlining the process for researchers and developers.

What are Decision Trees?

Decision Trees are a type of supervised learning algorithm utilized for tasks involving classification and regression. They represent decisions and their potential outcomes in a hierarchical tree structure. Nodes in the tree correspond to attribute tests, edges depict the results of these tests, and leaf nodes indicate class labels or final decision outcomes.

Conclusion

To prepare for an interview, these AI interview questions will help you better answer these questions. To prepare for an AI interview, you need to have a LinkedIn profile which will help you get hired. If you want to learn in-demand data science skills and get hands-on in-depth learning, you can join our 12-month executive data science and AI course. This course will provide you with complete details about becoming a data science professional. You will also get AI project experience with this course. By familiarizing yourself with these top 9 AI-related interview questions, you'll face your next interview with confidence. Remember, the key to success is continuous learning and staying updated with the latest knowledge.

Get Free Consultation

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