Once the stuff of science fiction novels and futuristic movies, Artificial Intelligence (AI) is now very real to us. From business applications to everyday life, we are almost unaware that many of us interact with AI every day. Nearly 60 percent (57.9) of organizations with Big Data solutions are using AI in some way; it’s predicted that AI and machine learning will impact all segments of our daily lives by 2025 with huge implications for industries ranging from transport and logistics to healthcare, home maintenance, and customer service. 

With that dramatic increase in reliance on AI, massive investments are being made in both the technology and the skilled professionals needed to enable implementing and benefitting from the technology. According to Tractica, the market for enterprise AI will increase from $202.5 million in 2015 to $11.1 billion by 2024. Meaning, the need for professionals skilled in Artificial Intelligence exists in just about every field imaginable, which leads to a stable job outlook and high-paying salaries. According to Indeed.com, the average salary for a professional with an AI certification is $110k a year in the U.S. Growing adoption, increased demand for certified professionals, and substantial salaries make a move into AI a wise choice for someone interested in this career field.

AI Interview Questions for Those Moving into the AI Domain

Whether you’re considering a career move into the AI domain, or you’re already there and want to move up the career ladder, the future looks bright. However, there are also plenty of other professionals who will recognize the opportunities and move into the field. To position yourself for success as a job candidate who stands out from the crowd, you should be pursuing certifications in AI, as well as preparing ahead of time for crucial job AI interview questions. 

Once you’ve lined up a job interview with a potential employer, you’ll have an opportunity to study that particular organization and its use of AI. That can help you to prepare for specific Artificial Intelligence interview questions relevant to that employer. Until then, you can make for more general Artificial Intelligence interview questions by knowing how to demonstrate your broader knowledge of the implications and applications of AI. The 11 questions and answers below will help.

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1. What Are the Common Uses and Applications of AI?

Your answer here should show that you recognize the far-reaching and practical applications of AI. Still, your answer is up to you because of your understanding of the AI field is what the interviewer is trying to ascertain. If possible, mention those uses most relevant to the potential employer. Possibilities include contract analysis, object detection, and classification for avoidance and navigation, image recognition, content distribution, predictive maintenance, data processing, automation of manual tasks, or data-driven reporting. 

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

Intelligent agents are autonomous entities that use sensors to know what is going on, and then use actuators to perform their tasks or goals. They can be simple or complex and can be programmed to learn to accomplish their jobs better. 

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

TensorFlow is an open-source software library initially developed by the Google Brain Team for use in machine learning and neural networks research. It is used for data-flow programming. TensorFlow makes it much easier to build certain AI features into applications, including natural language processing and speech recognition. 

4. What Is Machine Learning, and How Does It Relate to AI? 

Machine learning is a subset of AI. The idea is that machines will “learn” and get better at tasks over time rather than having humans continually having to input parameters. Machine learning is a practical application of AI. 

5. What Are Neural Networks, and How Do They Relate to AI? 

Neural networks are a class of machine learning algorithms. The neuron part of the neural is the computational component, and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more efficiently.

6. What Is Deep Learning, and How Does It Relate to AI? 

Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly sophisticated ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data for continual improvement in the ability to recognize and process information. Layers of neural networks stacked on top of each for use in deep learning are called deep neural networks.

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7. Why Is Image Recognition a Key Function of AI?

Humans are visual, and AI is designed to emulate human brains. Therefore, teaching machines to recognize and categorize images is a crucial part of AI. Image recognition also helps machines to learn (as in machine learning) because the more images that are processed, the better the software gets at recognizing and processing those images. 

8. What Is Automatic Programming?

Automatic programming is describing what a program should do, and then having the AI system “write” the program.

9. What Is a Bayesian Network, and How Does It Relate to AI?

A Bayesian network is a graphical model for probabilistic relationships among a set of variables. It mimics the human brain in processing variables.

10. What Are Constraint Satisfaction Problems?

Constraint Satisfaction Problems (CSPs) are mathematical problems defined as a set of objects, the state of which must meet several constraints. CSPs are useful for AI because the regularity of their formulation offers commonality for analyzing and solving problems. 

11. What Is Supervised Versus Unsupervised Learning?

Supervised learning is a machine learning process in which outputs are fed back into a computer for the software to learn from, for more accurate results the next time. With supervised learning, the “machine” receives initial training to start. In contrast, unsupervised learning means a computer will learn without initial training to base its knowledge.

12. What are some common misunderstandings about AI?

Since the beginning of the development of artificial intelligence, there have been a number of misunderstandings regarding it. The following are examples of some of these common misunderstandings:

  • AI Does Not Need Humans

AI's initial misunderstanding is that it can function without the assistance of humans. But in practice, each AI-based system still relies on humans, and they will continue to do so for the foreseeable future. Human-gathered data is needed to get insight into the information.

  • AI Is Harmful to Humanity

As long as AI isn't able to outperform humans, it isn't a threat to our survival. It is impossible for a strong technology to be destructive if it is handled properly.

  • AI Has Attained Its Pinnacle

However, there is still a significant distance between us and the most advanced level of AI. Getting to the peak of the ridge will be an extremely difficult and lengthy trek.

  • AI Will Overtake Your Job

One of the most common misconceptions is that artificial intelligence will eliminate most of the employment, yet the technology is really creating more opportunities for new professions.

  • AI Is a Novel Technological Advance

This technology was originally conceived for the first time in the year 1840 via an English newspaper, despite the fact that some individuals believe that it is a new kind of technology.

13. What role does computer vision play in AI?

Artificial intelligence (AI) is broken down into a number of subfields, one of which is known as computer vision. Computer vision is the process of teaching computers to understand and collect data from the visual environment, such as graphics. Therefore, AI technology is used by computer vision in order to address complicated challenges such as image analysis, object identification, and other similar issues.

14. How does the Strong AI differ from the Weak AI?

  • Strong AI

The goal of strong artificial intelligence is to create actual intelligence artificially, which refers to an intellect created by humans that possesses feelings, consciousness, and emotions comparable to those of humans. The idea of creating AI entities with perceiving, analyzing, and decision-making skills comparable to those of humans is still only an assumption at this point.

  • Weak AI

The present phase of artificial intelligence research is known as "weak AI," and it is concerned with the construction of expert systems and robots that can assist people and solve challenging real-world issues. Weak artificial intelligence systems like Alexa and Siri are examples.

15. Where does Artificial Intelligence go from here?

It is anticipated that artificial intelligence will continue to have a significant impact on a large number of people as well as almost every sector. Artificial intelligence has become the primary impetus behind the development of new technologies such as robots, the Internet of Things, and large data sets. AI is capable of making an ideal judgment in a split second, which is almost difficult for a person to do. 

Cancer treatment, cutting-edge global climate solutions, smart transportation, and space research are all being aided by AI. We don't expect it to renounce its position as the driving force behind computer innovation and progress any time soon. Artificial Intelligence will have a greater influence on the globe than any other technological advancement in human history.

16. What do you comprehend by the phrase "reward maximization"?

Reinforcement learning uses the phrase "reward maximization" to describe the purpose of the agent, which is to maximize rewards. Real-world rewards are positive feedback for doing an action that results in a change in a state. A reward is given to the agent if he uses optimum policies to complete a good deed, and a reward is deducted if he fails to do so. Rewards are maximized by using the best rules possible, which is known as reward maximization.

17. How many different kinds of agents exist in Artificial Intelligence?

  • Simple Reflex Agents

Simple reflex agents behave only in response to the present circumstance without taking into account the previous record of the surroundings or the ways in which the ecosystem has interacted with it.

  • Simulation-Based Reflex Agents

These models form their perceptions of the environment based on the models that have been established. This model also maintains track of the internal circumstances, which may be altered based on the changes that are done to the surrounding environment.

  • Goal-Based Agents

The actions taken by agents of this kind are dictated by the objectives that have been assigned to them. Their entire aim is to accomplish that objective. If the agent is given a choice between many different options, it will choose the one that brings it one step closer to achieving its objective.

  • Utilitarian Agents

Reaching a goal isn't always enough. To reach your destination, you must choose the quickest, safest route possible. Agents use utility-based decision-making decisions based on the utility (preferences) of various options.

  • Educative Agents

These sorts of agents are able to gain knowledge from their previous encounters.

18. What is your comprehension of hyperparameters?

The training process is controlled by hyperparameters. Model train performance is directly influenced by these factors, which may be changed to one's liking. They are made known in advance. Algorithm hyperparameters that have no influence on simulation results but can influence the efficiency and acquisition of skills are the other two categories of hyperparameters that may be inferred when accommodating the machine to the learning algorithm.

19. What are the various Expert System components?

The following are the primary components that make up an expert system:

  • User Interface

It allows a person to engage with or interact with an expert system in order to discover a solution to a problem they are experiencing.

  • Inference Engine

It is known as the expert system's central processing unit or brain. In order to reach a conclusion based on the existing knowledge, it applies a variety of different rules of inference to the data. An inference engine is used by the system in order to get the information that is included inside the KB.

  • Knowledge Base

Domain-specific and high-quality information is stored in a knowledge base.

20. What is a Chatbot?

A chatbot is a computer program with artificial intelligence (AI) that can converse with humans using natural language processing. The communication may take place on a website, via an application, or through one of the several messaging applications. These chatbots, which are often referred to as digital assistants, are capable of interacting with people either via the exchange of text or by voice commands. The majority of companies now make extensive use of AI chatbots in order to provide round-the-clock, virtual customer service to their clientele.

21. How can artificial intelligence be used to identify fraud?

It is possible to use artificial intelligence in fraud detection utilizing various machine learning techniques (e.g., supervised and unsupervised). Machine learning's rule-based algorithms may be used to identify and stop fraudulent transactions. Machine learning is used to identify fraud in the following ways:

  • Extracting Data

Data extraction is the initial stage. Web scraping technologies and surveys are used to collect data. The sort of model we want to build dictates the type of data we gather. Personal information, transactions, and shopping may all be found here.

  • Data Cleaning

This stage eliminates any information that was deemed unnecessary or duplicated. Because of the data's inherent unreliability, incorrect predictions may be made.

  • Data Analysis and Exploration

This is a critical step in determining the relationship between various predictor variables.

  • Building Models

The very last thing that has to be done is to construct the model by applying various machine learning algorithms to it, and this will depend on the requirements of the company.

22. Why do we utilize an inference engine in AI?

AI's inference engine extracts valuable learning from its knowledge base by following a set of predefined logical rules. For the most part, it operates in two distinct modes:

  • Backward Chaining

It starts with the end aim and then works backward to figure out the evidence that points in that direction.

  • Forward Chaining

It begins with facts that are already known and then claims new facts.

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How to Ace AI Job Interviews

Artificial intelligence learns, in part, using “if-then” rules, so if you’re not sure your AI education is at the level it should be before you start job hunting, then consider pursuing Artificial Intelligence Certification Course or even a Post Graduate AI and Machine Learning Course that can prepare you for a career as an Artificial Intelligence Engineer. With the right program, learning can be done on your own time, yet provide you with plenty of hands-on experience you can talk about in your AI job interview.

About the Author

Eshna VermaEshna Verma

Eshna writes on PMP, PRINCE2, ITIL, ITSM, & Ethical Hacking. She has done her Masters in Journalism and Mass Communication and is a Gold Medalist in the same. A voracious reader, she has penned several articles in leading national newspapers like TOI, HT, and The Telegraph. She loves travelling and photography.

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