The tech industry is continuously making waves, with several innovations developed left and right. As such, many are considering shifting to technology-focused careers, which are now highly geared toward automation. Adapting to changes means gaining all the necessary information about artificial intelligence (AI).

While you may have the necessary educational background and skills to land the job, you still need to articulate these during your interview. How do you prepare for one? Knowing the answers to the most frequently asked artificial intelligence questions can do the trick. Let’s dive in.

Artificial Intelligence Questions Potential Employers Are Likely to Ask

Here are 10 of the most commonly asked artificial intelligence questions by hiring managers.

Question #1: How do you define AI?

AI is a branch of computer science that focuses on developing intelligent machines that function and react in ways that are highly similar to humans. AI will reach its peak when machines that can not just imitate human behaviors and intelligence but also become self-aware. 

Question #2: What are the different AI applications?

AI has been widely used in various areas, notably in banking and finance, healthcare, marketing, agriculture, space exploration and aeronautics, gaming, and computing. Almost all applications that we see today, such as chatbots, speech recognition, visual recognition, voice search, and predictive text, are powered by AI.

Question #3: Can you differentiate strong from weak AI?

Strong AI uses complex algorithms that permit systems to act depending on a situation—as if they think like humans, allowing them to make independent decisions. Weak AI, on the other hand, needs human preprogramming. Often, these machines can only simulate human behavior.

Question #4: Which AI search method consumes the least memory?

The method that consumes the least amount of memory is the depth-first search, which uses an algorithm to look at graph data or a searching tree. This traversal technique does not consume much memory because only the nodes within its current path are stored. It follows a recursive algorithm (i.e., calling itself with smaller input values using simple operations) and takes advantage of backtracking or building a solution one at a time. 

Question #5: Can you explain the main difference between statistical and classical AI?

Their main difference lies in the process they follow. Statistical AI systems go through an inductive thought process (i.e., creating generalizations based on observations) when given a set of patterns or trends. Classical AI machines, meanwhile, are more concerned about deductive thought, wherein the reasoning is based on taking several statements to draft a conclusion. They aim to represent human intelligence by simply declaring an end result rather than providing an explanation of how it was achieved. For an AI system to be truly “intelligent,” it must be able to do both inductive and deductive thought. That said, the best AI systems synthesized the two concepts.

Question #6: What process is involved in partial-order planning?

Partial-order planning is an automated planning process where the search method involves looking at possible plans instead of potential scenarios. The aim is to create a program piece by piece. As the name implies, actions need to follow a partial order even if the desired one is not specified. For example, a dog must finish an obstacle course comprising hurdles, zigzagging cones, and ladders (arranged from nearest to farthest from the starting point). Without telling the dog, he/she must first go through the hurdles, proceed to the cones, and then tackle the ladders last.

Question #7: When can you say an algorithm is complete?

An algorithm can be considered complete when it can handle all possible inputs without missing anything. For example, a sorting algorithm that returns a sorted list according to the desired outcome can be deemed complete.

Question #8: What are the different AI domains?

For this, you may need to enumerate and explain the following:

  • Expert systems: Machines that aim to mimic human decision-making abilities. They use AI technologies to imitate the behavior and judgment of a person or an organization known who is an expert in a given field. You can watch this video for more details:
  • Fuzzy logic systems: These focus on the different degrees of truth instead of merely using true or false logic. This domain can process distorted and imprecise inputs.
  • Machine learning (ML): One of the major domains, this lets computers act on data fed to them to learn tricks even without programming. Augment your knowledge by reading our informative article that differentiates AI, ML, and deep learning
  • Natural language processing (NLP): This refers to the method of analyzing natural human language to get insights about solving problems. Watch this video about NLP to learn about the concept quickly:
  • Neural networks: Neural networks refer to sets of techniques and algorithms based on how the human brain functions. When applied, they allow systems to solve advanced and sophisticated ML issues. You can get a backgrounder in neural networks from this Techslang piece.
  • Robotics: This subset includes the use of robots as agents of AI in the real-world setting. Impress your interviewer by enumerating the top 3 robots by watching this video:

Question #9: How is ML related to AI?

As you may have read in our AI, ML, and deep learning post, ML is a subset of AI. Not all AI systems learn from experience but ML-enabled devices do. Very simply put, all ML systems are AI but not all AI machines are ML-capable.

ML-enabled systems can improve their performance as they enrich their experience. They can be said to subscribe to the adage “Practice makes perfect.”

A concrete example to show how ML and AI differ is a chatbot. A chatbot is an AI system but is not ML-capable. It can’t answer customer queries by learning from experience. No matter how many times it is asked a particular question, it can’t provide the answer if it is not in its database.

Question #10: What are Bayesian networks for?

Bayesian networks are statistical models that comprise a set of variables. During an event, these networks are useful in predicting possible causes that act as contributing factors. For instance, one can use Bayesian networks to establish the relationship between symptoms and diseases. They can be used for diagnostics, time-series predictions, anomaly detection, and automated insights.

Question #11: What is Q-learning?

Q-learning is a form of model-free reinforcement learning.

Reinforcement learning works similar to giving pets treats when they learn new tricks and punishing them when they do something wrong. The process follows these steps:

  1. Observe the environment.
  2. Decide how to act based on a chosen strategy.
  3. Act.
  4. Get rewarded or penalized.
  5. Learn from the experience and refine the strategy.
  6. Repeat the process until the best solution is found.

Q-learning is a kind of reinforcement learning. The “Q” in the term refers to “quality.” A device with Q-learning capability follows the same reinforcement learning process but it determines its success based on the quality of the reward or penalty it gets in step 4. Its aim is not necessarily to find the best solution but to get the ultimate reward.

Question #12: What are the significant contributions of AI in society? What are its limitations?

AI has made impressive advances in society. In fact, almost all of the tech we see today is powered by AI. When you drive a car, its driver-assist features work because of AI. When you send a file over the cloud, AI makes that happen, too. When you ask your Google Home assistant to schedule your meeting, it does so because of AI.

While AI has dramatically improved how we live by significantly reducing repetitive, time-consuming, and dangerous tasks and allowing us to focus on enhancing our creativity, it has certain limitations. Because it relies on data, it can’t use incomplete data sets, as that would result in an incomplete learning process. Inaccuracies would be reflected in the output. In instances where data is incomplete, AI would rely on historical information, which may result in outdated and irrelevant results.

These artificial intelligence questions are merely here to guide you in preparing for your interview. The key to landing the job is ensuring that you can answer the questions with confidence. If you don’t know the answer, don’t bluff and be honest. Remember, hiring managers can spot if you are faking your knowledge.