Interview Prep: Artificial Intelligence
The following article provides an outline for Artificial Intelligence Interview Questions. Artificial Intelligence is slowly shaping modern life; it is helping Wall Street to decide the stock market trades, Netflix to recommend movies and many other usabilities. AI is a science of copying or mimicking human behavior. Artificial Intelligence technology describes how to program computers to exhibit and function like humans in terms of intelligence and decision making. Fellow readers would love to witness some facts about this latest hit buzz in the tech industry.
- Just 15% of the enterprises are using AI currently.
- 45% of matured organizations (tech giants) have an advanced and defined AI strategy.
- Only 33% of consumers think they are using AI in some other ways, but the reality is that 77% of them are using it.
- 38% believe that AI will enhance customer services.
- 61% believe AI will make the world smarter and better.
- India ranked 3rd in the research of AI.
Now, if you are looking for a job which is related to Artificial Intelligence, then you need to prepare for the 2022 Artificial Intelligence interview questions. It is true that every interview is different as per the different job profiles. Here, we have prepared the important artificial Intelligence interview questions which will help you get success in your interview.
This 2022 Artificial Intelligence interview questions article will present 14 most important and frequently used AI interview questions.
Part 1 – Artificial Intelligence Interview Questions (Basic)
This first part covers the basic AI interview questions and answers:
Q1. What is AI?
Answer:
AI is a branch of computer science that stresses and finds a way of creating an intelligent machine that has the ability to work, think, and reacts like humans.
Q2. How aware do you think you are in terms of using AI-enabled devices and services?
Answer:
Like I said, AI is everywhere and has a deep impact on our surroundings. We can see AI touch in the below-listed things:
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- Smartphones
- Smart Cars and Drones
- Social Media Feeds
- Media Players
- Video Games and many more Areas
Q3. What do you think are the areas where AI has a great impact?
Answer:
AI has a great influence on numerous areas.
At present, it is:
- Computing Field
- Speech Recognition
- Bioinformatics
- Humanoid Robots
- Space and Aeronautics
- Weather Forecasting
Q4. Which programming languages are preferred for AI?
Answer:
These are the common Artificial Intelligence interview questions asked in an interview. The programming language which is preferred for AI is Python, R, Lisp, Prolog, and Java.
Q5. Narrate the formula for the coefficient for logistic regression?
Answer:
The logistic regression is given by:
- πi=Pr(Yi=1|Xi=xi)=exp(β0+β1xi)1+exp(β0+β1xi)
Q6. What do you mean by overfitting and underfitting algorithms?
Answer:
- Overfitting and Underfitting are responsible for poor performance.
- Overfitting gives a good performance on the trained data, and poor generalization to other data.
- Underfitting gives a poor performance on the training data and good generalization to other data.
Q7. Explain tree topology?
Answer:
As the name suggests, “Tree” topology has several connected elements arranged like the branches of a tree. The structure has at least three specific levels in the hierarchy. These are scalable and accessible while troubleshooting and are so preferred. A common drawback in this topology is the hindrance or malfunctioning of the primary node.
Part 2 – Artificial Intelligence Interview Questions (Advanced)
Let us now have a look at the advanced AI Interview Questions and Answers:
Q8. Narrate some of the branches of AI?
Answer:
There are some branches of AI as follows:
- Automatic Programming
- Constraint Satisfaction
- Bayesian Networks
- Knowledge Representations
- Machine Learning
- Natural Language Processing (NLP)
- Neural Networks
- Robotics
- Speech Recognition
Q9. Explain Karl Pearson’s coefficient of correlation?
Answer:
Karl Pearson’s correlation coefficient is a measure of the strength of a linear association between two variables. It is denoted by r or rxy (where x and y are the two variables involved). This method of correlation draws a line of best fit through the data of two variables.
The value of the Pearson correlation coefficient (r) indicates how far away all these data points are to this line of best fit.
Formula:
Where,
* cov(X, Y): is the covariance between X and Y.
Q10. How to select the best hyperparameters in a tree-based model?
Answer:
There is two best Hyperparameter in a tree-based model:
- Measure the performance over training data.
- Measure the performance over validation data.
We have to consider the validation result while comparing it with the test results, so the answer is B.
Q11. What did you know about the Agent in AI? Tell me all the relevant details about it?
Answer:
In my understanding, first, we have an agent formula.
Agent = Architecture + Agent program
Let’s see a practical example of an agent. In our human body, three are the eyes, ears, which act as a sensor, and the legs, hands, and other body parts act as actuators.
So, an AI system is composed of an agent and its environment. All the time, an agent acts in its environment.
So anything can be considered an agent if it has:
- Perceiving the environment using its sensors.
- Acting or behaving upon a related environment using actuators.
Q12. List down the techniques or algorithms mostly used in AI?
Answer:
In general, certain algorithms are mostly used, or we can say that they are the first ones to approach understanding complex scenarios.
- Neural Network
- Generic Algorithms
- Reinforcement Learning
Q13. Explain the objective and related terminology used in the search algorithms of AI?
Answer:
These are the most popular Artificial Intelligence Interview Questions asked in an interview. Searching is the universal technique used in AI problem techniques. This algorithm is used to search for a particular position.
Every search terminology has some components.
- Problem space: This is the environment in which the search takes place.
- Problem Instance: It’s a result of the Initial State + Goal state.
- Problem Space Graph: This is used to represent a problem state.
- The depth of a problem: Here, we can define the shortest path length.
- Space Complexity: We can calculate this by the maximum number of nodes that are stored in memory.
- Time Complexity: It is defined as the maximum number of nodes that are created.
- Admissibility: This is the property of the algorithms that are used to find the optimal solutions.
- Branching Factors: This can be calculated by the average number of child nodes in the problem space graph.
- Depth: It is the length of the shortest path from inception to the goal state.
Here are some of the search algorithms:
- Breadth-first search
- Depth-first search
- Bidirectional search
- Uniform cost search
Q14. List down some of the best AI software platforms?
Answer:
Following are the best AI software platforms:
- Tensor Flow
- Azure Machine Learning
- Ayasdi
- Playment
- Salesforce Einstein
- Cloud Machine Learning
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