Top 50 Amazing Artificial Intelligence Interview Questions And Answers

Artificial Intelligence is a bunch of many different technologies working together, enabling the machines to sense, act and learn with human-level intelligence.

AI has grown a lot in the past few years. Many companies are also offering the best jobs in this field of AI.

However, with AI’s growth in the industry, cracking out the interview has become a big task. This is because AI is a growing industry, and with this, the questions keep on changing now and then.

What is Artificial Intelligence?

Artificial intelligence (AI) refers to the reflection of human intelligence in machines programmed to think like humans and mimic their actions.

The term may also be applied to any device that demonstrates traits associated with a human mind, such as learning and problem-solving.

AI is a vast field, and the career options available in this field are multiple.

Some AI-related jobs include Software analysts and developers, while some include computer scientists, computer engineers, algorithm specialists, and electrical engineers.

It’s always good to keep some sure-shot interview hacks handy before you aim for artificial intelligence jobs.

You need to gain expertise in AI skills to crack the interview and get a job. Excellent communication is essential for clearing any interview, so work on the way you interact with people.

You should talk with clarity about what you say, make eye contact with each of the interviewers, pick relevant points, talk just what’s required, speak without stammering and use the right body language. This all comes under good communication skills. He or she also needs to have a level of expertise in statistics and even mathematics.

However, we have compiled this list of some of the most updated Artificial Intelligence Interview Questions you can reference in the future or the next job interview you would appear in.

Also Check Out: How To Use Artificial Intelligence For Lead Generation

top 50 artificial intelligence interview questions and answers

What are some of the most common misconceptions about AI that you have heard?

In today’s world, misconceptions about AI are spreading like wildfire. AI is getting an increasing amount of attention as its applications and capabilities grow. There are still many misconceptions about what AI is and what it can do.

Some of the most common misconceptions that I’ve heard are:

• With the growth of AI, it is likely going to replace humans in due course of time.
• AI can’t get smarter than Human Intelligence.
• AI works precisely like a human brain.

Even though these stories are viral amongst the newbies and pretty common to hear but to be very honest, these are all fake. SAI — Based technology can do a lot like human tasks, but it can not ever replace humans.

According to you, what’s the difference between classical AI and statistical AI?

Statistical AI comes from machine learning, which is more concerned with inductive thought. There are a set of patterns, and it also understands a specific trend. The language used with statistical AI is C++.

Whereas Classical AI is concerned with deductive thought, where it can study a set of constraints and finalize a conclusion. The language used here is LISP.

A system can only be intelligent and be called a totally AI Influenced System, where there is a blend of inductive and deductive programming simultaneously.

Suppose all of us here are non-technical people. Define Machine Learning to us

Machine learning is based on algorithms that can learn from data without relying on rules-based programming. This algorithm collects data from the users, observes the patterns, and uses the same to get fresh information and study the user's behaviour. This helps the program learn itself and provide better results with every attempt.

The most common and relevant example of this is online streaming platforms like Netflix, Amazon Prime Video,

etc. which recommend you what to watch next or the top shows according to your taste and preferences.

Why do you think AI is needed?

AI has a lot of significance, especially during this pandemic, i.e. COVID-19. This technology is helpful for proper screening, tracking, and predicting current and future patients. It could also be used for early detection and diagnosis of the infection. Development of drugs and vaccines could be made with the help of AI, which would ultimately reduce the workload on health workers.

AI enhances the speed, precision, and effectiveness of human efforts. This technology gives us better vision, understanding, memory, and much more.

Tell us some of the branches of AI

Some Branches of AI are:

• Knowledge Representations
• Machine Learning
• Speech Recognition
• Natural language processing
• Neural Networks and Robotics
• Fuzzy Logic
• Common Sense Knowledge and Reasoning

What is Fuzzy Logic? Can you mention some of its applications?

In simple words, Fuzzy Logic could be referred to as a subset of AI that encodes human learning into artificial processing. It is represented as IF-THEN rules and in the form of many-valued logic.

Some of its applications are:

• Control of automatic exposure in video cameras
• Vacuum cleaners, microwave oven, air conditioning systems, washing machines
• Control of water purification plants
• Optimization of milk production and cheese production.
• Automatic underground train operation

What is an expert system, and what are its characteristics?

An expert system could easily be considered an AI program that comes with extensive and expert knowledge about a particular area. The same can also be used to react to a given number of situations. These systems are fully mastered enough to replace a human expert.

Some of the characteristics of an expert system are:

• Responsiveness
• Reliable and effective
• Understandable
• Enough response time
• High performance

Explain how AI and game theory are related?

Game theory can be applied to different territories of Artificial Intelligence like multi-agent AI systems, Imitation and Reinforcement Learning, and Adversary training in Generative Adversarial Networks (GANs). Basically, AI systems are known to use game theory for enhancement.

What do you think FOPL Language consists of?

The FOPL Language consists of a set of constant symbols. These include:

• A whole set of variables.
• Functional symbols.
• Predicate symbols.
• A binary relation of equality.
• A universal and an existential quantifier.

Tell us some of the roles in the AI career

Some of the roles in the AI Career are:

• Computer scientists and engineers.
• Engineering consultants.
• Software developers.
• Algo specialists.
• Software analysts.
• Research scientist.
• Surgical technicians.
• Manufacture engineers.

Explain a bidirectional search algorithm

A bidirectional search algorithm runs two simultaneous searches, one from an initial state called forward-search and the other from a goal node called backwards-search, to find the goal node.

Bidirectional search replaces one single search graph with two small subgraphs in which one starts the search from an initial vertex and the other starts from the goal vertex. Finally, the search stops when these two graphs intersect each other at a certain point.

For any kind of game-playing problem, what’s the best kind of approach?

For any kind of game-playing problem, a heuristic should be the best kind of approach we could follow. The reason behind this approach is that it uses both comprehensible and effective techniques.

A common example of this is a chess game between a human and a computer that uses brute force computation and has thousands of possible positions.

Can you tell us how computer vision and AI are related?

Computer Vision is a specific field of AI used to gather information and other kinds of data from images or other such resources. Machine Learning algorithms like K-means are used for the segmentation of images. Along with that, the SVM (Support Vector Machine) is also used for image classification.

Computer Vision uses a particular type of method for working. Well, it uses AI technology to solve all kinds of tough problems. These include image processing, object detection, etc.

Suppose you have missing data in your program. How would you deal with it?

Missing data could still be retrieved. We can find the same in a data-set ‘&either’ drop the rows or columns. Or we can replace the same with any other kind of value.

For the python library Pandas, there are two useful functions that could prove to be helpful. These are IsNull() and drop ().

What is Pruning in Decision Trees?

Pruning allows you to remove the branches. They have weak predictive power. This is because it reduces the complexity and also increases the predictive accuracy of the decision tree model. Pruning also reduces the final classifier’s complexness, hence improving prophetic accuracy by reducing overfitting.

Mention some of the advantages of fuzzy logic systems

• A fuzzy logic system gives the freedom to do modifications to rules and is also flexible.
• The systems can be very easily constructed.
• You can delete the rules according to your convenience.
• Leverage to make inaccurate and clangorous input information.

What are some of the most common algorithm techniques in Machine Learning?

• Supervised and unsupervised learning
• Transduction
• Learning to learn
• Reinforcement learning
• Semi-supervised learning

What is MxNet used for?

MxNet is usually used to define, train and deploy deep neural networks. This system is lean, flexible, and ultra-scalable, i.e. it allows fast model training and it also supports a flexible programming model and multiple languages like — C++, Python etc. Basically, it is used for easy programming and has the fastest training capabilities.

What is the main focus of Artificial Intelligence?

The main focus of AI (Artificial Intelligence) is to solve all kinds of real-world problems as well as artificial problems. AI is also used to extract scientific causes. It explains all other kinds of intelligence also. AI simplifies human efforts and helps them to make better decisions.

Most companies also use Artificial Intelligence to improve their process efficiencies and automate resource-heavy tasks.

How do you think AI is going to impact application development?

Artificial Intelligence had a massive impact on application development in a variety of sectors. In the near future, AI is expected to be more involved in the application-building process as it can change how we manage the system and use the whole infrastructure at different levels. In other words, the word AIOps (Artificial Intelligence for IT operations) might be replacing DevOps (Development and Operations) because it allows the developers to analyze the root cause by combining ML (Machine Language), visualization and Big Data.

AIOps is a multilayered platform that automates and improves IT operations. This could be significant for developers as they can leverage analytics to collect/process data from various sources. This information could further be analyzed in real-time for identifying and solving problems of all kinds.

Explain the Turing test and TensorFlow

The Turing test could be referred to as a mechanical testing machine’s human-level intelligence.

Whereas TensorFlow is an open-source framework that’s dedicated to Machine Learning.

Some of the common examples of these are Google and AlphaGo.

Explain Breadth-First Search Algorithm

BFS (Breadth-First Search) algorithm is when you should start traversing from a single node and traverse the whole graph in layers. By this method, other nodes can also be explored. You must then move forward towards the next-level neighbouring nodes.

It also assigns a couple of values to every node — distance and predecessor.

What do you consider regularization in AI?

Regularization in AI is used to reduce the error by fitting appropriately on the given training set, and it also avoids overfitting. Methods like cross-fitting also help in overfitting.

Do you have any kind of research experience in AI?

The organizations you are interviewing for would be digging deep into your understanding and your field of knowledge. If you have contributed to any research papers in the past, make sure you share all that information. Take them through the experience you had in the research process.

And in case you don’t have any, keep an explanation ready. You can also mention how you started your journey with AI and how you grew during this time.

Explain the role of frameworks such as Keras, TensorFlow, and PyTorch

Keras: It is known to be an open-source neural network that’s written in Python. It has been designed to allow fast experimentation with deep neural networks.

TensorFlow: It is an open-source software library that is dedicated to dataflow programming. It is also used for ML applications. A common example is neural networks.

PyTorch: It is a common open-source ML library. It’s dedicated to Python and is based on Torch. It is commonly used for natural language processing.

For any kind of game-playing problem, what’s the best kind of approach?

The heuristic approach would be the best approach to be followed for any game-playing problem. The reason behind following this theory is that this approach uses intelligent guesswork. It thoroughly evaluates the situation and comes up with a more explained and likeable solution. A common example of this is chess gameplay between humans and computers.

What are the Overfitting and underfitting algorithms?

The overfitting and underfitting algorithms are usually responsible for the poor performance of a program. Overfitting usually is known to give a good performance on the trained data and gives poor generalization to the other kinds of data. Whereas underfitting gives a poor performance on the training data and a good generalization to other kinds of data.

Explain the role of random forests in AI

A random forest is defined as a data construct that could be applied to different Machine Learning projects for developing a variety of random decision trees while analyzing further variables. It can be used for both classification and regression problems in ML. Random Forest takes the average to improve the predictive accuracy of a dataset.

Can you differentiate between strong and weak AI?

STRONG AIWEAK AIIt could be applied across a variety of domains. It could be used to perform simple tasks. It has human-level intelligence. It has limited intelligence. Some of the methods of processing data are clustering and association. It uses supervised and unsupervised methods of learning. It has a lot of scopes. The scope is significantly less.

What is the methodology of Inheritable Knowledge in AI?

The methodology of Inheritable Knowledge in AI means that all the data should be stored in a hierarchy of classes. The classes should be arranged in a generalized manner.

Can you differentiate between L1 and L2 regularization?

A model that uses L1 regularization is called Lasso Regression, and the model which uses L2 regularization is called Ridge Regression. The main difference between these two is the penalty term. Lasso Regression shrinks the less important feature’s coefficient to zero. Thus it removes some of the features. These techniques are a great alternative when we are working with a lot of features.

What do you mean by p-value?

P-value regulates getting a particular result when the null hypothesis is assumed to be true. It is 0 to 1 and is described as (typically ≤ 0.05), which means a big mark against this null hypothesis. Therefore you refuse the null data Value. A common example of this is tossing a coin.

What are some of the disadvantages related to linear models?

Some of the disadvantages related to linear models are:

• It lacks autocorrelation.
• It has frequent errors in linearity equations.
• It hardly solves overfitting problems.
• It can’t be used for calculating binary outcomes.

What is LSTM?

LSTM (Long Short-Term Memory) network is a type of recurrent neural network capable of learning order dependence in prediction problems. This type of network is required in complex problem domains like machine translation, speech recognition, and more.

In our modern approaches, why do we prefer LSTM over RNN?

We prefer LSTM over RNN because of the vanishing gradient. The problem depends on the choice of the activation function. The activation functions are usually known to squash input in any small number range in a non-linear fashion.

What are the layers of a deep neural network?

• Input layer
• Hidden layer
• Output layer

Give some common examples of AI in use

• Self-driving cars
• Speech/voice recognition
• Chatbots
• Prediction systems
• Facial recognition system
• Tags on images

What is the F1 Score?

The F1 score is a weighted average of precision and recall. It takes into account false positive/negative values and measures a model’s performance.

What’s the philosophy behind AI that you can explain to us?

As we progress towards a more tech-savvy world, humans have become more curious if machines could actually do their tasks.

‘Can a machine be graceful or hostile?’ or ‘Can a machine think like a human?’

So, AI was started with the intention of creating some of the most intelligent machines that could potentially reduce the workload of humans.

What is the most popular programming language that’s used in AI?

The most popular programming language that is used in AI is Python. There are also some other programming languages like — Julia, Java, Lisp, and Haskell.

Tell us something about the keys in AI?

Here are a few keys which can be integrated into Artificial Intelligence:

Alternate Keys: All the candidate keys, excluding the primary keys, are known as Alternate Keys.

Artificial Keys: If none of the obvious keys is available for disposal, then if you can create a key as a last resort, it’s an artificial one. It is made by assigning a number to every record.

Natural Keys: It is one of the data elements that’s stored within a construct and could also be used as a primary key.

Compound Keys: When no single data element that defines the occurrence within the construct is left, then different elements are integrated to create a unique identifier known as a compound key.

What are the different methods for sequential supervised learning?

The different methods for sequential supervised learning are:

• Sliding Windows methods.
• Hidden Markov models.
• Recurring Sliding Windows method.
• Maximum Entropy Markov models.
• Graph Transformer networks.

Explain what hyperparameters in Deep Neural Networks are

Hyperparameters are such kinds of variables that define the whole structure of a particular network. A variable like learning rate defines how the entire network is trained. The same is used to determine the number of hidden layers in any network. A lot of hidden units can also increase the accuracy of the whole network. In case there is a lesser number of units, underfitting might occur.

Tell us some of the different algorithm techniques that you can use in AI and ML?

Different algorithms techniques that can be used in AI and ML are:

• Learning to learn.
• Reinforcement Learning.
• Supervised Learning.
• Semi-Supervised Learning.
• Unsupervised Learning.

What are some of the best techniques to represent Knowledge?

Relational Knowledge: Here, knowledge is described as a set of relations where they are related to bonds done in the database.

Inferential knowledge: The knowledge here is expressed as first-order word logic.

Inheritable Knowledge: Knowledge here is represented as managing objects along with their attributes.

Procedural knowledge: Knowledge is represented as commands where each command describes an action that needs to be executed whenever a condition is met.

What are some of the different types of AI?

There are 4 different types of AI:

Reactive Machines: It’s the most basic type of AI and uses previous experience to form the decisions it has to. It consistently updates the information.

Self-Awareness: This kind of AI can work according to human reactions and inputs. Machines here can perform self-driven actions.

Limited Memory: Limited memory allows certain functions in an AI program. A common example is self-driven cars, as the movements are detected automatically and added to the memory.

Theory of Mind: This type allows the AI to understand feelings and other types of emotions.

What are intelligent agents?

In AI, intelligent agents refer to autonomous entities that use the sensors to evaluate a situation and make a specific decision. The same entities can solve all kinds of complex tasks. Along with that, intelligent agents are also programmed to accomplish tasks in a better way.

What’s the Greedy Best First Search Algorithm?

This algorithm ensures the process of the node closest to the goal that would be expanded first.The same explanation of nodes goes by f(n) = h(n). The same technique is applied at a larger stage where the same priority queue comes into consideration.

Mention the benefits of an expert system

The benefits of an expert system are:

• Logic and Memory.
• Reasoning.
• Unbiased.
• Diligence.
• Multiple Expertise.
• Faster Response.

How does AI perform against Frames and Scripts?

Frames are a variant of all kinds of semantic networks that are one of the most popular ways to present non-procedural knowledge in any expert system. Frames are artificial data structures that are used to divide knowledge into substructures. The same is done by representing stereotyped situations.

Scripts, on the other hand, are similar to frames. The only difference is in the values filling the slots that must be ordered. Scripts are usually used in natural language understanding systems to organise a knowledge base for the situation that the system needs to understand.

Final Words

We hope these questions will help you for your next interview. These were some of the theoretical approaches. For the Technical ArtificiaI Intelligence Interview Questions, I recommend you go through all these questions and get a good insight into what could be asked in your next AI interview.

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