However, with the growth of jobs in AI, it’s fairly obvious that cracking the interviews is also one hell of a job, we get that!
The reason for this is there are a lot of advancements in this field and with such advancements, the questions also keep on changing every now and then.
This makes most of the previous AI interview questions outdated.
However, we have compiled this list of some of the most updated AI interview questions that you can take reference from in the future or in the next job interview that you would be appearing in.
Q. What is the most popular programming language that’s used in AI?
Python is one of the most popular programming languages that leads the AI industry due to the simplicity and the predictable coding behaviour that it has.
Python is one of the most popular programming languages.
It can easily be attributed to the open-source libraries. These include Matplotlib as well as NumPy.
Some of the common examples of these programming languages include the following:
Q. What’s the philosophy behind AI that you can explain to us?
As we are progressing towards a more tech-savvy world, humans have started to become more curious if machines could actually do their tasks.
‘Can a machine think and behave like that of humans?’
So, AI was started with the intention of creating some of the most intelligent machines that could potentially do their work for them.
Q. What are some of the most common misconceptions about AI that you have heard?
AI is a very popular concept and eventually, there are a lot of things around as well that contribute to the ‘fake news’ of AI.
Some of the most common misconceptions that I’ve heard are:
- The AI systems aren’t safe and are vulnerable most of the times.
- With the growth of AI, it is likely going to replace humans in due course of time
- If AI is integrated into workplaces, it might lead to a global rise of unemployment.
Even though these stories are very popular and pretty common to hear, they are totally fake.
AI-based technology, for sure, is able to complete a lot of human tasks, but it’s still not able to replace humans and won’t ever be able to do so.
The reason is that AI needs consistent guidance from humans and can’t work on their own. This means that even if some of the sectors of a workplace are influenced by AI, there still isn’t any chance of major unemployment.
AI needs data and that data is provided by humans, not machines.
So, we are definitely safe with AI as it doesn’t have the potential to replace us or even the jobs we have.
Q. Can you differentiate between strong and weak AI?
Strong and weak AI are exactly what they sound like.
Typically speaking, strong AI is able to imitate human intelligence very well. It is known as the core of the advanced robotics and has its implementation across a variety of sectors and fields.
On the other hand, weak AI is able to predict all the specific characteristics that are known to have similarities with human intelligence.
Here’s a table to help you understand better:
|STRONG AI||WEAK AI|
|Could be applied across a variety of domains.||Could be used to perform simple tasks.|
|It has human-level intelligence.||Has limited intelligence.|
|Some of the methods of processing data are by clustering and association.||Uses the supervised and unsupervised methods of learning.|
|Has a lot of scopes.||The scope is very less.|
Q. According to you, what’s the difference between classical AI and statistical AI?
Statistical AI arises from Machine Learning which is more concerned about the inductive thought which thoroughly studies a given pattern, a set of patterns or even understands a specific trend.
At the same time, classical AI is concerned with the deductive thought where it can study a set of constraints and finalize a conclusion.
For statistical AI, C++ is the preferred language. And with Classical AI, LISP is the preferred language.
For a system to be intelligent and be called a totally AI influenced system, it needs to be inductive and deductive at the same time.
Q. Tell us something about the keys in AI?
There are a few keys that could be integrated into AI:
Alternate Keys - All the candidate keys excluding the primary keys are known as Alternate Keys.
Artificial Keys - In case none of the obvious keys are available for disposal, then if you can create a key as a last resort, it’s an artificial key. 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 for the sake of creating a unique identifier that is known as a compound key.
Q. Suppose all of us here are non-technical people. Define ML to us.
ML is more driven towards pattern recognition over anything else. It collects the user data and provides them with personalized suggestions. A common example of this is movie streaming sites like Netflix.
The ML algorithms collect data from the users, observe the patterns and use the same for getting new information and studying the behaviour of the user.
An ML program helps you by learning itself and providing better results with every attempt.
Some of the common integrations of Machine Learning programs are:
- Fraud detection.
- Spam detection.
- Spell detection.
- E-commerce product recommendations.
Q. Why do you think AI is needed?
AI has a lot of significance, at least in 2020.
Between the difference in traditional computer programs and human intelligence, AI lies somewhere.
Normal humans have the same intellectual mechanism as that of the AI that we witness now.
The difference in intelligence is also related to quantitative biochemical and psychological conditions.
Computing is needed for the sake of getting through mechanical computations by definite procedures.
And AI is the major source of helping us solve these problems.
Q. Tell us some of the branches of AI
AI is a very diverse field. Some of the most significant branches of AI are:
- Bayesian Networks and automatic programming.
- Constraint Satisfaction.
- Machine Learning.
- Neural networks and robotics.
- Knowledge representations.
- Natural Language Processing.
- Speech Recognition.
Q. Explain the Turing test and TensorFlow.
Turing test could be referred to as a method of testing a machine’s human-level intelligence.
This could be explained well with the help of an example
Suppose a human is compared to a machine. A judge is asked to identify what terminal would be occupied by the human and what’s occupied by the computer-based on individual performance
As soon as a computer passes off as a human, it is deemed as intelligent over the competitor. Even though this whole process has evolved, but the ground rules remain the same.
On the other hand, TensorFlow is an open-source framework that’s dedicated to Machine Learning.
It fuels a very adaptable ecosystem of libraries and community resources that help the developers in building different kinds of applications that are powered by ML.
Some of the common examples of these are Google and AlphaGo.
Q. How do you think AI is going to impact application development?
AI has had a massive impact on application development over a variety of sectors.
In the near future, AI is expected to be more involved in the process of application building as it has the ability to change how we manage and use the whole infrastructure at different levels.
In other words, word is that AIOps might be replacing DevOps because it allows the developers to analyze the root cause by combining ML, 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 the analytics to collect/process the data that’s derived from a variety of sources.
This information could further be analyzed in real-time for identifying and solving problems of all kinds.
Q. Explain the role of random forest in AI.
Random forest could be defined as a data construct that could be applied to the ML projects for developing a variety of random decision trees while analyzing variables.
The same algorithms could be leveraged for improving the way all kinds of technologies consider different kinds of complex data sets.
A lot of weak learners could also be combined for building a strong learner.
A random forest acts as an amazing tool for the AI and ML projects.
The reason for this is that it can work with labelled and unlabelled data sets that have a wide set of attributes.
It can also maintain the accuracy and fill the void of any kind of missing data. It can also model the importance of all attributes.
Q. Explain Breadth-First Search Algorithm.
The Breadth-First Search Algorithm involves the transversion of a binary search tree, one at a time. Right from the root node to the neighbouring nodes and adjacent nodes, it moves to the top.
This process is performed with the First In First Out (FIFO) method and gives the shortest part to the solutions.
It also assigns a couple of values to every node - distance and predecessor.
The distance is calculated with the maximum number of edges in any kind of a path from source to node ‘v’. While as the predecessor node of ‘v’ along with the shortest path from the source node.
In case there’s no path from the source node to the node ‘v’, then the distance between is infinite and it’s assumed that the predecessor comes with the same special value as that of the source’s predecessor.
Q. What do you consider regularization in AI?
This process could be defined considering a small scenario -
When we have an underfitting/overfitting issue in a statistical model, this technique resolves the same for us.
Techniques like LASSO penalize some of the model parameters in case they lead to such issues.
Methods like cross-validation also help in avoiding overfitting. A common technique is k-fold.
Another common approach is to take only a few variables into account as it helps us in removing any kind of noise while training data.
Q. What is Fuzzy Logic? Can you mention some of its applications?
Fuzzy Logic could basically be referred to as a subset of AI that encodes human learning for artificial processing.
It is represented as IF-THEN rules and is a form of many-valued logic.
Some of its common applications are:
- Facial Recognition
- Stock Trading
- Systems for forecasting weather
- AC’s, washing machines.
- Controlling subways.
- Projecting Risk Assessment.
Q. Explain to us what a Hash table means?
Hash table basically is a data structure that is known to implement an associative array abstract data type. It maps the key values.
It could also compute an index into an array of slots where the desired value could be found.
Hash table has two parts.
The first part is an array where the data is stored. It is the main part of the table.
On the other hand, the second part is a mapping function that’s known as the has function.
Q. What is an expert system and what are its characteristics?
An expert system could easily be considered as an AI program that comes with extensive and expert knowledge about a particular area.
The same is used to react to a given number of situations.
These very systems have the full expertise that is enough to replace a human expert.
Some of the characteristics of an expert system are:
- High performance.
- Enough response time.
Q. Do you have any kind of research experience in AI?
AI is going through a lot of research-based processes. As such, organizations you are interviewing for would be digging deep into your understanding and your field of knowledge.
In case you have contributed to the research papers, make sure you share all that information.
Provide some valued feedback to the interviewers and take them through the experience that you had in the research.
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.
Q. 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 for allowing 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.
Q. Explain how AI and game theory are related?
AI systems are known to use the game theory for enhancement.
The number of participants needed is two or more for narrowing down the field. Along with that, the two fundamental roles are:
Mechanism Design - The inverse game theory also designs a game for a particular group of potential participants.
Participant Design - The game theory enhances the decision of a participant for getting the maximum utility.
Q. For any kind of game playing problem, what’s the best kind of approach?
For any kind of game playing problem, a heuristic approach would be the best kind of approach we could go for.
The reason for this is because it uses techniques based on intelligent guesswork.
This doesn’t mean that it’s totally based on random choices. It thoroughly evaluates the situation and comes up with a more explained and likeable solution.
A common example of this is a chess game between a human and a computer which uses brute force computation and has thousands of possible positions.
Q. Can you tell us how computer vision and AI are related?
Computer Vision is a specific field of AI that’s used to obtain the information and other kinds of data from images or other such resources.
ML algorithms like K-means are used for the segmentation of images.
Along with that, the Support Vector Machine is also used for image classification.
Computer Vision uses a particular type of method for working.
Well, it uses AI technology for solving all kinds of complex problems. These include image processing, object detection etc.
Q. The FOPL language, what do you think it consists of?
The language of FOPL 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.
Q. 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 ().
Q. Tell us some of the roles in the AI career.
The field of AI has been growing significantly and promises a lot of careers at the same time.
Some of the major roles in the AI career are:
- Computer Scientists and engineers.
- Algo specialists.
- Software analysts.
- Software developers.
- Manufactural engineers.
- Research scientists.
- Engineering consultants.
- Surgical technicians
Q. Explain a bidirectional search algorithm.
A bidirectional search algorithm helps in making the search in forward from the beginning state. The same is done in reverse from the objective state.
All these searches finally meet for identifying a common state.
The initial state is linked in a reverse way with the objective state and each search is done up to half of the total way.
Q. 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.
Q. 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 increase the predictive accuracy has a decision tree model.
Q. Mention some of the advantages of fuzzy logic systems.
The Fuzzy logic system has a few advantages, like:
- Easy to understand.
- Effortlessly constructible logics.
- Leverage to take inaccurate and clangorous input information.
- Add or delete the rules as per convenience.
Q. What are some of the most common algorithm techniques in Machine Learning?
- Supervised and unsupervised learning.
- Sem-supervised learning.
- Learning to learn.
- Reinforcement Learning.
Q. What is MxNet used for?
MxNet is used for easy programming.
It provides scalability, portability, and flexibility to a program.
It also supports a variety of carriers and languages like C++, R, Perl, Python, etc. Thus, it eliminates the need for learning a new language. It also has the fastest training capabilities.
Q. How does AI perform against Frames and Scripts?
Frames are a variant of all kinds of semantic networks that’s one of the most popular ways to present the non-procedural knowledge in any expert system.
Frames are an artificial data structure that’s used to divide knowledge into substructures. The same is done by representing in stereotyped situations.
Scripts, on the other hand, are similar to frames. The only difference is in the values that fill the slots which must be ordered.
Scripts are usually used in natural language understanding systems for the sake of organizing a knowledge base for the situation that the system needs to understand.
Q. What is the main focus of Artificial Intelligence?
The main focus of AI are:
- Solving all kinds of real-world problems.
- Solving all kinds of artificial problems.
- Extracting scientific causes.
- Explaining all sorts of intelligence.
Q. What is the methodology of Inheritable Knowledge in AI?
Inheritable knowledge in AI is a representation scheme that can be represented in the form of objects. Along with that, their attributes and the corresponding values of the attributes are also included.
The whole relation between object defined using a is a property in it.
Suppose two entities in a game are presented as objects over the relation between the two is that one of them is a person.
Q. Can you differentiate between L1 and L2 regularization?
Regularization is the technique that helps in solving overfitting all kinds of problems in Machine Learning.
Just like we mentioned, when we have an underfitting/overfitting issue in a statistical model, this technique resolves the same for us.
Techniques like LASSO penalize some of the model parameters in case they lead to such issues.
It also inclines into spreading error among all the terms.
L1 is more binary with a lot of variables either being assigned a 1 or 0 in weighting.
It corresponds to setting a Laplacian prior on terms and L2 corresponds into a Gaussian prior.
Q. What do you mean by p-value?
p-Value performs to the hypothesis analysis in statistics and supports in determining the importance of all your results.
It is the 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.
Q. What are some of the disadvantages related to the linear models?
There are a few disadvantages to the use of linear models. Some of the major ones are:
- It lacks autocorrelation.
- It has usual errors in linearity assumptions.
- It barely solves overfitting problems.
- It can’t be used to calculate outcomes or any binary outcomes as well.
Q. What is LSTM?
LSTM or Long Short Term Memory refers to the approach to address the long time dependency problem.
It provides a state to remember everything and keep items in store.
Some of the major components of LSTM are:
- tanh (x)
- Sigmoid (x)
Q. In our modern approaches, why do we prefer LSTM over RNN?
Because of the vanishing gradient, the problem depends on the choice of the activation function.
The activation functions usually are known to squash input in any small number range in a non-linear fashion.
Q. What are the different methods for sequential supervised learning?
Some of the most common methods for sequential supervised learning are:
- Recurring Sliding Windows Methods.
- Sliding window methods.
- Hidden Markov models.
- Maximum entropy Markov models.
- Graph transformer networks.
Q. 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 whole network is trained.
The same is used for the purpose of defining the number of hidden layers that could be present in any of the networks.
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.
Q. Tell us some of the different algorithm techniques that you can use in AI and ML?
Some of the algorithm techniques that can be leveraged are:
- Learning to learn
- Reinforcement learning.
- Supervised learning
- Semi-supervised learning
- Unsupervised learning.
Q. 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.
Q. 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 the previous experience to form the decisions it has to. It consistently updates the information.
Self-Awareness - This kind of AI can work as per the human reactions and inputs of course. Machines here can perform the 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 the feelings and other types of emotions.
Q. What are intelligent agents?
In AI, the intelligent agents refer to autonomous entities that use the sensors for the purpose of evaluation of a situation and making a specific decision.
The same entities can solve all kinds of complex tasks.
Along with that, the intelligent agents are also programmed for accomplishing the tasks in a better way.
Q. What’s the Greedy Best First Search Algorithm?
This algorithm ensures the process of 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.
Q. What are the layers of a deep neural network?
- Input Layer.
- Hidden Layer.
- Output Layer.
Q. Give some common examples of AI in use.
Some of the common examples of AI in use are:
- Speech Recognition.
- Image tagging.
- Self-driving cars.
- Prediction systems.
- Facial Expression Recognition.
Q. Mention the benefits of an expert system.
The common benefits of having an expert system are:
- Logic and memory.
- Multiple expertise.
- Faster response.
Q. What is 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.
We hope these questions would help you in your next interview.
These were some of the theoretical approaches.
For the technical AI interview questions, I recommend you go through all these questions and get a good insight of what could be asked in your next AI interview.
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