Artificial Intelligence Interview Questions and Answers …

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Artificial Intelligence (AI) has made a huge impact across several industries, such as healthcare, finance, telecommunication, business, education, etc., within a short period. Today, almost every company is looking for AI professionals to implement Artificial Intelligence in their systems and provide better customer experience, along with other features. In this Artificial Intelligence Interview Questions blog, we have compiled a list of some of the most frequently asked questions by interviewers during AI-based job interviews:

Q1. What is the difference between Strong Artificial Intelligence and Weak Artificial Intelligence?Q2. What is Artificial Intelligence?Q3. List some applications of AI.Q4. List the programming languages used in AI.Q5. What is Tower of Hanoi?Q6. What is Turing test?Q7. What is an expert system? What are the characteristics of an expert system?Q8. List the advantages of an expert system.Q9. What is an A* algorithm search method?Q10. What is a breadth-first search algorithm?

The Artificial Intelligence Interview Questions blog is widely categorized in the following three types:1. Basic

2. Intermediate

3. Advanced

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Artificial Intelligence is a field of computer science wherein the cognitive functions of the human brain are studied and tried to be replicated on a machine/system. Artificial Intelligence is today widely used for various applications like computer vision, speech recognition, decision-making, perception, reasoning, cognitive capabilities, and so on.

Tower of Hanoi is a mathematical puzzle that shows how recursion might be utilized as a device in building up an algorithm to take care of a specific problem. Using a decision tree and a breadth-first search (BFS) algorithm in AI, we can solve the Tower of Hanoi.

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The Turing test is a method to test a machines ability to match the human-level intelligence. A machine is used to challenge human intelligence, and when it passes the test it is considered intelligent. Yet a machine could be viewed as intelligent without sufficiently knowing how to mimic a human.

An expert system is an Artificial Intelligence program that has expert-level knowledge about a specific area and how to utilize its information to react appropriately. These systems have the expertise to substitute a human expert. Their characteristics include:

A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.

A breadth-first search (BFS) algorithm,used for searching tree or graph data structures, starts from the root node, then proceeds through neighboring nodes, and further moves toward the next level of nodes.

Till the arrangement is found, it produces one tree at any given moment. As this pursuit can be executed utilizing the FIFO (first-in, first-out) data structure, this strategy gives the shortest path to the solution.

Depth-first search (DFS) is based on LIFO (last-in, first-out). A recursion is implemented with LIFO stack data structure. Thus, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.

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In a bidirectional search algorithm, the search begins in forward from the beginning state and in reverse from the objective state. The searches meet to identify a common state. The initial state is linked with the objective state in a reverse way. Each search is done just up to half of the aggregate way.

The repetitive search processes of level 1 and level 2 happen in this search. The search processes continue until the solution is found. Nodes are generated until a single goal node is created. Stack of nodes is saved.

The uniform cost search performs sorting in increasing the cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has the same cost. It investigates ways in the expanding order of cost.

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AI system uses game theory for enhancement; it requires more than one participant which narrows the field quite a bit. The two fundamental roles are as follows:

AlphaBeta pruning is asearch algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to n depths and can prune the entire subtrees and leaves.

Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF-THEN rules.

A problem has to be solved in a sequential approach to attain the goal. The partial-order plan specifies all actions that need to be undertaken but specifies an order of the actions only when required.

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First-order predicate logic is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.

Deep Learning is a subset of Machine Learning which is used to create an artificial multi-layer neural network. It has self-learning capabilities based on previous instances, and it provides high accuracy.

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Linear and logistic regression, support vector machine, and Naive Bayes

Flexibility, power, and performance

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Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.

Perceptron is an algorithm that is able to simulate the ability of the human brain to understand and discard; it is used for the supervised classification of the input into one of the several possible non-binary outputs.

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Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, function approximation, etc. of a model.

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A hash table is a data structure that is used to produce an associative array which is mostly used for database indexing.

Regularization comes into the picture when a model is either overfit or underfit. It is basically used to minimize the error in a dataset. A new piece of information is fit into the dataset to avoid fitting issues.

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Model accuracy, a subset of model performance, is based on the model performance of an algorithm. Whereas, model performance is based on the datasets we feed as inputs to the algorithm.

F1 score is the weighted average of precision and recall. It considers both false positive and false negative values into account. It is used to measure a models performance.

A recommendation system is an information filtering system that is used to predict user preference based on choice patterns followed by the user while browsing/using the system.

Dimensionality reduction is the process of reducing the number of random variables. We can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc.

Bias error is used to measure how much on an average the predicted values vary from the actual values. In case a high-bias error occurs, we have an under-performing model.

Variance is used to measure how the predictions made on the same observation differ from each other. A high-variance model will overfit the dataset and perform badly on any observation.

TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experimental learning architectures and to work on them to produce desired outputs.

TensorFlow Installation Guide:

CPU : pip install tensorflow-cpu

GPU : pip install tensorflow-gpu

A cost function is a scalar function that quantifies the error factor of the neural network. Lower the cost function better the neural network. For example, while classifying the image in the MNIST dataset, the input image is digit 2, but the neural network wrongly predicts it to be 3.

Number of epochs:The number of times the entire training data is fed to the network while training is referred to as the number of epochs. We increase the number of epochs until the validation accuracy starts decreasing, even if the training accuracy is increasing (overfitting).

As we add more and more hidden layers, backpropagation becomes less useful in passing information to the lower layers. In effect, as information is passed back, the gradients begin to vanish and become small relative to the weights of the network.

Dropout is a simple way to prevent a neural network from overfitting. It is the dropping out of some of the units in a neural network. It is similar to the natural reproduction process, where the nature produces offsprings by combining distinct genes (dropping out others) rather than strengthening the co-adapting of them.

Long short-term memory (LSTM) is explicitly designed to address the long-term dependency problem, by maintaining a state of what to remember and what to forget.

An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are given below:

Components of GAN:

Deployment Steps:

Gradient descent is an optimization algorithm that is used to find the coefficients of parameters that are used to reduce the cost function to a minimum.

Step 1: Allocate weights (x,y) with random values and calculate the error (SSE)

Step 2: Calculate the gradient, i.e., the variation in SSE when the weights (x,y) are changed by a very small value. This helps us move the values of x and y in the direction in which SSE is minimized

Step 3: Adjust the weights with the gradients to move toward the optimal values where SSE is minimized

Step 4: Use new weights for prediction and calculating the new SSE

Step 5: Repeat Steps 2 and 3 until further adjustments to the weights do not significantly reduce the error

Syntax: Class Session

It is a class for running TensorFlow operations. The environment is encapsulated in the session object wherein the operation objects are executed and Tensor objects are evaluated.

TensorFlow cluster is a set of tasks that participate in the distributed execution of a TensorFlow graph. Each task is associated with a TensorFlow server, which contains a master that can be used to create sessions and a worker that executes operations in the graph. A cluster can also be divided into one or more jobs, where each job contains one or more tasks.

To use HDFS with TensorFlow, we need to change the file path for reading and writing data to an HDFS path. For example:

The intermediate tensors are tensors that are neither inputs nor outputs of theSession.run()call, but are in the path leading from the inputs to the outputs; they will be freed at or before the end of the call.

Sessions can own resources, few classes like tf.Variable, tf.QueueBase, and tf.ReaderBase, and they use a significant amount of memory. These resources (and the associated memory) are released when the session is closed, by calling tf.Session.close.

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When we first run the tf.Variable.initializer operation for a variable in a session, it is started. It is destroyed when we run the tf.Session.close operation.

Yes, logical inference can easily be solved in propositional logic by making use of three concepts:

Face verification is used by a lot of popular firms these days. Facebook is famous for the usage of DeepFace for its face verification needs.

There are four main things you must consider when understanding how face verification works:

There are many algorithms that are used for hyperparameter optimization, and following are the three main ones that are widely used:

Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.

Overfitting can be prevented by using the below-mentioned methods:

Overfitting is avoided in neural nets by making use of a regularization technique called dropout.

By making use of the concept of dropouts, random neurons are dropped when the neural network is being trained to use the model doesnt overfit. If the dropout value is too low, it will have a minimal effect. If it is too high, the model will have difficulty in learning.

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Artificial Intelligence Interview Questions and Answers ...