Artificial Intelligence Interview Questions and Answers
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Best Artificial Intelligence Interview Questions and Answers
Various industries nowadays are making Artificial Intelligence a priority in the workflow which includes education, telecommunication, healthcare, banking and finance and several others. And, so there is a huge demand for professional, certified and experienced AI professionals. Here, we have brought to light some of the top 50 Artificial Intelligence Questions and Answers. Aspirants can prepare these interview questions and crack AI job interviews at ease.
Top Artificial Intelligence Interview Questions and Answers
These top 50 Artificial Intelligence Questions and Answers are prepared in keen observance of the expert interviewers of the top multinational companies and certified training experts of Artificial Intelligence. All these top 50 Artificial Interview questions are most frequently asked in most of the artificial intelligence interviews. Both freshers who are willing to set a path in the AI career and experienced professionals to wish to boost your career in the field of Artificial Intelligence can make of these questions.
Now start preparing confidently for AI jobs interviews by going through our top 50 Artificial Intelligence Interview Questions and Answers. We wish you all success in your career search.
Artificial Intelligence can be explained in many different ways while some are listed below:
- Artificial Intelligence is actually a branch of computer science that creates highly intellectual machines that imitates and behaves like the human.
- Artificial Intelligence (AI) studies the cognitive functions performed by the human brain and tries to replicate the same intellectual human activity in the form of a machine.
Artificial Intelligence is widely used in the below listed areas or applications:
- Speech Recognition
- Cognitive Capabilities
- Decision making
- Reasoning
- Computing
- Space and Aeronautics
- Bio-informatics
- Humanoid Robot and more
Artificial Intelligence is divided into two types namely Strong Artificial Intelligence and Weak Artificial Intelligence. The difference between Strong AI and Weak AI are as follows:
Strong Artificial Intelligence | Weak Artificial Intelligence |
It has extensive scope and can be applied to wide applications | It has only limited scope and can be applied only to fewer applications |
It ensures maximum intelligence in line with the human activity | It performs well only with specific tasks |
To process any particular data, it makes use of the clustering and association technology | To process any particular data, it makes use of the supervised and unsupervised technology |
Advanced Robotics are the examples of Strong AI | Siri and Alexa are some of the examples of Weak AI |
Listed below are some of the programming languages used in Artificial Intelligence or AI:
- Python
- R
- Lisp
- Prolog
- Java
Some of the major applications of Artificial Intelligence are as follows:
- It can be used to process natural language
- It can be used in effective Chatbots creation
- It can be used analyze sentiments and to predict sales
- Artificial Intelligence is used to develop Self-driving cars
- It is used to recognize facial expression and image tagging
Artificial Intelligence | Machine Learning | Deep Learning |
AI was established in the year of 1950s | Machine Learning was established in the year of 1960s | Deep Learning was established in the year of 1970s |
The process of representing the simulated machine intelligence is known as the artificial intelligence | Machine Learning equip machines to take decisions on their own without being programmed | Deep Learning makes use of artificial neural networks to resolve any of the complex tasks |
Artificial Intelligence is actually a subset of recognized data science | Machine Learning is actually a subset of recognized Artificial Intelligence and data science | Artificial Intelligence is actually a subset of recognized Artificial Intelligence, Machine Learning and data science |
It develops machines which can act and work like a human | It develops machines which solves problems from the information gained from data | It develops neural network that determines patterns automatically that are used to detect features accurately |
A calculative paradox that explains the way how recursion is used to develop an algorithm which can be used to solve complex issues is known as a Tower of Hanoi, it can be solved using a breadth-first search (BFS) algorithm or decision tree algorithm in Artificial Intelligence.
The algorithm that is used to find the data structures present in the form of a graph or tree that begins from the root node and extends to the neighbor nodes and continues to the further node levels available is known as the BFS algorithm or breadth-first search algorithm in AI.
The algorithm search method that is used to determine the need of analyzing the path of a graph to estimate the essential route that is available between several points which are known as nodes is called as a *A algorithm search method.
A search that starts from the beginning state in the forward action and that turns to a reverse action in the objective state is known as a bidirectional search algorithm. These searches actually meet at a common point to determine the common state. Every search operated by a bidirectional search algorithm will be carried to half a way of the essential aggregate way. The initial state of the search is actually connected in a reverse manner with the objective state.
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The algorithm that undergoes the process of last in and first out (LIFO) is known as the depth-first search algorithm. In DFS, a recursion has been established with the help of Last in, First our data structure model and so the nodes available here will be in an order that is different from BFS.
In this type of search, you will find the repetitive searches of the levels 1 and 2. This search will be carries out until the solution that you seek for is estimated. Until and otherwise the single goal node is created, you will find numerous nodes generating simultaneously and all node stacks will be stored promptly.
In this type of search algorithm, the sort process is established by enhancing the cost of each available path to the available node and uniform cost search algorithm finally expands the lowest node cost. In case if all the iteration costs similar, then this search type will be identical to the breadth-first search algorithm (BFS).
A complete statistical model that renders a particular variable set and the conditional dependencies associated with the set of variables which might be similar to the directed acyclic graph.
A method of inquiry known as Turing Test which can be used to determine whether a computer can think and execute like a human being or not.
One of the mathematical approaches that are being used to map any specific solution in the reinforcement learning is known as the Markov’s decision process in AI.
Listed below are some of the important domains that are available in AI:
- Machine Learning
- Neural Networks
- Robotics
- Expert Systems
- Fuzzy Logic
- Natural Language Processing
One of the Artificial Intelligence programs with sufficient or expert-level information about a particular field and that explains how to use that information to execute appropriately is known as expert systems. This will be actually a prompt alternative to a human expertise.
Mentioned below are some of the characteristics of expert systems:
- It ensures maximum performance
- It gives a sufficient time to respond
- It ensures maximum understandability and reliability
There are three main components in expert systems namely knowledge base, inference engine and user interface.
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Listed below are some of the major advantages of expert systems:
- Consistency
- Memory
- Diligence
- Logic
- Multiple expertise
- Ability to reason
- Fast response
- Unbiased in nature
A form of many-valued logic and a subset of Artificial Intelligence are known as a fuzzy logic which can be illustrated as the IT-THEN rule actually encodes the human learning to accomplish an effective artificial processing.
Listed below are of the applications of fuzzy logic in AI:
- It is used recognize facial patterns and forecast weather
- This logic is highly used in vacuum cleaners, washing machines and air conditioners
- It is used to diagnose and plan accurate medical treatments
- It is also used to assess the risks involved in certain projects
The neural networks in AI are used to mathematically portray the activities of a human brain and induce the machine to learn and execute in the same way as the human brain does, this makes the machine recognize speech, animals, and objects as human being predict.
Mentioned below are some of the important advantages of neural network in Artificial Intelligence AI:
- Even with low level statistical training, users can work on neural networks effectively
- It can be highly used to predict the nonlinear relationships available between several variables
- It can also be used to analyze the interactions or relations between the available predictor variables
- It ensures multiple training algorithms at ease
Listed below are some of the algorithm techniques that are being used in Machine Learning:
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
- Transduction
- Learning to Learn
Supervised Learning | Unsupervised Learning | Reinforcement Learning |
The types of problem involved in supervised learning are classification and regression | The types of problem involved in unsupervised learning are association and clustering | Reward based problems are found in reinforcement learning |
External supervision training is available in supervised learning | No supervision training is involved | No supervision training is involved |
Labelled data is used in supervised learning | Unlabelled data is used in unsupervised learning | No-pre defined data is available in reinforcement learning |
These are some of the major algorithms used in Machine Learning:
- Logistic regression
- Linear regression
- Decision trees
- Support vector machines
- Naive Bayes
Some of the steps involved in Machine Learning are as follows:
- Data collection
- Data preparation
- Choosing an appropriate model
- Training the dataset
- Evaluation
- Parameter tuning
- Predictions
The most robust algorithm that is used for accurate predictive modeling is known as the Naïve Bayes Machine Learning algorithm. This algorithm actually works on the basis of Bayes theorem. In Naïve Bayes algorithm, each of the specialized highlight turns self-reliant and each of those features contributes for the resultant.
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Perceptron induces the human brain activity like accepting and rejecting things instantly. It actually classifies an input data using supervised learning technology and reproduces non-binary outcomes.
When a model turns overfit or unfit, then regularization comes into act which actually decreases the dataset errors. Regularization is used to affix useful information to the dataset which in turn avoids the related fitting problems.
There are three best feature selection techniques used in Machine Learning and they are:
- Univariate Selection
- Feature Importance
- Correlation Matrix with Heatmap
When the noise of the data is captured by any of the statistical model or Machine Learning then data overfitting occurs which ensure outcome that is of low bias and high variance.
Listed below are some of the methodologies that can be used to avoid data overfitting in Machine Learning:
- Cross validation
- More training data
- Remove features
- Early stopping
- Regularization
- Use ensemble methods
One of the regularization techniques known as Dropout is used in neural networks to avoid data overfitting where neurons that are randomly picked can be dropped off during the ongoing training.
The main objective of natural language processing is to predict the information conveyed via speech and it can be acquired with the help of deep neural networks, advanced machine learning models and more.
The two important components of natural language processing are natural language understanding and natural language generation.
Stemming algorithm in natural language processing is actually the process of separating and cutting off the beginning and end of a particular word by analyzing a list of general prefixes and suffixes which are commonly determined in some of the specialized inflected words. This process is mostly done better in some of the occasions and it does not reproduce successful results always.
Lemmatization in natural language processing is used to analyze a word morphologically and to perform this perfectly a holistic dictionary is required for the particular algorithm to glance the details and connect the essential form again with its associated lemma without hassles.
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Game theory is actually used to activate some of the principal components or elements that are highly required for a multi-agent environment in which various Artificial Intelligence programs has to be interacted to complete or fulfill a task successfully.
It is a recursive algorithm which is used to pick a reliable optimal move for one of the players considering that the other player is also performing optimally is known as a minimax algorithm in Artificial Intelligence while the game in which the players are performing is defined to be as a search problem which consists of the following exceptional components:
- Game tree
- Initial state
- Successor function
- Terminal state
- Utility function
One of the search algorithms that attempt to decrease the overall nodes in a search tree that are being searched by the minimax algorithm is known as the alpha-beta pruning which can be applied to ‘n’ depths and so it can prune the overall trees and sub trees effectively.
A partial-order plan defines all the actions that to be executed but the action’s order is specified only when it turns highly necessary.
A wide collection of several formal systems in which every statement is reliably divided into two segments called subject and predicate is known as FOPL or first order predicate logic. The predicate actually represents a single subject which can either define or modify any particular subject’s properties at ease.
Some of the expert extraction techniques that are being used for dimensionality reduction are known as:
- Independent component analysis
- Principal component analysis
- Kernel-based principal component analysis
The data structure that is predominantly used to generate an associative array which can be highly used for database indexing is known as the hash table.
The weighted average of recall and precision is known as the F1 score which determines both the false negative and false positive values and is used to calculate or measure the performance of a particular model.
F1 score can be calculated with the formula illustrated below:
F1 score = 2 * Precision * Recall/Precision + Recall
An information filtering system that can be utilized to analyze the preference of the users which are in line with the choice patterns is known as the recommendation system.
The technique that is used to understand the compressed format of a specific data is known as an auto-encoder in Artificial Intelligence.
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