Top & Best 99 Interview Questions on Artificial Intelligence

Top & Best 99 Interview Questions in Artificial Intelligence

Top & Best 99 Interview Questions on Artificial Intelligence


What is Artificial Intelligence?


Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also the name of the academic field of study which studies how to create computers and computer software that are capable of intelligent behavior.

What is the difference between strong AI and weak AI?


                                                                                          source: Andrew McKeever youtube

Strong AI (a machine with consciousness, sentience, and mind): The idea behind Strong AI is that the machines could represent human minds in the future. If that is the case, those machines will have the ability to reason, think and do all functions that a human is capable of doing.  Nanobots, which can help us fight diseases and also make us more intelligent, are being designed. The development of an artificial neural network, which can function as a proper human being, is being looked at as a future application of Strong AI.

Weak AI or “narrow” AI: The idea behind Weak AI is the fact that machines can be made to act as if they are intelligent. For example, when a human player plays any game against a computer, the human player may feel as if the computer is actually making impressive moves. But the game application is not thinking and planning at all. All the moves it makes are previously fed into the computer by a human and that is how it is ensured that the software will make the right moves at the right times.

What is an artificial intelligence Neural Networks?


An artificial neuron network (ANN) is a computational model based on the structure and functions of biological neural networks. Information that flows through the network affects the structure of the ANN because a neural network changes – or learns, in a sense – based on that input and output.

What are the different areas where AI (Artificial Intelligence) can be utilized?


Here are some.

game playing

speech recognition

understanding natural language

computer vision

expert systems

heuristic classification etc

Which is not commonly used programming language for AI?


There is no authoritative answer to this question, as it really depends on what languages you like programming in. AI programs have been written in just about every language ever created. The most common seem to be Lisp, Prolog, C/C++, recently Java, and even more recently, Python.

Which Language is Best for Artificial Intelligence?


Python. Python is one of the most widely used programming languages in the AI field because of its simplicity. …

Java. Java is also the best choice.




Final thoughts.

What is a LISP Programming?


LISP, an acronym for list processing, is a programming language that was designed for easy manipulation of data strings. Developed in 1959 by John McCarthy, it is a commonly used language for artificial intelligence (AI)programming. It is one of the oldest programming languages still in relatively wide use.

What kind of Language is ProLog?


Unlike traditional programming languages that are based on performing sequences of commands, Prolog is based on defining and then solving logical formulas. Prologis sometimes called a declarative language or a rule-based language because its programs consist of a list of facts and rules.

Can Java be used for AI?


Pretty much any language can be used to code pretty much anything – given the effort and will. But Python has more functional programming constructs which may be more useful when you are coding AI. … Never use PHP for AI. Java or C/C++ is the best, but Python for fast development.

Which Programming Language is used in AI?


Prolog and Lisp are ‘old’ languages used in A.I. But nowadays almost all ‘A.I’ is written in standard programming languages like C, C++, Python, etc.

What is meant by Learning in AI?


Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.

What is AI in computer?


Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans.

What is AI in computer science?


Artificial Intelligence (AI) is the science of mimicking human intelligence using computers. The Computer Science and Artificial Intelligence degree course is a computing degree that allows students to specialize in AI through their project work and a number of specialist AI modules.

What is AIML?


AIML (Artificial Intelligence Markup Language) is an XML-compliant language that’s easy to learn and makes it possible for you to begin customizing an Alicebot or creating one from scratch within minutes. … <aiml>: the tag that begins and ends an AIML document.

What is the Best Programming Language for Robotics?


There has been a huge resurgence of Python in recent years, especially in robotics. One of the reasons for this is probably that Python (and C++) are the two main programming languages found in ROS. Like Java, it is an interpretive language. Unlike Java, the prime focus of the language is ease of use.

What is inference in AI?


Inferences are steps in reasoning, moving from premises to conclusions. … Human inference (i.e. how humans draw conclusions) is traditionally studied within the field of cognitive psychology; artificial intelligence researchers develop automated inference systems to emulate human inference.

Is AI Science Or is it Engineering?


Artificial Intelligence (AI) combines science and engineering in order to build machines capable of intelligent behavior. … Intelligent robotics is a related discipline in which the machines can manipulate objects in the physical world, (see ROBOT).

How does an Artificial Intelligence work?


There is no single way in which artificial intelligence works. One definition of AI is when a computer can solve a problem that normally requires a level of intelligence. Over the past 60 or so years, many different approaches to solving a wide variety of problems have been discovered by AI researchers.

When did the idea of AI start?


After 2nd World War, a number of people independently started to work on intelligent machines. The English mathematician Alan Turing may have been the first. He gave a lecture on it in 1947. He also may have been the first to decide that AI was best researched by programming computers rather than by building machines.

Who invented Artificial Intelligence?


Allen Newell, J. C. Shaw, and Herbert A. Simon pioneered the newly created artificial intelligence field with the Logic Theory Machine (1956), and the General Problem Solver in 1957. In 1958, John McCarthy and Marvin Minsky started the MIT Artificial Intelligence lab with $50,000

When was the first AI Program written?


The first working AI programs were written in 1951 to run on the Ferranti Mark 1 machine of the University of Manchester: a checkers-playing program written by Christopher Strachey and a chess-playing program written by Dietrich Prinz. 1952–1962.

Who is the Father of AI?


Contributions in computer science. John McCarthy is one of the “founding fathers” of artificial intelligence, together with Marvin Minsky, Allen Newell and Herbert Simon. McCarthy coined the term “artificial intelligence”, and organized the famous Dartmouth Conference in summer 1956.

Who coined the term AI?


John McCarthy, a professor emeritus of computer science at Stanford, the man who coined the term “artificial intelligence” and subsequently went on to define the field for more than five decades, died suddenly at his home in Stanford in the early morning Monday, Oct. 24. He was 84.

What is meant by Machine Learning?


Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data. … Both systems search through data to look for patterns.

What is Google’s AI?

Reply: DeepMind Technologies Limited is a British artificial intelligence company founded in September 2010. It was acquired by Google in 2014. … The company made headlines in 2016 after its AlphaGo program beat a human professional Go player for the first time.

What are Machine Learning techniques?


Evolved from the study of pattern recognition and computational learning theory in artificial intelligence, machine learning explores the study and construction of algorithms that can learn from and make predictions on data – such algorithms overcome following strictly static program instructions by making data-driven …

What is Big data and Machine Learning?


Freed from the limitations of human scale thinking and analysis, machine learning is able to discover and display the patterns buried in the data. “Data analytics is about discovering knowledge from large volumes data and applying it to the business.

What is Alpha Go?


AlphaGo is a computer program developed by Google DeepMind in London to play the board game Go. … In March 2016, it beat Lee Sedol in a five-game match, the first time a computer Go program has beaten a 9-dan professional without handicaps.

What is Google’s AI name?


In the past, the American technology giant has given its AI projects futuristic names like TensorFlow and DeepDream, to convey their awesome potential and weighty purpose. But no, you’ve guessed it. This time, Google has called it… Parsey McParseface.

What is an Artificial Intelligence System?


Artificial Intelligence System (AIS) was a distributed computing project undertaken by Intelligence Realm, Inc. with the long-term goal of simulating the human brain in real time, complete with artificial consciousness and artificial general intelligence.

What is meant by Learning Algorithm?


A learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task.

What is Classification in Machine Learning?


In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.

What is the meaning of Supervised Learning?


In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal). A supervised learning algorithm analyzes the training data and produces an inferred function, which can be used for mapping new examples.


Mention the difference between statistical AI and Classical AI?


Statistical AI is concerned with “inductive” thought like given a set of pattern, induce the trend. While, classical AI, on the other hand, is more concerned with “deductive” thought given as a set of constraints, deduce a conclusion etc.

What is Deep Blue?


Deep Blue was a chess-playing computer developed by IBM. It is known for being the first piece of artificial intelligence to win both a chess game and a chess match against a reigning world champion under regular time controls.

What is Artificial Intelligence Techniques?


Artificial intelligence is a science and technology based on disciplines such as Computer Science, Biology, Psychology, Linguistics, Mathematics, and Engineering. A major thrust of AI is in the development of computer functions associated with human intelligence, such as reasoning, learning, and problem-solving.

What is meant by Intelligent Systems?


An intelligent system is a machine with an embedded, Internet-connected computer that has the capacity to gather and analyze data and communicate with other systems. … In IT, a system is defined as a collection of connected elements or components that are organized for a common purpose.

How do I start learning Machine Learning?


My best advice for getting started in machine learning is broken down into a 5-step process:

Step 1: Adjust Mindset. Believe you can practice and apply machine learning. …

Step 2: Pick a Process. Use a systemic process to work through problems. …

Step 3: Pick a Tool. …

Step 4: Practice on Datasets. …

Step 5: Build a Portfolio.


What is Classification problem?


The classification problem is the problem that for many real-world objects and systems; coming up with an iron-clad classification system (to determine if an object is a member of a set or not, or which of several sets) is a difficult problem.

What is alternate, artificial, compound and natural key?


Alternate Key:  Excluding primary keys all candidate keys are known as Alternate Keys.

Artificial Key: If no obvious key either stands alone or compound is available, then the last resort is to, simply create a key, by assigning a number to each record or occurrence.  This is known as artificial key.

Compound Key:  When there is no single data element that uniquely defines the occurrence within a construct, then integrating multiple elements to create a unique identifier for the construct is known as Compound Key.

Natural Key:  Natural key is one of the data element that is stored within a construct, and which is utilized as the primary key.

What is the main difference between Classification and Clustering?


Clustering and Classification are the absolute basics of machine learning. Let’s look at the difference between them. … Because of this difference in learning, Clustering is called an unsupervised learning method and Classification is called a supervised learning method.

What is the difference between Supervised Learning and Unsupervised Learning?


Supervised learning is the Data mining task of inferring a function from labeled training data.The training data consist of a set of training examples. In supervised learning, each example is a pair consisting of an input object (typically a vector) and a desired output value (also called the supervisory signal)

What is meant by training data in Machine Learning?


More formally, a training set is a set of data used to discover potentially predictive relationships. A test set is a set of data used to assess the strength and utility of a predictive relationship. Test and training sets are used in intelligent systems, machine learning, genetic programming, and statistics.

How does an Artificial Intelligence work?


There is no single way in which artificial intelligence works. One definition of AI is when a computer can solve a problem that normally requires a level of intelligence. Over the past 60 or so years, many different approaches to solving a wide variety of problems have been discovered by AI researchers.

What is an Intelligent Agent?


In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals (i.e. it is “rational”, as defined in economics).

What is an Expert system in computing?


In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented mainly as if–then rules rather than through conventional procedural code.

Is Weka Open source?


Weka is a collection of machine learning algorithms for data mining tasks. The algorithms can either be applied directly to a dataset or called from your own Java code. … Weka is open source software issued under the GNU General Public License.

What is the difference between Classification and Regression?


Regression is used to predict continuous values. Classification is used to predict which class a data point is a part of (discrete value).

Is clustering machine learning?


Clustering is the assignment of a set of observations into subsets (called clusters) so that observations in the same cluster are similar in some sense. Clustering is a method of unsupervised learning and a common technique for statistical data analysis used in many fields.

What is Unsupervised Machine Learning?


Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Why is Clustering known as unsupervised learning?


The basic difference in layman terms: In supervised learning, the output datasets are provided which are used to train the machine and get the desired outputs whereas in unsupervised learning no datasets are provided, instead the data is clustered into different classes.

What is a rational agent in AI?


In economics, game theory, decision theory, and artificial intelligence, a rational agent is an agent that has clear preferences, models uncertainty via expected values of variables or functions of variables, and always chooses to perform the action with the optimal expected outcome for itself from among all feasible …

What is knowledge base in AI?


A knowledge base (KB) is a technology used to store complex structured and unstructured information used by a computer system. The initial use of the term was in connection with expert systems which were the first knowledge-based systems.

Is Linear regression a Supervised Learning?


For example, logistic regression is a commonly used technique in both statistics and supervised learning. However, despite its name, it is a classification technique in supervised learning, because the response variable in logistic regression is categorical.

What is Machine  Intelligence?


Artificial intelligence (AI) is intelligence exhibited by machines. … Colloquially, the term “artificial intelligence” is applied when a machine mimics “cognitive” functions that humans associate with other human minds, such as “learning” and “problem-solving” (known as Machine Learning).

Is neural network a supervised learning?


The learning algorithm of a neural network can either be supervised or unsupervised. A neural net is said to learn supervised if the desired output is already known. While learning, one of the input patterns is given to the net’s input layer.

What is Keras?


Keras is a high-level neural networks library, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation.

What is cuDNN?


The NVIDIA CUDA® Deep Neural Network library (cuDNN) is a GPU-accelerated library of primitives for deep neural networks. cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers.

What is CUDA Programming?


“CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model created by NVIDIA and implemented by the graphics processing units (GPUs) that they produce.” – quoting the first sentence of CUDA(Wikipedia). Also, see Parallel Programming and Computing Platform, NVIDIA’sCUDA home page.

What is a heuristic function?


The heuristic function is a way to inform the search about the direction to a goal. It provides an informed way to guess which neighbor of a node will lead to a goal. There is nothing magical about a heuristic function. It must use only information that can be readily obtained about a node.

What do you mean by heuristic search?


A heuristic function also called simply a heuristic, is a function that ranks alternatives in search algorithms at each branching step based on available information to decide which branch to follow. For example, it may approximate the exact solution.

What does it mean for a heuristic to be admissible?


In computer science, specifically in algorithms related to pathfinding, a heuristic function is said to be admissible if it never overestimates the cost of reaching the goal, i.e. the cost it estimates to reach the goal is not higher than the lowest possible cost from the current point in the path.

What is Manhattan distance heuristic?


The Manhattan Distance is the distance between two points measured along axes at right angles. The name alludes to the grid layout of the streets of Manhattan, which causes the shortest path a car could take between two points in the city.

What is a heuristic approach?


A heuristic technique often called simply a heuristic, is any approach to problem-solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals.

What is an Algorithm in AI?


In computer science, A* is a computer algorithm that is widely used in pathfinding and graph traversal, the process of plotting an efficiently traversable path between multiple points, called nodes.

What are Expert systems in AI?


In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning about knowledge, represented mainly as if–then rules rather than through conventional procedural code.

What is fringe in AI?


In computer science, the fringe search is a recent graph search algorithm that finds the least-cost path from a given initial node to one goal node. In essence, the fringe search is a middle ground between A* and the iterative deepening A* variant.

What are best graduate schools for AI?


Carnegie Mellon University (Pittsburgh, PA) …

Stanford University (Stanford, CA) …

Massachusetts Institute of Technology “MIT” (Cambridge, MA) …

The University of California, Berkeley (Berkeley, CA) …

Harvard University (Cambridge, MA) …

Yale University (New Haven, CT) …

Cornell University (Ithaca, NY) etc


What are the branches of AI?


Automatic Programming

Machine Learning

Natural Language Processing(NLP)

Neural Networks(NN)

Bayesian Networks


Speech Recognition 

Constraint Satisfaction 

Knowledge Engineering/Representation 

Genetic Programming





What does it mean to say an algorithm is sound and complete?


Basically, soundness (of an algorithm) means that the algorithm doesn’t yield any results that are untrue.

Completeness, on the other hand, means that the algorithm addresses all possible inputs and doesn’t miss any.

It is complete and sound if it works on all inputs (semantically valid in the world of the program) and always gets the answer right.

What is the Turing test?


The Turing test is a one-sided test. A machine that passes the test should certainly be considered intelligent, but a machine could still be considered intelligent without knowing enough about humans to imitate a human.

What about parallel machines?


Machines with many processors are much faster than single processors can be. Parallelism itself presents no advantages, and parallel machines are somewhat awkward to program. When extreme speed is required, it is necessary to face this awkwardness.

How is Artificial Intelligence is related to human based nature?


Artificial Intelligence (AI) critics repeatedly ask whether humans can be replaced by machines: Can “human nature” be duplicated by machines and, if so, are humans then just a special sort of machine? By examining the present and history of AI criticism it is possible to identify moments where specific critics have fixated on particular qualities as the “essential” qualities of “human nature”. Reason, perception, emotion, and the body are four qualities that have been championed by AI critics (and proponents) as “essential” and implementable as hardware or software machinery.

Does AI aim to put the human mind into the computer?


Some researchers say they have that objective, but maybe they are using the phrase metaphorically. The human mind has a lot of peculiarities, and I’m not sure anyone is serious about imitating all of theirs.

What is the function of the third component of the planning system?


In a planning system, the function of the third component is to recognize when an answer for issue has been found.

What is “Generality” in AI?


Generality is the measure of simplicity with which the technique can be adjusted to various domains of use.

What is a top-down parser?


A top-down parser starts by conjecturing a sentence and progressively anticipating lower level constituents until individual pre-terminal images are composed.

What is FOPL stands for in AI?


FOPL stands for First Order Predicate Logic.

Which search algorithm will use a limited amount of memory in online search?


RBFE and SMA* will solve any kind of problem that A* can’t by using limited amount of memory.

In AI where you can utilize the Bayes rule?


In Artificial Intelligence to answer the probabilistic inquiries conditioned on one bit of proof, Bayes rule can be utilized.

 For building a Bayes model what a number of terms are required?


For building a Bayes display in AI, three terms are required; they are one conditional probability and two unconditional probability.

While making Bayesian Network what is the outcome between a node and its predecessors?


While making Bayesian Network, the outcome between a node and its predecessors is that a node can be restrictively free of its predecessors.

To answer any inquiry how the Bayesian system can be utilized?


In the event that a Bayesian Network is a representative of the joint distribution, then by summing all the applicable joint entries, it can solve any inquiry.

What combines inductive techniques with the power of first-order representations?


Inductive logic programming joins inductive methods with the power of first-order representations.

 In Inductive Logic Programming what should have been satisfied?


The target of an Inductive Logic Programming is to think of an arrangement of sentences for the hypothesis such that the entailment constraint is satisfied.

In top-down inductive learning techniques what a number of literals are accessible? What are they?


There are three literals accessible in top-down inductive learning strategies they are

a)      Predicates

b)      Equality and Inequality

c)       Arithmetic Literals

Which algorithm inverts an entire resolution strategy?


‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first-order hypotheses.

In speech recognition what kind of signal is utilized?


In speech recognition, Acoustic flag is utilized to identify a sequence of words.

In speech recognition which model gives the probability of each word following each word?


“Diagram” model gives the probability of each word following each other word in speech recognition.

Which algorithm is used for solving temporal probabilistic reasoning?


To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.

What is Hidden Markov Model (HMMs) is utilized?


Hidden Markov Models are a ubiquitous instrument for modelling time series data or to model sequence behavior.  They are utilized in almost all current speech recognition systems.

In Hidden Markov Model, how does the state of the process is depicted?


The state of the process in HMM’s model is depicted by a ‘Single Discrete Random Variable’.

In HMM’s, what are the possible values of the variable?


‘The Possible States of the World’ is the possible values of the variable in HMM’s.

In HMM, where does the extra variable is added?


While remaining inside the HMM network, the additional state variables can be added to a temporal model.

In Artificial Intelligence, what do semantic analyses utilized for?


In Artificial Intelligence, to extract the significance from the group of sentences semantic analysis is utilized.

What is meant by compositional semantics?


The way toward deciding the significance of PQ from P,Q and is known as Compositional Semantics.

Mention the difference between breadth first search and best first search in artificial intelligence?


These are the two strategies which are quite similar. In best first search, we expand the nodes in accordance with the evaluation function. While, in breadth first search a node is expanded in accordance to the cost function of the parent node.

What are frames and scripts in “Artificial Intelligence”?


Frames are a variant of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. A frame which is an artificial data structure is used to divide knowledge into substructure by representing “stereotyped situations’. Scripts are similar to frames, except the values that fill the slots must be ordered. Scripts are used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.

How can the logical inference be fathomed in Propositional Logic?


In Propositional Logic, Logical Inference algorithm can be tackled by utilizing

a)      Logical Equivalence

b)      Validity

c)       Satisfying ability

Which process makes different logical expression looks identical?


‘Unification’ process makes different logical expressions identical.  Lifted inferences require discovering substitute which can make an alternate expression looks identical.  This procedure is called unification.

Which algorithm in ‘Unification and Lifting’ takes two sentences and returns a unifier?


In ‘Unification and Lifting’ the algorithm that takes two sentences and returns a unifier is ‘Unify’ algorithm.

Which is the most straightforward approach for planning algorithm?


State space hunt is the most straight forward approach for arranging calculation because it takes account of everything for finding a solution.

How do I start learning Artificial Intelligence from scratch?


Where you start depends on what you already know.

Below you’ll find a list of resources to learn and practice. The sections are roughly organized in the order they will be useful.

If you are a total beginner in short your short path should look like this:

1.) Learn Python & SQL

2.) Learn Machine Learning from couple of courses.

I’ve listed Top 10 Artificial Intelligence & Machine Learning Courses that will help you turn into the following ML master Google or Apple employs.

3.) Learn probability theory, statistics, Data science and some computational mathematics.

4.) I have listed some of my favourite free machine learning/Data science ebooks from where you can download and kick start Machine Learning Basics/Statistics for developers to become good at building AI systems quickly.

5.) Practice few exercises on

Scikit learn website:

6.) Practice practice on your own, step by step you will slowly become AI programmer.

I have listed free open source AI tools which you can use to build your solutions


Any question? Ask in the comments.

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