AI Interview Question
Here we have mentioned most frequently asked Artificial Intelligence Interview Questions and Answers specially for freshers and experienced.
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Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.
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Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.
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Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.
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Perl language is not commonly used programming language for AI
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In AI, Prolog is a programming language based on logic.
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Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools.
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Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
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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.
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The production rule comprises of a set of rule and a sequence of steps.
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The “depth first search” method takes less memory.
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Heuristic approach is the best way to go for game playing problem, as it will use the technique based on intelligent guesswork. For example, Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.
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A* algorithm is based on best first search method, as it gives an idea of optimization and quick choose of path, and all characteristics lie in A* algorithm.
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A hybrid Bayesian network contains both a discrete and continuous variables.
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Anything perceives its environment by sensors and acts upon an environment by effectors are known as Agent. Agent includes Robots, Programs, and Humans etc.
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In partial order planning , rather than searching over possible situation it involves searching over the space of possible plans. The idea is to construct a plan piece by piece.
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a) Add an operator (action)
b) Add an ordering constraint between operators
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“Attachment” is considered as not a desirable property of a logical rule based system.
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In artificial intelligence, neural network is an emulation of a biological neural system, which receives the data, process the data and gives the output based on the algorithm and empirical data.
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An algorithm is said completed when it terminates with a solution when one exists.
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A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.
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In a planning system, the function of the third component is to detect when a solution to problem has been found.
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Generality is the measure of ease with which the method can be adapted to different domains of application.
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A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.
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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.
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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.
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FOPL stands for First Order Predicate Logic, Predicate Logic provides
a) A language to express assertions about certain “World”
b) An inference system to deductive apparatus whereby we may draw conclusions from such assertion
c) A semantic based on set theory
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a) A set of constant symbols
b) A set of variables
c) A set of predicate symbols
d) A set of function symbols
e) The logical connective
f) The Universal Quantifier and Existential Qualifier
g) A special binary relation of equality
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In online search, it will first take action and then observes the environment.
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RBFE and SMA* will solve any kind of problem that A* can’t by using a limited amount of memory.
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In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.
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For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability.
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While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.
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If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.
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Inductive logic programming combines inductive methods with the power of first order representations.
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The objective of an Inductive Logic Programming is to come up with a set of sentences for the hypothesis such that the entailment constraint is satisfied.
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There are three literals available in top-down inductive learning methods they are
a) Predicates
b) Equality and Inequality
c) Arithmetic Literals
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‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first order theories.
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In speech recognition, Acoustic signal is used to identify a sequence of words.
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Biagram model gives the probability of each word following each other word in speech recognition.
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To solve temporal probabilistic reasoning, HMM (Hidden Markov Model) is used, independent of transition and sensor model.
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Hidden Markov Models are a ubiquitous tool for modelling time series data or to model sequence behaviour. They are used in almost all current speech recognition systems.
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The state of the process in HMM’s model is described by a ‘Single Discrete Random Variable’.
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‘Possible States of the World’ is the possible values of the variable in HMM’s.
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While staying within the HMM network, the additional state variables can be added to a temporal model.
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In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.
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The process of determining the meaning of P*Q from P,Q and* is known as Compositional Semantics.
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In Propositional Logic, Logical Inference algorithm can be solved by using
a) Logical Equivalence
b) Validity
c) Satisfying ability
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‘Unification’ process makes different logical expressions identical. Lifted inferences require finding substitute which can make a different expression looks identical. This process is called unification.
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In ‘Unification and Lifting’ the algorithm that takes two sentences and returns a unifier is ‘Unify’ algorithm.
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State space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution.
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Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.
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Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to humans. Weak AI simply states that some “thinking-like” features can be added to computers to make them more useful tools… and this has already started to happen (witness expert systems, drive-by-wire cars and speech recognition software). What does ‘think’ and ‘thinking-like’ mean? That’s a matter of much debate.
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A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.
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Georg Thimm maintains a webpage that lets you search for upcoming or past conferences in a variety of AI disciplines.
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Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do.
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It depends what the game does. If it’s a two-player board game,look into the “Mini-max” search algorithm for games (see [4-1]). In most commercial games, the AI is is a combination of high-level scripts and low-level efficiently-coded, real-time, rule-based systems. Often, commercial games tend to use finite state machines for computer players. Recently, discrete Markov models have been used to simulate unpredictible human players (the buzzword compliant name being “fuzzy” finite state machines).
A recent popular game, “Black and White”, used machine learning techniques for the non-human controlled characters. Basic reinforcement learning, perceptrons and decision trees were all parts of the learning system. Is this the begining of academic AI in video games.
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Generality is the measure of ease with which the method can be adapted to different domains of application.
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It is a set of attributes that can uniquely identify weak entities and that are related to same owner entity. It is sometime called as Discriminator.
Alternate Key:
All Candidate Keys excluding the Primary Key are known as Alternate Keys.
Artificial Key:
If no obvious key, either stand alone or compound is available, then the last resort is to simply create a key, by assigning a unique number to each record or occurrence. Then this is known as developing an artificial key.
Compound Key:
If no single data element uniquely identifies occurrences within a construct, then combining multiple elements to create a unique identifier for the construct is known as creating a compound key.
Natural Key:
When one of the data elements stored within a construct is utilized as the primary key, then it is called the natural key.
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Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.
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A very misused term. Today, an agent seems to mean a stand-alone piece of AI-ish software that scours across the internet doing something “intelligent.” Russell and Norvig define it as “anything that can can be viewed a perceiving its environment through sensors and acting upon that environment through effectors.” Several papers I’ve read treat it as ‘any program that operates on behalf of a human,’ similar to its use in the phrase ‘travel agent’. Marvin Minsky has yet another definition in the book “Society of Mind.” Minsky’s hypothesis is that a large number of seemingly-mindless agents can work together in a society to create an intelligent society of mind. Minsky theorizes that not only will this be the basis of computer intelligence, but it is also an explaination of how human intelligence works. Andrew Moore at Carnegie Mellon University once remarked that “The only proper use of the word ‘agent’ is when preceded by the words ‘travel’, ‘secret’, or ‘double’.”
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In a planning system, the function of the third component is to detect when a solution to problem has been found.
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The short answer is: MIT, CMU, and Stanford are historically the powerhouses of AI and still are the top 3 today.
There are however, hundreds of schools all over the world with at least one or two active researchers doing interesting work in AI. What is most important in graduate school is finding an advisor who is doing something YOU are interested in. Read about what’s going on in the field and then identify the the people in the field that are doing that research you find most interesting. If a professor and his students are publishing frequently, then that should be a place to consider.
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Perl language is not commonly used programming language for AI
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Quite a bit, actually. In ‘Computing machinery and intelligence.’, Alan Turing, one of the founders of computer science, made the claim that by the year 2000, computers would be able to pass the Turing test at a reasonably sophisticated level, in particular, that the average interrogator would not be able to identify the computer correctly more than 70 per cent of the time after a five minute conversation. AI hasn’t quite lived upto Turing’s claims, but quite a bit of progress has been made, including:
Deployed speech dialog systems by firms like IBM, Dragon and Lernout&Hauspie
Financial software, which is used by banks to scan credit card transactions for unusual patterns that might signal fraud. One piece of software is estimated to save banks $500 million annually.
Applications of expert systems/case-based reasoning: a computerized Leukemia diagnosis system did a better job checking for blood disorders than human experts.
Machine translation for Environment Canada: software developed in the 1970s translated natural language weather forcasts between English and French. Purportedly stil in use.
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A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.
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Statistical AI, arising from machine learning, tends to be more concerned with “inductive” thought: given a set of patterns, induce the trend. Classical AI, on the other hand, is more concerned with “deductive” thought: given a set of constraints, deduce a conclusion. Another difference, as mentioned in the previous question, is that C++ tends to be a favourite language for statistical AI while LISP dominates in classical AI.
A system can’t be truely intelligent without displaying properties of both inductive and deductive thought. This lends many to beleive that in the end, there will be some kind of synthesis of statistical and classical AI.
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In AI, Prolog is a programming language based on logic.
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There are many, some are ‘problems’ and some are ‘techniques’.
Automatic Programming – The task of describing what a program should do and having the AI system ‘write’ the program.
Bayesian Networks – A technique of structuring and inferencing with probabilistic information. (Part of the “machine learning” problem).
Constraint Statisfaction – solving NP-complete problems, using a variety of techniques.
Knowledge Engineering/Representation – turning what we know about particular domain into a form in which a computer can understand it.
Machine Learning – Programs that learn from experience or data.
Natural Language Processing(NLP) – Processing and (perhaps) understanding human (“natural”) language. Also known as computational linguistics.
Neural Networks(NN) – The study of programs that function in a manner similar to how animal brains do.
Planning – given a set of actions, a goal state, and a present state, decide which actions must be taken so that the present state is turned into the goal state
Robotics – The intersection of AI and robotics, this field tries to get (usually mobile) robots to act intelligently.
Speech Recognition – Conversion of speech into text.
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An algorithm is said completed when it terminates with a solution when one exists.
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This topic can be somewhat sensitive, so I’ll probably tread on a few toes, please forgive me. There is no authoritative answer for 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.
LISP- For many years, AI was done as research in universities and laboratories, thus fast prototyping was favored over fast execution. This is one reason why AI has favored high-level langauges such as Lisp. This tradition means that current AI Lisp programmers can draw on many resources from the community. Features of the language that are good for AI programming include: garbage collection, dynamic typing, functions as data, uniform syntax, interactive environment, and extensibility. Read Paul Graham’s essay, “Beating the Averages” for a discussion of some serious advantages:
PROLOG- This language wins ‘cool idea’ competition. It wasn’t until the 70s that people began to realize that a set of logical statements plus a general theorem prover could make up a program. Prolog combines the high-level and traditional advantages of Lisp with a built-in unifier, which is particularly useful in AI. Prolog seems to be good for problems in which logic is intimately involved, or whose solutions have a succinct logical characterization. Its major drawback (IMHO)
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Strong AI makes strong claims that computers can be made to think on a level equal to humans while weak AI simply predicts that some features that are resembling to human intelligence can be incorporated to computer to make it more useful tools.
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Statistical AI is more concerned with “inductive” thought like given a set of pattern, induce the trend etc. While, classical AI, on the other hand, is more concerned with “ deductive” thought given as a set of constraints, deduce a conclusion etc.
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“Attachment” is considered as not a desirable property of a logical rule based system.
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Your answer here should show that you recognize the far-reaching and practical applications of AI, but your answer is up to you because your personal understanding of the AI field is what the interviewer is trying to ascertain. If possible, mention those uses most relevant to the potential employer. Possibilities include contract analysis, object detection and classification for avoidance and/or navigation, image recognition, content distribution, predictive maintenance, data processing, automation of manual tasks or data-driven reporting.
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Intelligent agents are autonomous entities that use sensors to know what is going on, and then use actuators to perform their tasks or goals. They can be simple or complex and can be programmed to learn to better accomplish their tasks.
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TensorFlow is an open-source software library originally developed by the Google Brain Team for use in machine learning and neural networks research. It is used for data-flow programming. TensorFlow makes it much easier to build certain AI features into applications, including natural language processing and speech recognition.
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Machine learning is a subset of AI. The idea is that machines will “learn” and get better at tasks over time rather than having humans constantly having to input parameters. Machine learning is a practical application of AI.
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Neural networks are a class of machine learning algorithms. The neuron part of neural is the computational component and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more easily.
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Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data, for continual improvement in the ability to recognize and process information. Layers of neural networks stacked on top of each for use in deep learning are called deep neural networks.
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Humans are visual and AI is designed to emulate human brains. Therefore, teaching machines to recognize and categorize images is a crucial part of AI. Image recognition also helps machines to learn (as in machine learning) because the more images that are processed, the better the software gets at recognizing and processing those images.
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Automatic programming is describing what a program should do and then having the AI system “write” the program.
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A Bayesian network is a graphical model for probabilistic relationships among a set of variables. It mimics the human brain in processing variables.
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Constraint Satisfaction Problems (CSPs) are mathematical problems defined as a set of objects the state of which must meet a number of constraints. CSPs are useful for AI because the regularity of their formulation offers a commonality for analyzing and solving problems.
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Supervised learning is a machine learning process in which outputs are fed back into a computer for the software to learn from, for more accurate results the next time. With supervised learning, the “machine” receives initial training to start. In contrast, unsupervised learning means a computer will learn without initial training to base its learning on.
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Artificial intelligence Neural Networks are composed of multiple nodes, which imitate biological neurons of human brain, empowering the machine to think and learn the same way the human’s do- making them fit for seeing things like speech, objects and animals like we do.
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Artificial Intelligence can be used in several areas like Computing, Bio-informatics, Speech recognition, Humanoid robot, Space, Computer software and Aeronautics’s etc.
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Strong AI: It makes solid claims that computers can be made to think on a level equivalent to humans
Weak AI: It predicts that some features that resemble to human intelligence can be united to computer to make it more useful tools.
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Statistical AI: It is concerned with “inductive” thought like given a set of pattern, induce the trend etc.
Classical AI: It is more concerned with “deductive” thought given as a set of constraints, deduce a conclusion etc.
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Alternate Key: All candidate keys are known as Alternate Keys except primary keys
Artificial Key: If no key either stands alone, then the last resort is to, simply create a key, by assigning a number to each record. This is known as artificial key.
Compound Key: When there is no single data element that exclusively defines the existence within a construct, then integrating various elements to create a unique identifier for the construct is known as Compound Key.
Natural Key: Natural key is one of the data elements that is stored within a construct and is used as the primary key.
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Heuristic approach is the best way to go for game playing issue, as it will use the method based on intelligent guesswork. For e.g. Chess between humans and computers as it will use brute force computation, looking at hundreds of thousands of positions.
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Anything identifies its environment with the use of sensors and acts upon an environment by effectors are known as Agent. Agent includes Programs, Robots and Humans etc.
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In partial order, rather than searching over possible situation it includes searching over the space of possible plans. The idea is to construct a plan piece by piece.
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Steps:
Add an operator (action)
Add an ordering constraint between operators
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In artificial intelligence, neural network is an emulation of a biological neural system, which receives the data, processes the data and gives the output based on the algorithm and empirical data.
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An algorithm is completed when it dismisses with a solution when one exists.
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A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.
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Generality is the amount of ease with which the method can be modified to various domains of application.
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A top-down parser begins by guessing a sentence and logically predicting lower level constituents until individual pre-terminal symbols are made.
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These are the two strategies which are quite similar.
Best first search: In this we expand the nodes in accordance with the evaluation function.
Breadth first search: In this a node is expanded in accordance to the cost function of the parent node.
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Frames are a modified of semantic networks which is one of the popular ways of presenting non-procedural knowledge in an expert system. These are used to share knowledge into substructure by representing “stereotyped situations’.
Scripts are similar to frames, except the values that fill the slots must be ordered. These are used in natural language understanding systems to establish a knowledge base in terms of the situation that the system should understand.
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A set of constant symbols
A set of predicate symbols
A set of variables
The logical connective
A set of function symbols
A special binary relation of equality
The Universal Quantifier and Existential Qualifier
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In Artificial Intelligence, Bayes rule can be used to answer the queries conditioned on one piece of evidence.
For building a Bayes model how many terms are required?
Three terms are required for building a Bayes model in AI. They are one conditional probability and two unconditional probabilities.
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While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.
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If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.
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Inductive logic programming combines inductive methods with the power of first order representations.
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The motto of an Inductive Logic Programming is to come up with the bunch of sentences for the hypothesis such that the entailment constraint is satisfied.
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Prolog in AI is a programming language based on logic.
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