50 Most Important Artificial Intelligence Interview Questions and Answers
Artificial Intelligence is one of the most happening fields today and the demand for AI jobs and professionals with the right skills is huge. Businesses are projected to invest heavily in artificial intelligence and machine learning in the coming years. This will lead to an increased demand for such professionals with AI skills who can help them revolutionize business operations for better productivity and profits. If you are preparing for an AI-related job interview, you can check out these AI interview questions and answers that will give you a good grip on the subject matter.
1. What is Artificial Intelligence?
Artificial intelligence, also known as machine intelligence, focuses on creating machines that can behave like humans. It is one of the wide-ranging branches of computer science which deals with the creation of smart machines that can perform tasks that usually need human intelligence. Google’s search engine is one of the most common examples of artificial intelligence.
2. What are the different domains of Artificial Intelligence?
Artificial intelligence mainly has six different domains. These are neural networks, machine learning, expert systems, robotics, fuzzy logic systems, natural language processing are the different domains of artificial intelligence. Together they help in creating an environment where machines mimic human behavior and do tasks that are usually done by them.
3. What are the different types of Artificial Intelligence?
There are seven different types of artificial intelligence. They are limited memory AI, Reactive Machines AI, Self Aware AI, Theory of Mind AI, Artificial General Intelligence (AGI), Artificial Narrow Intelligence (ANI) and Artificial Superhuman Intelligence (ASI). These different types of artificial intelligence differ in the form of complexities, ranging from basic to the most advanced ones.
4. What are the areas of application of Artificial Intelligence?
Artificial intelligence finds its application across various sectors. Speech recognition, computing, humanoid robots, computer software, bioinformatics, aeronautics and space are some of the areas where artificial intelligence can be used.
5. What is the agent in Artificial Intelligence ?
Agents can involve programs, humans and robots, and are something that perceives the environment through sensors and acts upon it with the help of effectors. Some of the different types of agents are goal-based agents, simple reflex agent, model-based reflex agent, learning agent and utility-based agent.
6. What is Generality in Artificial Intelligence?
It is the simplicity with which the method can be made suitable for different domains of application. It also means how the agent responds to unknown or new data. If it manages to predict a better outcome depending on the environment, it can be termed as a good agent. Likewise, if it does not respond to the unknown or new data, it can be called a bad agent. The more generalized the algorithm is, the better it is.
7. What is the use of semantic analyses in Artificial Intelligence?
Semantic analysis is used for extracting the meaning from the group of sentences in artificial intelligence. The semantic technology classifies the rational arrangement of sentences to recognize the relevant elements and recognize the topic.
8. What is an Artificial Intelligence Neural Network?
An artificial neural network is basically an interconnected group of nodes which takes inspiration from the simplification of neurons in a human brain. They can create models that exactly imitate the working of a biological brain. These models can recognize speech and objects as humans do.
It is a tool that prevents a neural network from overfitting. It can further be classified as a regularization technique that is patented by Google to reduce overfitting in neural networks. This is achieved by preventing composite co-adaptations on training data. The word dropout refers to dropping out units in a neural network.
10. How can Tensor Flow run on Hadoop?
The path of the file needs to be changed for reading and writing data for an HDFS path.
11. Where can the Bayes rule be used in Artificial Intelligence?
It can be used to answer probabilistic queries that are conditioned on one piece of evidence. It can easily calculate the subsequent step of the robot when the current executed step is given. Bayes' rule finds its wide application in weather forecasting.
12. How many terms are required for building a Bayes model?
Only three terms are required for building a Bayes model. These three terms include two unconditional probabilities and one conditional probability.
13. What is the result between a node and its predecessors when creating a Bayesian network?
The result is that a node can provisionally remain independent of its precursor. For constructing Bayesian networks, the semantics were led to the consequence to derive this method.
14. How can a Bayesian network be used to solve a query?
The network must be a part of the joint distribution after which it can resolve a query once all the relevant joint entries are added. The Bayesian network presents a holistic model for its variables and their relationships. Due to this, it can easily respond to probabilistic questions about them.
15. What is prolog in Artificial Intelligence?
Prolog is a logic-based programming language in artificial intelligence. It is also a short for programming logic and is widely used in the applications of artificial intelligence, especially expert systems.
17. How are artificial learning and machine learning related to each other?
Machine learning is a subset of artificial learning and involves training machines in a manner by which they behave like humans without being clearly programmed. Artificial intelligence can be considered as a wider concept of machines where they can execute tasks that humans can consider smart. It also considers giving machines the access to information and making them learn on their own.
18. What is the difference between best-first search and breadth-first search?
They are similar strategies in which best-first search involves the expansion of nodes in acceptance with the evaluation function. For the latter, the expansion is in acceptance with the cost function of the parent node. Breadth-first search is always complete and will find solutions if they exist. It will find the best solution based on the available resources.
19. What is a Top-Down Parser?
It is something that hypothesizes a sentence and predicts lower-level constituents until the time when individual pre-terminal symbols are generated. It can be considered as a parsing strategy through which the highest level of the parse tree is looked upon first and it will be worked down with the help of rewriting grammar rules. An example of this could be the LL parsers that use the top-down parsing strategy.
20. On which search method is A* algorithm based?
It is based on the best first search method because it highlights optimization, path and different characteristics. When search algorithms have optimality, they will always find the best possible solution. In this case, it would be about finding the shortest route to the finish state.
21. Which is not a popular property of a logical rule-based system?
Attachment is a property that is not considered desirable in a logical rule-based system in artificial intelligence.
22. When can an algorithm be considered to be complete?
When an algorithm terminates with an answer when one exists, it can be said to be complete. Further, if an algorithm can guarantee a correct answer for any random input, it can be considered complete. If answers do not exist, it should guarantee to return failure.
23. How can different logical expressions look identical?
They can look identical with the help of the unification process. In unification, the lifted inference rules need substitutions through which different logical expressions can look identical. The unify algorithm combines two sentences to return a unifier.
24. How Does Partial order involve?
It involves searching for possible plans rather than possible situations. The primary idea involves generating a plan piece by piece. A partial order can be considered a binary relation that is antisymmetric, reflexive and transitive.
25. What are the two steps involved in constructing a plan ?
The first step is to add an operator, followed by adding an ordering constraint between operators. The planning process in Artificial Intelligence is primarily about decision-making of robots or computer programs to achieve the desired objectives. It will involve choosing actions in a sequence that will work systematically towards solving the given problems.
26. What is the difference between classical AI and statistical AI?
Classical AI is related to deductive thought that is given as constraints, while statistical AI is related to inductive thought that involves a pattern, trend induction, etc. Another major difference is that C++ is the favorite language of statistical AI, while LISP is the favorite language of classical AI. However, for a system to be truly intelligent, it will require the properties of deductive and inductive thought.
27. What does a production rule involve?
It involves a sequence of steps and a set of rules. A production system, also known as a production rule system, is used to provide artificial intelligence. The rules are about behavior and also the mechanism required to follow those rules.
28 .What are FOPL and its role in Artificial Intelligence?
First Order Predicate Logic (FOPL) provides a language that can be used to express assertions. It also provides an inference system to deductive apparatus. It involves quantification over simple variables and they can be seen only inside a predicate. It gives reasoning about functions, relations and world entities.
29 What does FOPL language include?
It includes a set of variables, predicate symbols, constant symbols, function symbols, logical connective, existential quantifier and a universal quantifier. The wffs that are obtained will be according to the FOPL and will represent the factual information of AI studies.
30. What is the role of the third component in the planning system?
Its role is to detect the solutions to problems when they are found. search method is the one that consumes less memory. It is basically a traversal technique due to which less space is occupied in memory. The algorithm is recursive in nature and makes use of backtracking.
31. What are the components of a hybrid Bayesian network?
The hybrid Bayesian network components include continuous and discrete variables. The conditional probability distributions are used as numerical inputs. One of the common examples of the hybrid Bayesian network is the conditional linear Gaussian (CLG) model.
32. How can inductive methods be combined with the power of first-order representations?
Inductive methods can be combined with first-order representations with the help of inductive logic programming.
33. What needs to be satisfied in inductive logic programming?
Inductive logic programming is one of the areas of symbolic artificial intelligence. It makes use of logic programming that is used to represent background knowledge, hypotheses and examples. To satisfy the entailment constraint, the inductive logic programming must prepare a set of sentences for the hypothesis.
34. What is a heuristic function?
Also simply known as heuristic, a heuristic function is a function that helps rank alternatives in search algorithms. This is done at each branching step which is based on the existing information that decides the branch that must be followed. It involves the ranking of alternatives at each step which is based on the information that helps decide which branch must be followed.
35. What are scripts and frames in artificial intelligence?
Scripts are used in natural language systems that help organize a knowledge repository of the situations. It can also be considered a structure through which a set of circumstances can be expected to follow one after the other. It is very similar to a chain of situations or a thought sequence. Frames are a type of semantic networks and are one of the recognized ways of showcasing non-procedural information.
36. How can a logical inference algorithm be solved in Propositional Logic?
Logical inference algorithms can be solved in propositional logic with the help of validity, logical equivalence and satisfying ability.
37. What are the signals used in Speech Recognition?
Speech is regarded as the leading method for communication between human beings and dependable speech recognition between machines. An acoustic signal is used in speech recognition to identify a sequence of words that is uttered by the speaker. Speech recognition develops technologies and methodologies that help the recognition and translation of the human language into text with the help of computers.
38. Which model gives the probability of words in speech recognition?
In speech recognition, the Diagram model gives the probability of each word that will be followed by other words.
39. Which search agent in artificial intelligence operates by interleaving computation and action?
The online search would involve taking the action first and then observing the environment.
40. What are some good programming languages in artificial intelligence?
Prolog, Lisp, C/C++, Java and Python are some of the most common programming languages in artificial intelligence. These languages are highly capable of meeting the various requirements that arise in the designing and development of different software.
41. How can temporal probabilistic reasoning be solved with the help of algorithms?
The Hidden Markov Model can be used for solving temporal probabilistic reasoning. This model observes the sequence of emission and after a careful analysis, it recovers the state of sequence from the data that was observed.
42. What is the Hidden Markov Model used for?
It is a tool that is used for modelling sequence behavior or time-series data in speech recognition systems. A statistical model, the hidden Markov model (HMM) describes the development of events that are dependent on internal factors. Most of the time, these internal factors cannot be directly observed. The hidden states lead to the creation of a Markov chain. The underlying state determines the probability allocation of the observed symbol.
43. What are the possible values of the variables in HMM?
The possible values of the variable in HMM are the “Possible States of the World”.
44. Where is the additional variable added in HMM?
The additional state variables are usually added to a temporal model in HMM.
45 . How many literals are available in top-down inductive learning methods?
Equality and inequality, predicates and arithmetic literals are the three literals available in top-down inductive learning methods.
46. What does compositional semantics mean?
Compositional semantics is a process that determines the meaning of P*Q from P,Q and*. Also simply known as CS, the compositional semantics is also known as the functional dependence of the connotation of an expression or the parts of that expression. Many people might have the question if a set of NL expressions can have any compositional semantics.
47. How can an algorithm be planned through a straightforward approach?
The most straightforward approach is using state-space search as it considers everything that is required to find a solution. The state-space search can be solved in two ways. These include backward from the goal and forward from the initial state.
48. What is Tree Topology?
Tree topology has many connected elements that are arranged in the form of branches of a tree. There is a minimum of three specific levels in the hierarchy. Since any two given nodes can have only one mutual connection, the tree topologies can create a natural hierarchy between parent and child.
If you wish to learn an Artificial Intelligence Course, Great Learning is offering several advanced courses in the subject. An artificial intelligence Certification will provide candidates the AI skills that are required to grab a well-paying job as an AI engineer in the business world. There are several AI Courses that are designed to give candidates extensive hands-on learning experience. Great Learning is offering Machine Learning and Artificial Intelligence courses at great prices. Contact us today for more details. The future of AI is very bright, so get enrolled today to make a dream AI career.
