Latest 39 Artificial Intelligence Questions For beginners

Latest 39 Artificial Intelligence Questions For beginners

1. What are intelligent agents and how are they used in AI?
Answer: 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.

2. What is a Bayesian network and how does it relate to AI?
Answer: A Bayesian network is a graphical model for probabilistic relationships among a set of variables. It mimics the human brain in processing variables.

3. What Are Good Programming Languages For Ai?
Answer: This topic can be somewhat sensitive, so I’ll probably tread on a few toes, please forgive me. 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.

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 languages 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 a ‘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)

4. What Is Relational Knowledge?
Answer: It is a knowledge representation scheme in which facts are represented as a set of relations. For example knowledge about players can be represented using a relation called “player” having three fields: player name, height, and weight. This form of knowledge representation provides weak inferential capabilities when used as standalone but is useful as an input for sophisticated inferential procedures.

5. What is the agent in artificial intelligence?
Answer: Anything perceives its environment by sensors and acts upon an environment by effectors are known as Agent. The agent includes Robots, Programs, and Humans, etc.

6. Give some advantages of Artificial Intelligence?
a. Error Reduction

We use artificial intelligence in most of the cases. This helps us in reducing the risk. Also, increases the chance of reaching accuracy with a greater degree of precision.

b. Difficult Exploration

In mining, we use artificial intelligence and the science of robotics. Also, other fuel exploration processes. Moreover, we use complex machines for exploring the ocean. Hence, overcoming the ocean limitation.

c. Daily Application

As we know that computed methods and learning have become commonplace in daily life. Financial institutions and banking institutions are widely using AI. That is to organize and manage data. Also, AI is used in the detection of fraud users in a smart card-based system.

7. Why A.I is needed?
Answer: There are some reasons behind its need. So, let us first compare the differences between traditional Computer programs vs. Human Intelligence. As it’s identified that normal humans have the same intellectual mechanisms. Moreover, the difference in intelligence is related to “quantitative biochemical and physiological conditions.” Traditionally, we use computing for performing mechanical computations using fixed procedures. Also, there are more complex problems that we need to solve.

8. What is Single Agent Pathfinding Problems?
Answer: There are different types of games. Such as 3X3 eight-tile, 4X4 fifteen-tile puzzles are single-agent-path-finding challenges. As they are consisting of a matrix of tiles with a blank tile. Thus, to arrange the tiles by sliding a tile either vertically or horizontally into a blank space. Also, with the aim of accomplishing some objective.

9. What Brute-Force Search Strategies?
Answer: This strategy doesn’t require any domain-specific knowledge. Thus it’s so simple strategy. Hence, it works very smoothly and fine with a small number of possible states.

Requirements for Brute Force Algorithms

a. State description

b. A set of valid operators

c. Initial stated. Goal state description

10. What is the Depth-First Search Algorithm?
Answer: It is based on the concept of LIFO. As it stands for Last In First Out. Also, implemented in recursion with the LIFO stack data structure. Thus, It used to create the same set of nodes as the Breadth-First method, only in a different order. As the path is been stored in each iteration from root to leaf node. Thus, store nodes are linear with space requirements. With branching factor b and depth as m, the storage space is bm.

11. What are the roles in AI careers?
Software analysts and developers.
Computer scientists and computer engineers.
Algorithm specialists.
Research scientists and engineering consultants.
Mechanical engineers and maintenance technicians.
Manufacturing and electrical engineers.
Surgical technicians working with robotic tools.
Military and aviation electricians working with flight simulators, drones, and armaments.

12. What is a top-down parser?
Answer: A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.

13. Do Bots And Intelligent Agents Have Personalities And Emotions?
Answer: IA is used to develop bots… and moreover how u program it is very important. It uses NL and ML also. If a person uses proper ontology then it can answer out.

14. What is the difference between strong AI and weak AI?
Answer: 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.

15. What Is A Heuristic Function?
Answer: A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow. (artificial intelligence training)

16. In Inductive Logic Programming what needed to be satisfied?
Answer: 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.

17. How logical inference can be solved in Propositional Logic?
In Propositional Logic, Logical Inference algorithm can be solved by using

a) Logical Equivalence

b) Validity

c) Satisfying ability

18. Which algorithm in ‘Unification and Lifting’ takes two sentences and returns a unifier?
Answer: In ‘Unification and Lifting’ the algorithm that takes two sentences and returns a unifier is the ‘Unify’ algorithm.

19. Which is the most straight forward approach for planning algorithms?
Answer: State-space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution. (Interview Questions and Answers)

20. What Are Partial, Alternate, Artificial, Compound, And Natural Key?
Answer: It is a set of attributes that can uniquely identify weak entities and that are related to the same owner entity. It is sometimes 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.

21. What Are Best Graduate Schools For Ai?
Answer: 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 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.

22. What Is The Difference Between Classical Ai And Statistical Ai?
Answer: 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 favorite language for statistical AI while LISP dominates in classical AI.

A system can’t be truly intelligent without displaying properties of both inductive and deductive thought. This leads many to believe that in the end, there will be some kind of synthesis of statistical and classical AI.

23. Why is image recognition a key function of AI?
Answer: 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.

24. What is deep learning and how does it relate to AI?
Answer: 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.

25. What is machine learning and how does it relate to AI?
Answer: 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.

26. What is automatic programming?
Answer: Automatic programming is describing what a program should do and then having the AI system “write” the program.

27. What is Constraint Satisfaction Problems?
Answer: 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 commonality for analyzing and solving problems. (company)

28. What is the Hidden Markov Model (HMMs) is used?
Answer: Hidden Markov Models are a ubiquitous tool for modeling time series data or to model sequence behavior. They are used in almost all current speech recognition systems.

29. In HMM’s, what are the possible values of the variable?
Answer: ‘Possible States of the World’ is the possible values of the variable in HMM’s.

30. Which algorithm inverts a complete resolution strategy?
Answer: ‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first-order theories.

31. What Does A Production Rule Consist Of?
Answer: The production rule comprises a set of rule and a sequence of steps.

32. What does a hybrid Bayesian network contain?
Answer: A hybrid Bayesian network contains both discrete and continuous variables.

33. What is meant by compositional semantics?
Answer: The process of determining the meaning of P*Q from P, Q and* is known as Compositional Semantics.

34. Which property is considered as not a desirable property of a logical rule-based system?
“Attachment” is considered as not a desirable property of a logical rule-based system.

35. Suppose 2 Batsmen Each On 94. 7 Runs To Win In 3 Balls. Both Make Unbeaten 100. How?
Answer:  Case 1: A batsman can be given out 1st batsman hits a six….gets caught on d next ball…crease is changed….next batsman hits a six again…

Case 2: No batsman is out

1st batsman hits d ball n hits d keepers helmet kept behind…he also takes a single…6 runs are added to his total making it 100…on d next ball, 2nd batsman hits a six, making his score 100….as simple as dat…

36. What Are The Undesirable Properties Of Knowledge?

Following are the undesirable properties of knowledge:

Voluminous: Knowledge may become voluminous
Difficult to characterize: It is difficult to characterize the knowledge accurately
Variability: Knowledge has a property that it may change over time
Variation in usage: Knowledge may be used in some other way than the way in which data is organized

37. How Many Types Of Entities Are There In Knowledge Representation?

There are two types of entities in knowledge representation:

Facts: These are truths that need to be represented
Symbols: It is a form of representation of facts and it is manipulated by the programs to derive new facts

38. What Are The Properties Of A Good Knowledge Representation System?

A good knowledge representation system must have the following properties:

Representation Adequacy: It must be able to represent all knowledge required in a particular domain
Inferential Adequacy: It must be able to derive knowledge representation structures such as symbols when new knowledge is inferred from old knowledge
Inferential Efficiency: It must be able to incorporate additional information into knowledge structures which may help the inference process to move in a promising direction
Acquisitional Efficiency: It must be able to incorporate new information

39. Explain in brief Artificial Intelligence?
Answer: According to the father of Artificial Intelligence, John McCarthy, it is “The science and engineering of making intelligent machines, especially intelligent computer programs”. Also, intelligence distinguishes us from everything in the world. As it has the ability to understand, apply knowledge. Also, improve skills that played a significant role in our evolution. We can define AI as the area of computer science. Further, they deal with the ways in which computers can be made. As they made to perform cognitive functions ascribed to humans.


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