Interview Questions Artificial Intelligence
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 vanishing gradient?
Answer: As we add more and more hidden layers, backpropagation becomes less useful in passing information to the lower layers. In effect, as information is passed back, the gradients begin to vanish and become small relative to the weights of the network.
3. What is artificial intelligence Neural Networks?
Answer: Artificial intelligence Neural Networks can model mathematically the way the 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.
4. What Is 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.
5. What is TensorFlow and what is it used for?
Answer: 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.
5. 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.
6. 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.
7. What is Deep Learning?
Answer: Deep Learning is a subset of Machine Learning which is used to create an artificial multi-layer neural network. It has self-learning capabilities based on previous instances, and it provides high accuracy.
8. What is regularization in Machine Learning?
Answer: Regularization comes into the picture when a model is either overfit or underfit. It is basically used to minimize the error in a dataset. A new piece of information is fit into the dataset to avoid fitting issues.
9. What are the hyperparameters of ANN?
Answer: Learning rate: The learning rate is how fast the network learns its parameters.
Momentum: It is a parameter that helps to come out of the local minima and smoothen the jumps while gradient descent.
A number of epochs: The number of times the entire training data is fed to the network while training is referred to as the number of epochs. We increase the number of epochs until the validation accuracy starts decreasing, even if the training accuracy is increasing (overfitting).
10. In top-down inductive learning methods how many literals are available? What are they?
Answer:
There are three literals available in top-down inductive learning methods they are
a)Predicates
b)Equality and Inequality
c)arithmetic Literals
11. Which process makes different logical expression looks identical?
Answer: ‘Unification’ process makes different logical expressions identical. Lifted inferences require finding a substitute which can make a different expression looks identical. This process is called unification.
12. What Is An Agent?
Answer: 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 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 explanation 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’.”
13. What Are The Properties Of A Good Knowledge Representation System?
Answer: 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.
14. Mention the difference between statistical AI and Classical AI?
Answer: 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.
15. What is FOPL stands for and explain its role in Artificial Intelligence?
Answer: 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
16. 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.
17. What Is An Agent?
Answer: 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 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 explanation 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’.”
18. 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.
19. 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.
20. How Should Knowledge Be Represented To Be Used For An Ai Technique?
Answer:
Following are the requirements for knowledge to be used for an AI technique:
When two individual situations are represented, knowledge should provide generalization such that only common properties of both situations are represented rather than representing both situations individually
Knowledge should be represented such that it should be understood by the people who have provided it
Knowledge should be represented in a way that it can be easily modified
Knowledge should be represented such that it should still be applicable to one or more situations even if it is inaccurate or incomplete.
29. Give An Explanation On The Difference Between Strong Ai And Weak Ai?
Answer: 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 human intelligence can be incorporated to computer to make it more useful tools.
30. What are alternate, artificial, compound and natural key?
Answer:
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 an 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.
31. For online search in ‘Artificial Intelligence’ which search agent operates by interleaving computation and action?
Answer: In an online search, it will first take action and then observes the environment.
32. What are the various areas where AI (Artificial Intelligence) can be used?
Answer: Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s, etc. (company)
33. What Is Neural Network In Artificial Intelligence?
Answer: In artificial intelligence, the 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.
34. What are neural networks and how do they relate to AI?
Answer: 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.
35. What is the Iterative Deepening Depth-First Search Algorithm?
Answer: To perform this search we need to follow steps. As it performs the DFS starting to level 1, starts and then executes a complete depth-first search to level 2. Moreover, we have to continue searching process until we find the solution. We have to generate nodes to single nodes are created. Also, it saves only a stack of nodes. As soon as he finds a solution at depth d, the algorithm ends, The number of nodes created at depth d is bd and at depth d-1 is bd-1.
36. What Has Ai Accomplished?
Answer: 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 percent of the time after a five minute conversation. AI hasn’t quite lived up to 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 forecasts between English and French. Purportedly still in use.
37. What Are The Various Areas Where Ai (artificial Intelligence Can Be Used?
Answer: Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s, etc.
38. While creating Bayesian Network what is the consequence between a node and its predecessors?
Answer: While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.
39. What Are The Undesirable Properties Of Knowledge?
Answer:
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.
40. 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.
41. What Are The Branches Of Ai?
Answer: 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 conferencing with probabilistic information. (Part of the “machine learning” problem.
Constraint Satisfaction: solving NP-complete problems, using a variety of techniques.
Knowledge Engineering/Representation: turning what we know about the 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.
41. Mention the difference between statistical and classical artificial intelligence?
Answer: The classical AI is concerned with deductive thoughts like deducing the conclusion, set of constraints, etc. The preferred language for classical AI is LISP.
The statistical AI is more emphasized towards inductive thoughts such as induce the trend and set of patterns etc. C++ is the major programming language for statistical AI
42. Explain the Turing Test In Artificial Intelligence?
Answer: This is the methodology used to test the ability of the machine, whether it matches human intelligence or not. The machines that clear the Turing test is considered as an intelligent machine.
43. How AI and game theory are related?
Answer: game theory enables the capabilities that are necessary for multi-agent environments. In these environments, the AI programs interact to accomplish a particular goal or task.
44. Which domain study Artificial Included?
Answer:
- Computer Science
- Cognitive Science
- Engineering
- Ethics
- Linguistics
- Logic
- Mathematics
- Natural Sciences
- Philosophy
- Physiology
- Psychology
- Statistics
45. Explain artificial intelligence examples and applications?
Answer:
a. Virtual Personal Assistants
Basically, it is processed in which we have to collect a huge amount of data. That is collected from a variety of sources to learn about users. Also, one needs to be more effective in helping them organize and track their information. For Example, There are various platforms like iOS, Android, and Window mobile. We use intelligent digital personal assistants are like Siri, Google Now, and Cortana. AI plays an important role in these apps. If you demand they use to collect the information. And this information is used to recognize your request and serves your result.
b. Smart Cars
There are two examples: That is featured Google’s self-driving car project and Tesla’s “autopilot”. Also. the artificial intelligence is been used since the invention of the first video game.
c. Prediction
We call it the use of predictive analytics. Its main purpose is potential privacy. Also, we can use in many ways. As its also sending you coupons, offering you discounts. That is close to your home with products that you will like to buy. Further, we can call it the controversial use of artificial intelligence.
d. Fraud Detection
We use AI to detects fraud. As many frauds always happen in banks. Also, computers have a large sample of fraudulent and non-fraudulent purchases. As they asked to look for signs that a transaction falls into one category or another.
46. What is the Breadth-First Search Algorithm?
Answer: Basically, we have to start searching for the root node. And continue through neighboring nodes first. Further, moves towards the next level of nodes. Moreover, the solution is found, generates one tree at a time. As this search can be implemented using a FIFO queue data structure. This method provides the shortest path to the solution. FIFO(First in First Out). If the branching factor (average number of child nodes for a given node) = b and depth = d, the number of nodes at level d = bd. The total no of nodes created in the worst case is b + b2 + b3 + … + bd.
47. In ‘Artificial Intelligence’ where you can use the Bayes rule?
Answer: In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.
48. 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.
49. How to resolve a problem with the Game Playing Problem Methodology?
Answer: Game Playing Problem Methodology:
Heuristic Approach is the best way to proceed further for game playing problem, though it will use the technique based on intelligent guesswork. Let us say an example like a chess game – Chess between human and computer as it will proceed with brute force computation and looking at hundreds of thousands of positions.
50. What are the different NLP tasks deep learning can be applied?
Answer:
Machine translation, Sentiment Analysis, Question and Answer system
Machine translation: Sequence to sequence models are used for this.
Sentiment Analysis: Classification techniques on text using neural networks
Question and Answer system: This is again a Seq to seq model
51. What is the variance?
Answer: This, on the other hand, was an error due into way too much complexity in your learning algorithm. Mainly due to this complexity, the algorithm was highly sensitive into high degrees have variation, which can lead your model into overfitting the data. Additionally, you will be carrying too much noise from your training data for your model to be useful.
52. How would you approach the “Netflix Prize” competition?
Answer: The Netflix Prize was a famed competition where Netflix offered $1,000,000 for a better collaborative filtering algorithm. The team the won called BellKor had a 10% improvement & used an ensemble have different methods into a win. Some familiarity with the case & its solution will help demonstrate you’ve paid attention to machine learning for a while.
53. Why was “Naive” Bayes naive?
Answer: Despite its practical applications, especially in text mining, Naive Bayes was considered “Naive” because it makes an assumption the was virtually impossible into see in real-life data: the conditional probability was calculated as the pure product have the individual probabilities have components. This implies the absolute independence have features — a condition probably never met in real life.
54. What do you know about artificial intelligence? Give an example where AI is used in our regular lives?
Answer: Artificial Intelligence, also known as machine intelligence, is the field of computer science. It focuses on developing machines that demonstrate human intelligence. The AI gives the capability to the machines to mimic the human mind and behavior. Some of the most common examples of AI in our daily lives are smartphones, search engines, smart cars and drones, social media, etc.
55. Why A.I is needed?
Answer: There are some reasons behind its need. So, let us first compare 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 which we need to solve.
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