1. What is the difference between Strong Artificial Intelligence and Weak Artificial Intelligence?
|Weak AI||Strong AI|
|Narrow application, with very limited scope||Widely applied, with vast scope|
|Good at specific tasks||Incredible human-level intelligence|
|Uses supervised and unsupervised learning to process data||Uses clustering and association to process data|
|E.g., Siri, Alexa, etc.||E.g., Advanced Robotics|
2. What is Artificial Intelligence?
Artificial Intelligence is a field of computer science wherein the cognitive functions of the human brain are studied and tried to be replicated on a machine/system. Artificial Intelligence is today widely used for various applications like computer vision, speech recognition, decision-making, perception, reasoning, cognitive capabilities, and so on.
3. List some applications of AI.
- Natural language processing
- Sentiment analysis
- Sales prediction
- Self-driving cars
- Facial expression recognition
- Image tagging
4. List the programming languages used in AI.
5. What is an expert system? What are the characteristics of an expert system?
An expert system is an Artificial Intelligence program that has expert-level knowledge about a specific area and how to utilize its information to react appropriately. These systems have the expertise to substitute a human expert. Their characteristics include:
- High performance
- Adequate response time
6. What is an A* algorithm search method?
A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.
7. What is a breadth-first search algorithm?
A breadth-first search (BFS) algorithm, used for searching tree or graph data structures, starts from the root node, then proceeds through neighboring nodes, and further moves toward the next level of nodes. Till the arrangement is found, it produces one tree at any given moment. As this pursuit can be executed utilizing the FIFO (first-in, first-out) data structure, this strategy gives the shortest path to the solution.
8. What is a depth-first search algorithm?
Depth-first search (DFS) is based on LIFO (last-in, first-out). A recursion is implemented with LIFO stack data structure. Thus, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.
9. What is a uniform cost search algorithm?
The uniform cost search performs sorting in increasing the cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has the same cost. It investigates ways in the expanding order of cost.
10.List the different algorithm techniques in Machine Learning.
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
- Learning to Learn
11. What is Deep Learning?
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
12. What is Naive Bayes?
Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.
13. List the extraction techniques used for dimensionality reduction.
- Independent component analysis
- Principal component analysis
- Kernel-based principal component analysis
14. What is a hash table?
A hash table is a data structure that is used to produce an associative array which is mostly used for database indexing.
15.What is a recommendation system?
A recommendation system is an information filtering system that is used to predict user preference based on choice patterns followed by the user while browsing/using the system.
16. What are the advantages of neural networks?
Require less formal statistical training
Have the ability to detect nonlinear relationships between variables
Detect all possible interactions between predictor variables
Availability of multiple training algorithms
17. What is TensorFlow?
TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experimental learning architectures and to work on them to produce desired outputs.
18. What is a cost function?
A cost function is a scalar function that quantifies the error factor of the neural network. Lower the cost function better the neural network. For example, while classifying the image in the MNIST dataset, the input image is digit 2, but the neural network wrongly predicts it to be 3.
19. Define LSTM.
Long short-term memory (LSTM) is explicitly designed to address the long-term dependency problem, by maintaining a state of what to remember and what to forget.
20. What do you mean by TensorFlow cluster?
TensorFlow cluster is a set of ‘tasks’ that participate in the distributed execution of a TensorFlow graph. Each task is associated with a TensorFlow server, which contains a ‘master’ that can be used to create sessions and a ‘worker’ that executes operations in the graph. A cluster can also be divided into one or more ‘jobs’, where each job contains one or more tasks.
21. How to run TensorFlow on Hadoop?
To use HDFS with TensorFlow, we need to change the file path for reading and writing data to an HDFS path. For example:
filename_queue = tf.train.string_input_producer([
22. What are the various areas where AI (Artificial Intelligence) can be used?
Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.
23 .Mention the difference between statistical AI and Classical AI ?
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.
24. 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.
25. 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.
26. What is FOPL stands for and explain its role in Artificial Intelligence?
FOPL stands for First Order Predicate Logic, Predicate Logic provides
A language to express assertions about certain “World”
An inference system to deductive apparatus whereby we may draw conclusions from such assertion
A semantic based on set theory
27. Which is the most straight forward approach for planning algorithm?
State space search is the most straight forward approach for planning algorithm because it takes account of everything for finding a solution.
28. What Are Partial, Alternate, Artificial, Compound And Natural Key?
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.
All Candidate Keys excluding the Primary Key are known as Alternate Keys.
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.
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.
When one of the data elements stored within a construct is utilized as the primary key, then it is called the natural key.
29. Where To Find Specific Information On Search Bots?
Check out ALICE and ELIZA bots are very good …and we can get more info on how to build in respective websites
30. What Is Artificial Intelligence?
Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans.
31. What Are The Various Areas Where Ai (artificial Intelligence) Can Be Used?
Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc.
32. What Is Agent In Artificial Intelligence?
Anything perceives its environment by sensors and acts upon an environment by effectors are known as Agent. Agent includes Robots, Programs, and Humans etc.
33. What Is Neural Network In Artificial Intelligence?
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.
34.What Is A Heuristic Function?
A heuristic function ranks alternatives, in search algorithms, at each branching step based on the available information to decide which branch to follow.
35.What Is A Top-down Parser?
A top-down parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.
36. Mention Some Popular Domains of AI.
The most popular domains in AI are:
- Machine Learning
- Neural Networks
- Expert Systems
- Fuzzy Logic Systems
- Natural Language Processing
37. What is an expert system? What are its characteristics?
An expert system is an Artificial Intelligence program that has an expert-level knowledge about a specific area of data and its utilisation to react appropriately. These systems tend to have the capability to substitute a human expert. Their characteristics include:
- High performance
- Unbiased nature
38. What are the Advantages of an Expert System?
The advantages of an expert system are:
- Easy availability
- Low production costs
- Greater speed and reduced workload
- They avoid motions, tensions, and fatigue
- They reduce the rate of errors.
39. What is an Artificial Neural Network? Name some of the commonly used ones.
Artificial Neural Networks, as the name suggests, are brain-inspired systems that are intended to replicate the way humans learn. Neural networks consist of input and output layers, as well as a hidden layer consisting of units that transform the inputs into optimal outputs. They are excellent tools to find patterns that are far too complex or numerous for a human programmer to extract and teach the machine to recognize.
40.What is the Turing test?
The Turing test is a method that tests a machine’s ability to match human-level intelligence. It is only considered intelligent if it passes the Turing test. However, a machine can be considered as intelligent even without sufficiently knowing how to mimic a human, in specific scenarios.
41.What is an A* Algorithm search method?
A* is a computer algorithm in AI that is extensively used for the purpose of finding paths or traversing graphs – to obtain the most optimal route between nodes. It is widely used in solving pathfinding problems in video games. Considering its flexibility and versatility, it can be used in a wide range of contexts. A* is formulated with weighted graphs, which means it can find the best path involving the smallest cost in terms of distance and time. This makes A* an informed search algorithm for best-first search.
42. What is a breadth-first search algorithm?
A breadth-first search (BFS) algorithm is used to search tree or graph data structures. It starts from the root node, proceeds through neighbouring nodes, and finally moves towards the next level of nodes. Till the arrangement is found and created, it produces one tree at any given moment. As this pursuit is capable of being executed by utilising the FIFO (first-in, first-out) data structure, this strategy gives the shortest path to the solution.
43. What is a Depth-first Search Algorithm?
Depth-first search (DFS) is an algorithm that is based on LIFO (last-in, first-out). Since recursion is implemented with LIFO stack data structure, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.
44. Mention some popular Machine Learning Algorithms?
Some of the popular Machine Learning algorithms are:
- Logistic regression
- Linear regression
- Decision trees
- Support vector machines
45. What is Ensemble Learning?
Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, and function approximation of any model.
46. Which domain study Artificial Included?
- Computer Science
- Cognitive Science
- Natural Sciences
47. What is the philosophy behind Artificial Intelligence?
As if we see the powers that are exploiting the power of computer system, the curiosity of human lead him to wonder, “Can a machine think and behave like humans do?” Thus, AI was started with the intention of creating similar intelligence in machines. Also, that we find and regard high in humans.
48. Explain Goal of Artificial Intelligence?
To Create Expert Systems it is the type of system in which the system exhibit intelligent behavior, and advice its users. b. To Implement Human Intelligence in Machines It is the way of creating the systems that understand, think, learn, and behave like humans.
49.Name types of Artificial Intelligence?
Strong artificial intelligence
Weak artificial intelligence
50. Explain types of Artificial Intelligence?
There are two types of artificial intelligence such as:
a. Strong artificial intelligence
Basically, it deals with the creation of real intelligence artificially. Also, strong AI believes that machines can be made sentient.
There are two types of strong AI: Human-like AI In this computer program thinks and reasons to the level of human-being. Non-human-like AI In this computer program develops a non-human way of thinking and reasoning.
b. Weak artificial intelligence
As a result, it doesn’t believe creating human-level intelligence in machines is possible. Although, AI techniques can be developed to solve many real-life problems.
51. Why A.I is needed?
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.
52. What is AI technique?
Basically, its volume is huge, next to unimaginable. Although, it keeps changing constantly. As AI Technique is a manner to organize. Also, we use it efficiently in such a way that − Basically, it should be perceivable by the people who provide it. As it should be easily modifiable to correct errors. Moreover, it should be useful in many situations. Though it is incomplete or inaccurate.
53. What are applications of A.I?
a. Natural Language Processing
Basically, it is possible to interact with the computer. Also, they understand only natural language which human use to spoke.
In strategic games, AI plays a crucial role. Such as chess, poker, tic-tac-toe, etc., As applications presents which integrate machine, software to impart reasoning and advising. They provide explanation and advice to the users.
c. Speech Recognition
Basically, systems capable of hearing the language. And also their meanings while human talks to it.
54. Give some advantages of Artificial Intelligence?
a. Error Reduction
We use artificial intelligence in most of the cases. As this helps us in reducing the risk. Also, increases the chance of reaching accuracy with the greater degree of precision.
b. Difficult Exploration
In mining, we use artificial intelligence and 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.
55. Give some disadvantages of Artificial Intelligence?
High Cost Its creation requires huge costs as they are very complex machines. Also, repair and maintenance require huge costs.
56. What is a production rule consist of and the which search method take a less memory?
It is a rule which comprises of a set of rule and a sequence of steps in it.
Depth First Search is the method that would takes the less memory for any of the process to follow.
57. What is Heruistic Function & How Neural Networks working in AI?
It is an alternatives function for ranks in search algorithms and at each branching step based on the available information to decide which branch that needs to be follow.
Neural Networks In AI:
In general it is an biological term, but in artificial intelligence it is an emulation of a biological neural system,
which receives the data, process the data and gives output based on the algorithm and empirical data.
58. How to resolve a problem with the Game Playing Problem Methodology?
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 chess game – Chess between human and computer as it will proceed with brute force computation and looking at hundreds of thousands of positions.
59. Simple Explanation about Alternate, Artificial, Compound and Natural Key?
It is one of the data element that is stored within a construct, and it is optimized as the primary key.
Compound & Artificial Key:
If there is no single data element that uniquely defines the occurrences within a construct, then integrating multiple elements to create a unique identifier for the construct and it is called as compound key.
If there is no obvious key either stands alone or compound is available, then the last report is to simply create a key by assigning a number to each record or occurrence and it is called a artificial key.
60. What is an Agent and How Partial Order or Planning Involve?
Like, anything that preceives its environment by the sensors, and act upon an environment by effectors are called as
Agent. (e.g. Robots, Programs, Humans, HCI, HMI etc.)
61. How the Generality and Top-Down Parser works?
It is the ease measure with which the method can be adapted to different domains of application
It begins by hypothesizing a sentence and successively predicting lower level constituents until that the individual pre-terminal symbols are written.
62. How FOPL works in AI?
It’s nothing but a first order predicate logic we called as shortly FOPL.
It needs a language to express assertions about certain world
It needs an inteference system to deductive apparatus whereby we may draw conclusions from such assertions
It needs a semantic based on set theory
63. How AI perform against Frames and Scripts?
They are 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 the knowledge into substructure by representing in stereotyped-situations.
It is similar with respect to frames but except the values that fill the slots must be ordered.
Though, scripts used in natural language understanding systems to organize a knowledge base in terms of the situation that the system should understand.
64. Breif the Literal that help for top-down inductive learning methods?
The below are the few literal that currently used for inductive learning methodology:
Equality & In-Equality
65. Comment on batch Normalization?
To make the data standardized before sending it to the another layer. It reduces the impacts of previous layers by keeping the mean and variance constant, makes the layers independent of each other. The convergence becomes faster.
66. What are the different NLP tasks deep learning can be applied?
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
67. Simple explanation of one hot representation to lower dimension conversion?
Trained Neural Network with one hidden layer gives the lookup table. First of all train a model NN model with one hidden layer to predict the context words, after the training the actual weight matrix that is learnt by hidden layer is user for representing the words.
68. What is advantage of pooling layer in convolutional neural networks?
Statistical Average of the Output of the convolution layer, which is easy to compute on the further steps. This reduces the spatial size of the representation to reduce the amount of parameters and computation in the network. Pooling layer operates on each feature map independently.
69. Can RNN be unfolded into full CNN with infinite length?
TRUE, RNN’s neuron can be thought of as a neuron sequence of infinite length of time steps.
70. Size of Convolution kernel would necessarily increase the performance of CNN?
FALSE, it is hyperparameter so changing it we can increase or decrease performance. We initially randomly initialize the weights for these kernels and they learn the correct weight by back propagation. So it make more computation time and occupy resources.
71. Why do we prefer LSTM over RNN?
Answer: Due to vanishing gradient, Vanishing gradient problem depends on the choice of the activation function. Activation functions (e.g sigmoid or tanh) usually ‘squash’ input into a very small number range in a very non-linear fashion.
72. What is the major difference between CRF (Conditional Random Field) and HMM (Hidde Markov Model)?
HMMs are generative models , models the joint distribution P(y,x). Therefore, model the distribution of the data P(x). These computations might take longer time compared to directly computing the conditional probability.
CRFs are discriminative models which model conditional probability P(y|x). As such, they do not require P(x) to be modelled. This results in faster performance, as they need fewer parameters to be learned.
73. What is generally the sequence followed when building a neural network architecture for semantic segmentation for image?
Encoder network on input and Decoder network on output.
The encoder is a classification network which is pre trained and just like VGG/ResNet and followed by a decoder network.
The decoder is to project the lower resolution features learnt by encoder onto higher resolution space to get the dense classification.
74. Can Normal Neural Network has long term dependencies on the Sentences?
NO, RNNs and LSTMs can have them. This is because the hidden state information is also passed to the consecutive layers in RNN and LSTM.
75. What is main difference between AI and ML?
Just think of artificial intelligence as a broader umbrella under which machine learning and deep learning come. From the below diagram we can see that even deep learning is a subset of Machine Leaning. So you can see that all three AI, machine learning and deep learning are the subsets of each other.
76. How Branches are located in AI?
• Expert System
• Pattern Recognition
• Swarm Intelligence
• Data Mining
• Genetic Algorithm
• Neural Networks
• Statistical AI
• Fuzzy Logic
77. Describe Game Theory with AI Relation?
An AI System will use the game theory for the purpose of the requirement that enhance as the more than a participant. So, the relation between the game have two parts like,
• Participant Design
• Mechanism Design
78. How Relational Knowledge used in AI?
A knowledge representation scheme in which facts are represented as a set of relations. Let’s say e.g. Knowledge about a player can be represented using a relation called as player which consist of three fields,
79. Describe the methodology of Inheritable Knowledge in AI?
A knowledge repesentation scheme which can be represented in the form of objects, their attributes and the corresponding values of the attributes.
The relation between the object defined using a isa property in it.
Let’s say an e.g. In a game two entities like Amature Male & Person are presented as objects than the relation between the two is that Amature Male isa Person.
80. How NLP works against AI?
Natural Language Processing shortly called as NLP.
It’s nothing but an processing and prehaps based understanding.
Like, process an computational linguistics with the help of read the scenario by natural human recognizable language.
81. What is supervised machine learning?
It requires training using labelled data. Example: in order into do classification, which was a supervised learning task, you’ll first need into label the data you’ll use into train the model into classify data into your labelled groups.
82. What are the algorithms used in AI ?
• neural networks
• natural language processing
• support vector machine
83. How KNN was different from k-means clustering?
The difference between both is, K-Nearest Neighbor was a supervised classification algorithm, whereas k-means was a unsupervised clustering algorithm. The procedure may seem similar at first, what it really means was the in order into K-Nearest Neighbors into work, you need labelled data which you want into classify a unlabelled point into it.
84. What was Bayes Theorem?
Bayes Theorem gives you the posterior probability which have a event given what was known as prior knowledge. Again, it was the basis behind Naive Bayes classifier.
85. What is Type I error?
Type one error was false positive, while Type II was false negative. Type one error was claiming something has happened when it hasn’t.
86. What Deep Learning was exactly?
Most people didn’t know this, but Machine Learning & Deep Learning was not two different things, but Deep learning was a subset have Machine learning.
87. What DL deals with?
It mostly deals with neural networks: how into use back propagation & other certain principles from neuroscience into more accurately model large sets have unlabeled data.
88. How would you handle a imbalanced data-set?
It is, for example, you have 90% have the data in one class & 10% in other. Leads into problems such as, no predictive power on the other category have data.
89. How will you handle missing data?
One can find missing data in a data-set & either drop those rows or columns, or decide into replace them with another value. In python library Pandas there were two useful functions which will be helpful, isnull() & dropna().
90. Describe a hash table.
A hash table was a data structure the produces a associative array. A key was mapped into certain values through the use have a hash function. They were often used for tasks such as database indexing.
91. Why was “Naive” Bayes naive?
Despite its practical applications, especially in text mining, Naive Bayes was considered “Naive” because it makes a 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.
92. What was deep learning, & how do it contrast with other machine learning algorithms?
Deep learning was a subset have machine learning the was concerned with neural networks: how into use backpropagation & certain principles from neuroscience into more accurately model large sets have unlabelled or semi-structured data. In the sense, deep learning represents a unsupervised learning algorithm the learns representations have data through the use have neural nets.
93. What are the dis advantages of the Uniform Cost Search Algorithm?
There can be many remarkably long paths with the cost C*.
Uniform Cost quest wants to explore them all.
94. Defined as a Bidirectional Search Algorithm?
Basically, Bidirectional Search Algorithm is started searches front from an original view and behind from goal state. As till both of them appears to know a common state. Moreover, the active state path is concatenated including special goal post-inverse path. Each exploration is performed entirely up to half of the total path.
95. Definition of AI Single Agent Pathfinding Problems?
Such as 3X3 eight-tile, 4X4 fifteen-tile puzzles remain single-agent-path-finding challenges. As others move to consist from one collection from pipes including a modern tile. For, to get the tiles including beginning a tile both vertically and horizontally inside a new space. Also, including identity support from achieving some objective.
96. How do you decide between model accuracy and model performance?
Precision is the number of the True Positives value it’s divided by the product of Actual Positives value also False Positives value. Put another way, it is an important number from positive predictions broken with this total quantity of positive property benefits predicted. It is also called the (PPV) Positive Predictive Value.
97. How are the k-nearest Neigh-Bors (KNN) algorithms different from k-means clustering?
K-means is a learn for the unsupervised algorithm used to clustering the problem whereas KNN is a learn to the supervised algorithm used for analysis and regression problem. This is the fundamental difference between K-means also KNN algorithm. In unsupervised learning, the information is not labelled so reflect the unlabelled data.
98.What is p-value?
It’s perform to the hypothesis analysis in statistics, a p-value supports into determining the importance of your results. The P-value is the 0 to 1 and described into the next way: A small p-value (typically ≤ 0.05) means a big mark against this null hypothesis, therefore you refuse the null data Value.
99. How Name search For AI algorithm technology?
Problem Space Basically, it is the context in which the research takes place.
Problem Instance This is an end of Initial state + Goal state.
Problem Space Graph Us do this to design problem state. Also, we practice nodes to show states.
The depth of a problem Us can determine that length of that shortest path.
100. What is meant by Uniform Cost Search Algorithm?
Basically, it makes sorting in increasing the value of the path over a node. Also, always increases single least value node. Although, it is just to Breadth-First analysis if each turn becomes the same cost. It examines ways into particular developing order of cost.