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 distinguish 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.

Read more about Artificial Intelligence

Which domain study Artificial Included?

Computer Science
Cognitive Science
Engineering
Ethics
Linguistics
Logic
Mathematics
Natural Sciences
Philosophy
Physiology
Psychology
Statistics

What is Artificial Intelligence? Give an example of where AI is used on a daily basis.

“Artificial Intelligence (AI. is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans.” “The capability of a machine to imitate the intelligent human behavior.”

Google’s Search Engine

One of the most popular AI Applications is the google search engine. If you open up your chrome browser and start typing something, Google immediately provides recommendations for you to choose from. The logic behind the search engine is Artificial Intelligence.

AI uses predictive analytics, NLP and Machine Learning to recommend relevant searches to you. These recommendations are based on data that Google collects about you, such as your search history, location, age, etc. Thus, Google makes use of AI, to predict what you might be looking for.

What are the different types of AI?

Reactive Machines AI: Based on present actions, it cannot use previous experiences to form current decisions and simultaneously update their memory.
Example: Deep Blue
Limited Memory AI: Used in self-driving cars. They detect the movement of vehicles around them constantly and add it to their memory.
Theory of Mind AI: Advanced AI that has the ability to understand emotions, people and other things in the real world.
Self Aware AI: AIs that possess human like consciousness and reactions. Such machines have the ability to form self-driven actions.
Artificial Narrow Intelligence (ANI.: General purpose AI, used in building virtual assistants like Siri.
Artificial General Intelligence (AGI.: Also known as strong AI. An example is the Pillo robot that answers questions related to health.
Artificial Superhuman Intelligence (ASI.: AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.

Explain the different domains of Artificial Intelligence.

Machine Learning: It’s the science of getting computers to act by feeding them data so that they can learn a few tricks on their own, without being explicitly programmed to do so.
Neural Networks: They are a set of algorithms and techniques, modeled in accordance with the human brain. Neural Networks are designed to solve complex and advanced machine learning problems.
Robotics: Robotics is a subset of AI, which includes different branches and application of robots. These Robots are artificial agents acting in a real-world environment. An AI Robot works by manipulating the objects in it’s surrounding, by perceiving, moving and taking relevant actions.
Expert Systems: An expert system is a computer system that mimics the decision-making ability of a human. It is a computer program that uses artificial intelligence (AI. technologies to simulate the judgment and behavior of a human or an organization that has expert knowledge and experience in a particular field.
Fuzzy Logic Systems: Fuzzy logic is an approach to computing based on “degrees of truth” rather than the usual “true or false” (1 or 0. boolean logic on which the modern computer is based. Fuzzy logic Systems can take imprecise, distorted, noisy input information.

Natural Language Processing: Natural Language Processing (NLP. refers to the Artificial Intelligence method that analyses natural human language to derive useful insights in order to solve problems.

What is the difference between AI, Machine Learning and Deep Learning?

Artificial Intelligence	Machine Learning	Deep Learning
Originated in 1950s	Originated in 1960s	Originated in 1970s
AI represents simulated intelligence in machine	ML is the practice of getting machines to make decisions without being programmed	DL is the process of using Artificial Neural Networks to solve complex problems
AI is a subset of Data Science	ML is a subset of AI & Data Science	DL is a subset of ML, AI & Data Science
Aims to build machines which are capable of thinking like humans	Aims to build machines learning through data so that they can solve problems	Aims to build neural networks that automatically discover patterns for feature detection

How is Machine Learning related to Artificial Intelligence?

Artificial Intelligence is a technique that enables machines to mimic human behavior. Whereas, Machine Learning is a subset of Artificial Intelligence. It is the science of getting computers to act by feeding them data and letting them learn a few tricks on their own, without being explicitly programmed to do so.

Therefore Machine Learning is a technique used to implement Artificial Intelligence.

What are the different types of Machine Learning?

What is .Learning?

The .learning is a Reinforcement Learning algorithm in which an agent tries to learn the optimal policy from its past experiences with the environment. The past experiences of an agent are a sequence of state.action.rewards:

In the above state diagram, the Agent(a0. was in State (s0. and on performing an Action (a0., which resulted in receiving a Reward (r1. and thus being updated to State (s1

What is Deep Learning?

Deep learning imitates the way our brain works i.e., it learns from experiences. It uses the concepts of neural networks to solve complex problems.

Any Deep neural network will consist of three types of layers:

Input Layer: This layer receives all the inputs and forwards them to the hidden layer for analysis
Hidden Layer: In this layer, various computations are carried out and the result is transferred to the output layer. There can be n number of hidden layers, depending on the problem you’re trying to solve.
Output Layer: This layer is responsible for transferring information from the neural network to the outside world.

Explain how Deep Learning works.

Deep Learning is based on the basic unit of a brain called a brain cell or a neuron. Inspired from a neuron, an artificial neuron or a perceptron was developed.
A biological neuron has dendrites which are used to receive inputs.
Similarly, a perceptron receives multiple inputs, applies various transformations and functions and provides an output.
Just like how our brain contains multiple connected neurons called neural network, we can also have a network of artificial neurons called perceptron’s to form a Deep neural network.

An Artificial Neuron or a Perceptron models a neuron which has a set of inputs, each of which is assigned some specific weight. The neuron then computes some function on these weighted inputs and gives the output.

Explain the commonly used Artificial Neural Networks.

Feedforward Neural Network

The simplest form of ANN, where the data or the input travels in one direction.
The data passes through the input nodes and exit on the output nodes. This neural network may or may not have the hidden layers.

Convolutional Neural Network

Here, input features are taken in batch wise like a filter. This will help the network to remember the images in parts and can compute the operations.
Mainly used for signal and image processing

Recurrent Neural Network(RNN. – Long Short Term Memory

Works on the principle of saving the output of a layer and feeding this back to the input to help in predicting the outcome of the layer.
Here, you let the neural network to work on the front propagation and remember what information it needs for later use
This way each neuron will remember some information it had in the previous time. Step.

Autoencoders

These are unsupervised learning models with an input layer, an output layer and one or more hidden layers connecting them.
The output layer has the same number of units as the input layer. Its purpose is to reconstruct its own inputs.
Typically for the purpose of dimensionality reduction and for learning generative models of data.

What are Bayesian Networks?

A Bayesian network is a statistical model that represents a set of variables and their conditional dependencies in the form of a directed acyclic graph.

On the occurrence of an event, Bayesian Networks can be used to predict the likelihood that any one of several possible known causes was the contributing factor.

For example, a Bayesian network could be used to study the relationship between diseases and symptoms. Given various symptoms, the Bayesian network is ideal for computing the probabilities of the presence of various diseases.

Explain the assessment that is used to test the intelligence of a machine.

In artificial intelligence (AI., a Turing Test is a method of inquiry for determining whether or not a computer is capable of thinking like a human being.

How does Reinforcement Learning work? Explain with an example.

Generally, a Reinforcement Learning (RL. system is comprised of two main components:

An agent
An environment

The environment is the setting that the agent is acting on and the agent represents the RL algorithm.
The RL process starts when the environment sends a state to the agent, which then based on its observations, takes an action in response to that state.
In turn, the environment sends the next state and the respective reward back to the agent. The agent will update its knowledge with the reward returned by the environment to evaluate its last action.
The loop continues until the environment sends a terminal state, which means the agent has accomplished all his tasks.

To understand this better, let’s suppose that our agent is learning to play counterstrike. The RL process can be broken down into the below steps:

The RL Agent (Player1. collects state S⁰ from the environment (Counterstrike game.
Based on the state S⁰, the RL agent takes an action A⁰, (Action can be anything that causes a result i.e. if the agent moves left or right in the game Initially, the action is random
The environment is now in a new state S¹ (new stage in the game.
The RL agent now gets a reward R¹ from the environment. This reward can be additional points or coins
This RL loop goes on until the RL agent is dead or reaches the destination, and it continuously outputs a sequence of state, action, and reward.

To learn more about Reinforcement Learning you can go through this video recorded by our Machine Learning experts.

Explain Markov’s decision process with an example.

The mathematical approach for mapping a solution in Reinforcement Learning is called Markov’s Decision Process (MDP

The following parameters are used to attain a solution using MDP:

Set of actions, A
Set of states, S
Reward, R
Policy, π
Value, V

To briefly sum it up, the agent must take an action (A. to transition from the start state to the end state (S While doing so, the agent receives rewards (R. for each action he takes. The series of actions taken by the agent, define the policy (π. and the rewards collected define the value (V The main goal here is to maximize rewards by choosing the optimum policy.

To better understand the MDP, let’s solve the Shortest Path Problem using the MDP approach:

Given the above representation, our goal here is to find the shortest path between ‘A’ and ‘D’. Each edge has a number linked with it, this denotes the cost to traverse that edge. Now, the task at hand is to traverse from point ‘A’ to ‘D’, with minimum possible cost.

In this problem,

The set of states are denoted by nodes i.e. {A, B, C, D}
The action is to traverse from one node to another {A .> B, C .> D}
The reward is the cost represented by each edge
The policy is the path taken to reach the destination

You start off at node A and take baby steps to your destination. Initially, only the next possible node is visible to you, thus you randomly start off and then learn as you traverse through the network. The main goal is to choose the path with the lowest cost.

Since this is a very simple problem, I will leave it for you to solve. Make sure you mention the answer in the comment section.

Explain reward maximization in Reinforcement Learning.

The RL agent works based on the theory of reward maximization. This is exactly why the RL agent must be trained in such a way that, he takes the best action so that the reward is maximum.

The collective rewards at a particular time with the respective action is written as:

The above equation is an ideal representation of rewards. Generally, things don’t work out like this while summing up the cumulative rewards.

Let me explain this with a small game. In the figure you can see a fox, some meat and a tiger.

Our RL agent is the fox and his end goal is to eat the maximum amount of meat before being eaten by the tiger.
Since this fox is a clever fellow, he eats the meat that is closer to him, rather than the meat which is close to the tiger, because the closer he is to the tiger, the higher are his chances of getting killed.
As a result, the rewards near the tiger, even if they are bigger meat chunks, will be discounted. This is done because of the uncertainty factor, that the tiger might kill the fox.

The next thing to understand is, how discounting of rewards work?
To do this, we define a discount rate called gamma. The value of gamma is between 0 and 1. The smaller the gamma, the larger the discount and vice versa.

So, our cumulative discounted rewards is:

What is exploitation and exploration trade.off?

An important concept in reinforcement learning is the exploration and exploitation trade.off.

Exploration, like the name suggests, is about exploring and capturing more information about an environment. On the other hand, exploitation is about using the already known exploited information to heighten the rewards.

Consider the fox and tiger example, where the fox eats only the meat (small. chunks close to him but he doesn’t eat the bigger meat chunks at the top, even though the bigger meat chunks would get him more rewards.
If the fox only focuses on the closest reward, he will never reach the big chunks of meat, this is called exploitation.
But if the fox decides to explore a bit, it can find the bigger reward i.e. the big chunk of meat. This is exploration.

What is the difference between parametric & non-parametric models?

What is the difference between Hyperparameters and model parameters?

What are hyperparameters in Deep Neural Networks?

Hyperparameters are variables that define the structure of the network. For example, variables such as the learning rate, define how the network is trained.
They are used to define the number of hidden layers that must be present in a network.
More hidden units can increase the accuracy of the network, whereas a lesser number of units may cause underfitting.

Explain the different algorithms used for hyperparameter optimization.

Grid Search
Grid search trains the network for every combination by using the two set of hyperparameters, learning rate and the number of layers. Then evaluates the model by using Cross Validation techniques.

Random Search
It randomly samples the search space and evaluates sets from a particular probability distribution. For example, instead of checking all 10,000 samples, randomly selected 100 parameters can be checked.

Bayesian Optimization
This includes fine-tuning the hyperparameters by enabling automated model tuning. The model used for approximating the objective function is called surrogate model (Gaussian Process Bayesian Optimization uses Gaussian Process (GP. function to get posterior functions to make predictions based on prior functions.

How does data overfitting occur and how can it be fixed?

Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data. This causes an algorithm to show low bias but high variance in the outcome.

Overfitting can be prevented by using the following methodologies:

Cross-validation: The idea behind cross-validation is to split the training data in order to generate multiple mini train-test splits. These splits can then be used to tune your model.

More training data: Feeding more data to the machine learning model can help in better analysis and classification. However, this does not always work.

Remove features: Many times, the data set contains irrelevant features or predictor variables that are not needed for analysis. Such features only increase the complexity of the model, thus leading to possibilities of data overfitting. Therefore, such redundant variables must be removed.

Early stopping: A machine learning model is trained iteratively, this allows us to check how well each iteration of the model performs. But after a certain number of iterations, the model’s performance starts to saturate. Further training will result in overfitting; thus, one must know where to stop the training. This can be achieved by a mechanism called early stopping.

Regularization: Regularization can be done in n number of ways, the method will depend on the type of learner you’re implementing. For example, pruning is performed on decision trees, the dropout technique is used on neural networks and parameter tuning can also be applied to solve overfitting issues.

Use Ensemble models: Ensemble learning is a technique that is used to create multiple Machine Learning models, which are then combined to produce more accurate results. This is one of the best ways to prevent overfitting. An example is Random Forest, it uses an ensemble of decision trees to make more accurate predictions and to avoid overfitting.

Mention a technique that helps to avoid overfitting in a neural network.

Dropout is a type of regularization technique used to avoid overfitting in a neural network. It is a technique where randomly selected neurons are dropped during training.

The Dropout value of a network must be chosen wisely. A value too low will result in a minimal effect and a value too high results in under-learning by the network.

What are intelligent agents?

An intelligent agent is an autonomous entity that leverages sensors to understand a situation and make decisions. It can also use actuators to perform both simple and complex tasks.

In the beginning, it might not be so great at performing a task, but it will improve over time. The Roomba vacuum cleaner is an excellent example of this.

What’s the most popular programming language used in AI?

The open-source modular programming language Python leads the AI industry because of its simplicity and predictable coding behavior.

Its popularity can be attributed to open-source libraries like Matplotlib and NumPy, efficient frameworks such as Scikit-learn, and practical version libraries like TensorFlow and VTK.

There’s a chance that the interviewer might keep the conversation going and ask you for more examples. If that happens, you can mention the following:

Java
Julia
Haskell
Lisp

What are AI neural networks?

Neural networks in AI mathematically model how the human brain works. This approach enables the machine to think and learn as humans do. This is how smart technology today recognizes speech, objects, and more.

What’s the difference between strong AI and weak AI?

The difference between the two is just like the terms sound. Strong AI can successfully imitate human intelligence and is at the core of advanced robotics.

Weak AI can only predict specific characteristics that resemble human intelligence. Alexa and Siri are excellent examples of weak AI.
Strong AI

Can be applied widely
Extensive scope
Human-level intelligence
Processes data by using clustering and association

Weak AI

Can be great at performing some simple tasks
Uses both supervised and unsupervised learning
The scope can be minimal

What’s the difference between AI and ML?

AI and ML are closely related, but these terms aren’t interchangeable. ML actually falls under the umbrella of AI. It demands that machines carry out tasks in the same way that humans do.

The current application of ML in AI is based around the idea that we should enable access to data so machines can observe and learn for themselves.

What is the purpose of Deep Learning frameworks such as Keras, TensorFlow, and PyTorch?

Keras is an open-source neural network library written in Python. It is designed to enable fast experimentation with deep neural networks.
TensorFlow is an open-source software library for dataflow programming. It is used for machine learning applications like neural networks.
PyTorch is an open source machine learning library for Python, based on Torch. It is used for applications such as natural language processing.

Differentiate between NLP and Text mining.

What are the different components of NLP?

Natural Language Understanding includes:

Mapping input to useful representations
Analyzing different aspects of the language

Natural Language Generation includes:

Text Planning
Sentence Planning
Text Realization

What is Stemming & Lemmatization in NLP?

Stemming algorithms work by cutting off the end or the beginning of the word, taking into account a list of common prefixes and suffixes that can be found in an inflected word. This indiscriminate cutting can be successful on some occasions, but not always.

Lemmatization, on the other hand, takes into consideration the morphological analysis of the words. To do so, it is necessary to have detailed dictionaries which the algorithm can look through to link the form back to its lemma.

Explain Fuzzy Logic architecture.

Fuzzification Module− The system inputs are fed into the Fuzzifier, which transforms the inputs into fuzzy sets.
Knowledge Base− It stores analytic measures such as IF.THEN rules provided by experts.
Inference Engine− It simulates the human reasoning process by making fuzzy inference on the inputs and IF.THEN rules.
Defuzzification Module− It transforms the fuzzy set obtained by the inference engine into a crisp value.

Explain the components of Expert Systems.

Knowledge BaseIt contains domain.specific and high quality knowledge.
Inference EngineIt acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution.
User InterfaceThe user interface provides interaction between the user and the Expert System itself.

How is Computer Vision and AI related?

Computer Vision is a field of Artificial Intelligence that is used to obtain information from images or multi-dimensional data. Machine Learning algorithms such as K-means is used for Image Segmentation, Support Vector Machine is used for Image Classification and so on.

Therefore, Computer Vision makes use of AI technologies to solve complex problems such as Object Detection, Image Processing, etc.

Which is better for image classification? Supervised or unsupervised classification? Justify.

In supervised classification, the images are manually fed and interpreted by the Machine Learning expert to create feature classes.
In unsupervised classification, the Machine Learning software creates feature classes based on image pixel values.

Therefore, it is better to choose supervised classification for image classification in terms of accuracy.

Finite difference filters in image processing are very susceptible to noise. To cope up with this, which method can you use so that there would be minimal distortions by noise?

Image Smoothing is one of the best methods used for reducing noise by forcing pixels to be more like their neighbors, this reduces any distortions caused by contrasts.

How is Game theory and AI related?

“In the context of artificial intelligence (AI. and deep learning systems, game theory is essential to enable some of the key capabilities required in multiagent environments in which different AI programs need to interact or compete in order to accomplish a goal.”

What is the Minimax Algorithm? Explain the terminologies involved in a Minimax problem.

Minimax is a recursive algorithm used to select an optimal move for a player assuming that the other player is also playing optimally.

A game can be defined as a search problem with the following components:

Game Tree: A tree structure containing all the possible moves.
Initial state: The initial position of the board and showing whose move it is.
Successor function: It defines the possible legal moves a player can make.
Terminal state: It is the position of the board when the game ends.
Utility function: It is a function which assigns a numeric value for the outcome of a game.

Show the working of the Minimax algorithm using Tic.Tac.Toe Game.

There are two players involved in a game:

MAX: This player tries to get the highest possible score
MIN: MIN tries to get the lowest possible score

The following approach is taken for a Tic.Tac.Toe game using the Minimax algorithm:

Step 35: First, generate the entire game tree starting with the current position of the game all the way up to the terminal states.

Step 2: Apply the utility function to get the utility values for all the terminal states.

Step 3: Determine the utilities of the higher nodes with the help of the utilities of the terminal nodes. For instance, in the diagram below, we have the utilities for the terminal states written in the squares.

Let us calculate the utility for the left node(red. of the layer above the terminal:

MIN{3, 5, 10}, i.e. 3.
Therefore, the utility for the red node is 3.

Similarly, for the green node in the same layer:
MIN{2,2}, i.e. 2.

Step 4: Calculate the utility values.

Step 5: Eventually, all the backed.up values reach to the root of the tree. At that point, MAX has to choose the highest value:
i.e. MAX{3,2} which is 3.

Therefore, the best opening move for MAX is the left node(or the red one
To summarize,

Minimax Decision = MAX{MIN{3,5,10},MIN{2,2}}
= MAX{3,2}
= 3

Which method is used for optimizing a Minimax based game?

Alpha-beta Pruning
If we apply alpha-beta pruning to a standard minimax algorithm, it returns the same move as the standard one, but it removes all the nodes that are possibly not affecting the final decision.

In this case,
Minimax Decision = MAX{MIN{3,5,10}, MIN{2,a,b}, MIN{2,7,3}}
= MAX{3,c,2}
= 3

Hint: (MIN{2,a,b} would certainly be less than or equal to 2, i.e., c<=2 and hence MAX{3,c,2} has to be 3..

Which algorithm does Facebook use for face verification and how does it work?

Facebook uses DeepFace for face verification. It works on the face verification algorithm, structured by Artificial Intelligence (AI. techniques using neural network models.

Here’s how face verification is done:

Input: Scan a wild form of photos with large complex data. This involves blurry images, images with high intensity and contrast.
Process: In modern face recognition, the process completes in 4 raw steps:

Detect facial features
Align and compare the features
Represent the key patterns by using 3D graphs
Classify the images based on similarity

Output: Final result is a face representation, which is derived from a 9.layer deep neural net
Training Data: More than 4 million facial images of more than 4000 people
Result: Facebook can detect whether the two images represent the same person or not

Explain the logic behind targeted marketing. How can Machine Learning help with this?

Target Marketing involves breaking a market into segments & concentrating it on a few key segments consisting of the customers whose needs and desires most closely match your product.

It is the key to attracting new business, increasing your sales, and growing the company.

The beauty of target marketing is that by aiming your marketing efforts at specific groups of consumers it makes the promotion, pricing, and distribution of your products and/or services easier and more cost-effective.

Machine Learning in targeted marketing:

Text Analytics Systems: The applications for text analytics ranges from search applications, text classification, named entity recognition, to pattern search and replace applications.
Clustering: With applications including customer segmentation, fast search, and visualization.
Classification: Like decision trees and neural network classifiers, which can be used for text classification in marketing.
Recommender Systems: And association rules which can be used to analyze your marketing data
Market Basket Analysis: Market basket analysis explains the combinations of products that frequently co-occur in transactions.

How can AI be used in detecting fraud?

Artificial Intelligence is used in Fraud detection problems by implementing Machine Learning algorithms for detecting anomalies and studying hidden patterns in data.

The following approach is followed for detecting fraudulent activities:

Data Extraction: At this stage data is either collected through a survey or web scraping is performed. If you’re trying to detect credit card fraud, then information about the customer is collected. This includes transactional, shopping, personal details, etc.

Data Cleaning: At this stage, the redundant data must be removed. Any inconsistencies or missing values may lead to wrongful predictions; therefore such inconsistencies must be dealt with at this step.

Data Exploration & Analysis: This is the most important step in AI. Here you study the relationship between various predictor variables. For example, if a person has spent an unusual sum of money on a particular day, the chances of a fraudulent occurrence are very high. Such patterns must be detected and understood at this stage.

Building a Machine Learning model: There are many machine learning algorithms that can be used for detecting fraud. One such example is Logistic Regression, which is a classification algorithm. It can be used to classify events into 2 classes, namely, fraudulent and non-fraudulent.

Model Evaluation: Here, you basically test the efficiency of the machine learning model. If there is any room for improvement, then parameter tuning is performed. This improves the accuracy of the model.

A bank manager is given a data set containing records of 1000s of applicants who have applied for a loan. How can AI help the manager understand which loans he can approve? Explain.

This problem statement can be solved using the KNN algorithm, that will classify the applicant’s loan request into two classes:

Approved
Disapproved

K Nearest Neighbour is a Supervised Learning algorithm that classifies a new data point into the target class, depending on the features of its neighboring data points.

The following steps can be carried out to predict whether a loan must be approved or not:

Data Extraction: At this stage data is either collected through a survey or web scraping is performed. Data about the customers must be collected. This includes their account balance, credit amount, age, occupation, loan records, etc. By using this data, we can predict whether or not to approve the loan of an applicant.

Data Cleaning: At this stage, the redundant variables must be removed. Some of these variables are not essential in predicting the loan of an applicant, for example, variables such as Telephone, Concurrent credits, etc. Such variables must be removed because they will only increase the complexity of the Machine Learning model.

Data Exploration & Analysis: This is the most important step in AI. Here you study the relationship between various predictor variables. For example, if a person has a history of unpaid loans, then the chances are that he might not get approval on his loan applicant. Such patterns must be detected and understood at this stage.

Building a Machine Learning model: There are n number of machine learning algorithms that can be used for predicting whether an applicant loan request is approved or not. One such example is the K-Nearest Neighbor, which is a classification and a regression algorithm. It will classify the applicant’s loan request into two classes, namely, Approved and Disapproved.

You’ve won a 2-million-dollar worth lottery’ we all get such spam messages. How can AI be used to detect and filter out such spam messages?

To understand spam detection, let’s take the example of Gmail. Gmail makes use of machine learning to filter out such spam messages from our inbox. These spam filters are used to classify emails into two classes, namely spam and non-spam emails.

Let’s understand how spam detection is done using machine learning:

A machine learning process always begins with data collection. We all know the data Google has, is not obviously in paper files. They have data centers which maintain the customer’s data. Data such as email content, header, sender, etc are stored.
This is followed by data cleaning. It is essential to get rid of unnecessary stop words and punctuations so that only the relevant data is used for creating a precise machine learning model. Therefore, in this stage stop words such as ‘the’, ‘and’, ‘a’ are removed. The text is formatted in such a way that it can be analyzed.
After data cleaning comes data exploration and analysis. Many a time, certain words or phrases are frequently used in spam emails. Words like “lottery”, “earn”, “full-refund” indicate that the email is more likely to be a spam one. Such words and correlations must be understood in this stage.
After retrieving useful insights from data, a machine learning model is built. For classifying emails as either spam or non-spam you can use machine learning algorithms like Logistic Regression, Naïve Bayes, etc. The machine learning model is built using the training dataset. This data is used to train the model and make it learn by using past user email data.
This stage is followed by model evaluation. In this phase, the model is tested using the testing data set, which is nothing but a new set of emails. After which the machine learning model is graded based on the accuracy with which it was able to classify the emails correctly.
Once the evaluation is over, any further improvement in the model can be achieved by tuning a few variables/parameters. This stage is also known as parameter tuning. Here, you basically try to improve the efficiency of the machine learning model by tweaking a few parameters that you used to build the model.
The last stage is deployment. Here the model is deployed to the end users, where it processes emails in real time and predicts whether the email is spam or non-spam.

Let’s say that you started an online shopping business and to grow your business, you want to forecast the sales for the upcoming months. How would you do this? Explain.

This can be done by studying the past data and building a model that shows how the sales have varied over a period of time. Sales Forecasting is one of the most common applications of AI. Linear Regression is one of the best Machine Learning algorithms used for forecasting sales.

When both sales and time have a linear relationship, it is best to use a simple linear regression model.

Linear Regression is a method to predict dependent variable (Y. based on values of independent variables (X It can be used for the cases where we want to predict some continuous quantity.

Dependent variable (Y.:
The response variable whose value needs to be predicted.
Independent variable (X.:
The predictor variable used to predict the response variable.

In this example, the dependent variable ‘Y’ represents the sales and the independent variable ‘X’ represents the time period. Since the sales vary over a period of time, sales is the dependent variable.

The following euation is used to represent a linear regression model:

Y=𝒃𝟎+𝒃𝟏 𝒙+ⅇ

Here,

Y = Dependent variable
𝒃𝟎 = Y.Intercept
𝒃𝟏 = Slope of the line
x = Independent variable
e = Error

Therefore, by using the Linear Regression model, wherein Y.axis represents the sales and X.axis denotes the time period, we can easily predict the sales for the upcoming months.

Customers who bought this also bought this…’ we often see this when we shop on Amazon. What is the logic behind recommendation engines?

E-commerce websites like Amazon make use of Machine Learning to recommend products to their customers. The basic idea of this kind of recommendation comes from collaborative filtering. Collaborative filtering is the process of comparing users with similar shopping behaviors in order to recommend products to a new user with similar shopping behavior.

To better understand this, let’s look at an example. Let’s say a user A who is a sports enthusiast bought, pizza, pasta, and a coke. Now a couple of weeks later, another user B who rides a bicycle buys pizza and pasta. He does not buy the coke, but Amazon recommends a bottle of coke to user B since his shopping behaviors and his lifestyle is quite similar to user A. This is how collaborative filtering works.

What is market basket analysis and how can Artificial Intelligence be used to perform this?

Market basket analysis explains the combinations of products that frequently occur in transactions.

For example, if a person buys bread, there is a 40% chance that he might also buy butter. By understanding such correlations between items, companies can grow their businesses by giving relevant offers and discount codes on such items.

Market Basket Analysis is a well-known practice that is followed by almost every huge retailer in the market. The logic behind this is Machine Learning algorithms such as Association Rule Mining and Apriori algorithm:

Association rule mining is a technique that shows how items are associated with each other.
Apriori algorithm uses frequent itemsets to generate association rules. It is based on the concept that a subset of a frequent itemset must also be a frequent itemset.

For example, the above rule suggests that, if a person buys item A then he will also buy item B. In this manner the retailer can give a discount offer which states that on purchasing Item A and B, there will be a 30% off on item C. Such rules are generated using Machine Learning. These are then applied on items in order to increase sales and grow a business.

Place an agent in any one of the rooms (0,1,2,3,4. and the goal is to reach outside the building (room 5 Can this be achieved through AI? If yes, explain how it can be done.

In the above figure:

5 rooms in a building connected by doors
Each room is numbered 0 through 4
The outside of the building can be thought of as one big room (5.
Doors 1 and 4 directly lead into the building from room 5 (outside.

This problem can be solved by using the .Learning algorithm, which is a reinforcement learning algorithm used to solve reward based problems.

Let’s represent the rooms on a graph, each room as a node, and each door as a link, like so:

Next step is to associate a reward value to each door:

doors that lead directly to the goal have a reward of 100
Doors not directly connected to the target room have zero reward
Because doors are two-way, two arrows are assigned to each room
Each arrow contains an instant reward value

Now let’s try to understand how learning can be used to solve this problem. The terminology in learning includes the terms state and action:

The room (including room 5. represents a state
Agent’s movement from one room to another represents an action

In the figure, a state is depicted as a node, while “action” is represented by the arrows. Suppose, the Agent traverses from room 2 to room5, then the following path is taken:

Initial state = state 2
State 2 .> state 3
State 3 .> state (2, 1, 4.
State 4 .> state 5

Next, we can put the state diagram and the instant reward values into a reward table or a matrix R, like so:

The next step is to add another matrix , representing the memory of what the agent has learned through experience.

The rows of matrix represent the current state of the agent
columns represent the possible actions leading to the next state

The formula to calculate the matrix:

(state, action. = R(state, action. + Gamma * Max [(next state, all actions.]

Here, (state, action. and R(state, action. represent the state and action in the Reward matrix R and the Memory matrix

Note: The Gamma parameter has a range of 0 to 1 (0 <= Gamma > 1

If Gamma is closer to zero, the agent will tend to consider only immediate rewards.
If Gamma is closer to one, the agent will consider future rewards with greater weight

Finally, by following the below steps, the agent will reach room 5 by taking the most optimal path:

The crop yield in India is degrading because farmers are unable to detect diseases in crops during the early stages. Can AI be used for disease detection in crops? If yes, explain.

AI can be used to implement image processing and classification techniques for extraction and classification of leaf diseases.

This sounds complex, let me break it down into steps:

Image Acquisition : The sample images are collected and stored as an input database.

Image Preprocessing: Image preprocessing includes the following:

Improve image data that suppresses unwanted distortion
Enhance image features
Image clipping, enhancement, color space conversion
Perform Histogram equalization to adjust the contrast of an image

Image Segmentation: It is the process of partitioning a digital image into multiple segments so that image analysis becomes easier. Segmentation is based on image features such as color, texture. A popular Machine Learning method used for segmentation is the K-Means clustering algorithm.

Feature Extraction: This is done to extract information that can be used to find the significance of a given sample. The Haar Wavelet transform can be used for texture analysis and the computations can be done by using Gray-Level Co-Occurrence Matrix.

Classification: Finally, Linear Support Vector Machine is used for classification of leaf disease. SVM is a binary classifier which uses a hyperplane called the decision boundary between two classes. This results in the formation of two classes:

Diseased leaves
Healthy leaves

Therefore, AI can be used in Computer Vision to classify and detect disease by studying and processing images. This is one of the most profound applications of AI.

What is artificial intelligence?

AI can be described as an area of computer science that simulates human intelligence in machines. It’s about smart algorithms making decisions based on the available data.

Whether it’s Amazon’s Alexa or a self-driving car, the goal is to mimic human intelligence at lightning speed (and with a reduced rate of error

How would you describe ML to a non-technical person?

ML is geared toward pattern recognition. A great example of this is your Facebook newsfeed and Netflix’s recommendation engine.

In this scenario, ML algorithms observe patterns and learn from them. When you deploy an ML program, it will keep learning and improving with each attempt.

If the interviewer prods you to provide more real-world examples, you can list the following:

Amazon product recommendations
Fraud detection
Search ranking
Spam detection
Spell correction

What are some examples of AI in use?

Some compelling examples of AI applications are:

Chatbots
Facial recognition
Image tagging
Natural language processing
Sales prediction
Self-driving cars
Sentiment analysis

What’s a Turing test?

The Turing test, named after Alan Turing, is a method of testing a machine’s human-level intelligence. For example, in a human-versus-machine scenario, a judge will be tasked with identifying which terminal was occupied by a human and which was occupied by a computer based on individual performance.

Whenever a computer can pass off as a human, it’s deemed intelligent. The game has since evolved, but the premise remains the same.

What’s TensorFlow?

TensorFlow is an open-source framework dedicated to ML. It’s a comprehensive and highly adaptable ecosystem of libraries, tools, and community resources that help developers build and deploy ML powered applications. Both AlphaGo and Google Cloud Vision were built on the Tensorflow platform.

Why is game theory important to AI?

Game theory, developed by American mathematician Josh Nash, is essential to AI because it plays an underlying role in how these smart algorithms improve over time.

At its most basic, AI is about algorithms that are deployed to find solutions to problems. Game theory is about players in opposition trying to achieve specific goals. As most aspects of life are about competition, game theory has many meaningful real-world applications.

These problems tend to be dynamic. Some game theory problems are natural candidates for AI algorithms. So, whenever game theory is applied, multiple AI agents that interact with each other will only care about utility to itself.

Data scientists within this space should be aware of the following games:

Symmetric vs. asymmetric
Perfect vs. imperfect information
Cooperative vs. non-cooperative
Simultaneous vs. sequential
Zero-sum vs. non-zero sum

In your opinion, how will AI impact application development?

These types of questions help the interviewer ascertain your level of interest in the field. If you’re naturally passionate about AI and everything related to it, you should have some knowledge about current industry trends.

So, if you have been actively following this space, you’ll know all about AIOps. In the coming months, you can expect AI to be more involved in how we build applications. It has the potential to transform how we use and manage the infrastructure at a micro and macro level.

Some say that DevOps will be replaced by what they are calling AIOps because it allows developers to engage in accurate root cause analysis by combining big data, ML, and visualization.

AIOps can be described as a multilayered platform that can be used to automate and improve IT operations. In this scenario, developers can leverage analytics and ML to collect and process data from a variety of sources. This information can then be analyzed in real time to identify and rectify problems.

What would you say are common misconceptions about AI?

Many AI related misconceptions are making the rounds in the age of “fake news.” The most common ones are:

AI will replace humans
AI systems aren’t safe
AI will lead to significant unemployment

While these types of stories are common, they’re far from the truth. Even though some AI based technology is able to complete some tasks—for example, analyzing zettabytes of data in less than a second—it still needs humans to gather the data and define the patterns for identification.

So we aren’t near a reality where technology has the potential to replace us or our jobs.

Can you name the properties of a good knowledge representation system?

From the perspective of systems theory, a good knowledge representation system will have the following:

Acquisition efficiency to acquire and incorporate new data
Inferential adequacy to derive knowledge representation structures like symbols when new knowledge is learned from old knowledge
Inferential efficiency to enable the addition of data into existing knowledge structures to help the inference process
Representation adequacy to represent all the knowledge required in a specific domain

What are the different types of keys in a relational database?

There are a variety of keys in a relational database, including:

Alternate keys are candidate keys that exclude all primary keys.
Artificial keys are created by assigning a unique number to each occurrence or record when there aren’t any compound or standalone keys.
Compound keys are made by combining multiple elements to develop a unique identifier for a construct when there isn’t a single data element that uniquely identifies occurrences within a construct. Also known as a composite key or a concatenated key, compound keys consist of two or more attributes.
Foreign keys are groups of fields in a database record that point to a key field or a group of fields that create a key of another database record that’s usually in a different table. Often, foreign keys in one table refer to primary keys in another. As the referenced data can be linked together quite quickly, it can be critical to database normalization.
Natural keys are data elements that are stored within constructs and utilized as primary keys.
Primary keys are values that can be used to identify unique rows in a table and the attributes associated with them. For example, these can take the form of a Social Security number that’s related to a specific person. In a relational model of data, the primary key is the candidate key. It’s also the primary method used to identify a tuple in each possible relation.
Super keys are defined in the relational model as a set of attributes of a relation variable. It holds that all relations assigned to that variable don’t have any distinct tuples. They also don’t have the same values for the attributes in the set. Super keys also are defined as a set of attributes of a relational variable upon which all of the functionality depends.

AI, ML, and data science all have a great deal of overlap, so it’s crucial to cover all bases before your AI interview. However, it’s important to note that these fields aren’t interchangeable. Although everything is relative, AI produces actions, ML produces predictions, and data science produces insights.

So what kind of potential data science related AI questions should you be prepared for? Let’s take a look.

In Python’s standard library, what packages would you say are the most useful for data scientists?

Python wasn’t built for data science. However, in recent years it has grown to become the go.to programming language for the following:

Machine learning
Predictive analytics
Simple data analytics
Statistics

For data science projects, the following packages in the Python standard library will make life easier and accelerate deliveries:

NumPy (to process large multidimensional arrays, extensive collections of high-level mathematical functions, and matrices.
Pandas (to leverage built.in methods for rapidly combining, filtering, and grouping data.
SciPy (to extend NumPy’s capabilities and solve tasks related to integral calculus, linear algebra, and probability theory.

What is collaborative filtering?

Collaborative filtering can be described as a process of finding patterns from available information to build personalized recommendations. You can find collaborative filtering in action when you visit websites like Amazon and IMDB.

Also known as social filtering, this approach essentially makes suggestions based on the recommendations and preferences of other people who share similar interests.

Can you list some disadvantages related to linear models?

There are many disadvantages to using linear models, but the main ones are:

Errors in linearity assumptions
Lacks autocorrelation
It can’t solve overfitting problems
You can’t use it to calculate outcomes or binary outcomes

What’s a feature vector?

A feature vector is an n-dimensional vector that contains essential information that describes the characteristics of an object. For example, it can be an object’s numerical features or a list of numbers taken from the output of a neural network layer.

In AI and data science, feature vectors can be used to represent numeric or symbolic characteristics of an object in mathematical terms for seamless analysis.

Let’s break this down. A data set is usually organized into multiple examples where each example will have several features. However, a feature vector won’t have the same feature for numerous examples. Instead, each example will correspond to one feature vector that will contain all the numerical values for that example object.

Feature vectors are often stacked into a design matrix. In this scenario, each row will be a feature vector for one example. Each column will feature all the examples that correspond to that particular feature. This means that it will be like a matrix, but with just one row and multiple columns (or a single column and multiple rows. like [1,2,3,5,6,3,2,0].

What’s selection bias? What other types of biases could you encounter during sampling?

When you’re dealing with a non-random sample, selection bias will occur due to flaws in the selection process. This happens when a subset of the data is consistently excluded because of a particular attribute. This exclusion will distort results and influence the statistical significance of the test.

Other types of biases include survivorship bias and under coverage bias. It’s important to always consider and reduce such biases because you’ll want your smart algorithms to make accurate predictions based on the data.

What’s a random forest? Could you explain its role in AI?

A random forest is a data construct that’s applied to ML projects to develop a large number of random decision trees while analyzing variables.

These algorithms can be leveraged to improve the way technologies analyze complex data sets. The basic premise here is that multiple weak learners can be combined to build one strong learner.

This is an excellent tool for AI and ML projects because it can work with large labeled and unlabeled data sets with a large number of attributes. It can also maintain accuracy when some data is missing. As it can model the importance of attributes, it can be used for dimensionality reduction.

What’s an eigenvalue? What about an eigenvector?

The directions along which a particular linear transformation compresses, flips, or stretches is called eigenvalue. Eigenvectors are used to understand these linear transformations.

For example, to make better sense of the covariance of the covariance matrix, the eigenvector will help identify the direction in which the covariances are going. The eigenvalues will express the importance of each feature.

Eigenvalues and eigenvectors are both critical to computer vision and ML applications. The most popular of these is known as principal component analysis for dimensionality reduction (e.g., eigenfaces for face recognition

Would you use batch normalization? If so, can you explain why?

The idea here is to standardize the data before sending it to another layer. This approach helps reduce the impact of previous layers by keeping the mean and variance constant. It also makes the layers independent of each other to achieve rapid convergence. For example, when we normalize features from 0 to 1 or from 1 to 100, it helps accelerate the learning cycle.

What’s a hash table?

There are two parts to a hash table. The first is an array, or the actual table where the data is stored, and the other is a mapping function that’s known as the hash function.

It’s a data structure that implements an associative array abstract data type that can map key values. It can also compute an index into an array of slots or buckets where the desired value can be found.

What are the different algorithm techniques you can use in AI and ML?

Some algorithm techniques that can be leveraged are:

Learning to learn
Reinforcement learning (deep adversarial networks, learning, and temporal difference.
Semi-supervised learning
Supervised learning (decision trees, linear regression, naive bayes, nearest neighbor, neural networks, and support vector machines.
Transduction
Unsupervised learning (association rules and k-means clustering.

How would you go about choosing an algorithm to solve a business problem?

First, you have to develop a “problem statement” that’s based on the problem provided by the business. This step is essential because it’ll help ensure that you fully understand the type of problem and the input and the output of the problem you want to solve.

The problem statement should be simple and no more than a single sentence. For example, let’s consider enterprise spam that requires an algorithm to identify it.

The problem statement would be: “Is the email fake/spam or not?”
In this scenario, the identification of whether it’s fake/spam will be the output.

Once you have defined the problem statement, you have to identify the appropriate algorithm from the following:

Any classification algorithm
Any clustering algorithm
Any regression algorithm
Any recommendation algorithm

Which algorithm you use will depend on the specific problem you’re trying to solve. In this scenario, you can move forward with a clustering algorithm and choose a k-means algorithm to achieve your goal of filtering spam from the email system.

While examples aren’t always necessary when answering questions about artificial intelligence, sometimes it will help make it easier for you to get your point across.

When is it necessary to update an algorithm?

You should update an algorithm when the underlying data source has been changed or whenever there’s a case of nonstationary. The algorithm should also be updated when you want the model to evolve as data streams through the infrastructure.

What’s regularization?

When you have underfitting or overfitting issues in a statistical model, you can use the regularization technique to resolve it. Regularization techniques like LASSO help penalize some model parameters if they are likely to lead to overfitting.

If the interviewer follows up with a question about other methods that can be used to avoid overfitting, you can mention cross-validation techniques such as k-folds cross validation.

Another approach is to keep the model simple by taking into account fewer variables and parameters. Doing this helps remove some of the noise in the training data.

What’s the difference between inductive, deductive, and abductive learning?

Inductive learning describes smart algorithms that learn from a set of instances to draw conclusions. In statistical ML, k-nearest neighbor and support vector machine are good examples of inductive learning.

There are three literals in (top-down. inductive learning:

Arithmetic literals
Equality and inequality
Predicates

In deductive learning , the smart algorithms draw conclusions by following a truth-generating structure (major premise, minor premise, and conclusion. and then improve them based on previous decisions. In this scenario, the ML algorithm engages in deductive reasoning using a decision tree.
Abductive learning is a DL technique where conclusions are made based on various instances. With this approach, inductive reasoning is applied to causal relationships in deep neural networks.

What steps would you take to evaluate the effectiveness of your ML model?

You have to first split the data set into training and test sets. You also have the option of using a cross-validation technique to further segment the data set into a composite of training and test sets within the data.

Then you have to implement a choice selection of the performance metrics like the following:

Confusion matrix
Accuracy
Precision
Recall or sensitivity
Specificity
F1 score

For the most part, you can use measures such as accuracy, confusion matrix, or F1 score. However, it’ll be critical for you to demonstrate that you understand the nuances of how each model can be measured by choosing the right performance measure to match the problem.

What would you do if data in a data set were missing or corrupted?

Whenever data is missing or corrupted, you either replace it with another value or drop those rows and columns altogether. In Pandas, both isNull(. and dropNA(. are handy tools to find missing or corrupted data and drop those values. You can also use the fillna(. method to fill the invalid values in a placeholder—for example, “0.”

Do you know how to build a simple neural network? Can you demonstrate how you would do it with Python code?

These types of questions are designed to ascertain your programming skills. If you’re a coding ninja, you should be able to achieve this with nine lines of Python code.

Can you write a Python program to draw the flower shown below?

Again, the interviewer is trying to test your coding skills. It’s always good to perceive these types of questions as an opportunity to show a potential employer what you can do. So, demonstrate your programming skills without hesitation and with confidence.

The tech talent shortage has created fierce demand for your skills, but to land your “dream job” it’ll help if you can demonstrate your passion for the field. Whether your next scheduled AI interview is with a startup or an established tech giant, be ready for wide-ranging questions like the ones listed below.

Do you have research experience in AI?

At present, a lot of work within the AI space is research-based. As a result, many organizations will be digging into your background to ascertain what kind of experience you have in this area. If you authored or co-authored research papers or have been supervised by industry leaders, make sure to share that information.

In fact, take it a step further and have a summary of your research experience along with your research papers ready to share with the interviewing panel.

However, if you don’t have any formal research experience, have an explanation ready. For example, you can talk about how your AI journey started as a weekend hobby and grew into so much more within a space of two or three years.

What’s the last AI related research paper you read? What were your conclusions?

If you’re passionate about AI, you have to keep up with scientific research within the field. An excellent place to start is by following ScienceDirect to keep track of published research papers along with what’s in the pipeline.

What’s your favorite use case?

Just like research, you should be up to date on what’s going on in the industry. As such, if you’re asked about use cases, make sure that you have a few examples in mind that you can share. Whenever possible, bring up your personal experiences.

You can also share what’s happening in the industry. For example, if you’re interested in the use of AI in medical images, Health IT Analytics has some interesting use cases:

Detecting Fractures and Other Musculoskeletal Injuries
Aiding In The Diagnosis Neurological Diseases
Flagging Thoracic Complications and Conditions
Screening for Common Cancers

What conferences are you hoping to attend this year? Any keynote speeches you’re hoping to catch?

Conferences are great places to network, attend workshops, learn, and grow. So if you’re planning to stick to a career in artificial intelligence, you should be going to some of these. For example, Deep Learning World has a great one every summer.

This year’s event in Las Vegas will feature keynote speakers like Dr. Dyann Daley (founder and CEO of Predict Align Prevent., Siddha Ganju (solutions architect at Nvidia., and Dr. Alex Glushkovsky (principal data scientist at BMO Financial Group, and others

How is Google training data for self-driving cars?

If you’re interested and heavily involved within this space, this question should be a no-brainer. If you know the answer, it’ll demonstrate your knowledge about a variety of ML methods and how ML is applied to autonomous vehicles. But even if you don’t know the answer, take a stab at it as it will show your creativity and inventive nature.

Google has been using reCAPTCHA to source labeled data on storefronts and traffic signs for many years now. The company also has been using training data collected by Sebastian Thrun, CEO of the Kitty Hawk Corporation and the co-founder (and former CEO. of Udacity.

Such information, although it might not seem significant, will show a potential employer that you’re interested and excited about this field.

Where do you usually source your data sets?

If you talk about AI projects that you’ve worked on in your free time, the interviewer will probably ask where you sourced your data sets. If you’re genuinely passionate about the field, you would have worked on enough projects to know where you can find free data sets.

For example, here are some freely available public data sets that you should know about (without conducting a Google search.:

CelebFaces(with 200,000 celebrity images along with 40 attribute annotations.
CIFAR (with 60,000 images that map 10 different classes.
YouTube.8M(with over 4,000 annotated entities taken from an enormous data set of YouTube videos.

Researchers have released hundreds of free resources like these along with the actual network architecture and weights used in their examples. So it will serve you well to explore some of these data sets and run some experiments before heading out for an AI interview.

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.

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.

What contributes to Artificial Intelligence?

Basically, artificial intelligence relates to following disciplines such as –

Computer Science
Biology
Psychology
Linguistics
Mathematics and
Engineering

Name types of Artificial Intelligence?

Strong artificial intelligence
Weak artificial intelligence

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.

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.

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.

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.
b. Gaming
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.

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.

Read about Robotic and Artificial Intelligence

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.
No Replicating Humans As intelligence is believed to be a gift of nature. An ethical argument continues, whether human intelligence is to be replicated or not.
Lesser Jobs As we are aware that machines do routine and repeatable tasks much better than humans. Moreover, we use machines instead of humans. As to increase their profitability in businesses.
Lack of Personal Connections We can’t rely too much on these machines for educational oversights. That hurt learners more than help.

Explain artificial intelligence examples and applications?

a. Virtual Personal Assistants
Basically, it is processed in which we have to collect a huge amount 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 this 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 are 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 as 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 as 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.

What are Educational Requirements for Career in Artificial Intelligence?

Various level of math, including probability, statistics, algebra, calculus, logic, and algorithms.
Bayesian networking or graphical modeling, including neural nets.
Physics, engineering, and robotics.
Computer science, programming languages, and coding.
Cognitive science theory.

What are artificial intelligence career domains?

A career in this can be realized within a variety of settings including: private companies public organizations education the arts healthcare facilities government agencies and the military.

What are roles in AI career?

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.

What is future of Artificial intelligence?

Artificial Intelligence is used by one another after the company for its benefits. Also, it’s fact that artificial intelligence is reached in our day-to-day life. Moreover, with a breakneck speed. On the basis of this information, arises a new question: Is it possible that artificial Intelligence outperforms human performance? If yes, then is it happens and how much does it take? Only when Artificial Intelligence is able to do a job better than humans.

What is AI according to the survey results?

Machines are predicted to be better than humans in translating languages;

Working in the retail sector, and can completely outperform humans by 2060.

As a result, MI researchers believed that AI will become better than humans in the next 40 year time frame.

To build AI smarter, companies have already acquired around 34 AI startups. These companies are reinforcing their leads in the world of Artificial Intelligence.

In every sphere of life, AI is present. We use AI to organize big data into different patterns and structures. Also, patterns help in a neural network, machine learning, and data analytics.

From 80’s to now, Artificial intelligence is now part of our everyday lives, it’s very hard to believe. Moreover, it is becoming more intelligent and accepted every day. Also, with lots of opportunities for business.

What are steps to ensure the business stays relevant to the AI revolution?

A finger on the pulse Maybe the time is going on it’s not right for your business to harness the value of AI. Although, doesn’t mean you should stop keeping up like others are using AI. Only reading IT journal trade is a good place to start. Rather start focusing on how businesses are leveraging AI.
Piggyback on the innovators to implement AI, there are so many resources present from an industry that will help you. For example, Google has developed a machine learning system, TensorFlow. That has released as open-source software.
Brainstorm potential uses with your team If you want, a team must be engaged in encouraging in the areas of business, AI could be deployed. Data-heavy, inefficient are processes that are likely benefit. Moreover, find where these exist.
Start small and focus on creating real value It’s not mandatory to move forward for the sake only. Rather, it’s necessary to focus on objectives and start finding the best solution for it. Moreover, mean finding the specific process to run AI pilot. Also, see how it goes, learn and build from there.
Prepare the ground Before, to maximize the value of AI, its good to ensure your current process. I.e working in a best possible way.

What are jobs in artificial intelligence?

Computational philosopher: To ensure human-aligned ethics are embedded in AI algorithms
Robot personality designer
Robot obedience trainer
Autonomous vehicle infrastructure designer: New road and traffic signs to be read by computer
Algorithm trainers include the growing army of so-called “click workers”. That, help algorithms learn to recognize images or analyze sentiment, for instance.

Give some best books to A.I?

a. Artificial Intelligence (3rd edition. –
Patrick Henry Winston has written this book. As it is an introduction to AI. Also, this book is best for the non-programmers. As they can easily understand the explanations and concepts. Moreover, advanced AI topics are covered but haven’t been explained in depth. Further, it teaches to build intelligent systems using various real-life examples.
b. Artificial Intelligence: A Modern Approach –
If you have opted a course from Norvig to understand his style of teaching. Hence, you will long for it! Stuart Russell and Peter Norvig have written this book. This is the best book for newcomers for A.I. Also, covers subjects from search algorithm, working with logic to more advanced topics. Moreover, make this book your first choice for A.I.
c. Artificial Intelligence For Humans.
Jeff Heaton has written this book. This book will help to understand the basic artificial intelligence algorithms. Such as dimensionality, distance metrics, clustering, and linear regression. Interesting examples and cases were used to explain these algorithms. Although, to understand this book you need a good command of math. Otherwise, you’ll require more time to learn the equations.
d. Paradigm of Artificial Intelligence Programming.
Another one by Peter Norvig! This book will help you to understand the advanced common lisp techniques to build major A.I systems. It is all about practical aspects. Also, it teaches readers the method to build and debug robust practical programs. Moreover, It gives better understanding superior programming style and essential AI concepts. Further, if you are serious about a career, this book is best for you.

What is Single Agent Pathfinding Problems?

There are different types of games. Such as 3X3 eight.tile, 4X4 fifteen.tilepuzzles 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.

Name search algorithm technology?

Problem Space Basically, it is the environment in which the search takes place. (A set of states and set of operators to change those states.
Problem Instance It is a result of Initial state + Goal state.
Problem Space Graph We use it to represent problem state. Also, we use nodes to show states.
The depth of a problem We can define a length of the shortest path.

What Brute.Force Search Strategies?

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

State description
A set of valid operators
Initial state
Goal state description

What is Breadth-first Search Algorithm?

Basically, we have to start searching for the root node. And continue through neighboring nodes first. Further, moves towards next level of nodes. Moreover, till the solution is found, generates one tree at a time. As this search can be implemented using FIFO ueue 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 worst case is b + b2 + b3 + … + bd.

What is Depth-first Search Algorithm?

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

What is Bidirectional Search Algorithm?

Basically, starts searches forward from an initial state and backward from goal state. As till both meets to identify a common state. Moreover, initial state path is concatenated with the goal state inverse path. Each search is done only up to half of the total path.

What is the Uniform Cost Search Algorithm?

Basically, it performs sorting in increasing the cost of the path to a node. Also, always expands the least cost node. Although, it is identical to Breadth-First search if each transition has the same cost. It explores paths in the increasing order of cost.

What is Iterative Deepening Depth-First Search Algorithm?

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 till we find the solution. We have to generate nodes till single nodes are created. Also, it saves only 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.

What are disadvantages Uniform Cost Search Algorithm?

There can be multiple long paths with the cost ≤ C*.

Uniform Cost search must explore them all.

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.

What is an artificial intelligence Neural Networks?

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.

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.

Which is not commonly used programming language for AI?

Perl language is not commonly used programming language for AI

What is Prolog in AI?

In AI, Prolog is a programming language based on logic.

Give an explanation on the difference between strong AI and weak AI?

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.

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.

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.

What does a production rule consist of?

The production rule comprises of a set of rule and a sequence of steps.

Which search method takes less memory?

The “depth first search” method takes less memory.

Which is the best way to go for Game playing problem?

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.

A* algorithm is based on which search method?

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.

What does a hybrid Bayesian network contain?

A hybrid Bayesian network contains both a discrete and continuous variables.

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.

What does Partial order or planning involve?

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.

What are the two different kinds of steps that we can take in constructing a plan?

Add an operator (action.
Add an ordering constraint between operators

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.

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.

When an algorithm is considered completed?

An algorithm is said completed when it terminates with a solution when one exists.

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.

What is the function of the third component of the planning system?

In a planning system, the function of the third component is to detect when a solution to problem has been found.

What is “Generality” in AI ?

Generality is the measure of ease with which the method can be adapted to different domains of application.

What is a top-down parser?

A top.-own parser begins by hypothesizing a sentence and successively predicting lower level constituents until individual pre-terminal symbols are written.

Mention the difference between breadth first search and best first search in artificial intelligence?

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.

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.

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

What does the language of FOPL consists of

A set of constant symbols
A set of variables
A set of predicate symbols
A set of function symbols
The logical connective
The Universal quantifier and Existential qualifier
A special binary relation of equality

For online search in ‘Artificial Intelligence’ which search agent operates by interleaving computation and action?

In online search, it will first take action and then observes the environment.

Which search algorithm will use a limited amount of memory in online search?

RBFE and SMA* will solve any kind of problem that A* can’t by using a limited amount of memory.

In ‘Artificial Intelligence’ where you can use the Bayes rule?

In Artificial Intelligence to answer the probabilistic queries conditioned on one piece of evidence, Bayes rule can be used.

For building a Bayes model how many terms are required?

For building a Bayes model in AI, three terms are required; they are one conditional probability and two unconditional probability.

While creating Bayesian Network what is the consequence between a node and its predecessors?

While creating Bayesian Network, the consequence between a node and its predecessors is that a node can be conditionally independent of its predecessors.

To answer any query how the Bayesian network can be used?

If a Bayesian Network is a representative of the joint distribution, then by summing all the relevant joint entries, it can solve any query.

What combines inductive methods with the power of first order representations?

Inductive logic programming combines inductive methods with the power of first order representations.

In Inductive Logic Programming what needed to be satisfied?

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.

In top-down inductive learning methods how many literals are available? What are they?

There are three literals available in top-down inductive learning methods they are

Predicates
Equality and Inequality
Arithmetic Literals

Which algorithm inverts a complete resolution strategy?

‘Inverse Resolution’ inverts a complete resolution, as it is a complete algorithm for learning first order theories.

In speech recognition what kind of signal is used?

In speech recognition, Acoustic signal is used to identify a sequence of words.

In speech recognition which model gives the probability of each word following each word?

Biagram model gives the probability of each word following each other word in speech recognition.

Which algorithm is used for solving temporal probabilistic reasoning?

To solve temporal probabilistic reasoning, HMM (Hidden Markov Model. is used, independent of transition and sensor model.

What is Hidden Markov Model (HMMs. is used?

Hidden Markov Models are a ubiquitous tool for modelling time series data or to model sequence behavior. They are used in almost all current speech recognition systems.

In Hidden Markov Model, how does the state of the process is described?

The state of the process in HMM’s model is described by a ‘Single Discrete Random Variable’.

In HMM’s, what are the possible values of the variable?

‘Possible States of the World’ is the possible values of the variable in HMM’s.

In HMM, where does the additional variable is added?

While staying within the HMM network, the additional state variables can be added to a temporal model.

In Artificial Intelligence, what do semantic analyses used for?

In Artificial Intelligence, to extract the meaning from the group of sentences semantic analysis is used.

What are some of the algorithms used for hyperparameter optimization?

There are many algorithms that are used for hyperparameter optimization, and following are the three main ones that are widely used:

Bayesian optimization
Grid search
Random search

What is overfitting? How is overfitting fixed?

Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.

Overfitting can be prevented by using the below-mentioned methods:

Early stopping
Ensemble models
Cross-validation
Feature removal
Regularization

How is overfitting avoided in neural networks?

Overfitting is avoided in neural nets by making use of a regularization technique called ‘dropout.’

By making use of the concept of dropouts, random neurons are dropped when the neural network is being trained to use the model doesn’t overfit. If the dropout value is too low, it will have a minimal effect. If it is too high, the model will have difficulty in learning.

What are the typical characteristics of elements in a list and a dictionary?

In lists, elements maintain their order unless they are explicitly commanded to re-order. These can be made up of any data type that can be all the same or mixed. However, elements in lists can only be accessed via numeric, zero-based indices.

In a dictionary, the order isn’t guaranteed. However, each entry will be assigned a key and a value. As a result, elements within a dictionary can be accessed by using their individual key.

So whenever you have a set of unique keys, you have to use a dictionary. Whenever a collection of items are in order, you can use a list.

It’s difficult to predict how an AI interview will unfold, so if they follow up by asking you how to get a list of all the keys in a dictionary, respond with the following:

To obtain a list of keys in a dictionary, you’ll have to use the following function keys (.:

mydict={‘a’:1,’b’:2,’c’:3,’e’:5}
mydict.keys(.
dict_keys([‘a’, ‘b’, ‘c’, ‘e’].

What is meant by compositional semantics?

The process of determining the meaning of P* from P, and* is known as Compositional Semantics.

How logical inference can be solved in Propositional Logic?

In Propositional Logic, Logical Inference algorithm can be solved by using

Logical Equivalence
Validity
Satisfying ability

Which process makes different logical expression looks identical?

‘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.

Which algorithm in ‘Unification and Lifting’ takes two sentences and returns a unifier?

In ‘Unification and Lifting’ the algorithm that takes two sentences and returns a unifier is ‘Unify’ algorithm.

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.

What do you understand about Artificial Intelligence?

In Artificial Intelligence, you study the cognitive functions of the human brain and try to replicate them on a system (or machine It’s a branch of computer science and has applications in many industries and areas. You can also say that Artificial Intelligence focuses on creating intelligent machines that perform functions like humans.

Name some areas which can benefit from Artificial Intelligence (AI

Artificial Intelligence is useful in many industries. Computer science, software, robotics, speech recognition, aeronautics, game development, bioinformatics, and many other areas are using Artificial Intelligence to make.

Can you distinguish between Weak AI and Strong AI?

There are many differences between Weak Artificial Intelligence and Strong Artificial Intelligence. Weak AI has minimal application and doesn’t have much scope in comparison to strong AI. The former is useful for performing specific tasks, but the latter can match human-like Intelligence.

Another difference between the two is the method of processing data. Weak AI uses unsupervised and supervised learning for processing data. On the other hand, strong AI uses association and clustering for this purpose.

What programming languages do you use in AI?

The prominent programming languages we use in AI are Lisp, Python, R, Java, and Prolog.

What do you mean by an expert system? What are its qualities?

An expert system is an AI-based programthat has a lot of knowledge (expert-level of a particular field. This program can use its expertise to solve real problems as well. Expert systems are capable of replacing human experts in their areas.

The qualities of an AI expert system are:

Reliable
Fast
Productive
Understandable

What is A* algorithm search?

When you want to find the best route between two nodes, you will use an A* algorithm search. Its purpose is to traverse a graph or find a path for this purpose.

What is a Turing Test? Explain.

A Turing test allows you to check your machine’s Intelligence in comparison to human Intelligence. In a Turing test, a computer would challenge human Intelligence, and if it passes the test, only then can you term it as intelligent. Even a smart machine might not be able to replicate humans also though it passes the test.

What are depth-first search algorithms?

A depth-first search is a kind of algorithm which searches graph or tree data structures. It starts at the root node and searches as far as possible along every branch before it performs backtracking. It is based on LIFO. And the order of nodes in DFS algorithms is different from that of BFS algorithms. The system stores the path in every iteration linearly and requires space for the same.

What are iterative deepening depth-first search algorithms?

In iterative deepening DFS algorithms, the search process of level 1 and 2 takes place. It continues the exploration until it finds the solution. It generates nodes until it finds the goal node and saves the stack of nodes it had created.

Explain a bidirectional search algorithm. What is it?

A bidirectional search algorithm runs two simultaneous searches. The first go forward from the initial state, and the second goes backward from the goal state. They both meet at a common point, and that’s when the search ends—the goal state links with the initial state in a reverse manner.

How would you explain a uniform cost search algorithm?

In a uniform cost search algorithm, you start from the initial state and go to the neighboring states to choose the ‘least costly’ state. From there, you’ll select the next least costly state from the unvisited neighboring states and the visited states. You’d keep looking for the goal state in this manner, and even if you do, you’ll look for other potential states. If every iteration of a breadth-first search algorithm had the same cost, it would become a uniform cost search algorithm.

What are breadth-first search algorithms?

A BFS (breadth-first search. algorithm starts its search from the root node, then to its adjacent nodes, and keeps going to the next group of nodes until it finds the arrangement. You can create a tree until you find the arrangement. You can use it through the FIFO data structure, so you’ll get to see the shortest solution as well.

What is the difference between Classical Artificial Intelligence and Statistical Artificial Intelligence?

Classical AI focuses on deductive thought, such as a group of constraints. On the other hand, Statistical AI focuses on inductive thought like a pattern or trend.

What do you understand by fuzzy logic?

Fuzzy logic is a method of encoding human learning for AI. It imitates the decision making process of humans through IF.THEN instances and the digital values of YES and NO. It is based on degrees of truth. Dr. Lotfi Zadeh of the University of California at Berkeley was the first person to put forth the idea of fuzzy logic.

What do you know about FOPL?

It’s one of the critical AI interview questions, so be sure to prepare it. FOPL stands for First-Order Predicate Logic. It’s a collection of formal systems, and each statement has a subject and a predicate. A predicate can have only one subject, and it has the ability to modify the subject.

What is the relationship between AI and game theory?

Game Theory is a specialized branch of mathematics that deals with opposing players trying to achieve a particular set of goals. It’s about choosing from a group of rational choices when you have multiple agents. Experts use this algorithm in AI when they have various agents in a problem.

Can you state some applications of fuzzy logic?

Fuzzy logic finds applications in multiple industries. Subway systems, unscrewed vehicles, air conditioners, vacuum cleaners, project risk assessment, and facial pattern recognition are just some of the areas which use fuzzy logic.

What is Machine Learning?

Machine learning (ML. is the study of algorithms and models, which computers use for performing specific tasks without having particular instructions. It is a subset of artificial Intelligence and enables a machine to learn from its experience. In ML, computers can access data and use it to advance their knowledge of the problem.

What is the difference between abductive, deductive, and inductive machine learning?

Abductive machine learning is when you derive conclusions according to different instances. Its basis is deep neural networks. In deductive machine learning, you first obtain the result and improve it according to your past results. The machine learning algorithm, in this case, uses a decision tree.

Inductive machine learning learns from a group of instances and draws its conclusion accordingly. It uses KNN, SVM, and other Statistical machine learning algorithms.

What do you understand by Deep Learning?

Deep Learning is a subset of Machine Learning. It has neural networks that can perform unsupervised learning from unstructured data. They learn through representation learning, and it could be unsupervised, supervised, or semi-supervised.

In simple words, deep learning is a technique of machine learning that teaches computer systems to do activities which humans can do naturally. An example of deep learning is unscrewed cars and how they can recognize stop signs on the road.

What is an agent in artificial Intelligence?

In Artificial Intelligence, you study the relationship between an agent and its environment. Those agents have sensors that allow them to understand (sense. their settings. They also have actuators for acting on the environment. They have specified goals, and they can learn and use their knowledge to achieve their goals.

What is a Partial-Order Plan?

When a plan specifies all the actions you need to perform but specifies the order of the steps only when necessary, it’s called a partial-order plan.

What Are the Common Uses and Applications of AI?

Your answer here should show that you recognize the far-reaching and practical applications of AI. Still, your answer is up to you because of your 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 navigation, image recognition, content distribution, predictive maintenance, data processing, automation of manual tasks, or data-driven reporting.

What Are Intelligent Agents, and How Are They Used in AI?

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 accomplish their jobs better.

What Is TensorFlow, and What Is It Used For?

TensorFlow is an open-source software library initially 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.

What Is Machine Learning, and How Does It Relate to AI?

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 continually having to input parameters. Machine learning is a practical application of AI.

What Are Neural Networks, and How Do They Relate to AI?

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 efficiently.

What Is Deep Learning, and How Does It Relate to AI?

Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly sophisticated 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.

Why Is Image Recognition a Key Function of AI?

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.

What Is Automatic Programming?

Automatic programming is describing what a program should do, and then having the AI system “write” the program.

What Is a Bayesian Network, and How Does It Relate to AI?

A Bayesian network is a graphical model for probabilistic relationships among a set of variables. It mimics the human brain in processing variables.

What Is Constraint Satisfaction Problems?

Constraint Satisfaction Problems (CSPs. are mathematical problems defined as a set of objects, the state of which must meet several constraints. CSPs are useful for AI because the regularity of their formulation offers commonality for analyzing and solving problems.

What Is Supervised Versus Unsupervised Learning?

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 knowledge.

What is AI?

Artificial Intelligence is a field of computer science wherein the cognitive functions of the human brain are studied and replicated on a machine or a system. Artificial Intelligence today is widely used in various sectors of the economy including science and technology, healthcare, telecommunications, energy and so on. AI has three different levels:

Narrow AI: AI is narrow when the machine performs a specific task better than a human. The current research of AI is taking place at this level.
General AI: AI reaches the general state when it can perform any intellectual task equivalent to the accuracy of that of a human.
Active AI: AI is active when it can completely beat humans in all performed tasks.

What is the difference between Artificial Intelligence, Machine Learning, and Deep Learning?

DL is a subset of ML, which is the subset of AI. Hence, AI is the all-encompassing concept that initially erupted in computer science. It was then followed by ML that thrived later, and lastly DL, that is now promising to escalate the advances of AI to another level.

What are the Different Types of AI?

Reactive Machines AI: Based on present actions, it is not capable of using previous experiences to form current decisions whilst simultaneously updating their memory.
Limited Memory AI: This type of AI is used in self-driving cars – they detect the movement of vehicles around them constantly and add it to their memory.
Theory of Mind AI: Advanced levels of AI have the ability to understand emotions and people.
Self-Aware AI: This type of AI possesses human.like consciousness and reactions. Such machines have the ability to form self-driven actions.

Artificial Narrow Intelligence (ANI.: This type of AI is a general-purpose AI, essentially used in building virtual assistants like Siri or Alexa.
Artificial General Intelligence (AGI.: AGI is also known as strong AI. Example: Pillo robot – that answers questions related to health.
Artificial Superhuman Intelligence (ASI.: This is the AI that possesses the ability to do everything that a human can do and more. An example is the Alpha 2 which is the first humanoid ASI robot.

Name some popular programming languages in AI.

Some commonly used programming languages in AI include:

Python
R
Lisp
Prolog
Java

List some AI Applications and Common uses.

AI-powered tools are applied in various spheres of the economy, including:

Natural Language Processing
Chatbots
Sentiment analysis
Sales prediction
Self-driving cars
Facial expression recognition
Image tagging

Mention Some Popular Domains of AI.

The most popular domains in AI are:

Machine Learning
Neural Networks
Robotics
Expert Systems
Fuzzy Logic Systems
Natural Language Processing

What is the difference between Weak AI and Strong AI?

Weak AI	Strong AI
It is a narrow application with a limited scope.	It is a wider application with a more vast scope.
This application is good at specific tasks.	This application has an incredible human-level intelligence.
It uses supervised and unsupervised learning to process data.	It uses clustering and association to process data.
Example: Siri, Alexa.	Example: Advanced Robotics

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 utilization to react appropriately. These systems tend to have the capability to substitute a human expert. Their characteristics include:

High performance
Consistency
Reliability
Diligence
Unbiased nature

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.

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.

Some of the commonly used ones include:

Feed Forward Neural Nets
Multiple Layered Perceptron Neural Nets
Convolution Neural Nets
Recurrent Neural Nets
Modular Neural Network

What are the Hyper Parameters of ANN?

Learning rate: The learning rate implies how fast the network learns its parameters.
Momentum: This parameter helps in coming out of the local minima and smoothening the jumps while gradient descents.
Number of epochs: This shows the number of times the entire training data is fed to the network. Here, the training is referred to as the number of epochs.

What is the Tower of Hanoi?

Tower of Hanoi essentially is a mathematical puzzle that displays how recursion is utilized as a device in building up an algorithm to solve a specific problem. The Tower of Hanoi can be solved using a decision tree and a breadth-first search (BFS. algorithm in AI. With 3 disks, a puzzle can essentially be solved in 7 moves. However, the minimal number of moves required to solve a Tower of Hanoi puzzle is 2ⁿ − 1, where n is the number of disks.

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.

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.

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 neighboring 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 utilizing the FIFO (first.in, first-out. data structure, this strategy gives the shortest path to the solution.

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.

What is fuzzy logic? List its Applications.

Fuzzy logic is a subset of AI. It is a way of encoding human learning for artificial processing. It is represented as IF.THEN rules. Some of its important applications include:

Facial pattern recognition
Air conditioners, washing machines, and vacuum cleaners
Anti Skid braking systems and transmission systems
Control of subway systems and unmanned helicopters
Weather forecasting systems
Project risk assessment
Medical diagnosis and treatment plans
Stock trading

Mention some popular Machine Learning Algorithms?

Some of the popular Machine Learning algorithms are:

Logistic regression
Linear regression
Decision trees
Support vector machines

Differentiate between parametric and non.parametric models.

Differentiation	Parametric Model	Non.parametric Model
Features	A finite number of parameters to predict new data	Unbounded number of parameters to predict new data.
Algorithm	Logistic regression. Linear discriminant analysis Perceptron Naive Bayes	K.nearest neighbours .Decision trees (E.g.CART and C4.5.Support vector machines
Benefits	Easy to use quick in functioning Less data	Flexibility Power Performance
Limitations	Constrained Limited complexity Poor fit	More data Slower Overfit

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.

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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

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.

List some applications of AI.

Natural language processing
Chatbots
Sentiment analysis
Sales prediction
Self-driving cars
Facial expression recognition
Image tagging

List the programming languages used in AI.

Python
R
Lisp
Prolog
Java

What is Tower of Hanoi?

Tower of Hanoi is a mathematical puzzle that shows how recursion might be utilized as a device in building up an algorithm to take care of a specific problem. Using a decision tree and a breadth-first search (BFS. algorithm in AI, we can solve the Tower of Hanoi.

What is Turing test?

The Turing test is a method to test a machine’s ability to match the human-level intelligence. A machine is used to challenge human intelligence, and when it passes the test it is considered intelligent. Yet a machine could be viewed as intelligent without sufficiently knowing how to mimic a human.

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
Reliability
Understandability

List the advantages of an expert system.

Consistency
Memory
Diligence
Logic
Multiple expertise
Ability to reason
Fast response
Unbiased in nature

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.

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.

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.

What is a bidirectional search algorithm?

In a bidirectional search algorithm, the search begins in forward from the beginning state and in reverse from the objective state. The searches meet to identify a common state. The initial state is linked with the objective state in a reverse way. Each search is done just up to half of the aggregate way.

What is an iterative deepening Depth-first search algorithm?

The repetitive search processes of level 1 and level 2 happen in this search. The search processes continue until the solution is found. Nodes are generated until a single goal node is created. Stack of nodes is saved.

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.

How are game theory and AI related?

AI system uses game theory for enhancement; it requires more than one participant which narrows the field quite a bit. The two fundamental roles are as follows:

Participant design: Game theory is used to enhance the decision of a participant to get maximum utility.
Mechanism design: Inverse game theory designs a game for a group of intelligent participants, e.g., auctions.

Explain Alpha–Beta pruning.

Alpha–Beta pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to ‘n’ depths and can prune the entire subtrees and leaves.

What is a fuzzy logic?

Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF.THEN rules.

List the applications of fuzzy logic.

Facial pattern recognition
Air conditioners, washing machines, and vacuum cleaners
Antiskid braking systems and transmission systems
Control of subway systems and unmanned helicopters
Weather forecasting systems
Project risk assessment
Medical diagnosis and treatment plans
Stock trading

What is a partial-order planning?

A problem has to be solved in a sequential approach to attain the goal. The partial-order plan specifies all actions that need to be undertaken but specifies an order of the actions only when required.

What is FOPL?

First-order predicate logic is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.

What is the difference between inductive, deductive, and abductive Machine Learning?

Inductive Machine Learning	Deductive Machine Learning	Abductive Machine Learning
Learns from a set of instances to draw the conclusion	Derives the conclusion and then improves it based on the previous decisions	It is a Deep Learning technique where conclusions are derived based on various instances
Statistical Machine Learning such as KNN (K-nearest neighbor. or SVM (Support Vector Machine.	Machine Learning algorithm using a decision tree	Deep neural networks
A ⋀ B ⊢ A → B (Induction.	A ⋀ (A → B. ⊢ B (Deduction.	B ⋀ (A → B. ⊢ A (Abduction.

the different algorithm techniques in Machine Learning.

Supervised Learning
Unsupervised Learning
Semi-supervised Learning
Reinforcement Learning
Transduction
Learning to Learn

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.

Differentiate between supervised, unsupervised, and reinforcement learning.

Differentiation Based on	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Features	The training set has both predictors and predictions.	The training set has only predictors.	It can establish state-of-the-art results on any task.
Algorithms	Linear and logistic regression, support vector machine, and Naive Bayes	K-means clustering algorithm and dimensionality reduction algorithms	.learning, state-action-reward-state-action (SARSA., and Deep Network (DN.
Uses	Image recognition, speech recognition, forecasting, etc.	Preprocessing data, pretraining supervised learning algorithms, etc.	Warehouses, inventory management, delivery management, power system, financial systems, etc.

Differentiate between parametric and non-parametric models.

Differentiation Based on	Parametric Model	Non-parametric Model
Features	A finite number of parameters to predict new data	Unbounded number of parameters
Algorithm	Logistic regression, linear discriminant analysis, perceptron, and Naive Bayes	K-nearest neighbors, decision trees like CART and C4.5, and support vector machines
Benefits	Simple, fast, and less data	Flexibility, power, and performance
Limitations	Constrained, limited complexity, and poor fit	More data, slower, and overfitting

Name a few Machine Learning algorithms you know.

Logistic regression
Linear regression
Decision trees
Support vector machines
Naive Bayes, and so on

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.

What is perceptron in Machine Learning?

Perceptron is an algorithm that is able to simulate the ability of the human brain to understand and discard; it is used for the supervised classification of the input into one of the several possible non-binary outputs.

List the extraction techniques used for dimensionality reduction.

Independent component analysis
Principal component analysis
Kernel-based principal component analysis

Is KNN different from K.-means Clustering?

KNN	K-means Clustering
Supervised	Unsupervised
Classification algorithms	Clustering algorithms
Minimal training model	Exhaustive training model
Used in the classification and regression of the known data	Used in population demographics, market segmentation, social media trends, anomaly detection, etc.

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, function approximation, etc. of a model.

List the steps involved in Machine Learning.

Data collection
Data preparation
Choosing an appropriate model
Training the dataset
Evaluation
Parameter tuning
Predictions

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.

What is regularization in Machine Learning?

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.

What are the components of relational evaluation techniques?

Data acquisition
Ground truth acquisition
Cross validation technique
query type
Scoring metric
Significance test

What is model accuracy and model performance?

Model accuracy, a subset of model performance, is based on the model performance of an algorithm. Whereas, model performance is based on the datasets we feed as inputs to the algorithm.

Define F1 score.

F1 score is the weighted average of precision and recall. It considers both false positive and false negative values into account. It is used to measure a model’s performance.

List the applications of Machine Learning.

Image, speech, and face detection
Bioinformatics
Market segmentation
Manufacturing and inventory management
Fraud detection, and so on

Can you name three feature selection techniques in Machine Learning?

Univariate Selection
Feature Importance
Correlation Matrix with Heatmap

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.

What methods are used for reducing dimensionality?

Dimensionality reduction is the process of reducing the number of random variables. We can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc.

List different methods for sequential supervised learning.

Sliding window methods
Recurrent sliding windows methods
Hidden Markov models
Maximum entropy Markov models
Conditional random fields
Graph transformer networks

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

What is Bias–Variance tradeoff?

Bias error is used to measure how much on an average the predicted values vary from the actual values. In case a high-bias error occurs, we have an under-performing model.

Variance is used to measure how the predictions made on the same observation differ from each other. A high-variance model will overfit the dataset and perform badly on any observation.

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.

How to install TensorFlow?

TensorFlow Installation Guide:

CPU : pip install tensorflow.cpu

GPU : pip install tensorflow.gpu

What are the TensorFlow objects?

Constants
Variables
Placeholder
Graph
Session

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.

List different activation neurons or functions.

Linear neuron
Binary threshold neuron
Stochastic binary neuron
Sigmoid neuron
Tanh function
Rectified linear unit (ReLU).

What are the hyper parameters of ANN?

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.
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

What is vanishing gradient?

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.

What are dropouts?

Dropout is a simple way to prevent a neural network from overfitting. It is the dropping out of some of the units in a neural network. It is similar to the natural reproduction process, where the nature produces offspring’s by combining distinct genes (dropping out others. rather than strengthening the co-adapting of them.

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.

List the key components of LSTM.

Gates (forget, Memory, update, and Read.
Tanh(x. (values between −1 and 1.
Sigmoid(x. (values between 0 and 1.

List the variants of RNN.

LSTM: Long Short-term Memory
GRU: Gated Recurrent Unit
End-to-end Network
Memory Network

What is an autoencoder? Name a few applications.

An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are given below:

Data denoising
Dimensionality reduction
Image reconstruction
Image colorization

What are the components of the generative adversarial network (GAN.? How do you deploy it?

Components of GAN:

Generator
Discriminator

Deployment Steps:

Train the model
Validate and finalize the model
Save the model
Load the saved model for the next prediction

What are the steps involved in the gradient descent algorithm?

Gradient descent is an optimization algorithm that is used to find the coefficients of parameters that are used to reduce the cost function to a minimum.
Step 1 : Allocate weights (x,y. with random values and calculate the error (SSE.
Step 2: Calculate the gradient, i.e., the variation in SSE when the weights (x,y. are changed by a very small value. This helps us move the values of x and y in the direction in which SSE is minimized
Step 3: Adjust the weights with the gradients to move toward the optimal values where SSE is minimized
Step 4: Use new weights for prediction and calculating the new SSE
Step 5: Repeat Steps 2 and 3 until further adjustments to the weights do not significantly reduce the error

What do you understand by session in TensorFlow?

Syntax: Class Session

It is a class for running TensorFlow operations. The environment is encapsulated in the session object wherein the operation objects are executed and Tensor objects are evaluated.

# Build a graph

x = tf.constant(2.0.

y = tf.constant(5.0.

z = x * y

# Launch the graph in a session

sess = tf.Session(.

# Evaluate the tensor `z`

print(sess.run(z..

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.

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_ueue = tf.train.string_input_producer([

“hdfs://namenode:8020/path/to/file1.csv”,

“hdfs://namenode:8020/path/to/file2.csv”,

What are intermediate tensors? Do sessions have lifetime?

The intermediate tensors are tensors that are neither inputs nor outputs of the Session-run(. call, but are in the path leading from the inputs to the outputs; they will be freed at or before the end of the call.

Sessions can own resources, few classes like tf.Variable, tf.ueueBase, and tf.ReaderBase, and they use a significant amount of memory. These resources (and the associated memory. are released when the session is closed, by calling tf.Session.close.

What is the lifetime of a variable?

When we first run the tf.Variable.initializer operation for a variable in a session, it is started. It is destroyed when we run the tf.Session.close operation.

Is it possible to solve logical inference in propositional logic?

Yes, logical inference can easily be solved in propositional logic by making use of three concepts:

Logical equivalence
Process satisfaction
Validation checking

How does face verification work?

Face verification is used by a lot of popular firms these days. Facebook is famous for the usage of DeepFace for its face verification needs.

There are four main things you must consider when understanding how face verification works:

Input: Scanning an image or a group of images
Process:
- Detection of facial features
- Feature comparison and alignment
- Key pattern representation
- Final image classification

Output : Face representation, which is a result of a multilayer neural network
Training data: Involves the usage of thousands of millions of images

So, this brings us to the end of the Artificial Intelligence Interview Questions blog.

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What you will Learn in this Course?

Introduction to Deep Learning and AI

What is Deep Learning?
Advantage of Deep Learning over Machine learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Pre-requisites for AI & DL
Python Programming Language
Installation & IDE

Environment Set Up and Essentials

Installation
Python – NumPy
Python for Data Science and AI
Python Language Essentials
Python Libraries – Numpy and Pandas
Numpy for Mathematical Computing

More Prerequisites for Deep Learning and AI

Pandas for Data Analysis
Machine Learning Basic Concepts
Normalization
Data Set
Machine Learning Concepts
Regression
Logistic Regression
SVM – Support Vector Machines
Decision Trees
Python Libraries for Data Science and AI

Introduction to Neural Networks

Creating Module
Neural Network Equation
Sigmoid Function
Multi-layered perception
Weights, Biases
Activation Functions
Gradient Decent or Error function
Epoch, Forward & backword propagation
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables

Multi-layered Neural Networks

Error Back propagation issues
Drop outs

Regularization techniques in Deep Learning

Deep Learning Libraries

Tensorflow
Keras
OpenCV
SkImage
PIL

Building of Simple Neural Network from Scratch from Simple Equation

Training the model

Dual Equation Neural Network

TensorFlow
Predicting Algorithm

Introduction to Keras API

Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and loading a model with Keras
Customizing the Training Process
Using TensorBoard with Keras
Use-Case Implementation with Keras

GPU in Deep Learning

Introduction to GPUs and how they differ from CPUs
Importance of GPUs in training Deep Learning Networks
The GPU constituent with simpler core and concurrent hardware
Keras Model Saving and Reusing
Deploying Keras with TensorBoard

Keras Cat Vs Dog Modelling

Activation Functions in Neural Network

Optimization Techniques

Some Examples for Neural Network

Convolutional Neural Networks (CNN)

Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and visualizing a CNN

RNN: Recurrent Neural Networks

Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Backpropagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model

Application of Deep Learning in image recognition, NLP and more
Real world projects in recommender systems and others

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