Most Frequently Asked Artificial Intelligence Interview Questions

“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.

• 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 posses 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.

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.

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.

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.

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.

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.

• 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.

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.

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.

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.

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

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.

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.

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.

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