Artificial Intelligence Basic Level Interview Questions and Answers
by Sathish, on Nov 12, 2020 12:05:25 PM
1. What is Artificial Intelligence? Give an example of where AI is used on a daily basis.
Ans: 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.
2.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 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.
3.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
4.How is Machine Learning related to Artificial Intelligence?
Ans: 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.
5.What is Q-Learning?
Ans: The Q-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
6.What is Deep Learning?
Ans: 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.
7.Explain the assessment that is used to test the intelligence of a machine?
Ans: 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.
8.Explain how Deep Learning works?
Ans: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.
9.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
- 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.
- 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.
10.What are Bayesian Networks?
Ans: 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.
Artificial Intelligence Scenario Based Interview Question
11.What is market basket analysis and how can Artificial Intelligence be used to perform this?
Ans: Market basket analysis explains the combinations of products that frequently co-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
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.
12.Which algorithm does Facebook use for face verification and how does it work?
Ans: Facebook uses DeepFace for face verification. It works on the face verification algorithm, structured by Artificial Intelligence (AI) techniques using neural network models.
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
13.How can AI be used in detecting fraud?
Ans: 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.
14.Explain the logic behind targeted marketing. How can Machine Learning help with this?
Ans: 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.
15. 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.
Ans: 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.