Artificial intelligence, or AI, has become one of the hottest subjects in the tech industry today. With the rise of machine learning and big data, AI has become an essential part of many businesses and industries. It has revolutionized the way we live, work, and interact with each other. As the demand for AI professionals grows, so does the demand for people who can create machines that can work independently. Anyone seeking an AI developer job needs to practice commonly asked Artificial Intelligence interview questions.
How to Prepare for an AI Interview?
Preparing for an Artificial Intelligence interview can be daunting, but you can do well with the right resources and practice. Firstly, you must have a solid understanding of the fundamentals of AI, such as Machine Learning Algorithms, natural language processing, data analysis, computer vision, etc. To strengthen your skills, you can practice solving AI problems on websites like Kaggle, which offer datasets and challenges that mimic real-world scenarios.
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Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. AI is an interdisciplinary science with multiple approaches, but advancements in machine learning and deep learning are creating a paradigm shift in virtually every sector of the tech industry.
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The goal of Artificial intelligence is to create intelligent machines that can mimic human behavior. We need AI for today’s world to solve complex problems, make our lives more smoothly by automating the routine work, saving the manpower, and to perform many more other tasks.
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Artificial intelligence can be divided into different types on the basis of capabilities and functionalities.
Based on capabilities, AI is divided into three parts:
- Weak AI or Narrow AI: Weak AI is capable of performing some dedicated tasks with intelligence. Siri is an example of Weak AI.
- General AI: The intelligent machines that can perform any intellectual task with efficiency as a human.
- Strong AI: It is the hypothetical concept that involves the machine that will be better than humans and will surpass human intelligence.
- Reactive Machines: Purely reactive machines are the basic types of AI. These focus on the present actions and cannot store the previous actions.
- Limited Memory: As its name suggests, it can store the past data or experience for the limited duration. The self-driving car is an example of such AI types.
- Theory of Mind: It is the advanced AI that is capable of understanding human emotions, people, etc., in the real world.
- Self-Awareness: Self Awareness AI is the future of Artificial Intelligence that will have their own consciousness, emotions, similar to humans.
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AI covers lots of domains or subsets, and some main domains are given below:
- Machine Learning
- Deep Learning
- Neural Network
- Expert System
- Fuzzy Logic
- Natural Language Processing
- Robotics
- Speech Recognition.
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Below are the top five programming languages that are widely used for the development of Artificial Intelligence:
- Python
- Java
- Lisp
- R
- Prolog
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The intelligent agent can be any autonomous entity that perceives its environment through the sensors and act on it using the actuators for achieving its goal.
These Intelligent agents in AI are used in the following applications:
- Information Access and Navigations such as Search Engine
- Repetitive Activities
- Domain Experts
- Chatbots, etc.
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Strong AI: Strong AI is about creating real intelligence artificially, which means a human-made intelligence that has sentiments, self-awareness, and emotions similar to humans. It is still an assumption that has a concept of building AI agents with thinking, reasoning, and decision-making capabilities similar to humans.
Weak AI: Weak AI is the current development stage of artificial intelligence that deals with the creation of intelligent agents and machines that can help humans and solve real-world complex problems.
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Turing test is one of the popular intelligence tests in Artificial intelligence. The Turing test was introduced by Alan Turing in the year 1950. It is a test to determine that if a machine can think like a human or not. According to this test, a computer can only be said to be intelligent if it can mimic human responses under some particular conditions.
In this test, three players are involved, the first player is a computer, the second player is a human responder, and the third player is the human interrogator, and the interrogator needs to find which response is from the machine on the basis of questions and answers.
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NLP stands for Natural Language Processing, which is a branch of artificial intelligence. It enables machines to understand, interpret, and manipulate the human language.
Components of NLP:
There are mainly two components of Natural Language processing, which are given below:
- Natural Language Understanding (NLU):
It involves the below tasks:
- To map the input to useful representations.
- To analyze the different aspects of the language.
- Natural Language Generation (NLG)
It involves:
- Text Planning
- Sentence Planning
- Text Realization
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An expert system mainly contains three components:
- User Interface: It enables a user to interact or communicate with the expert system to find the solution for a problem.
- Inference Engine: It is called the main processing unit or brain of the expert system. It applies different inference rules to the knowledge base to draw a conclusion from it. The system extracts the information from the KB with the help of an inference engine.
- Knowledge Base: The knowledge base is a type of storage area that stores the domain-specific and high-quality knowledge.
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Computer vision is a field of Artificial Intelligence that is used to train the computers so that they can interpret and obtain information from the visual world such as images. Hence, computer vision uses AI technology to solve complex problems such as image processing, object detections, etc.
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Game theory is the logical and scientific study that forms a model of the possible interactions between two or more rational players. Here rational means that each player thinks that others are just as rational and have the same level of knowledge and understanding. In the game theory, players deal with the given set of options in a multi-agent situation, it means the choice of one player affects the choice of the other or opponent players.
Game theory and AI are much related and useful to each other. In AI, the game theory is widely used to enable some of the key capabilities required in the multi-agent environment, in which multiple agents try to interact with each other to achieve a goal.
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Artificial neural networks are computational models that work similarly to the functioning of a human nervous system. There are several kinds of artificial neural networks. These types of networks are implemented based on the mathematical operations and a set of parameters required to determine the output.
Following are some commonly used Artificial Neural networks:
- Feedforward Neural Network – Artificial Neuron.
- Radial basis function Neural Network.
- Kohonen Self Organizing Neural Network.
- Recurrent Neural Network(RNN)
- Convolutional Neural Network.
- Long / Short Term Memory.
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Knowledge representation is the part of AI, which is concerned with the thinking of AI agents. It is used to represent the knowledge about the real world to the AI agents so that they can understand and utilize this information for solving the complex problems in AI.
Following elements of Knowledge that are represented to the agent in the AI system:
- Objects
- Events
- Performance
- Meta-Knowledge
- Facts
- Knowledge-base
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Following are the knowledge representation techniques:
- Logical Representation
- Semantic Network Representation
- Frame Representation
- Production Rules
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The artificial intelligence can be broadly helpful in fraud detection using different machine learning algorithms, such as supervised and unsupervised learning algorithms. The rule-based algorithms of Machine learning help to analyze the patterns for any transaction and block the fraudulent transactions
Below are the steps used in fraud detection using machine learning:
- Data extraction: The first step is data extraction. Data is gathered through a survey or with the help of web scraping tools. The data collection depends on the type of model, and we want to create. It generally includes the transaction details, personal details, shopping, etc.
- Data Cleaning: The irrelevant or redundant data is removed in this step. The inconsistency present in the data may lead to wrong predictions.
- Data exploration & analysis: This is one of the most crucial steps in which we need to find out the relation between different predictor variables.
- Building Models: Now, the final step is to build the model using different machine learning algorithms depending on the business requirement. Such as Regression or classification.
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In artificial intelligence, the inference engine is the part of an intelligent system that derives new information from the knowledge base by applying some logical rules.
It mainly works in two modes:
- Backward Chaining: It begins with the goal and proceeds backward to deduce the facts that support the goal.
- Forward Chaining: It starts with known facts, and asserts new facts.
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There are various real-world applications of AI, and some of them are:
- Google Search Engine: When we start writing something on the google search engine, we immediately get the relevant recommendations from google, and this is because of different AI technologies.
- Ridesharing Applications: Different ride-sharing applications such as Uber uses AI and machine learning to determine the type of ride, minimize the time once the car is hailed by the user, price of the ride, etc.
- Spam Filters in Email: The AI is also used for email spam filtering so that you can get the important and relevant emails only in your inbox. As per the studies, Gmail successfully filters 99.9% of spam mails.
- Social Networking: Different social networking sites such as Facebook, Instagram, Pinterest, etc., use the AI technology for different purposes such as face recognition and friend suggestions, when you upload a photograph on Facebook, understanding the contextual meaning of an emoji in Instagram, and so on.
- Product recommendations: When we search for a product on Amazon, we get the recommendation for similar products, and this is because of different ML algorithms. Similarly, on Netflix, we get personalized recommendations for movies and web series.
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Hidden Markov model is a statistical model used for representing the probability distributions over a chain of observations. In the hidden markov model, hidden defines a property that it assumes that the state of a process generated at a particular time is hidden from the observer, and Markov defines that it assumes that the process satisfies the Markov property. The HMM models are mostly used for temporal data.
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Minimax algorithm is a backtracking algorithm used for decision making in game theory. This algorithm provides the optimal moves for a player by assuming that another player is also playing optimally.
This algorithm is based on two players, one is called MAX, and the other is called the MIN.
Following terminologies that are used in the Minimax Algorithm:
- Game tree: A tree structure with all possible moves.
- Initial State: The initial state of the board.
- Terminal State: Position of the board where the game finishes.
- Utility Function: The function that assigns a numeric value for the outcome of the game.