Artificial Intelligence Questions – Basic Level:

What is Artificial Intelligence?

Artificial Intelligence is a branch of computer science focusing on developing intelligent machines replicating human behavior. Intelligent machines may be characterized as machines that can behave like humans, think like humans, and make decisions. It consists of two words: “Artificial” and “Intelligence,” which signify “man-made thinking capacity.”

We don’t need to pre-program the machine to accomplish a task using artificial intelligence; instead, we can design a machine with pre-programmed algorithms that can function independently.

What are the different types of Artificial Intelligence systems based on their functions?

Here are the different types of Artificial Intelligence systems based on their functions:

• Limited Memory – It can save previous data and experience quickly. A self-driving automobile is an example of this form of AI.
• Self Awareness – It is the future of artificial intelligence, with consciousness and emotions akin to humans.
• Reactive Machines – The most basic kinds of AI are pure reactive machines. These are limited to current activities and cannot store previous actions.
• Theory of Mind – It is a sophisticated AI that can grasp human emotions, people, and so on in the actual world.

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

Here is the difference between Machine Learning, Artificial Intelligence, and Deep Learning:

Machine Learning

Artificial Intelligence

Deep Learning

Arthur Samuel launched this term in 1959. 

John McCarthy launched this term in 1956.

Igor Aizenberg launched this term in 2000.

Machine learning works with both organized and semi-structured data.

AI is capable of dealing with organized and semi-structured data.

Deep learning is capable of dealing with both organized and unstructured data.

It is a subtype of artificial intelligence that learns from previous data and experiences.

It is a technique used to develop intelligent machines that can duplicate human behavior.

It is a branch of machine learning and AI inspired by human brain cells, known as neurons, and mimics how the human brain works.

The purpose of machine learning is to allow the computer to learn from previous experiences.

The objective of AI is to allow machines to think for themselves without the need for human involvement.

The objective of deep learning is to use diverse algorithms to solve complicated problems in the same way that the human brain does.

Difference between Machine Learning, AI, and Deep Learning

What is the relationship between AI and ML?

Machine learning is a branch of artificial intelligence. As both are distinct ideas, the relationship between them might be interpreted as – AI employing various Machine Learning methods and concepts to tackle complicated issues.

What are the uses of Deep Learning in the real world?

Deep learning is a class of machine learning that replicates how the human brain works. It is based on the notion of neural networks to tackle difficult real-world issues and is inspired by human brain cells called neurons. It’s also called a deep neural network or deep neural learning.

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Artificial Intelligence Questions – Intermediate Level:

What is the Hidden Markov Model?

The hidden Markov model (HMM) is a statistical model used to depict probability distributions across a series of observations. The hidden layer in the model establishes a property that implies the state of a process is created at a specific moment and is concealed from the observer. It is assumed that the process is similar to the Markov property. In nearly all modern voice recognition systems, the HMM is employed in many applications, such as reinforcement learning and temporal pattern identification.

What is Q-learning?

The Q-learning algorithm is a well-known reinforcement learning method. In this case, the agent attempts to learn the optimum rules that can give the best behaviors to maximize the environment’s rewards. The agent learns these optimum rules through previous encounters.

Explain the Tower of Hanoi!

Tower of Hanoi is a mathematical problem demonstrating how recursion is used to construct an algorithm to solve a specific issue. A decision tree and a breadth-first search (BFS) method can be used to solve the Tower of Hanoi in artificial intelligence.

What is an ensemble technique?

An ensemble method’s main premise is to train many models and integrate their predictions while enhancing robustness over a single model. On a dataset, the approach learns many weak predictors. These provide somewhat diverse outcomes; some models learn specific patterns better than others and then combine their predictions to achieve superior performance.

Enquire Upcoming Batch Details of our Artificial Intelligence CourseWhat is the Gradient Descent technique?

Gradient descent is an optimization technique used to minimize the error term, which is the cost function. It is an iterative strategy that converges to the best solution by advancing in the direction of the steepest fall defined by the gradient’s negative. The gradient descent approach features a hyperparameter learning rate, which defines the algorithm’s number of jumps to get to the best solution.

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Artificial Intelligence Questions – Advanced Level:

What is the Alpha-beta Pruning algorithm?

The minimax algorithm is used in Alpha-Beta Pruning to reduce the number of nodes explored within a search tree. It may be used to prune whole subtrees and leaves at ‘n’ depths.

What is Perceptron?

Perceptron is a method for categorizing input into several non-binary outputs. Based on a set of weights and a feature vector, it is a linear classifier that predicts based on a linear predictor function.

Is Game Theory related to AI?

Game theory is a subfield of mathematics that deals with opposing and rational players attempting to achieve a given set of goals. A reasonable player is one who has the same understanding and information as the others. In this case, AI is used. In a multi-agent scenario where one player’s choice influences the choice of the other or opposing players, the players must select from a given set of possibilities.

Conclusion:

Career options in Artificial Intelligence are immense, and Training Basket will help students prepare with the best possible courses. The above article lists all the important questions asked in Artificial Intelligence interviews. Before appearing for the interview, students must do basic research about the organization and assess the role they want to apply for. They must prepare well for questions about artificial intelligence. Honest and integral answers will definitely help them land their Dream Jobs.