Artificial Intelligence Interview Questions and Answers
Question - 11 : - How to choose an algorithm for a problem?
Answer - 11 : -
To solve a problem, there can be multiple Machine Learning algorithms with different approaches and constraints. However, a generic approach can be applied to most of the problems and find a suitable algorithm. Below are the steps you need to consider while choosing an algorithm:
Categorize the Problem
The first is finding the algorithm, which is to categorize it based on the type of input you have and the output you want from it. If the data is labeled, it’s a problem for supervised learning. If the data is not labeled, then it’s an unsupervised learning problem. At last, if the problem aims to optimize a model, then it’s a reinforcement learning problem.
Similarly, you can categorize a problem based on the outcome you want from the algorithm. If the output is expected to be numerical then it’s a regression problem. Is class is the output of a model, it’s a classification problem, and grouping of the input values can be categorized as the clustering problems.
Understanding the Data
Data plays an important role in the process of selecting the right algorithm for your problem. This is because, some algorithms can process tons of data, while some work better with smaller samples. Analyzing and transforming your data will also help you to know the constraints and the challenges you t=want to overcome while solving the problem.
Find the available Algorithms
Identify the available algorithms you can apply for solving the problem in a reasonable timeframe. Some of the factors that may affect your choice of the right algorithm include the accuracy of the algorithm, complexity, scalability interpretability, build & training time, space, and the time it takes to solve the problem.
Implement the Algorithm
After selecting the algorithm, you have to make an evaluation criteria by carefully selecting the testing values and subgroups of the datasets. Also, check the time taken by each algorithm to solve the problem. The algorithm that provides accurate results in the given time while acquiring less space, would be the best algorithm for your problem.
Question - 12 : - Difference between AI, ML, and DL?
Answer - 12 : -
Below is the difference between AI, ML, and DL:
Artificial Intelligence: AI consists of the algorithms and techniques that enable a machine to perform the tasks commonly associated with human intelligence. The AI applications are trained to process large amounts of complex information and right decisions without human intervention. Some of the popular examples of AI applications are chatbots, Autonomous Vehicles, Space rovers, and Simulators for mathematical and scientific purposes.
Machine Learning: Machine Learning is a subset of Artificial Intelligence and is mainly used to improve computer programs through experience and training on different models. There are three main methods of Machine Learning:
- Supervised Learning: In supervised learning, the machine gets the input for twitch the output is already known. After the processing is completed, the algorithm compared the output produced from the original output and measure the degree of errors in it.
- Unsupervised Learning: Here, the instructor has no output or historical labels for the input data. So, the algorithm is expected to figure out the right path and extract the features from the given dataset. The goal is to allow the algorithm to search the data and s some structure in it.
- Reinforcement Learning: In this method of learning there are three components, the agent, environment, and actions. An agent is a decision-maker whose goal is to choose the right actions and maximize the expected reward within a set timeframe. Reinforcement learning is mainly used in robotics where the machine learns about the environment through trial and error.
Deep Learning: In Machine Learning, where the model tends to surrender to environmental changes, Deep Learning adapts to the changes by updating the models based on constant feedback. It’s facilitated by Artificial Neural Networks that mimic the cognitive behavior of the human brain.
Question - 13 : - List the advantages of an expert system.
Answer - 13 : -
- Consistency
- Memory
- Diligence
- Logic
- Multiple expertise
- Ability to reason
- Fast response
- Unbiased in nature
Question - 14 : - What is an A* algorithm search method?
Answer - 14 : -
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.
Question - 15 : - What is a bidirectional search algorithm?
Answer - 15 : -
In a bidirectional search algorithm, the search begins 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.
Question - 16 : - What is an iterative deepening depth-first search algorithm?
Answer - 16 : -
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. The stack of nodes is saved.
Question - 17 : - List the different algorithm techniques in Machine Learning.
Answer - 17 : -
Here are some of the most commonly used Machine Learning Algorithms
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
- Transduction
- Learning to Learn
Question - 18 : - What is the difference between inductive, deductive, and abductive Machine Learning?
Answer - 18 : -
| 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) |
Question - 19 : - What is FOPL?
Answer - 19 : -
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.
Question - 20 : - What is a partial-order planning?
Answer - 20 : -
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.