Artificial Intelligence Interview Questions and Answers
Question - 21 : - 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.
Answer - 21 : -
- Facial pattern recognition
- Air conditioners, washing machines, and vacuum cleaners
- Antiskid braking systems and transmission systems
- Control of subway systems and unmanned helicopters
- Weather forecasting systems
- Project risk assessment
- Medical diagnosis and treatment plans
- Stock trading
Question - 22 : - What is a fuzzy logic?
Answer - 22 : -
Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF-THEN rules.
Question - 23 : - Explain Alpha–Beta pruning.
Answer - 23 : -
Alpha–Beta pruning is a search algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to ‘n’ depths and can prune the entire subtrees and leaves.
Question - 24 : - How are game theory and AI related?
Answer - 24 : -
AI system uses game theory for enhancement; it requires more than one participant which narrows the field quite a bit. The two fundamental roles are as follows:
- Participant design: Game theory is used to enhance the decision of a participant to get maximum utility.
- Mechanism design: Inverse game theory designs a game for a group of intelligent participants, e.g., auctions.
Question - 25 : - What is a uniform cost search algorithm?
Answer - 25 : -
The uniform cost search performs sorting in increasing the cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has the same cost. It investigates ways in the expanding order of cost.
Question - 26 : - What is Deep Learning?
Answer - 26 : -
Deep Learning is a subset of Machine Learning which is used to create an artificial multi-layer neural network. It has self-learning capabilities based on previous instances, and it provides high accuracy.
Question - 27 : - Name a few Machine Learning algorithms you know.
Answer - 27 : -
- Logistic regression
- Linear regression
- Decision trees
- Support vector machines
- Naive Bayes, and so on
Question - 28 : - What is Naive Bayes?
Answer - 28 : -
Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on the Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.
Question - 29 : - What is a Backpropagation Algorithm?
Answer - 29 : -
Backpropagation is a Neural Network algorithm that is mainly used to process noisy data and detect unrecognized patterns for better clarification. It’s a full-state algorithm and has an iterative nature. As an ANN algorithm, Backpropagation has three layers, Input, hidden, and output layer.
The input layers receive the input values and constraints from the user or the outside environment. After that, the data goes to the Hidden layer where the processing is done. At last, the processed data is transformed into some values or patterns that can be shared using the output layer.
Before processing the data, the following values should be there with the algorithm:
- Dataset: The dataset which is going to be used for training a model.
- Target Attributes: Output values that an algorithm should achieve after processing the data.
- Weights: In a neural network, weights are the parameters that transform input data within the hidden layer.
- Biases: At each node, some values called bias are added to the sum calculated(except input nodes).
Backpropagation is simple ANN algorithm that follows a standard approach for training ML models. It doesn’t require high computational performance and is widely used in speed recognition, image processing, and optical character recognition(OCR).
Question - 30 : - How route weights are optimized to reduce the error in the model?
Answer - 30 : -
Weights in AI determine how much influence the input is going to have on the output. In neural networks, algorithms use weights to process the information and train the model. The output is expected to be the same as the target attributes.
However, the output may have some errors, which need to be rectified to produce the exact output. For example, in the Backpropagation algorithm when there is an error in the output, the algorithm will backpropagate to the hidden layer and reroute the weights to get an optimized output.