Veracity refers to the accuracy and trustworthiness of data, which is not a defining characteristic of Big Data.

The ultimate goal of AI is to create machines that can think and act like humans.

Logistic Regression is widely used for binary classification problems, where the output is either 0 or 1.

The process of training a Machine Learning model involves feeding it data and allowing it to learn patterns and relationships.

Transfer Learning is a technique used to transfer knowledge from one Machine Learning model to another, rather than a distinct type of Machine Learning.

The sheer volume and complexity of Big Data make it challenging to extract meaningful insights and make informed decisions.

Quantum Computing is a field of computer science that explores the use of quantum-mechanical phenomena to perform computations.

Predictive Analytics aims to use historical data to make predictions about future events or outcomes.

Climate Change is an environmental issue, not directly related to the ethical implications of Artificial Intelligence.

Machine Learning algorithms are used to train models that can learn from data and make predictions.

Data Overload refers to the challenge of managing and analyzing large volumes of data, which is not a benefit of Big Data Analytics.

Clustering is a technique used in Unsupervised Learning to group similar data points together.

Decision Tree is a tree-like structure used for classification and regression tasks, not a type of Artificial Neural Network.

Testing involves using a held-out dataset to assess the performance of a Machine Learning model.

MySQL is a relational database management system, not specifically designed for Big Data processing.