The role of a hidden layer in a neural network is to extract and transform features from the input data. Hidden layers learn to represent the data in a way that makes it easier for the network to make predictions or classifications. They are essential for capturing the underlying patterns and relationships in the data.

Data Scientists are typically proficient in advanced statistical modeling. They use statistical techniques to analyze data and create predictive models. While Data Analysts may also have statistical skills, Data Scientists specialize in this area.

Ensemble methods, like Random Forest and Gradient Boosting, combine multiple models (decision trees in the case of Random Forest) to improve overall predictive performance. These models are trained independently and then aggregated to make predictions. The combination of models is what enhances the accuracy and robustness of the ensemble.

The process of transforming skewed data into a more Gaussian-like distribution is called "standardization." It involves shifting the data's distribution to have a mean of 0 and a standard deviation of 1, making it more amenable to certain statistical techniques.

Convolutional Neural Networks (CNNs) are well-suited for translation-invariant tasks, such as image processing, due to their ability to capture local patterns and features. CNNs can automatically learn and detect features in images, making them effective for tasks like object recognition and image classification.

The "right to explanation" is important as it promotes transparency in AI decision-making. In ethical AI, users should have insight into how AI algorithms arrive at their decisions. This transparency is vital to prevent bias, discrimination, and unethical decision-making, making it a critical aspect of AI ethics.

To combat the vanishing gradient problem in RNNs, a common approach is to use Long Short-Term Memory (LSTM) units. LSTMs are designed to alleviate the vanishing gradient issue by allowing gradients to flow over longer sequences.

Standardization is the process of transforming data to have a mean of 0 and a standard deviation of 1. This helps in making data more interpretable and suitable for various machine learning algorithms, as it removes the scale effect.

NewSQL databases like Google Spanner are designed to combine the scalability of NoSQL databases with strong transactional guarantees, making them suitable for microservices transitioning from monolithic systems.

Pre-processing in computer vision typically includes steps like resizing, filtering, and transforming an image. It's during this phase that an image is converted into an array of pixel values, making it ready for subsequent analysis and feature extraction.