In RNNs, the function that retains information from previous inputs in the sequence is typically referred to as the "Memory Cell" or "Hidden State." This element allows RNNs to maintain a form of memory that influences their predictions at each step in the sequence, making them suitable for sequential data processing.

When data is not missing at random, it's often referred to as "data leakage." Data leakage can occur when missing data is not random but systematically related to the target variable, which can lead to biased results in data analysis.

Transparency is an essential ethical principle in AI, emphasizing that AI systems should be open and transparent about how they make decisions. It ensures that users and stakeholders can understand the logic behind AI-generated outcomes and trust the system.

Machine Translation is the most beneficial NLP technique for handling multiple languages with a single model. It allows the chatbot to translate user queries from various languages to a common language for processing. NER, Sentiment Analysis, and POS tagging are useful for different tasks but do not directly address multilingual support.

When dealing with spatial hierarchies in images, Convolutional Neural Networks (CNNs) are the most suitable choice. CNNs are designed to capture local patterns and spatial information in images, making them highly effective for tasks like facial recognition, where spatial hierarchies are crucial.

Data Engineers are responsible for the efficient collection, storage, and processing of data. They create the infrastructure necessary for Data Scientists and Analysts to work with data effectively.

When dealing with skewed datasets, the median is preferred for imputation. The median is robust to extreme values and is less affected by outliers than the mean. Using the median as the measure of central tendency helps maintain the integrity of the dataset in the presence of outliers.

The "Transformation" stage in the ETL (Extract, Transform, Load) process is responsible for cleaning, validating, and transforming data to ensure its quality. This phase involves data cleaning, data type conversion, and other operations to make the data suitable for analysis and reporting.

The issue where adding more layers to a deep neural network exacerbates the training problem due to diminishing gradients is called "Vanishing Gradient." It occurs when gradients become too small during backpropagation, making it challenging for deep networks to learn intricate patterns in the data.

To address the vanishing gradient problem in RNNs, one common technique is to use Long Short-Term Memory (LSTM) networks. LSTMs are a type of RNN that helps mitigate the vanishing gradient problem by preserving and updating information over long sequences. LSTMs are designed to capture long-term dependencies and are more effective than traditional RNNs for tasks where data from distant time steps is important.

The Transformation stage is where data discrepancies are often addressed. During transformation, data is cleaned, normalized, and validated to ensure consistency and reliability. This stage is critical for data quality and consistency in the data warehouse. Extraction involves collecting data, Loading is about data loading into the warehouse, and Data Ingestion is the process of bringing data into the system.

"Interpretability" in AI ethics is about understanding how AI systems make decisions. It's crucial for accountability, transparency, and identifying and addressing potential biases in AI algorithms. AI being right or fast is important but not the primary concern in this context.