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In artificial intelligence, the process of adding labels or tags to datasets to categorize and classify the data is called data annotation.

In machine learning, Boosting is an integrated meta-algorithm used to reduce bias and variance in supervised learning, as well as a family of machine learning algorithms that convert weak learners into strong learners.

Music Information Retrieval (MIR) is an interdisciplinary field concerned with the extraction of information from music and its analysis, aiming to study the processes, systems, and knowledge representations required to retrieve information from music.

Reinforcement Learning with AI Feedback (RLAIF) is a hybrid learning approach that allows the learning agent to refine its behavior not only based on rewards from the environment, but also based on insights gained from other AI systems, thus enriching the learning process.

Pattern Recognition uses machine learning algorithms to automatically identify patterns and regularities in data. This data can be anything from text, images to sound or other definable qualities.

Active learning is a special case of machine learning in which the learning algorithm can interactively query the user (or some other information source) to label new data points with the desired output.

Predictive Analytics is the process of using data analysis, machine learning, artificial intelligence, and statistical models to find patterns that may predict future behavior.

Sentiment Analysis, also known as opinion mining, refers to the use of natural language processing, text mining, and computational linguistics to identify and extract subjective information from original materials.

Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores of multiple previously ranked results to produce a unified set of results.

Grid computing pools all the unused resources on multiple computers and uses them to perform a single task. Organizations use grid computing to perform large tasks or solve complex problems that are difficult to handle on a single computer. 

Backward Chaining is a reasoning method that is often used in expert systems and rule engines in the field of artificial intelligence.

Forward Chaining is a reasoning method used to gradually derive conclusions based on known facts. In a rule-based reasoning system, it starts from a known starting fact or rule, gradually derives new conclusions by matching the conditional part of the rule and performing corresponding operations based on the matching results.

The AI Framework represents the backbone of AI, providing the infrastructure for developing and deploying AI models.

Autonomous AI refers to artificial intelligence systems that are able to perform tasks without human intervention.

Bounding Box, also known as bounding volume or bounding area, is a rectangular box used to describe the location and range of an object in an image.

RAG is a technique that uses facts obtained from external sources to improve the accuracy and reliability of generative AI models. It optimizes the output of large language models to reference authoritative knowledge bases outside the training data source before generating a response.

In computer science, distributed computing is a method of having multiple computers work together to solve a common problem.

Neural Radiance Fields (NeRF) is a neural network that can reconstruct complex 3D scenes from partial 2D image sets.

The Raspberry Pi is a small, credit card-sized computer that can be used in conjunction with any input and output hardware device.

A significant advantage of Mixture of Experts (MoE) models is that they can be effectively pre-trained with far fewer computational resources than dense models. This means that the size of the model or dataset can be significantly scaled up under the same computational budget.

Data augmentation is one of the commonly used techniques in deep learning, which involves making minor changes to the dataset or using deep learning to generate new data points. 

Autoregressive models are a class of machine learning (ML) models that automatically predict the next component in a sequence by measuring previous inputs in the sequence.

The Transformer model is a deep learning model that uses a self-attention mechanism, which assigns different weights to different parts of the input data according to their importance. This model is mainly used in the fields of natural language processing (NLP) and computer vision (CV).

TensorFlow is an open source software library for machine learning for various perception and language understanding tasks. It is currently widely used in research and production, such as Google commercial products such as speech recognition, Gmail, Google Photos, and Search.