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In this multi-modal task natural language processing and machine learning are utilized to produce human-like speech from text.

A multi-modal Task that utilizes computer vision algorithms in combination with language generation models to recognize objects, characters, scenes, or activities within images and then generating relevant textual descriptions or identifications.

An AI system that operates exclusively through a single mode of input or output, such as voice or text, can interact with a range of services and apps to enrich the output's recency or relevancy as part of its processing.

An artificial intelligence model can understand and respond to one or various forms of input, such as text, voice, and visual cues. This type of assistant can integrate information from multiple sources and modalities to provide a more comprehensive and personalized experience for the user.

Combines retrieval and generation models to find an present information in big corpora. In RAG, a retrieval model first selects relevant pieces of information from a large corpus, which are then used by a generation model to produce a coherent and contextually relevant response. It may or not be Q/A based.

Mathematical representation of text or data that captures the underlying relationships and context within the information. Text embeddings are often used in natural language processing tasks and can also be applied in other areas of machine learning or functional challenges such as search. By transforming data into a dense numerical format, embeddings allow models to better capture the meaning and nuances of the information, leading to more accurate predictions and insights.