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Data is typically distinguished in spatial data and time-series data, the former can be things like images, maps, graphs, etc. the latter can be e.g. stock-price or a voice recording. Document AI combines text data, which has a time dimension, with other types of data, such as the position of an address in a business letter, which is spatial.
[33] [34] Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use, [35] process plans for manufacturing [33] and decision plans such as in prototype autonomous ...
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An example of an AI-generated conversation by NotebookLM (January 2025) NotebookLM (Google NotebookLM) is a research and note-taking online tool developed by Google Labs that uses artificial intelligence (AI), specifically Google Gemini, to assist users in interacting with their documents. It can generate summaries, explanations, and answers ...
Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.
Document automation (also known as document assembly) is the design of systems and workflows that assist in the creation of electronic documents. These include logic-based systems that use segments of pre-existing text and/or data to assemble a new document.
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