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Automatic summarization. Automatic summarization is the process of shortening a set of data computationally, to create a subset (a summary) that represents the most important or relevant information within the original content. Artificial intelligence algorithms are commonly developed and employed to achieve this, specialized for different ...
Prompt engineering is the process of structuring an instruction that can be interpreted and understood by a generative AI model. [1] [2] A prompt is natural language text describing the task that an AI should perform: [3] a prompt for a text-to-text language model can be a query such as "what is Fermat's little theorem?", [4] a command such as "write a poem about leaves falling", [5] or a ...
Artificial intelligence. Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is artificial intelligence capable of generating text, images, videos, or other data using generative models, [2] often in response to prompts. [3][4] Generative AI models learn the patterns and structure of their input training data and then generate ...
Question answering. Question answering (QA) is a computer science discipline within the fields of information retrieval and natural language processing (NLP) that is concerned with building systems that automatically answer questions that are posed by humans in a natural language. [1]
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabelled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labelled dataset.
Creators will input text prompts to create AI images, which can then become the basis of the six-second clips. Mohan teased it with an AI-generated video of a dog and a sheep becoming friends.
To do so technically would require a more sophisticated grammar, like a Chomsky Type 1 grammar, also termed a context-sensitive grammar. However, parser generators for context-free grammars often support the ability for user-written code to introduce limited amounts of context-sensitivity. (For example, upon encountering a variable declaration ...
LangChain was launched in October 2022 as an open source project by Harrison Chase, while working at machine learning startup Robust Intelligence. The project quickly garnered popularity, [3] with improvements from hundreds of contributors on GitHub, trending discussions on Twitter, lively activity on the project's Discord server, many YouTube tutorials, and meetups in San Francisco and London.