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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.
A question and answer system (or Q&A system) is an online software system that attempts to answer questions asked by users.Q&A software is frequently integrated by large and specialist corporations and tends to be implemented as a community that allows users in similar fields to discuss questions and provide answers to common and specialist questions.
Whisper is a machine learning model for speech recognition and transcription, created by OpenAI and first released as open-source software in September 2022. [2]It is capable of transcribing speech in English and several other languages, and is also capable of translating several non-English languages into English. [1]
The high-level architecture of IBM's DeepQA used in Watson [9]. Watson was created as a question answering (QA) computing system that IBM built to apply advanced natural language processing, information retrieval, knowledge representation, automated reasoning, and machine learning technologies to the field of open domain question answering.
Command and Control Systems: Semantic parsing aids in the accurate interpretation of voice or text commands used to control systems in applications such as software interfaces or smart homes. Content Categorization: It is a useful tool for online publishing and digital content management as it aids in the classification and organization of vast ...
Prompt engineering is the process of structuring or crafting an instruction in order to produce the best possible output from a generative artificial intelligence (AI) model. [ 1 ] A prompt is natural language text describing the task that an AI should perform. [ 2 ]
Piazza is a learning management system which allows students to ask questions in a forum-type format. Instructors are able to moderate the discussion, along with endorsing accurate answers. Instructors are able to moderate the discussion, along with endorsing accurate answers.
The capabilities of a generative AI system depend on the modality or type of the data set used. Generative AI can be either unimodal or multimodal; unimodal systems take only one type of input, whereas multimodal systems can take more than one type of input. [59] For example, one version of OpenAI's GPT-4 accepts both text and image inputs. [60]