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Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] 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.
GPT may refer to: Computing. Generative pre-trained transformer, a type of artificial intelligence language model ChatGPT, a chatbot developed by OpenAI, based on ...
Generative AI systems trained on words or word tokens include GPT-3, GPT-4, GPT-4o, LaMDA, LLaMA, BLOOM, Gemini and others (see List of large language models). They are capable of natural language processing, machine translation, and natural language generation and can be used as foundation models for other tasks. [62]
The term was first logged on Urban Dictionary, a crowdsourced English language online dictionary, in December 2017 with the definition, "what you would say if something was really good."
The new model, called GPT-4o, is an update from the company’s previous GPT-4 model, which launched just over a year ago. The model will be available to unpaid customers, meaning anyone will have ...
AI slop, commonly referred to simply as slop, is low-quality media—including writing and images—made using generative artificial intelligence technology. [4] [5] [1] Coined in the 2020s, the term has a derogatory connotation akin to "spam".
Internet slang (also called Internet shorthand, cyber-slang, netspeak, digispeak or chatspeak) is a non-standard or unofficial form of language used by people on the Internet to communicate to one another. [1] An example of Internet slang is "lol" meaning "laugh out loud."
GPT-2 was pre-trained on a dataset of 8 million web pages. [2] It was partially released in February 2019, followed by full release of the 1.5-billion-parameter model on November 5, 2019. [3] [4] [5] GPT-2 was created as a "direct scale-up" of GPT-1 [6] with a ten-fold increase in both its parameter count and the size of its training dataset. [5]