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ChatGPT is a chatbot and virtual assistant developed by OpenAI and launched on November 30, 2022. Based on large language models (LLMs), it enables users to refine and steer a conversation towards a desired length, format, style, level of detail, and language. Successive user prompts and replies are considered at each conversation stage as context.
This is a list of free and open-source software packages, computer software licensed under free software licenses and open-source licenses.Software that fits the Free Software Definition may be more appropriately called free software; the GNU project in particular objects to their works being referred to as open-source. [1]
FOSDEM —Free and Open-source Software Developers' European Meeting. FOSI —Formatted Output Specification Instance. FOSS —Free and Open-Source Software. FP —Function Programming. FP —Functional Programming. FPGA —Field Programmable Gate Array. FPS —Floating Point Systems. FPU —Floating-Point Unit. FRU —Field-Replaceable Unit.
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e. Generative Pre-trained Transformer 2 ( GPT-2) is a large language model by OpenAI and the second in their foundational series of GPT models. 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]
CAPTCHA. This CAPTCHA ( reCAPTCHA v1) of "smwm" obscures its message from computer interpretation by twisting the letters and adding a slight background color gradient. A CAPTCHA ( / ˈkæp.tʃə / KAP-chə) is a type of challenge–response test used in computing to determine whether the user is human in order to deter bot attacks and spam.
GPT-J. GPT-J or GPT-J-6B is an open-source large language model (LLM) developed by EleutherAI in 2021. [1] As the name suggests, it is a generative pre-trained transformer model designed to produce human-like text that continues from a prompt. The optional "6B" in the name refers to the fact that it has 6 billion parameters.
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.