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The Pile is an 886.03 GB diverse, open-source dataset of English text created as a training dataset for large language models (LLMs). It was constructed by EleutherAI in 2020 and publicly released on December 31 of that year. [1] [2] It is composed of 22 smaller datasets, including 14 new ones. [1]
Re-captioning is used to augment training data, by using a video-to-text model to create detailed captions on videos. [ 7 ] OpenAI trained the model using publicly available videos as well as copyrighted videos licensed for the purpose, but did not reveal the number or the exact source of the videos. [ 5 ]
Adaptive web design (AWD) promotes the creation of multiple versions of a web page to better fit the user's device, as opposed to a single static page which loads (and looks) the same on all devices or a single page which reorders and resizes content responsively based on the device/screen size/browser of the user.
Make web pages easy to read for you! With simple keyboard shortcuts, you can zoom in or out to make text larger or smaller. In an instant, these commands improve the readability of the content you're viewing. • Zoom in - Press Ctrl (CMD on a Mac) + the plus key (+) on your keyboard.
Perplexity AI is a conversational search engine that uses large language models (LLMs) to answer queries using sources from the web and cites links within the text response. [3] Its developer, Perplexity AI, Inc., is based in San Francisco, California .
Since OpenAI has not released source code for any of the three models, there have been several attempts to create open-source models offering similar capabilities. [ 64 ] [ 65 ] Released in 2022 on Hugging Face 's Spaces platform, Craiyon (formerly DALL-E Mini until a name change was requested by OpenAI in June 2022) is an AI model based on the ...
AI is effectively ‘useless’—and it’s created a ‘fake it till you make it’ bubble that could end in disaster, veteran market watcher warns Will Daniel July 8, 2024 at 3:40 PM
The number of neurons in the middle layer is called intermediate size (GPT), [55] filter size (BERT), [35] or feedforward size (BERT). [35] It is typically larger than the embedding size. For example, in both GPT-2 series and BERT series, the intermediate size of a model is 4 times its embedding size: =.