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DeepSeek-V2 was released in May 2024. In June 2024, the DeepSeek-Coder V2 series was released. [32] The DeepSeek login page shortly after a cyberattack that occurred following its January 20 launch. DeepSeek V2.5 was released in September and updated in December 2024. [33] On 20 November 2024, DeepSeek-R1-Lite-Preview became accessible via API ...
Hugging Face, Inc. is an American company incorporated under the Delaware General Corporation Law [1] and based in New York City that develops computation tools for building applications using machine learning.
In January 2025, DeepSeek released DeepSeek R1, a 671-billion-parameter open-weight model that performs comparably to OpenAI o1 but at a much lower cost. [19] Since 2023, many LLMs have been trained to be multimodal, having the ability to also process or generate other types of data, such as images or audio. These LLMs are also called large ...
There is free software on the market capable of recognizing text generated by generative artificial intelligence (such as GPTZero), as well as images, audio or video coming from it. [99] Potential mitigation strategies for detecting generative AI content include digital watermarking , content authentication , information retrieval , and machine ...
Daphne Koller (Hebrew: דפנה קולר; born August 27, 1968) is an Israeli-American computer scientist. She was a professor in the department of computer science at Stanford University [4] and a MacArthur Foundation fellowship recipient. [1]
T5 (Text-to-Text Transfer Transformer) is a series of large language models developed by Google AI introduced in 2019. [1] [2] Like the original Transformer model, [3] T5 models are encoder-decoder Transformers, where the encoder processes the input text, and the decoder generates the output text.
For many years, sequence modelling and generation was done by using plain recurrent neural networks (RNNs). A well-cited early example was the Elman network (1990). In theory, the information from one token can propagate arbitrarily far down the sequence, but in practice the vanishing-gradient problem leaves the model's state at the end of a long sentence without precise, extractable ...
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.