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  2. Residual neural network - Wikipedia

    en.wikipedia.org/wiki/Residual_neural_network

    In 1961, Frank Rosenblatt described a three-layer multilayer perceptron (MLP) model with skip connections. [16]: 313, Chapter 15 The model was referred to as a "cross-coupled system", and the skip connections were forms of cross-coupled connections. During the late 1980s, "skip-layer" connections were sometimes used in neural networks.

  3. Generative pre-trained transformer - Wikipedia

    en.wikipedia.org/wiki/Generative_pre-trained...

    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.

  4. Retrieval-augmented generation - Wikipedia

    en.wikipedia.org/wiki/Retrieval-augmented_generation

    Retrieval-Augmented Generation (RAG) is a technique that grants generative artificial intelligence models information retrieval capabilities. It modifies interactions with a large language model (LLM) so that the model responds to user queries with reference to a specified set of documents, using this information to augment information drawn from its own vast, static training data.

  5. Impact of self-driving cars - Wikipedia

    en.wikipedia.org/wiki/Impact_of_self-driving_cars

    According to a 2020 Annual Review of Public Health review of the literature, self-driving cars "could increase some health risks (such as air pollution, noise, and sedentarism); however, if properly regulated, AVs will likely reduce morbidity and mortality from motor vehicle crashes and may help reshape cities to promote healthy urban environments."