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  2. Andrej Karpathy - Wikipedia

    en.wikipedia.org/wiki/Andrej_Karpathy

    Andrej Karpathy (born 23 October 1986 [2]) is a Slovak-Canadian computer scientist who served as the director of artificial intelligence and Autopilot Vision at Tesla. He co-founded and formerly worked at OpenAI , [ 3 ] [ 4 ] [ 5 ] where he specialized in deep learning and computer vision .

  3. AutoGPT - Wikipedia

    en.wikipedia.org/wiki/AutoGPT

    AutoGPT can be used to develop software applications from scratch. [5] AutoGPT can also debug code and generate test cases. [ 9 ] Observers suggest that AutoGPT's ability to write, debug, test, and edit code may extend to AutoGPT's own source code, enabling self-improvement.

  4. Former OpenAI, Tesla engineer Andrej Karpathy starts AI ... - AOL

    www.aol.com/news/former-openai-tesla-engineer...

    Karpathy - who received a PhD from Stanford University - started posting tutorial videos on how to solve Rubik's cubes and over the years has published content online exploring concepts related to AI.

  5. 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.

  6. Sam Altman - Wikipedia

    en.wikipedia.org/wiki/Sam_Altman

    Altman was born on April 22, 1985, in Chicago, Illinois, [8] [9] into a Jewish family, [10] and grew up in St. Louis, Missouri.His mother is a dermatologist, and his father was a real estate broker.

  7. 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.