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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 .
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.
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.
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.
Generative Pre-trained Transformer 1 (GPT-1) was the first of OpenAI's large language models following Google's invention of the transformer architecture in 2017. [2] In June 2018, OpenAI released a paper entitled "Improving Language Understanding by Generative Pre-Training", [ 3 ] in which they introduced that initial model along with the ...
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.
Good news for generative AI fans, and bad news for those who fear an age of cheap, procedurally-generated content: OpenAI's GPT-4 is a better language model than GPT-3, the model that powered ...
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] GPT-2 was created as a "direct scale-up" of GPT-1 [6] with a ten-fold increase in both its parameter count and the size of its training dataset. [5]