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Generative artificial intelligence (generative AI, GenAI, [1] or GAI) is a subset of artificial intelligence that uses generative models to produce text, images, videos, or other forms of data. [ 2 ] [ 3 ] [ 4 ] These models learn the underlying patterns and structures of their training data and use them to produce new data [ 5 ] [ 6 ] based on ...
Liang Zhao is a computer scientist and academic. He is an associate professor in the Department of Computer Science at Emory University. [1]Zhao's research focuses on data mining, machine learning, and artificial intelligence, with particular interests in deep learning on graphs, societal event prediction, interpretable machine learning, multi-modal machine learning, generative AI, and ...
[6] [7] At the time, the focus of the research was on improving Seq2seq techniques for machine translation, but the authors go further in the paper, foreseeing the technique's potential for other tasks like question answering and what is now known as multimodal Generative AI. [1] The paper's title is a reference to the song "All You Need Is ...
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.
His joint paper published in 2017, provided a detailed account of the design of the DeepQ AI-powered symptom checker's key components, such as the optical sense and vital sense modules. These modules were integrated into a mobile device that was designed to offer a user-friendly experience.
A February 2019 article in The Verge argued that the threat posed by GPT-2 had been exaggerated; [21] Anima Anandkumar, a professor at Caltech and director of machine learning research at Nvidia, said that there was no evidence that GPT-2 had the capabilities to pose the threats described by OpenAI, and that what they did was the "opposite of ...