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Wu Dao – Wen Hui, an 11.3-billion-parameter generative language model, was designed for "essential problems in general artificial intelligence from a cognitive perspective"; Synced says that it can "generate poetry, make videos, draw pictures, retrieve text, perform complex reasoning, etc".
Deep learning speech synthesis refers to the application of deep learning models to generate natural-sounding human speech from written text (text-to-speech) or spectrum . Deep neural networks are trained using large amounts of recorded speech and, in the case of a text-to-speech system, the associated labels and/or input text.
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.
AI cannot kill poetry. The intersection of people who write poetry and people who read poetry is very nearly a circle. As Warpland 2.0 puts it, " I am going to keep writing poetry until the day I die.
Gato is a deep neural network for a range of complex tasks that exhibits multimodality. It can perform tasks such as engaging in a dialogue, playing video games, controlling a robot arm to stack blocks, and more. It was created by researchers at London-based AI firm DeepMind. It is a transformer, like GPT-3. [1]
For a vast number of book writers, artificial intelligence is a threat to their livelihood and the very idea of creativity. More than 10,000 of them endorsed an open letter from the Authors Guild ...
Edmond de Belamy, an artwork generated by a generative adversarial network. Computational creativity (also known as artificial creativity, mechanical creativity, creative computing or creative computation) is a multidisciplinary endeavour that is located at the intersection of the fields of artificial intelligence, cognitive psychology, philosophy, and the arts (e.g., computational art as part ...
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.
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