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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 ...
An image conditioned on the prompt an astronaut riding a horse, by Hiroshige, generated by Stable Diffusion 3.5, a large-scale text-to-image model first released in 2022. A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description.
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.
Gen-2 is a multimodal AI system that can generate novel videos with text, images or video clips. The model is a continuation of Gen-1 and includes a modality to generate video conditioned to text. Gen-2 is one of the first commercially available text-to-video models. [31] [32] [33] [34]
Google AI is a division of Google dedicated to artificial intelligence. [1] It was announced at Google I/O 2017 by CEO Sundar Pichai. [2]This division has expanded its reach with research facilities in various parts of the world such as Zurich, Paris, Israel, and Beijing. [3]
In 2019 October, Google started using BERT to process search queries. [36] In 2020, Google Translate replaced the previous RNN-encoder–RNN-decoder model by a Transformer-encoder–RNN-decoder model. [37] Starting in 2018, the OpenAI GPT series of decoder-only Transformers became state of the art in natural language generation.
Google’s former CEO Eric Schmidt has a complaint about his old stomping ground—and it's one that workers have heard on repeat for the past two years: They aren’t working in the office enough.
As originally proposed by Google, [11] each CoT prompt included a few Q&A examples. This made it a few-shot prompting technique. However, according to researchers at Google and the University of Tokyo , simply appending the words "Let's think step-by-step", [ 21 ] has also proven effective, which makes CoT a zero-shot prompting technique.
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