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A text-to-image model is a machine learning model which takes an input natural language description and produces an image matching that description. Text-to-image models began to be developed in the mid-2010s during the beginnings of the AI boom, as a result of advances in deep neural networks. In 2022, the output of state-of-the-art text-to ...
Together with results from HDBSCAN, users can generate topic hierarchies, or groups of related topics and subtopics. Furthermore, a user can use the results of top2vec to infer the topics of out-of-sample documents. After inferring the embedding for a new document, must only search the space of topics for the closest topic vector.
DALL·E, DALL·E 2, and DALL·E 3 are text-to-image models developed by OpenAI using deep learning methodologies to generate digital images from natural language descriptions known as "prompts". The first version of DALL-E was announced in January 2021. In the following year, its successor DALL-E 2 was released. DALL·E 3 was released natively ...
Google (GOOG, GOOGL) is bringing generative AI to its Android phones, rolling out new capabilities for text messaging with a feature called Magic Compose.Launching later this summer as a Beta ...
Generative AI planning systems used symbolic AI methods such as state space search and constraint satisfaction and were a "relatively mature" technology by the early 1990s. They were used to generate crisis action plans for military use, [65] process plans for manufacturing [63] and decision plans such as in prototype autonomous spacecraft. [66]
Adobe Illustrator is a vector graphics editor and design software developed and marketed by Adobe. Originally designed for the Apple Macintosh , development of Adobe Illustrator began in 1985 . Along with Creative Cloud (Adobe's shift to a monthly or annual subscription service delivered over the Internet ), Illustrator CC was released.
Token type: The token type is a standard embedding layer, translating a one-hot vector into a dense vector based on its token type. Position: The position embeddings are based on a token's position in the sequence. BERT uses absolute position embeddings, where each position in sequence is mapped to a real-valued vector.
Generative pretraining (GP) was a long-established concept in machine learning applications. [16] [17] [18] 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.