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A small image of Clippit can be found in Microsoft Office 2013 and newer, which can be seen by going to Options and changing the theme (or Office Background) to "School Supplies". Clippit would then appear on the ribbon. Clippit appeared as an Office Assistant in Office Online as part of an April Fools' Day 2014 joke. [34]
DALL-E was revealed by OpenAI in a blog post on 5 January 2021, and uses a version of GPT-3 [5] modified to generate images.. On 6 April 2022, OpenAI announced DALL-E 2, a successor designed to generate more realistic images at higher resolutions that "can combine concepts, attributes, and styles". [6]
Later IUIs, such as Clippy, are more statistically-based, using machine learning methods to decide how to tune the interactive experience to the individual user. In the 2000s this strand of research often is labeled personalization , most often employing various recommender system techniques to adapt the behavior of an interface or an entire ...
David Paul Morris/Bloomberg—Getty Images. During the PC boom of the 1990s, Microsoft introduced Clippy as a friendly face to help users hone their word-processing skills. An animated paper clip ...
Ideogram was founded in 2022 by Mohammad Norouzi, William Chan, Chitwan Saharia, and Jonathan Ho to develop a better text-to-image model. [3]It was first released with its 0.1 model on August 22, 2023, [4] after receiving $16.5 million in seed funding, which itself was led by Andreessen Horowitz and Index Ventures.
Diagram of the latent diffusion architecture used by Stable Diffusion The denoising process used by Stable Diffusion. The model generates images by iteratively denoising random noise until a configured number of steps have been reached, guided by the CLIP text encoder pretrained on concepts along with the attention mechanism, resulting in the desired image depicting a representation of the ...
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.
In text-to-image retrieval, users input descriptive text, and CLIP retrieves images with matching embeddings. In image-to-text retrieval, images are used to find related text content. CLIP’s ability to connect visual and textual data has found applications in multimedia search, content discovery, and recommendation systems. [31] [32]
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