Abstract: Methods and systems are provided to edit or update images or videos based on instructions. A system may analyze an input image and may determine an instruction associated with the input image. The instruction may include content to edit or update the input image. The system may select an edit task, among predetermined edit tasks associated with changes to images, based on a description of the content of the instruction. The system may generate an output image, based on implementing the selected edit task, including an update to the input image depicting the description of the content of the instruction.

Abstract: Various systems, methods, and devices are described for utilizing artificial intelligence (AI) bot (e.g., a chatbot) to fetch or create content associated with a third-party platform based on an input associated with an electronic device. In an example, systems and methods of AI bot fetching or creating content may include receiving an input, via a user device. The input may be textual, audible, or any other suitable method. Based on the input, one or more content items may be fetched or created. The machine learning model may be utilized to determine context associated with the input. The machine leaning model may determine a number of content items associated with the input and data sources related to the retrieval generators. A result may be presented to a user, where the result may comprise the one or more content items determined.

Abstract: In accordance with techniques for context-driven generation of diverse questions, a machine learning model receives a first target question, a second target question, and a context. Based on the context and one or more words of the first target question, a first decoder of the machine learning model outputs a first representation of candidate words to follow the one or more words of the first target question. Based on the context and one or more words of the second target question, a second decoder of the machine learning model outputs a second representation of candidate words to follow the one or more words of the second target question. The machine learning model is fine-tuned to generate diverse questions for a given context based on a diversity loss that captures a degree of variance between the first representation and the second representation.

Abstract: A method and system for text-to-video generation. The method includes receiving a text input, generating a representation frame based on the text input using a model trained on text-image pairs, generating a set of frames based on the representation frame and a first frame rate, interpolating the set of frames to a higher frame rate, generating a first video based on the interpolated set of frames, increasing a resolution of the first video based on a first and second super-resolution model, and generating an output video based on a result of the super-resolution models.

Abstract: Methods, systems, and storage media for generating audio data includes receiving a text input. The method also includes receiving a plurality of representative audio sources and encoding the plurality of representative audio sources into a plurality of audio tokens. The method includes encoding the text input into a plurality of text representations. The method comprises mapping each audio tokens of the plurality of audio tokens to a text representation of the plurality of text representations. The method also comprises determining a relationship score based on mapping each audio tokens to the text representation, wherein the relationship score identifies a distribution of audio tokens from the plurality of audio tokens. The method and systems can also comprise decoding the subgroup of audio tokens to yield a reconstructed audio source.