Abstract: Implementations described herein relate to causing a client device to initiate streaming of multimedia content and causing the client device to render dialog content before and/or during the streaming of the multimedia content. Processor(s) can generate a structured large language model (LLM) query that can be processed to generate LLM output. The LLM output can include, for example, an indication of the multimedia content and the dialog content. In some implementations, the processors(s) can determine when to generate an additional structured LLM query to continue the streaming of the multimedia content, and proactively cause additional LLM output to be generated based on the additional structured LLM query. In additional or alternative implementations, the processor(s) can determine when to cause the dialog content to be rendered with respect to the streaming of the multimedia content.

Abstract: Implementations are described herein for using LLMs to modify less than the entirety of rendered LLM outputs. In various implementations, a first LLM response is used by a client application to provide first rendered LLM output. The client application may provide (i) an indication of a subportion of the first rendered LLM output that is selected by a user, and (ii) a request to modify the selected subportion. A subportion of the first LLM response corresponding to the selected subportion of the first rendered LLM output may be used to assemble a second LLM prompt, which may be processed using one or more LLMs to generate a second LLM response. The second LLM response may be operable to provide second rendered LLM output that includes at least part of the first rendered LLM output outside of the selected subportion and the modified selected subportion.

Abstract: Methods and systems for training a machine learning model include generating pairs of training pixel patches from a dataset of training images, each pair including a first patch representing a part of a respective training image, and a second patch, centered at the same location as the first, representing a larger part of the training image, being resized to a same size of as the first patch. A detection model is trained using the first pixel patches, to detect and locate cells in the images. A classification model is trained using the first pixel patches, to classify cells according to whether the detected cells are cancerous, based on cell location information generated by the detection model. A segmentation model is trained using the second pixel patches, to locate and classify cancerous arrangements of cells in the images.

Abstract: Implementations described herein relate to causing a client device to initiate streaming of multimedia content and causing the client device to render dialog content before and/or during the streaming of the multimedia content. Processor(s) can generate a structured large language model (LLM) query that can be processed to generate LLM output. The LLM output can include, for example, an indication of the multimedia content and the dialog content. In some implementations, the processors(s) can determine when to generate an additional structured LLM query to continue the streaming of the multimedia content, and proactively cause additional LLM output to be generated based on the additional structured LLM query. In additional or alternative implementations, the processor(s) can determine when to cause the dialog content to be rendered with respect to the streaming of the multimedia content.

Abstract: Methods and systems for training a machine learning model include generating pairs of training pixel patches from a dataset of training images, each pair including a first patch representing a part of a respective training image, and a second patch, centered at the same location as the first, representing a larger part of the training image, being resized to a same size of as the first patch. A detection model is trained using the first pixel patches, to detect and locate cells in the images. A classification model is trained using the first pixel patches, to classify cells according to whether the detected cells are cancerous, based on cell location information generated by the detection model. A segmentation model is trained using the second pixel patches, to locate and classify cancerous arrangements of cells in the images.