Abstract: Implementations relate to managing multimedia content that is obtained by large language model(s) (LLM(s)) and/or generated by other generative model(s). Processor(s) of a system can: receive natural language (NL) based input that requests multimedia content, generate a response that is responsive to the NL based input, and cause the response to be rendered. In some implementations, and in generating the response, the processor(s) can process, using a LLM, LLM input to generate LLM output, and determine, based on the LLM output, at least multimedia content to be included in the response. Further, the processor(s) can evaluate the multimedia content to determine whether it should be included in the response. In response to determining that the multimedia content should not be included in the response, the processor(s) can cause the response, including alternative multimedia content or other textual content, to be rendered.

Abstract: Implementations relate to generating multi-modal response(s) through utilization of large language model(s) (LLM(s)). Processor(s) of a system can: receive natural language (NL) based input, generate a multi-modal response that is responsive to the NL based output, and cause the multi-modal response to be rendered. In some implementations, and in generating the multi-modal response, the processor(s) can process, using a LLM, LLM input (e.g., that includes at least the NL based input) to generate LLM output, and determine, based on the LLM output, textual content for inclusion in the multi-modal response and multimedia content for inclusion in the multi-modal response. In some implementations, the multimedia content can be obtained based on a multimedia content tag that is included in the LLM output and that is indicative of the multimedia content. In various implementations, the multimedia content can be interleaved between segments of the textual content.

Abstract: There is disclosed herein examples of system and procedure for generating a model to be implemented within autonomous vehicles for identifying assertive vehicles at an intersection having stop indicators. The model can be generated via data-driven procedure where captures of the movement of vehicles through an intersection are analyzed and utilized for generating the model. The model may be provided to the autonomous vehicles and may be implemented by the autonomous vehicles for identifying assertive vehicles.

Abstract: There is disclosed herein examples of system and procedure for generating a model to be implemented within autonomous vehicles for identifying assertive vehicles at an intersection having stop indicators. The model can be generated via data-driven procedure where captures of the movement of vehicles through an intersection are analyzed and utilized for generating the model. The model may be provided to the autonomous vehicles and may be implemented by the autonomous vehicles for identifying assertive vehicles.

Abstract: There is disclosed herein examples of system and procedure for generating a model to be implemented within autonomous vehicles for identifying assertive vehicles at an intersection having stop indicators. The model can be generated via data-driven procedure where captures of the movement of vehicles through an intersection are analyzed and utilized for generating the model. The model may be provided may be provided to the autonomous vehicles and may be implemented by the autonomous vehicles for identifying assertive vehicles.