Abstract: A computing system is disclosed that includes a processor and memory. The memory stores instructions that, when executed by the processor, cause the processor to perform several acts. The acts include generating a prompt that is to be input to a generative language model. The prompt includes conversational input set forth by a user. The acts further comprise providing the prompt as input to the generative language model, and receiving conversational output from the generative language model, where the generative language model generated the conversational output based upon the prompt. Additionally, the acts comprise streaming the conversational output on one of a SERP or webpage to which the user has navigated from the SERP.

Abstract: A computing system is disclosed that includes a processor and memory. The memory stores instructions that, when executed by the processor, cause the processor to perform several acts. The acts include generating a prompt that is to be input to a generative language model. The prompt includes conversational input set forth by a user. The acts further comprise providing the prompt as input to the generative language model, and receiving conversational output from the generative language model, where the generative language model generated the conversational output based upon the prompt. Additionally, the acts comprise streaming the conversational output on one of a SERP or webpage to which the user has navigated from the SERP.

Abstract: The present disclosure relates to generating semantic search engine results. Aspects of the present disclosure retrieve relevant information from a search engine based on user's search query. The query can be a classic search query (keyword or short phrase) or a conversational query (e.g., a chat messages between users and/or chatbots), a query based upon an email or other type of message, or a query generate based upon a content item (e.g., a webpage, image, video, document, etc.). Aspects of the disclosure leverage a large language model (LLM), such as, for example, a generative model, to summarizes the content according to the intent detected from the query. In some cases, aspects of the present disclosure may generate a direct answer to the query and provide relevant references to support the information.

Abstract: Methods, systems, and media for storing entries in and/or retrieving information from an object memory are provided. In some examples, a content item is received that has content data. The content data associated with the content item may be provided to one or more semantic models that generate semantic objects. From one or more of the semantic models, one or semantic objects may be received. The one or more semantic objects may then be inserted into the object memory. The semantic objects may be associated with respective indications corresponding to a reference to source data associated with the semantic objects. Further, the insertion may trigger a memory storage operation to store the semantic objects. A plurality of collections of stored objects may be received from the object memory, based on a provided input, to determine a result.

Abstract: A computing system described herein includes a processor and memory storing instructions that, when executed by the processor, cause the processor to perform several acts. The acts include generating a prompt that is to be input to a generative language model, where the prompt includes content of a webpage being presented to the user. The acts also include providing the prompt as input to the generative language model. The acts further include receiving output from the generative language model, where the generative language model generated the output based upon the prompt. The acts additionally include causing the output to be presented to the user by way of a client computing device concurrently with the webpage being presented to the user.

Abstract: A computing system is disclosed that includes a processor and memory. The memory stores instructions that, when executed by the processor, cause the processor to perform several acts. The acts include generating a prompt that is to be input to a generative language model. The prompt includes conversational input set forth by a user. The acts further comprise providing the prompt as input to the generative language model, and receiving conversational output from the generative language model, where the generative language model generated the conversational output based upon the prompt. Additionally, the acts comprise receiving an indication that the user has performed an interface mode change action and updating a search engine results page (SERP) to provide information related to the conversational output generated by the generative language model. The acts further comprise presenting the updated SERP to the user on a client computing device.

Abstract: A first exemplary method implementation for designing an application programming interface (API) includes: preparing multiple code samples for a core scenario, each respective code sample of the multiple code samples corresponding to a respective programming language of multiple programming languages; and deriving the API from the core scenario responsive to the multiple code samples. A second exemplary method for designing an API includes: selecting a core scenario for a feature area; writing at least one code sample for the core scenario; and deriving an API for the core scenario responsive to the at least one code sample. A third exemplary method for designing an API includes: deriving an API for a scenario responsive to at least one code sample written with regard to the scenario; performing one or more usability studies on the API utilizing multiple developers; and revising the API based on the one or more usability studies.

Abstract: Described is a mechanism for enabling software applications to be upgraded from a remote location without forcing an immediate termination or restart of the application. A starter component is associated with the application such that the starter component is executed on behalf of the application. When executed, the starter component launches an executable file associated with a current version of the application. An updater component then periodically polls a remote location to determine if a newer version of the application is available for download. If so, the updater component downloads the newer version of the application to local storage. The updater component then updates configuration information such that the newer version of the application is executed on subsequent launches of the application by the application starter component.