Abstract: The present disclosure relates to a recommendation information processing method and apparatus, an electronic device, a storage medium and a product, and relates to the field of artificial intelligence and computer technologies. The recommendation information processing method of the present disclosure includes: acquiring recommendation information; generating colloquial summary information according to the recommendation information; and generating audio corresponding to the summary information and playing the audio through an audio playback device.

Abstract: Circuits and methods involve an integrated circuit (IC) device, a plurality of application-specific sub-circuits, and a plurality of instances of a measuring circuit. The application-specific sub-circuits are disposed within respective areas of the IC device. Each instance of the measuring circuit is associated with one of the application-specific sub-circuits and is disposed within a respective one of the areas of the device. Each instance of the measuring circuit further includes a ring oscillator and a register for storage of a value indicative of an interval of time. Each instance of the measuring circuit is configured to measure passage of the interval of time based on a first clock signal, count oscillations of an output signal of the ring oscillator during the interval of time, and output a value indicating a number of oscillations counted during the interval of time.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing machine learning using a query engine. One of the methods includes obtaining, from a user device and by a query engine that is configured to access one or more databases, a command to execute a user-defined function, wherein the user-defined function includes an inference call to a machine learning model, wherein the command comprises one or more model inputs to the machine learning model; obtaining, by the query engine and from the one or more databases, trained parameter values for the machine learning model; executing, by the query engine, the user-defined function, comprising processing the one or more model inputs using the machine learning model according to the obtained parameter values of the machine learning model to generate respective model outputs; and providing, to the user device and by the query engine, the generated model outputs.

Abstract: Disclosed herein is a framework to organize functions to scenarios in order to guide users to complete processes. In accordance with one aspect, the framework determines an initial scenario. Particular functions may be assigned to the initial scenario. The initial scenario may be performed and other scenarios that follow the initial scenario may be presented.