Abstract: Systems and methods are directed to providing multilevel chained testing. A modeling manager receives a request for data associated with an experience having multiple levels of testing, whereby each lower level of testing has a set of one or more variants chained to a variant of a higher level. Based on the request, the model manager determines which variant of the multiple levels of testing to provide to a user. The determining comprises detecting a lowest segment the user is a member of, whereby each segment level corresponds to a level of testing, and selecting a variant from a corresponding set of one or more variants of the lowest sub-segment, a chained variant of a parent segment, or a control value. The modeling manager transmits a response to an experience component that includes the selected variant, and the experience component causes presentation of the experience with the selected variant.

Abstract: Aspects of the disclosure include methods for leveraging a universal embedding based entity retrieval deep learning model for candidate recommendations. A method can include receiving a request for a candidate pair having a first entity and a second entity and generating a filtered candidate pool including a first number of candidates. The filtered candidate pool can include a subset of an initial candidate pool having a second number of candidates larger than the first number of candidates. A learned distance function is selected from a plurality of distance functions. At least one distance function was predetermined prior to receiving the request and at least one distance function is generated in response to receiving the request. A distance measure is determined for each candidate in the filtered candidate pool using the learned distance function and a response is returned including top K candidates according to the determined distance measures.

Abstract: Systems and methods are directed to providing multilevel chained testing. A modeling manager receives a request for data associated with an experience having multiple levels of testing, whereby each lower level of testing has a set of one or more variants chained to a variant of a higher level. Based on the request, the model manager determines which variant of the multiple levels of testing to provide to a user. The determining comprises detecting a lowest segment the user is a member of, whereby each segment level corresponds to a level of testing, and selecting a variant from a corresponding set of one or more variants of the lowest sub-segment, a chained variant of a parent segment, or a control value. The modeling manager transmits a response to an experience component that includes the selected variant, and the experience component causes presentation of the experience with the selected variant.

Abstract: Methods, systems, and computer programs are presented for implementing an artificial-intelligence modeling utility system. One method includes receiving, by a modeling manager, a schema from an experience module that implements features of an online service. The modeling manager manages a plurality of machine-learning (ML) models, provides a user interface (UI) based on the schema for entering experiment parameter values, and configures one or more ML models for the experiment. The experiment is initialized, and during the experiment, the modeling manager receives a request from the experience module for data associated with the experiment and selects one of the configured ML models for providing a response to the request. The response is obtained from the selected ML model based on input provided to the ML model based on the request, and the modeling manager sends the response to the experience. Further, results of the experiment are presented.

Abstract: Disclosed is a gravity transforming method which comprises: establishing a circumference body (1); installing a driving device of the circumference body (1) on the top of the circumference body (1) for moving the circumference body (1); installing a gravity output device in the potential energy area of the circumference body (1) for transferring the gravity of the circumference body (1) to gear box, a receiving wheel, a lever, a convex point or a flywheel. The gravity energy of the circumference body (1) is outputted and transformed into an available power by the gravity output device. During the movement of the circumference body, the output energy is greater than the input energy.