Abstract: Methods, systems, and computer programs are presented for automated hypothesis generation and evaluation. One method includes an operation for generating a user interface (UI) for identifying a baseline segment and a target segment of users of a product or service. The UI further provides at least one option for configuring parameters for generating a hypothesis. The method further includes an operation for generating the hypothesis based on the configured parameters. The hypothesis defines a campaign to reach members of the baseline segment in order to transfer members from the baseline segment to the target segment. The method further includes estimating, by a machine-learning model, at least one performance metric value that would result from implementing the hypothesis to transfer members from the baseline segment to the target segment. Further, the method includes an operation for causing presentation on the UI of the hypothesis and the at least one performance metric.

Abstract: Techniques for automatically manufacturing mechanical parts are described. A first estimate of manufacturing cost for a first mechanical part is generated using a first machine learning model. In response to determining that the first estimate of manufacturing cost for the first mechanical part falls within a range of effectivity for the first machine learning model, a second estimate of manufacturing cost for the first mechanical part is generated using a second machine learning model. An expected cost error in the second estimate of manufacturing cost for the first mechanical part is determined, and upon determining that the expected cost error falls within a pre-determined acceptable range, automatic manufacturing of the first mechanical part is facilitated.

Abstract: A method includes receiving a component history for a processor module, the component history including identities of host vehicles in a set of host vehicles in which the processor module was installed; receiving an operational history for the set of host vehicles for a time period the processor module was installed on the set of host vehicles; receiving indirect sensor measurements related to the set of host vehicles for the time period; receiving a part survival model that is based at least in part on a part status of a plurality of historical processor modules, the plurality of historical processor modules having a historical component history, a historical operational history, and historical indirect sensor measurements; and determining a survival probability of the processor module based at least in part on the component history, the operational history, the indirect sensor measurements, and the part survival model.

Abstract: A method includes receiving a component history for a processor module, the component history including identities of host vehicles in a set of host vehicles in which the processor module was installed; receiving an operational history for the set of host vehicles for a time period the processor module was installed on the set of host vehicles; receiving indirect sensor measurements related to the set of host vehicles for the time period; receiving a part survival model that is based at least in part on a part status of a plurality of historical processor modules, the plurality of historical processor modules having a historical component history, a historical operational history, and historical indirect sensor measurements; and determining a survival probability of the processor module based at least in part on the component history, the operational history, the indirect sensor measurements, and the part survival model.

Abstract: Techniques for automatically manufacturing mechanical parts are described. A first estimate of manufacturing cost for a first mechanical part is generated using a first machine learning model. In response to determining that the first estimate of manufacturing cost for the first mechanical part falls within a range of effectivity for the first machine learning model, a second estimate of manufacturing cost for the first mechanical part is generated using a second machine learning model. An expected cost error in the second estimate of manufacturing cost for the first mechanical part is determined, and upon determining that the expected cost error falls within a pre-determined acceptable range, automatic manufacturing of the first mechanical part is facilitated.