Abstract: Embodiments of the disclosed technologies include creating a first set of title prompts by applying a first set of title prompt templates to a seed, where the seed includes a topic descriptor, applying a first generative language model to the first set of title prompts, outputting, by the first generative language model, based on the first set of title prompts, a first set of document titles, creating a first set of document prompts by applying a first set of document prompt templates different from the first set of title prompt templates to the first set of document titles, applying a second generative language model to the first set of document prompts, and outputting, by the second generative language model, based on the first set of document prompts, a first set of documents.

Abstract: Embodiments of the disclosed technologies include, in response to input of a first prompt to a generative language model, outputting, by the generative language model, a first document including a first piece of writing, where the first piece of writing is based on the first prompt. Feedback is received for the first document, where the feedback includes a rating for the first piece of writing. Using the generative language model, a second prompt different from the first prompt is generated, where the second prompt is based on the feedback. In response to input of the second prompt to the generative language model, the generative language model outputs a second document different from the first document, where the second document includes a second piece of writing based on the second prompt. The second document is published to a network.

Abstract: Embodiments of the disclosed technologies include, in response to input of a first prompt to a generative language model, outputting, by the generative language model, a first document including a first piece of writing, where the first piece of writing is based on the first prompt. Feedback is received for the first document, where the feedback includes a rating for the first piece of writing. Using the generative language model, a second prompt different from the first prompt is generated, where the second prompt is based on the feedback. In response to input of the second prompt to the generative language model, the generative language model outputs a second document different from the first document, where the second document includes a second piece of writing based on the second prompt. The second document is published to a network.

Abstract: A method for assisting a user in an interaction with an apparatus including at least one automated and/or partially automated and/or manual operating state by way of a control device. A possible change or a compulsory change of operating state of the apparatus is ascertained, an escalation level for the change of operating state of the apparatus is ascertained, the ascertained change of operating state and the escalation level are signaled to the user by a dynamic indicator. A control device, a computer program, a machine-readable storage medium, and an indicator are also described.

Abstract: In example embodiments, specialized machine learning techniques may be utilized to automatically create summaries for viewers based at least partially on viewer intent. Viewer intent refers to the intention of the viewer with respect to performing a particular action in a computer system, namely what the viewer is attempting to accomplish. In some example embodiments, this viewer intent may be expressed in the form of a plurality of different intent categories, each providing, at a high level, what the viewer intends to accomplish. Examples of such categories in a social networking service include “job seeker,” “information gatherer,” “salesperson,” and “recruiter.

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: Embodiments of the disclosed technologies include creating a first set of title prompts by applying a first set of title prompt templates to a seed, where the seed includes a topic descriptor, applying a first generative language model to the first set of title prompts, outputting, by the first generative language model, based on the first set of title prompts, a first set of document titles, creating a first set of document prompts by applying a first set of document prompt templates different from the first set of title prompt templates to the first set of document titles, applying a second generative language model to the first set of document prompts, and outputting, by the second generative language model, based on the first set of document prompts, a first set of documents.

Abstract: Embodiments of the disclosed technologies include, in response to input of a first prompt to a generative language model, outputting, by the generative language model, a first document including a first piece of writing, where the first piece of writing is based on the first prompt. Feedback is received for the first document, where the feedback includes a rating for the first piece of writing. Using the generative language model, a second prompt different from the first prompt is generated, where the second prompt is based on the feedback. In response to input of the second prompt to the generative language model, the generative language model outputs a second document different from the first document, where the second document includes a second piece of writing based on the second prompt. The second document is published to a network.

Abstract: A method for assisting a user in an interaction with an apparatus including at least one automated and/or partially automated and/or manual operating state by way of a control device. A possible change or a compulsory change of operating state of the apparatus is ascertained, an escalation level for the change of operating state of the apparatus is ascertained, the ascertained change of operating state and the escalation level are signaled to the user by a dynamic indicator. A control device, a computer program, a machine-readable storage medium, and an indicator are also described.