Abstract: A request associated with reviewing content for a field of interest is received. A confidence is determined associated with the content including the field of interest. A machine learning (ML) model determines a first confidence associated with the content includes the field of interest. The field of interest is transmitted for review in instances where the first confidence is less than a confidence threshold. After review, an indication associated with a reviewer reviewing the content and the first confidence associated with the ML model identifying the field of interest is updated to a second confidence.

Abstract: Machine-learned networks provide generative design. Rather than emulate the typical human design process, an inverse model is machine trained to generate a design from requirements. A simulation model is machine trained to recover performance relative to the requirements for generated designs. These two machine-trained models are used in an optimization that creates further designs from the inverse model output design and tests those designs with the simulation model. The use of machine-trained models in this loop for exploring many different designs decreases the time to explore, so may result in a more optimal design or better starting designs for the design engineer.

Abstract: A method, and corresponding systems and computer-readable mediums, for implementing a hierarchical multi-agent control system for an environment. A method includes generating an observation of an environment by a first agent process and sending a first message that includes the observation to a meta-agent process. The method includes receiving a second message that includes a goal, by the first agent process and from the meta-agent process. The method includes evaluating a plurality of actions, by the first agent process and based on the goal, to determine a selected action. The method includes applying the selected action to the environment by the first agent process.