Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using learnable robotic control plans. One of the methods comprises obtaining a learnable robotic control plan comprising data defining a state machine that includes a plurality of states and a plurality of transitions between states, wherein: one or more states are learnable states, and each learnable state comprises data defining (i) one or more learnable parameters of the learnable state and (ii) a machine learning procedure for automatically learning a respective value for each learnable parameter of the learnable state; and processing the learnable robotic control plan to generate a specific robotic control plan, comprising: obtaining data characterizing a robotic execution environment; and for each learnable state, executing, using the obtained data, the respective machine learning procedures defined by the learnable state to generate a respective value for each learnable parameter of the learnable state.

A system and method for automatic coding out biases in applications, systems, and processes are disclosed. A processor operatively connected to a memory via a communication interface applies an intake process based on received inventory data to applications, systems, and processes and implements a machine learning model in response to applying the intake process. The processor also identifies areas of the potential bias data within the applications, systems, and processes by utilizing the machine learning model based on analyzing response data received during the intake process; generates output data that includes bias data and exceptions data identified for the applications, systems, and processes; and mitigates the bias data and exceptions data in response to the output data by implementing a mitigation process.

A system and method for automatic coding out biases in applications, systems, and processes are disclosed. A processor operatively connected to a memory via a communication interface applies an intake process based on received inventory data to applications, systems, and processes and implements a machine learning model in response to applying the intake process. The processor also identifies areas of the potential bias data within the applications, systems, and processes by utilizing the machine learning model based on analyzing response data received during the intake process; generates output data that includes bias data and exceptions data identified for the applications, systems, and processes; and mitigates the bias data and exceptions data in response to the output data by implementing a mitigation process.

A device may analyze data associated with a conference call. The device may provide at least a portion of the data to a machine learning model. The device may receive an output from the machine learning model in response to the machine learning model processing at least the portion of the data. The output may include a probability score associated with a determination made by the machine learning model with respect to capturing multimedia content associated with the conference call. The device may output a notification associated with capturing the multimedia content based on the output from the machine learning model.

A device may analyze data associated with a conference call. The device may provide at least a portion of the data to a machine learning model. The device may receive an output from the machine learning model in response to the machine learning model processing at least the portion of the data. The output may include a probability score associated with a determination made by the machine learning model with respect to capturing multimedia content associated with the conference call. The device may output a notification associated with capturing the multimedia content based on the output from the machine learning model.

In some implementations, a device may monitor incoming messages to at least one message account of a user. The device may determine, based on monitoring the incoming messages, that one or more messages, of the incoming messages, are associated with resetting authentication information for one or more accounts of the user. The device may determine, based on determining that the one or more messages are associated with resetting authentication information, whether the one or more messages are indicative of abnormal authentication information resetting activity. The device may perform one or more actions based on determining that the one or more messages are indicative of abnormal authentication information resetting activity.

A machine learning system includes a training platform and an inference platform, where the inference platform is coupled to receive the output of the training platform. Based upon an updating of hyperparameters in the training platform, an optimized inference model is configured to be deployed to the inference platform from the training platform. The optimized inference model is further optimized in the inference platform by using an observation difference between a client observation and a prediction response to update the optimized inference model. The updated optimized inference model is used to provide a prediction response to a client.

Commercial interactions with non-discretized items such as liquids in carafes or other dispensers are detected and associated with actors using images captured by one or more digital cameras including the carafes or dispensers within their fields of view. The images are processed to detect body parts of actors and other aspects therein, and to not only determine that a commercial interaction has occurred but also identify an actor that performed the commercial interaction. Based on information or data determined from such images, movements of body parts associated with raising, lowering or rotating one or more carafes or other dispensers may be detected, and a commercial interaction involving such carafes or dispensers may be detected and associated with a specific actor accordingly.

Commercial interactions with non-discretized items such as liquids in carafes or other dispensers are detected and associated with actors using images captured by one or more digital cameras including the carafes or dispensers within their fields of view. The images are processed to detect body parts of actors and other aspects therein, and to not only determine that a commercial interaction has occurred but also identify an actor that performed the commercial interaction. Based on information or data determined from such images, movements of body parts associated with raising, lowering or rotating one or more carafes or other dispensers may be detected, and a commercial interaction involving such carafes or dispensers may be detected and associated with a specific actor accordingly.

A computer-implemented method for improving efficiency in an electronic procurement system for sourcing resources, comprising, during digital electronic interactions of a buyer computer with one or more software platforms and without receiving explicit request for recommendations from the buyer computer: automatically generating, at a coding computer, implicit observation data of the buyer computer; automatically determining, at the coding computer, one or more active sourcing events from a plurality of sourcing events, based on at least the implicit observation data of the buyer computer; using the coding computer, causing to display at least one of the one or more active sourcing events in a graphical user interface.