Abstract: An example electronic device includes a display device, and a sensor to detect position data of a user of the electronic device. The position data indicates a position and orientation of the user relative to the display device. In addition, the electronic device includes a controller coupled to the sensor and the display device. The controller is to: receive the position data from the sensor; use a machine learning model and the position data to classify an interaction of the user with the electronic device in a first ergonomic category or a second ergonomic category; and adjust an angular position of a display device or an output from the display device responsive to a classification of the interaction in the first ergonomic category.

Abstract: In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: generate a model of activity for the computing device, determine a time period for performing a calculation based on the model, wherein the calculation utilizes performance metrics associated with the computing device, activate an agent at a start time of the time period to perform the calculation, send, by the agent, a result of the calculation to a remote computing device, and deactivate the agent in response to sending the result.

Abstract: An image forming apparatus may include a receiving unit to receive a print request to print a document. Further, the image forming apparatus may include an extraction unit to extract content from the document. Furthermore, the image forming apparatus may include a categorization unit to determine a type of the content by applying a machine learning model to the extracted content. Further, the image forming apparatus may include a controller to manage the print request based on the type of the content.

Abstract: In some examples, a computing device can include a processor resource and a non-transitory memory resource storing machine-readable instructions stored thereon that, when executed, cause the processor resource to: generate a model of activity for the computing device, determine a time period for performing a calculation based on the model, wherein the calculation utilizes performance metrics associated with the computing device, activate an agent at a start time of the time period to perform the calculation, send, by the agent, a result of the calculation to a remote computing device, and deactivate the agent in response to sending the result.

Abstract: In one example, a non-transitory machine-readable storage medium encoded with instructions that, when executed by a processor, may cause the processor to obtain historical recruitment data associated with an enterprise for a period, pre-process the historical recruitment data, filter the pre-processed historical recruitment data based on a set of recruitment parameters, build a timeseries machine learning model with the filtered historical recruitment data associated with a portion of the period, test the time-series machine learning model with the filtered historical recruitment data associated with a remaining portion of the period, and predict a resource demand for an upcoming period using the timeseries machine learning model based on successful testing.

Abstract: A method of electronically supporting a meeting includes, with a computerized agent having a mailbox, receiving a meeting invite from a meeting organizer, the meeting invite including meeting participants, meeting attendance information and a meeting agenda; and with the agent, in response to receiving the meeting invite, automatically communicating with the meeting participants in advance of the meeting to prepare for the meeting. The agent may answer questions during the meeting. A computerized agent includes a bot for supporting a meeting using artificial intelligence to process an electronic recording of the meeting to extract information about the meeting for a meeting organizer or participant. The hot is deployed as a docker containerized service.

Abstract: In an example in accordance with the present disclosure, a system is described. The system includes a database. The data base includes 1) forecasted values of an event indexed by date and 2) actual values of the event indexed by date. The system also includes a non-transitory machine-readable storage medium to store instructions. The system also includes a processor to execute the instructions. The instructions to cause the processor to determine, from a current date and based on a forecasting frequency and forecasting category for forecasted values, previous dates for which there is both a forecasted value and an actual value. The instructions also cause the processor to identify those previous dates for which there is both an actual value and a forecasted value as dates by which a forecasting model accuracy is to be determined.