Disclosed are various embodiments for generating an artificial intelligence neural network predictive of performance. A hardware processor can process the artificial intelligence neural network to determining whether a validity value for the artificial intelligence neural network meets a validity threshold. A predictive bias can be computed for the artificial neural network based on non-factored inputs. Nodes of the artificial neural network can be scored to compute an effect on the predictive bias. Another artificial intelligence neural network predictive of performance can be generated excluding a combination of parameters associated with a highest scored node of the artificial intelligence neural network.

An adjustment necessity determination device is an adjustment necessity determination device that makes a determination, after a workpiece is machined, about a necessity to make an adjustment of a deviation of the axis position of each axis of a machine tool that has performed the machining and includes: a data acquisition unit that acquires a physical quantity relating to a cause of a deviation of the axis position of each axis of the machine tool, the physical quantity observed at the time of the machining; a time-series data storage unit that stores the physical quantity as time-series data; and an adjustment necessity judgement unit that makes a judgment about a necessity to make an adjustment of a deviation of the axis position of each axis of the machine tool based on the time-series data.

A numerical controller has a machine learning device that performs machine learning of the adjustment of a setting value used in override control. The machine learning device acquires state data showing states of the numerical controller and a machine, sets reward conditions, calculates a reward based on the state data and the reward conditions, performs the machine learning of the adjustment of the setting value used in override control, and determines the adjustment of the setting value used in override control, based on a machine learning result and the state data.

A numerical controller has a machine learning device that performs machine learning of the adjustment of a setting value used in override control. The machine learning device acquires state data showing states of the numerical controller and a machine, sets reward conditions, calculates a reward based on the state data and the reward conditions, performs the machine learning of the adjustment of the setting value used in override control, and determines the adjustment of the setting value used in override control, based on a machine learning result and the state data.

A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive sets of inputs individually responsive to a respective subset of queries. The hardware processor can generate a predictive model using the set of input data. The hardware processor can calculate predictive outcomes individually associated with a respective user by applying the predictive model to each respective set of inputs of the sets of inputs. The hardware processor can generate a list ranked according to the predictive outcomes for the particular asset.

A hardware processor can receive a set of input data individually describing a particular asset associated with an entity. The hardware processor can receive a set of inputs individually responsive to a respective subset of a plurality of queries for a particular user. The hardware processor can generate a predictive model based on the set of input data. The hardware processor can calculate a predictive outcome for the particular user by applying the predictive model to the set of inputs. The hardware processor can identify a target score impacting the predictive outcome for the particular user. The hardware processor can assign a training program to the particular user corresponding to the target score.