Abstract: A sets of capability data can be received by a computing device. The each set of capability data can describe a user. The computing device can apply an artificial intelligence model to the sets of capability data to generate sets of vectors individually representing a respective one of the sets of capability data. The computing device can generate summary vectors individually corresponding to a particular set of the sets of capability data. The computing device can receive requirement data associated with a particular asset. The computing device can generate an asset vector for the particular asset based on the requirement data. The computing device can determine similarity scores individually based on a comparison of a corresponding summary vector of the summary vectors to the asset vector. The computing device can generate a ranking of the users for the particular asset based on the similarity scores.

Abstract: 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.

Abstract: 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.

Abstract: A hardware processor can receive sets of input data describing assets associated with an entity. The hardware processor can receive inputs responsive to queries of a user. The hardware processor can individually generate predictive models based on a respective set of input data. The hardware processor can calculate predicted outcomes for the user by applying each of models to the inputs. The hardware processor can generate a user interface comprising the predictive outcomes for the user for each of the predictive models.

Abstract: A hardware processor can generate an artificial intelligence neural network that is predictive of performance. The 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.