Abstract: A computer-readable recording medium has stored therein a machine learning program executable by one or more computers, the machine learning program including: an instruction for comparing a first plurality of relationship information pieces with a second plurality of relationship information pieces, the first plurality of relationship information pieces being determined in terms of an inputted configuration of a first Artificial Intelligence (AI) system and each including a plurality of attributes, the second plurality of relationship information pieces being determined in terms of a second AI system; an instruction for determining priorities of the first plurality of relationship information pieces, the priorities being based on a result of the comparing; and an instruction for outputting, as a checklist of the first AI system, one or more check items selected in accordance with the determined priorities from among a plurality of check items associated with the plurality of attributes.

Abstract: A machine learning method includes acquiring data including attendance records of employees and information indicating which employee has taken a leave of absence from work, in response to determining that a first employee of the employees has not taken a leave of absence in accordance with the data, generating a first tensor on a basis of an attendance record of the first employee and parameters associated with elements included in the attendance record, in response to determining that a second employee of the employees has taken a leave of absence in accordance with the data, modifying the parameters, and generating a second tensor on a basis of an attendance record of the second employee and the modified parameters, and generating a model by machine learning based on the first tensor and the second tensor.

Abstract: A learning device receives, for each target, learning data that represents the source of generation of a tensor including a plurality of elements which multi-dimensionally represent the features of the target over a period of time set in advance. When the target satisfies a condition set in advance, the learning device identifies the period of time corresponding to the condition in the learning data. Subsequently, the learning device generates a weighted tensor corresponding to the learning data that is at least either before or after the concerned period of time.

Abstract: A non-transitory computer-readable recording medium storing a risk analysis program for an artificial intelligence (AI) system, the analysis program being a program for causing a computer to execute processing, the processing including: acquiring a plurality of pieces of relational information that include at least two attributes among an attribute of a type of an object person, an attribute of a type of processing, and an attribute of a type of data, wherein the relational information is determined on a basis of a configuration of the AI system; determining a priority of the plurality of pieces of relational information on a basis of the attribute of the type of the object person; and outputting one or a plurality of check items selected on a basis of the determined priority from among a plurality of check items associated with each attribute as a checklist for the AI system.

Abstract: A machine learning method includes acquiring data including attendance records of employees and information indicating which employee has taken a leave of absence from work, in response to determining that a first employee of the employees has not taken a leave of absence in accordance with the data, generating a first tensor on a basis of an attendance record of the first employee and parameters associated with elements included in the attendance record, in response to determining that a second employee of the employees has taken a leave of absence in accordance with the data, modifying the parameters, and generating a second tensor on a basis of an attendance record of the second employee and the modified parameters, and generating a model by machine learning based on the first tensor and the second tensor.

Abstract: A learning device receives, for each target, learning data that represents the source of generation of a tensor including a plurality of elements which multi-dimensionally represent the features of the target over a period of time set in advance. When the target satisfies a condition set in advance, the learning device identifies the period of time corresponding to the condition in the learning data. Subsequently, the learning device generates a weighted tensor corresponding to the learning data that is at least either before or after the concerned period of time.

Abstract: A content utilization support method executed by a computer, including detecting a section of a content based on operation information on the content and user information, the detected section being a section whose play frequency by single user is more than a predetermined value, comparing a first distribution that is a distribution of attribute information of users in a first group and a second distribution that is a distribution of attribute information of users in a second group, the first group being a group of the users whose play frequency of the detected section is more than predetermined value, the second group being a group of the users whose play frequency of the detected section is equal to or less than predetermined value, and outputting information that indicates the detected section and attribute information whose difference between the first distribution and the second distribution is larger than a predetermined threshold.

Abstract: A content utilization support method executed by a computer, including detecting a section of a content based on operation information on the content and user information, the detected section being a section whose play frequency by single user is more than a predetermined value, comparing a first distribution that is a distribution of attribute information of users in a first group and a second distribution that is a distribution of attribute information of users in a second group, the first group being a group of the users whose play frequency of the detected section is more than predetermined value, the second group being a group of the users whose play frequency of the detected section is equal to or less than predetermined value, and outputting information that indicates the detected section and attribute information whose difference between the first distribution and the second distribution is larger than a predetermined threshold.