Creating Diversity in Artificial Intelligence and Machine Learning
AssigneeSony Interactive Entertainment Inc.
InventorAlbhy Galuten
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Abstract
The present invention concern systems and methods for maintaining diversity in AI and ML environments through the cooperation of various AI and ML systems such that they optimize for social and cultural diversity. The examination of behavior, infrastructure, and governance, mimicking of genetic biodiversity, and application of the foregoing to machine reasoning mitigates the tendency of systems to find optimized or single best solutions. AI and ML environments may thus derive multiple diverse solutions that contribute to richer ecosystems in which human beings may function and thrive.
Claims25
A system for dynamic reconfiguration of machine learning datasets, the system comprising:
- memory that maintains information including one or more different datasets concerning behaviors, infrastructure, and governance;
- and
- a software engine maintained in a non-transitory storage medium and executable by a processing device to:
- receive a selection of a training set from among the different datasets, wherein the training set is selected as a starting point;
- identify one or more groupings including at least one niche grouping within the training set and one or more relationships between the groupings, wherein two or more of the groupings share data in common, and wherein the data is maintained separately for each of the groupings;
- continuously monitor one or more social networks using artificial intelligence to identify one or more social trends and to extrapolate the social trends into one or more factors;
- apply the extrapolated factors to the training set and one or more commonalities;
- and
- generate a prediction regarding behaviors, infrastructure, and governance at another point in time based on a trend using one or more neural networks, wherein the trend includes one or more changes in the one or more groupings and the relationships based on the extrapolated factors.
A method for dynamic reconfiguration of machine learning datasets, the method comprising:
- maintaining information including one or more different datasets concerning behaviors, infrastructure, and governance;
- receiving a selection of a training set from among the different datasets, wherein the training set is selected as a starting point;
- identifying one or more groupings including at least one niche grouping within the training set and one or more relationships between the groupings, wherein two or more of the groupings share data in common, and wherein the data is maintained separately for each of the groupings;
- continuously monitoring one or more social networks using artificial intelligence to identify one or more social trends and to extrapolate the social trends into one or more factors;
- applying the extrapolated factors to the training set and one or more commonalities;
- and
- generating a prediction regarding behaviors, infrastructure, and governance at another point in time based on a trend using one or more neural networks, wherein the trend includes one or more changes in the one or more groupings and the relationships based on the extrapolated factors.
A non-transitory, computer-readable storage medium, having embodied there on a program executable by a processor to perform a method for dynamic reconfiguration of machine learning datasets, the method comprising:
- maintaining information including one or more different datasets concerning behaviors, infrastructure, and governance;
- receiving a selection of a training set from among the different datasets, wherein the training set is selected as a starting point;
- identifying one or more groupings including at least one niche grouping within the training set and one or more relationships between the groupings, wherein two or more of the groupings share data in common, and wherein the data is maintained separately for each of the groupings;
- continuously monitoring one or more social networks using artificial intelligence to identify one or more social trends and to extrapolate the social trends into one or more factors;
- apply the extrapolated factors to the training set and one or more commonalities;
- and
- generating a prediction regarding behaviors, infrastructure, and governance at another point in time based on a trend using one or more neural networks, wherein the trend includes one or more changes in the one or more groupings and the relationships based on the extrapolated factors.
Dependencies are inferred from the claim text.