Abstract: In some aspects, a server trains an artificial intelligence (AI) to create a trained AI. The training includes: selecting a content creator from multiple content creators to create a selected content creator, selecting a plurality of content items associated with the content creator to create selected content items, training the AI using the selected content items, determining a creator influence of the selected content creator on the trained AI based on an aggregate influence of the selected content items on the AI during the training, and including the creator influence in a static attribution vector. After the training is completed, the trained AI receives an input and generates an output. The server creates an attribution determination based at least in part on the static attribution vector and initiates providing compensation to one or more of the multiple content creators based at least in part on the attribution determination.

Abstract: The technology disclosed relates to a provenance system for tracing repurposing of targeted artificial intelligence (AI) systems. A training logic is configured to train an AI system on a training dataset that satisfies a target artist configuration by requiring that at least some training samples in the training dataset are sourced from a target artist, and to generate a trained version of the AI system (trained AI system). The target artist configuration characterizes a work of the target artist. The trained AI system is configured to construct an output that satisfies that target artist configuration. A deployment logic is configured to make available the trained AI system via a blockchain network. A provenance logic is configured to provide access to the trained AI system, and to validate that new outputs generated by repurposing the trained AI system satisfy the target artist configuration.

Abstract: The technology disclosed relates to a provenance system for tracing licensing of targeted artificial intelligence (AI) systems. A training logic is configured to train an AI system on a training dataset that satisfies a target artist configuration by requiring that at least some training samples in the training dataset are sourced from a target artist, and to generate a trained version of the AI system. The target artist configuration characterizes a work of the target artist. The trained AI system is configured to construct an output that satisfies that target artist configuration. A deployment logic is configured to make available the trained AI system via a blockchain network. A provenance logic is configured to license the trained version of the artificial intelligence system to a plurality of licensees, and to validate that licensed copies of the trained version of the artificial intelligence system satisfy the target artist configuration.

Abstract: The technology disclosed relates to identity designations (“akas”) for metaverse environments. In particular, the technology disclosed relates to a system that comprises a metaverse environment configured to render a plurality of digital avatars, memory storing a plurality of identity labels (“akas”), identity labels in the plurality of identity labels configured for designation to digital avatars in the plurality of digital avatars, identity designation logic configured to designate an identity label from the plurality of identity labels to an avatar in the plurality of avatars in response to a user selection of the identity label, and rendering logic configured to render the avatar in the metaverse environment with the designated identity label.

Abstract: The technology disclosed relates to a fractional ownership system for artificial intelligence systems. A fractionalization logic is configured to specify respective fractions of ownership for respective owners of an artificial intelligence system. The fractions of ownership are specified based on a plurality of architectural resolutions of an architecture of the artificial intelligence system. A credit flow logic is configured to fractionalize and allocate credit results from licensing of the artificial intelligence system to the respective owners based on the fractions of ownership.

Abstract: Devices, systems, and methods herein relate to non-invasive monitoring of a patient. These systems and methods may receive one or more image signals corresponding to a skin of the patient, process the one or more image signals using a first machine learning model, and predict a physiological parameter based on the processed one or more image signals using a second machine learning model.